Reviewerly/exHarmony huggingface dataset:
Minimal Explanations in ReLU-based Neural Network via Abduction

Abduction is a well-known reasoning approach to compute plausible explanations for an observation. It has recently been employed to explain the machine learning prediction of samples from a data set by generating subset-minimal or cardinality-minimal explanations with respect to features. In this paper, we study some complexity properties of such minimal explanations in explaining predictions of neural networks. This paper also extends existing works by proposing a randomized subset-minimal procedure as a strategy to compute subset-minimal explanations. The experiment results on a number of benchmarks validate that the resulting explanations are generally smaller than subset-minimal ones. On the other hand, this strategy is not as expensive as computing cardinality-minimal explanations. It thus serves as a trade-off between the existing strategies of cardinality-and subset-minimal explanations.
A Study on the Adequacy of Paraphrasing Test of Proficiency in Korean(TOPIK) Reading Questions in Testing Literal Comprehension Ability

The purpose of this research is to set appropriate standards for paraphrasing when writing multiple-choice reading questions for testing literal comprehension ability and to make suggestions for appropriate paraphrasing based on the results obtained through test-item analysis. Three main criteria were set for evaluating the level of adequacy in paraphrasing, which includes the adequacy of the paraphrased unit, the consistency of meaning between the original and the paraphrased texts, and the linguistic level of paraphrased text. The unit of the paraphrasing should be done at the syntactic level rather than at the lexical level, and the meanings of the original and the paraphrased texts must exactly match. In addition, the language level of the paraphrased text ought to be equal to or lower than that of the original text. Based on these criteria, the items measuring literal comprehension ability in the TOPIK(Test of Proficiency in Korean) were analysed. First, observing the adequacy of the paraphrased unit, it was found that although paraphrasing was done mostly at the syntactic level, there were more lexical-level paraphrasing in the beginner level than the intermediate and advanced level. Next, the results showed that there were more discrepancies of meaning in the intermediate and advanced level than the beginner level, and more cases of inconsistencies in syntactic-level paraphrasing than thelexical-level paraphrasing. Lastly, it revealed that the ratio of the linguistic level of the paraphrased text being higher than that of the original text were somewhat higher in the beginner level than the intermediate and advanced level. Thus, when creating multiple-choice items for assessing literal comprehension ability, it is necessary to construct the questions based upon these criteria of the adequacy and to check the items against the criteria in the editing stage.
Impact of data normalization methods and clustering model in the problem of automatic grouping of industrial products

Abstract We compare the results of splitting batches of industrial products (semiconductor devices) into several prospective homogeneous production batches using the standard k-means and p-median clustering models with various normalization methods. In the case of clustering problems, quadratic Euclidean distances are the most popular. We use them as well as the Mahalanobis distances to calculate the differences (distances) in the normalized space of the industrial product features, and compare the clustering results with the use of the Rand index, and empirically establish the advantage of the p-median model.
Modeling Cross-Modal Interaction in a Multi-detector, Multi-modal Tracking Framework

Different modalities have their own advantages and disadvantages. In a tracking-by-detection framework, fusing data from multiple modalities would ideally improve tracking performance than using a single modality, but this has been a challenge. This study builds upon previous research in this area. We propose a deep-learning based tracking-by-detection pipeline that uses multiple detectors and multiple sensors. For the input, we associate object proposals from 2D and 3D detectors. Through a cross-modal attention module, we optimize interaction between the 2D RGB and 3D point clouds features of each proposal. This helps to generate 2D features with suppressed irrelevant information for boosting performance. Through experiments on a published benchmark, we prove the value and ability of our design in introducing a multi-modal tracking solution to the current research on Multi-Object Tracking (MOT).
Qsimulation V2.0: An Optimized Quantum Simulator

Qsimulation is a tool for simulating quantum computation on classical computers, which allows a user to write quantum programs in a simple quantum programming language, draw quantum circuits, and view the results of executing them. Similar to many other quantum simulation tools, the performance of Qsimulation largely depends on its capacity of dealing with matrix operations. In this paper we present Qsimulation V2.0, an optimized quantum simulator that implements a new algorithm for accelerating matrix-vector multiplications. The algorithm is based on matrix decomposition using tensor products and suitable for simulating the execution of quantum circuits. Experimental results show that Qsimulation V2.0 outperforms the open source frameworks Qiskit and ProjectQ.
Smart earpieces that know who you are quietly

