A Systematic Review of Mobile Phone Data in Crime Applications: A Coherent Taxonomy Based on Data Types and Analysis Perspectives, Challenges, and Future Research Directions.

Digital technologies have recently become more advanced, allowing for the development of social networking sites and applications. Despite these advancements, phone calls and text messages still make up the largest proportion of mobile data usage. It is possible to study human communication behaviors and mobility patterns using the useful information that mobile phone data provide. Specifically, the digital traces left by the large number of mobile devices provide important information that facilitates a deeper understanding of human behavior and mobility configurations for researchers in various fields, such as criminology, urban sensing, transportation planning, and healthcare. Mobile phone data record significant spatiotemporal (i.e., geospatial and time-related data) and communication (i.e., call) information. These can be used to achieve different research objectives and form the basis of various practical applications, including human mobility models based on spatiotemporal interactions, real-time identification of criminal activities, inference of friendship interactions, and density distribution estimation. The present research primarily reviews studies that have employed mobile phone data to investigate, assess, and predict human communication and mobility patterns in the context of crime prevention. These investigations have sought, for example, to detect suspicious activities, identify criminal networks, and predict crime, as well as understand human communication and mobility patterns in urban sensing applications. To achieve this, a systematic literature review was conducted on crime research studies that were published between 2014 and 2022 and listed in eight electronic databases. In this review, we evaluated the most advanced methods and techniques used in recent criminology applications based on mobile phone data and the benefits of using this information to predict crime and detect suspected criminals. The results of this literature review contribute to improving the existing understanding of where and how populations live and socialize and how to classify individuals based on their mobility patterns. The results show extraordinary growth in studies that utilized mobile phone data to study human mobility and movement patterns compared to studies that used the data to infer communication behaviors. This observation can be attributed to privacy concerns related to acquiring call detail records (CDRs). Additionally, most of the studies used census and survey data for data validation. The results show that social network analysis tools and techniques have been widely employed to detect criminal networks and urban communities. In addition, correlation analysis has been used to investigate spatial-temporal patterns of crime, and ambient population measures have a significant impact on crime rates.

Real-Time Human Detection and Gesture Recognition for On-Board UAV Rescue.

Unmanned aerial vehicles (UAVs) play an important role in numerous technical and scientific fields, especially in wilderness rescue. This paper carries out work on real-time UAV human detection and recognition of body and hand rescue gestures. We use body-featuring solutions to establish biometric communications, like yolo3-tiny for human detection. When the presence of a person is detected, the system will enter the gesture recognition phase, where the user and the drone can communicate briefly and effectively, avoiding the drawbacks of speech communication. A data-set of ten body rescue gestures (i.e., Kick, Punch, Squat, Stand, Attention, Cancel, Walk, Sit, Direction, and PhoneCall) has been created by a UAV on-board camera. The two most important gestures are the novel dynamic Attention and Cancel which represent the set and reset functions respectively. When the rescue gesture of the human body is recognized as Attention, the drone will gradually approach the user with a larger resolution for hand gesture recognition. The system achieves 99.80% accuracy on testing data in body gesture data-set and 94.71% accuracy on testing data in hand gesture data-set by using the deep learning method. Experiments conducted on real-time UAV cameras confirm our solution can achieve our expected UAV rescue purpose.

How children learn to communicate discriminatively.

How do children learn to communicate, and what do they learn? Traditionally, most theories have taken an associative, compositional approach to these questions, supposing children acquire an inventory of form-meaning associations, and procedures for composing / decomposing them; into / from messages in production and comprehension. This paper presents an alternative account of human communication and its acquisition based on the systematic, discriminative approach embodied in psychological and computational models of learning, and formally described by communication theory. It describes how discriminative learning theory offers an alternative perspective on the way that systems of semantic cues are conditioned onto communicative codes, while information theory provides a very different view of the nature of the codes themselves. It shows how the distributional properties of languages satisfy the communicative requirements described in information theory, enabling language learners to align their expectations despite the vastly different levels of experience among language users, and to master communication systems far more abstract than linguistic intuitions traditionally assume. Topics reviewed include morphological development, the acquisition of verb argument structures, and the functions of linguistic systems that have proven to be stumbling blocks for compositional theories: grammatical gender and personal names.

