Reviewerly/exHarmony huggingface dataset:
Attitudes Toward Blockchain Technology in Managing Medical Information: Survey Study

Background The recently developed blockchain technology uses a peer-to-peer network to distribute data to all participants for storage. This method enhances data safety, reliability, integrity, and transparency. To successfully introduce blockchain technology to medical data management, it is essential to obtain consent from medical doctors and patients. Objective The aim of this study was to examine medical doctors’ and patients’ attitudes toward the use of blockchain technology and interpret the findings within the framework of expectancy theory. Methods In this questionnaire survey, we examined medical doctors’ (n=90) and patients’ (n=90) attitudes toward the use of blockchain technology in the management and distribution of medical information. The questionnaire comprised 8 questions that assessed attitudes toward new means of managing and distributing medical information using blockchain technology. Responses were rated on a scale that ranged from 1 (very negative) to 7 (very positive). Results Medical doctors (mean 3.7-5.0) reported significantly more negative attitudes than patients (mean 6.3-6.8). Furthermore, self-employed doctors reported more negative attitudes than employed doctors and university professors. Conclusions To successfully introduce blockchain technology to medical data management, it is necessary to promote positive attitudes toward this technology among medical doctors, especially self-employed doctors.
Enhanced Power and Electromagnetic SCA Resistance of Encryption Engines via a Security-Aware Integrated All-Digital LDO

This article demonstrates enhanced power (P) and electromagnetic (EM) side-channel analysis (SCA) attack resistance of standard (unprotected) 128-bit advanced encryption standard (AES) engines with parallel (P-AES, 128-bit) and serial (S-AES, 8-bit) datapaths and a 128-bit SIMON engine with the bit-serial (1-bit) datapath by an on-die security-aware all-digital low-dropout (DLDO) regulator. The proposed DLDO improves SCA resistance using control-loop-induced perturbations in a nominal DLDO, enhanced by a random switching noise injector (SNI) by power-stage control and a randomized reference voltage (R-VREF) generator coupled with all-digital clock modulation (ADCM). SCA performed on the measured power/EM signatures acquired from a 130-nm CMOS testchip demonstrates up to 25× reduction in test vector leakage assessment (TVLA) leakage for P-AES and 3579×, 2182×, and 500× increase in minimum-traces-to-disclose (MTD) 80% of the subkeys for P-AES, S-AES, and SIMON cores, respectively, with respect to correlation power analysis (CPA) and correlation EM analysis (CEMA).
Multi-Domain Goal-Oriented Dialogues (MultiDoGO): Strategies toward Curating and Annotating Large Scale Dialogue Data

The need for high-quality, large-scale, goaloriented dialogue datasets continues to grow as virtual assistants become increasingly widespread.However, publicly available datasets useful for this area are limited either in their size, linguistic diversity, domain coverage, or annotation granularity.In this paper, we present strategies toward curating and annotating large scale goal oriented dialogue data.We introduce the MultiDoGO dataset to overcome these limitations.With a total of over 81K dialogues harvested across six domains, MultiDoGO is over 8 times the size of MultiWOZ, the other largest comparable dialogue dataset currently available to the public.Over 54K of these harvested conversations are annotated for intent classes and slot labels.We adopt a Wizard-of-Oz approach wherein a crowd-sourced worker (the "customer") is paired with a trained annotator (the "agent").The data curation process was controlled via biases to ensure a diversity in dialogue flows following variable dialogue policies.We provide distinct class label tags for agents vs. customer utterances, along with applicable slot labels.We also compare and contrast our strategies on annotation granularity, i.e. turn vs. sentence level.Furthermore, we compare and contrast annotations curated by leveraging professional annotators vs the crowd.We believe our strategies for eliciting and annotating such a dialogue dataset scales across modalities and domains and potentially languages in the future.To demonstrate the efficacy of our devised strategies we establish neural baselines for classification on the agent and customer utterances as well as slot labeling for each domain.
Comparing the roles of landmark visual salience and semantic salience in visual guidance during indoor wayfinding

Landmark visual salience (characterized by features that contrast with their surroundings and visual peculiarities) and semantic salience (characterized by features with unusual or important meaning and content in the environment) are two important factors that affect an individual’s visual attention during wayfinding. However, empirical evidence regarding which factor dominates visual guidance during indoor wayfinding is rare, especially in real-world environments. In this study, we assumed that semantic salience dominates the guidance of visual attention, which means that semantic salience will correlate with participants’ fixations more significantly than visual salience. Notably, in previous studies, semantic salience was shown to guide visual attention in static images or familiar scenes in a laboratory environment. To validate this assumption, first, we collected the eye movement data of 22 participants as they found their way through a building. We then computed the landmark visual and semantic salience using computer vision models and questionnaires, respectively. Finally, we conducted correlation tests to verify our assumption. The results failed to validate our assumption and show that the role of salience in visual guidance in a real-world wayfinding process is different from the role of salience in perceiving static images or scenes in a laboratory. Visual salience dominates visual attention during indoor wayfinding, but the roles of salience in visual guidance are mixed across different landmark classes and tasks. The results provide new evidence for understanding how pedestrians visually interpret landmark information during real-world indoor wayfinding.
Realizing digital identity in government: Prioritizing design and implementation objectives for Aadhaar in India

