Reviewerly/exHarmony huggingface dataset:
Forecasting of COVID-19 Cases in Jakarta using Poisson Autoregression

COVID-19 is currently become a global problem, including in Jakarta, Indonesia. There have been many approaches to predict COVID-19 occurrence, including the forecasting approach. However, the traditional forecasting method, particularly machine learning, often does not consider the condition of the data, although it has forms of the count, such as the number of cases. This study employs an autoregression model using Poisson distribution in predicting the COVID-19 future cases, namely the positive and recovery number. We compare the Poisson Autoregression with several well-known forecasting methods, namely ARIMA, Exponential Smoothing, BATS, and Prophet. This study found that Poisson Autoregression could create an accurate prediction with MAPE below 20% and tend to follows the actual data for the next 8 to 14 days to the future. Thus, this approach can forecast the future cases of COVID-19 and other cases that use count data in Jakarta, like the number of citizen complaints or transportation context.
Competing ratio loss for discriminative multi-class image classification

The development of deep convolutional neural network architecture is critical to the improvement of image classification task performance. Many image classification studies use deep convolutional neural network and focus on modifying the network structure to improve image classification performance. Conversely, our study focuses on loss function design. Cross-entropy Loss (CEL) has been widely used for training deep convolutional neural network for the task of multi-class classification. Although CEL has been successfully implemented in several image classification tasks, it only focuses on the posterior probability of the correct class. For this reason, a negative log likelihood ratio loss (NLLR) was proposed to better differentiate between the correct class and the competing incorrect ones. However, during the training of the deep convolutional neural network, the value of NLLR is not always positive or negative, which severely affects the convergence of NLLR. Our proposed competing ratio loss (CRL) calculates the posterior probability ratio between the correct class and the competing incorrect classes to further enlarge the probability difference between the correct and incorrect classes. We added hyperparameters to CRL, thereby ensuring its value to be positive and that the update size of backpropagation is suitable for the CRL’s fast convergence. To demonstrate the performance of CRL, we conducted experiments on general image classification tasks (CIFAR10/100, SVHN, ImageNet), the fine-grained image classification tasks (CUB200–2011, and Stanford Car), and the challenging face age estimation task (using Adience). Experimental results showed the effectiveness and robustness of the proposed loss function on different deep convolutional neural network architectures and different image classification tasks. Code is released at https://github.com/guoyurong0104/CRL-code.
How Do Practitioners Interpret Conditionals in Requirements?

Context: Conditional statements like "If A and B then C" are core elements for describing software requirements. However, there are many ways to express such conditionals in natural language and also many ways how they can be interpreted. We hypothesize that conditional statements in requirements are a source of ambiguity, potentially affecting downstream activities such as test case generation negatively. Objective: Our goal is to understand how specific conditionals are interpreted by readers who work with requirements. Method: We conduct a descriptive survey with 104 RE practitioners and ask how they interpret 12 different conditional clauses. We map their interpretations to logical formulas written in Propositional (Temporal) Logic and discuss the implications. Results: The conditionals in our tested requirements were interpreted ambiguously. We found that practitioners disagree on whether an antecedent is only sufficient or also necessary for the consequent. Interestingly, the disagreement persists even when the system behavior is known to the practitioners. We also found that certain cue phrases are associated with specific interpretations. Conclusion: Conditionals in requirements are a source of ambiguity and there is not just one way to interpret them formally. This affects any analysis that builds upon formalized requirements (e.g., inconsistency checking, test-case generation). Our results may also influence guidelines for writing requirements.
Exponential Clustering of Bipartite Quantum Entanglement at Arbitrary Temperatures

Macroscopic quantum effects play central roles in the appearance of inexplicable phenomena in low-temperature quantum many-body physics. Such macroscopic quantumness is often evaluated using long-range entanglement, i.e., entanglement in the macroscopic length scale. The long-range entanglement not only characterizes the novel quantum phases but also serves as a critical resource for quantum computation. Thus, the problem that arises is under which conditions can the long-range entanglement be stable even at room temperatures. Here, we show that bi-partite long-range entanglement is unstable at arbitrary temperatures and exponentially decays with distance. Our theorem provides a no-go theorem on the existence of the long-range entanglement. The obtained results are consistent with the existing observations that long-range entanglement at non-zero temperatures can exist in topologically ordered phases, where tripartite correlations are dominant. In the derivation of our result, we introduce a quantum correlation defined by the convex roof of the standard correlation function. We establish an exponential clustering theorem for generic quantum many-body systems for such a quantum correlation at arbitrary temperatures, which yields our main result by relating quantum correlation to quantum entanglement. As a simple application of our analytical techniques, we derived a general limit on the Wigner-Yanase-Dyson skew information and the quantum Fisher information, which will attract significant attention in the field of quantum metrology. Our work reveals novel general aspects of low-temperature quantum physics and sheds light on the characterization of long-range entanglement.
Implications of Blockchain Technology in the Health Domain

