Reviewerly/exHarmony huggingface dataset:
Predicting Semantic Textual Similarity of Arabic Question Pairs using Deep Learning

Question pairing is the task of listing similar question-answer pairs to a query question automatically. This technique is used by famous online question-answering forums like Quora and Stack exchange, and has gotten a lot of attention lately as it reduces the amount of duplicate questions on such topics. That is why there are many researchers working on this task on the English language, but there is not much focus on other languages like the Arabic language. This paper introduces a deep neural networks based solution to the question pairing task on Arabic questions using minimal pre-processing. We investigate the best settings for the proposed DNN model. We show a thorough set of experiments and demonstrate that our proposed model outperforms the few existing implementations reported in literature on benchmark datasets by no less than 10% in prediction accuracy measure. Namely, the NSURL 2019 question-question dataset and the SemEval 2017 question-answer dataset. Our proposed model achieved 77% and 58% prediction accuracy on both datasets, respectively.
PHY-Based Key Agreement Scheme using Audio Networking

The widespread use of wireless technologies goes hand in hand with the need for finding practical and less expensive solutions for securing communication. Establishing a shared secret key is considered to be the first step in any security system. In this regard, many solutions have been offered from classical cryptography (i.e. computational security) based solutions to physical layer (i.e. information-theoretic security) based ones. Recently, some of the physical characteristics of the wireless channel have been leveraged for the purpose of secret key establishment. Most of the proposed works in this domain consider exploiting the properties of the wireless radio channel. However, motivated by the high prevalence of smart-phones and their applications, and differently from previous works, in this paper we introduce an alternative solution for PHY-based key agreement over a limited communication range, more specifically, room range audio communication between smartphones. The use of audio channel allows implementation on off the shelf smartphones. We validate and analyse the proposed system through real world experiments. We find that our system produces significantly high key generation rate in comparison with the existing radio based solutions. In addition, the system is robust against eavesdropping and brute force attacks.
Some structural results on D -finite functions

D-finite (or holonomic) functions satisfy linear differential equations with polynomial coefficients. They form a large class of functions that appear in many applications in Mathematics or Physics. It is well-known that these functions are closed under certain operations and these closure properties can be executed algorithmically. Recently, the notion of D-finite functions has been generalized to differentially definable or Dn-finite functions. Also these functions are closed under operations such as forming (anti)derivative, addition or multiplication and, again, these can be implemented. In this paper we investigate how Dn-finite functions behave under composition and how they are related to algebraic and differentially algebraic functions.
DREAM Principles from the PORTAL-DOORS Project and NPDS Cyberinfrastructure

The PORTAL-DOORS Project (PDP) has been pursued to develop the Nexus-PORTAL-DOORS-Scribe (NPDS) cyberinfrastructure as a distributed network system of data repositories to manage lexical and semantic data and metadata from and/or about online and offline resources. Designed with the Hierarchically Distributed Mobile Metadata (HDMM) architectural style in a manner analogous to IRIS-DNS, the NPDS cyberinfrastructure provides distributed multilevel metadata management as an open, flexible, and extensible networked system of independent community customizable who-what-where registries, directories, and diristries for identifying, describing, locating, and linking things on the internet, web and grid. In the current work reported here, we combined our original principles from PDP, HDMM, and NPDS together with additional principles for scientific reproducibility and social engineering related to our family of quantitative metrics with acronym FAIR for Fair Attribution to Indexed Reports and Fair Acknowledgment of Information Records. We call this new consolidated collection of principles the DREAM principles with acronym DREAM for the phrase Discoverable Data with Reproducible Results for Equivalent Entities with Accessible Attributes and Manageable Metadata. To codify these DREAM principles as a concrete artifact for the semantic web, and thus to operationalize their use, we developed an OWL 2.0 ontology that we named the PDP-DREAM ontology.
Sentiment Analysis as an Indicator to Evaluate Gender disparity on Sexual Violence Tweets in South Africa

South Africa has one of the highest rates of sexual violence in the world, and with this technology era being the information age, it is not suprising that people are turning to social media to voice their views on this matter. As tweets on sexual violence grow continually, extracting insights from such data demands a robust, real-time, and scalable tools with flexibility in the database schema. Elasticsearch (ES) is an example of a free-license search engine written in Java and developed on Apache Lucene that meets the stated requirements. On the other hand, Kibana facilitates intuitive dashboard development, visual exploration, and real-time analysis of an index in ES through an intuitive graphical user interface. This study demonstrates how gender was inferred and evaluated through the integration of deep neural networks and Google's TensorFlow. We used the AFINN model to infer sentiment analysis as our measure of gender disparity in this instance. The Indexer, built of Node. js, and defined as the hub of the system connects with the Twitter streaming API to ingest tweets found within the boundaries of sexual violence. This system runs persistently with tweets through the ES search engine and visualised in Kibana.
On the Use of Cyber Threat Intelligence (CTI) in Support of Developing the Commander's Understanding of the Adversary

