Reviewerly/exHarmony huggingface dataset:
"All Rise for the AI Director"

How might the capabilities of voice assistants several decades in the future shape human society? To anticipate the space of possible futures for voice assistants, we asked 149 participants to each complete a story based on a brief story stem set in the year 2050 in one of five different contexts: the home, doctor's office, school, workplace, and public transit. Story completion as a method elicits participants' visions of possible futures, unconstrained by their understanding of current technological capabilities, but still reflective of current sociocultural values. Through a thematic analysis, we find these stories reveal the extremes of the capabilities and concerns of today's voice assistants---and artificial intelligence---such as improving efficiency and offering instantaneous support, but also replacing human jobs, eroding human agency, and causing harm through malfunction. We conclude by discussing how these speculative visions might inform and inspire the design of voice assistants and other artificial intelligence.
Drawing on Experiences of Self

In this paper, we present a method of Dialogical Sketching. We introduce the development of this method as a discursive aid to understanding design probe responses within participatory co-design engagements but also articulate its potential more broadly within participatory research. Situated within a research study into the potential of digital jewellery to support self, we focus on how sketching can elucidate reflection on layers of meaning conveyed both explicitly and implicitly in participants' probe responses. The method enabled an iterative dialogue not bound by certainty, but more by inference, interpretation and suggested meanings. Systems of sketching scaffolded conversations about personal issues and feelings that were difficult to articulate in a way that was imaginative, rather than descriptive. We argue that the method firstly enriches the potential of probes, secondly encourages discourse in open and often uncertain ways and thirdly can enable sustained participatory engagement even through challenging circumstances.
Detecting Off-Task Behavior from Student Dialogue in Game-Based Collaborative Learning

Collaborative game-based learning environments integrate game-based learning and collaborative learning. These environments present students with a shared objective and provide them with a means to communicate, which allows them to share information, ask questions, construct explanations, and work together toward their shared goal. A key challenge in collaborative learning is that students may engage in unproductive discourse, which may affect learning activities and outcomes. Collaborative game-based learning environments that can detect this off-task behavior in real-time have the potential to enhance collaboration between students by redirecting the conversation back to more productive topics. This paper investigates the use of dialogue analysis to classify student conversational utterances as either off-task or on-task. Using classroom data collected from 13 groups of four students, we trained off-task dialogue models for text messages from a group chat feature integrated into Crystal Island: EcoJourneys, a collaborative game-based learning environment for middle school ecosystem science. We evaluate the effectiveness of the off-task dialogue models, which use different word embeddings (i.e., word2vec, ELMo, and BERT), as well as predictive off-task dialogue models that capture varying amounts of contextual information from the chat log. Results indicate that predictive off-task dialogue models that incorporate a window of recent context and represent the sequential nature of the chat messages achieve higher predictive performance compared to models that do not leverage this information. These findings suggest that off-task dialogue models for collaborative game-based learning environments can reliably recognize and predict students' off-task behavior, which introduces the opportunity to adaptively scaffold collaborative dialogue.
Exploring pre-service teachers’ justifications for one-to-one technology use in schools: implications for initial teacher education

Set against the backdrop of a succession of educational technology policies in Ireland, influenced by international discourses, this study aimed to explore how Irish pre-service teachers justify the use of mobile technologies in schools.In order to achieve this, 23 preservice teachers were presented with a vignette that asked them to justify the use of a one-toone tablet initiative in school.The research found that pre-service teachers tended to justify the initiative, as they saw the increasing technification of schools and society as an inevitable process.In addition, they presented pragmatic reasons for using the technology rather than highlighting their educational/pedagogic value.This study points to the need to challenge pre-service teachers' innovation-centric and techno-centric attitudes towards technology use.It also highlights the need for teacher educators, as a whole, to take a more active role in addressing this issue in teacher education programmes.
A Maze Matrix-Based Secret Image Sharing Scheme with Cheater Detection

