Reviewerly/exHarmony huggingface dataset:
Cross-Domain Aspect Extraction using Transformers Augmented with Knowledge Graphs

The extraction of aspect terms is a critical step in fine-grained sentiment analysis of text. Existing approaches for this task have yielded impressive results when the training and testing data are from the same domain. However, these methods show a drastic decrease in performance when applied to cross-domain settings where the domain of the testing data differs from that of the training data. To address this lack of extensibility and robustness, we propose a novel approach for automatically constructing domain-specific knowledge graphs that contain information relevant to the identification of aspect terms. We introduce a methodology for injecting information from these knowledge graphs into Transformer models, including two alternative mechanisms for knowledge insertion: via query enrichment and via manipulation of attention patterns. We demonstrate state-of-the-art performance on benchmark datasets for cross-domain aspect term extraction using our approach and investigate how the amount of external knowledge available to the Transformer impacts model performance.
Bounds for Coding Theory over Rings

Coding theory where the alphabet is identified with the elements of a ring or a module has become an important research topic over the last 30 years. It has been well established that, with the generalization of the algebraic structure to rings, there is a need to also generalize the underlying metric beyond the usual Hamming weight used in traditional coding theory over finite fields. This paper introduces a generalization of the weight introduced by Shi, Wu and Krotov, called overweight. Additionally, this weight can be seen as a generalization of the Lee weight on the integers modulo 4 and as a generalization of Krotov’s weight over the integers modulo 2s for any positive integer s. For this weight, we provide a number of well-known bounds, including a Singleton bound, a Plotkin bound, a sphere-packing bound and a Gilbert–Varshamov bound. In addition to the overweight, we also study a well-known metric on finite rings, namely the homogeneous metric, which also extends the Lee metric over the integers modulo 4 and is thus heavily connected to the overweight. We provide a new bound that has been missing in the literature for homogeneous metric, namely the Johnson bound. To prove this bound, we use an upper estimate on the sum of the distances of all distinct codewords that depends only on the length, the average weight and the maximum weight of a codeword. An effective such bound is not known for the overweight.
Data-Model-Circuit Tri-Design for Ultra-Light Video Intelligence on Edge Devices

In this paper, we propose a data-model-hardware tri-design framework for high-throughput, low-cost, and high-accuracy multi-object tracking (MOT) on High-Definition (HD) video stream. First, to enable ultra-light video intelligence, we propose temporal frame-filtering and spatial saliency-focusing approaches to reduce the complexity of massive video data. Second, we exploit structure-aware weight sparsity to design a hardware-friendly model compression method. Third, assisted with data and model complexity reduction, we propose a sparsity-aware, scalable, and low-power accelerator design, aiming to deliver real-time performance with high energy efficiency. Different from existing works, we make a solid step towards the synergized software/hardware co-optimization for realistic MOT model implementation. Compared to the state-of-the-art MOT baseline, our tri-design approach can achieve 12.5x latency reduction, 20.9x effective frame rate improvement, 5.83x lower power, and 9.78x better energy efficiency, without much accuracy drop.
Detection of a quantum phase transition in a spin-1 chain through multipartite high-order correlations

We design a Bell inequality that is violated by correlations obtained from the ground states of XXZ spin-1 chain with on site anisotropies at the region of phase transition. In order to detect such correlations in spin-1 systems we exploit the formalism of generalized Bell inequality via the use of multipartite and high order correlations. We observe sharp violation in the vicinity of quantum phase transition between the so called large D and AFM phase. Interestingly, the violation of our Bell inequality is manifested by the change of the XXZ spin-1 chain ground state to a Greenberger-Horne-Zeilinger (GHZ)-like state at the critical region. Our results provide the first characterization of quantum phase transition via the violation of Bell-type constraint by correlations in the XXZ spin-1 chain with multi-body correlations and high-order measurements.
Detecting 3D syndromic faces as outliers using unsupervised normalizing flow models

Many genetic syndromes are associated with distinctive facial features. Several computer-assisted methods have been proposed that make use of facial features for syndrome diagnosis. Training supervised classifiers, the most common approach for this purpose, requires large, comprehensive, and difficult to collect databases of syndromic facial images. In this work, we use unsupervised, normalizing flow-based manifold and density estimation models trained entirely on unaffected subjects to detect syndromic 3D faces as statistical outliers. Furthermore, we demonstrate a general, user-friendly, gradient-based interpretability mechanism that enables clinicians and patients to understand model inferences. 3D facial surface scans of 2471 unaffected subjects and 1629 syndromic subjects representing 262 different genetic syndromes were used to train and evaluate the models. The flow-based models outperformed unsupervised comparison methods, with the best model achieving an ROC-AUC of 86.3% on a challenging, age and sex diverse data set. In addition to highlighting the viability of outlier-based syndrome screening tools, our methods generalize and extend previously proposed outlier scores for 3D face-based syndrome detection, resulting in improved performance for unsupervised syndrome detection.
Combining an explainable model based on ontologies with an explanation interface to classify images