User authentication and identification on smart devices has great significance in keeping data privacy and recommending personalized services. Existing few research works propose active sensing systems that emit and receive inaudible acoustic signals to authenticate users. But they share shortcomings of intrusiveness to users, high power consumption, and purely focusing on authentication. Instead, in this paper, we propose a passive sensing system called EarID with low-cost customized earpieces which attains user authentication and identification simultaneously. It makes use of a embedded microphone to sense body sounds spread out through ear canals and extract 'fingerprints' as a novel biometric feature. With self-designed earpieces, we design a deep learning-based real-time data processing pipeline. Extensive experiments under different real-world settings show that EarID can achieve a rather low false acceptance rate less than 5% for user authentication and a high F1 score of 96% for user identification.
A V2X-Assisted Intelligent Vehicle Target Tracking Method

Abstract In the target tracking scene of connected intelligent vehicles, besides onboard sensors, the V2X is an important approach to acquire target information. In this paper, a target tracking method which introduces the targets’ identity provided by V2X to improve the data association is proposed. The measurements are associated with targets according to conventional method firstly, and then the position data from V2X is used to update the association probability between tracks and measurements. The simulation results show that the proposed method can improve the performance of the target tracking.
Contactless Environment Map Construction with Ultrasonic Sensors

This paper presents a contactless environment mapping system for autonomous navigation of mobile robots. Based on ultrasonic sensing, the proposed system obtains reliable and accurate maps of unknown environments without the need of physical contact and regardless of the scene's illumination. These maps include the location of obstacles and objects in the environment; therefore, they can be used for autonomous navigation and trajectory planning of mobile robots. This paper introduces the design, implementation, and preliminary results showing feasibility of the proposed system.
Formal Verification of Atomicity Requirements for Smart Contracts

Smart contracts are notoriously vulnerable to bugs and loopholes. This is due largely to an unusual combination of features: re-entrant calls, transfer-triggered code execution, the way exceptions are propagated, etc. Numerous validation techniques have been developed to ensure the safety and security of smart contracts. An important class of problems dealt with is related to the atomic performance of actions such as contract calls and state updates. In this paper, we examine the major existing atomicity-related criteria for the safety and security of smart contracts. We then propose an atomicity criterion and argue about its advantages. Furthermore, we develop a Hoare-style program logic that is capable of verifying the fulfillment of safety requirements by smart contracts, including the satisfaction of the proposed criterion. The program logic is developed and proven sound for a core Solidity-like language, which supports reentrant calls, ether transfers, and exception handling.
The Role of Penetration Testing in Forensic Multimedia Retrieval Process

Digital forensic investigators are faced with multimedia retrieval and discovery challenges that require innovation and application of evolving methodologies. This work is made more difficult in critical infra-structure environments where the acquired evidence is in many formats, types and presentations. Penetration testing is one of the techniques used to focus an investigation and to target the potential case information from the vulnerability identification phase, through to the media identification phase. In this chapter a review of these processes is made and a framework example developed to show how the investigator discovers relevant evidence. The problem for the digital investigator is the vast array of media in which evidence is stored or transmitted. Some work is from live retrieval and others static. A framework of methods that is flexible and adaptable to the context of investigation is proposed and the discovery methods for multimedia environments elaborated.
A Practical Exercise System Using Virtual Machines for Learning Cross-Site Scripting Countermeasures

Cross-site scripting (XSS) is an often-occurring major attack that developers should consider when developing web applications. We develop a system that can provide practical exercises for learning how to create web applications that are secure against XSS. Our system utilizes free software and virtual machines, allowing low-cost, safe, and practical exercises. By using two virtual machines as the web server and the attacker host, the learner can conduct exercises demonstrating both XSS countermeasures and XSS attacks. In our system, learners use a web browser to learn and perform exercises related to XSS. Experimental evaluations confirm that the proposed system can support learning of XSS countermeasures.
Pushing and Bounding Loss for Training Deep Super-Resolution Network

As deep neural networks (DNNs) are hard to be trained due to gradient vanishing, intermediate supervision is typically used to help earlier layers to be better optimized. Such deeply supervised methods have proved to be beneficial to various tasks such as classification and pose estimation, but it is rarely used for image super-resolution (SR). This is because intermediate supervision needs a set of intermediate labels, but in SR, these labels are hard to be defined. Experiments show that identity labels across the whole network, which are used for classification, will cause inconsistence and harm the final performance. We argue that ‘mediately accurate’ labels, i.e. relatively soft labels, are more suitable for intermediate supervision on SR networks. But labels in SR networks are of either completely high resolution or completely low resolution. To address this problem, we propose what we call pushing and bounding loss, which forces the network to learn better features as it goes deeper. In this way, we do not need to explicitly give any ‘mediately accurate’ labels but all internal layers can also be directly supervised. Extensive experiments show that deep SR networks trained in this scheme will receive a stable gain without adding any extra modules.
RETRACTED: Industrial Supply Chain Optimization Based on 5G Network and Markov Model

