Reviewerly/exHarmony huggingface dataset:
Implementing Group-Based Authentication Methods On The Internet Of Things Standards

The improvement and widespread internet of things concept, the end devices which require low energy, resources, and cost; has become the most important part of many solutions all around us. The security of end devices that transmit or process data that requires high confidentiality, security, and precision is very critical. In various studies, group-based authentication and authorization methods have been proposed to solve existing authentication and authorization problems. However, these studies remained at the protocol level and were not carried out at the application layer. In this paper, we implement group-based authentication and authorization methods on the Mobius, which is the open-source IoT server platform based on the oneM2M standard. Also, we design multiple test scenarios to show the performance of the implemented authentication and authorization methods. The results have shown the efficiency of the implemented methods.
Performance of Biometrics Recognition System Using Multiple Scales Analysis

Biometrics trait can recognize individual based on the special characteristics unique to his/herself. New soft biometrics trait was introduced such as androgenic hair to overcome problem where the face of individual was hidden by the camera. In this work, performance of biometric recognition system was analyzed further using multiple scales methods such as the difference of Gaussian and discrete wavelet transform using Haar and Daubechies order2. The difference of Gaussians was built using 6 octaves and 4 levels in each octave while discrete wavelet transform was built using nine levels of decomposition. There were two databases of biometric characteristics, androgenic hair pattern and face. Total of 400 images were studied from each database and performance results were calculated. The best performance of accuracy recognition was obtained, 85,25% for androgenic hair database and 92,5% for face database.
The State of the Public Cloud: Security Concerns with Cloud Computing

Cloud computing continues to grow in popularity; however, concerns about increased risk to sensitive data continues to limit adoption. This paper explores the current state of cloud computing security. It investigates specific security concerns with cloud computing, providing examples of security threats that continue to make such concerns relevant, and examines mitigations to these threats described in the current literature. Finally, it summarizes specific areas where additional research is needed to address security concerns.
A Data Cleansing Approach In Smart Home Environments Using Artificial Neural Networks

A smart home is generally equipped with a set of sensors/devices able to provide intelligent and personalized services to end users. These sensors/devices can sense multiple information related to the physical environment and the residents. This information is then transmitted to a central station for further processing through wireless communication. However, the wireless medium is considered vulnerable and the sensors can fail in providing correct measurements. Moreover, a smart home system should also be able to implement a cleaning system of its sensed data and discard those instances that are erroneous or incoherent. To achieve the data quality improvements, this paper proposes a new approach that uses an Artificial Neural Network (ANN) to detect faulty measurements. The proposed scheme can prematurely and efficiently detect outlier data before forwarding it to a central station. The performance of the solution is validated through simulations, using realistic datasets, and compared with other well-known models. Our findings demonstrate that the proposed approach outperforms the compared models in terms of accuracy, f-score, recall and precision metrics.
Construction of an accurate automatic lexicon for Arabic sentiment analysis

Sentiment analysis has aroused the interest of many studies in recent years. Regarding to its high importance in taking and extracting decisional information, the light of research is still shed on it. The first step of a sentiment analysis system is the construction of the basic knowledge, namely the linguistic resources. The classical methods of lexicon building are manual, semi-automatic, or automatic. Both the manual and semi-automatic methods need a manual check that is time and effort consuming whereas the automatic approach neglects word semantic. Herein, we intend to automate the lexicon extraction method as well as giving accurate polarity. In order to perform this task and achieve satisfying results, we extract a Bag-of-Words and we then apply many filters on it to keep only clean and sentimental terms. This paper also explores the effectiveness of a supervised approach based on the Bag-of-Words model in defining sentiment polarity of the processed reviews in order to shed the light on its usefulness.
Chinese Long and Short Form Choice Exploiting Neural Network Language Modeling Approaches

Lexicalisation is one of the most challenging tasks of Natural Language Generation (NLG). This paper presents our work in choosing between long and short forms of elastic words in Chinese, which is a key aspect of lexicalisation. Long and short forms is a highly frequent linguistic phenomenon in Chinese such as (laohu-hu, tiger). The choice of long and short form task aims to properly choose between long and short form for a given context to producing high-quality Chinese. We tackle long and short form choice as a word prediction question with neural network language modeling approaches because of their powerful language representation capability. In this work, long and short form choice models based on the-state-of-art Neural Network Language Models (NNLMs) have been built, and a classical n-gram Language Model (LM) is constructed as a baseline system. A well-designed test set is constructed to evaluate our models, and results show that NNLMs-based models achieve significantly improved performance than the baseline system.
Phase error analysis of clipped waveforms in surface topography measurement using projected fringes

