Reviewerly/exHarmony huggingface dataset:
Partial neighborhood local searches

Abstract In this work, we study partial neighborhood local search (PNLS) techniques, which consist of adaptive walks where moves are chosen in a random subset of the current solution neighborhood. PNLSs balance between intensification and diversification is mainly determined by its single parameter λ designing the subset size. We analyze and discuss three PNLSs variants, using the abstraction of several combinatorial optimization problems into fitness landscapes: NK landscapes, Unconstrained Binary Quadratic Programming, Flow‐shop scheduling, and Quadratic Assignment. Our empirical study first analyses the structure of these landscapes through indicators. Then, we perform a parameter study of PNLSs for two computational budgets to study the impact of the sample size on the balance between intensification and diversification on different landscapes. Moreover, these experiments allow us to set an appropriate parameter value to compare the ability of PNLSs to reach good‐quality solutions accurately. Finally, we compare PNLS variants with two classical metaheuristics, identifying links between landscape characteristics and algorithms behavior.
An efficient multi-grid method for TV minimization problems

&lt;p style='text-indent:20px;'&gt;We propose an efficient multi-grid domain decomposition method for solving the total variation (TV) minimization problems. Our multi-grid scheme is developed based on the piecewise constant function spanned subspace correction rather than the piecewise linear one in [&lt;xref ref-type="bibr" rid="b17"&gt;17&lt;/xref&gt;], which ensures the calculation of the TV term only occurs on the boundaries of the support sets. Besides, the domain decomposition method is implemented on each layer to enable parallel computation. Comprehensive comparison results are presented to demonstrate the improvement in CPU time and image quality of the proposed method on medium and large-scale image denoising and reconstruction problems.&lt;/p&gt;
Infrastructural Power: Dealing with Abuse, Crime, and Control in the Tor Anonymity Network

This chapter reports on the first empirical criminological research on the Tor Project, the organisation which develops the Tor anonymity network. There has been little focus as yet by cybercrime researchers on the human factors shaping the platforms and infrastructures on which the Internet depends. These are emerging as powerful technologies of control and profound sites of resistance in contemporary societies, increasingly taking on responsibility for enormous user communities and the crime and abuse which come with them. Of these, I focus on Tor, an international anonymity infrastructure which offers its users extremely strong protections against online surveillance and censorship. Tor has become a particularly important subject of criminological research on online crime. However, there is as yet no criminological research which deals with how the people who develop and maintain Tor understand these issues. Through interviews and archival research, I study how this community perceive Tor’s use for crime and harm and how they navigate these issues in practice, identifying three distinct sites at which Tor deals with crime, and three concomitant ways of making sense of Tor’s crime problem (conceptualised as ‘social worlds’ of Tor). I explore how Tor has developed from a disruptive character to an increasingly governmental one and the implications of this for understanding the role of platforms and infrastructures in the governance of online crime more broadly.
Large-Scale Multi-agent Reinforcement Learning Based on Weighted Mean Field

Deep reinforcement learning is an emerging approach to solve the decision making of multi-agent systems in recent years, and currently has achieved good results in small-scale decision problems. However, when the number of agents increases, the dynamic of the other agent strategies and the proportional enlargement of information between the agent lead to "non-stationarity", "dimensional catastrophe" and many other problems. In order to solve Multi-Agent Deep Deterministic Policy Gradient (MADDPG) are difficult to converge when the size of multi-agent systems exceeds a certain number, a deep reinforcement learning collaboration algorithm for multi-agent systems based on weighted mean field is proposed. The mean field is used to reconstruct the dynamic decision action of other agent involved in the decision making, while assigning different weights to each agent action based on the set of relevant attributes, transforming the joint action of the agent into the mean action of the other agent formed through the weighted mean field, and serving as an update function of the actor network and state function in the multi-agent deep deterministic policy gradient algorithm parameters to simplify the scale of interaction. In this paper, the effectiveness of the algorithm is validated by Battle game scenarios from convergence, win rates at different scenario sizes, win rates of different algorithms, and other game performance.
Investigation of Student Performance with Contextual Factors Using Association Rules in Higher Educational Institutions (HEIs)

