Reviewerly/exHarmony huggingface dataset:
Blockchain Based Secure Communication for IoT Devices in Smart Cities

In smart city technologies we have witnessed advanced technological improvements in small computing devices, which can be connected to the Internet and named as Internet of Thing (IoT) devices, and cooperatively working complex systems. With this increased use of new technologies, the security problem is becoming more and more important because complex systems lead to unpredictable security vulnerabilities, which result in financial and private information losses. As a recently emerged technology, Blockchain was emerged as an alternative solution to security breaches of a different application environment. In contrast to the central structure used by most systems, it is preferred especially in the area of security by its distributed structure and the cryptographic hash algorithm it uses. Today, structures such as Smart Home, Smart City, Smart Environment and Smart Agriculture, which are created by using IoT are seen as active research areas with more security shortages. The reason for the security weakness in these areas arises from the hardware restriction on the IoT devices used. In the proposed system, an authorization system for IoT devices has been tried to be set up by using the distributed node structure of Blockchain system and blocks kept in these nodes. UDP (User Datagram Protocol), which uses a simple communication model without establishing a connection to the minimum protocol mechanism for communication of nodes in the system, was preferred. The communication between the nodes has been encrypted using encryption methods, thus creating a secure environment.
Exploring the Dark Web for Cyber Threat Intelligence using Machine Leaning

In recent years, cyber attack techniques are increasingly sophisticated, and blocking the attack is more and more difficult, even if a kind of counter measure or another is taken. In order for a successful handling of this situation, it is crucial to have a prediction of cyber attacks, appropriate precautions, and effective utilization of cyber intelligence that enables these actions. Malicious hackers share various kinds of information through particular communities such as the dark web, indicating that a great deal of intelligence exists in cyberspace. This paper focuses on forums on the dark web and proposes an approach to extract forums which include important information or intelligence from huge amounts of forums and identify traits of each forum using methodologies such as machine learning, natural language processing and so on. This approach will allow us to grasp the emerging threats in cyberspace and take appropriate measures against malicious activities.
Test-Time Training for Out-of-Distribution Generalization

We introduce a general approach, called test-time training, for improving theperformance of predictive models when test and training data come fromdifferent distributions. Test-time training turns a single unlabeled testinstance into a self-supervised learning problem, on which we update the modelparameters before making a prediction on the test sample. We show that thissimple idea leads to surprising improvements on diverse image classificationbenchmarks aimed at evaluating robustness to distribution shifts. Theoreticalinvestigations on a convex model reveal helpful intuitions for when we canexpect our approach to help.
An ensemble approach for spam detection in Arabic opinion texts

Nowadays, individuals express experiences and opinions through online reviews. These has an influence on online marketing and obtaining real knowledge about products and services. However, some of the online reviews can be unreal. They may have been written to promote low-quality products/services or sabotage a product/service reputation to mislead potential customers. Such misleading reviews are known as spam reviews and require crucial attention. Prior spam detection research focused on English reviews, with less attention to other languages. The detection of spam reviews in Arabic online sources is a relatively new topic despite the relatively huge amount of data generated. Therefore, this paper contributes to such topic by presenting four different Arabic spam reviews detection methods, while putting more focus towards the construction and evaluation of an ensemble approach. The proposed ensemble method is based on integrating a rule-based classifier with machine learning techniques, while utilizing content-based features that depend on N-gram features and Negation handling. The four proposed methods are evaluated on two datasets of different sizes. The results indicate the efficiency of the ensemble approach where it achieves a classification accuracy of 95.25% and 99.98% for the two experimented datasets and outperforming existing related work by far of 25%.
Optimal Path Planning Using Swarm Intelligence Based Hybrid Techniques