Thinking about the idea of consent in data science genomics: How 'informed' is it?

In this paper we argue that 'informed' consent in Big Data genomic biobanking is frequently less than optimally informative. This is due to the particular features of genomic biobanking research which render it ethically problematic. We discuss these features together with details of consent models aimed to address them. Using insights from consent theory, we provide a detailed analysis of the essential components of informed consent which includes recommendations to improve consent performance. In addition, and using insights from philosophy of mind and language and psycholinguistics we support our analyses by identifying the nature and function of concepts (ideas) operational in human cognition and language together with an implicit coding/decoding model of human communication. We identify this model as the source of patients/participants poor understanding. We suggest an alternative, explicit model of human communication, namely, that of relevance-theoretic inference which obviates the limitations of the code model. We suggest practical strategies to assist health service professionals to ensure that the specific information they provide concerning the proposed treatment or research is used to inform participants' decision to consent. We do not prescribe a standard, formal approach to decision-making where boxes are ticked; rather, we aim to focus attention towards the sorts of considerations and questions that might usefully be borne in mind in any consent situation. We hope that our theorising will be of real practical benefit to nurses and midwives working on the clinical and research front-line of genomic science.

Positive transformation towards the Fourth Industrial Revolution: empirical evidence from a technology company.

This article explores the question of how organizations can transform constructively and positively towards the Fourth Industrial Revolution (4IR). It presents insights into the state of the art on 4IR, positive psychology movements PP1.0 and PP2.0 and particularly on German organizations in the 4IR within the South African context. The study uses a qualitative research approach and presents findings from a study conducted with 16 managers across top, middle and lower management levels in a German engineering organization, based in South Africa, operating in Southern Africa. Findings, discussion, conclusions and recommendations provide insights into emerging themes on the 4IR from perspectives that take the context of discourses on the 4IR in developed and developing countries into account. Findings show the importance of five main themes when transforming into the 4IR: (1) Employee management; (2) Innovative technological and systemic change; (3) Work organization; (4) Environment and (5) Network and cooperation. Human communication and connectivity and a balanced human-machine interaction seem to build the core framework for constructive socio-technological change and a meaningful work environment. Thereby, a focus on the positive transformation requires working through the challenges and dark sides of the 4IR as well as a contextual and culture-specific approach to finally create a meaningful, healthy and optimal functioning work environment for the employees.

Instantaneous Conventions.

Humans can communicate even with few existing conventions in common (e.g., when they lack a shared language). We explored what makes this phenomenon possible with a nonlinguistic experimental task requiring participants to coordinate toward a common goal. We observed participants creating new communicative conventions using the most minimal possible signals. These conventions, furthermore, changed on a trial-by-trial basis in response to shared environmental and task constraints. Strikingly, as a result, signals of the same form successfully conveyed contradictory messages from trial to trial. Such behavior is evidence for the involvement of what we term joint inference, in which social interactants spontaneously infer the most sensible communicative convention in light of the common ground between them. Joint inference may help to elucidate how communicative conventions emerge instantaneously and how they are modified and reshaped into the elaborate systems of conventions involved in human communication, including natural languages.

Ontogenetic effects on gazing behaviour: a case study of kennel dogs (Labrador Retrievers) in the impossible task paradigm.

