Reviewerly/exHarmony huggingface dataset:
A joint adaptive evolutionary model towards optical image contrast enhancement and geometrical reconstruction approach in underwater remote sensing

Nowadays, the underwater optical imaging is used by a wide range of sciences including marine scientists for studying underwater structures and organisms, oil and gas companies, telecommunications to monitor fuel transmission lines and cables, and military centers to detect the sea mines and submarines. Images with non-uniform brightness are visually limited due to low ambient light during imaging or excessive exposure, fogging, hazing, or a combination of these factors. As a result, a process of imaging improvement is highly complex with some conditions. Particularly, this issue is harmful for the underwater environments as well as its highly complexity. In this paper, we improve the underwater images from different environments by exploiting image processing techniques including elimination of fogging in the image, and improvement of contrast with combining super resolution methods which result in significant enhancement of the image quality. To remove fogging in the image enhancement method, a variational-based fusion method was proposed without increasing or decreasing image resolution to increase the adaptability of non-uniform backlight images. This approach is optimized by the bee colony algorithm. Additionally, weighting coefficient selection method is used for super resolution of image after adjusting contrast. The main reason for using hybrid contrast techniques includes the adaptive enhancement and color correction. The bee colony algorithm and weighting coefficient in super resolution due to the lack of hybrid methods in visually improved water images has been the subject of recent researches. The hybrid set has provided more effect on different images at various conditions. Having applied the algorithm set for low-quality visual image set of underwater, some analyses such as PSNR, MSE and SSIM are tested. Compared to similar solutions, the quality is significantly improved and highlighted the exhaustive operation of the algorithm. Applying the presented algorithm in the software part of underwater imaging systems is widely effective for the accurate analysis of underwater images.
CleanGame

Refactoring is the process of transforming the internal structure of existing code without changing its observable behavior. Many studies have shown that refactoring increases program maintainability and understandability. Due to these benefits, refactoring is recognized as a best practice in the software development community. However, prior to refactoring activities, developers need to look for refactoring opportunities, i.e., developers need to be able to identify code smells, which essentially are instances of poor design and ill-considered implementation choices that may hinder code maintainability and understandability. However, code smell identification is overlooked in the Computer Science curriculum. Recently, Software Engineering educators have started exploring gamification, which entails using game elements in non-game contexts, to improve instructional outcomes in educational settings. The potential of gamification lies in supporting and motivating students, enhancing the learning process and its outcomes. We set out to evaluate the extent to which such claim is valid in the context of post-training reinforcement. To this end, we devised and implemented CleanGame, which is a gamified tool that covers one important aspect of the refactoring curriculum: code smell identification. We also carried out an experiment involving eighteen participants to probe into the effectiveness of gamification in the context of post-training reinforcement. We found that, on average, participants managed to identify twice as much code smells during learning reinforcement with a gamified approach in comparison to a non-gamified approach. Moreover, we administered a post-experiment attitudinal survey to the participants. According to the results of such survey, most participants showed a positive attitude towards CleanGame.
Network Cost Based Node Selection Strategy for Multiple Target Tracking in Netted Radar System

In this paper, a network cost based node selection (NCBNS) strategy is proposed to address the node selection problem for multiple target tracking (MTT) in netted radar system (NRS). In the NCBNS, we first establish a network cost model based on the clustering framework with both considering the cost of node scheduling and network transmission. Herein, the node scheduling cost is determined by the node we select when forming the cluster while the network transmission cost depends on the selection of the cluster head (CH) in a specific cluster. Then, the proposed strategy is formulated as a mathematical optimization problem of minimizing the network cost with the constraints of node number and the requirement of tracking accuracy. It is shown that the optimization problem is multi-objective and non-convex and we propose a sequential heuristic node selection algorithm (SHNSA) to solve it suboptimally. Finally, numerical results demonstrate the superior performance of the proposed strategy compared with a conventional strategy. Besides, it has great controllability in sensor scheduling.
Empirical Evaluation of the Interplay of Emotion and Visual Attention in Human-Virtual Human Interaction

We examined the effect of rendering style and the interplay between attention and emotion in users during interaction with a virtual patient in a medical training simulator. The virtual simulation was rendered representing a sample from the photo-realistic to the non-photorealistic continuum, namely Near-Realistic, Cartoon or Pencil-Shader. In a mixed design study, we collected 45 participants’ emotional responses and gaze behavior using surveys and an eye tracker while interacting with a virtual patient who was medically deteriorating over time. We used a cross-lagged panel analysis of attention and emotion to understand their reciprocal relationship over time. We also performed a mediation analysis to compare the extent to which the virtual agent’s appearance and his affective behavior impacted users’ emotional and attentional responses. Results showed the interplay between participants’ visual attention and emotion over time and also showed that attention was a stronger variable than emotion during the interaction with the virtual human.
Multistatic Multiband Passive Radar - Architecture and Sensor Cluster Results

