Reviewerly/exHarmony huggingface dataset:
Ensemble Adaptive Streaming – A New Paradigm to Generate Streaming Algorithms via Specializations

Video streaming is now ubiquitous in the mobile Internet. This motivated intense research in adaptive streaming algorithms to tackle mobile networks' fluctuating conditions. Our investigations revealed that while existing algorithms can perform well in their intended operating environments, their performance can degrade substantially in other environments. This work tackles this challenge by developing a novel Ensemble Adaptive Streaming (EAS) paradigm to mobile video streaming. As opposed to designing a single streaming algorithm for all network conditions, we argue that different network conditions require different algorithms. We introduce the notion of network differentiators to segregate network conditions into different classes where each class has its own adaptation algorithm designed and optimized specifically for it. An EAS mobile streaming client then selects at runtime the matching adaptation algorithm using the same network differentiators on a per session basis for streaming. We show how EAS can be applied to existing machine-learning approaches to improve their performances. Moreover, to fully exploit EAS's potential we developed the first Genetic Programming approach to evolve adaptive streaming algorithms. The resultant EAS-GP algorithms not only outperformed state-of-the-art algorithms substantially, but also exhibited remarkable robustness over time, location, mobile operators, as well as quality-of-experience metrics.
An SMT-Based Approach to Motion Planning for Multiple Robots With Complex Constraints

In this paper, we propose a new method for solving multirobot motion planning problems with complex constraints. We focus on an important class of problems that require an allocation of spatially distributed tasks to robots, along with efficient paths for each robot to visits their task locations. We introduce a framework for solving these problems that naturally couples allocation with path planning. The allocation problem is encoded as a Boolean Satisfiability problem (SAT) and the path planning problem is encoded as a traveling salesman problem (TSP). In addition, the framework can handle complex constraints such as battery life limitations, robot carrying capacities, and robot-task incompatibilities. We propose an algorithm that leverages recent advances in Satisfiability Modulo Theory (SMT) to combine state-of-the-art SAT and TSP solvers. We characterize the correctness of our algorithm and evaluate it in simulation on a series of patrolling, periodic routing, and multirobot sample collection problems. The results show our algorithm significantly outperforms state-of-the-art mathematical programming solvers.
Random grid based color visual cryptography scheme for black and white secret images with general access structures

To reduce the pixel expansion problem in color–black-and-white visual cryptography schemes (CBW-VCSs) and to provide flexible sharing strategy simultaneously, a random grid based CBW-VCS (RG-CBW-VCS) for general access structure (GAS) is proposed in this paper. First of all, the concept of random grid is extended to color pixel domain, and the XOR operation for color pixels is defined. By adopting this operation, a scheme for RG-CBW-VCS which implements the GAS is introduced. The proposed method is further extended to realize the generalized GAS (GGAS), where implementing complicated sharing strategy becomes available. Theoretical analysis on the proposed method are given to prove the security and contrast conditions, as well as the calculation of contrast. Experimental results are exhibited to show that optimal pixel expansion is achieved by the proposed method.
Neural collision avoidance system for biomimetic autonomous underwater vehicle

Autonomous underwater vehicles (AUVs) are underwater robots which are able to perform certain tasks without the help of a human operator. The key skill of each AUV is the capability to avoid collisions. To this end, appropriate devices and software are necessary with the potential to detect obstacles and to take proper decisions from the point of view of both the task and safety of the vehicle. The paper presents a neural collision avoidance system (NCAS) designed for the biomimetic autonomous underwater vehicle (BAUV). The NCAS is a component of the path following and collision avoidance system (PFCAS), which as the name implies is responsible for safely leading the vehicle along a desired path with collision avoidance. The task of NCAS is to make decisions regarding vehicle maneuvers in the horizontal plane, but only in the close proximity of the obstacles. It is implemented as an evolutionary artificial neural network designed by means of a neuro-evolutionary technique called assembler encoding with evolvable operations (AEEO). The paper outlines operation and construction of the BAUV as well as the PFCAS, the role of the NCAS in the entire system, and briefly presents AEEO as well as reporting on the experiments performed in simulation.
Achieving Safety Incrementally with Checked C

