Reviewerly/exHarmony huggingface dataset:
Deep Multi-path Low-Light Image Enhancement

Noise and color shifts are prone to occur in low-light conditions, which can significantly affect imaging quality. Simply stretching the pixel intensity to improve the illumination level is not able to eliminate noise and color shifts, and it may even lead to artifacts amplification. Various methods have been developed to avoid noise and color shifts in low-light conditions over the past few years. However, most of them may fail to handle different exposure conditions at the same time. In this paper, we propose a novel multi-path convolutional neural network architecture for this task. Luminance and chroma were impacted in two different ways in low-light conditions. By using different well-designed custom networks and loss functions, luminance and chroma can be well restored. Extensive experiments have been performed to demonstrate the superiority of our approach to the state-of-art methods using multiple metrics under different exposure conditions.
Minimal Complexity Support Vector Machines

Minimal complexity machines (MCMs) minimize the VC (Vapnik-Chervonenkis) dimension to obtain high generalization abilities. However, because the regularization term is not included in the objective function, the solution is not unique. In this paper, to solve this problem, we propose fusing the MCM and the standard support vector machine (L1 SVM). This is realized by minimizing the upper bound on the decision function for the training data in the L1 SVM. We call the machine Minimum complexity L1 SVM (ML1 SVM). We compare the ML1 SVM with other types of SVMs including the L1 SVM using several benchmark data sets and show that the ML1 SVM performs comparable to or better than the L1 SVM.
Serious Game Design of Virtual Reality Balance Rehabilitation with a Record of Psychophysiological Variables and Emotional Assessment

Virtual Reality (VR) offers an alternative to complement physical or neurological rehabilitation based on the efficacy of Gamification as a technique to get compromised, enjoy, and turn treatment periods less annoying through an immersive task. This work presents the design of a VR environment and takes into a count three levels of presence: self-presence, physical, and social. Thirteen healthy participants performed a traditional balance session with a wobble board through a parachute game. The direction of the fall is controlled with the inclination of a wobble board platform instrumented with a Gyroscope, electrocardiography (ECG), electrodermal activity (EDA), and respiratory rate (RESP) signals are simultaneously recorded. These psychophysiological responses of the Autonomous Nervous System (SNA) are contrasted with a standardized emotional self-report questionnaire to gather information about the experience and was filled out by the participant with the eye selection of the Oculus Rift device. The integrated Unity-Matlab system stored and processed some of the extraction data of 15 characteristics associated with the biosignals and three emotional levels, In addition to proposing a severe game as a solution to problems of conventional methodology such as low attention, monotonous and predictable sessions, combining computerized simulation and innovative technologies for more pleasant recovery therapy.
An Observational Investigation of Reverse Engineers’ Processes

Reverse engineering is a complex process essential to software-security tasks such as vulnerability discovery and malware analysis. Significant research and engineering effort has gone into developing tools to support reverse engineers. However, little work has been done to understand the way reverse engineers think when analyzing programs, leaving tool developers to make interface design decisions based only on intuition. This paper takes a first step toward a better understanding of reverse engineers' processes, with the goal of producing insights for improving interaction design for reverse engineering tools. We present the results of a semi-structured, observational interview study of reverse engineers (N=16). Each observation investigated the questions reverse engineers ask as they probe a program, how they answer these questions, and the decisions they make throughout the reverse engineering process. From the interview responses, we distill a model of the reverse engineering process, divided into three phases: overview, sub-component scanning, and focused experimentation. Each analysis phase's results feed the next as reverse engineers' mental representations become more concrete. We find that reverse engineers typically use static methods in the first two phases, but dynamic methods in the final phase, with experience playing large, but varying, roles in each phase. % and the role of experience varies between phases. Based on these results, we provide five interaction design guidelines for reverse engineering tools.
Algoritma K-Nearest Neighbor dengan Euclidean Distance dan Manhattan Distance untuk Klasifikasi Transportasi Bus

K-Nearest Neighbor is a data mining algorithm that can be used to classify data. K-Nearest Neighbor works based on the closest distance. This research using the Euclidean and Manhattan distances to calculate the distance of Lhokseumawe-Medan bus transportation. Data that used in this research was obtained from the Organisasi Angkutan Darat Kota Lhokseumawe. The results of the test with k = 3 has obtained the percentage of 44.94% for Precision, 37.06% Recall, and 81.96% Accuracy for the performance of K-NN with Euclidean Distance. Whereas by using Manhattan Distance the result obtained was 45.49% for Precision, 36.39% Recall, and 84.00% Accuracy. The result shown that Manhattan Distance obtained the highest accuracy, with the difference of 2.04% higher than Euclidean Distance. It indicates that Manhattan Distance is more accurate than Euclidean Distance to classify the bus transportation.
Election Verifiability Revisited: Automated Security Proofs and Attacks on Helios and Belenios

