Reviewerly/exHarmony huggingface dataset:
Effect of Person-specific Biometrics in Improving Generic Stress Predictive Models

Because stress is subjective and is expressed differently from one person to another, generic stress prediction models (i.e., models that predict the stress of any person) perform crudely.Only person-specific models (i.e., ones that predict the stress of a preordained person) yield reliable predictions, but they are not adaptable and are costly to deploy in realworld environments.For illustration, in an office environment, a stress monitoring system that uses person-specific models would require the collection of new data and the training of a new model for every employee.Moreover, once deployed, the models would deteriorate and need expensive periodic upgrades because stress is dynamic and depends on unforeseeable factors.We propose a simple, yet practical and cost-effective calibration technique that derives an accurate and personalized stress prediction model from physiological samples collected from a large population.We validate our approach on two stress datasets.The results show that our technique performs much better than a generic model.For instance, a generic model achieved only 42.5 ± 19.9% accuracy.However, with only 100 calibration samples, we raised its accuracy to 95.2 ± 0.5%.We also propose a blueprint for a stress monitoring system based on our strategy, and we debate its merits and limitations.Finally, we made our source code and the relevant datasets public to allow other researchers to replicate our findings.
Modified Bidirectional Encoder Representations From Transformers Extractive Summarization Model for Hospital Information Systems Based on Character-Level Tokens (AlphaBERT): Development and Performance Evaluation

Background Doctors must care for many patients simultaneously, and it is time-consuming to find and examine all patients’ medical histories. Discharge diagnoses provide hospital staff with sufficient information to enable handling multiple patients; however, the excessive amount of words in the diagnostic sentences poses problems. Deep learning may be an effective solution to overcome this problem, but the use of such a heavy model may also add another obstacle to systems with limited computing resources. Objective We aimed to build a diagnoses-extractive summarization model for hospital information systems and provide a service that can be operated even with limited computing resources. Methods We used a Bidirectional Encoder Representations from Transformers (BERT)-based structure with a two-stage training method based on 258,050 discharge diagnoses obtained from the National Taiwan University Hospital Integrated Medical Database, and the highlighted extractive summaries written by experienced doctors were labeled. The model size was reduced using a character-level token, the number of parameters was decreased from 108,523,714 to 963,496, and the model was pretrained using random mask characters in the discharge diagnoses and International Statistical Classification of Diseases and Related Health Problems sets. We then fine-tuned the model using summary labels and cleaned up the prediction results by averaging all probabilities for entire words to prevent character level–induced fragment words. Model performance was evaluated against existing models BERT, BioBERT, and Long Short-Term Memory (LSTM) using the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) L score, and a questionnaire website was built to collect feedback from more doctors for each summary proposal. Results The area under the receiver operating characteristic curve values of the summary proposals were 0.928, 0.941, 0.899, and 0.947 for BERT, BioBERT, LSTM, and the proposed model (AlphaBERT), respectively. The ROUGE-L scores were 0.697, 0.711, 0.648, and 0.693 for BERT, BioBERT, LSTM, and AlphaBERT, respectively. The mean (SD) critique scores from doctors were 2.232 (0.832), 2.134 (0.877), 2.207 (0.844), 1.927 (0.910), and 2.126 (0.874) for reference-by-doctor labels, BERT, BioBERT, LSTM, and AlphaBERT, respectively. Based on the paired t test, there was a statistically significant difference in LSTM compared to the reference (P&lt;.001), BERT (P=.001), BioBERT (P&lt;.001), and AlphaBERT (P=.002), but not in the other models. Conclusions Use of character-level tokens in a BERT model can greatly decrease the model size without significantly reducing performance for diagnoses summarization. A well-developed deep-learning model will enhance doctors’ abilities to manage patients and promote medical studies by providing the capability to use extensive unstructured free-text notes.
Towards Human-centric Model-driven Software Engineering

Many current software systems suffer from a lack of consideration of the human differences between end users.This includes age, gender, language, culture, emotions, personality, education, physical and mental challenges, and so on.We describe our work looking to consider these characteristics by incorporation of human centric-issues throughout the model-driven engineering process lifecycle.We propose the use of the co-creational "living lab" model to better collect human-centric issues in the software requirements.We focus on modelling these human-centric factors using domain-specific visual languages, themselves humancentric modelling artefacts.We describe work to incorporate these human-centric issues into model-driven engineering design models, and to support both code generation and run-time adaptation to different user human factors.We discuss continuous evaluation of such human-centric issues in the produced software and feedback of user reported defects to requirements and model refinement.
ClarQ: A large-scale and diverse dataset for Clarification Question Generation

