Reviewerly/exHarmony huggingface dataset:
A surrogate-assisted GA enabling high-throughput ML by optimal feature and discretization selection

Novel lookup-based classification approaches allow machine-learning (ML) to be performed at extremely high classification rates for suitable low-dimensional classification problems. A central aspect of such approaches is the crucial importance placed on the optimal selection of features and discretized feature representations. In this work we propose and study a hybrid-genetic algorithm (hGAm) approach to solve this optimization problem. For the considered problem the fitness evaluation function is expensive, as it entails training a ML classifier with the proposed set of features and representations, and then evaluating the resulting classifier. We have here devised a surrogate problem by casting the feature selection and representation problem as a combinatorial optimization problem in the form of a multiple-choice quadratic knapsack problem (MCQKP). The orders of magnitude faster evaluation of the surrogate problem allows a comprehensive hGAm performance evaluation to be performed. The results show that a suitable trade-off exists at around 5000 fitness evaluations, and the results also provide a characterization of the parameter behaviors as input to future extensions.
A parallel two-stage genetic algorithm for route planning

Robotic path planning is an area of increasing importance given the interest in developing autonomous vehicles of all types and sizes. Consider, for example, a warehouse robot for a large internet commerce company. The robot's task is to retrieve a large number of items from warehouse shelves and deliver them to the packing area. Because efficiency is important, it is desirable for the robot to travel as short a distance as possible in completing its task. Though related to path planning, this is a distinct problem as there are multiple, perhaps many, destinations. We call this problem route planning. We present a parallel genetic algorithm that runs in two stages to solve the route planning problem. The two-stage approach significantly improves results over a similar singe-stage parallel genetic algorithm for this problem.
Behavior Analysis of a Deep Feedforward Neural Network by Varying the Weight Initialization Methods

An artificial neural network is one of the principal machine learning models that are successfully able to model complex data by appending a greater number of layers to an artificial neural network; the model can minimize the cost over the training epochs. Weight initialization techniques play a vital role in the network’s performance. Initializing the weight matrix for the network model affects the way the network enables the process of learning from the input data. This allows the model to achieve the optimal set of weights and faster convergence which further governs the network’s performance over the defined problem. This paper explores the behavior of a deep neural network by varying weight initializing methods on the MNIST dataset for the classification problem. Our experimentation shows that He initialization can outclass other methods like Xavier initialization as well as more conventional methods like random initialization while employing ReLU nonlinear activation.
gBeam-ACO

Beam-ACO, a modification of the traditional Ant Colony Optimization (ACO) algorithms that incorporates a modified beam search, is one of the most effective ACO algorithms for solving the Traveling Salesman Problem (TSP). Although adding beam search to the ACO heuristic search process is effective, it also increases the amount of work (in terms of partial paths) done by the algorithm at each step. In this work, we introduce a greedy variant of Beam-ACO that uses a greedy path selection heuristic. The exploitation of the greedy path selection is offset by the exploration required in maintaining the beam of paths. This approach has the added benefit of avoiding costly calls to a random number generator and reduces the algorithms internal state, making it simpler to parallelize. Our experiments demonstrate that not only is our greedy Beam-ACO (gBeam-ACO) faster than traditional Beam-ACO, in some cases by an order of magnitude, but it does not sacrifice quality of the found solution, especially on large TSP instances. We also found that our greedy algorithm, which we refer to as gBeam-ACO, was less dependent on hyperparameter settings.
Improving Customer Relationship Management using Machine Learning techniques: A Tunisian Case Study

In the CRM domain, predicting interesting customer profiles relies on studying their different characteristics and behavior. Data mining tools provide functions and algorithms that allow such knowledge extraction. We investigate a research study of data mining concepts and an application of its different functions for a Tunisian hotel case study. The proposed data mining application results in successful knowledge extraction that will be very precious to enhance the loyalty of the hotel customers.
A Comparative Analysis on Bangla Handwritten Digit Recognition with Data Augmentation and Non-Augmentation Process

Determination of Bangla handwritten digit is a momentous image classification task. Though object recognition technology is getting smarter day by day, still Bangla handwritten digit recognition remains inconclusive. Researchers are becoming more concerned about handwritten digit recognition for it's educational and advantageous importance. But it is a matter of trouble that the improvement in Bangla handwritten digit recognition is significantly less as compared to the other languages. To improve the performance of the Bangla handwritten digit recognition system, we have designed a model, in which all basic Bangla digits have been classified. Furthermore, we have also demonstrated Densenet121 architecture in our system. For recognizing Bangla handwriting digits, we proposed CNN (Convolution Neural Network) model. Our system has been experimented on the NumtaDB dataset for recognizing Bangla digit both with augmentation and non-augmentation.
Efficient Cache Strategy for Face Recognition System

