Reviewerly/exHarmony huggingface dataset:
Enhancing example-based code search with functional semantics

As the quality and quantity of open source code increase, effective and efficient search for code implementing certain semantics, or semantics-based code search, has become an emerging need for software developers to retrieve and reuse existing source code. Previous techniques in semantics-based code search encode the semantics of loop-free Java code snippets as constraints and utilize an SMT solver to find encoded snippets that match an input/output (IO) query. We present in this article the Quebio approach to semantics-based search for Java methods. Quebio advances the state-of-the-art by supporting important language features like invocation to library APIs and enabling the search to handle more data types like array/List, Set, and Map. Compared with existing approaches, Quebio also integrates a customized keyword-based search that uses as the input a textual, behavioral summary of the desired methods to quickly prune the methods to be checked against the IO examples. To evaluate the effectiveness and efficiency of Quebio, we constructed a repository of 14,792 methods from 723 open source Java projects hosted on GitHub and applied the approach to resolve 47 queries extracted from StackOverflow. Quebio was able to find methods correctly implementing the specified IO behaviors for 43 of the queries, significantly outperforming the existing semantics-based code search techniques. The average search time with Quebio was 213.2 seconds for each query.
Knowledge-Based Sensors for Controlling A High-Concentration Photovoltaic Tracker

To reduce the cost of generated electrical energy, high-concentration photovoltaic systems have been proposed to reduce the amount of semiconductor material needed by concentrating sunlight using lenses and mirrors. Due to the concentration of energy, the use of tracker or pointing systems is necessary in order to obtain the desired amount of electrical energy. However, a high degree of inaccuracy and imprecision is observed in the real installation of concentration photovoltaic systems. The main objective of this work is to design a knowledge-based controller for a high-concentration photovoltaic system (HCPV) tracker. The methodology proposed consists of using fuzzy rule-based systems (FRBS) and to implement the controller in a real system by means of Internet of Things (IoT) technologies. FRBS have demonstrated correct adaptation to problems having a high degree of inaccuracy and uncertainty, and IoT technology allows use of constrained resource devices, cloud computer architecture, and a platform to store and monitor the data obtained. As a result, two knowledge-based controllers are presented in this paper: the first based on a pointing device and the second based on the measure of the electrical current generated, which showed the best performance in the experiments carried out. New factors that increase imprecision and uncertainty in HCPV solar tracker installations are presented in the experiments carried out in the real installation.
A Non-Intrusive Deep Learning Based Fall Detection Scheme Using Video Cameras

Video-based wellness monitoring is relatively new and attractive field in eHealth, as it enables personalized healthcare with improved accuracy. In this paper we propose a non-intrusive deep learning based real-time scheme to detect falls using video cameras. Our end-to-end model is composed of Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) modules, which automatically learn discriminative features from training data. We have trained and tested our system on a video dataset we have collected, and have achieved very promising results.
Performance Evaluation of ROS Local Trajectory Planning Algorithms to Social Navigation

Accuracy and safety are necessary characteristics in social navigation and still constitute a challenge. The ROS Navigation Stack (RNS) allows the variation of local path planning methods through plugins for navigation. This paper brings you the comparison of those methods, which are directly connected with the safety and naturalness of the robot. Therefore, four different methods were compared by varying the sensors and the simulated environment. A thousand experiments were performed for each combination using the standard parameters of each method in a total of 24000 experiments. This paper concluded that the Elastic Band (EBand) method presents more safety and accuracy than the Dynamic window approach (DWA), method commonly used in several robots that participate in RoboCup@home, so it is more suitable for social navigation - reaching 90% accuracy in some cases and collision rate below 5%.
A Fast Reference-Free Genome Compression Using Deep Neural Networks

Recent development of DNA sequencing technologies has led to a significant increase in genomic data volume. Such a big amount of genome data needs appropriate data storage, data management, and data transfer policies. Compressing genomes can be used for efficient data management. Auto-encoder is a kind of deep neural networks, due to its ability to reduce the dimension of data is suitable for this purpose. In this paper, a new method for genome compression with auto-encoders based on deep neural networks is proposed. It is the first time that an auto-encoder is used to compress the genomes. Experimental results showed that the proposed method can achieve a compression ratio of up to 5 and 92 percent compression accuracy in case of reference-free genome compression. Moreover, after the auto-encoder training stage, the trained network will have a very short compression time which makes it suitable for real-time applications.
Exploiting CSI-MIMO for Accurate and Efficient Device Identification

