Reviewerly/exHarmony huggingface dataset:
Public Sentiment on User Reviews about Application in Handling COVID-19 using Naive Bayes Method and Support Vector Machine

Another crisis has emerged in the shape of widespread anxiety and panic, fueled by imprecise and frequently incorrect information, during the Coronavirus pandemic. As a result, there is a critical need to address and better comprehend COVID-19&#x0027;s informational crisis, as well as evaluate public mood, in order to adopt effective communications and policy decisions. This study aims to classify the results of PIKOBAR&#x0027;s review sentiments, PIKOBAR is a Center for Information and Coordination of Diseases and Disasters in West Java. A total of 371 reviews were taken, each of which was labeled positive, negative or neutral. The data first goes through a pre-processing process before conducting a sentiment review analysis using the Naive Bayes Classifier and Support Vector Machine processes. The results from 80&#x0025; testing data and 20&#x0025; training data obtained the Naive Bayes accuracy rate of 75.67&#x0025; and the Support Vector Machine was 71.62&#x0025;. Furthermore, in the text association process, information was obtained that the PIKOBAR application users mostly talked about words &#x201C;Jabar&#x201D; for positive class and the word &#x201C;aplikasi&#x201D; for negative class and the word &#x201C;data&#x201D; for neutral class.
PerKGQA: Question Answering over Personalized Knowledge Graphs

Previous studies on question answering over knowledge graphs have typically operated over a single knowledge graph (KG).This KG is assumed to be known a priori and is leveraged similarly for all users' queries during inference.However, such an assumption is not applicable to real-world settings, such as healthcare, where one needs to handle queries of new users over unseen KGs during inference.Furthermore, privacy concerns and high computational costs render it infeasible to query the single KG that has information about all users while answering a specific user's query.The above concerns motivate our question answering setting over personalized knowledge graphs (PERKGQA) where each user has restricted access to their KG.We observe that current state-of-the-art KGQA methods that require learning prior node representations fare poorly.We propose two complementary approaches, PATHCBR and PATHRGCN for PERKGQA.The former is a simple non-parametric technique that employs case-based reasoning, while the latter is a parametric approach using graph neural networks.Our proposed methods circumvent learning prior representations, can generalize to unseen KGs, and outperform strong baselines on an academic and an internal dataset by 6.5% and 10.5%.
OsmoticGate: Adaptive Edge-Based Real-Time Video Analytics for the Internet of Things

Edge computing has gained momentum in recent years, and can provide more immediate analysis of streaming video data. However, the edge devices often lack the computing capabilities (processing power, memory) to guarantee reasonable performance (e.g., accuracy, latency, throughput) for complex video analytics tasks. To alleviate this critical problem, the prevalent trend is to offload some video analytics tasks from the edge devices to the cloud. However, existing offloading approaches fail to consider the dynamic nature of the video analytical tasks (e.g., varying encoding format for different video content) and are unable to adapt system dynamics (e.g., varying workload between the edge and the cloud). To overcome the limitation of existing approaches, we develop an edge-cloud offloading performance model based on the concept of hierarchical queues. The resource constraints (e.g., computing capacity and network bandwidth) of each edge nodes and dynamic edge-cloud network conditions are used to parameterize the performance model. Since finding optimal solutions for the performance model is NP-hard, we develop a two-stage gradient-based algorithm and compare it with some state-of-the-art (SOTA) solutions (e.g., FastVA, DeepDecision, Hill Climbing). Experiments have shown our performance model's advantages and the stability of the proposed offloading approach given different systems (edge-cloud) and video analytics application dynamics.
A Hybrid Parallelization Approach for Distributed and Scalable Deep Learning

Recently, Deep Neural Networks (DNNs) have recorded significant success in handling medical and other complex classification tasks. However, as the sizes of DNN models and the available datasets increase, the training process becomes more complex and computationally intensive, usually taking longer to complete. In this work, we have proposed a generic full end-to-end hybrid parallelization approach combining model and data parallelism for efficiently distributed and scalable training of DNN models. We have also proposed a Genetic Algorithm Based Heuristic Resources Allocation (GABRA) mechanism for optimal distribution of partitions on the available GPUs for computing performance optimization. We have applied our proposed approach to a real use case based on 3D Residual Attention Deep Neural Network (3D-ResAttNet) for efficient Alzheimer Disease (AD) diagnosis on multiple GPUs and compared with the existing state-of-the-art parallel methods. The experimental evaluation shows that our proposed approach is 20% averagely better than existing parallel methods in terms of training time and achieves almost linear speedup with little or no differences in accuracy performance when compared with the existing non-parallel DNN models.
SimStu-Transformer: A Transformer-Based Approach to Simulating Student Behaviour