Blockchains are tamper-evident and tamper-resistant digital ledgers. Due to their distributed and shared nature, and their cryptographic functions, blockchains are resilient to alterations. They provide a trustworthy environment for the data stored in the ledgers. However, each new technology brings its own challenges along with the opportunities. Our minds are still busy with the question of “how could blockchain technology potentially benefit us?” In this paper, we approach to this question from the health domain perspective. Based on a systematic literature review, we discuss to what extent blockchain could provide solutions for the challenges inherited in the health domain and if blockchain technology introduces new challenges for development of health applications. The review included 27 publications which share experiences of practitioners from 2016 to 2020.
Secure Communication Framework for Blockchain-Based Internet of Drones-Enabled Aerial Computing Deployment

Internet of Drones (IoD)-enabled aerial computing can effectively reduce communication delay and energy consumption as compared to conventional computing environments. It seems very useful for its deployment in disaster areas, emergency relief and battlefield communications. However, different types of attacks, such as replay, man-in-the-middle, impersonation, credential leakage, and other types of data modifications, are possible in an IoD-based communication environment. Therefore, we need a highly robust security protocol to prevent the occurrence of these types of attacks in IoD-enabled aerial computing environments. Moreover, blockchain, a tamper-proof technology, adds more security in the designed security protocol. To cope with these issues, we propose a “blockchain-based secure communication framework for Internet of Drones enabled aerial computing deployment (BCF-IoDAC)”. We also provide a security analysis of the proposed BCF-IoDAC which proves its resilience against various types of passive and active attacks. The security and performance comparison of the proposed BCF-IoDAC is conducted with the closely related existing schemes, and its performance is also better than the compared schemes, as BCF-IoDAC provides more security and functionality features with low computation and communication costs. In addition, the blockchain-based practical demonstration of the proposed BCF-IoDAC is provided to measure its impact on the computational time on a varied number of transactions per block and a varied number of blocks mined in the blockchain.
TimNet: A text-image matching network integrating multi-stage feature extraction with multi-scale metrics

Text image comparison is a core component in web consistency testing across browsers. Previous studies mainly focused on general image comparison, which could not best suit the proposed task since text images share unique properties. In this paper, we introduce a novel Text-Image Matching Network (TimNet) to detect text differences in image pairs. A Multi-Stage Feature Extraction module (MFE) is integrated in TimNet to extract not only non-textual content features but also text-like features that are temporally aligned. Moreover, TimNet is composed of a Multi-Scale Metric module (MM) to measure the similarity between two text images with various scales and aspect ratios. Accordingly, a Text-Image Similarity Database (TISDB) consisting of 615.6 k text-image pairs of English characters, Chinese characters, and Arabic numbers was established. Extensive experiments were conducted to demonstrate that our TimNet outperforms existing state-of-the-art methods.
On Multi-Channel Huffman Codes for Asymmetric-Alphabet Channels

Zero-error single-channel source coding has been studied extensively over the past decades. Its natural multi-channel generalization is however seldom investigated. While the special case with multiple symmetric-alphabet channels was studied a decade ago, codes in such setting have no advantage over single-channel codes in data compression, making them worthless in most applications. With essentially no development since the last decade, in this paper, we break the stalemate by showing that it is possible to beat single-channel source codes in terms of compression assuming asymmetric-alphabet channels. We present the multi-channel analogs of several classical results in single-channel source coding, e.g., a multi-channel Huffman code is an optimal tree-decodable code. We also show evidences that finding an efficient construction of multi-channel Huffman codes may be hard. Nevertheless, we propose a construction whose redundancy is guaranteed to be no larger than that of an optimal single-channel source code.
On the reliability of a voting system under cyber attacks

