Reviewerly/exHarmony huggingface dataset:
Spatial modeling for the optimum site selection of solar photovoltaics power plant in the northwest coast of Egypt

The unbalanced distribution of the Egyptian population causes serious social and economic problems. Redistributing population density to fully utilize the uninhabited areas like desert regions is very critical. This requires discovering renewable energy and water resources, to achieve an optimal goal of the sustainable national strategy. Therefore, this paper aims to define the most suitable locations for establishing the photovoltaic (PV) power plants considering the techno-economic and environmental conditions, assuring the maximum power achievement with minimizing project cost. To achieve this, the integration of Geographic Information System (GIS) tools, Remote Sensing technology (RS) with the Multi-Criteria Decision Making (MCDM) technique was performed. Among MCDM techniques, the Analytic Hierarchy Process (AHP) method has been used to determine the weights of the multi-criteria (techno-economic and environmental) as a more suitable tool to solve site selection problems. The obtained results showed that the entire region's surface receives a large amount of radiation, as the maximum and the minimum values of solar radiation for 2018 were 5.9 - 4.7 kWh/m2/day, respectively. The Land Suitability Index (LSI) map was created to evaluate the potentiality of the sites. LSI was classified into five categories: “most suitable,” “highly suitable,” “moderately suitable,” “marginally suitable,” and “least suitable”. As a result, 24.9% (261.1747 km2) of the investigation area is more suitable and promising for deploying photovoltaic (PV) power plants.
Location Privacy-Preserving Distance Computation for Spatial Crowdsourcing

Data privacy, especially location privacy, is paramountly important for protecting individual's information in smart cities in the big data era. One of the examples is in spatial crowdsourcing (SC). It enables people not only to issue spatiotemporal tasks to ask for help as requesters but also to solve others' tasks as workers on the SC platform. While SC brings convenience to people, it also produces severe location privacy problems, which have been recently paid more attention from both academia and industries. In this article, we address the location privacy problem in SC in a practical and secure way. We propose a location privacy-preserving framework for almost all existed mainstream distance computations in the SC system, namely, Euclidean-L3P, Minkowski-L3P, Manhattan-L3P, and Chebyshev-L3P, among which the first two are constructed based on homomorphic encryption and composite-order multilinear mapping while the latter two on the homomorphic encryption and prefix membership verification approach. Location privacy is resolved because of the above techniques having enabled that all distance computations are evaluated through ciphertexts without disclosing any location information. Security analysis shows that our framework can prevent a strong adversary from obtaining participants' location privacy. Performance analysis evaluates computation and communication overheads between protocols. The results show that Euclidean-L3P is more efficient than Manhattan-L3P and Chebyshev-L3P in terms of computation overheads when the SC applications require a small number of participants, a large plaintext space, and a small number of base stations. Moreover, compared with Manhattan-L3P and Chebyshev-L3P, Euclidean-L3P is a better choice in terms of communication overhead.
To What Extent Will Blockchain Drive the Machine Economy? Perspectives From a Prospective Study

The advancements in the fields of IT and robotics are provoking the fourth industrial revolution and transforming our economy toward the machine economy. Billions of economically autonomous machines engaging in business relationships raise novel requirements regarding security, privacy, regulation, business models, trustful transaction processing, and interoperability. A technology that promises to provide solutions to these challenges is blockchain. Neither practice has spawned productive solutions nor has research been able to assess the phenomenon profoundly. First investigations focus on specific use cases, but existing work does not examine the overall impact and role of blockchain in the machine economy. However, this knowledge is of particular importance for various stakeholders. In this article, we address this research and knowledge gap by conducting a study with 50 blockchain and machine economy experts. We establish a sound research fundament by following a literature assessment, group discussion, interviews, and qualitative data analysis. As main research instrument, we chose a Delphi study, which has a long-proven record of application in prospective studies and has been applied to examine future premises, estimates, and challenges across nearly all disciplines. This allows us to derive six key findings leading to a transparent picture of blockchain's role in the machine economy.
Learning Asr-Robust Contextualized Embeddings for Spoken Language Understanding

Employing pre-trained language models (LM) to extract contextualized word representations has achieved state-of-the-art performance on various NLP tasks. However, applying this technique to noisy transcripts generated by automatic speech recognizer (ASR) is concerned. Therefore, this paper focuses on making contextualized representations more ASR-robust. We propose a novel confusion-aware fine-tuning method to mitigate the impact of ASR errors on pre-trained LMs. Specifically, we fine-tune LMs to produce similar representations for acoustically confusable words that are obtained from word confusion networks (WCNs) produced by ASR. Experiments on multiple benchmark datasets show that the proposed method significantly improves the performance of spoken language understanding when performing on ASR transcripts.
Lightweight and Efficient End-To-End Speech Recognition Using Low-Rank Transformer

