Reviewerly/exHarmony huggingface dataset:
Internet of Things Based Blockchain for Temperature Monitoring and Counterfeit Pharmaceutical Prevention

The top priority of today’s healthcare system is delivering medicine directly from the manufacturer to end-user. The pharmaceutical supply chain involves some level of commingling of a collection of stakeholders such as distributors, manufacturers, wholesalers, and customers. The biggest challenge associated with this supply chain is temperature monitoring as well as counterfeit drug prevention. Many drugs and vaccines remain viable within a specific range of temperatures. If exposed beyond this temperature range, the medicine no longer works as intended. In this paper, an Internet of Things (IoT) sensor-based blockchain framework is proposed that tracks and traces drugs as they pass slowly through the entire supply chain. On the one hand, these new technologies of blockchain and IoT sensors play an essential role in supply chain management. On the other hand, they also pose new challenges of security for resource-constrained IoT devices and blockchain scalability issues to handle this IoT sensor-based information. In this paper, our primary focus is on improving classic blockchain systems to make it suitable for IoT based supply chain management, and as a secondary focus, applying these new promising technologies to enable a viable smart healthcare ecosystem through a drug supply chain.
Evaluation of Sentiment Analysis in Finance: From Lexicons to Transformers

Financial and economic news is continuously monitored by financial market participants. According to the efficient market hypothesis, all past information is reflected in stock prices and new information is instantaneously absorbed in determining future stock prices. Hence, prompt extraction of positive or negative sentiments from news is very important for investment decision-making by traders, portfolio managers and investors. Sentiment analysis models can provide an efficient method for extracting actionable signals from the news. However, financial sentiment analysis is challenging due to domain-specific language and unavailability of large labeled datasets. General sentiment analysis models are ineffective when applied to specific domains such as finance. To overcome these challenges, we design an evaluation platform which we use to assess the effectiveness and performance of various sentiment analysis approaches, based on combinations of text representation methods and machine-learning classifiers. We perform more than one hundred experiments using publicly available datasets, labeled by financial experts. We start the evaluation with specific lexicons for sentiment analysis in finance and gradually build the study to include word and sentence encoders, up to the latest available NLP transformers. The results show improved efficiency of contextual embeddings in sentiment analysis compared to lexicons and fixed word and sentence encoders, even when large datasets are not available. Furthermore, distilled versions of NLP transformers produce comparable results to their larger teacher models, which makes them suitable for use in production environments.
Exploring the impact of blockchain on the performance of agri-food supply chains

The implementation of the blockchain technology in the agri-food supply chains is in its introductory phase. Lead companies, often retailers, introduce this technology for specific objectives, such as assuring traceability or improving sales and reputation. At the same time, the technology could impact much more broadly the performances of food chains. Little is known about this impact as the evidence provided in the literature is scarce and mostly focused on specific indicators. This paper addresses this gap assessing the impact of the blockchain technology on food supply chains from an explorative perspective. An integrated conceptual framework is proposed which includes a broad set of performance dimensions discussed in the literature: efficiency, flexibility, responsiveness, food quality, and transparency of supply chains. These dimensions are assessed using a case study, consisting of three supply chains where a large European retailer has promoted the blockchain adoption. Data was collected through semi-structured interviews with key managers at different stages of the three supply chains and were systematically analysed through a thematic analysis. Results reveal that blockchain technology impacts positively on the profit and/or return on investment of supply chains, it leads to an increase of extrinsic food quality attributes and it fosters a better information management along the food chains due to an improved information accessibility, availability and sharing. The current analysis also suggests an improved management of behavioural uncertainty among the agents of the supply chains and an increase of firm's knowledge as well as supply chain management competencies. While the study remains of explorative nature, it offers a basis for the selection of theoretical approaches and the formulation of new hypotheses for future blockchain studies.
A Scoping Review of Immersive Virtual Reality in STEM Education

Although virtual reality (VR) simulation training has gained prominence, review studies to inform instructors and educators on the use of this technology in science, technology, engineering, and mathematics (STEM) are still scarce. This article presents various VR-supported instructional design practices in K-12 (primary and secondary) and higher education in terms of participants' characteristics, methodological features, and pedagogical uses in alignment with applications, technological equipment, and instructional design strategies. During the selection and screening process, 41 (n = 41) studies published in the period 2009-2019 were included for a detailed analysis and synthesis. This article's results indicate that many studies were focused on the description and evaluation of the appropriateness or the effectiveness of applied teaching practices with VR support. Several studies pointed out improvements in learning outcomes or achievements, positive perspectives on user experience, and perceived usability. Nevertheless, fewer studies were conducted to measure students' learning performance. The current scoping review aims to encourage instructional designers to develop innovative VR applications or integrate existing approaches in their teaching procedures. It will also inform researchers to conduct further research for an in-depth understanding of the educational benefits of immersive-VR applications in STEM fields.
Blockchain in operations management and manufacturing: Potential and barriers