Lacking behavioural data between students and an Intelligent Tutoring System (ITS) has been an obstacle for improving its personalisation capability. One feasible solution is to train "sim students", who simulate real students' behaviour in the ITS. We can then use their generated behavioural data to train the ITS to offer real students personalised learning strategies and trajectories. In this paper, we thus propose SimStu-Transformer, developed based on the Decision Transformer algorithm, to generate learning behavioural data.
Linguistic Frameworks Go Toe-to-Toe at Neuro-Symbolic Language Modeling

We examine the extent to which, in principle, different syntactic and semantic graph representations can complement and improve neural language modeling.Specifically, by conditioning on a subgraph encapsulating the locally relevant sentence history, can a model make better next-word predictions than a pretrained sequential language model alone?With an ensemble setup consisting of GPT-2 and ground-truth graphs from one of 7 different formalisms, we find that the graph information indeed improves perplexity and other metrics.Moreover, this architecture provides a new way to compare different frameworks of linguistic representation.In our oracle graph setup, training and evaluating on English WSJ, semantic constituency structures prove most useful to language modeling performance-outpacing syntactic constituency structures as well as syntactic and semantic dependency structures.
FabKG: A Knowledge graph of Manufacturing Science domain utilizing structured and unconventional unstructured knowledge source

As the demands for large-scale information processing have grown, knowledge graph-based approaches have gained prominence for representing general and domain knowledge. The development of such general representations is essential, particularly in domains such as manufacturing which intelligent processes and adaptive education can enhance. Despite the continuous accumulation of text in these domains, the lack of structured data has created information extraction and knowledge transfer barriers. In this paper, we report on work towards developing robust knowledge graphs based upon entity and relation data for both commercial and educational uses. To create the FabKG (Manufacturing knowledge graph), we have utilized textbook index words, research paper keywords, FabNER (manufacturing NER), to extract a sub knowledge base contained within Wikidata. Moreover, we propose a novel crowdsourcing method for KG creation by leveraging student notes, which contain invaluable information but are not captured as meaningful information, excluding their use in personal preparation for learning and written exams. We have created a knowledge graph containing 65000+ triples using all data sources. We have also shown the use case of domain-specific question answering and expression/formula-based question answering for educational purposes.
eyeSay: Brain Visual Dynamics Decoding With Deep Learning &amp; Edge Computing

Brain visual dynamics encode rich functional and biological patterns of the neural system, and if decoded, are of great promise for many applications such as intention understanding, cognitive load quantization and neural disorder measurement. We here focus on the understanding of the brain visual dynamics for the Amyotrophic lateral sclerosis (ALS) population, and propose a novel system that allows these so- called 'lock-in' patients to 'speak' with their brain visual movements. More specifically, we propose an intelligent system to decode the eye bio-potential signal, Electrooculogram (EOG), thereby understanding the patients' intention. We first propose to leverage a deep learning framework for automatic feature learning and classification of the brain visual dynamics, aiming to translate the EOG to meaningful words. We afterwards design and develop an edge computing platform on the smart phone, which can execute the deep learning algorithm, visualize the brain visual dynamics, and demonstrate the edge inference results, all in real-time. Evaluated on 4,500 trials of brain visual movements performed by multiple users, our novel system has demonstrated a high eye-word recognition rate up to 90.47%. The system is demonstrated to be intelligent, effective and convenient for decoding brain visual dynamics for ALS patients. This research thus is expected to greatly advance the decoding and understanding of brain visual dynamics, by leveraging machine learning and edge computing innovations.
A Study on Small-Scale Ship Detection Based on Attention Mechanism

In ship detection based on optical images, the system typically needs to handle small-scale targets in complex environments owing to special application scenarios, and small-scale targets are easily ignored after a multi-layer convolution is applied in deep learning. A small-ship detection method based on an attention mechanism is therefore proposed in this study. The local attention module acts on the bottom feature prediction layer to highlight the key features and improve the detection ability of small target objects. Meanwhile, a high-level feature prediction layer is combined to classify, detect, and improve the recognition accuracy of the model. In this study, training was conducted on the SeaShips dataset. Because the SeaShips dataset is of a single type and consists of ships of roughly the same size, we changed the size of the default box, which not only improves the detection speed, it also significantly improves the detection accuracy compared with a conventional SSD algorithm.
Insider Threats and Individual Differences: Intention and Unintentional Motivations