With the increasing levels of digital transformation, focus on digital identities of individuals is increasingly getting prominence. It is the information captured as part of the identity surrounding the citizen which decides what services and products one is entitled to and can access. At present, there are still around 1.1 billion people in the world without any official identity and to address this concern, United Nations through its 16th Sustainable Development Goals (SDGs) recommended governments to provide their citizens with unique identities by 2030. India's Aadhaar is one consequential step in this direction as it has already reached over 1 billion enrolments in India. However establishing a national identity program successfully requires expertise, time, and huge financial commitments. This paper takes Aadhaar as a case study and uses Design Theory (DT) and Critical Success Factor theory (CSF) as a theoretical lens and attempts to evaluate design and execution choices made during the tenure of the project. The study also identifies and prioritizes primary goals of Aadhaar based on the secondary data, expert opinion through a Focus Group Discussion (FGD) and subsequently systematic prioritization using mixed research methodologies. The expert opinion from the FGD was analyzed using the Best-Worst method (BWM), followed by the Total Interpretive Structural Modeling (TISM) method and Matrix of Cross Impact Multiplications Applied to Classification (MICMAC) analysis. The study identified uniqueness, security and privacy as the top priority goals in an identity system and is 11 times more crucial than scalability which is identified to be of lesser priority. These findings from this study could be considered as a reference for other countries that aim to develop and implement digital identity for its citizens.
DeepMAL - Deep Learning Models for Malware Traffic Detection and Classification

Robust network security systems are essential to prevent and mitigate the harming effects of the ever-growing occurrence of network attacks. In recent years, machine learning-based systems have gain popularity for network security applications, usually considering the application of shallow models, which rely on the careful engineering of expert, handcrafted input features. The main limitation of this approach is that handcrafted features can fail to perform well under different scenarios and types of attacks. Deep Learning (DL) models can solve this limitation using their ability to learn feature representations from raw, non-processed data. In this paper we explore the power of DL models on the specific problem of detection and classification of malware network traffic. As a major advantage with respect to the state of the art, we consider raw measurements coming directly from the stream of monitored bytes as input to the proposed models, and evaluate different raw-traffic feature representations, including packet and flow-level ones. We introduce DeepMAL, a DL model which is able to capture the underlying statistics of malicious traffic, without any sort of expert handcrafted features. Using publicly available traffic traces containing different families of malware traffic, we show that DeepMAL can detect and classify malware flows with high accuracy, outperforming traditional, shallow-like models.
Virtual reality and modern tourism

Purpose The purpose of this paper is to provide an insight as to how recent trends in virtual reality (VR) have changed the way tourism and hospitality industry communicates their offerings and meets the tourists’ needs. Design/methodology/approach The approach is based on systematic literature review, where the relations between focal concepts are given to analyze potential future developments concerning VR. Findings The paper identifies and analyzes how VR technology affected tourism and hospitality industry through three main touch points – future tourism planning and management, technology-based marketing of tourism destinations and VR potential in changing consumer requirements. These concepts were analyzed to identify changing forces and suggest potential paradigm shifts that tourism and hospitality suppliers and marketers need to consider. These included realistic virtual travel replacements, the importance of interactive experiences and innovation in future tourism systems. Originality/value While there has been increasingly larger number of discussions on how tourists and tourism and hospitality industry have been using information and communication technology recently, there is little evidence of scholars and practitioners applying such methods. This paper used systematic literature review to illustrate means in which VR could be ingrained into tourism and hospitality services to meet the needs of tourists. It suggests that VR can and probably will fundamentally change the way in which tourists’ experiences and requirements are managed entirely.
Elliptic Curve Cryptography Point Multiplication Core for Hardware Security Module