Cyber Threat Intelligence (CTI) is a rapidly developing field which has evolved in direct response to exponential growth in cyber related crimes and attacks. CTI supports Communication and Information System (CIS)Security in order to bolster defenses and aids in the development of threat models that inform an organization's decision making process. In a military organization like NATO, CTI additionally supports Cyberspace Operations by providing the Commander with essential intelligence about the adversary, their capabilities and objectives while operating in and through cyberspace. There have been many contributions to the CTI field; a noteworthy contribution is the ATT&CK® framework by the Mitre Corporation. ATT&CK® contains a comprehensive list of adversary tactics and techniques linked to custom or publicly known Advanced Persistent Threats (APT) which aids an analyst in the characterization of Indicators of Compromise (IOCs). The ATT&CK® framework also demonstrates possibility of supporting an organization with linking observed tactics and techniques to specific APT behavior, which may assist with adversary characterization and identification, necessary steps towards attribution. The NATO Allied Command Transformation (ACT) and the NATO Communication and Information Agency (NCI Agency) have been experimenting with the use of deception techniques (including decoys) to increase the collection of adversary related data. The collected data is mapped to the tactics and techniques described in the ATT&CK® framework, in order to derive evidence to support adversary characterization; this intelligence is pivotal for the Commander to support mission planning and determine the best possible multi-domain courses of action. This paper describes the approach, methodology, outcomes and next steps for the conducted experiments.
Ultra-thin MobileNet

Convolutional Neural Networks (CNNs) are deep learning architectures which play an important role in object detection, image classification, face recognition, autonomous driving applications, etc. MobileNet is a light CNN model which was developed especially for embedded vision applications. But still, it is quite challenging to deploy the baseline model into memory constrained micro-controller units. Design Space Exploration of the above-mentioned model can make it less memory and computationally intensive. This paper proposes some modifications to the existing baseline MobileNet architecture to make it more efficient and suitable to be deployed on real-time embedded platforms. The intent behind developing such an architecture is to reduce the size, the number of parameters, computation time per epoch and the overfitting problem considerably without letting the accuracy drop below the baseline accuracy level. We achieve good accuracy levels by using the Swish activation function instead of the standard activation function ReLU and introducing a regularization method called random erasing instead of Drop out into the network. We decrease the model size by using Separable Convolutions in place of Depthwise Separable Convolutions, changing the channel depth, choosing an optimum width multiplier value and eliminating some layers with the same output shape, without much drop in the accuracy levels. We train the model with the above-mentioned modifications from scratch on the CIFAR-10 dataset and obtain a much lighter architecture as compared to the baseline MobileNet V1. We name the new DNN architecture as Ultra-thin MobileNet having a size of 3.9 MB only which is deployable in real-time embedded processors with limited memory and power.
Robust Attack on Deep Learning based Radar HRRP Target Recognition

In the past few years, deep learning have attracted increasing attention for HRRP-based radar automatic target recognition(RATR) because of their powerful ability to learn features from training data automatically. However, recent studies show that deep learning models are vulnerable to adversarial examples. In this paper, we verified adversarial examples also exist in the deep learning based HRRP target recognition. A novel adversarial attack algorithm called Robust HRRP Attack(RHA) is proposed to generate robust adversarial perturbations in realworld. Experimental results on measured HRRP data show that RHA decrease HRRP recognition performance significantly which indicate our method is efficient and robust.
Tracking Targets with Known Spatial Extent Using Experimental Marine Radar Data

Endeavours to have a reliable and robust maritime traffic situation assessment are today leading to exploit the improved sensor resolution technology of radars. In such concerned tracking scenarios, multiple noisy scattered measurements arise from targets of interest&#x0027;s surfaces at each observation step. In this paper, we present a special version of the Multiplicative Error Model-Extended Kalman Filter&#x002A; (MEM-EKF*) approach to recursively track the orientation and kinematics of targets having known physical dimensions from a maritime- and radar-based dataset. Altogether, we also introduce and thereby propose an open dataset part of a marine radar benchmark dedicated to extended target tracking to the community. The benefits and performance of the proposed approach adopted on our dataset are discussed taking into consideration the non-ideal but nonetheless prevailing effects of the quotidian marine radar such as beam spreading and false negatives. As compared to its original version, the proposed approach proved to be more computationally efficient.
Petrol 18 946: Downhole failures revealed through ontology engineering

