Reviewerly/exHarmony huggingface dataset:
Optimizing the Electrical Wire Routing through Multiple Points using Multi-Objective Ant Colony Algorithms for Electrical Wire Routing (MOACS-EWR)

Ant colony optimization algorithm is a remarkable nature-inspired algorithm which produces outstanding optimization solutions. During the recent years these algorithms have been applied to various difficult combinatorial optimization problems and proved successes. In this paper, we present a novel approach of ant colony optimization algorithm to optimize the electrical wire routes through multiple points. In this study, Multi-Objective Ant Colony Algorithms for Electrical Wire Routing (MOACS-EWR) is used to optimize multiple objectives: length of the path and the number of bends in the path. The study was done using two approaches with modifications to local pheromone updating rule. The results of the study shown that the proposed modifications are performed well in optimizing electrical wire routes through multiple points.
IOT Based Question Paper Delivery Box: A Solution towards Preventing Question Paper Leakage in Public Exams of Bangladesh

A possible low cost solution to prevent the question paper leakage in public exams of Bangladesh has been illustrated in this paper. A GSM based multi-node security system has been designed for delivering the questions to different centers. However, this system can also serve the purpose of delivering other important or confidential deliveries as well. An Arduino Mega micro-controller has been used to control the hardware devices of the system. IR Proximity Sensor, Light Dependent Resistors and Laser Lights have been used to build the prototype sensors for the security system of the devices. GSM Module has been used to send notification of any unusual or suspicious activities during the delivery period via SMS using mobile network to the particular authority. The SMS is then forwarded and illustrated in the admin portal built as a webapp. The architecture of the system has been designed to keep costs low. This system aims to ensure the maximum minimization of question paper leakage during the delivery period to the exam centers.
Solving Method for Multi-level Programming Problems Based on Gravity Search Algorithm

The researches on multi-level programming models have gradually become one of the most promising research fields in operational research, and have been widely used in many fields. In this paper the theoretical background, basic definitions, and basic properties of multi-level programming models are introduced. Then, the basic principles, basic models, solution processes and characteristics of gravity search algorithm are introduced in detail. Taking a bi-level programming model as an example, the basic method of solving multi-level programming problem is proposed based on gravity search algorithm. In order to verify the validity and the feasibility of the proposed solving method, the multiple test functions, including continuous linear bi-level programming problem, integer linear bi-level programming problem and nonlinear bi-level programming problems, are used to investigate the performances of gravity search algorithm to solve multi-level programming models. The results show the high performances of the proposed method.
Geological and petrophysical applications of imaging infrared spectroscopy for mineralogical analysis of core and cuttings: examples from the North Sea, Norwegian Sea and Barents Sea

Abstract The most recent advance in infrared spectroscopy is in the use of real-time imaging reflectance spectrometers to study cores and cuttings. These are non-contact and non-destructive, and acquire continuous mineral and hydrocarbon data in a detailed sub-millimetre pixel image format. The main strength of this approach is the unique ability to accurately discriminate and quantify the clays, carbonates and sulfates, along with hydrocarbon information. Three hyperspectral core-scanning projects from the UK and Norwegian Continental Shelf highlight how these detailed, continuous mineral and hydrocarbon data can be used in geological and petrophysical evaluations. In the Dunbar Field of the Northern North Sea, UK, the spectral recognition of illite and kaolinite polytypes associated with faulted sandstone units contributed to a successful revision of lithostratigraphic correlation between wells with core material and those with only cuttings. These had been hitherto problematical. In Norway, hyperspectral mineral data from mixed carbonate–siliciclastic sequences across the Permo-Triassic boundary in the Alta Field, Barents Sea, helped in the delineation of a karstified dolomitic reservoir. A kaolinite cyclicity associated with an Upper Triassic stacked alluvial fan sequence was also identified in the Lorry Prospect, Norwegian Sea. Finally, it is demonstrated how hyperspectral data can be applied quantitatively to help to calibrate downhole petrophysical data, improve gamma log scaling for shale volume calculations and link mineralogy to permeability.
Comparing Performances of Cypress Block Cipher and Modern Lighweight Block Ciphers on Different Platforms

The paper is devoted to the comparison of performance of prospective lightweight block cipher Cypress with performances of the known modern lightweight block ciphers such as AES, SPECK, SPARX etc. The measurement was done on different platforms: Windows, Linux and Android. On all platforms selected, the block cipher Cypress showed the best results. The block cipher Cypress-256 showed the highest performance on Windows x32 (almost 3.5 Gbps), 64-bit Linux (over 8 Gbps) and Android (1.3 Gbps). On Windows x64 the best result was obtained by Cypress- 512 (almost 5 Gbps).
A Dynamic QoS Negotiation Framework for IoT Services