In today's technology, a sheer number of Internet of Things applications use hardware security modules for secure communications. The widely used algorithms in security modules, for example, digital signatures and key agreement, are based upon elliptic curve cryptography (ECC). A core operation used in ECC is the point multiplication, which is computationally expensive for many Internet of things applications. In many IoT applications, such as intelligent transportation systems and distributed control systems, thousands of safety messages need to be signed and verified within a very short time-frame. Considerable research has been conducted in the design of a fast elliptic curve arithmetic on finite fields using residue number systems (RNS). In this article, we propose an RNS-based ECC core hardware for the two families of elliptic curves that are short Weierstrass and twisted Edwards curves. Specifically, we present RNS implementations for SECP256K1 and ED25519 standard curves. We propose an RNS hardware architecture supporting fast elliptic curve point-addition (ECPA), point-doubling (ECPD), and point-tripling (ECPT). We implemented different ECC point multiplication algorithms on the Xilinx FPGA platform. The test results confirm that the performance of our fully RNS ECC point multiplication is better than the fastest ECC point multiplication cores in the literature.
Individual-Level Fatality Prediction of COVID-19 Patients Using AI Methods

The global covid-19 pandemic puts great pressure on medical resources worldwide and leads healthcare professionals to question which individuals are in imminent need of care. With appropriate data of each patient, hospitals can heuristically predict whether or not a patient requires immediate care. We adopted a deep learning model to predict fatality of individuals tested positive given the patient's underlying health conditions, age, sex, and other factors. As the allocation of resources toward a vulnerable patient could mean the difference between life and death, a fatality prediction model serves as a valuable tool to healthcare workers in prioritizing resources and hospital space. The models adopted were evaluated and refined using the metrics of accuracy, specificity, and sensitivity. After data preprocessing and training, our model is able to predict whether a covid-19 confirmed patient is likely to be dead or not, given their information and disposition. The metrics between the different models are compared. Results indicate that the deep learning model outperforms other machine learning models to solve this rare event prediction problem.
Detail-Preserving Multi-Exposure Fusion With Edge-Preserving Structural Patch Decomposition

The multi-exposure fusion (MEF) methods have received much attention in recent years due to the importance of constructing high dynamic range images. Among most of the existing studies, multi-scale structural-patch-decomposition-based MEF (MSPD-MEF) has achieved state-of-the-art fusion quality and the fastest running time. However, this method still suffers from detail loss in the fused images. To tackle this issue, we first incorporate the edge-preserving factors into this method to preserve the details in the fused images in a single-scale setting. Then, we develop the novel and flexible bell curve function, which can further preserve the details in both bright and dark regions. After that, we also show that our method can seamlessly plug in to this multi-scale framework. Extensive experimental results indicate that the proposed method can produce pleasing fusion results with little artifacts and low computational cost in both static and dynamic scenes.
Network-Aware Locality Scheduling for Distributed Data Operators in Data Centers

Large data centers are currently the mainstream infrastructures for big data processing.As one of the most fundamental tasks in these environments, the efficient execution of distributed data operators (e.g., join and aggregation) are still challenging current data systems, and one of the key performance issues is network communication time.State-of-the-art methods trying to improve that problem focus on either application-layer data locality optimization to reduce network traffic or on network-layer data flow optimization to increase bandwidth utilization.However, the techniques in the two layers are totally independent from each other, and performance gains from a joint optimization perspective have not yet been explored.In this paper, we propose a novel approach called NEAL (NEtwork-Aware Locality scheduling) to bridge this gap, and consequently to further reduce communication time for distributed big data operators.We present the detailed design and implementation of NEAL, and our experimental results demonstrate that NEAL always performs better than current approaches for different workloads and network bandwidth configurations.
Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach

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Chapter 33. Satisfiability Modulo Theories

Applications in artificial intelligence, formal verification, and other areas have greatly benefited from the recent advances in SAT. It is often the case, however, that applications in these fields require determining the satisfiability of formulas in more expressive logics such as first-order logic. Also, these applications typically require not general first-order satisfiability, but rather satisfiability with respect to some background theory, which fixes the interpretations of certain predicate and function symbols. For many background theories, specialized methods yield decision procedures for the satisfiability of quantifier-free formulas or some subclass thereof. Specialized decision procedures have been discovered for a long and still growing list of theories with practical applications. These include the theory of equality, various theories of arithmetic, and certain theories of arrays, as well as theories of lists, tuples, records, and bit-vectors of a fixed or arbitrary finite size. The research field concerned with determining the satisfiability of formulas with respect to some background theory is called Satisfiability Modulo Theories (SMT). This chapter provides a brief overview of SMT together with references to the relevant literature for a deeper study. It begins with an overview of techniques for solving SMT problems by encodings to SAT. The rest of the chapter is mostly concerned with an alternative approach in which a SAT solver is integrated with a separate decision procedure (called a theory solver) for conjunctions of literals in the background theory. After presenting this approach as a whole, the chapter provides more details on how to construct and combine theory solvers, and discusses several extensions and enhancements.
A MapReduce Opinion Mining for COVID-19-Related Tweets Classification Using Enhanced ID3 Decision Tree Classifier