Association rules provide an important clue on how hidden knowledge and patterns can be extracted and the attributes extracted are associated when big data is mined by means of a particular data mining algorithm. The context of HEIs, where big data mining is uncommon, there is a need to know how association rules could be utilized to discover hidden knowledge and forecast student performance based on time-to-degree and cumulative grade point average (CGPA) and also make decisions. This research has demonstrated this by taking the example of the dataset of 337 graduated students of a university in Bahrain belonging to the Bachelor's degree in Accounting and Finance. Two types of data mining concepts were applied, regression and a priori algorithm. Regression results showed that there is a statistically substantial link between time-to-degree and course-difficulty and not CGPA. Similarly, association rules showed the link between time-to-degree, course-taking pattern, no. of courses and course-difficulty pattern but not CGPA. The association rules generated provided the basis to predict optimum time-to-degree by controlling the no. of courses, course-taking pattern, and course-taking difficulty. The findings of this research provide an important window of opportunity for HEIs to enhance student performance using data mining techniques. This investigation has contributed theoretically showing the application of data mining theory using apriori algorithm and regression, for HEI big data mining where student factors and organizational factors are said to be fairly different from that of the HEIs where research on big data is seen to be a well-known activity.
An Unsupervised Multi-Shot Person Re-Identification Method via Mutual Normalized Sparse Representation and Stepwise Learning

Due to abundant prior information and widespread applications, multi-shot based person re-identification has drawn increasing attention in recent years. In this paper, the high labeling cost and huge unlabeled data motivate us to focus on the unsupervised scenario and a unified coarse-to-fine framework is proposed, named by Mutual Normalized Sparse Representation (MNSR). Our method is an iteration procedure and each iteration involves two key steps: label estimation and metric model learning. In the former, we present a MNSR model to infer the pairwise labels of cross-camera by endowing sparse representation coefficient with the probability property. MNSR explicitly takes the mutually correlation between cameras into consideration and thus produces more accurate results. Meanwhile, we propose a probability-guided positive pairwise label prediction method to mine hard positive samples. For the latter, we learn a metric model with the estimated pairwise labels as supervision. In this procedure, we select some reliable labels for training by configuring with a stepwise learning method, rather than use all the estimated pair samples. This procedure helps to prevent the noise samples damaging the learning of discriminative metric model, especially for the initial iterations. Extensive experiments are conducted on four publicly available datasets, including PRID 2011, iLIDS-VID, SAIVT-SoftBio and MARS, and the results demonstrate the superior performance of the MNSR method in comparison with state-of-the-art unsupervised multi-shot person re-identification methods.
Commonsense Reasoning Using WordNet and SUMO: a Detailed Analysis.

We describe a detailed analysis of a sample of large benchmark of commonsense reasoning problems that has been automatically obtained from WordNet, SUMO and their mapping. The objective is to provide a better assessment of the quality of both the benchmark and the involved knowledge resources for advanced commonsense reasoning tasks. By means of this analysis, we are able to detect some knowledge misalignments, mapping errors and lack of knowledge and resources. Our final objective is the extraction of some guidelines towards a better exploitation of this commonsense knowledge framework by the improvement of the included resources.
Feature Engineering and Deep Learning for Stereo Matching Under Adverse Driving Conditions

Stereo matching is a challenging research topic in driving assistance systems. Existing stereo matching methods work well under normal day-light conditions. However, they fail to operate under adverse driving conditions, such as at night and during snowfall. This paper proposes a robust stereo matching framework using both deep-learning-based features and feature engineering. The proposed method investigates the benefits of features based on feature engineering and deep learning for solving stereo matching problems. Robust feature engineering is proposed for handling specific driving under adverse weather conditions, and a robust feature based on deep learning is considered for handling unspecific driving under extreme weather conditions. The proposed study has shown significantly improved accuracy by 8.31% for the state-of-the-art census based on semi-global matching under the reflection regions using the KITTI Stereo 2012 benchmark. Moreover, the experimental results demonstrate that the proposed system obtains more stable results than existing stereo methods based on deep learning on various stereo datasets, such as the Middlebury, EISAT, HCI, and CCD datasets.
Heterogeneous Graph-based Knowledge Transfer for Generalized Zero-shot Learning