As the Internet of Things (IoT) ecosystem expands, accommodating a large number of heterogeneous and resource-constrained devices, there have been concerns about the Quality of Service (QoS) provided by these devices. The IoT ecosystem supports the discovery and integration of services hosted by IoT devices to perform tasks without human intervention. This process allows user-applications to achieve their goal in the dynamic and constantly evolving IoT environment. In such a complex environment, one of the problems is how to resolve QoS contention between IoT service providers and consumer as they spontaneously interact with each other. To address this challenge, this paper presents a dynamic negotiation framework that defines the QoS contract of IoT services based on the current constraints and needs of all the participants in an IoT-aware task. It uses our proposed negotiation model that adapts to the dynamics of the IoT environment. A prototype negotiation framework has been developed and the results illustrate its practicality and the negotiation performance of the adopted negotiation model.
Improved Real-Time Visual Tracking via Adversarial Learning

The high accuracy and fast running speed are two goals of tracking algorithms. In recent years, many trackers based on deep learning have improved in both aspects respectively, but it is difficult to balance these two indicators. For example, a real-time tracking algorithm named RT-MDNet has greatly increased speed on the MDNet, but the accuracy is still limited to some extent. On the contrary, a state-of-the-art visual tracker based on adversarial learning named VITAL achieves a significant improvement in performance by alleviating the imbalance between positive and negative samples of tracking data. However, its running speed is seriously limited. In this paper, we attempt to combine the advantages from both methods and propose an improved real-time visual tracking algorithm via adversarial learning to get a more balanced result in accuracy and tracking speed. Specifically, we base on the framework of RT-MDNet and introduce a random feature map masking with adversarial learning to improve the quality of feature maps. Experiments on OTB2015 show our algorithm runs at 17 FPS with a precision of 87.6%.
Continuous and Adaptive Learning over Big Streaming Data for Network Security

Continuous and adaptive learning is an effective learning approach when dealing with highly dynamic and changing scenarios, where concept drift often happens. In a continuous, stream or adaptive learning setup, new measurements arrive continuously and there are no boundaries for learning, meaning that the learning model has to decide how and when to (re)learn from these new data constantly. We address the problem of adaptive and continual learning for network security, building dynamic models to detect network attacks in real network traffic. The combination of fast and big network measurements data with the re-training paradigm of adaptive learning imposes complex challenges in terms of data processing speed, which we tackle by relying on big data platforms for parallel stream processing. We build and benchmark different adaptive learning models on top of a novel big data analytics platform for network traffic monitoring and analysis tasks, and show that high speed-up computations (as high as × 6) can be achieved by parallelizing off-the-shelf stream learning approaches.
Evaluation of the Effects of Russian–Chinese Transboundary Projects in the Far East

The theoretical and methodological issues of measurement and spatial localization of system effects from the implementation of transboundary projects have been investigated. The feasibility of applying the cognitive approach to analyze and predict the dynamics of socio-economic systems under the influence of quantitative and qualitative factors has been substantiated. Research stages and a set of factors have been developed, informational and methodological approaches and results of experimental calculations in the framework of cognitive modeling for the implementation of a Russian–Chinese transboundary project in the Far East of Russia are presented. The quantitative and qualitative results of cognitive modeling of the impact of the transboundary project Kimkano-Sutarsky Mining and Processing Plant (MPP) on the economy of the Jewish Autonomous Region have been meaningfully interpreted. The sustainability of the positive impact of the evaluated effects over time has been evaluated, the need for the development of a set of controlling factors and managerial influences for its prolongation has been revealed. The obtained results allow the authors to draw an inference of the practical applicability of imitational cognitive models to evaluate the systemic effects of the implementation of transboundary investment projects localized in the participating regions.
A Multi-language Platform for Generating Algebraic Mathematical Word Problems

The following topics are dealt with: learning (artificial intelligence); feature extraction; Internet of Things; convolutional neural nets; mobile computing; natural language processing; mobile robots; text analysis; power grids; image classification.
NMT Enhancement based on Knowledge Graph Mining with Pre-trained Language Model