Recently, the need for real-time deep learning applications, such as face recognition system, implement on the embedded devices is increasing. At the system level, caching system is one of the most effective ways to reduce response time. However, embedded front-end devices can hardly guarantee effective system overhead for complex cache system while deploying deep learning model. In this paper, this paper presented an efficient cache strategy for deep learning applications such as face recognition system. This paper first established a distributed embedded system model to describe to functions of the face recognition systems. Then, this paper figures out the goal and idea of optimizing the cache design theoretically. Finally, our cache strategy is experimented and verified better than the traditional methods.
Constrained sampling from deep generative image models reveals mechanisms of human target detection

The first steps of visual processing are often described as a bank of oriented filters followed by divisive normalization. This approach has been tremendously successful at predicting contrast thresholds in simple visual displays. However, it is unclear to what extent this kind of architecture also supports processing in more complex visual tasks performed in naturally looking images. We used a deep generative image model to embed arc segments with different curvatures in naturalistic images. These images contain the target as part of the image scene, resulting in considerable appearance variation of target as well as background. Three observers localized arc targets in these images, with an average accuracy of 74.7%. Data were fit by several biologically inspired models, four standard deep convolutional neural networks (CNNs), and a five-layer CNN specifically trained for this task. Four models predicted observer responses particularly well; (1) a bank of oriented filters, similar to complex cells in primate area V1; (2) a bank of oriented filters followed by tuned gain control, incorporating knowledge about cortical surround interactions; (3) a bank of oriented filters followed by local normalization; and (4) the five-layer CNN. A control experiment with optimized stimuli based on these four models showed that the observers’ data were best explained by model (2) with tuned gain control. These data suggest that standard models of early vision provide good descriptions of performance in much more complex tasks than what they were designed for, while general-purpose non linear models such as convolutional neural networks do not.
Technology and Audiovisual Translation

Without technology, it could be argued, there would be no audiovisual translation (AVT), as the source material for such translation is generally a piece of audiovisual media of some description, itself technical in nature, and audiovisual translators need technology to create their translations and to enable their translations to be received by viewers. This chapter provides an overview of the current relationship between technology and the various areas of AVT, covering its application in subtitling, dubbing, workflow software and the technological considerations involved in working with audiovisual source and target texts. A critical overview of the application of machine translation (MT) and machine learning to this sector is also offered.
Consistency Verification of a Rule-Based Smart Home Reasoning System with Satisfiability Modulo Theories

The following topics are dealt with: Internet; Internet of Things; health care; geriatrics; medical signal processing; brain-computer interfaces; electroencephalography; ubiquitous computing; optimisation; indoor radio.
On the Numerical Distortion-Mutual Information Function of Image Denoising with Deep Convolutional Networks

This paper presents a new perspective on the image denoising problem. The architecture of image denoising algorithm used in this paper is the Deep Convolutional Neural Networks (DCNN). Designing DCNN models relates tuning hyperparameters such as a number of layers of DCNN. To choose suitable hyperparameters, it would has an advantage to examine the best achievable image denoising performance under different a number of DCNN layers. Moreover realizing how close the image denoising algorithm performance to the optimal result allows us to design the efficient image denoising algorithm. The Blahut-Arimoto algorithm is used to derive numerically distortion-mutual information function of image denoising algorithm. The derived function is the distortion lower bound given the mutual information between the original image and the denoised image. The noise environment in this paper is relied on the Poisson noise. The denoised image qualities of different DCNN configurations are compared with the results from the numerical Blahut-Arimoto algorithm. The experimental results indicate that the DCNN models provide near optimal denoised image qualities given mutual information, even though there are some rooms to further improve image denoising algorithm.
A corpus study of outgrouping in Greek radical right computer-mediated discourses

Abstract This paper reports on a large-scale study on how “enemies” are linguistically constructed by Greece’s radical right. The research combines corpus linguistics approaches and insights from critical discourse analysis, with the aim of analysing the referential/nomination and predication strategies used to delineate “others” as outgroups. Drawing on a 90 million-word corpus comprising the full set of texts from 13 radical right web-based platforms from 2001 to 2019, the research identifies and statistically classifies principal designators and qualifiers. By closely examining their diachronic variations and correlation with significant sociopolitical events, we critically categorise and discuss the empirical findings and thus unveil topics, as well as aspects of the argumentation, pooled by Greece’s radical right in their discursive constructions.
Selection of Appliance Using Skeletal Tracking of Hand to Hand-tip for a Gesture Controlled Home Automation