Numerous explainability methods have appeared thanks to the surge of popularity of the explainable AI (XAI) domain. The DARPA depicted an explainable system, where an explainable model interacts with an explanation interface to generate explanations adapted to a user. We propose an explainable image classification system that follows this combination of explainable model and explanation interface as described by the DARPA. It takes advantage of the explainability of ontologies as well as the performance of machine learning models. This system is able to predict the class and properties of an object in an image. The results of this classification system are displayed in an explanation interface to help users understand and analyze the predictions of the proposed system. Our system exploits an ontology to build models that classify an object and its properties. The class and properties predicted by these models are instantiated in the ontology and added as assertions to an individual in order to verify the consistency of these predictions. Therefore, the system is able to warn the user when a prediction is uncertain and explain why, by using the ontology. These capacities will help users trust this system and better understand the predictions.
A Novel Blockchain Approach for Improving the Security and Reliability of Wireless Sensor Networks Using Jellyfish Search Optimizer

For the past few years, centralized decision-making is being used for malicious node identification in wireless sensor networks (WSNs). Generally, WSN is the primary technology used to support operations, and security issues are becoming progressively worse. In order to detect malicious nodes in WSN, a blockchain-routing- and trust-model-based jellyfish search optimizer (BCR-TM-JSO) is created. Additionally, it provides the complete trust-model architecture before creating the blockchain data structure that is used to identify malicious nodes. For further analysis, sensor nodes in a WSN collect environmental data and communicate them to the cluster heads (CHs). JSO is created to address this issue by replacing CHs with regular nodes based on the maximum remaining energy, degree, and closeness to base station. Moreover, the Rivest–Shamir–Adleman (RSA) mechanism provides an asymmetric key, which is exploited for securing data transmission. The simulation outcomes show that the proposed BCR-TM-JSO model is capable of identifying malicious nodes in WSNs. Furthermore, the proposed BCR-TM-JSO method outperformed the conventional blockchain-based secure routing and trust management (BSRTM) and distance degree residual-energy-based low-energy adaptive clustering hierarchy (DDR-LEACH), in terms of throughput (5.89 Mbps), residual energy (0.079 J), and packet-delivery ratio (89.29%).
Google SEO score and accessibility rank on the American University Libraries’ websites: one comparative analysis

Purpose This study aims to present a comparative study of university library websites (in the USA) from the standpoint of “Google SEO” and “Accessibility”. Furthermore, correlation analysis between these two done. Design/methodology/approach By opting for a webometric approach, the present study analyzed university library websites in the USA. The Lighthouse add-on for the Google Chrome browser has been used as a data collection tool, by writing and implementing a computer program in Bash language automatically (May 2020). Data analysis tools used were “Libre-Office-Calc”, “SPSS22” and “Excel”. Findings In all 81 university library websites in the USA, Google search engine optimization (SEO) scores have been observed the amount higher than 60 (Total Score = 100). The accessibility rank obtained lay between 0.56 and 1 (Total Score = 1). A weak correlation relationship between “SEO score” and “accessibility rank” ( P -value = 0.02, Spearman Correlation Coefficient = 0.345) was observed. This weak relationship can be explained due to the impact of several components affecting Google’s SEO score, one of them being having a high “accessibility rank”. Practical implications Given the increasing automation of library processes, SEO tools can help libraries in achieving their digital marketing goals. Originality/value Accurate measurement of the Google SEO score and accessibility rank for the university library websites (in the USA) were obtained by Lighthouse add-on for Google Chrome browser. Moreover, data extraction by the implementation of one program computer without the direct observation of human resources is the innovation of this study.
Entanglement emerges from dissipation-driven quantum self-organization

Entanglement is a holistic property of multipartite quantum systems, which is accompanied by the establishment of nonclassical correlations between subsystems. Most entanglement mechanisms can be described by a coherent interaction Hamiltonian, and entanglement develops over time. In other words, the generation of entanglement has a time arrow. Dissipative structure theory directs the evolving time arrow of a non-equilibrium system. By dissipating energy to the environment, the system establishes order out of randomness. This is also referred to as self-organization. Here, we explore a new mechanism to create entanglement, utilizing the wisdom of dissipative structure theory in quantum systems. The entanglement between subsystems can emerge via the dissipation-structured correlation. This method requires a non-equilibrium initial state and cooperative dissipation, which can be implemented in a variety of waveguide-coupled quantum systems.
Sessionvideo: A Novel Approach for Encrypted Traffic Classification via 3D-CNN Model