Pre-trained language models like Bert, RoBERTa, GPT, etc. have achieved SOTA effects on multiple NLP tasks (e.g. sentiment classification, information extraction, event extraction, etc.). We propose a simple method based on knowledge graph to improve the quality of machine translation. First, we propose a multi-task learning model that learns subjects, objects, and predicates at the same time. Second, we treat different predicates as different fields, and improve the recognition ability of NMT models in different fields through classification labels. Finally, beam search combined with L2R, R2L rearranges results through entities. Based on the CWMT2018 experimental data, using the predicate's domain classification identifier, the BLUE score increased from 33.58% to 37.63%, and through L2R, R2L rearrangement, the BLEU score increased to 39.25%, overall improvement is more than 5 percentage.
Addressing Challenges in Building Web-Scale Content Classification Systems

Understanding the semantic meaning of content on the web through the lens of a taxonomy has many practical advantages. However, when building large-scale content classification systems, practitioners are faced with unique challenges involving finding the best ways to leverage the scale and variety of data available on internet platforms. We present learnings from our efforts in building a content classification system for multiple document types at Facebook using Multi-modal Transformers. We empirically demonstrate the effectiveness of multi-lingual, multi-modal and cross-document type learning. We describe effective strategies for exploiting weakly supervised signals as a pre-training step and show that they lead to significant gains in downstream classification accuracy. We also discuss label collection schemes that help minimize the amount of noise in collected data.
Interactive example-based finding of text items

We consider the problem of identifying within a given document all text items which follow a certain pattern to be specified by a user. In particular, we focus on scenarios in which the task is to be completed very quickly and the user is not able to specify the exact pattern of interest. The key use case corresponds to the interactive exploration of documents in search of snippets that do not fit Boolean, word-based search expressions. We propose an interactive framework in which the user provides examples of the items he is interested in, the system identifies items similar to those provided by the user and progressively refines the similarity criterion by submitting selected queries to the user, in an active learning fashion. The fact that the search is to be executed very quickly places severe requirements on the algorithms that can be used by the system, both for identifying the items and for constructing the queries. We propose and assess experimentally in detail a number of different design options for the components of the learning machinery. The results demonstrate the ability of our approach to achieve effectiveness close to state-of-the-art approaches based on regular expressions, while requiring an execution time which is orders of magnitude shorter.
Evaluation of transfer learning techniques for classifying small surgical dataset

Deep learning is the key technology used in a large variety of applications such as self-driving cars, image recognition, automatic machine translation, automatic handwriting generation. The success was fueled due to accessibility of huge datasets, GPUs, max pooling. Earlier machine learning techniques employed two phases: features extraction and classification. The performance of such algorithms was highly dependent on how well the features are extracted and that was the major bottleneck of these techniques. Deep learning techniques employ Convolutional Neural Networks (CNNs) with numerous layers of non-linear processing for extracting the features automatically and classification that solves the previous problem. In the real time applications most of the time, either the dataset is unavailable or has less amount of data which makes it difficult to achieve accurate results for classifying the images. CNNs are hard to be trained using the small datasets. Transfer learning has emerged as a very powerful technique where in the knowledge gained from the larger dataset is transferred to the new dataset. Data augmentation and dropout are also powerful techniques that are useful for dealing with small datasets. In this paper, different techniques using the VGG16 pretrained model are compared on the small dataset.
Visual Tracking Algorithm Based on Color Name Histogram

Abstract Aimed at the problem that traditional histogram is sensitive to illumination changes in visual tracking, combined with the CN(Color Name) feature, we proposed a new feature(denotes CNH, Color Name Histogram) based on color name. Firstly, the method projected the original RGB image to CN space to obtain robust 11 feature layers. Then, we counted the each pixel numbers of feature layers. Finally, normalizing the amount of pixels in each layer. In addition, we adopted a feature adaptive fusion method to combine CNH and HOG(Histogram of Oriented Gradient). In order to prove validity of the proposed algorithm, we use Staple(Sum of Template And Pixel-wise Learners) algorithm frame to make a controlled trial. In contrast with the reference algorithms, the success of our algorithm increases by 1.5% and the precision increases by 1.7%. The results show that this method retains the advantages of traditional histogram which is insensitive to target deformation, but also enhances the robustness to illumination change.
An Efficient Convolutional Neural Network Approach for Facial Recognition

Data security being the main concern now a days, has faced a lot of threat in terms of breaching of information which requires immediate attention. Biometrics have served a long-run for this purpose which is a part of Deep Learning. In the recent past, face recognition has become a very important tool for safety and security purposes. This paper presents the application of face recognition technique, making use of Convolutional Neural Network (CNN) with Python and a comparison is drawn between the other techniques such as Principal Component Analysis (PCA), Local Binary Pattern (LBP) and K Nearest Neighbour (KNN). Unlike conventional methods, the proposed scheme uses four Convolutional layers with ReLu layers, four pooling layers, a fully connected layer and a Softmax Loss Layer to normalize the probability distribution. The dataset consists of 1500 images with different facial expressions and the model is trained and tested in order to acquire an accuracy using CNN method. Experimental results show that the proposed Neural Network scored an accuracy of 96.96%.
Automated Railway Security System