This study aims to utilize the hand and hand tip points detected by the Kinect for Microsoft Xbox One used for a gesture controlled home automation. Also, the appliance positional points are generated in order to form a relation between the hand point and the appliance to be selected. By gathering these points, a vector is formed between the hand point and the appliance positional point, creating the appliance vector. Also, a vector is formed from the hand to the hand tip point, creating the pointing vector. This allows the users to aim at the appliance with their hand tip. The proponents used angle comparison with a set angle threshold between the two vectors formed in order to determine if an appliance is being selected. A user must raise the left hand and open the palm to turn an appliance on, and palms closed to turn an appliance off. With this, the proponents have utilized the hand and hand tip points for selecting a user's desired appliance.
Analyzing learners’ English learning process involving educational robots and IoTbased toys through the lens of zone of proximal development

This paper reports an analysis of elementary school students' learning process as they participate in English learning activities using educational robots and IoT-based toys. Grounded on Vygotsky's notion of zone of proximal development, the analysis is conducted through the lenses of obstacles learners encounter in real learning situations as well as the causes of and the solutions to the encountered obstacles. The techniques of video and qualitative analysis are adopted to conduct this naturalistic inquiry, extracting insight from real-world data for enhancing language learning with emerging technology.
Provable Consent for Voice User Interfaces

The proliferation of acoustic human-computer interaction raises privacy concerns since it allows Voice User Interfaces (VUI) to overhear human speech and to analyze and share content of overheard conversation in cloud datacenters and with third parties. This process is non-transparent regarding when and which audio is recorded, the reach of the speech recording, the information extracted from a recording and the purpose for which it is used. To return control over the use of audio content to the individual who generated it, we promote intuitive privacy for VUIs, featuring a lightweight consent mechanism as well as means of secure verification (proof of consent) for any recorded piece of audio. In particular, through audio fingerprinting and fuzzy cryptography, we establish a trust zone, whose area is implicitly controlled by voice loudness with respect to environmental noise (Signal-to-Noise Ratio (SNR)). Secure keys are exchanged to verify consent on the use of an audio sequence via digital signatures. We performed experiments with different levels of human voice, corresponding to various trust situations (e.g. whispering and group discussion). A second scenario was investigated in which a VUI outside of the trust zone could not obtain the shared secret key.
Towards a Language for Defining Human Behavior for Complex Activities

This paper outlines the steps for a specification to create a language for defining human behavior in the context of complex activities, with a specific focus on Activities of Daily Living (ADLs). The proposed specification to create a language for defining human behavior is used to develop a framework for representation of (1) macro-level tasks or actions associated with any complex activity, (2) the context parameters on which these tasks or actions are performed and (3) sequence of movements of various body parts associated with performing these actions on the context parameters in the context of the given complex activity. To evaluate the efficacy of this proposed work, it has been implemented on a dataset of ADLs. The results presented and discussed uphold the relevance for practical implementation of the proposed human behavior definition language in real-time settings for addressing various challenges and utilizing the full potential of activity centric computing for improving the quality of life and user experience during ADLs as well as for various other applications.
Morphological Operations For denoising of White Gaussian Noise Corrupted MR Images

MRI scan is an extremely accurate method for pathological disease detection and it provides crucial information of tissues inside the human body. It is a painless technique that uses strong magnetic fields along with radio waves to generate precise images that provide accuracy in detecting structural abnormalities within the body. Very often, these MR images get corrupted by different noises of different probability density functions, and to get accurate information, denoising needs to be performed on these corrupted images. The most common types of noises associated with MR images are White Gaussian Noise, Salt and Pepper Noise and Speckle Noise. This paper discusses the denoising of MR images corrupted with White Gaussian Noise with a zero mean and uniform normal distribution, i.e, distributed independently and identically over an MR image. The denoising of corrupted grayscale MR images is carried out using five different morphological operations- Erosion, Dilation, Opening, Closing and Averaging alongside seven most commonly used structuring elements of size 3×3 and 5×5. After the analysis, it is observed that the Erosion and Opening operations are best suited for MR Images corrupted with white Gaussian noise as they give the most efficient results in terms of Signal-to-Noise ratio, Correlation coefficient, and computation time. Pertaining to structuring elements, 5×5 proves to be better at denoising the said Image than the commonly used 3×3 structuring elements.
On the Use of Attention Map for Land Cover Mapping

