Reviewerly/exHarmony huggingface dataset:
Effort Estimation using Bayesian Networks for Agile Development

This article proposes an automatic method to estimate the effort of development based on narrative texts. Narrative used the agile method. We propose techniques of symbolic analysis of natural language were used for extraction of verbs and nouns, and verbal reduction (verbs in infinitive), and the standardization of keywords through synonyms. For the machine learning was used if the naive Bayesian classifier model. Apply and test the model in real environment that used the narratives in the Portuguese language in the form of BDD (Behavior Driven Development). In these tests, obtaining an accuracy of 83% in the estimates of the story points.
Perceived quality measurement of stereoscopic 3D images based on sparse representation and binocular combination

Measurement of the perceived quality of stereoscopic three-dimensional (S3D) images has attracted an increasing amount of research interest in recent years. This paper proposes a S3D image quality measurement (IQM) metric based on sparse representation and binocular combination. The proposed method involves learning binocular and monocular dictionaries from a training database such that the sparse features of binocular combination can be expressed by a linear combination of a few selected basis feature vectors. Following this, scores for the similarity of these sparse features between reference and distorted S3D images are measured. Based on the observation that sparse features are invariant against weak degradations, similarity scores of the features of the gradient magnitude of binocular combination are then computed and used as a complementary feature. Finally, by using kernel-based support vector regression (SVR), these similarity scores are integrated into an overall quality value. Experimental results on three public S3D-IQM datasets show that in comparison with the relevant existing metrics, the devised metric attains significantly high consistency alignment with subjective quality assessment.
Methodology for Autonomous Crossing Narrow Passages Applied on Assistive Mobile Robots

The assistive robotics field works with technologies aimed at assisting people with physical impairments. Simple tasks like passing through doorways can be a significant challenge for motored wheelchairs users. Besides, depending on the disability degree, the task may be impossible to complete without help. Therefore, mobile robotics technologies have been implemented in wheelchairs to give them intelligence, and a supervisory interface inserts the user in the control loop to complete the objectives. Thus, this research proposes an innovative method to assist the user to cross narrow passages automatically. The methodology works in known and unknown environments. Besides, it does not require mapping and global localization algorithms. In this technique, a depth camera recognizes the edges between a narrow path. A Kalman filter method is used to improve the precision of these points even when the passage leaves the camera field of view. Finally, a nonlinear controller performs the narrow passage crossing. This methodology was analyzed using a MATLAB model, Gazebo simulation platform, and a real mobile robot Pioneer-P3DX. The results demonstrated promising applicability.
CodeArena: Inspecting and Improving Code Quality Metrics using Minecraft

To minimize technical debt and increase the maintainability of software, it is essential to make developers aware of what factors influence the maintainability of a codebase. To make the field of code quality and maintenance intuitive to developers, we propose CodeArena, which is an extension to the popular 3D sandbox game called Minecraft. CodeArena converts patterns in a codebase that are considered harmful to monsters in Minecraft, which can then be fought to improve the codebase. This way, the developer can gradually improve the quality of the code, while learning about code quality in an engaging way.
Dense-Connected Residual Network for Video Super-Resolution

Recent research has shown that the performances of super-resolution methods can be significantly boosted using deep convolutional neural networks. However, current superresolution methods continue to exhibit relatively low performances for video, partly because they ignore certain crucial inter-frame information from the original low-resolution frame sequence or the hierarchical features of deep networks. In this paper, we propose a novel method for video super resolution named dense-connected residual network (DCRnet) to address the above drawbacks. The DCRnet can preserve the low -frequency contents of motion compensated frames, and facilitate the restoration of high-frequency details by exploiting the hierarchical features from all the convolutional layers. Specifically, we propose a dense-connected residual block (DCRB) as a basic component. The output of one DCRB is the compressed concatenation of all preceding DCRBs features and each residual block features of the current DCRB. Extensive experimentation demonstrates that our method is superior to the current state-of-the-art methods in both quantitative and qualitative metrics.
Footstep power generation using Arduino Uno