Election verifiability aims to ensure that the outcome produced by electronic voting systems correctly reflects the intentions of eligible voters, even in the presence of an adversary that may corrupt various parts of the voting infrastructure. Protecting such systems from manipulation is challenging because of their distributed nature involving voters, election authorities, voting servers and voting platforms. An adversary corrupting any of these can make changes that, individually, would go unnoticed, yet in the end will affect the outcome of the election. It is, therefore, important to rigorously evaluate whether the measures prescribed by election verifiability achieve their goals. We propose a formal framework that allows such an evaluation in a systematic and automated way. We demonstrate its application to the verification of various scenarios in Helios and Belenios, two prominent internet voting systems, for which we capture features and corruption models previously outside the scope of formal verification. Relying on the Tamarin protocol prover for automation, we derive new security proofs and attacks on deployed versions of these protocols, illustrating trade-offs between usability and security.
Cross-point calibration method for the Scheimpflug measurement system

The Scheimpflug measurement system has broad application prospects in the fields of tomographic particle image velocimetry (PIV), ophthalmology, and three-dimensional reconstruction. Additionally, its special imaging structure makes the theoretical model of other traditional calibration methods no longer applicable. A cross-point calibration method in which the real-world coordinate system is transferred from an in-kind calibrator to displacement, achieving great improvements in the quantity and accuracy of the feature points, is proposed. This method, which makes up for the defects introduced by the distorted imaging plane, also in turn simplifies the calibration process. Finally, the proposed method is tested via both simulation and calibration experiments. The high calibration accuracy verifies that the method is feasible.
Big Data Analysis Model of Customer Appeal Based on Power Enterprise Service Platform

Today is the era of Internet information. Affected by economic development and living standards, power users have higher and higher expectations for power supply services. Although customer complaints are inevitable, as an important part of customer feedback in high-quality services, how to use customer complaint information efficiently, reasonably, and scientifically has become a problem that every power grid company must face in the Internet + era. The purpose of this article is to study the big data analysis model of customer demands based on the electric power enterprise service platform. This article first introduces the meaning of customer satisfaction and customer demands, and then analyzes the development status of the power enterprise service platform, and proposes the need to improve customer demand management. Based on this, this article establishes a big data analysis model of customer demands. The experimental results prove that the analysis model designed in this paper can not only solve the needs of power enterprises, but also improve the economic benefits of enterprises. In this paper, the economic benefits obtained from the four indicators of failure repair service, power outage information management norms, business process norms and customer service representatives' work efficiency improvement, and the statistical results of a power company using the model one year later are 5.25 million yuan.
Detecting falls and estimation of daily habits with depth images using machine learning algorithms

Different approaches have been proposed in the literature to detect the fall of an elderly person. In this paper, we propose a fall detection method based on the classification of parameters extracted from depth images. Three supervised learning methods are compared: decision tree, K-Nearest Neighbors (K-NN) and Random Forests (RF). The methods have been tested on a database of depth images recorded in a nursing home over a period of 43 days. The Random Forests based method yields the best results, achieving 93% sensitivity and 100% specificity when we restrict our study around the bed. Furthermore, this paper also proposes a 37 days follow-up of the person, to try and estimate his or her daily habits.
On the Use of Answer Set Programming for Model-Based Diagnosis

Model-based diagnosis has been an active area of AI for several decades leading to many applications ranging from automotive to space. The underlying idea is to utilize a model of a system to localize faults in the system directly. Model-based diagnosis usually is implemented using theorem provers or constraint solvers combined with specialized diagnosis algorithms. In this paper, we contribute to research in model-based diagnosis and present a way of using answer set programming for computing diagnoses. In particular, we discuss a specific coding of diagnosis problems as answer set programs, and answer the research question whether answer set programming can be used for diagnosis in practice. For this purpose, we come up with an experimental study based on Boolean circuits comparing diagnosis using answer set programming with diagnosis based on a specialized diagnosis algorithm. Although, the specialized algorithm provide diagnoses in shorter time on average, answer set programming offers additional features making it very much attractive to be used in practice.
Research on Typical Architecture and Application of Distribution Internet of Things Based on Blockchain