Question answering and conversational systems are often baffled and need help clarifying certain ambiguities.However, limitations of existing datasets hinder the development of large-scale models capable of generating and utilising clarification questions.In order to overcome these limitations, we devise a novel bootstrapping framework (based on self-supervision) that assists in the creation of a diverse, large-scale dataset of clarification questions based on post-comment tuples extracted from stackexchange.The framework utilises a neural network based architecture for classifying clarification questions.It is a two-step method where the first aims to increase the precision of the classifier and second aims to increase its recall.We quantitatively demonstrate the utility of the newly created dataset by applying it to the downstream task of question-answering.The final dataset, ClarQ, consists of ∼2M examples distributed across 173 domains of stackexchange.We release this dataset 1 in order to foster research into the field of clarification question generation with the larger goal of enhancing dialog and question answering systems.
BERT-Based Chinese Relation Extraction for Public Security

The past few years have witnessed some public safety incidents occurring around the world. With the advent of the big data era, effectively extracting public security information from the internet has become of great significance. Up to hundreds of TBs of data are injected into the network every second, and thus it is impossible to process them manually. Natural Language Processing (NLP) is dedicated to the development of an intelligent system for effective text information mining. By analysing the text and quickly extracting the relationships between the relevant entities, NLP can establish the knowledge graph (KG) of public security, which lays the foundation for safety case analysis, information monitoring, and activity tracking and locating. One of the current pre-training relation extraction models is the Word2Vec model. The Word2vec model is single mapped, and it produces a static, single representation of the words in sentences. Then, the BERT model considers contextual information and provides more dynamic, richer vector representations of generated words. Therefore, in this paper, we propose a Bidirectional Encoder Representation from Transformers (BERT) based on the Chinese relation extraction algorithm for public security, which can effectively mine security information. The BERT model is obtained by training the Masked Language Model and predicting the next sentence task, which is based on the Transformer Encoder and the main model structure is the stacked Transformers. Extensive simulations are conducted to evaluate our proposed algorithm in comparison to some state-of-the-art schemes.
Laparoscopy with augmented reality adaptations

One of the most promising applications of Optical See-Through Augmented Reality is minimally laparoscopic surgery, which currently suffers from problems such as surgeon discomfort and fatigue caused by looking at a display positioned outside the surgeon’s visual field, made worse by the length of the procedure. This fatigue is especially felt on the surgeon’s neck, as it is strained from adopting unnatural postures in order to visualise the laparoscopic video feed. Throughout this paper, we will present work in Augmented Reality, as well as developments in surgery and Augmented Reality applied to both surgery in general and laparoscopy in particular to address these issues. We applied user and task analysis methods to learn about practices performed in the operating room by observing surgeons in their working environment in order to understand, in detail, how they performed their tasks and achieved their intended goals. Drawing on observations and analysis of video recordings of laparoscopic surgeries, we identified relevant constraints and design requirements. Besides proposals to approach the ergonomic issues, we present a design and implementation of a multimodal interface to enhance the laparoscopic procedure. Our method makes it more comfortable for surgeons by allowing them to keep the laparoscopic video in their viewing area regardless of neck posture. Also, our interface makes it possible to access patient imaging data without interrupting the operation. It also makes it possible to communicate with team members through a pointing reticle. We evaluated how surgeons perceived the implemented prototype, in terms of usefulness and usability, via a think-aloud protocol to conduct qualitative evaluation sessions which we describe in detail in this paper. In addition to checking the advantages of the prototype as compared to traditional laparoscopic settings, we also conducted a System Usability Scale questionnaire for measuring its usability, and a NASA Task Load Index questionnaire to rate perceived workload and to assess the prototype effectiveness. Our results show that surgeons consider that our prototype can improve surgeon-to-surgeon communication using head pose as a means of pointing. Also, surgeons believe that our approach can afford a more comfortable posture throughout the surgery and enhance hand-eye coordination, as physicians no longer need to twist their necks to look at screens placed outside the field of operation.
Service architecture of IoT terminal connection based on blockchain identity authentication system

