Reviewerly/exHarmony huggingface dataset:
Optimal Online Coverage Path Planning with Energy Constraints

We consider the problem of covering an unknown polygonal environment possibly containing obstacles using a robot of square size $L\times L$. The environment is structured as a grid with resolution proportional to the robot size $L\times L$, imposed on it. The robot has a limited energy budget -- it has to visit a charging station before it runs out of its energy; there is a single charging station in the environment. In a single time step, the robot can move from one grid cell to one of its four adjacent cells. The energy budget B allows the robot to travel at most B distance, i.e., B grid cells. The objective of the robot is to minimize both total distance traveled to cover the environment (visit each cell of the environment not occupied by obstacles) and the number of visits to the charging station. In this paper, we present thefirst online coverage path planning algorithm that achieves O(log (B/L))-approximation for both objectives. Our bound is optimal since there exists a lower bound of Omega(log (B/L)) for this problem for both objectives. Simulation results show the efficiency of our approach.
Mixed reality for industrial applications: interactions in human-machine system and modelling in immersive virtual environment

The authors propose an innovative mixed reality solution representing an immersive intuitive and interoperable environment to support service in industrial plants. These methodologies are related to concepts of Industry 4.0. Proposed solutions are based on a mix of virtual and augmented reality. Indeed the authors propose an overview of this approach and other synergistic techniques. Moreover, alternative instruments are presented and their specific advantages and disadvantages are described. Particularly, the paper proposes an innovative approach based on the SPIDER, an advanced interoperable interactive CAVE developed by the authors which supports cooperative work of several users involved in training, troubleshooting and supervision. Also, the use of such technology and approach in other fields is presented, such as the validation of training equipment for bus driving and related motion platform integrated in the SPIDER; in this case the solution is running a simulator, named 'BBBuS', used for training and RsD activities.
Value Orientations Development in Adolescents by Means of Folk Music

The paper focuses on the pedagogical aspect of developing value orientations in adolescents by means of folk music. It provides an outline of various views on the issue; reveals the meaning of the value orientations concept; analyses the pedagogical settings necessary for the development of value orientations in adolescents and personality-oriented pedagogical technologies. The author describes her method of ‘value actualisation’ which is based on the use of some of the best pieces of folk music in contemporary arrangements that embody the values of the modern society and nourish universal human as well as cultural-historical national values. When working with adolescents, it’s important to take into account their musical interests and needs and to use contemporary arrangements of traditional music, for instance, folk-rock and folk-pop, gradually expanding the focus. The paper gives examples of how traditional music can be used in working with value orientations in adolescents. The outcomes of the study show that developing value orientations by means of folk music can be effective if personality-oriented pedagogical technologies are employed (which promote mastering and incorporating in the adolescent’s activity the entire spectrum of traditional music) and if the process of educating is humane and democratic enough.
A Method of Detecting Webshell Based on Multi-layer Perception

WebShell is a commonly used tool for network intrusion. It has the characteristics of high, concealment, great harm and so on. The existing WebShell detection method has higher detection accuracy when detecting a known WebShell, but the accuracy of detection is low when it faces complex and flexible unknown and variant WebShell. To solve this problem, a WebShell detection method based on Multi-Layer Perceptron (MLP) neural network is proposed. Firstly, the sample source code is converted into a sample byte code by a compiler tool, and then the sample byte code is divided into byte code sequences using Bi-Gram. Secondly, TF-IDF is used to calculate the word frequency matrix, and on this basis, the feature matrix of trained sample set is selected. Finally, the detection model is obtained through multi-layer neural network training. The experimental results indicate that compared with the existing methods, the constructed detection model can significantly improve the detection accuracy, accuracy, and recall rate, and the detection, accuracy of unknown and variant samples can reach over 90%.
The Study of the Effectiveness of the Secure Software Development Life-Cycle Models in IT Project Management

Software security is an important and a prevalent element in today’s society. The System Development Life-Cycle (SDLC) process that is currently used for most of software development does not address any security components until after the software is developed. The Secure Software Development Life-Cycle (SSDLC) is similar to the SDLC but includes security components into the phases. There have been many models proposed that are primary modified from preexisting SSDLC models. A study was conducted to survey different SSDLC models and their effectiveness. The authors first identified four popular SSDLC models in the IT industry, and then analyzed their common characteristics to derived four sets of Criteria for comparison. These criteria are: Focus Areas of Application, Implementation of model, Security Implementations and Enhancements, and Security training and Staff. The comparison results demonstrate that the Rastogi and Jones model is an effective one for being used for many IT projects. However, it is worthy to mentioned that one specific model cannot be used for all types of IT projects because IT projects are different.
Giving IoT Services an Identity and Changeable Attributes