The use of machine learning technology with remote sensing image analysis, especially for the land cover mapping requires experts and huge resources because every pixel in the training set must be labeled. This task is time-consuming and tedious. Therefore, a better strategy is to only identify what classes are present in an image without specifying where they are. In this way, a large number of remote sensing images can be labeled quickly. To achieve this goal, we employed the attention layer to create the attention map. The attention map is then further segmented to produce the final l and c over m ap where every pixel in an image will be labeled. We have tested the performance of our proposed algorithm with UC Merced Dataset and achieved 79.7 % in identifying the presence of land cover classes and 71.2 % accuracy in the labeling of all pixels.
Assessing textual source code comparison: split or unified?

Evaluating source code differences is an important task in software engineering. Unified and split are two popular textual representations supported by clients for source code management. Whether these representations differ in supporting source code commit assessment is still unknown, despite its ubiquity in software production environments.
Evaluation Model of Integrated English Course Online Guiding Intelligent Evaluation based on Evolutionary Algorithm and Data Mining

Evaluation model of An Integrated English Course online Guiding intelligent evaluation based on the evolutionary algorithm and the data mining is designed in this paper. The proposed framework has listed novelties. (1) If multi-objective evolutionary algorithm can only have better solving ability and performance on specific problem characteristics, multi-objective evolutionary algorithm is not robust. A robust multi-objective evolutionary algorithm should be able to solve different problems with some different characteristics, and show good stability when solving problems. (2) On the premise of ensuring the quality of the solution, the operating efficiency of the core multi-objective evolutionary algorithm is also an important index for the investigation, the traditional EA model is revised to construct the novel efficient framework. The simulation compared with the state-of-the-art approaches has proven the effectiveness.
A double-blockchains based Digital Archives Management Framework and Implementation

BlockChain have seen many significant adoptions in many sectors because of its characteristics such as tamper-proof, easy traceable, decentralized and so on. The BlockChain could be considered as a super database that cannot be tampered with, any data that does not want to be tampered can be recorded on the blockchain. The characteristics of blockchain are very suitable for the scene of digital archives protection. This paper proposes a double-blockchains framework concerning the management of archives. The framework can concatenate different kinds of isolated digital archive management systems to provide a public and dependable archive platform. The double-blockchains technology is easily integrated into the digital archives management system by calling the public interfaces of this framework. At last, we give an implementation of this framework. The experiment shows that this framework can resolve the problems encountered in current digital archive management system.
Unsupervised learning of visual invariant features for person re-identification

The recent successful methods of person re-identification (person Re-ID) involving deep learning have mostly adopted supervised learning algorithms, which require large amounts of manually labelled data to achieve good performance. However, there are two important unresolved problems, dataset annotation is an expensive and time-consuming process, and the performance of recognition model is seriously affected by visual change. In this paper, we primarily study an unsupervised method for learning visual invariant features using networks with temporal coherence for person Re-ID; this method exploits unlabelled data to learn expressions from video. In addition, we propose an unsupervised learning integration framework for pedestrian detection and person Re-ID for practical applications in natural scenarios. In order to prove the performance of the unsupervised person re-identification algorithm based on visual invariance features, the experimental results were verified on the iLIDS-VID, PRID2011 and MARS datasets, and a better performance of 57.5% (R-1) and 73.9% (R-5) was achieved on the iLIDS-VID and MARS datasets, respectively. The efficiency of the algorithm was validated by using BING + R-CNN as the pedestrian detector, and the person Re-ID system achieved a computation speed of 0.09s per frame on the PRW dataset.
Steal Your Life Using 5 Cents: Hacking Android Smartphones with NFC Tags

Retracted.
Exploring the Bitcoin Network

Bitcoin is a peer-to-peer (P2P) payment network running on a cryptographic protocol. Nodes of Bitcoin communicate to each other by broadcasting digitally signed messages to the network. Transactions are listed in a distributed, decentralized, public database referred to as the block, using a proof of work to reach consensus. The term “Bitcoin network” refers to a set of nodes running the P2P protocol. The structure of the Bitcoin network is minimal with a decentralized network of volunteers. This chapter presents the overall process of Bitcoin networks including transactions, digital signatures, relay networks, and Bitcoin script.