Life experiences and living conditions can influence the problem-solving strategies and the communicative abilities of dogs with humans. The goals of this study were to determine any behavioural differences between Labrador Retrievers living in a kennel and those living in a house as pets and to assess whether kennel dogs show preferences in social behaviours for their caretaker relative to a stranger when they are faced with an unsolvable task. Nine Labrador Retrievers living in a kennel from birth and ten Labrador Retrievers living in a family as pets were tested. The experimental procedure consisted of three "solvable" tasks in which the dogs could easily retrieve food from a container followed by an "unsolvable" task in which the container was hermetically locked. Dogs of both groups spent the same amount of time interacting with the experimental apparatus. Kennel dogs gazed towards people for less time and with higher latency than pet dogs; however, there were no significant preferences in gazing towards the stranger versus the caretaker in both groups. These findings demonstrated that kennel dogs are less prone to use human-directed gazing behaviour when they are faced with an unsolvable problem, taking the humans into account to solve a task less than do the pet dogs.

The Sound of Emotional Prosody: Nearly 3 Decades of Research and Future Directions.

Emotional voices attract considerable attention. A search on any browser using "emotional prosody" as a key phrase leads to more than a million entries. Such interest is evident in the scientific literature as well; readers are reminded in the introductory paragraphs of countless articles of the great importance of prosody and that listeners easily infer the emotional state of speakers through acoustic information. However, despite decades of research on this topic and important achievements, the mapping between acoustics and emotional states is still unclear. In this article, we chart the rich literature on emotional prosody for both newcomers to the field and researchers seeking updates. We also summarize problems revealed by a sample of the literature of the last decades and propose concrete research directions for addressing them, ultimately to satisfy the need for more mechanistic knowledge of emotional prosody.

Language Artificial Intelligences' Communicative Performance Quantified Through the Gricean Conversation Theory.

This study pragmatically investigates an artificial intelligence (AI) speaker (AIS)'s verbal communicative performance based on real AI-human conversation data. Specifically, this study explores Grice's conversation theory, which enables the categorization of an AIS's mistaken utterances as violations of specific conversational maxims. Twenty native Korean-speaking participants recorded at least 50 conversations with Kakao Mini AISs, provided by Daum Kakao, Inc., in Korea. Each conversation, either for information sharing or as daily dialogue, was required to contain at least two turn-taking instances. A total of 1,026 recorded dialogues were decomposed into adjacency pairs based on turn-taking. The dialogues were arranged into 3,365 adjacency pairs, and each pair was then classified as a conversational success or failure based on whether the AIS answered the user's utterance appropriately. Language users' evaluations of the AIS's mistaken expressions were also quantified via an additional acceptability rating test with 1,024 adjacency pairs. The overall results indicate that Grice's "maxim of relation" is most frequently flouted by AISs and is considered to be the least natural to language users. These findings suggest that to improve AISs' natural communication capacity, more detailed AI algorithms that generate utterances relevant to either the partner's preceding utterance or a broader conversational context should be created. Although the verbal communicative capacities of the AIS we test are substantially overtaken by those of recent large language models, such as generative pretrained transformer, the pragmatic evaluation described in the current study will remain useful for more precise linguistic quantification of current/future language AI's communicative performance/competence.

Visualization and Semantic Labeling of Mood States Based on Time-Series Features of Eye Gaze and Facial Expressions by Unsupervised Learning.

This study is intended to develop a stress measurement and visualization system for stress management in terms of simplicity and reliability. We present a classification and visualization method of mood states based on unsupervised machine learning (ML) algorithms. Our proposed method attempts to examine the relation between mood states and extracted categories in human communication from facial expressions, gaze distribution area and density, and rapid eye movements, defined as saccades. Using a psychological check sheet and a communication video with an interlocutor, an original benchmark dataset was obtained from 20 subjects (10 male, 10 female) in their 20s for four or eight weeks at weekly intervals. We used a Profile of Mood States Second edition (POMS2) psychological check sheet to extract total mood disturbance (TMD) and friendliness (F). These two indicators were classified into five categories using self-organizing maps (SOM) and U-Matrix. The relation between gaze and facial expressions was analyzed from the extracted five categories. Data from subjects in the positive categories were found to have a positive correlation with the concentrated distributions of gaze and saccades. Regarding facial expressions, the subjects showed a constant expression time of intentional smiles. By contrast, subjects in negative categories experienced a time difference in intentional smiles. Moreover, three comparative experiment results demonstrated that the feature addition of gaze and facial expressions to TMD and F clarified category boundaries obtained from U-Matrix. We verify that the use of SOM and its two variants is the best combination for the visualization of mood states.