Opinion Mining (OM) is a field of Natural Language Processing (NLP) that aims to capture human sentiment in the given text.With the ever-spreading of online purchasing websites, micro-blogging sites, and social media platforms, OM in online social media platforms has picked the interest of thousands of scientific researchers.Because the reviews, tweets and blogs acquired from these social media networks, act as a significant source for enhancing the decision making process.The obtained textual data (reviews, tweets, or blogs) are classified into three different class labels which are negative, neutral and positive for analyzing and extracting relevant information from the given dataset.In this contribution, we introduce an innovative MapReduce improved weighted ID3 decision tree classification approach for OM, which consists mainly of three aspects: Firstly We have used several feature extractors to efficiently detect and capture the relevant data from the given tweets, including N-grams or character-level, Bag-Of-Words, word embedding (GloVe, Word2Vec), FastText, and TF-IDF.Secondly, we have applied a multiple feature selector to reduce the high feature's dimensionality, including Chi-square, Gain Ratio, Information Gain, and Gini Index.Finally, we have employed the obtained features to carry out the classification task using an improved ID3 decision tree classifier, which aims to calculate the weighted information gain instead of information gain used in traditional ID3.In other words, to measure the weighted information gain for the current conditioned feature, we follow two steps: First, we compute the weighted correlation function of the current conditioned feature.Second, we multiply the obtained weighted correlation function by the information gain of this current conditioned feature.This work is implemented in a distributed environment using the Hadoop framework, with its programming framework MapReduce and its distributed file system HDFS.Its primary goal is to enhance the performance of a well-known ID3 classifier in terms of accuracy, execution time, and ability to handle the massive datasets.We have carried out several experiences that aims to assess the effectiveness of our suggested classifier compared to some other contributions chosen from the literature.The experimental results demonstrated that our ID3 classifier works better on COVID-19_Sentiments dataset than other classifiers in terms of Recall (85.72 %), specificity (86.51 %), error rate (11.18 %), false-positive rate (13.49%), execution time (15.95s),kappa statistic (87.69 %), F1-score (85.54 %), classification rate (88.82 %), false-negative rate (14.28 %), precision rate (86.67 %), convergence (it convergent towards the iteration 90), stability (it is more stable with mean deviation standard equal to 0.12 %), and complexity (it requires much lower time and space computational complexity).
CASS

Chatbots systems, despite their popularity in today's HCI and CSCW research, fall short for one of the two reasons: 1) many of the systems use a rule-based dialog flow, thus they can only respond to a limited number of pre-defined inputs with pre-scripted responses; or 2) they are designed with a focus on single-user scenarios, thus it is unclear how these systems may affect other users or the community. In this paper, we develop a generalizable chatbot architecture (CASS) to provide social support for community members in an online health community. The CASS architecture is based on advanced neural network algorithms, thus it can handle new inputs from users and generate a variety of responses to them. CASS is also generalizable as it can be easily migrate to other online communities. With a follow-up field experiment, CASS is proven useful in supporting individual members who seek emotional support. Our work also contributes to fill the research gap on how a chatbot may influence the whole community's engagement.
ViolenceNet: Dense Multi-Head Self-Attention with Bidirectional Convolutional LSTM for Detecting Violence

Introducing efficient automatic violence detection in video surveillance or audiovisual content monitoring systems would greatly facilitate the work of closed-circuit television (CCTV) operators, rating agencies or those in charge of monitoring social network content. In this paper we present a new deep learning architecture, using an adapted version of DenseNet for three dimensions, a multi-head self-attention layer and a bidirectional convolutional long short-term memory (LSTM) module, that allows encoding relevant spatio-temporal features, to determine whether a video is violent or not. Furthermore, an ablation study of the input frames, comparing dense optical flow and adjacent frames subtraction and the influence of the attention layer is carried out, showing that the combination of optical flow and the attention mechanism improves results up to 4.4%. The conducted experiments using four of the most widely used datasets for this problem, matching or exceeding in some cases the results of the state of the art, reducing the number of network parameters needed (4.5 millions), and increasing its efficiency in test accuracy (from 95.6% on the most complex dataset to 100% on the simplest one) and inference time (less than 0.3 s for the longest clips). Finally, to check if the generated model is able to generalize violence, a cross-dataset analysis is performed, which shows the complexity of this approach: using three datasets to train and testing on the remaining one the accuracy drops in the worst case to 70.08% and in the best case to 81.51%, which points to future work oriented towards anomaly detection in new datasets.