The planning of optimal path is an important research domain due to vast applications of optimal path planning in the robotics, simulation and modeling, computer graphics, virtual reality estimation and animation, and bioinformatics. The optimal path planning application demands to determine the collision free shortest and optimal path. There can be numerous possibilities that to find the path with optimal length based on different types of available obstacles during the path and different types of workspace environment. This research work aims to identify the optimum path from the initial source-point to final point for the unknown workspace environment consists of static obstacles. For this experimentation, swarm intelligence based hybrid concepts are considered as the work collaboration and intelligence behavior of swarm agents provides the resourceful solution of NP hard problems. Here, the hybridization of concepts makes the solution of problem more efficient. Among swarm intelligence concepts, cuckoo search (CS) algorithm is one of the efficient algorithms due to clever behavior and brood parasitic property of cuckoo birds. In this research work, two hybrid concepts are proposed. First algorithm is the hybridized concept of cuckoo search with bat algorithm (BA) termed as CS-BAPP. Another algorithm is the hybridized concept of cuckoo search with firefly algorithm (FA) termed as CS-FAPP. Both algorithms are initially tested on the benchmarks functions and applied to the path planning problem. For path planning, a real time dataset area of Alwar region situated at Rajasthan (India) is considered. The selected region consists of urban and dense vegetation land cover features. The results for the optimal path planning on Alwar region are assessed using the evaluation metrics of minimum number of iterations, error rate, success rate, and simulation time. Moreover, the results are also compared with the individual FA, BA, and CS along with the comparison of hybrid concepts.
BackPACK: Packing more into Backprop

Automatic differentiation frameworks are optimized for exactly one thing: computing the average mini-batch gradient. Yet, other quantities such as the variance of the mini-batch gradients or many approximations to the Hessian can, in theory, be computed efficiently, and at the same time as the gradient. While these quantities are of great interest to researchers and practitioners, current deep learning software does not support their automatic calculation. Manually implementing them is burdensome, inefficient if done naively, and the resulting code is rarely shared. This hampers progress in deep learning, and unnecessarily narrows research to focus on gradient descent and its variants; it also complicates replication studies and comparisons between newly developed methods that require those quantities, to the point of impossibility. To address this problem, we introduce BackPACK, an efficient framework built on top of PyTorch, that extends the backpropagation algorithm to extract additional information from first-and second-order derivatives. Its capabilities are illustrated by benchmark reports for computing additional quantities on deep neural networks, and an example application by testing several recent curvature approximations for optimization.
Revision in Continuous Space: Unsupervised Text Style Transfer without Adversarial Learning

Typical methods for unsupervised text style transfer often rely on two key ingredients: 1) seeking the explicit disentanglement of the content and the attributes, and 2) troublesome adversarial learning. In this paper, we show that neither of these components is indispensable. We propose a new framework that utilizes the gradients to revise the sentence in a continuous space during inference to achieve text style transfer. Our method consists of three key components: a variational auto-encoder (VAE), some attribute predictors (one for each attribute), and a content predictor. The VAE and the two types of predictors enable us to perform gradient-based optimization in the continuous space, which is mapped from sentences in a discrete space, to find the representation of a target sentence with the desired attributes and preserved content. Moreover, the proposed method naturally has the ability to simultaneously manipulate multiple fine-grained attributes, such as sentence length and the presence of specific words, when performing text style transfer tasks. Compared with previous adversarial learning based methods, the proposed method is more interpretable, controllable and easier to train. Extensive experimental studies on three popular text style transfer tasks show that the proposed method significantly outperforms five state-of-the-art methods.
Histographs: graphs in histopathology

Spatial arrangement of cells of various types, such as tumor infiltrating lymphocytes and the advancing edge of a tumor, are important features for detecting and characterizing cancers. However, convolutional neural networks (CNNs) do not explicitly extract intricate features of the spatial arrangements of the cells from histopathology images. In this work, we propose to classify cancers using graph convolutional networks (GCNs) by modeling a tissue section as a multi-attributed multi-relational spatial graph of its constituent cells. Cells are detected using their nuclei in H and E stained tissue image, and each cell’s appearance is captured as a multi-attributed high-dimensional vertex feature. The spatial relations between neighboring cells are captured as edge features based on their distances in a multi-relational graph. We demonstrate the utility of this approach by obtaining classification accuracy that is competitive with CNNs, specifically, Inception-v3, on two tasks – cancerous versus non-cancerous and in situ versus invasive – on the BACH breast cancer dataset.
A novel harmony search algorithm and its application to data clustering