Passive radar technology is a demanding technology for air surveillance and homeland defence. This paper discusses briefly the fundamentals of multistatic passive radar and explains the system architecture of a state-of-the-art multistatic multiband passive radar developed by Hensoldt Sensors. The system architecture and relevant system components like the antenna, the receiver and the multi-hypothesis tracking system are introduced. Thereafter single-sensor results and results of a measurement campaign with a sensor cluster, comprising four passive radar sensors, are presented. The shown results are proving the feasibility of passive radar as a powerful sensor network for future air surveillance systems.
Aggregation of Multiple Pseudo Relevance Feedbacks for Image Search Re-Ranking

Image retrieval effectiveness can be improved by pseudo relevance feedback (PRF), which automatically uses top-$k$ images of the initial retrieval result as the pseudo feedback. Since there are several different strategies for performing PRF leading to different search results, in this paper we focus on image search re-ranking by search result aggregation as a hybrid approach. In particular, different combinations of the original retrieval result with the result of PRF and the result of PRF by pseudo positive and negative feedbacks, using a strategy based on the Borda count, are compared. Our experiments, carried out on the NUS-WIDE-LIT and Caltech 256 datasets, demonstrate that search result aggregation can provide better retrieval performance than PRF. Specifically, the combination of the original result and the result of PRF by pseudo positive feedback performs the best.
On Interval Valued Intuitionistic Fuzzy Sets

In this chapter, the basic definitions of the concepts of Interval Valued Fuzzy Sets (IVFSs) and Interval Valued Intuitionistic Fuzzy Sets (IVIFSs) will be introduced. The relation between IFSs and IVFSs will be discussed.
Co-design of deep neural nets and neural net accelerators for embedded vision applications

Deep Learning is arguably the most rapidly evolving research area in recent years. As a result, it is not surprising that the design of state-of-the-art deep neural net models often proceeds without much consideration of the latest hardware targets, and the design of neural net accelerators proceeds without much consideration of the characteristics of the latest deep neural net models. Nevertheless, in this article, we show that there are significant improvements available if deep neural net models and neural net accelerators are co-designed. In particular, we show that a co-designed neural net model can yield an improvement of 2.6/8.3× in inference speed and 2.25/7.5× in energy as compared to SqueezeNet/AlexNet, while improving the accuracy of the model. We also demonstrate that a careful tuning of the neural net accelerator architecture to a deep neural net model can lead to a 1.9–6.3× improvement in inference speed.
A Sequence-to-Sequence Text Summarization Model with Topic Based Attention Mechanism

One of the limitation of automatic summarization is that how to take into account and reflect the implicit information conveyed between different text and the scene influence. In particularly, the generation of news headlines should under specific scene and topic in the field of journalism. Traditionally, Sequence-to-Sequence (Seq2Seq) with attention model has shown great success in summarization. However, Sequence-to-Sequence (Seq2Seq) with attention model is focusing on features of the text only, not on implicit information between different text and scene influence. In this work, we present a combination of techniques that harness scene information which reflects by word topic distribution to improve abstractive sentence summarization. This model combines word topic distribution of LDA topic model as an external attention mechanism to better text summarization result. This model contains an RNN network as an encoder and decoder part, encoder is used to embed original text in a low dimensional dense vector as previous works, and decoder uses attention mechanism to incorporate word-topic distribution and low dimensional dense vector of encoder. The proposed approach is evaluated by datasets of CNN/Daily Mail and citation. The result shows that it is better than the aforementioned methods.
Proving semantic properties as first-order satisfiability

The semantics of computational systems (e.g., relational and knowledge data bases, query-answering systems, programming languages, etc.) can often be expressed as (the specification of) a logical theory Th. Queries, goals, and claims about the behavior or features of the system can be expressed as formulas φ which should be checked with respect to the intended model of Th, which is often huge or even incomputable. In this paper we show how to prove such semantic properties φ of Th by just finding a model A of Th∪{φ}∪Zφ, where Zφ is an appropriate (possibly empty) theory depending on φ only. Applications to relational and deductive databases, rewriting-based systems, logic programming, and answer set programming are discussed.
PIR Schemes With Small Download Complexity and Low Storage Requirements

