Reviewerly/exHarmony huggingface dataset:
Blind Deconvolution for Poissonian Blurred Image With Total Variation and <i>L</i> <sub>0</sub>-Norm Gradient Regularizations

This paper proposes a regularized blind deconvolution method for restoring Poissonian blurred image. The problem is formulated by utilizing the L0 -norm of image gradients and total variation (TV) to regularize the latent image and point spread function (PSF), respectively, and combining them with the negative logarithmic Poisson log-likelihood. To solve the problem, we propose an approach which combines the methods of variable splitting and Lagrange multiplier to convert the original problem into three sub-problems, and then design an alternating minimization algorithm which incorporates the estimation of PSF and latent image as well as the updation of Lagrange multiplier into account. We also design a non-blind deconvolution method based on TV regularization to further improve the quality of the restored image. Experimental results on both synthetic and real-world Poissonian blurred images show that the proposed method can achieve restored images of very high quality, which is competitive with or even better than some state of the art methods.
Binary Political Optimizer for Feature Selection Using Gene Expression Data

DNA Microarray technology is an emergent field, which offers the possibility of obtaining simultaneous estimates of the expression levels of several thousand genes in an organism in a single experiment. One of the most significant challenges in this research field is to select high relevant genes from gene expression data. To address this problem, feature selection is a well-known technique to eliminate unnecessary genes in order to ensure accurate classification results. This paper proposes a binary version of Political Optimizer (PO) to solve feature selection problem using gene expression data. Two transfer functions are used to design a binary PO. The first one is based on Sigmoid function and will be noted as BPO-S, while the second one is based on V-shaped function and will be noted as BPO-V. The proposed methods are evaluated using 9 biological datasets and compared with 8 binary well-known metaheuristics. The comparative results show the prevalent performance of the BPO methods especially BPO-V in comparison with other techniques.
E2PP: An Energy-Efficient Path Planning Method for UAV-Assisted Data Collection

Using an unmanned aerial vehicle (UAV) to collect data from wireless sensor networks deployed in the field, one of the key tasks is to plan the path for the collection so as to minimize the energy consumption of the UAV. At present, most of the existing methods generally take the shortest flight distance as the optimal objective to plan the optimal path. They simply believe that the shortest path means the least energy consumption of the UAV and ignore the fact that changing direction (heading) can also consume the UAV’s energy in its flight. If the path can be planned based on the UAV’s energy consumption closer to the real situation, the energy consumption of the UAV can be really reduced and its working energy efficiency can be improved. Therefore, this paper proposes a path planning method for UAV-assisted data collection, which can plan an energy-efficient flight path. Firstly, by analyzing the experiment data, we, respectively, model the relationship between the angle of heading change and the energy consumption of the UAV and the relationship between the distance of straight flight and the energy consumption of the UAV. Then, an energy consumption estimation model based on distance and the angle of heading change (ECEMBDA) is put up. By using this model, we can estimate or predict the energy consumption of a UAV to fly from one point (or node) to another (including the start point). Finally, the greedy algorithm is used to plan the path for UAV-assisted data collection according to the above estimated energy consumption. Through simulation and experiments, we compare our proposed method with the conventional method based on pure distance index and greedy algorithm. The results show that this method can obtain data collection path with lower energy consumption and smoother path trajectory, which is more suitable for actual flight.
Evolving Hierarchical and Tag Information via the Deeply Enhanced Weighted Non-Negative Matrix Factorization of Rating Predictions

Identifying the hidden features of items and users of a modern recommendation system, wherein features are represented as hierarchical structures, allows us to understand the association between the two entities. Moreover, when tag information that is added to items by users themselves is coupled with hierarchically structured features, the rating prediction efficiency and system personalization are improved. To this effect, we developed a novel model that acquires hidden-level hierarchical features of users and items and combines them with the tag information of items that regularizes the matrix factorization process of a basic weighted non-negative matrix factorization (WNMF) model to complete our prediction model. The idea behind the proposed approach was to deeply factorize a basic WNMF model to obtain hidden hierarchical features of user’s preferences and item characteristics that reveal a deep relationship between them by regularizing the process with tag information as an auxiliary parameter. Experiments were conducted on the MovieLens 100K dataset, and the empirical results confirmed the potential of the proposed approach and its superiority over models that use the primary features of users and items or tag information separately in the prediction process.
Detection and localization of inter-frame forgeries in videos based on macroblock variation and motion vector analysis