The present study extends a cybersecurity questionnaire (CSEC) by including items that differentiate cyber hygiene behavior, self-disclosure vulnerability, intrusion vulnerability, and persuasion vulnerability. Using individual difference measures that are related to performance in experimental tasks, the present study provides evidence that individuals high in emotionality (i.e., fear and anxiety) and low in moral motivations (i.e., fairness and morality) are more likely to report engaging in behaviors related to unintentional insider threats. Consequently, this study provides further evidence that CSEC+ can be used to assess critical cybersecurity practices from the perspective of insiders.
How Item and Learner Characteristics Matter in Intelligent Tutoring Systems Data

AutoTutor-ARC (adult reading comprehension) is an intelligent tutoring system that uses conversational agents to help adult learners improve their comprehension skills. However, in such a system, not all lessons and items optimally serve the same purposes. In this paper, we describe a method for classifying items that are instructive, evaluative, motivational, versus potentially flawed based on analyses of items' psychometric properties. Further, there is no a priori way of determining which lessons are optimal given the learner's reading profile needs. To address this, we evaluate how assessing learner component reading skills can inform various aspects of learner needs on AutoTutor lessons. More specifically, we compare learners who were classified as proficient, underengaged, conscientious, versus struggling readers based on their experiences with AutoTutor. Together, these analyses suggest the utility of integrating assessments with instruction: efficient, adaptive learning at the lesson level, more efficient and valid post-testing, and consequently, recommendations for more targeted, adaptive pathways through the instructional program/system.
Single-shot optical surface profiling using extended depth of field 3D microscopy

The measurement of three-dimensional samples at high speed is essential for many applications, either due to the requirement for measuring samples that change fast over time, or due to the requirement of reducing the scanning time, and therefore inspection cost, in industrial environments. Conventionally, the measurement of surface topographies at high resolution typically requires an axial scanning of the sample. We report the implementation of a technique able to reconstruct surface topographies at high resolution, only from the acquisition of a single camera shot, dropping the need to perform an axial scan. A system prototype is reported and assessed as an ultra-fast optical surface profiler. We propose robust calibration and operation methods and algorithms to reconstruct surface topographies of optically-rough samples, and compare the experimental results with a reference confocal optical profiler.
<i>MobEdge</i>: Mobile blockchain‐based privacy‐edge scheme for healthcare Internet of Things‐based ecosystems

Summary Centralized healthcare Internet of Things (HIoT)‐based ecosystems are challenged by high latency, single‐point failures, and privacy‐based attacks due to data exchange over open channels. To address the challenges, the shift has progressed toward decentralized HIoT setups that infuse computation closer to a patient node via edge services. As HIoT data are critical and sensitive, trust among stakeholders is a prime concern. To address the challenges, researchers integrated blockchain (BCH) into edge‐based HIoT models. However, the integration of lightweight BCH is required with an edge for proper interplay and leverage effective, scalable, and energy‐efficient computational processes for constrained HIoT applications. Owing to the existing gap, this article proposes a scheme MobEdge , that fuses lightweight BCH, and edge computing to secure HIoT. A local BCH client model is set up that forwards data to edge sensor gateways. The shared data are secured through an access tree control lock scheme that preserves the privacy of health records. For security and signing purposes, we have considered signcryption, and the validated records meta‐information are stored in an on‐chain structure. The scheme is compared on two grounds, security and simulation grounds. On the security front, we do cost evaluation and present a formal analysis model using the Automated Validation of Internet Security Protocols and Applications tool. An edge‐based BCH setup use‐case is presented, and parameters like mining cost, storage cost, edge servicing latency, energy consumption, BCH network usage, and transaction signing costs are considered. In the simulation, the mining cost is 0.6675 USD, and improvement of storage costs are improved by 18.34%, edge‐servicing latency is 384 ms, and signcryption improves the signing cost by 36.78% against similar schemes, that indicates the scheme viability in HIoT setups.
Comparison of response behavior in karate kumite between real world and virtual reality