Voting systems have been widely used in many areas due to their technical decision making abilities. However, when a voting system operates on any internet platform, it faces a number of potential vulnerabilities including malware and denial of service attacks. In this work, we study the reliability of a majority voting system under cyber threats. Three multiple attack scenarios are considered including targeted, random, and dynamic attacks. We show that under some conditions, the reliability of a voting system decreases with the number of attacked voting components for the targeted attacks; however, for the random attacks, the reliability of a voting system can increase with the number of attacked voting components. For the dynamic attacks, it is discovered that the reliability of a voting system is significantly impacted by the attack rate and the random proposition time. We further develop efficient algorithms for computing the reliability of voting systems which can be employed in practice. • Three multiple attack scenarios are considered. • The decreasing is rigorously shown for the system reliability under some conditions. • The increasing is rigorously shown for the system reliability under some conditions.
Optimization Study of Multidimensional Big Data Matrix Model in Enterprise Performance Evaluation System

This paper uses a multidimensional big data matrix model to optimize the analysis and conduct a systematic construction of the enterprise performance evaluation system. The adoption of new research methods and perspectives to promote the study of the use of performance information is of great significance to achieve the effectiveness, science, and sustainability of corporate performance management. To solve the problem of objectivity and scientificity of performance information use, this part attempts to analyze performance information use from the perspective of the multidimensional big data matrix, focusing on the techniques and methods in the process of promoting performance information use from the multidimensional big data matrix and tries to construct a system model of enterprise performance information use from two dimensions: the use of performance information sources and the use of performance information results. Based on multiple theoretical hypotheses, a theoretical and empirical basis is provided for the division of demand dimensions of enterprise performance evaluation system. Through social capital theory, three dimensions of network social capital, cognitive social capital, and structural social capital are hypothesized, and the logistic regression method is applied for empirical study. The results show that these three dimensions have significant effects on the knowledge demand of enterprise performance evaluation systems. It is verified that the multidimensional big data matrix can enhance the quality of performance information sources and improve the objectivity of performance information. In the performance information source use dimension, the analysis verified that the collection and preprocessing technology of big data can realize the automation, real-time, and diversification of information collection and preprocessing, and enhance the objectivity of performance information. Big data helps to improve the quality and effectiveness of performance information results use. In the dimension of using performance information results, the distributed computing and analysis processing technology of big data can assist the decision support system, and the use of information can be shifted from micromanagement to decision support, to realize the scientific use of performance information and improve the quality of enterprise management decisions.
Cryptocurrency and Blockchain

As the technologies are evolving day by day, they are able to rejuvenate any sector either individually or by incorporating other technologies. There are many prominent sectors in the market such as healthcare, education, entertainment, business, information technology, retail, etc. Every sector has its own set of profits and consequences, but apart from all, the banking or finance sector is the only sector that provides dynamicity to all other sectors and helps them to generate maximum revenue from their principal investment. In this chapter, the authors are focusing on the traditional and modern ways of banking, currencies such as cryptocurrency like Bitcoin, Ethereum, Litecoin, and how the modern currency will change the transaction procedure in the global banking system, creating an amalgamation of such currency with a current transaction system with the role of technology such as Blockchain in the betterment of the global banking system making the system fully decentralized, distributed, transparent, fast, immutable, and efficient.
Sum-Rate-Distortion Function for Indirect Multiterminal Source Coding in Federated Learning

One of the main focus in federated learning (FL) is the communication efficiency since a large number of participating edge devices send their updates to the edge server at each round of the model training. Existing works reconstruct each model update from edge devices and implicitly assume that the local model updates are independent over edge devices. In FL, however, the model update is an indirect multi-terminal source coding problem, also called as the CEO problem where each edge device cannot observe directly the gradient that is to be reconstructed at the decoder, but is rather provided only with a noisy version. The existing works do not leverage the redundancy in the information transmitted by different edges. This paper studies the rate region for the indirect multiterminal source coding problem in FL. The goal is to obtain the minimum achievable rate at a particular upper bound of gradient variance. We obtain the rate region for the quadratic vector Gaussian CEO problem under unbiased estimator and derive an explicit formula of the sum-rate-distortion function in the special case where gradient are identical over edge device and dimension. Finally, we analyse communication efficiency of convex Mini-batched SGD and non-convex Minibatched SGD based on the sum-rate-distortion function, respectively.
Data Augmentation for Graph Convolutional Network on Semi-supervised Classification