Highly performing deep neural networks come at the cost of computational complexity that limits their practicality for deployment on portable devices. We propose the low-rank transformer (LRT), a memory-efficient and fast neural architecture that significantly reduces the parameters and boosts the speed of training and inference for end-to-end speech recognition. Our approach reduces the number of parameters of the network by more than 50% and speeds up the inference time by around 1.35x compared to the baseline transformer model. The experiments show that our LRT model generalizes better and yields lower error rates on both validation and test sets compared to an uncompressed transformer model. The LRT model outperforms those from existing works on several datasets in an end-to-end setting without using an external language model or acoustic data.
Outlier-robust Kalman filters with mixture correntropy

We consider the robust filtering problem for a nonlinear state-space model with outliers in measurements. To improve the robustness of the traditional Kalman filtering algorithm, we propose in this work two robust filters based on mixture correntropy, especially the double-Gaussian mixture correntropy and Laplace-Gaussian mixture correntropy. We have formulated the robust filtering problem by adopting the mixture correntropy induced cost to replace the quadratic one in the conventional Kalman filter for measurement fitting errors. In addition, a tradeoff weight coefficient is introduced to make sure the proposed approaches can provide reasonable state estimates in scenarios where measurement fitting errors are small. The formulated robust filtering problems are iteratively solved by utilizing the cubature Kalman filtering framework with a reweighted measurement covariance. Numerical results show that the proposed methods can achieve a performance improvement over existing robust solutions.
Disease prediction via Bayesian hyperparameter optimization and ensemble learning

Abstract Objective Early disease screening and diagnosis are important for improving patient survival. Thus, identifying early predictive features of disease is necessary. This paper presents a comprehensive comparative analysis of different Machine Learning (ML) systems and reports the standard deviation of the results obtained through sampling with replacement. The research emphasises on: (a) to analyze and compare ML strategies used to predict Breast Cancer (BC) and Cardiovascular Disease (CVD) and (b) to use feature importance ranking to identify early high-risk features. Results The Bayesian hyperparameter optimization method was more stable than the grid search and random search methods. In a BC diagnosis dataset, the Extreme Gradient Boosting (XGBoost) model had an accuracy of 94.74% and a sensitivity of 93.69%. The mean value of the cell nucleus in the Fine Needle Puncture (FNA) digital image of breast lump was identified as the most important predictive feature for BC. In a CVD dataset, the XGBoost model had an accuracy of 73.50% and a sensitivity of 69.54%. Systolic blood pressure was identified as the most important feature for CVD prediction.
Egocentric Hand Track and Object-Based Human Action Recognition

Egocentric vision is an emerging field of computer vision that is characterized by the acquisition of images and video from the first person perspective. In this paper we address the challenge of egocentric human action recognition by utilizing the presence and position of detected regions of interest in the scene explicitly, without further use of visual features. Initially, we recognize that human hands are essential in the execution of actions and focus on obtaining their movements as the principal cues that define actions. We employ object detection and region tracking techniques to locate hands and capture their movements. Prior knowledge about egocentric views facilitates hand identification between left and right. With regard to detection and tracking, we contribute a pipeline that successfully operates on unseen egocentric videos to find the camera wearer's hands and associate them through time. Moreover, we emphasize on the value of scene information for action recognition. We acknowledge that the presence of objects is significant for the execution of actions by humans and in general for the description of a scene. To acquire this information, we utilize object detection for specific classes that are relevant to the actions we want to recognize. Our experiments are targeted on videos of kitchen activities from the Epic-Kitchens dataset. We model action recognition as a sequence learning problem of the detected spatial positions in the frames. Our results show that explicit hand and object detections with no other visual information can be relied upon to classify hand-related human actions. Testing against methods fully dependent on visual features, signals that for actions where hand motions are conceptually important, a region-of-interest-based description of a video contains equally expressive information with comparable classification performance.
27.3 EM and Power SCA-Resilient AES-256 in 65nm CMOS Through &gt;350× Current-Domain Signature Attenuation