The Internet of Things (IoT) pervades our surroundings. It softwarizes our physical environments. Software controls devices that interface their physical environments. The IoT is often privacy, safety, and security critical. Consequently, it requires adequate mechanisms for securing its services. For reasons such as heterogeneity, complexity, and lack of deployment there is little research on IoT service security.Our work creates a base for IoT service security. We give IoT services secure identities and attributes. Using site-local X.509v3 certificates with short lifetimes, we show how service attributes can securely be changed at runtime. This enables enforcing security policies even on distributed, loosely coupled IoT nodes. Our central mechanisms are pinning certificates to service executables, and autonomously managing the short certificate lifetimes. We assess the resulting renewal traffic and power consumption.
Comparison of original and weighted singularity index in separation of Pb- Zn mineralized zone in the Haft Savaran district, central Iran

The Haft Saravan area is located in 90 km SE Arak in the central Iran. This area is a part of Malyer-Isfahan metallogenic belt. The article's objective is determining the most suitable method for identifying mineralized zones using original singularity and weighted singularity methods in three-dimensional space. In weighted singularity method, mineralized zones have a greater volume relative to original singularity method. Coefficient of areal association showed that percentage of overlapping of two methods is less than 50% for Pb, Fe and Mn and less than 55% for Zn. Two methods were compared by modified singularity method in order to select the best method. Percentage of overlapping is more than 98% both original and modified singularity methods for Pb, Zn and Mn and is less than 45% for weighted singularity method. Therefore, it can be said that original singularity method is suitable relative to weighted singularity in identifying mineralized zones due to the high overlapping of original and modified singularity method.
Development of Airbag Fabrication Machine and Process for Physical Human Machine Interaction

In this paper, we present an airbag fabrication machine and its process to make airbags which can be utilized for human-machine interaction. The proposed machine uses a CNC-style gantry with XYZ axes to seal layers of thermoplastic sheets in intricate patterns. A custom designed ball transfer is mounted on this machine to control temperature to press, heat, and fuse the thermoplastic sheets. We also implemented a path planning algorithm for heat sealing based on user's drawing. Using the developed machine and process, we demonstrate fabricated airbags can be pressed more than our target pressure and durable to be used in multiple applications over time.
Gentzen sequent calculi for some intuitionistic modal logics

Abstract Intuitionistic modal logics are extensions of intuitionistic propositional logic with modal axioms. We treat with two modal languages ${\mathscr{L}}_\Diamond $ and $\mathscr{L}_{\Diamond ,\Box }$ which extend the intuitionistic propositional language with $\Diamond $ and $\Diamond ,\Box $, respectively. Gentzen sequent calculi are established for several intuitionistic modal logics. In particular, we introduce a Gentzen sequent calculus for the well-known intuitionistic modal logic $\textsf{MIPC}$. These sequent calculi admit cut elimination and subformula property. They are decidable.
Image Completion Based on Gans with a New Loss Function

Abstract Recently, many approaches based on deep learning have demonstrated amazing capabilities in varieties of challenging image tasks, such as image classification, object detection, semantic and instance segmentation, and so on. These methods are more capable of extracting deeper features than traditional methods, which is critical for different kinds of image tasks. Similarly, these methods are gradually applied to the work of image completion of natural images. In consideration of the fact that most of the current methods would lead to blurring and fake results, we propose an image completion method based on the generation adversarial network. We use the network structure of the encoder and decoder to obtain the high-level feature information of the image and generate reasonable pixel values to fill the missing regions. Besides, we construct a new joint loss function based on SSIM evaluation indicators, which can retain the similarity between two images as much as possible. Our proposed method can keep the completion regions consistent with the surrounding pixels, which makes the images look more realistic. We evaluate on our datasets with our proposed method and compare with other methods in this paper, our results are sharper and more realistic than the previous ones.
EXPLORING THE COGNITIVE LEARNING PROCESS BY MEASURING COGNITIVE LOAD AND EMOTIONAL STATES

