Reviewerly/exHarmony huggingface dataset:
Teacher Training Effectiveness in Self-Regulation in Virtual Environments

Higher education in the 21st century faces the challenge of changing the way in which knowledge is conveyed and how teachers and students interact in the teaching-learning process. The current pandemic caused by SARS-CoV-2 has hastened the need to face up to this challenge and has furthered the need to approach the issue from the perspective of digitalisation. To achieve this, it is necessary to design training programmes geared towards teaching staff and which address both the use of technology and instructional design aimed at promoting the development of self-regulated learning (SRL) and automatic feedback systems. In this study, work was carried out with 23 teachers (8 inexperienced and 15 experienced teachers) in a training programme conducted through Moodle. The aims were: (1) to test whether there were any significant differences between the behaviour patterns of new teachers compared to experienced teachers, (2) to determine whether clusters of behaviour patterns corresponded to the type of teacher and (3) to ascertain whether the level of teacher satisfaction with the training activity in digital teaching will depend on the type of teacher. A quantitative as well as a qualitative design was applied. Differences were found in the behaviour patterns in the training activities for the development of rubrics and use of learning analytics systems in virtual learning environments. It was also found that the type of teacher did not correspond exactly to the behaviour cluster in the learning platform. In addition, no significant differences were found in the level of satisfaction between the two kinds of teacher. The main contribution this study makes is to provide a detailed description of the training stage as well as the materials required for its repetition. Further analytical studies are required on teacher perception of training programmes in digital teaching in order to provide personalised training proposals that lead to an effective use of teaching in digital environments.
Opportunistic Transmission for Video Streaming over Wild Internet

The video streaming system employs adaptive bitrate (ABR) algorithms to optimize a user’s quality of experience. However, it is hard for ABR algorithms to choose the right bitrate consistently under highly dynamic bandwidth fluctuations in wild Internet. In this article, we propose a building block on the client side named Opportunistic Chunk Replacement Mechanism (OCRM) to help existing ABR algorithms make full use of the available bandwidth to improve the network utilization and viewing experience of users. Specifically, the servers take advantages of the spare bandwidth to opportunistically transmit high-quality chunks (called opportunistic chunks ) with low priority to the client, without incurring any extra delay. Then, the client player replaces the low-quality chunks with the opportunistic ones that have high quality. We compare OCRM with state-of-the-art ABR algorithms by using trace-driven experiments spanning a wide variety of quality of experience metrics and network conditions. The test results show that OCRM effectively achieves high network utilization and improves the user’s viewing experience by up to 35%.
Deep learning-based pose prediction for visual servoing of robotic manipulators using image similarity

The accuracy of pose prediction is crucial in learning-based visual servoing. Motivated by the fact that the more similar observed images are, the closer the camera poses, we propose a joint training strategy with a two-part loss function in this paper. One part is the least absolute deviation (L1) loss function, which is defined by the error between the predicted pose and the pose label. The other is the mean similarity image measurement loss function (MSIM), which is related to the image’s brightness, contrast, and structure similarity and is determined by the differences between the input image and the image corresponding to the predicted pose. Meanwhile, a data generator based on spherical projection is created to generate data uniformly for training a CNN model, and position-based visual servoing (PBVS) is designed for a robotic manipulator after pose prediction. A numeric simulation and real experiments are conducted in a virtual environment and with a UR3 manipulator. The results show that the proposed method can realize more accurate pose prediction and is robust to occlusion disturbance, and PBVS is achieved by using monocular images.
Developing Persona Analytics Towards Persona Science

Much of the reported work on personas suffers from the lack of empirical evidence. To address this issue, we introduce Persona Analytics (PA), a system that tracks how users interact with data-driven personas. PA captures users’ mouse and gaze behavior to measure users’ interaction with algorithmically generated personas and use of system features for an interactive persona system. Measuring these activities grants an understanding of the behaviors of a persona user, required for quantitative measurement of persona use to obtain scientifically valid evidence. Conducting a study with 144 participants, we demonstrate how PA can be deployed for remote user studies during exceptional times when physical user studies are difficult, if not impossible.
Reflecting on an Integrated Approach to Understanding Pathways for Socially Inclusive Agricultural Intensification