Due to the inherent broadcast nature of the wireless medium, Wireless Local Area Networks (WLANs) are targets of a variety of malicious attacks, for example, MAC identity spoofing, rogue AP attack, and network freeloading. These attacks invite security and privacy threats and hinder the worry-free deployment of WLAN networks. To thwart these attacks, existing research has proposed to use hardware-specific imperfections as a unique unforgeable fingerprint for the APs and/or clients. Unfortunately, existing solutions are limited to static and stable environments or use customized hardware preventing their wide-scale adoption. To overcome the limitations, in this work, we propose to use the distribution of relative phase differences between MIMO-radio transmitter oscillators as a distinguishing trait or fingerprint. More specifically, we show that the nonidealities of the multiple RF chains on a single MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) transmitter can be extracted and utilized as a reliable device fingerprint. Each transmitter RF chain has a random initial phase offset, and their difference relative to one another is stable over time, differs uniquely for each transmitter device and cannot be altered by the adversary without significant effort and cost. Our functional prototype measures these unknown phase differences using PHY-layer Channel State Information (CSI) of the in-band channel obtained from off-the-shelf hardware. Our design eliminates expensive custom-built hardware, is invariant to environmental variations and supports device mobility making it practical and deployable in real indoor settings. Experimental evaluation using 17 Intel Network Interface Cards (NICs) resulted in 97 % and 92 % device identification accuracy for static and mobile device states respectively. Such promising results with identical model and manufacturer devices wherein underlying manufacturing variations are typically low showcase the effectiveness of our design and suggest even higher accuracy across multi-model and multi-manufacturer cards because of the higher manufacturing variations.
Air-Writing for Smart Glasses by Effective Fingertip Detection

This research investigates real-time fingertip detection in RGB images/frames captured from such wearable devices as smart glasses. A modified Mask Regional Convolutional Neural Network (Mask R-CNN) is proposed with one region-based CNN for hand detection and another three-layer CNN for locating the fingertip. The processing speed is high enough to facilitate several interesting applications. One application is to trace the location of a user's fingertip from first-person perspective to form writing trajectories. A text input mechanism for smart glasses can thus be implemented to enable a user to write letters/characters in air as the input and even interact with the system using simple gestures. Experimental results demonstrate the feasibility of this new text input methodology.
Distillation of an End-to-End Oracle for Face Verification and Recognition Sensors

Face recognition functions are today exploited through biometric sensors in many applications, from extended security systems to inclusion devices; deep neural network methods are reaching in this field stunning performances. The main limitation of the deep learning approach is an inconvenient relation between the accuracy of the results and the needed computing power. When a personal device is employed, in particular, many algorithms require a cloud computing approach to achieve the expected performances; other algorithms adopt models that are simple by design. A third viable option consists of model (oracle) distillation. This is the most intriguing among the compression techniques since it permits to devise of the minimal structure that will enforce the same I/O relation as the original model. In this paper, a distillation technique is applied to a complex model, enabling the introduction of fast state-of-the-art recognition capabilities on a low-end hardware face recognition sensor module. Two distilled models are presented in this contribution: the former can be directly used in place of the original oracle, while the latter incarnates better the end-to-end approach, removing the need for a separate alignment procedure. The presented biometric systems are examined on the two problems of face verification and face recognition in an open set by using well-agreed training/testing methodologies and datasets.
Can Exposure, Noise and Compression Affect Image Recognition? An Assessment of the Impacts on State-of-the-Art ConvNets

Convolutional Neural Networks stand the current state-of-the-art in image recognition, as well as many computer vision tasks. Nevertheless, these architectures have been shown to be vulnerable to image manipulations, which may undermine the reliability and safety of CNN-based models in autonomous and robotic applications. We present a rigorous evaluation of the robustness of several high-level image recognition models and investigate their performance under distinct image distortions. We propose a testing framework which emulates ill exposure conditions, low-range image sensors, lossy compression, as well as commonly observed noise types. One one side results measured in terms of accuracy, precision, and F1-Score, indicate that most CNN models are marginally affected by mild miss-exposure, heavy compression, and Poisson noise. Severe miss-exposure, impulse noise, or signal-dependent noise, on the other side, show a substantial drop in accuracy and precision. A careful evaluation of some typical image distortions, commonly observed in computer vision and machine vision pipelines, provides insights and directions for further developments in the field.
Qubit unitary lattice algorithm for spin-2 Bose–Einstein Condensates. I – Theory and Pade initial conditions

