Reviewerly/exHarmony huggingface dataset:
Failure of One, Fall of Many: An Exploratory Study of Software Features for Defect Prediction

Software defect prediction represents an area of interest in both academia and the software industry. Thus, software defects are prevalent in software development and might generate numerous difficulties for users and developers apart. The current literature offers multiple alternative approaches to predict the likelihood of defects in the source code. Most of these studies concentrate on predicting defects from a broad set of software features. As a result, the individual discriminating power of software features is still unknown as some perform well only with specific projects or metrics. In this study, we applied machine learning techniques in a popular dataset. This data has information about software defects in five Java projects, containing 5,371 classes and 37 software features. To this aim, we convey an exploratory investigation that produced hundreds of thousands of machine learning models from a diverse collection of software features. These models are random in the sense that they promptly select the features from the entire pool of features. Even though the immense majority of models are ineffective, we could produce several models that yield accurate predictions, thus classifying defects from Java project classes. Among these accurate models, our results indicate that change metric features are more present than entropy or class-level metrics. We concentrated our analysis on models that rank a randomly chosen defective class higher than a casually selected clean class with over 80% accuracy. We also report and discuss some features contributing to the explanation of model decisions. Therefore, our study promotes reasoning on which features support predicting defects in these projects. Finally, we present the implications of our work to practitioners.
Detection of Facial Features Using Statistical Machine Learning

Classification modules can be used in multiple and diverse applications. The general objective of this work is the classification of facial features using statistical learning algorithms, this means being able to detect and classify the largest possible number of features that can be found on a face using only examples of images of these, without using a priori no information of the given characteristics. In the present work, detectors and classifiers of the characteristics that are considered most significant were developed, Excellent results were reported in mouth detection, with detection rates greater than 99% and errors comparable to the error from manual mouth marking. The lens sorter also obtained excellent results, with detection rates of 95% for databases with controlled environments and of the order of 90% for databases with uncontrolled environments. Beard and mustache classifiers after using the mouth detector obtained very good results, with a detection rate of over 95% in databases with uncontrolled environments.
Cyber Trainground: Building-Scale Virtual Reality for Immersive Presentation Training

Trainground" is a word we coined inspired by the word "playground." It means a place for humans to perform various kinds of training and to also use machine learning to scientifically search for points they need to improve. The cyber trainground is an extension of the trainground to cyberspace. It enables training in virtual spaces that captures and reconstructs various types of information appearing in the real world, such as atmosphere and presence, to achieve an improvement effect that is the same or better than in the real world. In this paper, we build an immersive training space using building-scale VR, a technology that makes a virtual space based on an entire building existing in the real world. The space is used for presentations, allowing students to self-train. The results of a presentation are automatically evaluated by using machine learning or the like and fed back to the user. In this space, users can meet their past selves (more accurately, their avatars), so they can objectively observe their presentations and recognize weak points. We developed a mechanism for recording and reproducing activities in virtual space in detail and a mechanism for applying machine learning to activity records. With these mechanisms, a system for recording, reproducing, and automatically evaluating presentations was developed.
Inner product encryption from ring learning with errors

Abstract The functional encryption scheme designed using the lattice can realize fine-grained encryption and it can resist quantum attacks. Unfortunately, the sizes of the keys and ciphertexts in cryptographic applications based on learning with errors are large, which makes the algorithm inefficient. Therefore, we construct a functional encryption for inner product predicates scheme by improving the learning with errors scheme of Agrawal et al. [Asiacrypt 2011], and its security relies on the difficulty assumption of ring learning with errors. Our construction can reduce the sizes of the keys and ciphertexts compared with the learning with errors scheme.
Research and Application of Clustering Algorithm in Battlefield Scheduling Genetic Optimization

Combat mission system is an organic combination of scheduling command system and collaborative control system. The integrity and reliability of the system not only depend on the optimal control algorithm used by the system but also depend on the reasonable scheduling combination of battlefield resources. In this paper, the combat capability load fitness function, the scheduling strategy model integrated with the combined clustering method, and the code association structure model are proposed. The simulation results show that this method can accelerate the convergence speed of the algorithm, optimize the objective function of the model, cluster and combine the platform equipment set well, and improve the robustness of the model and the effectiveness of the battlefield application under different mission sizes.
Mechanism Analysis of Power Grid Cascading Failures Based on Data Mining Algorithm