Frequency of failures occurring during offshore oil-well drilling do not diminish over time, probably due to increased operational complexity. The volume and frequency of information generated during the drilling process are high. Every few seconds around 30 drilling parameters are recorded and stored. There will always be a need for new, smart solutions to drilling challenges. Present approaches to drilling challenges apply various types of knowledge generated by the operation; wellbore geometry, fluid info, lithology of the sediments, time-based drilling parameters, drilling equipment data, etc. In our research said knowledge is generalized into general concepts, and structured to form a knowledge model of the drilling process. This model is referred to as a drilling ontology, and we report how methods of knowledge modeling and ontology engineering have been used in developing the model, and subsequently how the model has been applied to predict downhole failures during drilling. The knowledge model and the drilling data are combined in the following manner: Data agents are surveilling drilling data. Deviatoric behavior of the drilling parameters are being detected and formed into symptoms. Symptoms trigger other concepts embedded in the ontology by means of linked cause-effect relationships. The end concept of the relationship path will always be one or several failure states. Tests show that reasoning within the ontology produces the highest probability of the failure identical with the real failure. The causes behind the failure can be retrieved from the ontology and applied in a useful manner in combatting the failure. The testing process also shows that this program is a potential supplement to warning against threatening failures before they occur.
Usability Evaluation of Lexicographic e-Services

Although the field of usability evaluation is a well-established discipline, there are no studies on how the usability of lexicographic e-services can be evaluated. This includes, for examples efficiency, effectiveness and user satisfaction when looking up for synonyms, meanings, or translations using online lexicons. In this paper, we propose to combine two types of usability evaluations to assess the usability of such services: a subjective user-experience evaluation and a more objective controlled experiment - demonstrating how both methods complement each other. We applied our proposed approach to evaluate two important online lexicographic e-services: a lexicographic search engine developed at Birzeit University (https://ontology.birzeit.edu) as well as Google Translate. The user-experience evaluation was conducted through a survey that involved 622 users, and was designed to measure effectiveness, efficiency, satisfaction and learnability. The controlled experiment involved a set of defined tasks, which were carried out by four teams (12 people) in two laboratories, and their performance was monitored. The tasks were designed to measure effectiveness and efficiency.
3D Reconstruction of 2D X-Ray Images

Today 2D X-Ray Images are an integral part of immediate medical centers and hospitals. They play an important role in diagnosis and treatment planning in orthopedics. This technique is globally available and is considered as fast and inexpensive in contrast to more advanced 3D imaging methods like CT (Computerised Tomography) Scan and MRI (Magnetic Resonance Imaging) Scan. The doctors use these methods to take photographs of the patient's body by placing a photographic film behind the body, with the X-Ray machine turned on. These rays take longer to pass through the bones in comparison to the skin that is why bones appear much lighter. The musculoskeletal analysis is by nature 3D, therefore, accurate assessment of a patient's anatomy is widely dependent upon a surgeon's ability to analyze the 2D images and make assumptions from the data available. To overcome the limitations of the surgeons understanding and for effective planning of surgical involvement. The goal of this project is the 3D reconstruction of images from 2D X-Ray images. Thus, in this paper, we have proposed a approach using machine learning for conversion which is independent of the experiment setup. First, Bone extraction from the image was done. Secondly, the 3D image has been obtained using stl conversion. At last the data are validated using patient data.
BlockChain based Fertilizer Distribution System

To ensure the soil fertility and crop production fertilizer is the key concern for the agriculture system. Currently, in Bangladesh, the fertilizer distribution system follows the pen and paper-based process which is not involving any digitalization. Following this manual process, the main issue is ensuring the supply and availability of the fertilizers to the farmer doorstep. In this paper, we have proposed a framework with the use of blockchain technology-based fertilizer distribution supply chain management process. The tamper-proof data storage, retrieval, and user management system guarantee persistent monitoring and evaluation process with the strong management information system which employs precise reference information that can be associated with the board.
Deep Learning for Opinion Mining: A Systematic Survey

The hype of sharing the thoughts, plans, opinions, sentiments, reviews what-so-ever comes in our mind on Social networking sites are increasing day by day and is at an all-time high. The data collected and made from this is enormous, and using them for analyzing, reviewing and as a guiding light by the calculative minds shows the advent of this era and the tremendous growth of technology. It also helps the industries, organizations in various forms. One of them is to gather the thoughts of the people globally, which is termed as opinion mining or sentimental analysis. This helps them to understand the market demand and help make the things consumers want and to separate the goods that are undesired or to fix a specific issue. It enables growth at a much higher pace. This study focuses on the literature done in the years 2015-2019 and will help the researchers and scholars to analyze the recent works in the field of Opinion mining.
An Automated Solution for Improving the Efficiency of Cryptocurrency Mining