Secret image sharing is a technique for sharing a secret message in such a fashion that stego image shadows are generated and distributed to individual participants. Without the complete set of shadows shared among all participants, the secret could not be deciphered. This technique may serve as a crucial means for protecting private data in massive Internet of things applications. This can be realized by distributing the stego image shadows to different devices on the Internet so that only the ones who are authorized to access these devices can extract the secret message. In this paper, we proposed a secret image sharing scheme based on a novel maze matrix. A pair of image shadows were produced by hiding secret data into two distinct cover images under the guidance of the maze matrix. A two-layered cheat detection mechanism was devised based on the special characteristics of the proposed maze matrix. In addition to the conventional joint cheating detection, the proposed scheme was able to identify the tampered shadow presented by a cheater without the information from other shadows. Furthermore, in order to improve time efficiency, we derived a pair of Lagrange polynomials to compute the exact pixel values of the shadow images instead of resorting to time-consuming and computationally expensive conventional searching strategies. Experimental results demonstrated the effectiveness and efficiency of the proposed secret sharing scheme and cheat detection mechanism.
Deep Convolutional Neural Network for Identifying Seam-Carving Forgery

Seam carving is a representative content-aware image retargeting approach to adjust the size of an image while preserving its visually prominent content. To maintain visually important content, seam-carving algorithms first calculate the connected path of pixels, referred to as the seam, according to a defined cost function and then adjust the size of an image by removing and duplicating repeatedly calculated seams. Seam carving is actively exploited to overcome diversity in the resolution of images between applications and devices; hence, detecting the distortion caused by seam carving has become important in image forensics. In this paper, we propose a convolutional neural network (CNN)-based approach to classifying seam-carving-based image retargeting for reduction and expansion. To attain the ability to learn low-level features, we designed a CNN architecture comprising five types of network blocks specialized for capturing subtle signals. An ensemble module is further adopted to both enhance performance and comprehensively analyze the features in the local areas of the given image. To validate the effectiveness of our work, extensive experiments based on various CNN-based baselines were conducted. Compared to the baselines, our work exhibits state-of-the-art performance in terms of three-class classification (original, seam inserted, and seam removed). In addition, our model with the ensemble module is robust for various unseen cases. The experimental results also demonstrate that our method can be applied to localize both seam-removed and seam-inserted areas.
Understanding Moderation in Online Mental Health Communities

Online Mental Health Communities (OMHCs) enable individuals to seek and provide support, and serve as a safe haven to disclose and share stigmatizing and sensitive experiences. Like other online communities, OMHCs are not immune to bad behavior and antisocial activities such as trolling, spamming, and harassment. Therefore, these communities are oftentimes guided by strict norms against such behavior, and moderated to ensure the quality and credibility of the content being shared. However, moderation within these communities is not only limited to ensuring content quality. It is far more complex—providing supportive spaces for disclosure, ensuring individuals' privacy, etc.—because of the sensitive population that they cater to. By interviewing 19 moderators across 12 such OMHCs on Reddit, this paper studies the practices and structure of moderation in these communities to better understand their functioning and effectiveness. Our research questions primarily revolve around three major themes—moderation, support, and self-disclosure. We find practices of moderation hierarchy, and several distinctions in motivations and responsibilities of the moderators individually and as a group. We also notice that these communities predominantly encourage emotional support, and provide supportive spaces that encourage self-disclosure on stigmatized concerns. Our findings highlight the necessity of awareness corresponding to (currently lacking) privacy concerns, and raises the importance of the presence of mental health experts (counselors and psychiatrists) in these communities. On the basis of the insights drawn from this work, we discuss the implications and considerations for designing OMHCs.
Understanding stack overflow code quality: A recommendation of caution

Community Question and Answer (CQA) platforms use the power of online groups to solve problems, or gain information. While these websites host useful information, it is critical that the details provided on these platforms are of high quality, and that users can trust the information. This is particularly necessary for software development, given the ubiquitous use of software across all sections of contemporary society. Stack Overflow is the leading CQA platform for programmers, with a community comprising over 10 million contributors. While research confirms the popularity of Stack Overflow, concerns have been raised about the quality of answers that are provided to questions on Stack Overflow. Code snippets often contained in these answers have been investigated; however, the quality of these artefacts remains unclear. This could be problematic for the software engineering community, as evidence has shown that Stack Overflow snippets are frequently used in both open source and commercial software. This research fills this gap by evaluating the quality of code snippets on Stack Overflow. We explored various aspects of code snippet quality, including reliability and conformance to programming rules, readability, performance and security. Outcomes show variation in the quality of Stack Overflow code snippets for the different dimensions; however, overall, quality issues in Stack Overflow snippets were not always severe. Vigilance is encouraged for those reusing Stack Overflow code snippets.
Minimum-variance hedging of Bitcoin inverse futures