The defects of the present fault chain model of power grid cascading failures have been summarized and a defined method for forecasting cascading failures is presented based on a fault chain model and Fuzzy C-Means. First, in order to reduce the workload and overcome the limitation of present means, this method selects a number of lines with the high value of predictive index to be the next outage lines during fault chain forecasting. Then, this paper analyzes the correlations among lines. Finally, taking IEEE 39-bus system as example, the rationality of the method proposed is verified based on comparison with other means.
Distinguishability and Disturbance in the Quantum Key Distribution Protocol Using the Mean Multi-Kings’ Problem

We introduce a quantum key distribution protocol using mean multi-kings’ problem. Using this protocol, a sender can share a bit sequence as a secret key with receivers. We consider a relation between information gain by an eavesdropper and disturbance contained in legitimate users’ information. In BB84 protocol, such relation is known as the so-called information disturbance theorem. We focus on a setting that the sender and two receivers try to share bit sequences and the eavesdropper tries to extract information by interacting legitimate users’ systems and an ancilla system. We derive trade-off inequalities between distinguishability of quantum states corresponding to the bit sequence for the eavesdropper and error probability of the bit sequence shared with the legitimate users. Our inequalities show that eavesdropper’s extracting information regarding the secret keys inevitably induces disturbing the states and increasing the error probability.
Multiclass Geospatial Object Detection using Machine Learning-Aviation Case Study

There is growing interest to explore the autonomous taxiing that can sense its environment and maneuver safely with little or no human input. This technology is like the one developed for driver less cars that synthesize information from multiple sensors, which sense surrounding environment to detect road surface, lanes, obstacles and signage. This paper presents application of computer vision and machine learning to autonomous method for the surface movement of an air vehicle. We present a system and method that uses pattern recognition which aids unmanned aircraft system (UAS) and enhance the manned air vehicle landing and taxiing. Encouraged with our previous results, we extend upon our research to include multiple object relevant to taxiing. The objective of the current project is to build training dataset of annotated objects acquired from overhead perspective. It is useful for training a deep neural network to learn to detect, count specific airport objects in a video or image. This paper details the procedure and parameters used to create training dataset for running convolutional neural networks (CNNs) on a set of aerial images for efficient and automated object recognition. In this method, multiple airport surface signage dataset from satellite images are subjected to training for pattern recognition. This trained system learns and then identifies and locates important visual references from imaging sensors and could help in decision making during taxiing phase.
Method for increasing the dynamic range of digital images based on modular arithmetic

Abstract The article proposes a new method of synthesis of digital images from several digital images to increase the dynamic range of intensity. The method is based on modular arithmetic. The increased range is divided into several residual classes. The full value of the intensity is calculated as the solution of the system of comparisons of residues for given modules. An analysis is made of the stability of the proposed method to the nonlinearity of the transfer characteristic and digital image recording devices under various types of interference. To this end, the maximum range for changing the brightness of a digital image is limited. By selecting the magnitude of the modules and the maximum brightness value, the distance between the diagonals in the decision table is selected so that this distance is greater than the level of distortion and (or) interference. Experimental validation of the method has been performed.
LHRM: A LBS Based Heterogeneous Relations Model for User Cold Start Recommendation in Online Travel Platform

Most current recommender systems used the historical behaviour data of user to predict user' preference. However, it is difficult to recommend items to new users accurately. To alleviate this problem, existing user cold start methods either apply deep learning to build a cross-domain recommender system or map user attributes into the space of user behaviour. These methods are more challenging when applied to online travel platform (e.g., Fliggy), because it is hard to find a cross-domain that user has similar behaviour with travel scenarios and the Location Based Services (LBS) information of users have not been paid sufficient attention. In this work, we propose a LBS-based Heterogeneous Relations Model (LHRM) for user cold start recommendation, which utilizes user's LBS information and behaviour information in related domains (e.g., Taobao) and user's behaviour information in travel platforms (e.g., Fliggy) to construct the heterogeneous relations between users and items. Moreover, an attention-based multi-layer perceptron is applied to extract latent factors of users and items. Through this way, LHRM has better generalization performance than existing methods. Experimental results on real data from Fliggy's offline log illustrate the effectiveness of LHRM.
Software Defect-Proneness Prediction with Package Cohesion and Coupling Metrics Based on Complex Network Theory