Checked C is a new effort working toward a memory-safe C. Its design is distinguished from that of prior efforts by truly being an extension of C: Every C program is also a Checked C program. Thus, one may make incremental safety improvements to existing codebases while retaining backward compatibility. This paper makes two contributions. First, to help developers convert existing C code to use so-called checked (i.e., safe) pointers, we have developed a preliminary, automated porting tool. Notably, this tool takes advantage of the flexibility of Checked C's design: The tool need not perfectly classify every pointer, as required of prior all-or-nothing efforts. Rather, it can make a best effort to convert more pointers accurately, without letting inaccuracies inhibit compilation. However, such partial conversion raises the question: If safety violations can still occur, what sort of advantage does using Checked C provide? We draw inspiration from research on migratory typing to make our second contribution: We prove a blame property that renders so-called checked regions blameless of any run-time failure. We formalize this property for a core calculus and mechanize the proof in Coq.
Learning of complex event processing rules with genetic programming

Complex Event Processing (CEP) is an established software technology to extract relevant information from massive data streams. Currently, domain experts have to determine manually CEP rules that define a situation of interest. However, often CEP rules cannot be formulated by experts, because the relevant interdependencies and relations between the data are not explicitly known, but inherently hidden in the data streams. To cope with this problem, we present a new learning approach for CEP rules, which is based on Genetic Programming. We discuss in detail the different building blocks of Genetic Programming and how to adjust them to CEP rule learning. Extensive evaluations with synthetic and real world data demonstrate the high potential of the approach and give some hints about the choice of suitable process parameters.
Digging into data management in public‐funded, international research in digital humanities

Path‐breaking in theory and practice alike, digital humanities (DH) not only secures a larger public audience for humanities and social sciences research, but also permits researchers to ask novel questions and to revisit familiar ones. Public‐funded, international, and collaborative research in DH furthers institutional research missions and enriches networked knowledge. The Digging into Data 3 challenge (DID3) (2014–2016), an international and interdisciplinary grant initiative embracing big data, included 14 teams sponsored by 10 funders from four nations. A qualitative case study that relies on purposive sampling and grounded analysis, this article centers on the information practices of DID3 participants. Semistructured interviews were conducted with 53 participants on 11 of the 14 DID3 projects. The study explores how Data Management Plan requirements affect work practices in public‐funded DH, how scholars grapple with key data management challenges, and how they plan to reuse and share their data. It concludes with three recommendations and three directions for future research.
Towards Robust Text Classification with Semantics-Aware Recurrent Neural Architecture

Deep neural networks are becoming ubiquitous in text mining and natural language processing, but semantic resources, such as taxonomies and ontologies, are yet to be fully exploited in a deep learning setting. This paper presents an efficient semantic text mining approach, which converts semantic information related to a given set of documents into a set of novel features that are used for learning. The proposed Semantics-aware Recurrent deep Neural Architecture (SRNA) enables the system to learn simultaneously from the semantic vectors and from the raw text documents. We test the effectiveness of the approach on three text classification tasks: news topic categorization, sentiment analysis and gender profiling. The experiments show that the proposed approach outperforms the approach without semantic knowledge, with highest accuracy gain (up to 10%) achieved on short document fragments.
On Learning Density Aware Embeddings

Deep metric learning algorithms have been utilized to learn discriminative and generalizable models which are effective for classifying unseen classes. In this paper, a novel noise tolerant deep metric learning algorithm is proposed. The proposed method, termed as Density Aware Metric Learning, enforces the model to learn embeddings that are pulled towards the most dense region of the clusters for each class. It is achieved by iteratively shifting the estimate of the center towards the dense region of the cluster thereby leading to faster convergence and higher generalizability. In addition to this, the approach is robust to noisy samples in the training data, often present as outliers. Detailed experiments and analysis on two challenging cross-modal face recognition databases and two popular object recognition databases exhibit the efficacy of the proposed approach. It has superior convergence, requires lesser training time, and yields better accuracies than several popular deep metric learning methods.
Educating Future Multidisciplinary Cybersecurity Teams

We present a vision and the curricular foundations needed for the multidisciplinary cybersecurity teams of the future, which are made up of diverse cybersecurity experts, each contributing unique abilities and perspectives that emerged from their own discipline-centric methodological approaches. Examples demonstrating the effectiveness of current and emerging multidisciplinary cybersecurity teams are included.
Towards Blockchain-based Electricity Trading System and Cyber Resilient Microgrids

With the impetuous growth of modernization electricity demand is at its highest peak. So power market becomes a major issue in terms of trading symmetry and security. The traditional power market is unidirectional in nature, i.e. power plant supplies electricity to the customers which leads to centralization and monopoly. As the prosumers (producer and consumer) arises, microgrid becomes popular with its bidirectional energy supply configuration. Yet, there exist several challenges in energy demand management, trading and security. This paper proposes a blockchain-based approach to address those challenges with minimal effort. A double chained model of blockchain is proposed to facilitate trading among prosumers; algorithms are designed to deploy smart contracts and to form coalition and negotiate electricity trading; several techniques and scenarios are outlined to ease implementation.
Local Force Pattern (LFP): Descriptor for Heterogeneous Face Recognition