This paper presents a variant of harmony search algorithm (HS), called best–worst-mean harmony search (BWM_HS). The main difference between the proposed algorithm and the canonical HS is that it employs a modified memory consideration procedure to utilize more efficiently the accumulated knowledge and experience in harmony memory (HM). To this aim, the random harmony selection scheme of this procedure is replaced with three novel pitch selection and production rules. These rules use the information of the current best and worst harmonies as well as the mean of all harmonies to guide the search process. To further utilize the valuable information of HM, two new harmonies are generated at each iteration where the better one will compete with the current worst harmony. The mean of all harmonies is always employed to produce a new harmony. On the other hand, each pitch of the second one is obtained by the rules that consider the information of the best and worst harmonies. These rules can present either explorative or exploitative search behaviors at different stages of search. Thus, a probabilistic self-adaptive selection scheme decides to choose between them to properly balance the exploration and exploitation abilities. The general performance of BWM_HS for solving optimization problems is evaluated against CEC 2017 benchmark functions and its results are compared with HS and eight state-of-the-art variants of HS. The comparison indicates that the performance of BWM_HS is better than or equal to the compared algorithms with respect to the accuracy, robustness, and convergence speed criteria. Moreover, the performance of BWM_HS in solving clustering problems is investigated by applying it for clustering several well-known benchmark datasets. The experimental results show that, in general, the BWM_HS outperforms other well-known algorithms in the literature and in particular, it significantly improves the statistical results for one dataset.
Empirical investigation of herding in cryptocurrency market under different market regimes

Purpose Our study focuses on analyzing the trading behaviour of the investors who invest in these currencies to review their trading patterns which may help us to understand the price formation of cryptocurrencies in this market. Design/methodology/approach We used Chang et al. (2000) measure to calculate herding that is based on cross-section absolute dispersion of stock returns (CSAD). We further analyse the nature of the same in different market regimes, that is up market, down market, high volatile market, low volatile market etc. Findings Applying different methodologies both static and time varying, we find that herding is pronounced when the market is either passing through stress or has become highly volatile. Anti-herding is found in a less volatile market or in a bullish market. Practical implications Our results are also helpful for the policy makers in designing stricter regulations to provide safe investment environment to the investors. Originality/value Our study in an extension of the literature in same direction and contribute in numerous ways. As the number of digital currencies is growing day by day and we have around 2,200 digital currencies trading across the world, we increased our sample size up to 100 most traded currencies. While majority of the studies cover the period 2015–2018, our study comprises the largest sample size starting from August 2013 to April 2019. We use the static model to find herding and simultaneously try to detect herding under different market regimes: up market and down market.
Toward Robust Sensing for Autonomous Vehicles: An Adversarial Perspective

Autonomous Vehicles rely on accurate and robust sensor observations for safety critical decision-making in a variety of conditions. Fundamental building blocks of such systems are sensors and classifiers that process ultrasound, RADAR, GPS, LiDAR and camera signals~\cite{Khan2018}. It is of primary importance that the resulting decisions are robust to perturbations, which can take the form of different types of nuisances and data transformations, and can even be adversarial perturbations (APs). Adversarial perturbations are purposefully crafted alterations of the environment or of the sensory measurements, with the objective of attacking and defeating the autonomous systems. A careful evaluation of the vulnerabilities of their sensing system(s) is necessary in order to build and deploy safer systems in the fast-evolving domain of AVs. To this end, we survey the emerging field of sensing in adversarial settings: after reviewing adversarial attacks on sensing modalities for autonomous systems, we discuss countermeasures and present future research directions.
Holistic digital forensic readiness framework for IoT-enabled organizations

Internet of Things (IoT) are becoming commonplace in homes, buildings, cities, and nations, and IoT networks are also getting more complex and interconnected. The complexity, interconnectivity, and heterogeneity of IoT systems, however, complicate digital (forensic) investigations. The challenge is compounded due to the lack of holistic and standardized approaches. Hence, building on the ISO/IEC 27043 international standard, we present a holistic digital forensic readiness (DFR) framework. We also qualitatively evaluate the utility of the proposed DFR framework.
Pre-Impact Fall Detection with CNN-Based Class Activation Mapping Method

In this paper, we report our improvement on the prediction accuracy of pre-impact fall detection by applying a learning-based method on the real-time data from an IMU (inertial measurement unit)-sensor mounted on the waist, making it possible to achieve a high accuracy on a wearable device with the extracted features. Using the fixed threshold method is difficult for achieving satisfactory detection accuracy, due to various characteristics and behaviors in the movement of different individuals. In contrast, one could realize high-accuracy detection with machine learning-based methods, but it is difficult to apply them in the wearable devices due to the high hardware requirement. Our method merges the two methods above. We build a convolutional neural network (CNN) with a class activation mapping (CAM) method, which could highlight the class-specific region in the data and obtain a hot map of the fall data. After training on the MobiAct dataset, the model could achieve high-accuracy detection (95.55%) and obtain the region with high contributions to the classification. Then, we manually extract effective features and characteristics of this region and form our special threshold method, achieving pre-impact fall detection in real-world data. Consequently, our method achieves accuracy of 95.33% and a detection time of within 400 ms.
Extreme Dimensionality Reduction with Quantum Modeling