In the classical model for (information theoretically secure) Private Information Retrieval (PIR) due to Chor, Goldreich, Kushilevitz and Sudan, a user wishes to retrieve one bit of a database that is stored on a set of n servers, in such a way that no individual server gains information about which bit the user is interested in. The aim is to design schemes that minimise the total communication between the user and the servers. More recently, there have been moves to consider more realistic models where the total storage of the set of servers, or the per server storage, should be minimised (possibly using techniques from distributed storage), and where the database is divided into R-bit records with R 1, and the user wishes to retrieve one record rather than one bit. When R is large, downloads from the servers to the user dominate the communication complexity and so the aim is to minimise the total number of downloaded bits. Work of Shah, Rashmi and Ramchandran shows that at least R + 1 bits must be downloaded from servers in the worst case, and provides PIR schemes meeting this bound. Sun and Jafar have considered the download cost of a scheme, defined as the ratio of the message length R and the total number of bits downloaded. They determine the best asymptotic download cost of a PIR scheme (as R → ∞) when a database of k messages is stored by n servers. This paper provides various bounds on the download complexity of a PIR scheme, generalising those of Shah et al. to the case when the number n of servers is bounded, and providing links with classical techniques due to Chor et al. The paper also provides a range of constructions for PIR schemes that are either simpler or perform better than previously known schemes. These constructions include explicit schemes that achieve the best asymptotic download complexity of Sun and Jafar with significantly lower upload complexity, and general techniques for constructing a scheme with good worst case download complexity from a scheme with good download complexity on average.
Plateaued functions, partial geometric difference sets, and partial geometric designs

Abstract Plateaued functions on finite fields have been studied in many papers in recent years. As a generalization of plateaued functions on finite fields, we introduce the notion of a plateaued function on a finite abelian group. We will give a characterization of a plateaued function in terms of an equation of the matrix associated to the function. Then we establish a one‐to‐one correspondence between the ‐valued plateaued functions and partial geometric difference sets (with specific parameters) in finite abelian groups. We will also discuss two general methods (extension and lifting) for the construction of new partial geometric difference sets from old ones in (abelian or nonabelian) finite groups, and construct many partial geometric difference sets and plateaued functions. A one‐to‐one correspondence between partial geometric difference sets (in arbitrary finite groups) and partial geometric designs will be proved.
Towards a Typology of Microsyntactic Constructions

This contribution outlines an international research effort for creating a typology of syntactic idioms on the borderline of the dictionary and the grammar. Recent studies focusing on the adequate description of such units, especially for modern Russian, have resulted in two types of linguistic resources: a microsyntactic dictionary of Russian, and a microsyntactically annotated corpus of Russian texts. Our goal now is to discover to what extent the findings can be generalized cross-linguistically in order to create analogous multilingual resources. The initial work consists in constructing a typology of relevant phenomena. The empirical base is provided by closely related languages which are mutually intelligible to various degrees. We start by creating an inventory for this typology for four representative Slavic languages: Russian (East Slavic), Bulgarian (South Slavic), Polish and Czech (West Slavic). Our preliminary results show that the aim is attainable and can be of relevance to theoretical, comparative and applied linguistics as well as in NLP tasks.
Experiences of Using Linked Data and Ontologies for Operational Data Sharing in Systems-of-Systems

This paper addresses the problem of exchanging complex data between the constituent systems in a system-of-systems. This is necessary to ensure that they have compatible understandings of the world surrounding them and entails a need for semantic interoperability between the constituents. Through a case study of a road construction system-of-systems, the world wide web technologies of linked data and ontologies are explored as a framework for data representation and exchange. This data includes several broad categories, such as assets, interfaces, organizations, capabilities, missions, and observations, as well as various properties of those. It is also discussed how the constituents can use this data for reasoning and decision making. The results have been validated through a simulation-based research prototype of the road construction case, from which experiences are reported.
Assessing Discriminating Capability of Geometrical Descriptors for 3D Face Recognition by Using the GH-EXIN Neural Network