Abstract The new round of energy reform is marked by large-scale development and utilization of new energy and re-electrification as the fundamental path, to cope with the pressure of absorbing renewable energy and stupendous access to variable loads such as electric vehicle charging piles creates several problems such as a large number of equipment failure and low standardization in the distribution network, to overcome this, the distribution Internet of Things (D-IoT) came into existence. In this paper an equivalent architecture of blockchain and D-IoT is built based on master-slave multi-chain. The combined equivalent architecture proposes the crucial application of blockchain technologies as consensus mechanism and smart contract in the D-IoT in order to explore the broad application prospects of blockchain in the D-IoT.
Deep signature-based isolated and large scale continuous gesture recognition approach

In recent years, recognition of hand gestures has received considerable attention from the research community, with an increased interest in advanced decision support systems. In applications of this type of system, recognition of hand gestures based on vision is used in the design and development of interfaces such as that of automobile users. This type of interface allows drivers to interact with the vehicle, without affecting their concentrations. This also increases driver comfort, without compromising safety. However, the video-based gesture recognition problem is not trivial and poses problems due to: (i) intra and inter-person variations in the movement of human hand gestures; (ii) variations between people in the shape and size of the human hand; and (iii) variations in lighting; and background noise. In this paper, a new recognition system is proposed to address the problem of large scale continuous gesture recognition with depth and gray-scale input videos. The proposed recognition system contains 3 main phases. Firstly continuous gesture sequences are segmented into isolated gesture using mean of velocity information calculated based on the estimation of deep optical flow. For each isolated segment a set of relevant descriptors named deep signature features is extracted in order to characterize different intensity and spatial information describing the location, the velocity and the orientation of the movement. The features constructed for both depth and gray-scale sequences are fed to linear SVM for classification. To evaluate the effectiveness of our recognition model, it was compared with other methods using two challenging datasets: KTH and Chalearn. Indeed, a first experimental study has shown that, using the KTH dataSet and the SVM classifier, results given by our method outperforms previously published methods and it reaches 95.6% in term of accuarcy and 96% in term of precision. The second experimental study, carried out on the ChaLearn LAP ConGD data set, also showed a superiority of our method from the point of view of precision by codelivery over other methods with mean Jaccard Index of 0.5011.
Recommendations for Different Tasks Based on the Uniform Multimodal Joint Representation

Content curation social networks (CCSNs), such as Pinterest and Huaban, are interest driven and content centric. On CCSNs, user interests are represented by a set of boards, and a board is composed of various pins. A pin is an image with a description. All entities, such as users, boards, and categories, can be represented as a set of pins. Therefore, it is possible to implement entity representation and the corresponding recommendations on a uniform representation space from pins. Furthermore, lots of pins are re-pinned from others and the pin’s re-pin sequences are recorded on CCSNs. In this paper, a framework which can learn the multimodal joint representation of pins, including text representation, image representation, and multimodal fusion, is proposed. Image representations are extracted from a multilabel convolutional neural network. The multiple labels of pins are automatically obtained by the category distributions in the re-pin sequences, which benefits from the network architecture. Text representations are obtained with the word2vec tool. Two modalities are fused with a multimodal deep Boltzmann machine. On the basis of the pin representation, different recommendation tasks are implemented, including recommending pins or boards to users, recommending thumbnails to boards, and recommending categories to boards. Experimental results on a dataset from Huaban demonstrate that the multimodal joint representation of pins contains the information of user interests. Furthermore, the proposed multimodal joint representation outperformed unimodal representation in different recommendation tasks. Experiments were also performed to validate the effectiveness of the proposed recommendation methods.
Perspectives on Adversarial Classification

Adversarial classification (AC) is a major subfield within the increasingly important domain of adversarial machine learning (AML). So far, most approaches to AC have followed a classical game-theoretic framework. This requires unrealistic common knowledge conditions untenable in the security settings typical of the AML realm. After reviewing such approaches, we present alternative perspectives on AC based on adversarial risk analysis.
Weighted majority rule ensemble classifier for sensor fault classification for plasma position control in Tokamaks