Generalized zero-shot learning (GZSL) tackles the problem of learning to classify instances involving both seen classes and unseen ones. The key issue is how to effectively transfer the model learned from seen classes to unseen classes. Existing works in GZSL usually assume that some prior information about unseen classes are available. However, such an assumption is unrealistic when new unseen classes appear dynamically. To this end, we propose a novel heterogeneous graph-based knowledge transfer method (HGKT) for GZSL, agnostic to unseen classes and instances, by leveraging graph neural network. Specifically, a structured heterogeneous graph is constructed with high-level representative nodes for seen classes, which are chosen through Wasserstein barycenter in order to simultaneously capture inter-class and intra-class relationship. The aggregation and embedding functions can be learned through graph neural network, which can be used to compute the embeddings of unseen classes by transferring the knowledge from their neighbors. Extensive experiments on public benchmark datasets show that our method achieves state-of-the-art results.
Online Fall Detection Using Attended Memory Reference Network

Falls cause serious injuries that make daily activities difficult; therefore, they are a common target action for intelligent monitoring systems. Existing vision-based methods for fall actions classify well-trimmed short videos as either fall or non-fall actions. However, critical limitations exist when applying these methods to untrimmed videos including fall and non-fall actions as well as background. These methods can determine whether there is a fall or not for an input video with many frames related to either fall or non-fall actions. In addition, these methods require offline processing for a whole video as input, while there is strong demand for quicker responses to fall injuries provided by online fall detection. To this end, we introduce an attended memory reference network that detects a current action online for a given video segment consisting of past and current frames. To integrate contextual information used for detecting a current action, we propose a new recurrent unit, called an attended memory reference unit, which accumulates input information based on visual memory attended by current information. In an experiment using a fall detection dataset obtained from the abnormal event detection dataset for CCTV videos publicized by AI Hub, the proposed method outperforms state-of-the-art online action detection methods. By conducting ablation studies, we also demonstrate the effectiveness of the proposed modules related to the attended visual memory.
Fault Diagnosis Strategy of Substation Electrical Equipment Based on Image Segmentation

Due to the different forms of equipment fault in substation, the required treatment methods are also different, and there is no applicable standard treatment method. Aiming at the problems in infrared detection, this paper analyzes the structure, fault types and infrared thermal image characteristics of several kinds of key equipment in substation. Then, the infrared image standard processing flow which is suitable for these kinds of equipment is proposed, and the corresponding optimization is made according to the specific structure characteristics of each kind of equipment to realize the segmentation and extraction of target area. A convolution neural network model is constructed to realize the recognition of infrared image. Finally, an infrared detection method of substation key equipment based on image recognition and neural network is proposed. The experimental results show that the proposed infrared image processing process and the established fault recognition network and image classification network have high practicability, and they are expected to further promote the application of infrared detection work.
Design and implementation of English composition automatic evaluation system based on B/S architecture

In order to solve the problems of low accuracy and efficiency of traditional English composition automatic evaluation system, a new English composition automatic evaluation system based on B/S architecture is proposed and designed. This paper analyzes the operation process of the English composition automatic evaluation system. The hardware part designs the English composition image recognition module, the English composition automatic evaluation module and the English composition repetition rate detection module. The software part constructs the English composition text matrix, and uses singular value decomposition method to solve the English composition text matrix, and calculates the relevant indicators of English composition automatic evaluation. The experimental results show that, compared with the traditional English composition automatic evaluation system, the evaluation results of the designed system are more accurate and efficient.
Securing Blockchain Wallets Efficiently Based on Threshold ECDSA Scheme Without Trusted Center

Using the private key stored in the blockchain wallet to sign the transaction is the only way to trade on the blockchain. Once the private key stored in the wallet is lost or leaked, it will cause irreparable loss. At present, the mainstream wallet management schemes store the wallet in a single location or rely on the participation of trusted center, which can not resist single point failure. Aiming at the protect the security of blockchain wallet, this paper proposes a blockchain wallet protection scheme against single point failure based on threshold elliptic curve digital signature without trusted center. In this scheme, participants cooperate to generate public and private keys and sharing private key without the participation of trusted center. The participants who exceed the threshold number can sign the transaction by constant rounds, which can effectively resist single point attack and ensure the security of wallet. And the security of the scheme meets the requirements of unforgeability, anonymity and robustness.
Multi-scale Relational Reasoning with Regional Attention for Visual Question Answering

One of the main challenges of visual question answering (VQA) lies in properly reasoning relations among visual regions involved in the question. In this paper, we propose a novel neural network to perform question-guided relational reasoning in multi-scales for visual question answering, in which each region of image is enhanced by regional attention. Specifically, we present regional attention module, which consists of a soft attention module and a hard attention module, to select informative regions of the image according to informative evaluations implemented by question-guided soft attention. Combinations of different informative regions are then concatenated with question embedding in different scales to capture relational information. Relational reasoning module can extract question-based relational information among regions, in which multi-scale mechanism gives it the ability to model scaled relationships with diversity making it sensitive to numbers. We conduct experiments to show that our proposed architecture is effective and achieves a new state-of-the-art on VQA v2.
The Perspective of Cyclists on Current Practices with Digital Tools and Envisioned Services for Urban Cycling