In pattern recognition, neural networks canCiravegna, Gabriele be used not only for the classificationCirrincione, Giansalvo task, but also for feature selection and other intermediateMarcolin, Federica steps. This paper addresses the 3D faceBarbiero, Pietro recognition problem in order to select the most meaningful geometric descriptors. At this aim, theDagnes, Nicole classification results are directly integrated in aPiccolo, Elio biclustering process in order to select the best leaves of a neural hierarchical tree. This tree is created by a novel neural network GH-EXIN. This approach results in a new criterion for the feature selection. This technique is applied to a database of face expressions where both traditional and novel geometric descriptors are used. The results state the importance of the curvedness novel descriptors and only of a few Euclidean distances.
Design and Experiment of Facial Expression Recognition Method Based on LBP and CNN

Aiming at the poor stability of traditional facial expression recognition methods, the feature extraction method is affected by the external environment such as illumination and posture, and an improved convolutional neural network (CNN) model is proposed. A local binary pattern (LBP) image is extracted from the facial expression image, Combine original face image and LBP image as training data set. Firstly, the expression features are implicitly extracted by means of continuous convolution. Then the extracted implicit features are subsampled by the maximum pooling method. Finally, the Softmax classifier is used to classify the facial expressions. The experimental results show that the improved CNN model trained by adding LBP feature information in the dataset has high recognition accuracy and robustness.
Analysis of Different Supervised Techniques for Named Entity Recognition

The enormous growth of information available on the internet poses a great challenge to Information Extraction tasks. Named Entity Recognition is one of such an Information Extraction task which works on locating the presence of entities belonging to some predefined categories. It is a crucial pre-processing tool assisting in diverse Natural Language Processing applications such as Automatic Text Summarization, Machine Translation, etc. In this article, we analyze the performance of different supervised machine learning algorithms worked for Named Entity Recognition task. For this, we use five diverse learning algorithms namely, Conditional Random Field (CRF), Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT) and Multilayer Perceptron (MLP) based neural network to create a variety of models based upon several combinations of different features that are independent of any domain-specific knowledge. These algorithms are evaluated mainly for the Hindi language. The evaluation of experimental results shows how Multilayer Perceptron based neural network approach performs better than other supervised learning methods used in this task.
Face Tracking Robot testbed for Performance Assessment of Machine Learning Techniques

Computer vision is an essential part of robotics. Face detection and tracking has improved with improvements in machine learning techniques. Various techniques has been proposed which work well with high performance computing facilities. We assess the comparative performance of various machine learning techniques to improve face tracking and implement a robot which can respond to user behaviour. Simulations have been done prior to construction of robot to obtain smooth movement. Earlier computer vision experiments using robots used simpler image processing algorithms. Machine learning and internet connected face tracking would suffer from processing delays affecting real time behavior. Here our experimental results show performance comparison of popular face recognition techniques running on an offline computing machine. Given the accuracy benefits, we feel adopting machine learning based techniques as opposed to image processing is acceptable even in resource constrained environment.
Disease Propagation Prediction using Machine Learning for Crowdsourcing Mobile Applications

Being a tropical island, Mauritius is prone to many communicable diseases. Relevant departments of the Ministry of Health and Quality of Life have as mission to monitor and control the propagation of diseases. The primary objective of this paper is to provide the ministry with a system that displays in real time the disease status over the island. The system also predicts the propagation of diseases allowing the ministry to better plan remedial actions. The system consists of three mobile crowdsourcing applications that allow the public, doctors and pharmacies to report diseases and drugs sales in real time. Data regarding diseases for the year 2017 were retrieved and the corresponding daily weather information namely as temperature, humidity and wind for that year was then extracted and added to this dataset. An Artificial Neural Network (ANN) was then trained with this dataset and then used to predict the propagation of the diseases which can be monitored by the Ministry of Health and Quality of Life through another application. The prediction was performed based on the number of reported diseases on the current day along with weather forecasts for the forthcoming days and the results were promising. The model has been evaluated resulting in an accuracy of 90%. Finally, we believe that such a system can be very beneficial to the ministry which can then take informed decisions to counteract the possible propagation of diseases.
Fake News Detection Using Bayesian Inference

Given the huge volume of information available on social media, making a distinction between false information and a real one is a challenging task. In fact, several statistical models dealing with this problem are based on multinomial distributions. However, a new family of distributions that is an exponential family approximation to the Dirichlet Compound Multinomial (EDCM) has been introduced to be more adjustable to high-dimensional data and to overcome the drawbacks of the multinomial assumption. Thus, in this paper, we tackle the problem of fake news detection using finite mixture models of EDCM distributions. In particular, we develop a Bayesian approach based on Markov Chain Monte Carlo and Metropolis-Hastings algorithm for the learning of these mixture models. The proposed method is validated via extensive simulations and a comparison with multinomial-based mixture models is provided.