Computationally-secure cryptographic algorithms when implemented on physical platforms leak critical physical signals correlated with the secret key in the form of power consumption and electromagnetic (EM) emanations. This can be exploited by an adversary, leading to side-channel attacks (SCA) that can recover the secret key. Circuit-level on-chip countermeasures include a switched-capacitor current equalizer [1], charge-recovery logic [2], an integrated voltage regulator (IVR) [3], and an all-digital low-dropout (LDO) regulator [4], which suffer from performance degradation, high power/area overheads because of large embedded passives, as well as EM leakage from large metal-insulator-metal (MIM) capacitor top plates. Alternatively, simulations of shunt LDO-based regulators have been shown to be effective for power SCA resistance [5]. Noting that the correlated current is the source of both power (at supply pin) and EM leakage (radiation throughout current path), this work embraces current-domain `signature attenuation' (CDSA) as a low-overhead generic countermeasure against both EM and power side-channel attacks to achieve the highest minimum traces to disclosure (MTD reported to date.
A visual analytics system for multi-model comparison on clinical data predictions

There is a growing trend of applying machine learning methods to medical datasets in order to predict patients' future status. Although some of these methods achieve high performance, challenges still exist in comparing and evaluating different models through their interpretable information. Such analytics can help clinicians improve evidence-based medical decision making. In this work, we develop a visual analytics system that compares multiple models' prediction criteria and evaluates their consistency. With our system, users can generate knowledge on different models' inner criteria and how confidently we can rely on each model's prediction for a certain patient. Through a case study of a publicly available clinical dataset, we demonstrate the effectiveness of our visual analytics system to assist clinicians and researchers in comparing and quantitatively evaluating different machine learning methods.
Defense Against Adversarial Attacks on Spoofing Countermeasures of ASV

Various forefront countermeasure methods for automatic speaker verification (ASV) with considerable performance in anti-spoofing are proposed in the ASVspoof 2019 challenge. However, previous work has shown that countermeasure models are vulnerable to adversarial examples indistinguishable from natural data. A good countermeasure model should not only be robust against spoofing audio, including synthetic, converted, and replayed audios; but counteract deliberately generated examples by malicious adversaries. In this work, we introduce a passive defense method, spatial smoothing, and a proactive defense method, adversarial training, to mitigate the vulnerability of ASV spoofing countermeasure models against adversarial examples. This paper is among the first to use defense methods to improve the robustness of ASV spoofing countermeasure models under adversarial attacks. The experimental results show that these two defense methods positively help spoofing countermeasure models counter adversarial examples.
31.2 CIM-Spin: A 0.5-to-1.2V Scalable Annealing Processor Using Digital Compute-In-Memory Spin Operators and Register-Based Spins for Combinatorial Optimization Problems

Annealing processors [1]-[3] based on the convergence property of the Ising model offer an attractive means for solving combinatorial optimization problems [4]. A recently developed annealing processor exploiting the quantum tunneling effect [1] implemented 2048 qubits using 128,000+ Josephson junctions. However, the practical application of quantum annealers is limited by the extremely low temperature (15mK) for operating their superconducting circuits and the associated huge power consumption (25kW). Alternatively, low-power annealing processors [2]-[3] based on the simulated annealing have been developed recently using low-cost CMOS processes. However, previous annealing processors with SRAM-based spin and coefficient memories have limited scalability, and there is a significant room for improvement in energy efficiency and annealing time. In this paper, we propose a scalable annealing processor based on compute-in-memory spin operators and register-based spins, enabling >10× higher energy-efficiency and faster annealing time.
An Automated Method for Counting Red Blood Cells using Image Processing

In healthcare, blood testing is observed to be as one of the most significant medical examination tests. In pathology labs, different types of blood cells are counted to diagnose the diseases patient. By counting RBCs (Red Blood Cells) in images of blood cells can play a very great role in detection as well as to follow the treatment process of number of diseases such as anemia, leukemia etc. Counting and examination of blood cells manually by microscope is tedious, time intense and entails a lot of technical expertise. Hence arises a need to come across for automated blood cell detection and counting system that can facilitate physician for diagnosing diseases in fast and efficient way. According to present studies, the RBCs are classified in four types of abnormality, namely elliptocytes, echinocytes, tear drop cells and macrocytes. In this paper, technique has been introduced to count the RBCs automatically. In proposed work, images are classified on the basis of color, texture and morphology. Process of counting of cells is done into three parts: image processing including texture feature extraction using morphology, thresholding segmentation and counting of cells using Hough transformation. The proposed algorithm achieves overall accuracy of 91.667% and is computationally very efficient as it takes only 0.81432 seconds to count the number of red blood cells for different blood samples.
Quantum algorithms for estimating physical quantities using block encodings