Effective and efficient forecasting relies on identification of the relevant information contained in past observations---the predictive features---and isolating it from the rest. When the future of a process bears a strong dependence on its behavior far into the past, there are many such features to store, necessitating complex models with extensive memories. Here, we highlight a family of stochastic processes whose minimal classical models must devote unboundedly many bits to tracking the past. For this family, we identify quantum models of equal accuracy that can store all relevant information within a single two-dimensional quantum system (qubit). This represents the ultimate limit of quantum compression and highlights an immense practical advantage of quantum technologies for the forecasting and simulation of complex systems.
Keep Your Data Locally: Federated-Learning-Based Data Privacy Preservation in Edge Computing

Recently, edge computing has attracted significant interest due to its ability to extend cloud computing utilities and services to the network edge with low response times and communication costs. In general, edge computing requires mobile users to upload their raw data to a centralized data server for further processing. However, these data usually contain sensitive information about mobile users that the users do not want to reveal, such as sexual orientation, political stance, health status, and service access history. The transmission of user data increases the leakage risk of data privacy since many extra devices can get access to these data. In this article, we attempt to keep the data of edge devices and end users on their local storage to resist the leakage of user privacy. To this end, we integrate federated learning and edge computing to propose P2FEC, a privacy-preserving framework that can construct a unified deep learning model across multiple users or devices without uploading their data to a centralized server. Furthermore, we use membership inference attacks as a case study for the privacy analysis of edge computing. The experiments show that the model constructed by our framework can achieve similar prediction performance and stricter protection of data privacy, compared to the model trained by standard edge computing.
Macroeconomic factors and antidumping filings: evidence from four countries

This paper examines the relationship between antidumping filings and macroeconomic factors. We show that real exchange rate fluctuations affect the two criteria for dumping in opposite ways, making the overall effect on filings ambiguous in theory. Interestingly, no such ambiguity is evidenced in the data. Examining the filing patterns of the four major users of AD law during the 1980–98 period we find that real exchange rates and domestic real GDP growth both have statistically significant impacts on filings. Bilateral filing data indicate that a one-standard deviation real appreciation of the domestic currency increases filings by 33% while a one-standard deviation fall in domestic real GDP increases filings by 23%.
Level of Contamination Assessment of Potentially Toxic Elements in the Urban Soils of Volos City (Central Greece)

A three-year study, designed to record the level of potentially toxic elements within the urban complex in the city of Volos, Greece, was carried out between 2018 and 2020. For the needs of the aforementioned study, 62 surface (0–15 cm) soil samples were collected each year (i.e., 186 samples in total) from an urban area of 3.65 km2, and the average value of pseudo-total metal concentration was measured. Soil pollution indices, such as the contamination factor (CF) and the geo-accumulation index (Igeo), were estimated regarding each of the metals of interest. The respective thematic maps were constructed, and the spatial variability of the contamination degree was displayed. Higher values of the CF and Igeo were obtained near the heavy traffic roads and beside the railway station, the bus stations, and the commercial port. The maps based on the pollution indices, along with the database that was constructed using the appropriate mathematical tools of geostatistical analysis, may be a useful tool for monitoring, prediction, and continuous verification of contamination in the urban soils of Volos city.
Millisecond Coherence in a Superconducting Qubit

Improving control over physical qubits is a crucial component of quantum computing research. Here we report a superconducting fluxonium qubit with uncorrected coherence time T2*=1.48±0.13 ms, exceeding the state of the art for transmons by an order of magnitude. The average gate fidelity was benchmarked at 0.99991(1). Notably, even in the millisecond range, the coherence time is limited by material absorption and could be further improved with a more rigorous fabrication. Our demonstration may be useful for suppressing errors in the next generation quantum processors.Received 21 April 2021Revised 24 March 2023Accepted 10 May 2023DOI:https://doi.org/10.1103/PhysRevLett.130.267001© 2023 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasJosephson effectQuantum benchmarkingQuantum computationQuantum gatesQuantum memoriesPhysical SystemsJosephson junctionsLow-temperature superconductorsSQUIDSuperconducting devicesSuperconducting qubitsThin filmsCondensed Matter, Materials & Applied PhysicsQuantum Information