This paper proposes a novel local-appearance based feature descriptor namely Local Force Pattern (LFP), for robust heterogeneous face recognition. LFP features encode the directional information of the textures of the given face images compactly, producing a more discriminating code than the state of the art methods. We compute the structure of each micro-pattern with the aid of Van der Waal's force between a pair of pixels, which extracts the directional information, and encodes it using a 2-D vector. This directional information helps us to discriminate images having similar structural patterns with different modality based intensity transitions. We divide the face image into several small pixel boxes called 'cell' and extract the LFP features from them. Several cell forms 'block'. The resultant force obtained from the block is vector-summed to get the final feature vector. Then, we concatenate these features into a vector and use it as the face descriptor. Experiments show that the proposed descriptor consistently performs well over state-of-the-art methods for different heterogeneous face datasets like CUSF, IIIT-D and CASIA.
Privacy-preserving Online Human Behaviour Anomaly Detection Based on Body Movements and Objects Positions

Human behaviour anomaly detection is crucial for modern artifi-cial intelligence systems. However, privacy protection plays a great role in the realization. In this paper, an online privacy-preserving anomaly detector is presented. The proposed method is able to discriminate on human subject body movements, postures and interactions with the surrounding objects, preserving subject privacy in all the tuning, training and testing stages. ActionXPose, Single Shot MultiBox Detector and Support Vector Machine are exploited for the proposed semi-supervised anomaly detector. The method successfully detects abnormal human behaviours, including unexpected body movements and misplaced objects. A new dataset ISLD-A is also proposed <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , providing suitable benchmark for performance evaluation <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> .
Design and implementation of an edge computing platform architecture using Docker and Kubernetes for machine learning

Huge data sets and high resources consumption are the prominent features of machine learning services. At present, machine learning services are often deployed on large-scaled cloud servers. The cloud utilizes its rich resources to perform the model training and prediction tasks, but the performance of this method is often limited by the unstable network conditions. To combine the rich-resources advantage of the cloud server with the stable-network performance of the edge computing technology, this paper proposes a Cloud-training and Edge-predicting framework. By integrating the Docker container technology and Kubernetes container choreography technology, we build an edge computing platform, and deploy a machine learning model (Inception V3) on the platform. With this method, we implemented machine learning services on the edge side. In this paper, we have described the designing and building process of the edge computing platform and the deployment procedure of the machine learning model in detail, and we have taken an experiment to implement the service to prove the feasibility of our ideas.
Ethics Is a Software Design Concern

The IEEE and Association for Computing Machinery (ACM) joint report "Software Engineering Code of Ethics" summarizes the responsibilities of software engineers as the following: "Software engineers shall commit themselves to making the analysis, specification, design, development, testing and maintenance of software beneficial and respected profession."
An empirical study on the potential usefulness of domain models for completeness checking of requirements

Domain modeling is a common strategy for mitigating incompleteness in requirements. While the benefits of domain models for checking the completeness of requirements are anecdotally known, these benefits have never been evaluated systematically. We empirically examine the potential usefulness of domain models for detecting incompleteness in natural-language requirements. We focus on requirements written as "shall"-style statements and domain models captured using UML class diagrams. Through a randomized simulation process, we analyze the sensitivity of domain models to omissions in requirements. Sensitivity is a measure of whether a domain model contains information that can lead to the discovery of requirements omissions. Our empirical research method is case study research in an industrial setting. We have experts construct domain models in three distinct industry domains. We then report on how sensitive the resulting models are to simulated omissions in requirements. We observe that domain models exhibit near-linear sensitivity to both unspecified (i.e., missing) and under-specified requirements (i.e., requirements whose details are incomplete). The level of sensitivity is more than four times higher for unspecified requirements than under-specified ones. These results provide empirical evidence that domain models provide useful cues for checking the completeness of natural-language requirements. Further studies remain necessary to ascertain whether analysts are able to effectively exploit these cues for incompleteness detection.
Anomaly Detection and Tracking Based on Mean–Reverting Processes with Unknown Parameters