Cognitive load and emotional states may impact the cognitive learning process. Detection of reliable emotions and cognitive load would benefit the design of the emotional intelligent mobility system for visually impaired peoples (VIPs). Application of learning process using electroencephalography (EEG) offers novel and promising approaches to measure cognitive load and emotional states. EEG is used to identify the physiological index that can lead to detecting cognitive load and emotions which can help to explore the knowledge of learning processes. Basically, EEG is a record of ongoing electrical signals to represent the human brain activity due to external and internal stimuli. Therefore, in this study EEG signals are captured from participants with nine different degrees of sight loss people. EEG signals are then used to measure various cognitive load and emotional states to evaluate cognitive learning process for the VIPs. To support the argument of cognitive learning process, the complexity of the tasks in terms of cognitive load and emotional states are quantified considering diverse factors by extracting features from various well-established metrics such as permutation entropy, event related synchronization/desynchronization, arousal, and valence when VIPs are navigating unfamiliar indoor environments. A classification accuracy of door is 86.67% which is achieved by the proposed model. It has almost 10% of improvement compared to another state-of the-art method who have used same dataset. Moreover, we have achieved 10% and 1% more accuracy in the corridor and open space conditions compared to the existing method. Experimental results also demonstrated that learning process is significantly improved considering wide range of obstacles when they are navigating indoor environments.
Generating and Framing Content: Strategic Multi-Platform Content Marketing in Academic Libraries

This column is adapted from a presentation given at the 2018 Library Marketing and Communications Conference. In it, we describe our marketing team’s approach to developing sustainable marketing practices with our library’s strategic goals in mind. In an effort to create reusable, platform-flexible content, we developed social media posts that highlighted concepts from the ACRL Framework for Information Literacy. Our success on social media inspired us to consider how this content could be implemented on additional platforms. With this column, we invite you to engage with new ways of developing content for use on multiple marketing platforms.
Sequential Projection Selection Methods for Binary Tomography

Binary tomography reconstructs binary images from a low number of their projections. Often, there is a freedom how these projections can be chosen which can significantly affect the quality of reconstructions. We apply sequential feature selection methods to find the ‘most informative’ projection set based on a blueprint image. Using various software phantom images, we show that these methods outperform the previously published projection selection algorithms.
Analyzing and Optimizing the Dummy Rounds Scheme

The dummy rounds protection scheme, intended to offer resistance against Side Channel Attacks to Feistel and SP ciphers, has been introduced in earlier work. Its experimental evaluation revealed weaknesses, most notably in the first and last round. In this contribution, we show that the situation can be greatly improved by controlling the transition probabilities in the state space of the algorithm. We derived necessary and sufficient conditions for the round execution probabilities to be uniform and hence the minimum possible. The optimum trajectories over the state space are regular and easy to implement.
On the Fairness of Disentangled Representations.

Recently there has been a significant interest in learning disentangled representations, as they promise increased interpretability, generalization to unseen scenarios and faster learning on downstream tasks. In this paper, we investigate the usefulness of different notions of disentanglement for improving the fairness of downstream prediction tasks based on representations. We consider the setting where the goal is to predict a target variable based on the learned representation of high-dimensional observations (such as images) that depend on both the target variable and an \emph{unobserved} sensitive variable. We show that in this setting both the optimal and empirical predictions can be unfair, even if the target variable and the sensitive variable are independent. Analyzing the representations of more than \num{12600} trained state-of-the-art disentangled models, we observe that several disentanglement scores are consistently correlated with increased fairness, suggesting that disentanglement may be a useful property to encourage fairness when sensitive variables are not observed.
A Semantic Approach for Handling Probabilistic Knowledge of Fuzzy Ontologies

Today, there is a critical need to develop new solutions that enable classical ontologies to deal with uncertain knowledge, which is inherently attached to the most of the real world’s problems. For that need, several solutions have been proposed; one of them is based on fuzzy logic. Fuzzy ontologies were proposed as candidate solutions based on fuzzy logic. Indeed, they propose a formal representation and reason in presence of vague and imprecise knowledge in classical ontologies. Despite their indubitable success, they cannot handle the probabilistic knowledge, which is presented in most of the real world’s applications. To address this problem, this paper proposes a new solution based on fuzzy Bayesian networks, which aims at enhancing the expressivity of the fuzzy ontologies to handle probabilistic knowledge and benefits from the highlights of the fuzzy Bayesian networks to provide a fuzzy probabilistic reasoning based on vague knowledge stored in fuzzy ontologies.
ACOR Algorithm’s Efficiency for Electromagnetic Optimization Benchmark Problems

This article studies the efficiency of an Ant Colony Optimization (ACO) algorithm for the solving of electromagnetic optimization problems. A modified version of the ACO for continuous optimization, namely ACO R , is used to solve two benchmark electromagnetic problems referring to the optimization of coils configuration. After choosing the appropriate size of the population of ants, the ACO R algorithm performances are compared with results obtained with other evolutionary computation strategies such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). For one problem ACO R outperforms the variants of the GA and PSO used at all statistical criteria checked, whereas for the other problem ACO R is ranked as the second best after the PSO based algorithm tested.
Design of Lightweight Convolutional Neural Network Based on Dimensionality Reduction Module