We present quantum algorithms for the estimation of n-time correlation functions, the local and non-local density of states, and dynamical linear response functions. These algorithms are all based on block-encodings - a versatile technique for the manipulation of arbitrary non-unitary matrices on a quantum computer. We describe how to 'sketch' these quantities via the kernel polynomial method which is a standard strategy in numerical condensed matter physics. These algorithms use amplitude estimation to obtain a quadratic speedup in the accuracy over previous results, can capture any observables and Hamiltonians presented as linear combinations of Pauli matrices, and are modular enough to leverage future advances in Hamiltonian simulation and state preparation.
A Lightweight Privacy-Aware IoT-Based Metering Scheme for Smart Industrial Ecosystems

The smart grid emerges as a new era of the electronic power grid. It integrates advanced sensing technologies, communications, and controlling methods that tell how electricity travels from different generation points to consumers. In order to fulfill customers' satisfaction and two way communications, a huge number of smart meters are deployed in different countries for real-time consumption and presentation of the rigorous energy usage. The privacy of industrial ecosystems may require greater attention while considering minimum network load, lower computational resources, better energy efficiency, and accuracy of data. Different research works have been done to tackle customers' privacy, but at the cost of using more computational resources, communication overhead, and hiring of a trusting third party. In this article, we have proposed a symmetric encryption scheme for industrial ecosystems in the Internet of Thing (IoT) environment. The performance evaluation and security analysis demonstrate successful user privacy and integrity with lower computational resources and communication overhead.
Optimal breast tumor diagnosis using discrete wavelet transform and deep belief network based on improved sunﬂower optimization method

Breast cancer is one of the most widespread types of cancer among women, but it does not necessarily mean pre-death, such that timely diagnosis of it can make the patient get to survive. Due to the significance of breast cancer, early diagnosis of abnormal areas in breast helps to cure this cancer in the initial steps. This study presents a new computer-aided diagnosis system for the early detection of breast cancer. The proposed method contains five important stages including noise reduction, image segmentation, mathematical morphology, feature extraction based on the combination of discrete wavelet decomposition and GLCM, and finally classification based on Deep Belief Network (DBN). To improve the DBN efficiency, it is optimized by an enhanced version of the sunﬂower optimization algorithm. Simulation results are applied to the MIAS database and the achievements have been compared with three different methods. Simulation results showed that the rate of accuracy, specificity, and sensitivity for the proposed model are achieved 91.5%, 72.4%, and 94.1%, respectively for the MIAS benchmark which gives better achievements toward the previous methods.
Better Data Labelling With EMBLEM (and how that Impacts Defect Prediction)

Standard automatic methods for recognizing problematic development commits can be greatly improved via the incremental application of human+artificial expertise. In this approach, called EMBLEM, an AI tool first explore the software development process to label commits that are most problematic. Humans then apply their expertise to check those labels (perhaps resulting in the AI updating the support vectors within their SVM learner). We recommend this human+AI partnership, for several reasons. When a new domain is encountered, EMBLEM can learn better ways to label which comments refer to real problems. Also, in studies with 9 open source software projects, labelling via EMBLEM's incremental application of human+AI is at least an order of magnitude cheaper than existing methods ( <inline-formula><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> eight times). Further, EMBLEM is very effective. For the data sets explored here, EMBLEM better labelling methods significantly improved <inline-formula><tex-math notation="LaTeX">$P_{opt}20$</tex-math></inline-formula> and G-scores performance in nearly all the projects studied here.
Algorithm selection for software validation based on graph kernels

Abstract Algorithm selection is the task of choosing an algorithm from a given set of candidate algorithms when faced with a particular problem instance. Algorithm selection via machine learning (ML) has recently been successfully applied for various problem classes, including computationally hard problems such as SAT. In this paper, we study algorithm selection for software validation , i.e., the task of choosing a software validation tool for a given validation instance. A validation instance consists of a program plus properties to be checked on it. The application of machine learning techniques to this task first of all requires an appropriate representation of software. To this end, we propose a dedicated kernel function , which compares two programs in terms of their similarity, thus making the algorithm selection task amenable to kernel-based machine learning methods. Our kernel operates on a graph representation of source code mixing elements of control-flow and program-dependence graphs with abstract syntax trees. Thus, given two such representations as input, the kernel function yields a real-valued score that can be interpreted as a degree of similarity. We experimentally evaluate our kernel in two learning scenarios, namely a classification and a ranking problem: (1) selecting between a verification and a testing tool for bug finding (i.e., property violation), and (2) ranking several verification tools, from presumably best to worst, for property proving. The evaluation, which is based on data sets from the annual software verification competition SV-COMP, demonstrates our kernel to generalize well and to achieve rather high prediction accuracy, both for the classification and the ranking task.