Today, many organizations are forced to increase their global market share to survive and sustain growth in Industrial Supply Chain Optimization. At the same time, these same 5G Networks must protect their homeland upper class from global competitors. This Network is a tested way to increase coordination and outreach organizations globally, thereby delivering goods to clients who request them in a dynamic and rapidly changing channel setting. The 5G Network critical condition of the inventory is essential, so items are accessible when needed by the client‌; also, the chain emphasizes that it must be genuinely skilled and practical. The Industrial Supply Chain Optimization-based industry limits the property's use to achieve precise results, and until forced, dispersal routes are planned. Although Markov Model is assessed by administration quality and administration requirements, expertise is assessed by transport execution, commodity quality, purchases delay, and stock level in better efficiency.
Exploiting Graph Similarities with Clustering to Improve Long Tail Itens Recommendations

Techniques in recommendation systems generally focuses on recommending the most important items for a user. The purpose of this work is to generate recommendations focusing on long tail items, leading users to less popular and at the same time highly relevant products. Two techniques from the literature were applied in this study. The first technique is through graphs to calculate node similarity between users and items. The second technique applies clustering in the set of items in a dataset. This combination was adopted in order to give more visibility to long tail items. To evaluate the proposed approach an experiment was carried out to calculate the accuracy, diversity, and popularity of the generated recommendation. We compare the proposed approach with other 3 baselines where our approach achieved better results.
Feature Distillation of High Dimension Datasets: Dimension Contraction and Component Dilation

Handling high dimensional datasets is always a challenge in machine learning. The operations that deal with datasets containing vast amounts of features are extremely time-consuming and computationally expensive. In an even more challenging scenario, a data scientist may be given a dataset without any information about the target feature and he/she needs to find out the features that can be possibly predicted from the dataset without loss of interpretation. To satisfy this requirement we introduce a new technique, “Feature Distillation”. In addition to that a new algorithm, Dimension Contraction and Component Dilation (DCCD) is proposed. The idea is to return features that can be predicted with high accuracies from a dataset using a combination of information measurement, discretization, dimensionality reduction, supervised and unsupervised techniques. The DCCD algorithm is tested with various synthetic datasets and proven to be faster than traditional methods.
Crowd Management Using Ambient Intelligence

Ambient Intelligence (AmI) is the Artificial Intelligence which is completely human-centric. It deals with a new world where computing devices are spread everywhere, allowing the human being to interact in physical world environments in an intelligent and unobtrusive way. These environments should be aware of the needs of people, customizing requirements and forecasting behaviors. AmI involves integrating the technology invisible in day-to-day life. According to Information Society Technologies Advisory Group (ISTAG) 1999, AmI1 should be the result of the convergence of three key technologies: ‘Ubiquitous Computing,’ ‘Ubiquitous Communication’ and ‘Intelligent User-Friendly Interfaces.’ AmI is unobtrusive and often invisible, being embedded in everyday objects such as furniture, clothes, vehicles, roads and smart materials. People will be surrounded by intelligent and intuitive interfaces recognizing and responding to the presence of individuals. Intelligence embedded in everyday objects and the surrounding environment such that the use of these smart objects is intuitive to the inhabitants of the environment. Combining both mobile and sensing technologies for providing a pervasive and unobtrusive intelligence and environment in supporting the main activities and interactions of the users. This forms the base of Ambient Intelligence. Technologies like face-based interfaces and affective computing are inherent ambient intelligence technologies. Many features being the crucial element in setting up an effective and efficient Ambient Intelligence environment, intelligent user interface is the key component. A vision of society of the future, where the people will find themselves in an environment of intelligent and intuitively usable interfaces, ergonomic space in a broad sense, encompassing better, secure and active living environment around them, capable of aiding them with daily chores and professional duties by recognizing the presence of individuals, reacting to it in a non-disturbing, invisible way, fully integrated into the particular situation.
Combination of Genetic Algorithm and Neural Network for Independent Game Playing