Surveillance videos and footages are the primary sources of evidence for any event or crime in the court of law. However, with the rapid advent of low-cost, computationally cheap video manipulating software and tools, video manipulation has become a no-brainer task today. This introduces a major challenge in authenticating the sanctity/originality of videos before they can be produced in the court, or used in other sensitive application domains. In this paper, we propose a digital forensic technique to detect inter-frame forgeries in surveillance videos. The proposed technique utilizes compressed domain video footprints i.e, prediction footprint variation and variation of motion vectors in videos, for the purpose of video forgery detection and localization. Through this work, we identify the type of forgery that has taken place in a video. We have performed experiment over 43 authentic and 720 inter-frame forged videos. Our experimental results indicate that the proposed technique performs consistently efficiently, irrespective of the group of pictures length and degree of compression in videos.
Taming callbacks for smart contract modularity

Callbacks are an effective programming discipline for implementing event-driven programming, especially in environments like Ethereum which forbid shared global state and concurrency. Callbacks allow a callee to delegate the execution back to the caller. Though effective, they can lead to subtle mistakes principally in open environments where callbacks can be added in a new code. Indeed, several high profile bugs in smart contracts exploit callbacks. We present the first static technique ensuring modularity in the presence of callbacks and apply it to verify prominent smart contracts. Modularity ensures that external calls to other contracts cannot affect the behavior of the contract. Importantly, modularity is guaranteed without restricting programming. In general, checking modularity is undecidable—even for programs without loops. This paper describes an effective technique for soundly ensuring modularity harnessing SMT solvers. The main idea is to define a constructive version of modularity using commutativity and projection operations on program segments. We believe that this approach is also accessible to programmers, since counterexamples to modularity can be generated automatically by the SMT solvers, allowing programmers to understand and fix the error. We implemented our approach in order to demonstrate the precision of the modularity analysis and applied it to real smart contracts, including a subset of the 150 most active contracts in Ethereum. Our implementation decompiles bytecode programs into an intermediate representation and then implements the modularity checking using SMT queries. Overall, we argue that our experimental results indicate that the method can be applied to many realistic contracts, and that it is able to prove modularity where other methods fail.
MBSVR: Multiple birth support vector regression

Although the learning speed in twin support vector regression (TWSVR) is four times that in support vector regression (SVR), the computing time and fitting precision of TWSVR are limited. This paper develops multiple birth support vector regression (MBSVR), motivated by the multiple birth support vector machine (MBSVM) formulation. MBSVR constructs the final regressor from K hyperplanes, each of which is obtained by solving a small quadratic programming problem (QPP) with the associated constraints, in which all points in each of the corresponding class should be as far away as possible from its corresponding hyperplane. Since MBSVM can be seen as an extension of the twin support vector machine (TWSVM) and its computing time is less than that of TWSVM, the proposed MBSVR is also faster than TWSVR, especially when the number of classes K is large. To verify the performance of the proposed MBSVR, it is compared with TWSVR, TSVR (another form of twin support vector regression) and SVR on several synthetic datasets and UCI datasets.
You've Changed: Detecting Malicious Browser Extensions through their Update Deltas

In this paper, we conduct the largest to-date analysis of browser extensions, by investigating 922,684 different extension versions collected in the past six years, and using this data to discover malicious versions of extensions. We propose a two-stage system that first identifies malicious extensions based on anomalous extension ratings and locates the code that was added to a benign extension in order to make it malicious. We encode these code deltas according to the APIs that they abuse and search our historical dataset for other similar deltas of extensions which have not yet been flagged, neither by users nor by Chrome's Web Store. We were able to discover 143 malicious extensions belonging to 21 malicious clusters, exhibiting a wide range of abuse, from history stealing and ad injection, to the hijacking of new tabs and search engines. Our results show that our proposed techniques operate in an abuse-agnostic way and can identify malicious extensions that are evading detection.
Deep Detection for Face Manipulation

It has become increasingly challenging to distinguish real faces from their visually realistic fake counterparts, due to the great advances of deep learning based face manipulation techniques in recent years. In this paper, we introduce a deep learning method to detect face manipulation. It consists of two stages: feature extraction and binary classification. To better distinguish fake faces from real faces, we resort to the triplet loss function in the first stage. We then design a simple linear classification network to bridge the learned contrastive features with the real/fake faces. Experimental results on public benchmark datasets demonstrate the effectiveness of this method, and show that it generates better performance than state-of-the-art techniques in most cases.
Smart Contract Engineering