Being faster, larger and more reliable, today's trains have permeated the other means of transportation as the prime mode between several countries. However, railroad-related ac-cidents are also more perilous in damage and loss of lives. Therefore, in common railway security systems, more strivings are required for strengthening the safety. Moreover, manual observing of the tracks is actually impracticable and unreliable. Thus, to avoid collisions and allow a better communication between train, central control headquarters, and passengers, this paper, being focused in establishing an automated railway security prototype model using advanced electronic circuits, a communication module, and the proposed mobile interfaces. It is established on an Arduino UNO embedded platform to introduce automatic control crossing gates, switching train tracks and detecting line cracks with the aid of electronic sensors like IR sharp sensor, ultrasonic sensor, and gyroscopes. Moreover, by employing the web camera, the operator of the control room could get real status updating and have a continuous behavior monitoring during the train journey. Thus, the railway gate could be highly controlled so the road traffic will be wholly prophesied. Especially on dangerous trips, the train is supported by the developed dummy car which can detect and monitor any causes of risks on the railway lines and send that fault to the concerned authority. To offer different services for drivers and passengers, the proposed Android applications are responsible for obtaining the sensed data through the Bluetooth module and broadcasting urgent data to the control room. As the accuracy of automated operations is usually more accurate than manual operations, fatal errors due to human mistakes could be easily limited. Thus, compared to related systems, the proposed automatic railway security system which is cost-effective will be robust enough to facilitate much safety standards to Egypt railways.
Short and Medium Term Blood Glucose Prediction Using Multi-objective Grammatical Evolution

In this paper we investigate the benefits of applying a multi-objective approach for solving a symbolic regression problem by means of grammatical evolution. In particular, we continue with previous research about finding expressions to model the glucose levels in blood of diabetic patients. We use here a multi-objective Grammatical Evolution approach based on NSGA-II algorithm, considering the root mean squared error and an ad-hoc fitness function as objectives. This ad-hoc function is based on the Clarke Error Grid analysis, which is useful for showing the potential danger of mispredictions. Experimental results show that the multi-objective approach improves previous results in terms of Clarke Error Grid analysis reducing the number of dangerous mispredictions.
Towards a Taxonomy of Planning for Autonomous Systems

This paper presents a vision of planners, particularly for UAVs, from both an architectural and algorithmic perspective. It reviews key approaches and develops a taxonomy of planning methods. This paper explores various differences and similarities. A technical baseline is developed throughout this process to highlight current research interests, demand signals, and possible gaps in the research. Descriptions of the fundamental issues and limitations of present methods are also included. A common taxonomy will help researchers to coordinate efforts to build more general and powerful planning algorithms.
Face Recognition in an Unconstrained Environment using ConvNet

With the recent advancements in the discipline of Facial Recognition, it has made it easier to detect and identify multiple faces at a time in a situation where the subjects could have varying face posture, expressions, appearances in a dark or lighted background. In this paper, the detection of faces from the captured images having single or multiple faces in an unconstrained environment is done by Histogram of Oriented Gradients (HOG) feature descriptor and SVM classifier which is not only fast and accurate but also improves the accuracy of the proposed deep convolutional architecture which performs by learning feature representations to identify whether the subject is present in the image or not. The experiments are conducted on the DTU Biometric Research Lab, AT&T, and the Essex Face databases. In real-time, the proposed methodology performs excellent with high recognition accuracy.
Controlling Self-organization in Generative Creative Systems

We present a new tool which simulates the development of Artificial Chemistries (AChems) to produce real-time imagery for artistic/entertainment purposes. There have been many such usages of complex systems (CSs) for artistic purposes, but deciding which parameters to use for such unpredictable systems can lead to a feeling of lack of control. For our purposes, we struggled to gain enough control over the AChem real-time image generation tool to accompany music in a video-jockeying application. To overcome this difficulty, we developed a general-purpose clustering approach that attempts to produce sets of parameter configurations which lead to maximally distinct visualisations, thus ensuring users feel that they have influence over the AChem when controlled with a suitable GUI. We present this approach and its application to controlling the development of AChems, along with the results from experiments with different clustering approaches, aided by both machine vision analysis and human curation. We conclude by advocating an overfitting approach supplemented by a final check by a designer, and discuss potential applications of this in artistic and entertainment settings.