Transparency, visibility, and disintermediation are some of the prospects of the aspiring blockchain technology in the business-to-business context. The digital transformation and Industry 4.0 trends also facilitate blockchain applications in operations management (OM) and manufacturing. However, scientific contributions and successful industrial applications in this area are still scarce and mainly at a proof-of-concept stage. The empirical research in this article is based on an expert interview study to uncover and analyse the potential and barriers to the adoption of blockchain technology in OM and manufacturing from within the industry. Semi-structured interviews with industry experts are employed to elaborate on promising practices for the industry to efficiently promote blockchain adoption and meaningful research directions for scholars. Findings include unexplored potential regarding distributed production networks and collaboration, expected evolutionary steps of IoT, disintermediation leading to new business models like tokenisation, and short-term rather than long-term relationships. Current barriers include staff difficulties, legal uncertainties, missing infrastructure and standardisation, and unclear governance structures. Improving smart contract security and interoperability of private and public protocols will enable further dissemination of the technology. Managers and academic scholars can address these findings and new propositions of this study in future application development and implementation.
Mobile Robot Path Planning in Dynamic Environments Through Globally Guided Reinforcement Learning

Path planning for mobile robots in large dynamic environments is a challenging problem, as the robots are required to efficiently reach their given goals while simultaneously avoiding potential conflicts with other robots or dynamic objects. In the presence of dynamic obstacles, traditional solutions usually employ re-planning strategies, which re-call a planning algorithm to search for an alternative path whenever the robot encounters a conflict. However, such re-planning strategies often cause unnecessary detours. To address this issue, we propose a learning-based technique that exploits environmental spatio-temporal information. Different from existing learning-based methods, we introduce a globally guided reinforcement learning approach (G2RL), which incorporates a novel reward structure that generalizes to arbitrary environments. We apply G2RL to solve the multi-robot path planning problem in a fully distributed reactive manner. We evaluate our method across different map types, obstacle densities, and the number of robots. Experimental results show that G2RL generalizes well, outperforming existing distributed methods, and performing very similarly to fully centralized state-of-the-art benchmarks.
Emotions Don't Lie

We present a learning-based method for detecting real and fake deepfake multimedia content. To maximize information for learning, we extract and analyze the similarity between the two audio and visual modalities from within the same video. Additionally, we extract and compare affective cues corresponding to perceived emotion from the two modalities within a video to infer whether the input video is "real" or "fake". We propose a deep learning network, inspired by the Siamese network architecture and the triplet loss. To validate our model, we report the AUC metric on two large-scale deepfake detection datasets, DeepFake-TIMIT Dataset and DFDC. We compare our approach with several SOTA deepfake detection methods and report per-video AUC of 84.4% on the DFDC and 96.6% on the DF-TIMIT datasets, respectively. To the best of our knowledge, ours is the first approach that simultaneously exploits audio and video modalities and also perceived emotions from the two modalities for deepfake detection.
Recommendations for Reporting Machine Learning Analyses in Clinical Research

Use of machine learning (ML) in clinical research is growing steadily given the increasing availability of complex clinical data sets. ML presents important advantages in terms of predictive performance and identifying undiscovered subpopulations of patients with specific physiology and prognoses. Despite this popularity, many clinicians and researchers are not yet familiar with evaluating and interpreting ML analyses. Consequently, readers and peer-reviewers alike may either overestimate or underestimate the validity and credibility of an ML-based model. Conversely, ML experts without clinical experience may present details of the analysis that are too granular for a clinical readership to assess. Overwhelming evidence has shown poor reproducibility and reporting of ML models in clinical research suggesting the need for ML analyses to be presented in a clear, concise, and comprehensible manner to facilitate understanding and critical evaluation. We present a recommendation for transparent and structured reporting of ML analysis results specifically directed at clinical researchers. Furthermore, we provide a list of key reporting elements with examples that can be used as a template when preparing and submitting ML-based manuscripts for the same audience.
Learning From Multiple Experts: Self-paced Knowledge Distillation for Long-Tailed Classification