Harnessing the Power of Artificial Intelligence in Otolaryngology and the Communication Sciences.

Use of artificial intelligence (AI) is a burgeoning field in otolaryngology and the communication sciences. A virtual symposium on the topic was convened from Duke University on October 26, 2020, and was attended by more than 170 participants worldwide. This review presents summaries of all but one of the talks presented during the symposium; recordings of all the talks, along with the discussions for the talks, are available at https://www.youtube.com/watch?v=ktfewrXvEFg and https://www.youtube.com/watch?v=-gQ5qX2v3rg . Each of the summaries is about 2500 words in length and each summary includes two figures. This level of detail far exceeds the brief summaries presented in traditional reviews and thus provides a more-informed glimpse into the power and diversity of current AI applications in otolaryngology and the communication sciences and how to harness that power for future applications.

Text Analysis of Evolving Emotions and Sentiments in COVID-19 Twitter Communication.

Scientists and regular citizens alike search for ways to manage the widespread effects of the COVID-19 pandemic. While scientists are busy in their labs, other citizens often turn to online sources to report their experiences and concerns and to seek and share knowledge of the virus. The text generated by those users in online social media platforms can provide valuable insights about evolving users' opinions and attitudes. The objective of this research is to analyze text of such user disclosures to study human communication during a pandemic in four primary ways. First, we analyze Twitter tweet information, generated throughout the pandemic, to understand users' communications concerning COVID-19 and how those communications have evolved during the pandemic. Second, we analyze linguistic sentiment concepts (analytic, authentic, clout, and tone concepts) in different Twitter settings (sentiment in tweets with pictures or no pictures and tweets versus retweets). Third, we investigate the relationship between Twitter tweets with additional forms of internet activity, namely, Google searches and Wikipedia page views. Finally, we create and use a dictionary of specific COVID-19-related concepts (e.g., symptom of lost taste) to assess how the use of those concepts in tweets are related to the spread of information and the resulting influence of Twitter users. The analysis showed a surprisingly lack of emotion in the initial phases of the pandemic as people were information seeking. As time progressed, there were more expressions of sentiment, including anger. Further, tweets with and without pictures and/or video had statistically significant differences in text sentiment characteristics. Similarly, there were differences between the sentiment in tweets and retweets and tweets. We also found that Google and Wikipedia searches were predictive of sentiment in the tweets. Finally, a variable representing a dictionary of COVID-related concepts was statistically significant when related to users' Twitter influence score and number of retweets, illustrating the general impact of COVID-19 on Twitter and human communication. Overall, the results provide insights into human communication as well as models of human internet and social media use. These findings could be useful for the management of global challenges beyond, or different from, a pandemic.

Understanding Miscommunication: Speech Act Recognition in Digital Contexts.

Successful language use requires accurate intention recognition. However, sometimes this can be undermined because communication occurs within an interpersonal context. In this research, I used a relatively large set of speech acts (n = 32) and explored how variability in their inherent face-threat influences the extent to which they are successfully recognized by a recipient, as well as the confidence of senders and receivers in their communicative success. Participants in two experiments either created text messages (senders) designed to perform a specific speech act (e.g., agree) or interpreted those text messages (receivers) in terms of the specific speech act being performed. The speech acts were scaled in terms of their degree of face threat. In both experiments, speech acts that were more threatening were less likely to be correctly recognized than those that were less threatening. Additionally, the messages of the more threatening speech acts were longer and lower in clout than the less threatening speech acts. Senders displayed greater confidence in communicative success than receivers, but judgments of communicative success (for both senders and receivers) were unrelated to actual communicative success. The implications of these results for our understanding of actual communicative episodes are discussed.

Disentangling Pantomime From Early Sign in a New Sign Language: Window Into Language Evolution Research.