We formulate an optimal hedging problem of Bitcoin inverse futures under the minimum-variance framework. We obtain the optimal hedging strategy in closed forms for both short and long hedges and compute hedging effectiveness under the optimal strategy. Our empirical analyses show that the optimal hedging strategy achieves superior effectiveness in reducing risk and outperforms the naïve hedge in all scenarios.
Deep Learning for Arabic Error Detection and Correction

Research on tools for automating the proofreading of Arabic text has received much attention in recent years. There is an increasing demand for applications that can detect and correct Arabic spelling and grammatical errors to improve the quality of Arabic text content and application input. Our review of previous studies indicates that few Arabic spell-checking research efforts appropriately address the detection and correction of ill-formed words that do not conform to the Arabic morphology system. Even fewer systems address the detection and correction of erroneous well-formed Arabic words that are either contextually or semantically inconsistent within the text. We introduce an approach that investigates employing deep neural network technology for error detection in Arabic text. We have developed a systematic framework for spelling and grammar error detection, as well as correction at the word level, based on a bidirectional long short-term memory mechanism and word embedding, in which a polynomial network classifier is at the top of the system. To get conclusive results, we have developed the most significant gold standard annotated corpus to date, containing 15 million fully inflected Arabic words. The data were collected from diverse text sources and genres, in which every erroneous and ill-formed word has been annotated, validated, and manually revised by Arabic specialists. This valuable asset is available for the Arabic natural language processing research community. The experimental results confirm that our proposed system significantly outperforms the performance of Microsoft Word 2013 and Open Office Ayaspell 3.4, which have been used in the literature for evaluating similar research.
Experimental simulation of the four-dimensional Yang-Baxter equation on a spin quantum simulator

The Yang-Baxter equation (YBE) has created many applications in the integrable models of the statistical mechanics. It is recently shown that the YBE can be further applied in quantum information processing. Conversely, quantum simulation also provides novel approaches for effectively checking the YBE in quantum devices. Here, we adopted the method inspired by the previous work to experimentally perform the simulation of the YBE on a four-qubit nuclear magnetic resonance quantum processor. Whether the YBE is satisfied or not can be preliminarily determined by measuring the ancillary qubit without the assistance of quantum state tomography, and two hand sides of the YBE are concurrently simulated, not in separate forms involved in the existing protocols. The experimental result shows that the discrimination accuracy is over 98.1% when the condition of YBE is satisfied. The global and local entanglements in the YBE system are also studied by the numerical simulation and experiments. It is found that the YBE is not only a tool to yield the global entanglement but also the symptom when the local entanglement is equal to each other.
Impact of Sentence Length on the Readability of Web for Screen Reader Users

Readability of text is generally believed to be connected to sentence length. Most studies on readability are based on visual reading. Less is known about text readability for users relying on screen readers, such as users who are blind. This study therefore set out to investigate the effect of sentence length on the readability of web texts accessed using screen readers. A controlled within-subjects experiment was performed with twenty-one participants. Participants used a screen reader to read five texts with different sentence lengths. The participants' comprehension and perceived workload were measured. The findings reveal that there is a significant effect of sentence length and most participants exhibit the highest comprehension and lowest workload with sentences comprising 16–20 words. Implications of these results are that web content providers should strive for sentence length of 16–20 words to maximize readability.
MBBNet: An edge IoT computing-based traffic light detection solution for autonomous bus

Traffic light detection is a key module in the autonomous driving system to enhance the interactions between drivers and unmanned vehicles. In recent studies, deep neural networks are widely used for traffic light detection and resource/power consumption is a major concern for model deployment in vehicular edge devices. This paper proposes a novel light-weight deep CNN model that integrates the multi-backbone of state-of-the-art architectures for the self-driving traffic light detection. The MBBNet (Multi-BackBone Network) consists of three common convolutional backbones, i.e., the normal, residual and highway (DenseNet) convolutional modules. Simple ensemble of those backbones may incur high computational load. Therefore, channel compression is adopted to control the model parameters, while guaranteeing the accuracy for mobile and embedded hardware. Evaluation of a dataset collected from real road conditions demonstrate the robustness of our detection system, and it achieves higher accuracy (accuracy > 0.94 and Average_IOU>74.05%) for self-driving buses. In terms of resource consumption, the trained model size is 1.35 MB, and can process high-resolution images (1280 × 960) at 14 FPS (frames per second) on low-power edge devices.
Deep Eigen Space Based ASL Recognition System