In real-world scenarios, data tends to exhibit a long-tailed distribution, which increases the difficulty of training deep networks. In this paper, we propose a novel self-paced knowledge distillation framework, termed Learning From Multiple Experts (LFME). Our method is inspired by the observation that networks trained on less imbalanced subsets of the distribution often yield better performances than their jointly-trained counterparts. We refer to these models as ‘Experts’, and the proposed LFME framework aggregates the knowledge from multiple ‘Experts’ to learn a unified student model. Specifically, the proposed framework involves two levels of adaptive learning schedules: Self-paced Expert Selection and Curriculum Instance Selection, so that the knowledge is adaptively transferred to the ‘Student’. We conduct extensive experiments and demonstrate that our method is able to achieve superior performances compared to state-of-the-art methods. We also show that our method can be easily plugged into state-of-the-art long-tailed classification algorithms for further improvements.
Parallel entangling operations on a universal ion-trap quantum computer

The circuit model of a quantum computer consists of sequences of gate operations between quantum bits (qubits), drawn from a universal family of discrete operations1. The ability to execute parallel entangling quantum gates offers efficiency gains in numerous quantum circuits2-4, as well as for entire algorithms-such as Shor's factoring algorithm5-and quantum simulations6,7. In circuits such as full adders and multiple-control Toffoli gates, parallelism can provide an exponential improvement in overall execution time through the divide-and-conquer technique8. More importantly, quantum gate parallelism is essential for fault-tolerant error correction of qubits that suffer from idle errors9,10. However, the implementation of parallel quantum gates is complicated by potential crosstalk, especially between qubits that are fully connected by a common-mode bus, such as in Coulomb-coupled trapped atomic ions11,12 or cavity-coupled superconducting transmons13. Here we present experimental results for parallel two-qubit entangling gates in an array of fully connected trapped 171Yb+ ion qubits. We perform a one-bit full-addition operation on a quantum computer using a depth-four quantum circuit4,14,15, where circuit depth denotes the number of runtime steps required. Our method exploits the power of highly connected qubit systems using classical control techniques and will help to speed up quantum circuits and achieve fault tolerance in trapped-ion quantum computers.
Automatic Modulation Classification Using CNN-LSTM Based Dual-Stream Structure

Deep learning (DL) has recently aroused substantial concern due to its successful implementations in many fields. Currently, there are few studies on the applications of DL in the automatic modulation classification (AMC), which plays a critical role in non-cooperation communications. Besides, most previous work ignores the feature interaction, and only considers spatial or temporal attributes of signals. Combining the advantages of the convolutional neural network (CNN), and the long short-term memory (LSTM), this paper addresses the AMC using CNN-LSTM based dual-stream structure, which efficiently explores the feature interaction, and the spatial-temporal properties of raw complex temporal signals. Specifically, a preprocessing step is first implemented to convert signals into the temporal in-phase/quadrature (I/Q) format, and the amplitude/phase (A/P) representation, which facilitates the acquirement of more effective features for classification. To extract features from each signal pattern, each stream is composed of CNN, and LSTM (denoted as CNN-LSTM). Most importantly, the features learned from two streams interact in pairs, which increases the diversity of features and thereby further improves the performance. Finally, some comparisons with previous work are performed. The experimental results not only demonstrate the superior performance of the proposed method compared with the existing state-of-the-art methods, but also reveal the potential of DL-based approaches for AMC.
EtherTwin: Blockchain-based Secure Digital Twin Information Management

Digital Twins are complex digital representations of assets that are used by a variety of organizations across the Industry 4.0 value chain. As the digitization of industrial processes advances, Digital Twins will become widespread. As a result, there is a need to develop new secure data sharing models for a complex ecosystem of interacting Digital Twins and lifecycle parties. Decentralized Applications are uniquely suited to address these sharing challenges while ensuring availability, integrity and confidentiality. They rely on distributed ledgers and decentralized databases for data storage and processing, avoiding single points of trust. To tackle the need for decentralized sharing of Digital Twin data, this work proposes an owner-centric decentralized sharing model. A formal access control model addresses integrity and confidentiality aspects based on Digital Twin components and lifecycle requirements. With our prototypical implementation EtherTwin we show how to overcome the numerous implementation challenges associated with fully decentralized data sharing, enabling management of Digital Twin components and their associated information. For validation, the prototype is evaluated based on an industry use case and semi-structured expert interviews.
Deep Learning Based Vulnerability Detection: Are We There Yet?