Large investments in Research-for-Development (R4D) have occurred around agricultural intensification to improve social and economic outcomes for poor small and marginal farmer households. Mixed evidence for sustained and socially just impacts from these investments reflects that projects aimed at achieving social change are inherently complex and the pathways from intervention to impact are deeply uncertain. R4D projects are increasingly drawing on integrative approaches to explore solution spaces for these complex social-agroecological problems; albeit integration science is not yet mainstream in R4D. We reflect on one approach (integrated assessment, IA) in a project on socially inclusive agricultural intensification, namely on how the project team embraced integration tools and research approaches, translated knowledge and learnings of the community and broader research team into systems frameworks, and ensured that social inclusion and justice concepts were central to the IA tools and process. IA was valued for its participatory focus and for lessening ‘silo thinking’ in the design of community interventions and research activities. We argue that complexity-aware integration approaches like IA are needed to support the design, monitoring and evaluation of R4D projects to enhance outcomes and achieve sustained impact.
Applying a socioecological framework to chronic disease management: implications for social informatics interventions in safety-net healthcare settings

Vulnerable populations face numerous barriers in managing chronic disease(s). As healthcare systems work toward integrating social risk factors into electronic health records and healthcare delivery, we need better understanding of the interrelated nature of social needs within patients' everyday lives to inform effective informatics interventions to advance health equity.We conducted in-depth interviews, participant-led neighborhood tours, and clinic visit observations involving 10 patients with diabetes in underserved San Francisco neighborhoods and 10 community leaders serving those neighborhoods. We coded health barriers and facilitators using a socioecological framework. We also linked these qualitative data with early persona development, focusing on patients' experiences in these communities and within the healthcare system, as a starting place for our future informatics design.We identified social risk and protective factors across almost every socioecological domain and level-from physical disability to household context to neighborhood environment. We then detailed the complex interplay across domains and levels within two critical aspects of patients' lives: housing and food. Finally, from these data we generated 3 personas that capture the intersectional nature of these determinants.Drawing from different disciplines, our study provides a socioecological approach to understanding health promotion for patients with chronic disease in a safety-net healthcare system, using multiple methodologies. Future digital health research should center the lived experiences of marginalized patients to effectively design and implement informatics solutions for this audience.
Collaborative multi-agents in dynamic industrial internet of things using deep reinforcement learning

Sustainable cities are envisioned to have economic and industrial steps toward reducing pollution. Many real-world applications such as autonomous vehicles, transportation, traffic signals, and industrial automation can now be trained using deep reinforcement learning (DRL) techniques. These applications are designed to take benefit of DRL in order to improve the monitoring as well as measurements in industrial internet of things for automation identification system. The complexity of these environments means that it is more appropriate to use multi-agent systems rather than a single-agent. However, in non-stationary environments multi-agent systems can suffer from increased number of observations, limiting the scalability of algorithms. This study proposes a model to tackle the problem of scalability in DRL algorithms in transportation domain. A partition-based approach is used in the proposed model to reduce the complexity of the environment. This partition-based approach helps agents to stay in their working area. This reduces the complexity of the learning environment and the number of observations for each agent. The proposed model uses generative adversarial imitation learning and behavior cloning, combined with a proximal policy optimization algorithm, for training multiple agents in a dynamic environment. We present a comparison of PPO, soft actor-critic, and our model in reward gathering. Our simulation results show that our model outperforms SAC and PPO in cumulative reward gathering and dramatically improved training multiple agents.
Data Security Using Multiple Image Steganography and Hybrid Data Encryption Techniques

There are advances in data stealth and forgery with the rise of technology and advances in data transmission. This arises the need for better and developed methods for data transmission. Data Transmission is an essential task in the current era, and equally important is the secure and safe information of that data. In the paper, batch steganography is used to secure data transmission from one end to the other. Often a password can be used for encoding the payload into the cover image. Here the data is encrypted using hashing and encryption techniques, SHA-256 and AES. The passwords used for encryption have been used after the logical operation XOR. Thus, the information has been encrypted twice using the XORed password for first and second input password for the next time. It increases the security of the data and makes the decryption almost impossible without knowing both the passwords and the encryption method. The encoded data is then embedded within the pixels of the original image using the LSB method. This prevents data theft and any possibilities of Man-in-the-Middle attacks since the time required for decrypting the data is drastically high without the knowledge of the inputs and the techniques used.
Highlight Removal from A Single Grayscale Image Using Attentive GAN

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
A scalable association rule learning and recommendation algorithm for large-scale microarray datasets