In this paper, in order to tackle the challenge of a game playing agent, a combination of algorithms with the aim to play and win the game was introduced. The agent will learn to play a simplified version of a very popular game Super Mario. Even though launched almost four decades ago, it is considered as one of the most famous game, played until now by humans through years with many competitions also organized. Researchers argued that it is quite a hard game to win by humans. Our approach is to use genetic algorithm with the combination of neural networks to achieve the aim. The experiments conducted show promising results by reaching the requested score for the provided time.
A Deep Meta-Learning Neural Network for Single Image Rain Removal

Due to various interference factors, image degradation seriously retards the precision and efficiency of target tracking and recognition. Consequently, image restoration becomes a significant issue in the field of computer vision. In this paper, a novel de-rain network, called Meta-DerainNet was proposed to remove rain streaks in various conditions. With the strongly generation ability, Model-Agnostic Meta-Learning (MAML) has been proven to be able to accomplish the task of removing rain streaks from single image. However, in order to combine the MAML algorithm with rain removal tasks, there are many parts need to be improved. The datasets with specific sample methods were designed to train the generalization ability of the network, and the network structured of MAML was improved for more efficiently extract feature map. The modified network was successfully applied to remove rain streaks under various conditions. Experimental results on real-world data show that the method proposed in this paper performs better than other advanced methods on PSNR and SSIM. Furthermore, our method can also be applied to process other image interference removal factors, such as medical image recognition, self-driving and other fields.
Future Directions of Query Understanding

Query understanding bridges the gap and establishes a communication channel between the searcher and the search engine. An important challenge in question understanding is the enhancement of user interaction with the search engine in a more natural way, including spoken language querying, multi-turn search sessions and conversational question answering. This demands additional information sources, such as knowledge graphs, and advances in research areas, such as cross-language IR. Moreover, there are many open questions and settings in query understanding that have not yet been fully explored. We will review some of these directions in this chapter, and we hope that researchers interested in query understanding will find them challenging and inspiring for future research directions.
Merging Cloned Alloy Models with Colorful Refactorings

Likewise to code, clone-and-own is a common way to create variants of a model, to explore the impact of different features while exploring the design of a software system. Previously, we have introduced Colorful Alloy, an extension of the popular Alloy language and toolkit to support feature-oriented design, where model elements can be annotated with feature expressions and further highlighted with different colors to ease understanding. In this paper we propose a catalog of refactorings for Colorful Alloy models, and show how they can be used to iteratively merge cloned Alloy models into a single feature-annotated colorful model, where the commonalities and differences between the different clones are easily perceived, and more efficient aggregated analyses can be performed.
Hand Writing Recognition Model Enhancement Exploration Based on Skipped Connections

Handwriting recognition is a well-known problem utilizing a deep learning approach. Due to the variety of words and writing styles, a large data set is required to build the good model. In this research, we investigate the state-of-the-art handwriting model, namely Flôr model [1], with our data set, and explore the extensions to improve the accuracy. The improvement aims to use skipped connections and learn to pass through features properly. The possible skipped connection insertion approaches are studied. We also consider the model size along with the accuracy improvement. We use the modified data set originally from Research Group on Computer Vision and Artificial Intelligence INF, University of Bern [2], containing 112,7446 words. The results show that our refinement can lead to the reduction of 1.5 % in character base error and 2 % in word base error with the unseen test data set.
Impact of Layers Selective Approximation on CNNs Reliability and Performance

In this paper, we evaluate the impact on reliability and performance of the selective approximation of Convolutional Neural Networks (CNNs) layers on NVIDIA mixed-precision architectures. We found that, even without affecting accuracy, the approximation from single to half precision of each layer has a different impact on both performance and output error.
Three-Way Framework Using Fuzzy Concepts and Semantic Rules in Opinion Classification

Binary classification is a critical process for opinion mining, which classifies opinions or user reviews into positive or negative classes. So far many popular binary classifiers have been used in opinion mining. The problematic issue is that there is a significant uncertain boundary between positive and negative classes as user reviews (or opinions) include many uncertainties. Many researchers have developed models to solve this uncertainty problem. However, the problem of broad uncertain boundaries still remains with these models. This paper proposes a three-way decision framework using semantic rules and fuzzy concepts together to solve the problem of uncertainty in opinion mining. This framework uses semantic rules in fuzzy concepts to enhance the existing three-way decision framework proposed by authors. The experimental results show that the proposed three-way framework effectively deals with uncertainties in opinions using relevant semantic rules.