The Internet of Things is a highly integrated and integrated application of information technology that can better meet the needs of people’s development. With the popularity of the Internet of Things, the number of Internet of Things devices has increased, and the communication between devices and devices has become more complicated. Because the Internet of Things has some common problems with the Internet, a more effective, secure and trusted IoT platform has become the focus of many research scholars. Blockchain, as a decentralized, non-tamperable and highly credible technology, fits well with the development trend of networking. Identity authentication is an important part of the service architecture and can effectively solve the security in the Internet of Things. Hidden dangers. Therefore, in view of the low efficiency and lack of security in the traditional IoT server architecture, this paper proposes a blockchain-based networked identity authentication system. Firstly, according to the characteristics of the blockchain, the key technologies of the blockchain are briefly explained. Then, the overall design scheme design, the user end system design and the equipment end system design of the identity authentication system. Finally, the experimental platform is built, and the functional testing of the blockchain platform, device and client is carried out respectively The throughput of the system is obtained by the block rate and the outbound time of the blockchain under the condition of the privacy blockchain. The memory usage and the corresponding traffic consumption during the process of creating the wallet and data upload are analyzed to verify the stability of the system and to verify the rationality of the IoT identity authentication system based on the blockchain.
A Unified Theory of SGD: Variance Reduction, Sampling, Quantization and Coordinate Descent

In this paper we introduce a unified analysis of a large family of variants of proximal stochastic gradient descent ({\tt SGD}) which so far have required different intuitions, convergence analyses, have different applications, and which have been developed separately in various communities. We show that our framework includes methods with and without the following tricks, and their combinations: variance reduction, importance sampling, mini-batch sampling, quantization, and coordinate sub-sampling. As a by-product, we obtain the first unified theory of {\tt SGD} and randomized coordinate descent ({\tt RCD}) methods, the first unified theory of variance reduced and non-variance-reduced {\tt SGD} methods, and the first unified theory of quantized and non-quantized methods. A key to our approach is a parametric assumption on the iterates and stochastic gradients. In a single theorem we establish a linear convergence result under this assumption and strong-quasi convexity of the loss function. Whenever we recover an existing method as a special case, our theorem gives the best known complexity result. Our approach can be used to motivate the development of new useful methods, and offers pre-proved convergence guarantees. To illustrate the strength of our approach, we develop five new variants of {\tt SGD}, and through numerical experiments demonstrate some of their properties.
Efficient Anomaly Detection with Generative Adversarial Network for Breast Ultrasound Imaging

We aimed to use generative adversarial network (GAN)-based anomaly detection to diagnose images of normal tissue, benign masses, or malignant masses on breast ultrasound. We retrospectively collected 531 normal breast ultrasound images from 69 patients. Data augmentation was performed and 6372 (531 × 12) images were available for training. Efficient GAN-based anomaly detection was used to construct a computational model to detect anomalous lesions in images and calculate abnormalities as an anomaly score. Images of 51 normal tissues, 48 benign masses, and 72 malignant masses were analyzed for the test data. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of this anomaly detection model were calculated. Malignant masses had significantly higher anomaly scores than benign masses (p &lt; 0.001), and benign masses had significantly higher scores than normal tissues (p &lt; 0.001). Our anomaly detection model had high sensitivities, specificities, and AUC values for distinguishing normal tissues from benign and malignant masses, with even greater values for distinguishing normal tissues from malignant masses. GAN-based anomaly detection shows high performance for the detection and diagnosis of anomalous lesions in breast ultrasound images.
Cyber-Attacks on the Oil &amp; Gas Sector: A Survey on Incident Assessment and Attack Patterns

During the past two decades, oil and gas operational and information technology systems have experienced constant digital growth, closely followed by an increasing number of cyber-attacks on the newly interconnected systems.Adversaries exploit vulnerable accessible device or malware attacks networked services, in an attempt to gain access to critical systems and machinery that are interconnected over networks.Given the importance of the oil and gas sector on the global economy and the diversity of critical systems often being controlled over remote locations, it is highly important to understand and mitigate such attacks.In this paper, we survey cyber-attacks on all three domains of the oil and gas sector (upstream, midstream, downstream) starting from the early 90s up until 2020.For each domain, we document and analyze verified attacks based on real-world reports and published demo attacks on systems.We map and catalogue the attack types used in each case, in order to understand common and subliminal attack paths against oil and gas critical operations.Our aim is threefold, i.e., first, to assess documented attacks using standardized impact assessment techniques and highlight potential consequences of cyber-attacks on this sector, second, to build a vulnerability taxonomy based on technical knowledge gathered by all such incidents and connect each vulnerability with oil and gas systems and respective attack paths, and third, to map the documented knowledge and taxonomies with MITRE's international knowledge base of Adversary Tactics and Techniques, so as to provide a general guide for analyzing and protecting against cyber-attacks at oil and gas infrastructures.
Stance Prediction and Claim Verification: An Arabic Perspective