Data augmentation aims to generate new and synthetic features from the original data, which can identify a better representation of data and improve the performance and generalizability of downstream tasks. However, data augmentation for graph-based models remains a challenging problem, as graph data is more complex than traditional data, which consists of two features with different properties: graph topology and node attributes. In this paper, we study the problem of graph data augmentation for Graph Convolutional Network (GCN) in the context of improving the node embeddings for semi-supervised node classification. Specifically, we conduct cosine similarity based cross operation on the original features to create new graph features, including new node attributes and new graph topologies, and we combine them as new pairwise inputs for specific GCNs. Then, we propose an attentional integrating model to weighted sum the hidden node embeddings encoded by these GCNs into the final node embeddings. We also conduct a disparity constraint on these hidden node embeddings when training to ensure that non-redundant information is captured from different features. Experimental results on five real-world datasets show that our method improves the classification accuracy with a clear margin (+2.5%–+84.2%) than the original GCN model.
A Bottom-up method Towards the Automatic and Objective Monitoring of Smoking Behavior In-the-wild using Wrist-mounted Inertial Sensors

The consumption of tobacco has reached global epidemic proportions and is characterized as the leading cause of death and illness. Among the different ways of consuming tobacco (e.g., smokeless, cigars), smoking cigarettes is the most widespread. In this paper, we present a two-step, bottom-up algorithm towards the automatic and objective monitoring of cigarette-based, smoking behavior during the day, using the 3D acceleration and orientation velocity measurements from a commercial smartwatch. In the first step, our algorithm performs the detection of individual smoking gestures (i.e., puffs) using an artificial neural network with both convolutional and recurrent layers. In the second step, we make use of the detected puff density to achieve the temporal localization of smoking sessions that occur throughout the day. In the experimental section we provide extended evaluation regarding each step of the proposed algorithm, using our publicly-available, realistic Smoking Event Detection (SED) and Free-living Smoking Event Detection (SED-FL) datasets recorded under semi-controlled and free-living conditions, respectively. In particular, leave-one-subject-out (LOSO) experiments reveal an F1-score of 0.863 for the detection of puffs and an F1-score/Jaccard index equal to 0.878/0.604 towards the temporal localization of smoking sessions during the day. Finally, to gain further insight, we also compare the puff detection part of our algorithm with a similar approach found in the recent literature.
Secure Messaging Authentication against Active Man-in-the-Middle Attacks

Modern messaging applications often rely on out-of-band communication to achieve entity authentication, with human users verifying and attesting to long-term public keys. This is done primarily to reduce reliance on trusted third parties by replacing that role with the user. Despite a great deal of research focusing on analyzing the confidentiality aspect of secure messaging, the entity authenticity aspect of it, which relies on the user mediation, has been largely assumed away. Consequently, while many existing protocols provide some confidentiality guarantees after a compromise, such as post-compromise security (PCS), authenticity guarantees are generally lost, especially against an active attacker. This leads to potential man-in-the-middle (MitM) attacks. In this work, we address this gap by proposing a model to formally capture user-mediated entity authentication, as used by realworld protocols, that can be composed with any ratcheted key exchange. Our threat model captures active post-compromise entity authentication security. We demonstrate that the Signal application's user-mediated authentication protocol cannot be proven secure in this model and suggest a straightforward fix for Signal that allows the detection of an active adversary. Our results have direct implications for other existing and future ratcheted secure messaging applications.
Local Differential Privacy Is Equivalent to Contraction of an $f$-Divergence

We investigate the local differential privacy (LDP) guarantees of a randomized privacy mechanism via its contraction properties. We first show that LDP constraints can be equivalently cast in terms of the contraction coefficient of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathsf{E}_{\gamma}$</tex> -divergence. We then use this equivalent formula to express LDP guarantees of privacy mechanisms in terms of contraction coefficients of arbitrary <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$f$</tex> -divergences. When combined with standard estimation-theoretic tools (such as Le Cam's and Fano's converse methods), this result allows us to study the trade-off between privacy and utility in several testing and minimax and Bayesian estimation problems.
A Hybrid Machine Learning based Phishing Website Detection Technique through Dimensionality Reduction

Phishing attacks are generally launched through emails or websites to acquire unauthorized access to the user’s sensitive information. In recent times, many users face monetary losses due to phishing attacks. The motivation of our study is to present a prudent framework for detecting phishing websites to save users from being affected. Previous works used several supervised machine learning algorithms for classification to acquire higher accuracy for detection of phishing sites. In this paper, we have proposed a hybrid technique comprising of SVM, Decision tree, Random Forest, XGBoost by combining the idea of bagging and boosting. We have used the features of both phishing and legitimate website to mitigate the risk of phishing websites. We have evaluated classification algorithms using a number of feature subsets selected by various feature selection techniques to ascertain the most effective and efficient subset of features. Our hybrid technique achieved an accuracy of 98.28%, outperforming the state-of-the-art techniques.