Driven by functionality requirements, software codes are increasingly inflated, and invocations between codes are frequent and random. This makes it difficult for programmers to be thoughtful when modifying code, increasing the risk of defects. In an object-oriented software system, packages take the role of a middle tier that aggregates classes and limits class access. However, as the software system evolves, the logic and correctness of packages are weakened. In this paper, we explore the relation between package metrics and object-oriented software defect-proneness. We use two metrics of package cohesion and coupling based on complex network theory to verify the impact of code structure on software quality. On six Java software systems, the experimental result shows that the cohesion and coupling metrics play a positive role in software defect prediction, and they can correctly and effectively evaluate package organization structure. Meanwhile, our study confirms that compliance with the design principle of high cohesion and low coupling can reduce the risk of software defect-proneness and improve software quality.
Connect the people and the knowledge will flow – My recipe for building strong, knowledge sharing communities of peers

2020 has been described often as the year of disruption and I am confident I will look back on this year and be proud that knowledge sharing communities have helped to unlock new ways of working in organisations to adjust to the distruption. I have learned that these community connections have helped to leverage existing knowledge, enabled collaboration and co-creation of new knowledge. These achievements all done virtually, delivering quality results, faster than ever before. In my experience, the leaders of these communities might be subject matter experts, but often have fallen into their roles. As such they would benefit from some peer guidance and support. In this article, I share my experience, recommend some simple ways to help develop an open mindset to support knowledge sharing communities, and enable the flow of knowledge in your organisation.
Classification of Hand-Based and Non-Hand-Based Physical Activities Using Wearable Sensors

This study presents the application of machine learning techniques for physical activity recognition on data obtained from wearable sensors. For this purpose, it proposes the separate classification of hand-based (i.e., eating, writing, clapping) and non-hand-based (i.e., walking, jogging, sitting) human activities recorded by the accelerometer and gyroscope sensors of smartphones and watches. Different machine learning algorithms were compared to build the most appropriate model for the application. The experimental results showed that building two separate classification models for hand-based and non-hand-based activities achieved better accuracy (94.96%) than a single classification model (92.10%) which covered all activities.
A Method of Network Security Risk Measurement Based on Improved D-S Evidence Theory

Abstract This paper applies Dempster-Shafer (D-S) evidence theory to network security measurement and assessment, which improved the accuracy of network security measurement results. The main contents include improving the composition rules to solve the paradox, establishing a security measurement model, and proving the rationality of the improved method by three Distributed Denial of Service (DDoS) attacks experiment. The results show that this method is more accurate in detecting traffic attacks.
RCBO – A Big Data Classification Based On an Efficient RCBO Optimization Technique and Apache Spark

The peculiarity in enormous data is rising bit by bit so the current programming instruments faces inconvenience in management of tremendous data. Besides, the movement of the anomaly data in the tremendous datasets is a key basic to the investigation business. Thusly, this paper proposes a novel strategy for dealing with the huge data using Spark structure. The proposed strategy encounters two phases for orchestrating the gigantic data, which incorporates feature decision and game plan, which is acted in the hidden center points of Spark building. The proposed improvement estimation is named Rider Chaotic Biography smoothing out (RCBO) computation, which is the joining of the Rider Optimization Algorithm (ROA) and the standard confounded biogeography-based-progression (CBBO). The proposed RCBO-significant stacked auto-encoder using Spark structure effectively handles the huge data for accomplishing incredible colossal data course of action. Here, the proposed RCBO is used for picking sensible features from the colossal dataset.
The ontology-driven approach to intelligent support of requirements engineering in agile software development

The paper presents the ontology-driven approach to intelligent support of requirements engineering while agile software development. An ontology system is proposed that combines the ontology for information support of the requirements engineering process and the ontology of the software product application domain. So we offer to analyze the requirements as logical statements about the application domain of the software product. The method of extracting and analyzing the requirements from natural Russian language formulations is described. The requirements are first processed by the UDPipe as a part of the method. Production rules for extracting ontology concepts are applied to the dependency trees built by UDPipe. The consistency of the requirements set presented in the form of the ontology instances is checked according to the developed production rules. The paper also gives a brief description of the developed decision support system (DSS) prototype which involves the joint use of several OWL files presenting knowledge about the project, the application domain and the semantic relations between the key elements of the proposals with the requirements (actors, actions and objects). The ontology editor Protégé 5.2 is used to work with the ontology. The DSS is written in Python. DSS can exchange data with adjacent systems SWI-Prolog and UDPipe.
Encrypt Audio File using Speech Audio File As a key