This work explores the application of textual entailment in news claim verification and stance prediction using a new corpus in Arabic. The publicly available corpus comes in two perspectives: a version consisting of 4,547 true and false claims and a version consisting of 3,786 pairs (claim, evidence). We describe the methodology for creating the corpus and the annotation process. Using the introduced corpus, we also develop two machine learning baselines for two proposed tasks: claim verification and stance prediction. Our best model utilizes pretraining (BERT) and achieves 76.7 F1 on the stance prediction task and 64.3 F1 on the claim verification task. Our preliminary experiments shed some light on the limits of automatic claim verification that relies on claims text only. Results hint that while the linguistic features and world knowledge learned during pretraining are useful for stance prediction, such learned representations from pretraining are insufficient for verifying claims without access to context or evidence.
Teacher Competence and 21&lt;sup&gt;st&lt;/sup&gt; Century Skills in Transformation Schools 2025 (TS25)

The purpose of this study is to identify the relationship between teacher competence and 21 st century skills.Besides, the study also examines the influence of each dimension on teacher competence that contributes to predictive factors in 21 st century teachers' skills in teaching.A total of 242 secondary school teachers TS25 Cohort 1 North Zone of Peninsular Malaysia selected as study participants.The study also used quantitative approaches involving systematic random sampling.There are two instruments used in the study which are variables of teacher competence based on the Malaysian Teacher Standards by the Ministry of Education Malaysia (2009) and 21 st century skills based on the 21st Century Knowledge and Skills in Teacher Educator framework by Partnership For 21st Century Skills (2010).The analysis of the study shows that there is a robust and positive relationship between professional competence and 21 st century skills among teachers.The findings also predict that personal characteristics, pedagogy, professional, information and communication technology (ICT), as well as school management and development, are significant contributors to 21 st century skills.The findings also show that the dimensions of teacher competence have the potential to help further develop the potential of teachers in line with 21 st century learning (PAK-21) concepts.The 21 st century skills are the heartbeat for teachers to improve the quality of teaching in line with current educational developments.
A Probabilistic Self-Annealing Compute Fabric Based on 560 Hexagonally Coupled Ring Oscillators for Solving Combinatorial Optimization Problems

NP-hard combinatorial optimization problems (COPs) are very expensive to solve with traditional computers. COPs can be mapped to a coupled spin network where the ground state of the system is the solution. We propose a scalable truly coupled CMOS oscillator-based integrated system mimicking a spin network to solve COPs in hardware. Our simple latch-based coupling design finds solutions of max-cut problems with 85%-100% accuracy 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> -10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">6</sup> times faster than commercial software running on a CPU.
Citizen Seismology Without Seismologists? Lessons Learned From Mayotte Leading to Improved Collaboration

Starting the 10th May 2018, a series of earthquakes has hit Mayotte, a French island in the Indian Ocean. Facing a lack of seismic data, scientific information and communication from the authorities, the inhabitants took advantage of social media to develop, on their own, a citizen seismology group, composed of more than 10 000 people. Due to a particular cultural context, this was carried out mainly without the seismologist community. While some citizens did share seismological information (and eventually volcanology information when it was discovered that the earthquakes were caused by a new-born, undersea volcano), the lack of seismologists in the group also lead to the emergence of misinformation and even conspiracy theories. This mistrusting atmosphere had negative consequences for the way various seismological organisations were perceived, including LastQuake, a crowdsourced-based earthquake information app which allows eyewitnesses to share information about earthquakes they felt, combined with seismic data. However, due to the lack of seismic data for these earthquakes, some were not displayed in the app. This lack of information and understanding of how the system functioned led to additional mistrust towards this citizen seismology tool. This paper combines sociological observations with an empirical approach. First, a sociological analysis of this independent citizen science network enables an identification of the reasons for its creation and the pitfalls caused by the absence of collaboration with the scientific community. Then, an empirical case study of the LastQuake system exposes how it has been improved to offer information, while admittedly more incomplete, is nevertheless closer to citizens’ needs. It concludes that citizen seismology requires a stronger collaboration between citizens’ and scientists’ communities in order to be more efficient. It also advocates for scientific communication that takes into account cultural context from the beginning.
High-Dimensional Probability: An Introduction with Applications in Data Science

© 2018, Cambridge University Press Let us summarize our findings. A random projection of a set T in R n onto an m-dimensional subspace approximately preserves the geometry of T if m ⪆ d ( T ) . For...
Why Are We Like This?: The AI Architecture of a Co-Creative Storytelling Game

We present Why Are We Like This? (WAWLT), a mixed-initiative, co-creative storytelling game in which two players develop a story transcript by selecting and editing actions to perform and narrativize in an ongoing simulation. In this paper, we lay out the major technical features of WAWLT's AI architecture—including story sifting via Datalog queries, social simulation, action suggestions, and player-specified but system-understandable author goals—and discuss how these features work together to produce a play experience that facilitates player creativity.