Automated detection of software vulnerabilities is a fundamental problem in software security. Existing program analysis techniques either suffer from high false positives or false negatives. Recent progress in Deep Learning (DL) has resulted in a surge of interest in applying DL for automated vulnerability detection. Several recent studies have demonstrated promising results achieving an accuracy of up to 95 percent at detecting vulnerabilities. In this paper, we ask, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“how well do the state-of-the-art DL-based techniques perform in a real-world vulnerability prediction scenario?”</i> To our surprise, we find that their performance drops by more than 50 percent. A systematic investigation of what causes such precipitous performance drop reveals that existing DL-based vulnerability prediction approaches suffer from challenges with the training data (e.g., data duplication, unrealistic distribution of vulnerable classes, etc.) and with the model choices (e.g., simple token-based models). As a result, these approaches often do not learn features related to the actual cause of the vulnerabilities. Instead, they learn unrelated artifacts from the dataset (e.g., specific variable/function names, etc.). Leveraging these empirical findings, we demonstrate how a more principled approach to data collection and model design, based on realistic settings of vulnerability prediction, can lead to better solutions. The resulting tools perform significantly better than the studied baseline—up to 33.57 percent boost in precision and 128.38 percent boost in recall compared to the best performing model in the literature. Overall, this paper elucidates existing DL-based vulnerability prediction systems’ potential issues and draws a roadmap for future DL-based vulnerability prediction research.
VideoMoCo: Contrastive Video Representation Learning with Temporally Adversarial Examples

MoCo [11] is effective for unsupervised image representation learning. In this paper, we propose VideoMoCo for unsupervised video representation learning. Given a video sequence as an input sample, we improve the temporal feature representations of MoCo from two perspectives. First, we introduce a generator to drop out several frames from this sample temporally. The discriminator is then learned to encode similar feature representations regardless of frame removals. By adaptively dropping out different frames during training iterations of adversarial learning, we augment this input sample to train a tempo-rally robust encoder. Second, we use temporal decay to model key attenuation in the memory queue when computing the contrastive loss. As the momentum encoder updates after keys enqueue, the representation ability of these keys degrades when we use the current input sample for contrastive learning. This degradation is reflected via temporal decay to attend the input sample to recent keys in the queue. As a result, we adapt MoCo to learn video representations without empirically designing pretext tasks. By empowering the temporal robustness of the encoder and modeling the temporal decay of the keys, our VideoMoCo improves MoCo temporally based on contrastive learning. Experiments on benchmark datasets including UCF101 and HMDB51 show that VideoMoCo stands as a state-of-the-art video representation learning method.
Approaches to Automated Detection of Cyberbullying: A Survey

Research into cyberbullying detection has increased in recent years, due in part to the proliferation of cyberbullying across social media and its detrimental effect on young people. A growing body of work is emerging on automated approaches to cyberbullying detection. These approaches utilise machine learning and natural language processing techniques to identify the characteristics of a cyberbullying exchange and automatically detect cyberbullying by matching textual data to the identified traits. In this paper, we present a systematic review of published research (as identified via Scopus, ACM and IEEE Xplore bibliographic databases) on cyberbullying detection approaches. On the basis of our extensive literature review, we categorise existing approaches into 4 main classes, namely supervised learning, lexicon-based, rule-based, and mixed-initiative approaches. Supervised learning-based approaches typically use classifiers such as SVM and Naıve Bayes to develop predictive models for cyberbullying detection. Lexicon-based systems utilise word lists and use the presence of words within the lists to detect cyberbullying. Rule-based approaches match text to predefined rules to identify bullying, and mixed-initiatives approaches combine human-based reasoning with one or more of the aforementioned approaches. We found lack of labelled datasets and non-holistic consideration of cyberbullying by researchers when developing detection systems are two key challenges facing cyberbullying detection research. This paper essentially maps out the state-of-the-art in cyberbullying detection research and serves as a resource for researchers to determine where to best direct their future research efforts in this field.
QoS-aware cloud service composition using eagle strategy

In recent years, several cloud services have proliferated that conspicuously result in providing similar services having same functionality by multiple service providers, but varying in Quality of Service (QoS) properties. Thus, providing a cloud service composition with optimal QoS values that satisfy the requirements of an user becomes complex and challenging in a cloud environment. Several metaheuristics proposed in solving this problem. However, many of them fail to maintain a suitable balance between exploration and exploitation. We propose a novel Eagle Strategy with Whale Optimization Algorithm (ESWOA) that ensures the proper balance between exploration and exploitation.