Abstract Association rule learning algorithms have been applied to microarray datasets to find association rules among genes. With the development of microarray technology, larger datasets have been generated recently that challenge the current association rule learning algorithms. Specifically, the large number of items per transaction significantly increases the running time and memory consumption of such tasks. In this paper, we propose the Scalable Association Rule Learning (SARL) heuristic that efficiently learns gene-disease association rules and gene–gene association rules from large-scale microarray datasets. The rules are ranked based on their importance. Our experiments show the SARL algorithm outperforms the Apriori algorithm by one to three orders of magnitude.
Towards Textual Out-of-Domain Detection Without In-Domain Labels

In many real-world settings, machine learning models need to identify user inputs that are out-of-domain (OOD) so as to avoid performing wrong actions. This work focuses on a challenging case of OOD detection, where no labels for in-domain data are accessible (e.g., no intent labels for the intent classification task). To this end, we first evaluate different language model based approaches that predict likelihood for a sequence of tokens. Furthermore, we propose a novel representation learning based method by combining unsupervised clustering and contrastive learning so that better data representations for OOD detection can be learned. Through extensive experiments, we demonstrate that this method can significantly outperform likelihood-based methods and can be even competitive to the state-of-the-art supervised approaches with label information.
3Es for AI: Economics, Explanation, Epistemology

This article locates its roots/routes in multiple disciplinary formations and it seeks to advance critical thinking about an aspect of our contemporary socio-technical challenges by bracketing three knowledge formations-artificial intelligence (AI), economics, and epistemology-that have not often been considered together. In doing so, it responds to the growing calls for the necessity of further transdisciplinary engagements that have emanated from work in AI and also from other disciplines. The structure of the argument here is as follows. First, I begin by demonstrating how and why explanation is a problem in AI ("XAI problem") and what directions are being taken by recent research that draws upon social sciences to address this, noting how there is a conspicuous lack of reference in this literature to economics. Second, I identify and analyze a problem of explanation that has long plagued economics too as a discipline. I show how only a few economists have ever attempted to grapple with this problem and provide their perspectives. Third, I provide an original genealogy of explanation in economics, demonstrating the changing nature of what was meant by an explanation. These systematic changes in consensual understanding of what occurs when something is said to have been "explained", have reflected the methodological compromises that were rendered necessary to serve different epistemological tensions over time. Lastly, I identify the various relevant historical and conceptual overlaps between economics and AI. I conclude by suggesting that we must pay greater attention to the epistemologies underpinning socio-technical knowledges about the human. The problem of explanation in AI, like the problem of explanation in economics, is perhaps not only, or really, a problem of satisfactory explanation provision alone, but interwoven with questions of competing epistemological and ethical choices and related to the ways in which we choose sociotechnical arrangements and offer consent to be governed by them.
Confidentiality attacks against encrypted control systems

Encrypted control systems were introduced to enhance the security of cyber-physical systems, which outsource control action computations to a third-party platform. To protect the confidentiality of the transmitted data, homomorphic encryption schemes are particularly appealing for their capability of allowing computations on encrypted data. By considering the popular ElGamal and Paillier encryption schemes, this paper shows that encrypted control systems are vulnerable to attackers leveraging the inherently small domains of the plaintext data in control systems and the randomisation process required to make the utilised ciphers semantically secure. Finally, we present some countermeasures to defend against these attacks.
Learning Semantics-Consistent Stripes With Self-Refinement for Person Re-Identification

Aligning human parts automatically is one of the most challenging problems for person re-identification (re-ID). Recently, the stripe-based methods, which equally partition the person images into the fixed stripes for aligned representation learning, have achieved great success. However, the stripes with fixed height and position cannot well handle the misalignment problems caused by inaccurate detection and occlusion and may introduce much background noise. In this article, we aim at learning adaptive stripes with foreground refinement to achieve pixel-level part alignment by only using person identity labels for person re-ID and make two contributions. 1) A semantics-consistent stripe learning method (SCS). Given an image, SCS partitions it into adaptive horizontal stripes and each stripe is corresponding to a specific semantic part. Specifically, SCS iterates between two processes: i) clustering the rows to human parts or background to generate the pseudo-part labels of rows and ii) learning a row classifier to partition a person image, which is supervised by the latest pseudo-labels. This iterative scheme guarantees the accuracy of the learned image partition. 2) A self-refinement method (SCS+) to remove the background noise in stripes. We employ the above row classifier to generate the probabilities of pixels belonging to human parts (foreground) or background, which is called the class activation map (CAM). Only the most confident areas from the CAM are assigned with foreground/background labels to guide the human part refinement. Finally, by intersecting the semantics-consistent stripes with the foreground areas, SCS+ locates the human parts at pixel-level, obtaining a more robust part-aligned representation. Extensive experiments validate that SCS+ sets the new state-of-the-art performance on three widely used datasets including Market-1501, DukeMTMC-reID, and CUHK03-NP.
Integration of Blockchain Technology and Prioritization of Deployment Barriers in the Blood Supply Chain