Today encrypted traffic has been used widely on the internet, such as HTTPS and SSH. Network traffic classification plays an important role in network resource management and cyberspace security, and previous machine learning methods face the challenge of identifying and classifying the encrypted traffic. In this paper, we propose a deep learning method based on a 3D convolutional neural network (3D-CNN), called SessionVideo. The new approach integrates traffic payload and time feature extraction for application classification. Specifically, our proposed scheme converts the raw traffic into a simple grayscale video, which enables the model to capture the temporal characteristics of the traffic. To verify the effectiveness of this scheme, we build a traffic dataset of 20 applications. The experimental results demonstrate that our 3D-CNN model achieves an accuracy of 97.89% and a weighted average precision of 97.96%. Further, the evaluation metrics of the 3D-CNN model significantly outperform 1D-CNN and 2D-CNN models in the comparison experiments.
Exploring Indonesian Netizen's Emotional Behavior Through Investment Sentiment Analysis Using TextBlob-NLTK (Natural Language Toolkit)

The investment industry has recently continued to provide the greatest experience and has grown the number of investors to this day. It also increases the quantity of traded investment assets because of its varied central and decentralized operating processes. Yet another aspect that can't be isolated from the investment process is price volatility and monetary policy. This means that the present price movement is influenced by every market mood. According to this study, a three-month period of tweets from Indonesian citizens was used to analyze attitude towards investment patterns in Indonesia. Because of the large number of people throughout the world who use Twitter to voice their opinions on investments, Twitter was selected as the primary source for this study. Twint, an open-source Python library, is used to retrieve tweet data. To process and analyze each tweet's data, TextBlob will be used, which values subjectivity and polarity. There were 92% favorable feelings and 42% positive sentiments on Indonesian tweets after a succession of research stages. These results were obtained by the limitation of data preprocessing and data labeling has been used.
Secure secondary utilization system of genomic data using quantum secure cloud

Secure storage and secondary use of individual human genome data is increasingly important for genome research and personalized medicine. Currently, it is necessary to store the whole genome sequencing information (FASTQ data), which enables detections of de novo mutations and structural variations in the analysis of hereditary diseases and cancer. Furthermore, bioinformatics tools to analyze FASTQ data are frequently updated to improve the precision and recall of detected variants. However, existing secure secondary use of data, such as multi-party computation or homomorphic encryption, can handle only a limited algorithms and usually requires huge computational resources. Here, we developed a high-performance one-stop system for large-scale genome data analysis with secure secondary use of the data by the data owner and multiple users with different levels of data access control. Our quantum secure cloud system is a distributed secure genomic data analysis system (DSGD) with a "trusted server" built on a quantum secure cloud, the information-theoretically secure Tokyo QKD Network. The trusted server will be capable of deploying and running a variety of sequencing analysis hardware, such as GPUs and FPGAs, as well as CPU-based software. We demonstrated that DSGD achieved comparable throughput with and without encryption on the trusted server Therefore, our system is ready to be installed at research institutes and hospitals that make diagnoses based on whole genome sequencing on a daily basis.
Virtual reality stimulation and organizational neuroscience for the assessment of empathy

This study aimed to evaluate the viability of a new procedure based on machine learning (ML), virtual reality (VR), and implicit measures to discriminate empathy. Specifically, eye-tracking and decision-making patterns were used to classify individuals according to their level in each of the empathy dimensions, while they were immersed in virtual environments that represented social workplace situations. The virtual environments were designed using an evidence-centered design approach. Interaction and gaze patterns were recorded for 82 participants, who were classified as having high or low empathy on each of the following empathy dimensions: perspective-taking, emotional understanding, empathetic stress, and empathetic joy. The dimensions were assessed using the Cognitive and Affective Empathy Test. An ML-based model that combined behavioral outputs and eye-gaze patterns was developed to predict the empathy dimension level of the participants (high or low). The analysis indicated that the different dimensions could be differentiated by eye-gaze patterns and behaviors during immersive VR. The eye-tracking measures contributed more significantly to this differentiation than did the behavioral metrics. In summary, this study illustrates the potential of a novel VR organizational environment coupled with ML to discriminate the empathy dimensions. However, the results should be interpreted with caution, as the small sample does not allow general conclusions to be drawn. Further studies with a larger sample are required to support the results obtained in this study.
Blockchain meets lightweight cryptography