In this study, we aim to disentangle pantomime from early signs in a newly-born sign language: Sao Tome and Principe Sign Language. Our results show that within 2 years of their first contact with one another, a community of 100 participants interacting everyday was able to build a shared language. The growth of linguistic systematicity, which included a decrease in use of pantomime, reduction of the amplitude of signs and an increase in articulation economy, showcases a learning, and social interaction process that constitutes a continuum and not a cut-off system. The human cognitive system is endowed with mechanisms for symbolization that allow the process of arbitrariness to unfold and the expansion of linguistic complexity. Our study helps to clarify the role of pantomime in a new sign language and how this role might be linked with language itself, showing implications for language evolution research.

A Review of the Model/Rival (M/R) Technique for Training Interspecies Communication and Its Use in Behavioral Research.

In this paper, I will review the Model/Rival (M/R) technique that has been used to establish interspecies communication with Grey parrots (Psittacus erithacus). I will describe the original format developed by Todt, the relationship to other forms of observational learning outlined by other researchers, and the adaptations that I devised. I will describe how my undergraduate trainers and I isolated the various components that constitute the technique and explain how each is necessary, but how only the combination of all components is sufficient for successful implementation-and how improper implementation can lead to failure. I will briefly summarize the results of proper implementation-including the importance of interspecies communication itself as a technique for studying animal cognition.

Imagining How Lines Were Drawn: The Appreciation of Calligraphy and the Facilitative Factor Based on the Viewer's Rating and Heart Rate.

For this study, we examined how recognizing the writing process of calligraphy influences the cognitive and affective processes related to appreciating it, with the aim of contributing to both graphonomics and the psychology of aesthetics. To this end, we conducted two Web-based experiments in which some participants were instructed to view calligraphy by tracing it with their eyes (the tracing method), while others were told to feel free to think and imagine whatever they wanted. Study 1 (N = 103) revealed that the tracing method elicits stronger admiration, inspiration, and empathy in viewers. Study 2 (N = 87) showed that the tracing method decreases the average heart rate of those who do not frequently engage in calligraphy appreciation as they gaze at calligraphy for a minute-and-a-half (during the second half of the stimulus duration); this suggests that the tracing method could keep viewers from becoming bored while looking at calligraphy. In sum, the tracing method has positive effects on viewing calligraphy. From a broader perspective, the results imply that how in detail viewers recognize the process of creating an artwork will be a key determinant of art appreciation. In addition, our findings demonstrate how we can measure cardiac activities using the emerging technology of the photoplethysmogram (PPG).

Body synchrony in triadic interaction.

Humans subtly synchronize body movement during face-to-face conversation. In this context, bodily synchrony has been linked to affiliation and social bonding, task success and comprehension, and potential conflict. Almost all studies of conversational synchrony involve dyads, and relatively less is known about the structure of synchrony in groups larger than two. We conducted an optic flow analysis of body movement in triads engaged in face-to-face conversation, and explored a common measure of synchrony: time-aligned bodily covariation. We correlated this measure of synchrony with a diverse set of covariates related to the outcome of interactions. Triads showed higher maximum cross-correlation relative to a surrogate baseline, and 'meta-synchrony', in that composite dyads in a triad tended to show correlated structure. A windowed analysis also revealed that synchrony varies widely across an interaction. As in prior studies, average synchrony was low but statistically reliable in just a few minutes of interaction. In an exploratory analysis, we investigated the potential function of body synchrony by predicting it from various covariates, such as linguistic style matching, liking, laughter and cooperative play in a behavioural economic game. Exploratory results do not reveal a clear function for synchrony, though colaughter within triads was associated with greater body synchrony, and is consistent with an earlier analysis showing a positive connection between colaughter and cooperation. We end by discussing the importance of expanding and codifying analyses of synchrony and assessing its function.

What's in a Meow? A Study on Human Classification and Interpretation of Domestic Cat Vocalizations.