Nowadays, cryptocurrency's transmission volume increases continuously and rapidly. Bitcoin is the first and the most popular cryptocurrency. One of the ways to obtain Bitcoins is coin mining. Bitcoin mining is a mechanism, in which miner expend resources in a computation process to synchronize Bitcoin transactions for collecting Bitcoins as rewards. Currently, there are a number of crypto-mining marketplaces, in which allows selling or buying computing power on demand. In this paper, we focus on NiceHash platform, which is the largest crypto-mining marketplace having a large number of active miners. In NiceHash platform, sellers (also called, miners) mine coins to fulfill the selected buyers' orders. NiceHash offers a software which can automatically select the most profitable algorithm for miners, so miners do not need to monitor the market and their multiple wallets. However, the existing software usually select an algorithm for a miner to mine without any further update for a long time even the profit gained by using a mining algorithm is highly fluctuated. Moreover, many miners experienced lots of disconnects on the NiceHash pool. To address the above problems, we proposed a system which can maximize the Bitcoin profit of miners by automatically selecting the most profitable mining algorithms and keeping track on the connection between miners and the mining pool. Experiences show that our automated system can effectively maximize the mining profits by increasing 21% when compared with the original algorithm used in NiceHash.
A Comparative Study on the Numerical Performance of Kaizen Programming and Genetic Programming for Symbolic Regression Problems

Symbolic Regression (SR) is a problem that arises in the context of surrogate modeling and involves the fitting of a mathematical model to an input-output data set. Kaizen Programming (KP) is a novel algorithm for solving SR problems. This work presents a comparative analysis on the performance of KP and Genetic Programming (GP) for SR on 15 optimization benchmark functions and an industrial process application case. The experimental analysis shows that KP has a better performance than GP in almost all benchmark cases and in the application case. Also, the results of KP are competitive with state of the art algorithms reported in previous works. This work provides additional evidence on the benefits of KP and corroborates that KP represents a promising solver for SR problems.
Coupling energy efficiency and quality for consolidation of cloud workloads

Efficient usage of IT equipment in Data Centers requires modulating their power-consumption according to the actual workload. The most effective strategy to this aim consists in consolidating as many applications as possible on the smallest number of servers, so that idle devices can be shut down or put in low-power states. Usually, this process is driven by computing and networking resources requested by each application (e.g., CPU and RAM) and applies a certain degree of overcommitment, assuming that such resources are not fully used continuously. However, this approach is critical with real workload patterns, which usually change over time; as a matter of fact, consolidation in real scenarios often leads to either low efficiency or violations of Quality of Service (QoS) constraints, depending on the level of overcommitment. In this paper, we investigate a novel consolidation strategy based on an enhanced system model for the Infrastructure-as-a-Service cloud paradigm, which targets a better trade-off between Energy Efficiency and Quality of Service. We explicitly target modular cloud applications, which design is split into multiple components deployed in Virtual Machine (VM)s or containers. Our consolidation strategy allows to “freeze” parts of the application which are not currently used, making them available when requested with minimal latency. This improves energy saving with respect to other approaches, especially when idle VMs are present for backup or redundancy purposes, without degrading the service level. We compare multiple heuristics available in Optaplanner to solve our consolidation problem, and investigate improvements with respect to a more traditional approach. Our evaluation includes both simulations and experimentation in a real test-bed. The comparison shows that the Late Acceptance algorithm on average finds better solutions than other alternatives and energy efficiency improves up to 40% with respect to more conventional strategies, with deterioration of QoS indexes below 1%.
Facial Expression Recognition using Convolution Neural Network Enhancing with Pre-Processing Stages

Recognizing human expression is one of the most popular problems in the Human-Computer Interaction field. Facial Expression Recognition present a great challenge in a wide variety of areas due to varying conditions of the image, which influences expression recognition and makes this task a complex problem. The main difficulties depend on the irregular nature of the human face and the different conditions such as orientation, light and shadows. Lately, Deep learning obtained more attention as an intelligent technology to achieve robustness and offer best performance of expression recognition. Further investigations are still needed in this field in order to make the recognition process very efficient. For that, we present in this paper a new Convolutional Neural Networks model enhancing with pre-processing stages to recognize seven classes (six basic expressions and one neutral). Our approach contains two phases: normalization, and expression recognition. The result can achieve high accuracy compared to recent works with the popular facial expression databases such as CK+, JAFFE, and FER-2013.