A qubit unitary lattice algorithm is developed for the evolution of five coupled Gross–Pitaesvkii equations that govern the dynamics of a spin-2 Bose–Einstein Condensate. On introducing 2 qubits/scalar component of the order parameter, 10 qubits/lattice node are required. The interleaved sequence of unitary collide-stream operators relies on their non-commutivity in order that their be non-trivial evolution equations. This collisional entanglement followed by streaming of this entanglement throughout the lattice is a hallmark of the algorithm. The qubit algorithm for spin-2 is summarized and new Pade initial conditions are determined. Being unitary, the algorithm can be directly encoded onto a quantum-gate computer.
On the sparseness of certain linear MRD codes

We determine the proportion of linear [3×3;3]-MRD codes over Fq within the space of all 3-dimensional 3×3-rank-metric codes over the same field. This shows that for these parameters linear MRD codes are sparse in the sense that the proportion tends to 0 as q→∞. This is so far the only parameter case for which MRD codes are known to be sparse. The computation is accomplished by reducing the space of all such rank-metric codes to a space of specific bases and subsequently making use of a result by Menichetti (1973) on 3-dimensional semifields.
On Estimating Communication Delays using Graph Convolutional Networks with Semi-Supervised Learning

In large-scale communication networks consisting of many end hosts and routers, accurate acquisition, measurement, and estimation of communication delays between node pairs are essential for providing high-quality communication services. In several QoS-related performance metrics such as metrics for efficiency, metrics for availability, metrics for reliability, communication delay is one of the key metrics to realize several traffic control mechanisms. Conventional instrumentation and measurement techniques are suitable when the size of the network to be measured is relatively small, or when the number of node pairs to be measured is relatively small. However, in evolving and complex networks, it is not trivial to acquire, measure, and estimate the communication quality at a huge number of routers and end hosts. In this paper, as an initial step toward the realization of estimating communication quality (especially communication delays between node pairs) in a large-scale network, we investigate the potential of graph neural networks with semi- supervised learning for estimating communication delays between node pairs. Our findings include that the average relative error of estimated communication delays is around 10-35% depending on the fraction of the number of measurement nodes, and that communication delays for large-scale networks can be estimated with a high accuracy even if the fraction of the number of measurement nodes is not so large.
IoT-PEN: A Penetration Testing Framework for IoT

With the horizon of 5 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">th</sup> generation wireless systems (5G), Internet of Things (IoT) is expected to take the major portion of computing. The lack of inbuilt security and security protocols in cheap IoT devices give privilege to an attacker to exploit these device's vulnerabilities and break into the target device. IoT network security was initially perceived from the perspective of a single, or a few attacks surface only. However, attacks like Mirai, Wannacry, Stuxnet, etc. show that a cyber attack often comprises of a series of attacks on vulnerabilities of victim devices to reach the target device. Penetration testing is generally used to identify the vulnerabilities/ possible attacks on traditional systems periodically. A timely fix of these vulnerabilities can avoid future attacks. Traditional penetration testing methods focus on isolated and manual testing of a host that fails to detect attacks involving multi-hosts and multi-stages. In this paper, we introduced first-of-its-kind, IoT-PEN, a Penetration Testing Framework for IoT. The framework consists of server-client architecture with "a system with resources" as server and all "IoT nodes" as clients. IoT-PEN is an end-to-end, scalable, flexible, and automatic penetration testing framework for IoT. IoT-PEN seeks to discover all possible ways an attacker can breach the target system using target-graphs. It constructs prerequisite and postconditions for each vulnerability using the National Vulnerability Database (NVD). We also demonstrated that even if an individual system is secure under some threat model, the attacker can use a kill-chain (a sequence of exploitation of multiple vulnerabilities on different hosts) to reach the target system.
Analysis of QUIC Session Establishment and Its Implementations

In the recent years, the major web companies have been working to improve the user experience and to secure the communications between their users and the services they provide. QUIC is such an initiative, and it is currently being designed by the IETF. In a nutshell, QUIC originally intended to merge features from TCP/SCTP, TLS 1.3 and HTTP/2 into one big protocol. The current specification proposes a more modular definition, where each feature (transport, cryptography, application, packet reemission) are defined in separate internet drafts. We studied the QUIC internet drafts related to the transport and cryptographic layers, from version 18 to version 23, and focused on the connection establishment with existing implementations. We propose a first implementation of QUIC connection establishment using Scapy, which allowed us to forge a critical opinion of the current specification, with a special focus on the induced difficulties in the implementation. With our simple stack, we also tested the behaviour of the existing implementations with regards to security-related constraints (explicit or implicit) from the internet drafts. This gives us an interesting view of the state of QUIC implementations.
Characteristic Regularisation for Super-Resolving Face Images