Sign language is the art of communicating using hand gestures. The communication between an impaired person and the one who doesn't understand sign language could be easy using the computer as a tool. Inconsistent previous works in the field posed a need for more stable and effective model. We take an efficient step in order to tackle the problem of automatic fingerspelling recognition system using the con-cept that in order to efficiently perceive things, human brain alters some of the information. Moreover, the comparison between previous works and our model is mentioned systematically in this literature. Al-so, the model is capable to classify the real-time sign without dependency on environment.
Remote Working during the Pandemic: A Second Q&amp;A with Gillian Isaacs Russell

On the first anniversary of the beginning of the Covid‐19 pandemic lockdown, Gillian Isaacs Russell, author of the influential Screen Relations: The Limits of Computer‐Mediated Psychoanalysis and Psychotherapy, returns to respond to a second set of questions from the BJP . In this interview by email, she considers the challenges and issues that came up for clinicians and patients during the last year of working remotely. Looking back at the year as a whole, she explores the impact of ongoing trauma on the therapeutic couple. She discusses the creative ways that clinicians have found to navigate the losses and differences between co‐present and distance treatment, including holding an internal paradox of immersion in telepresence and the maintenance of a reflective distance, to be shared and explored with the patient. She examines the effects that differing hardware such as telephone or computer screen have on our intimate communication, how the intrusion of the personal environments of both clinician and patient may have affected the dynamics of the therapeutic couple, and the personal and global experience of loss and bereavement for both therapist and patient, particularly when it has to be processed remotely. Finally, the BJP asks her to give her thoughts on the future and whether the ‘new normal’ will include more hybrid forms of training and treatment.
Face Recognition from Video using Deep Learning

Face recognition (FR) and verification is the immeasurable technology to encounter any criminal activities nowadays. With the remarkable applications extending from criminal ID, security, and observation to amusement sites. This system (recognition of faces) is exceptionally helpful in banks, air terminals, and different associations for screening customers. In deep learning, convolutional neural networks (CNN) have gained attention for face recognition but to train CNN requires more data, which is very difficult in case of applications like criminal activities (robbery, murder, etc). Therefore, this paper proposed a face recognition system that makes searching for criminals easy and quick with less time and hence efficiently helps police and administration. In this paper, a pretrained model i.e FaceNet (FN) is used for face recognition from video. FN modifies the face images into a close-packed Euclidean space where separations extent the face nearness.
Intelligent teaching ability of contemporary college talents based on BP neural network and fuzzy mathematical model

In the evaluation of traditional college talents’ teaching ability, the importance of evaluation indicators lacks evaluation, and the evaluation results are relatively random. In order to improve the evaluation efficiency of university scientific research talents, this study combines BP neural network and fuzzy mathematical theory to build an evaluation model. Combining the talent training process and ability requirements of colleges and universities, a secondary index system is proposed, and the weight of the evaluation index is determined by combining data collection. This paper first normalizes the samples, determines the training and test samples, and then uses trial and error to determine the number of hidden layer neurons. Then use fuzzy mathematics theory to construct fuzzy similarity matrix to describe the fuzzy relationship between factor domain and judgement domain. Calculate membership to get comprehensive evaluation results. Finally, this paper uses statistical methods to draw the results into statistical charts and combines the simulation results to obtain performance comparison results. The feasibility of the model is verified by experimental research, and the model can be applied to practice, and can provide theoretical reference for subsequent related research.
An attention-based deep learning model for multiple pedestrian attributes recognition

The automatic characterization of pedestrians in surveillance footage is a tough challenge, particularly when the data is extremely diverse with cluttered backgrounds, and subjects are captured from varying distances, under multiple poses, with partial occlusion. Having observed that the state-of-the-art performance is still unsatisfactory, this paper provides a novel solution to the problem, with two-fold contributions: 1) considering the strong semantic correlation between the different full-body attributes, we propose a multi-task deep model that uses an element-wise multiplication layer to extract more comprehensive feature representations. In practice, this layer serves as a filter to remove irrelevant background features, and is particularly important to handle complex, cluttered data; and 2) we introduce a weighted-sum term to the loss function that not only relativizes the contribution of each task but also is crucial for performance improvement in multiple-attribute inference settings. Our experiments were performed on two well-known datasets (RAP and PETA) and point for the superiority of the proposed method with respect to the state-of-the-art. The code is available at https://github.com/Ehsan-Yaghoubi/MAN-PAR-.