Abstract The traditional CNN extracted image features is insufficient, so the classification accuracy is not ideal. It also brought overmuch model parameters and calculation. Based on these problems, this paper proposes a dimension reduction residual module. First, reducing the dimension of the output feature map. Then, feature extraction used two different sets of convolution kernels. It can get more sufficient and different characteristic information. Two sets constitute the cascaded layer. Finally, the cascaded layer act as the input of the next layer. This module can reduce parameters. Meanwhile, it increases the depth of the network and enriches the diversity of feature acquisition. A new convolution neural network is build through this module. The performance of new network and other recognition algorithms is compared on GTSRB and 101_food datasets. The new network model is reduced to about 6.2MB, and the classification accuracy can reach 98.2% on GTSRB, 72.3% on 101_food. The experimental results show that this module can effectively improve network performance and control model size.
Literation Hearing Impairment (I-Chat Bot): Natural Language Processing (NLP) and Naïve Bayes Method

Abstract A part from advances in Artificial Intelligence and Natural Language Processing, designed CHATBOT will try to understand user requests accurately, so that they do not give the wrong answer or no response. The difficulty of getting specific information about hearing impairments that can be asked properly is asking someone who understands that it still cannot be found on search engines on the internet which is a favorite tool for most people in obtaining information. By using Machine Learning and with the help of Artificial Intelligence, a question and answer (conversation) interaction mechanism is created to gain literacy knowledge that supports the educational process. Therefore, the CHATBOT application was developed in this study as a media for retrieving information about hearing loss. Using the NLP method and the Naïve Bayes Algorithm for classifications used to get input classes to I-Chat Bot, as well as to test hypotheses using the Technology Acceptance Model developed (extended). The result is an I-Chat Bot with artificial intelligence that understands user input and provides an appropriate response and produces a preferred and easy system model to be used in the search for information about required hearing impairments. This result paper also gets the value of the test accuracy with Precision 98.6%, Recall 88.75% and Accuracy 88.75%.
Twitter Sentiment Analysis of Juvenile Behaviour Deviations using LSA (Latent Semantic Analysis)

Abstract Sentiment analysis is an effort to mine data or can be called text mining. One of the uses from sentiment analysis is to analyze public opinion on a particular topic or product. Research related to sentiment analysis has been carried out with various methods and different accuracy results. The approach method in analyzing sentiment is divided into 2, namely with statistical approach and semantic approach. Research with a statistical approach has been developed previously. While the semantic approach is still quite rare. Based on previous study, The LSA ( Latent Semantic Analysis ) approach seems effective in analyzing data and further more for semantic understanding of sentiment orientation. In this study, we build two model, the first is statistic approach using TF-IDF for feature extraction then classified into SVM ( Support Vector Machine ) algorithm. Then compare it with semantic approach using TF-IDF and SVD ( Singular Value Decomposition ) for feature extraction then put into LSA ( Latent Semantic Analysis ) model. The LSA ( Latent Semantic Analysis ) model will convert the original feature space to a new lower-dimension space by calculate cosine similarity between sentence vectors with vector topics to produce a score for each sentence by measuring how closely the semantic meaning between the sentence and all the words on the topic. Then divided into three collection of words that sentiment oriented including its vector similarity value. The results of the study prove that the research with statistical approach using SVM ( Support Vector Machine ) algorithm gives results of precission average about 0.56 and recall about 0.54. While with semantic approach using LSA ( Latent Semantic Analysis ) gives a slight increase in result of precission about 0.57 and recall about 0.56.
Methods of Semantic Integrity Preservation in the Pattern Recognition Process

In this article, much attention is paid to pattern recognition quality, especially the visual information semantic integrity preservation. The main purpose is to find the ways of its possible improvement to the three basic stages of the pattern recognition process: image preparation, image processing, and classification. To avoid semantic integrity violations of information, in the initial stage of the image analysis, normalization is proposed. In the second stage, a new clustering method was developed, based on particle swarm optimization and the k-means algorithm. In the final stage of the pattern recognition process the Haar classifier was used with normalized training samples. The proposed algorithm and only Haar classifier with non-normalized samples were tested on 500 blurred images: in 8% of samples both algorithms provided semantic integrity preservation and in 64% only the developed algorithm worked effectively.