This project is to develop a new source of renewable energy with low-cost budget with the help of Arduino Uno as the microcontroller. The footstep power generation system is to capture the typically wasted energy surrounding a system and transforming it into electrical energy. The technique used in gaining the energy is via piezoelectric materials. This method employs piezoelectric components where deformations created by dissimilar means are directly transformed into electrical charge through piezoelectric effect. Afterwards, the electrical energy can be regulated or stored for further use. In this project, we are generating electrical power as a non-conventional method by simply walking or running as the input source. The piezoelectric sensor will then send the signal into the Arduino Uno and transform it into electrical energy. The LCD will then displayed the amount of voltage generated by the circuit. The highest voltage generated in this project is 8.29 V. Then, the voltage stored in the battery can be used to charge the mobile phone. The results shown that this footstep generation system is very important for utilization in today's world.
Towards an Ontology of Cognitive Assistants

Abstract Cognitive assistants such as IBM Watson and Siri are at the forefront of social and technological innovation and have the potential to solve many unique problems. However, the lack of standardization and classification within the field impedes critical analysis of existing cognitive assistants and may further inhibit their growth into more useful applications. This paper discusses the development of an ontology, its classes, and subclasses that may serve as a foundation for defining and differentiating CAs. Specifically, the four suggested classes include: learning, intelligence, autonomy, and communication. Various assistants are described and categorized using the proposed system. Our novel ontological framework is the first step towards a classification system for this burgeoning field.
European Genome-Phenome Archive (EGA) - Granular Solutions for the Next 10 Years

The European Genome-phenome Archive (EGA) is a repository that facilitates access and management for long-term archival of human biomolecular data. The EGA is co-managed by the European Bioinformatics Institute (EBI) and the Centre for Genomic Regulation (CRG). As the omics community awareness of data sharing and reproducibility increases, complex services and granular solutions are needed from the repositories such as EGA. Not only will we introduce the EGA environment but we will also present advanced features designed for a wide range of users. These new tools and technologies include the EGA Beacon (developed within the GA4GH and ELIXIR framework), infrastructures for data access and retrieval, as well as data quality control and visualisation projects.
Evaluation Approaches of Service Oriented Architecture (SOA) - A Survey

In Computer Science field, there are many established tools and methods to evaluate and test the software don't work well with all the systems, especially the systems that are created up for the services. For instance, for the systems integrators and the users, the service is just an interface. This shackle white box method that based on data flow knowledge and code structure. The lack of accessing the source code prevents the approaches that depend seeding the code with errors like classical mutation-testing approaches. Therefore, service-oriented system evaluation has been a challenge. There is many existing evaluation metrics, but none is efficient to perform a complete and effective evaluation. This survey paper discusses different evaluation methods for SOA software's, and then it summarizes their support and limitation in context of SOA's.
A 55nm 50nJ/encode 13nJ/decode Homomorphic Encryption Crypto-Engine for IoT Nodes to Enable Secure Computation on Encrypted Data

We present a 55nm test-chip prototype of a cryptosystem (encryption and decryption) implementing Homomorphic Encryption that can enable computation on encrypted data. Test-chip measurements show 50nJ/encode and 13nJ/decode thus making the cryptosystem suitable for sensor-nodes and IoT applications.
Traffic Classification for Mobile Video Streaming Using Dynamic Warping Network

Traditional traffic classification methods generally sort the Internet traffic for video streaming to the same category. However, video streaming should be treated differently according to different streaming techniques for the task of QoE evaluation. Meanwhile, end-to-end encryption and different encrypted forms make traffic classification even more challenging because of insufficient distinguishable characteristics. In this work, we propose a novel Dynamic Warping Network (DWN) model that allows us to differentiate different streaming techniques based on traffic patterns. We compute soft Dynamic Time Warping (DTW) distances between the download speed series and a set of warping series, which are further fed to Multi-Layer Perceptions (MLP) for traffic classification. We also show how to train the MLP and warping series jointly using back-propagation algorithm. The proposed model outperforms the start-of-the-art MaMPF model for distinguishing Internet traffic between different streaming techniques, where the accuracy for Video on Demand (VoD) using HTTP Adaptive Streaming (HAS) and live broadcasting (LB) reaches 90.51% and 88.84% respectively.
Legal Control over Big Data Criminal Investigation

The rapid growth of big data technology has become a major trend affecting the pattern of world development. Big data criminal investigation is a new type of criminal detection used extensively in the course of police practice at home and abroad. Its emergence indicates a trend in criminal justice towards ensuring security at the expense of privacy and exchanging rights for information. Big data criminal investigation highlights the backwardness and dysfunction of the traditional framework of legal norms, evident in doubts about the legal attributes of such investigation and the obvious limitations of techniques for distinguishing data content from metadata. This leaves a vacuum in the regulation of investigative power at the preliminary stage of investigation. Big data criminal investigation itself is a double-edge sword; in order to forestall the possible abuses it may entail in terms of deep and broad interventions in basic civil rights, big data criminal investigation should be brought under the necessary legal control. We therefore propose adopting a dual regulatory approach comprising investigative and data norms, selectively adopting the traditional normative framework of the principle of legality and the principle of proportionality, and at the same time supplementing it with other legal principles and mechanisms concerning the protection of personal information and data.
Person Re-identification Based on Feature Fusion