Abstract in this research, a new method for audio (.WAV) file encryption is propose using a random modification with two secret key applications. The proposed method provided a highly secure audio.WAV files and efficient audio files which have very high-frequency band. The proposed system is to convert speech audio file to text form, this text creates password seed with two keys using hash function, the first key encrypted using proposed algorithm then these keys used to encrypt the original audio (.WAV) file by using Rijndael algorithm.
Tradeoff Relations Between Accessible Information, Informational Power, and Purity

The accessible information and the informational power quantify the maximum amount of information that can be extracted from a quantum ensemble and by a quantum measurement, respectively. Here, we investigate the tradeoff between the accessible information (informational power, respectively) and the purity of the states of the ensemble (the elements of the measurement, respectively). Under any given lower bound on the purity, 1) we compute the minimum informational power and show that it is attained by the depolarized uniformly-distributed measurement; and 2) we give a lower bound on the accessible information. Under any given upper bound on the purity, 1) we compute the maximum accessible information and show that it is attained by an ensemble of pairwise commuting states with at most two distinct non-null eigenvalues; and 2) we give a lower bound on the maximum informational power. The present results provide, as a corollary, novel sufficient conditions for the tightness of the Jozsa-Robb-Wootters lower bound to the accessible information.
Deep Discriminative Embedding with Ranked Weight for Speaker Verification

Deep speaker-embedding neural network trained with a discriminative loss function is widely known to be effective for speaker verification task. Notably, angular margin softmax loss, and its variants, were proposed to promote intra-class compactness. However, it is worth noticing that these methods are not effective enough in enhancing inter-class separability. In this paper, we present a ranked weight loss which explicitly encourages intra-class compactness and enhances inter-class separability simultaneously. During the neural network training process, the most attention is given to the target speaker in order to encourage intra-class compactness. Next, its nearest neighbor who has the greatest impact on the correct classification gets the second most attention while the least attention is paid to its farthest neighbor. Experimental results on VoxCeleb1, CN-Celeb and the Speakers in the Wild (SITW) core-core condition show that the proposed ranked weight loss achieves state-of-the-art performance.
Accurate Damage Dimension Estimation in AI Driven Vehicle Inspection System

With the rise of Artificial Intelligence, research in Computer Vision and Deep Learning has made substantial advancement on understanding visual content. This has led industries across multiple sectors to upgrade and automate processes which require some visual understanding of the physical world. Damage assessment in vehicles is an important step for insurance claims and auto finance industry. The damage assessment involves granular part detection, damage localization and classification into different damage types such as dent, scratch, crush, tear, etc. Further, physical dimension estimation of the damages is required for computing cost to the customer. Currently, this process involves manual interventions, where an agent assesses the damages physically. This process leads to long turnaround time for the insurance majors and auto finance organizations. In this paper, we present a near real-time end-to-end solution which yields accurate damage detection and propose approaches for providing dimensions of the damages for accurate repair cost estimates for the vehicle. The solution further incorporates an ensemble of computer vision algorithms to calculate dimensions, generating bounding boxes, and consolidating the damage predictions to provide an overall damage estimate from multiple images captured by vehicle owner. Insurance majors and auto finance organizations can substantially reduce their turn around time for claims and charging penalties for damage incurred, respectively by integrating the developed solution in their existing process.
Privacy-Preserving Classification of Personal Data with Fully Homomorphic Encryption: An Application to High-Quality Ionospheric Data Prediction

In recent years, the problem of data leakage is really common, so designing a safe and efficient privacy-preserving machine learning protocol has become an urgent demand to many researchers. The fully homomorphic encryption algorithm has attracted the interest of many researchers. This algorithm can directly operate on the ciphertext without decryption, and the decrypting result is the same as that obtained by performing the same processing on the plaintext. At present, many scholars have designed privacy-preserving machine learning protocols, and have achieved good results. However, as one of the most frequent used algorithms, there are relatively few studies concentrating on the privacy-preserving protocols for logistic regression and fewer focusing on the prediction process. Therefore, we propose a privacy-preserving protocol to solve the data leakage problem during the logistic regression process. Based on the semi-honest assumption and BGV fully homomorphic encryption algorithm, our protocol is mainly focused on the prediction process of logistic regression. In this protocol, there are two parties (client and server), the client holds the test set data and label data, the server holds the model coefficients obtained from the training process. The two parties execute the protocol to finish the logistic regression prediction process in ciphertext. Finally, the client side obtains the prediction category of the dependent variable and calculates the accuracy of the prediction result. During this process, the server will not learn anything about the test set, nor will the client know any information about the model used by the server.