Existing facial image super-resolution (SR) methods focus mostly on improving "artificially down-sampled" lowresolution (LR) imagery. Such SR models, although strong at handling artificial LR images, often suffer from significant performance drop on genuine LR test data. Previous unsupervised domain adaptation (UDA) methods address this issue by training a model using unpaired genuine LR and HR data as well as cycle consistency loss formulation. However, this renders the model overstretched with two tasks: consistifying the visual characteristics and enhancing the image resolution. Importantly, this makes the end-to-end model training ineffective due to the difficulty of back-propagating gradients through two concatenated CNNs. To solve this problem, we formulate a method that joins the advantages of conventional SR and UDA models. Specifically, we separate and control the optimisations for characteristics consistifying and image super-resolving by introducing Characteristic Regularisation (CR) between them. This task split makes the model training more effective and computationally tractable. Extensive evaluations demonstrate the performance superiority of our method over state-of-the-art SR and UDA models on both genuine and artificial LR facial imagery data.
Fraud Prediction in Smart Supply Chains Using Machine Learning Techniques

In the domain of Big Data, the company's supply chain has a very high-risk exposure and this must be observed from a preventive perspective, that is, act before such situations occur. As a company grows and diversifies the number of suppliers, customers and therefore increases its number of daily transactions and associated risks. Despite the innovation and improvements that have been incorporated into financial management, credit and debit cards are the main means of exchanging cash online, with the expansion of e-commerce, online shopping has also increased number of extortion cases that have been identified and that continues to expand greatly. It takes a lot of time, effort and investment to restore the impact of these damages. In this paper, we work with machine learning techniques, used in predicting smart supply chain fraud, are valuable for estimating, classifying whether a transaction is normal or fraudulent, and mitigating future dangers.
MMSparse: 2D partitioning of sparse matrix based on mathematical morphology

Sparse matrix is any matrix with enough zeros that it pays to take advantage of them. The computational efficiency of sparse matrix–vector multiplication (SpMV) is significantly influenced by the distribution of non-zero elements in sparse matrix, which is not fully exploited by traditional one-dimensional and two-dimensional partitioning approaches. In this paper, we present a novel two-dimensional partitioning method based on mathematical morphology theory. The matrix is partitioned into dense and sparse areas utilizing some basic morphological transformations, including dilatation, filling, opening, and skeletonization. These dense areas are classified as rectangle, triangle, and diagonal according to their morphological features. We also propose a new MMSparse (Mathematical Morphological Sparse) format that stores each type of shapes in the most efficient format instead of one format for an entire matrix. We select different types of matrices from the SuiteSparse Matrix Collection and conduct a series of experiments on NVIDIA GTX 1080Ti GPU. The experimental results show that our approach achieves average speedup 2.47x over the best performance of the cuSPARSE library kernel, 2.54x over BCSR, 1.48x over yaSpMV, 1.85x over CSR5, 1.35x over the one-dimensional partition.
Chatbots — an organisation’s friend or foe?

In recent years, the use of artificial intelligence has increased tremendously and the hospitality industry has not gone unaffected. Nowadays, chatbots, which simulate human conversations, are almost indispensable in the customer service branch of hospitality. Where organisations started rapidly with the introduction of this new technology, they are now raising the question of whether or not this technological evolution is a good development for this industry. On the one hand, chatbots improve and accelerate customer service, saving time and labour costs. On the other hand, there are privacy and security concerns, lack of personality and lack of research resulting in errors and financial expenses. Presently, chatbots are seen as a technology to support human service, but due to rapid development this situation is open to change.
Aerial Target Detection Based on the Improved YOLOv3 Algorithm

The YOLOv3 algorithm is directly used to detect aerial targets with complex backgrounds, which leads to low detection accuracy and high missed detection rate. This paper proposes an improved YOLOv3 algorithm to detect aerial targets. Because the aerial target is small and its pixel is low, and its features are not obvious, this paper first uses the K-means clustering algorithm to analyze the dataset, selecting the appropriate number and size of the anchor boxes, and then improving the network detection scale to establish the feature fusion target detection layer. In the dataset of PETS200S-Tracking and DOTA, the improved YOLOv3 algorithm and the YOLOv3 algorithm are compared. The results show that the improved network can effectively detect the aerial target, and its precision and recall rate are significantly improved.