Abstract Virtual reality is increasingly applied to support physical training and improve athletes’ performance in sports. Nevertheless, there is a research deficit in that, especially in martial arts, it has not yet been shown to what extent the response behavior of athletes in virtual reality is the same as in the real world. If this can be confirmed, a transfer of VR-adapted skills to RW can be expected and sports training in a virtual environment can be applied to improve sports performance. Since the response behavior is essential for many sports, this study compares it in karate kumite to the competition-important attack (Kizami-Zuki) of a real and a virtual opponent. Experienced karate athletes wore a head-mounted display and were asked to respond quickly and efficiently to 22 karate attacks, of which eight were Kizami Zuki’s attacks. Using a video-based movement assessment, karate experts quantified the response behavior with the parameters ‘time for response’, ‘response quality’ and ‘kind of response’. Results show no significant differences in ‘time for response’ and ‘kind of response’ between both conditions (virtual reality vs. real world). Only the ‘response quality’ was rated better in real world than in virtual reality. It is concluded that the ‘time of response’ and ‘kind of response’ for karate kumite athletes in virtual reality are similar to that in the real world.
Explainable and secure artificial intelligence: taxonomy, cases of study, learned lessons, challenges and future directions

Explainable artificial intelligence (XAI) is an evolving discipline that mainly emphasises unboxing in these Black-Boxes. This study provides in-depth review of XAI literature together with a new taxonomy of categorising XAI methods. Moreover, the security of Deep learning (DL) against different attacks turned to be a critical concern for both industry and academia. This study presents a taxonomic overview of the attacks on DL solutions and methods for securing DL against these attacks. Experiments are performed to evaluate and analyse the cutting-edge methods for explaining and securing DL models on real-world case studies of Twitter sentimental analysis using state-of-the-art DL models.
Self-Admitted Technical Debt and comments’ polarity: an empirical study

Abstract Self-Admitted Technical Debt (SATD) consists of annotations—typically, but not only, source code comments—pointing out incomplete features, maintainability problems, or, in general, portions of a program not-ready yet. The way a SATD comment is written, and specifically its polarity, may be a proxy indicator of the severity of the problem and, to some extent, of the priority with which it should be addressed. In this paper, we study the relationship between different types of SATD comments in source code and their polarity, to understand in which circumstances (and why) developers use negative or rather neutral comments to highlight an SATD. To address this goal, we combine a manual analysis of 1038 SATD comments from a curated dataset with a survey involving 46 professional developers. First of all, we categorize SATD content into its types. Then, we study the extent to which developers express negative sentiment in different types of SATD as a proxy for priority, and whether they believe this can be considered as an acceptable practice. Finally, we look at whether such annotations contain additional details such as bug references and developers’ names/initials. Results of the study indicate that SATD comments are mainly used for annotating poor implementation choices ( $\simeq $ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>≃</mml:mo></mml:math> 41%) and partially implemented functionality ( $\simeq $ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo>≃</mml:mo></mml:math> 22%). The latter may depend from “waiting” for other features being implemented, and this makes SATD comments more negatives than in other cases. Around 30% of the survey respondents agree on using/interpreting negative sentiment as a proxy for priority, while 50% of them indicate that it would be better to discuss SATD on issue trackers and not in the source code. However, while our study indicates that open-source developers use links to external systems, such as bug identifiers, to annotate high-priority SATD, better tool support is required for SATD management.
A Fusion Biopsy Framework for Prostate Cancer Based on Deformable Superellipses and nnU-Net

In prostate cancer, fusion biopsy, which couples magnetic resonance imaging (MRI) with transrectal ultrasound (TRUS), poses the basis for targeted biopsy by allowing the comparison of information coming from both imaging modalities at the same time. Compared with the standard clinical procedure, it provides a less invasive option for the patients and increases the likelihood of sampling cancerous tissue regions for the subsequent pathology analyses. As a prerequisite to image fusion, segmentation must be achieved from both MRI and TRUS domains. The automatic contour delineation of the prostate gland from TRUS images is a challenging task due to several factors including unclear boundaries, speckle noise, and the variety of prostate anatomical shapes. Automatic methodologies, such as those based on deep learning, require a huge quantity of training data to achieve satisfactory results. In this paper, the authors propose a novel optimization formulation to find the best superellipse, a deformable model that can accurately represent the prostate shape. The advantage of the proposed approach is that it does not require extensive annotations, and can be used independently of the specific transducer employed during prostate biopsies. Moreover, in order to show the clinical applicability of the method, this study also presents a module for the automatic segmentation of the prostate gland from MRI, exploiting the nnU-Net framework. Lastly, segmented contours from both imaging domains are fused with a customized registration algorithm in order to create a tool that can help the physician to perform a targeted prostate biopsy by interacting with the graphical user interface.