Person re-identification is a matching task of person images captured from different camera views. In the real scene, This task is extremely challenging due to changes in pedestrian poses, camera angles and lighting. How to extract robust pedestrian features has become a key step in Person re-identification. In this paper, we propose an person re-identification model based on combined visual features. Our features consist of traditional visual features and convolutional neural network (CNN) features. We extract the CNN feature of person image and fuse them with robust Local Maximal Occurrence (LOMO) features. This fused feature has better performance. Before extracting features, we use the Retinex algorithm to preprocess person images. Finally adopt a random sampling softmax loss to effectively train the model. We experimentally show the effectiveness and accuracy of the proposed method on the VIPeR and PRID450s datasets.
Mind the Gap: Are Practitioners and Researchers in Software Testing Speaking the Same Language?

Context. Software testing is the area of software engineering focused on determining whether a software meets the planned requirements and on evaluating its quality. Lately, academic researchers have increased their attention in this topic due to the impact of its success on software projects. However, recent studies have discussed that practitioners and researchers might have different views regarding what is important to explore and study in order to improve the software testing process. Goal. This study aims to investigate the differences of interests between academic researchers and practitioners in software testing, pointing out observable convergences and divergences between the two communities. Method. A mixed-method approach based on a mapping study, a quantitative study and a focus group was applied to collect quantitative and qualitative data from professionals and academic sources. Results. Our results confirm the existence of a gap between the two communities and the findings suggest that, while researchers are mainly focused on the proposition of novel tools and techniques, practitioners are more interested in issues related to the evaluation and discussions of existing approaches, tools and techniques. Therefore, academic researchers might consider identify, understand and modify the existing tools and strategies, instead of building new ones. Conclusion. In general, the distinction between the two groups is noticeable and there is only one strong mutual interest between both practitioners and researchers, namely, test automation. Therefore, there is a need for the development of strategies that reduce the gap between academia and industrial practice and bring them closer in order to increase the quality of the software testing processes.
Towards Stateless RNIC for Data Center Networks

Because of small NIC on-chip memory, the massive connection states maintained on Remote Direct Memory Access (RDMA) NIC (RNIC) significantly limit its scalability. When the number of concurrent connections grows, RNICs have to frequently fetch connection states from host memory, leading to dramatic performance degradation. In this paper, we propose StaR, which fundamentally solves this scalability issue by making RNIC stateless. Leveraging the asymmetric communication pattern in data center applications, the StaR RNIC stores zero connection-related states by moving all the connection states to the other end. Through careful design, StaR RNICs can maintain unchanged RDMA semantics and avoid security issues even when processing traffic statelessly. Preliminary simulation results show that StaR can improve the aggregate throughput by more than 160x (stress test) and 4x (application) compared to original RNICs.
Measuring Students' Performance on Programming Tasks

Large scale learning systems for introductory programming need to be able to automatically assess the quality of students' performance on programming tasks. This assessment is done using a performance measure, which provides feedback to students and teachers, and an input to the domain, student and tutor models. The choice of a good performance measure is nontrivial, since the performance of students can be measured in many ways, and the design of measure can interact with the adaptive features of a learning system or imperfections in the used domain model. We discuss the important design decisions and illustrate the process of an iterative design and evaluation of a performance measure in a case study.
Effects of Immersive Virtual Reality on the Heart Rate of Athlete’s Warm-Up

An adequate warm-up prior to intensive exercise can bring benefits to athletes, these requirements may vary depending on the physical activity and the training needs. The immersive virtual reality could have benefits in the warm-up and it can be determined by physiological data of the athlete. This work presents a mobile virtual reality application to stimulate the warm-up of an athlete using a standard treadmill, where the developed application is composed of a pleasant and stimulating environment. A Smartphone and the Gear VR are used as an HMD device, and wireless headphones are placed in the users, in addition, the heart rate of the athletes is monitored using a Polar H7 sensor. Experimental results are obtained in athletes with similar characteristics and conditions, identified a direct relation of the virtual environment with the pulsations per minute (ppm), denoting pulsations greater than usual in case of the stimulating environment and lower pulsations for the pleasant environment. Finally, a usability test is performed that shows the level of sociability of the system.
Learning single-shot vehicle orientation estimation from large-scale street panoramas