Piecewise mean-reverting stochastic processes have been recently proposed and validated as an effective model for long-term object prediction. In this paper, we exploit the Ornstein-Uhlenbeck (OU) dynamic model to represent an anomaly as any deviation of the long-run mean velocity from the nominal condition. This amounts to modeling the anomaly as an unknown switching control input that can affect the dynamics of the object. Under this model, the problem of joint anomaly detection and tracking can be addressed within the Bayesian random set framework by means of a hybrid Bernoulli filter (HBF) that sequentially estimates a Bernoulli random set (empty under nominal behavior) for the unknown long-run mean velocity, and a random vector for the kinematic state of the object. An additional challenge is represented by the fact that two extra parameters, i.e. the reversion rate and the noise covariance of the underlying OU process, need to be specified for Bayes-optimal prediction. We propose a multiple-model adaptive filter (MMA-HBF) for anomaly detection, tracking and simultaneous estimation of the OU unknown parameters. The effectiveness of these tools is demonstrated on a simulated maritime scenario.
Two-Handed Bangla Sign Language Recognition Using Principal Component Analysis (PCA) And KNN Algorithm

Communication is the basic necessity for the existence in the society. Hearing-impaired people connect among themselves utilizing sign language, but typical people find it challenging to recognize it. The purpose of our paper is to decrease the communication gap between ordinary people and people with hearing and speech disabilities. This paper shows the sign language recognition framework prepared for recognizing 26 gestures from the Bangla Sign Language by utilizing PYTHON. In our proposed structure there are three phases: 1. Database formation phase 2. Training phase 3. Classification phase Preprocessing, segmentation, Eigenvector, and Eigenvalues are utilized as a part of the recognition. The Principal Component Analysis (PCA) was being used for recognizing images by extracting their principal component, and K-Nearest Neighbor algorithm is used for the classification phase. This method is very efficient with various backgrounds and effects.
Semantic Source Code Models Using Identifier Embeddings

The emergence of online open source repositories in the recent years has led to an explosion in the volume of openly available source code, coupled with metadata that relate to a variety of software development activities.As an effect, in line with recent advances in machine learning research, software maintenance activities are switching from symbolic formal methods to data-driven methods.In this context, the rich semantics hidden in source code identifiers provide opportunities for building semantic representations of code which can assist tasks of code search and reuse.To this end, we deliver in the form of pretrained vector space models, distributed code representations for six popular programming languages, namely, Java, Python, PHP, C, C++, and C#.The models are produced using fastText, a state-of-the-art library for learning word representations.Each model is trained on data from a single programming language; the code mined for producing all models amounts to over 13.000 repositories.We indicate dissimilarities between natural language and source code, as well as variations in coding conventions in between the different programming languages we processed.We describe how these heterogeneities guided the data preprocessing decisions we took and the selection of the training parameters in the released models.Finally, we propose potential applications of the models and discuss limitations of the models.
Improvements to N-gram Language Model Using Text Generated from Neural Language Model

Although neural language models have emerged, n-gram language models are still used for many speech recognition tasks. This paper proposes four methods to improve n-gram language models using text generated from a recurrent neural network language model (RNNLM). First, we use multiple RNNLMs from different domains instead of a single RNNLM. The final n-gram language model is obtained by interpolating generated n-gram models from each domain. Second, we use subwords instead of words for RNNLM to reduce the out-of-vocabulary rate. Third, we generate text templates using an RNNLM for template-based data augmentation for named entities. Fourth, we use both forward RNNLM and backward RNNLM to generate text. We found that these four methods improved performance of speech recognition up to 4% relative in various tasks.
Subject and Methodological Competencies in Teachers: Testing the Unified Federal Evaluation Tools

The article describes the main approaches to developing and testing the unified federal evaluation tools (UFET) used for studying subject and methodological competencies of teachers in the Russian Federation. The authors propose a level assessment model of teacher competencies based on the professional standard for teachers. Results of the research helped to identify the shortcomings of teacher training which make carrying out professional activities difficult for teachers. The article describes the terms of using UFET in the procedures of teacher certification and presents the main developmental directions for the current system of continuing teacher education. The authors argue that applying UFET at different stages of teacher training and professional development may ensure the formation of more effective individual educational routes for every teacher aimed at eliminating their professional deficits and improving the standards of education in general.
Electronic evidence preservation model based on blockchain

With the rapid development and application of the Internet, the amount of electronic data based on computer and computer network has exploded. Specific and effective electronic data play an increasingly important role in proving the facts of a case. The most important issue of electronic evidence is trust and security. However, electronic evidence is stored in the centralized database at present, and there are data security and trust problems. This paper proposes an electronic evidence preservation model based on blockchain to ensure the data safe and reliable.