Abstract Tokamaks are the most promising devices which employ magnetic confinement of Deuterium and Tritium plasma to achieve nuclear fusion for power generation. Magnetic flux sensors are employed to measure the position of the plasma column inside the device. Faults occurring in these sensors may cause the failure of control of the plasma position, thereby terminating the fusion process. In this paper, we present a comparison of different Machine Learning (ML) algorithms to detect and classify the sensors’ faults. We applied four different ML algorithms; namely, Logistic Regression based Multi-layer Perceptron Classifier (MLP), Support Vector Classifier (SVC), K-Nearest Neighbor (KNN), and Decision Tree (DT) as the individual ML classifiers for the task of sensor fault classification. We propose an ensemble classifier (ECF) using the classifiers, as mentioned earlier. We provide a comparative assessment of the classification accuracy of different classifiers, namely, MLP, SVC, KNN, DT, and ECF. The ECF utilized a weighted majority voting method to combine the decision of individual faults classifiers. Overall, robustness to the miss-classification of new data by the individual sensor fault classifiers by applying the ECF is reported.
Representativity and Consistency Measures for Deep Neural Network Explanations

The adoption of machine learning in critical contexts requires a reliable explanation of why the algorithm makes certain predictions. To address this issue, many methods have been proposed to explain the predictions of these black box models. Despite the choice of those many methods, little effort has been made to ensure that the explanations produced are objectively relevant. While it is possible to establish a number of desirable properties of a good explanation, it is more difficult to evaluate them. As a result, no measures are actually associated with the properties of consistency and generalization of explanations. We are introducing a new procedure to compute two new measures, Relative Consistency ReCo and Mean Generalization M eGe, respectively for consistency and generalization of explanations. Our results on several image classification datasets using progressively degraded models allow us to validate empirically the reliability of those measures. We compare the results obtained with those of existing measures. Finally we demonstrate the potential of the measures by applying them to different families of models, revealing an interesting link between gradient-based explanations methods and 1-Lipschitz networks.
Secure Online Banking With Biometrics

Online banking is a substantial part of daily routine of large enterprise businesses and individual users for making transactions. However, security in online banking is a major dilemma owing to the vulnerable authentication schemes. Online banking employs conventional methods of Username and Passwords for authenticating the user. However, these techniques only verify the passwords and not the end user who requests the services for which only legitimate person is privileged to use. Using these vulnerabilities of online banking, intruders tend to masquerade legitimate user for unauthorized access to the system. This paper presents three-factor authentication scheme, which includes username/password, familiar random images and fingerprint data of user in order to make user-authentication more secure. Subsequently, Match on Card technique is proposed to ensure the confidentiality and integrity of biometric data of user since the reference feature set of user once store in credit card would not be permitted to move out and matching is performed on the credit card itself. In addition, the concept of familiar random images is used in order to enhance the security, as humans are believed to have remarkable visual remembering capability in comparison to words. The results show that the incorporation of three-factor authentication in online banking application resists the intruder to illicitly use banking services of any authorized user. The proposed biometric online banking system tends to assist in lessening the cybercrime rate of online banking and tends to escalate the user confidence in using banking services online.
An Approach for Schema Extraction of NoSQL Graph Databases

Currently, a large volume of heterogeneous data is generated and consumed by several classes of applications, which raise a new family of database models called NoSQL. NoSQL graph databases is a member of this family. They provide high scalability and are schemaless, i.e., they do not require an implicit schema such as relational databases. However, the knowledge of how data is structured may be of great importance for data integration or data analysis processes. There are some works in the literature that extract the schema from graph structures or graph-based data sources. Different from them, this work proposes a comprehensive approach that consider all the common NoSQL database graph data model concepts, and generates a schema in the recent JSON Schema recommendation. Experimental evaluations show that our solution generates a suitable schema representation with a linear complexity.
Boosting Image-Based Localization Via Randomly Geometric Data Augmentation

Visual localization is a fundamental problem in computer vision and robotics. Recently, deep learning has shown to be effective for robust monocular localization. Most deep learning-based methods utilize convolution neural network (CNN) to regress global 6 degree-of-freedom (Dof) pose. However, these methods suffer from pose sparsity, leading to over-fitting during training and poor localization performance on unseen data. In this paper, we try to alleviate this issue by implementing randomly geometric augmentation (RGA) during training. Specifically, we firstly estimate the depth map using a depth estimation network for the initial training image. Combing the estimated depth, RGB image and its corresponding pose, we can randomly synthesize new images of different views. The synthesized and initial images are used to train the pose regression network. Experiment results show our geometric augmentation strategy can significantly improve the localization accuracy.