Although the domestic cat (Felis catus) is probably the most widespread companion animal in the world and interacts in a complex and multifaceted way with humans, the human-cat relationship and reciprocal communication have received far less attention compared, for example, to the human-dog relationship. Only a limited number of studies have considered what people understand of cats' human-directed vocal signals during daily cat-owner interactions. The aim of the current study was to investigate to what extent adult humans recognize cat vocalizations, namely meows, emitted in three different contexts: waiting for food, isolation, and brushing. A second aim was to evaluate whether the level of human empathy toward animals and cats and the participant's gender would positively influence the recognition of cat vocalizations. Finally, some insights on which acoustic features are relevant for the main investigation are provided as a serendipitous result. Two hundred twenty-five adult participants were asked to complete an online questionnaire designed to assess their knowledge of cats and to evaluate their empathy toward animals (Animal Empathy Scale). In addition, participants had to listen to six cat meows recorded in three different contexts and specify the context in which they were emitted and their emotional valence. Less than half of the participants were able to associate cats' vocalizations with the correct context in which they were emitted; the best recognized meow was that emitted while waiting for food. Female participants and cat owners showed a higher ability to correctly classify the vocalizations emitted by cats during brushing and isolation. A high level of empathy toward cats was significantly associated with a better recognition of meows emitted during isolation. Regarding the emotional valence of meows, it emerged that cat vocalizations emitted during isolation are perceived by people as the most negative, whereas those emitted during brushing are perceived as most positive. Overall, it emerged that, although meowing is mainly a human-directed vocalization and in principle represents a useful tool for cats to communicate emotional states to their owners, humans are not particularly able to extract precise information from cats' vocalizations and show a limited capacity of discrimination based mainly on their experience with cats and influenced by empathy toward them.

Repair Avoidance: When Faithful Informational Exchanges Don't Matter That Much.

Common-sense intuition suggests that, when people are engaged in informational exchanges, they communicate so as to be reasonably sure that they perform the exchanges faithfully. Over the years, we have found evidence suggesting that this intuition, which is woven into several influential theories of human communication, may be misleading. We first summarize this evidence and discuss its potential limitations. Then, we present a new study that addresses the potential limitations. A confederate instructed participants to "pick up the skask" from a tray containing six objects and move it to a specific location. Since skask is a non-word invented by us, participants had to ask for clarification to perform the instruction faithfully. In contradiction with the intuition that people pursue faithfulness when engaged in informational exchanges, 29 of the 48 participants we tested performed the instruction without asking for clarification. We identified a possible cause for this behavior, which occurred more frequently when avoiding the clarification was unlikely to result in an overt consequence (an error in the execution of the instruction that could be noticed by the confederate or the experimenter). Other factors such as individual differences and the specific interpersonal dynamics of the experimental settings, if they played a role at all, did it to an extent that is unlikely to be comparable to that of the role played by overt consequences. Considered together, our various assessments of the extent to which people engage in faithful informational exchanges converge on a simple conclusion: Communicating faithfully is a substantially demanding task, and people often fail at it. We discuss the implications of this conclusion and speculate on its relevance for understanding the evolutionary past of human communication.

Communicative misalignment in Autism Spectrum Disorder.

Communication deficits are a defining feature of Autism Spectrum Disorder (ASD), manifest during social interactions. Previous studies investigating communicative deficits have largely focused on the perceptual biases, social motivation, cognitive flexibility, or mentalizing abilities of isolated individuals. By embedding autistic individuals in live non-verbal interactions, we characterized a novel cause for their communication deficits. Adults with ASD matched neurotypical individuals in their ability and propensity to generate and modify intelligible behaviors for a communicative partner. However, they struggled to align the meaning of those behaviors with their partner when meaning required referencing their recent communicative history. This communicative misalignment explains why autistic individuals are vulnerable in everyday interactions, which entail fleeting ambiguities, but succeed in social cognition tests involving stereotyped contextual cues. These findings illustrate the cognitive and clinical importance of considering social interaction as a communicative alignment challenge, and how ineffective human communication is without this key interactional ingredient.