Background: This research aims to prioritize the blood supply hub for coping barriers of implementation blockchain (BC) in supply chain management (SCM). Nowadays, blood supply is a crucial matter that plays an essential role in people’s lives. Hence, tracing the supply of blood is very substantial. One of the ways for the SCM of blood supply is a blockchain system. It shows how all traces of the SCM stream can flow from raw material to receiving blood into end users. However, there are many barriers to the implementation of blockchain. None of the companies can design improvement projects to resolve these barriers because of a lack of rare resources, such as human resources (HR), budget, information, etc. Methods: Barriers are first extracted from previous studies and interviews with experts in this study. Then, these barriers are customized for this case study by the Delphi method. Then, these blood supply hubs are ranked by measurement alternatives and ranking according to the compromise solution (MARCOS) method. Since this method needs primary weight, the best–worst method (BWM) is applied to obtain this weight. Result: Results have pointed out that business owners’ unwillingness was the highest priority among the nine barriers. Conclusions: Additionally, implementing blockchain for SCM of blood supply requires paying more attention to business owners’ unwillingness barriers and resolving them. Furthermore, hub 4 is faced with many problems in tackling barriers to implementing SCM blockchain.
Two decades of automatic amortized resource analysis

Abstract This article gives an overview of automatic amortized resource analysis (AARA), a technique for inferring symbolic resource bounds for programs at compile time. AARA has been introduced by Hofmann and Jost in 2003 as a type system for deriving linear worst-case bounds on the heap-space consumption of first-order functional programs with eager evaluation strategy. Since then AARA has been the subject of dozens of research articles, which extended the analysis to different resource metrics, other evaluation strategies, non-linear bounds, and additional language features. All these works preserved the defining characteristics of the original paper: local inference rules, which reduce bound inference to numeric (usually linear) optimization; a soundness proof with respect to an operational cost semantics; and the support of amortized analysis with the potential method.
A Technique for Human Upper Body Parts Movement Tracking

In this digital world, technology mainly revolves around multimedia content, which is massive. The existing techniques are inadequate to capture the movement of the human upper body parts in real-time. Therefore, a system is urgently required to track the human upper body parts movements from image and video content. The Viola–Jones algorithm based on a novel technique is proposed to detect upper body parts like the eyes and face in this work. Moreover, a face tracking algorithm is employed to track the movement of the upper body parts in real-time. The proposed model improves 13%, 18%, and 10% accuracy for face, eye, and body on Grimace dataset, Face94 Dataset, and Milborrow University of Cape Town (MUCT) dataset, respectively. Furthermore, it is also tested directly on the MATLAB software on a new image or Cam video. It takes less than 25 milliseconds per frame; hence, applicable for real-time applications.
Relative Entropy of Correct Proximal Policy Optimization Algorithms with Modified Penalty Factor in Complex Environment

In the field of reinforcement learning, we propose a Correct Proximal Policy Optimization (CPPO) algorithm based on the modified penalty factor β and relative entropy in order to solve the robustness and stationarity of traditional algorithms. Firstly, In the process of reinforcement learning, this paper establishes a strategy evaluation mechanism through the policy distribution function. Secondly, the state space function is quantified by introducing entropy, whereby the approximation policy is used to approximate the real policy distribution, and the kernel function estimation and calculation of relative entropy is used to fit the reward function based on complex problem. Finally, through the comparative analysis on the classic test cases, we demonstrated that our proposed algorithm is effective, has a faster convergence speed and better performance than the traditional PPO algorithm, and the measure of the relative entropy can show the differences. In addition, it can more efficiently use the information of complex environment to learn policies. At the same time, not only can our paper explain the rationality of the policy distribution theory, the proposed framework can also balance between iteration steps, computational complexity and convergence speed, and we also introduced an effective measure of performance using the relative entropy concept.