A smart contract is the algorithmic description of a contractual transaction protocol that is automatically executed together with the information provided by its parties. It is written in a simplified programming language that is specific to a particular domain. Not only correctness and unambiguity are its essential formal properties, but also conformance to any legislation governing the matter of the transaction. Finally, and importantly, the trustworthiness, safety and security of the platform executing the transactions are its main attributes. An emerging challenge is to define a proper engineering process to meet the demanding requirements while supporting mass production and distribution. This paper proposes the concept of smart contract engineering (SCE) to facilitate the generation of smart legal contracts, which is the combination of software engineering, formal methods and computational law. SCE aims to reduce the potential errors and improve efficiency during the contract development process, meanwhile promote the standardization of contract design methodologies. In this paper, the roadmap of an iterative refinement-based, model-driven formal design methodology is introduced, not only to validate smart contracts but also to support the whole life cycle of their engineering.
Autonomous VTOL-UAV Docking System for Heterogeneous Multirobot Team

Heterogeneous robot teams offer significant advantages for exploration and navigation missions in unknown environments. Notably, the air-ground robot teams have superior efficiency due to their mixed abilities and shared sensor resources useful when completing missions. However, the high energy consumption and reduced payload capability of the vertical take-off and landing (VTOL) aerial platforms limit the complete robot team's behavior. At some point in a mission, the aircraft needs to return to the base, land, and replenish its batteries. This task restricts the team to explore the environment in-depth and to work for longer durations. To solve this problem, we present an autonomous system for docking a VTOL-unmanned aerial vehicles (UAVs) with a mobile manipulator. The central measurement-actuation unit consists of a robot manipulator mounted on a mobile platform with a visual sensor configured as an eye-in-hand device. The visual information is used to execute a stable UAV tracking to achieve air-ground robot contact firmly. This strategy allows the multirobot team to economize and potentially recover aircraft energy. It also enables the robust ground platform to protect and store the aircraft facilitating to continue the mission for a longer duration. The on-site autonomous docking can be initiated multiple times, allowing the team to explore larger areas and complete long-term missions. The proposed method has been tested in simulations and real environments with detailed experimental results.
Online supervised attention-based recurrent depth estimation from monocular video

Autonomous driving highly depends on depth information for safe driving. Recently, major improvements have been taken towards improving both supervised and self-supervised methods for depth reconstruction. However, most of the current approaches focus on single frame depth estimation, where quality limit is hard to beat due to limitations of supervised learning of deep neural networks in general. One of the way to improve quality of existing methods is to utilize temporal information from frame sequences. In this paper, we study intelligent ways of integrating recurrent block in common supervised depth estimation pipeline. We propose a novel method, which takes advantage of the convolutional gated recurrent unit (convGRU) and convolutional long short-term memory (convLSTM). We compare use of convGRU and convLSTM blocks and determine the best model for real-time depth estimation task. We carefully study training strategy and provide new deep neural networks architectures for the task of depth estimation from monocular video using information from past frames based on attention mechanism. We demonstrate the efficiency of exploiting temporal information by comparing our best recurrent method with existing image-based and video-based solutions for monocular depth reconstruction.
A New S-Box Generation Method and Advanced Design Based on Combined Chaotic System

The construction of substitute box (S-box) has always been an important research direction in cryptography. This paper proposes a new S-box generation method and advanced design based on combined chaotic system. Firstly, our paper proposes a new combined chaotic system and analyze its dynamic behavior. Next, we construct S-box by combining the generated pseudo-random sequence with the linear congruence random number generator, and the standard mapping is introduced to scramble the initial S-box. Then, the S-box optimization method based on advanced genetic algorithm is proposed in this paper. We design adaptive of S-box coding, selection operator, crossover operator and mutation operator to avoid the shortcomings of low calculation efficiency and non-convergence of optimization results in traditional genetic algorithm. Finally, through a lot of security analysis experiments and comparison with other papers, the optimized S-box has better cryptography characteristics and can resist linear attacks and differential attacks.
Kalman-filter-based sensor fusion applied to road-objects detection and tracking for autonomous vehicles