The Internet of Things (IoT) typically comprises sensors that have inadequate computational resources. On the contrary, blockchain technology is thought to be expensive in terms of resource consumption due to the consensus procedures involved. As a result, the application of blockchain to the IoT environment is a challenging task. The most appropriate solution lies in replacing the computationally heavy consensus algorithms with lightweight counterparts. Proof of authentication (PoAh) is the most suited replacement for such an environment. Several modifications have been proposed for PoAh during the last few years, and they are reviewed here.
Probing the Potential of Extended Reality to Connect Experts and Novices in the Garden

As extended reality (XR) systems become increasingly available, XR-based remote instruction is being adopted for diverse purposes in professional settings such as surgery and field servicing. Hobbyists have been well-studied in HCI and may similarly benefit from remote skill-sharing. However, little is known about how XR technologies might support expert-novice collaboration for skilled hobby activities. This paper examines the potential and limitations of XR to connect experts and novices for one such activity: gardening. Through two studies involving 27 expert and novice gardeners, we designed prototypes to understand 1) practitioner perceptions of XR and remote skill-sharing in the garden and 2) what kinds of interactions can be supported in XR for expert-novice groups. We discuss design opportunities and challenges for XR systems in supporting informal connecting interactions and meaningful sensory interactions with a remote environment during skill-sharing.
Who is gambling? Finding cryptocurrency gamblers using multi-modal retrieval methods

With the popularity of cryptocurrencies and the remarkable development of blockchain technology, decentralized applications emerged as a revolutionary force for the Internet. Meanwhile, decentralized applications have also attracted intense attention from the online gambling community, with more and more decentralized gambling platforms created through the help of smart contracts. Compared with conventional gambling platforms, decentralized gambling has transparent rules and a low participation threshold, attracting a substantial number of gamblers. In order to discover gambling behaviors and identify the contracts and addresses involved in gambling, we propose a tool termed ETHGamDet. The tool is able to automatically detect the smart contracts and addresses involved in gambling by scrutinizing the smart contract code and address transaction records. Interestingly, we present a novel LightGBM model with memory components, which possesses the ability to learn from its own misclassifications. As a side contribution, we construct and release a large-scale gambling dataset at https://github.com/AwesomeHuang/Bitcoin-Gambling-Dataset to facilitate future research in this field. Empirically, ETHGamDet achieves a F1-score of 0.72 and 0.89 in address classification and contract classification respectively, and offers novel and interesting insights.
Minimal action control method in quantum critical models

Abstract We present a general protocol to control closed quantum systems that is based on minimising the adiabatic action. Using tools based on the geometry of quantum evolutions through the quantum adiabatic brachistochrone, we show that high fidelity control of the ground state of critical quantum systems can be achieved and requires only a reasonable approximation for the ground state spectral gap. We demonstrate our results for three widely applicable systems: the Landau-Zener, Ising, and fully connected spin models.
Estimating the Uncertainty in Emotion Class Labels With Utterance-Specific Dirichlet Priors

Emotion recognition is a key attribute for artificial intelligence systems that need to naturally interact with humans. However, the task definition is still an open problem due to the inherent ambiguity of emotions. In this paper, a novel Bayesian training loss based on per-utterance Dirichlet prior distributions is proposed for verbal emotion recognition, which models the uncertainty in one-hot labels created when human annotators assign the same utterance to different emotion classes. An additional metric is used to evaluate the performance by detecting test utterances with high labelling uncertainty. This removes a major limitation that emotion classification systems only consider utterances with labels where the majority of annotators agree on the emotion class. Furthermore, a frequentist approach is studied to leverage the continuous-valued “soft” labels obtained by averaging the one-hot labels. We propose a two-branch model structure for emotion classification on a per-utterance basis, which achieves state-of-the-art classification results on the widely used IEMOCAP dataset. Based on this, uncertainty estimation experiments were performed. The best performance in terms of the area under the precision-recall curve when detecting utterances with high uncertainty was achieved by interpolating the Bayesian training loss with the Kullback-Leibler divergence training loss for the soft labels. The generality of the proposed approach was verified using the MSP-Podcast dataset which yielded the same pattern of results.
BBTCD: blockchain based traceability of counterfeited drugs

The supply chain is a complex network in healthcare that crosses organizational and geographical borders. The inherent complexity of such structures can introduce impurities inclusive of erroneous facts, lack of transparency, and restricted records provenance. In the healthcare business, counterfeit pills are one of the major reasons for the harmful impact on human health and also for financial losses. Thus, pharmaceutical supply chains and end-to-end tracking systems are the recent research in healthcare. In this paper, we propose blockchain based traceability of counterfeited drugs (BBTCD) that implements tracking of counterfeited drugs using smart contracts on the Ethereum blockchain. We propose a solution to fully decentralize the tracking in healthcare by storing BBTCD on IPFS (Inter Planetary File System) to provide transparency, cost-effectiveness.