As cycling becomes increasingly important in sustainable mobility policies, there is also an urge for new digital applications and services for urban cycling. This new generation of cycling applications should be able to connect cyclists with their local cycling ecosystem, promote cycling, and empower cyclists to become active agents of urban mobility. In this work, we aim to explore the new opportunity space of digital tools and applications designed specifically for urban cycling. We pursue this goal by trying to uncover current practices associated with digital tools that are already available and also by trying to uncover new information needs, even those that cyclists are not yet able to fully express. To explore these topics, we conducted 2 focus group sessions and 10 interviews with cyclists. The result is a set of design opportunities for the development of new applications, tools and methods for improving the cycling experience in the context of urban mobility. We expect this contribution might help to better define the design space of innovative digital tools for urban cyclists.
Scientific Document Summarization using Citation Context and Multi-objective Optimization

The rate of publishing scientific articles is increasing day by day which has created difficulty for the researchers to learn about the recent advancements in a faster way. Also, relying on the abstract of these published articles is not a good idea as they cover only broad ideas of the article. The summarization of scientific documents (SDS) addresses this challenge. In this paper, we propose a system for SDS having two components: identifying the relevant sentences in the article using citation context; generation of the summary by posing SDS as a binary optimization problem. For the purpose of optimization, a metaheuristic evolutionary algorithm is utilized. In order to improve the quality of summary, various aspects measuring the relevance of sentences are simultaneously optimized using the concept of multi-objective optimization. Inspired by the popularity of graph-based algorithms like LexRank which is popularly used in solving summarization problems of different real-life applications, its impact is studied in fusion with our optimization framework. An ablation study is also performed to identify the most contributing aspects for the summary generation. We investigated the performance of our proposed framework on two datasets related to the computational linguistic domain, CL-SciSumm 2016 and CL-SciSumm 2017, in terms of ROUGE measures. The results obtained illustrate that our framework effectively improves other existing methods. Further, results are validated using the statistical paired t-test.
Editorial for the Special Issue on “Bayesian Networks: Inference Algorithms, Applications, and Software Tools”

In the field of Artificial Intelligence, Bayesian Networks (BN) [...]
Similarity Detection of Natural-Language Questions and Answers using the VANiLLa dataset

Abstract Question Answering refers to the task of providing a matching answer for a given user’s natural-language question. We assume here that the received response from a Question Answering system is also given as a natural-language output. From this scenario the task is raised to validate if the received natural-language answer is valid w.r.t. the given question. In this paper, we will present our approach to compute the similarity of a question to the corresponding natural-language answer while using the features of the surface form of the question and the answer. Our metrics is the distinction between a matching answer and inappropriate answers to a given question. Hence, our research agenda is dedicated to improving the answer quality of Question Answering systems by using the natural-language representation of its input and output.
University Admissions Predictor Using Logistic Regression

Students applying for admissions to universities find it difficult to understand whether they have good chances of getting admission in a university or not. Keeping this in focus, we have used logistic regression techniques that have gained attention in software engineering field for its ability to be used for predictions. This is a novel work on a university admissions predictor using which students can evaluate their competitiveness for getting admission at a university. This is developed by collecting real student data. The data is stored in a form of a usable training data for the logistic regression classifier developed to make admissions predictions. We have collected the data from the Internet using a Selenium web scraper. The paper intensely discusses the methods, implementation and challenges faced in the process.
Replay Attack Anaysis Based on Acoustic Parameters of Overall Voice Quality

The replay attack detection has played an important role in the automatic speaker verification (ASV). From the perspective of the pronunciation mechanism, this paper proposes seven acoustic parameters reflecting the overall voice quality to improve the effectiveness of the replay attacks detection, including slope, SL, mACF, HNR, NHR, GNE and CPPS. The set of continuous speech samples from 108 subjects taken from three kinds of text content are used to evaluate the applicability and multi-condition sensitivity analysis of these parameters. The result confirms that five acoustic parameters including SL, mACF, HNR, NHR and CPPS provide reliable measurements for discriminating authentic recording and its playback versions.