In this article, a real-time road-Object Detection and Tracking (LR_ODT) method for autonomous driving is proposed. This method is based on the fusion of lidar and radar measurement data, where they are installed on the ego car, and a customized Unscented Kalman Filter is employed for their data fusion. The merits of both devices are combined using the proposed fusion approach to precisely provide both pose and velocity information for objects moving in roads around the ego car. Unlike other detection and tracking approaches, the balanced treatment of both pose estimation accuracy and its real-time performance is the main contribution in this work. The proposed technique is implemented using the high-performance language C++ and utilizes highly optimized math and optimization libraries for best real-time performance. Simulation studies have been carried out to evaluate the performance of the LR_ODT for tracking bicycles, cars, and pedestrians. Moreover, the performance of the Unscented Kalman Filter fusion is compared to that of the Extended Kalman Filter fusion showing its superiority. The Unscented Kalman Filter has outperformed the Extended Kalman Filter on all test cases and all the state variable levels (−24% average Root Mean Squared Error). The employed fusion technique shows how outstanding is the improvement in tracking performance compared to the use of a single device (−29% Root Mean Squared Error with lidar and −38% Root Mean Squared Error with radar).
The Effects of Body Tracking Fidelity on Embodiment of an Inverse-Kinematic Avatar for Male Participants

Many research studies have investigated avatar embodiment and its effects on self-location, agency, and body ownership. Researchers have also investigated the effects of various external stimuli and avatar appearances during embodiment. However, the effects of body tracking fidelity while embodying an inverse-kinematic avatar are relatively unexplored. In this paper, we present two studies using a set of six trackers that investigate four levels of body tracking fidelity during avatar embodiment for male participants only: Complete (head, hands, feet, and pelvis trackers), Head-and-Extremities (head, hands, and feet trackers), Head-and-Hands (head and hands trackers), and No-Avatar (head and hands trackers; only controllers visible). Our results indicate that tracking the head, hands, and feet significantly increases the sense of embodiment and the sense of spatial presence when embodying an inverse-kinematic avatar for male participants.
Source Code Authorship Identification Using Deep Neural Networks

Many open-source projects are developed by the community and have a common basis. The more source code is open, the more the project is open to contributors. The possibility of accidental or deliberate use of someone else’s source code as a closed functionality in another project (even a commercial) is not excluded. This situation could create copyright disputes. Adding a plagiarism check to the project lifecycle during software engineering solves this problem. However, not all code samples for comparing can be found in the public domain. In this case, the methods of identifying the source code author can be useful. Therefore, identifying the source code author is an important problem in software engineering, and it is also a research area in symmetry. This article discusses the problem of identifying the source code author and modern methods of solving this problem. Based on the experience of researchers in the field of natural language processing (NLP), the authors propose their technique based on a hybrid neural network and demonstrate its results both for simple cases of determining the authorship of the code and for those complicated by obfuscation and using of coding standards. The results show that the author’s technique successfully solves the essential problems of analogs and can be effective even in cases where there are no obvious signs indicating authorship. The average accuracy obtained for all programming languages was 95% in the simple case and exceeded 80% in the complicated ones.
Weight initialization based‐rectified linear unit activation function to improve the performance of a convolutional neural network model

Abstract Convolutional Neural Networks (CNNs) have made a great impact on attaining state‐of‐the‐art results in image task classification. Weight initialization is one of the fundamental steps in formulating a CNN model. It determines the failure or success of the CNN model. In this paper, we conduct a research based on the mathematical background of different weight initialization strategies to determine the one with better performance. To have smooth training, we expect the activation of each layer of the CNN model follow the standard normal distribution with mean 0 and SD 1. It prevents gradients from vanishing and leads to more smooth training. However, it was obtained that even with the appropriate weight initialization technique, a regular Rectified Linear Unit (ReLU) activation function increases the activation mean value. In this paper, we address this issue by proposing weight initialization based (WIB)‐ReLU activation function. The proposed method resulted in more smooth training. Moreover, the experiments showed that WIB‐ReLU outperforms ReLU, Leaky ReLU, parametric ReLU, and exponential linear unit activation functions and results in up to 20% decrease in loss value and 5% increase in accuracy score on both Fashion‐MNIST and CIFAR‐10 databases.
CenPSO: A Novel Center-based Particle Swarm Optimization Algorithm for Large-scale Optimization

Particle swarm optimization (PSO) has demonstrated a promising performance for solving challenging optimization problems, but its performance in solving large-scale optimization problems (LSGO) has drastically decreased. In the canonical PSO, velocity has a significant effect on the performance of PSO, which is updated based on cognitive and social factors. It can help particles to share information effectively. In this paper, a center-based velocity is proposed in which a new component, named opening "center of gravity factor", is added to velocity update rule to propose the center-based PSO (CenPSO). Center of gravity factor benefits from center-based sampling strategy, a new direction in population-based metaheuristics, especially to tackle LSGOs. The proposed method is evaluated on two benchmark functions, namely, CEC2010 and CEC2017, with dimensions 100 and 1000. The experimental results verify that CenPSO is significantly better than PSO over the majority of benchmark functions.