Modern autonomous driving techniques harness a variety of visual cues for performing key tasks like vehicle steering and collision avoidance. This work defines for the first time and proposes a solution to the following problem: can one precisely estimate an autonomous vehicle’s orientation relative to the road by taking a picture from within the vehicle? Vehicle orientation supplies crucial information for making various driving-related decisions. The key challenge of this problem is the lack of training data, since accurately annotating vehicle’s orientation information in an image is in general difficult. To circumvent the data scarcity issue, we propose to leverage publicly-available street panoramas as those found in GoogleMaps. Street panoramas are essentially defined by 2-D plenoptic functions that map the visual environment to a sphere. One can thus effortlessly collect annotated data for the interested task by specifying a direction and reading out corresponding pixels from a panorama. This way we collect a city-scale large benchmark TB500K for studying this problem. Our second contribution is the exposition of a two-stream deep network for attacking this problem. Intuitively, visual/semantic and geometric (e.g., the direction of road lanes) cues are both crucial and complement each other. Our proposed two-stream network can jointly learn from both cues and serves as a reasonable baseline for this novel problem. In addition, several experiments are also conducted to reveal key factors in estimating the vehicle orientation.
State Transfer via On-Line State Estimation and Lyapunov-Based Feedback Control for a N-Qubit System

In this paper, we propose a Lyapunov-based state feedback control for state transfer based on the on-line quantum state estimation (OQSE). The OQSE is designed based on continuous weak measurements and compressed sensing. The controlled system is described by quantum master equation for open quantum systems, and the continuous measurement operators are derived according to the dynamic equation of system. The feedback control law is designed based on the Lyapunov stability theorem, and a strict proof of proposed control laws are given. At each sampling time, the state is estimated on-line, which is used to design the control law. The simulation experimental results show the effectiveness of the proposed feedback control strategy.
Uniform versus Zipf distribution in a mixing collection process

We consider the variant of the (classic) collector’s problem where the family of coupon probabilities is the mixing of two subfamilies one of which is the uniform family, while the other belongs to the so-called Zipf family. Motivation comes from a question placed in a recent work of ours (A.V. Doumas and V.G. Papanicolaou, 2012b).
Splitting and Aggregating Signatures in Cryptocurrency Protocols

The blockchain technology and a vast amount of cryptocurrency related activities have generated an unprecedented level of interest among the public. However, even at the entry level, cryptocurrency users need to deal with the complex task of key management. In this paper, we propose a simple way to manage a user's private key, under a reasonable assumption that the user has two devices at his disposal (say a laptop and a mobile phone). We refer to our strategy as key splitting. Since these cryptographic keys are used for generating digital signatures, we should take a closer look at the signature schemes that would perform best under key splitting. At the operational level, scalability is one of the main challenges faced by the users and developers. While there are fundamental issues like consensus that challenge scalability, we focus on the computational efficiency in a block formation. Aggregation of signatures is one of the effective solutions to this problem. To this end, we observe that none of the existing signature schemes work well for BOTH key splitting and aggregation. The current popular schemes such as the ones used in Bitcoin or Schnorr's scheme implemented over Elliptic curves are neither suitable for aggregation nor can their keys be split in a convenient and meaningful way. A detailed theoretical and empirical analysis shows that the BLS short signature scheme is best suited for achieving both key splitting and aggregation.
Product Quality Assessment using Opinion Mining in Persian Online Shopping

Human opinions and decisions are mainly inspired by other people's beliefs and experiences which give them a strong background of anything they've heard or read about. Accordingly, extracting and knowing what others think about a special subject or product have become extremely important for different types of service providers as well as the consumers. Opinion mining is the area of analyzing user's sentiments through the available reviews on the internet and has the beneficial application of guiding users for online shopping. Unfortunately, very few studies has been done for sentiment analysis in Persian online shopping and the existing works have many limitations in their performances, esp. in differentiating between a weak and a strong mood in sentimental sentences. This paper proposes a new lexicon-based opinion mining method for Persian online shopping which considers the effect of intensifier adjectives in extracting the exact opinion of a review. It has been applied on a real dataset extracted from Digikala <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://www.digikala.com and has achieved promising results compared to those of expert evaluators.