When working with the method of projected fringes outside the optical laboratory one often encounters the problem of uncontrollable ambient light. This might cause saturation of the camera which in turn results in clipping of the fringes. Since standard theories describing phase-shifting techniques assume the projected fringes to be purely sinusoidal, such clipping will result in measurement error. In this paper a detailed analysis of this problem is given, and relations between phase errors, the amount of fringe clipping and the number of phase steps are found. Moreover, the phase difference between the clipped and the unclipped fringes is described. This investigation is based on Fourier- and phasor analysis.
PENGAPLIKASIAN MODEL BLENDED LEARNING PADA PERANGKAT PEMBELAJARAN BAHASA INDONESIA

Abstract This research is a research and development that aims to apply the Scientific Blended Learning (SBL) learning model through the development of valid, practical and effective SBL learning tools. Learning tools developed include: (1) Learning Implementation Plan (RPP); (2) Student Worksheets (LKS) (3) Handbook for Edodo Use; and (4) Content / contents of Edmodo virtual classroom facilities. The stages of development include four 4-D stages which are modified into 3 stages: defining, designing, and developing. Keywords: Scientific Blended Learning, Learning Tool
Implementation of Secure Work From Home System Based on Blockchain using NS3 Simulation

Work from Home (WFH) is an activity carrying out official duties, completing outputs, coordination, meetings, and other tasks from the residence of employees. Implement WFH many users use the zoom application has vulnerabilities. The network architecture used refers to the simple experiment network. In Secure WFH there are 3 offices connected through a router. Each client in each office is connected to the router via a Virtual Private Network (VPN) on a peer-to-peer (P2P). That architecture has 18 nodes that will be simulated. Secure WFH simulation with blockchain combines secure WFH with a bitcoin code simulator from Arthur Gervais's. Implementation of blockchain on secure WFH can increase security but the resulting speed decreases. The decrease in speed when implementing secure WFH is due to the generate block process and the verification process.
Voice-Based Biometric Authentication

Voice or speaker recognition has become an important source of security nowadays. It has a wide scope in security, information services, industries and voice dialing. This implementation is to extract the speech sample, categorize it and find out where the person speaking is a registered user or not. For feature extraction, Mel-frequency cepstral coefficients (MFCC) has been used, and for feature matching, vector quantization has been used. MFCC technique gives accurate reading and comparatively faster than other techniques. The implementation is text-independent speaker identification which means that the matching process will not depend on the spoken words of a particular speaker and when something is spoken that particular sample will be matched to all the stored samples, hence making it 1:N matching. The GUI implementation is the uniqueness in this research work which can be used by banks and many other companies in private or public sector for authenticating the customers based on their voice as biometric signature.
Scalable Taxonomy Generation and Evolution on Apache Spark

Big data mainly refers to a huge volume of rapidly growing data over size exabytes (1018). A major chunk of this data is unstructured text data produced from several sources. In order to use such data effectively, they need to be processed and organized. Taxonomy, a hierarchical structure, is considered an effective way of organizing the data. In the past, many techniques have been proposed to generate taxonomy automatically. Recently some attempts have also been made to evolve the static structure of taxonomy to deal with the rapidly changing nature of data. However, the voluminous nature of today's data currently exceeds the processing capabilities of conventional techniques. In this regard, there is a need for a scalable technique that potentially speeds up the process of taxonomy generation and evolution and caters to a large amount of unstructured big data. This paper presents a technique for both the generation and the evolution of taxonomy on the Apache Spark framework. The technique is tested on a text dataset belonging to a computing domain. The test results show that the scalable taxonomy generation and evolution technique proposed in this paper is not only time- efficient but also produces a good quality taxonomy as compared to state-of-the-art techniques.
Binary Signaling for Optical Wireless Channel based on Deep Learning

We propose a kind of parallel binary signaling for optical wireless channel based on deep learning. We develop an autoencoder structure for an optical wireless channel and it produces learned patterns of 2×2 light-emitting diode array for the binary signaling in the optical wireless channel, which is modeled with additive white Gaussian noise. We report the influence of received noise on learned pattern, which is assumed to experience variation of channel characteristics.
WITHDRAWN: Public key encryption scheme for internet of things based on seminearrings

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Cognitive Approaches to Human Computer Interaction

This chapter presents a brief overview of theories and concepts that ar some well-established and widely used cognitive architectures, such as ACT-R (Anderson et al (2004) Psychol Rev 111(4):1036–1060; Anderson (2007) How can the human mind occur in the physical universe? Oxford series on cognitive models and architectures. Oxford University Press, Oxford) and SOAR (Laird (2012) Soar cognitive architecture. MIT Press, Cambridge). These are computational attempts to model cognition for general and complete tasks rather than for single, small tasks. This chapter also reviews the most known and used cognitive models, KLM and GOMS, which are computational models used for simulations of human performance and behavior (Ritter et al (2000) ACM Trans Comput-Hum Interact 7(2):141–173. https://doi.org/10.1145/353485.353486). We will show how some cognitive architectures that originated within artificial intelligence (AI) have been developed to cover aspects of cognitive science, and vice versa. The relevance of the cognitive approach to HCI can be seen in the successful use of cognitive models in the HCI community to evaluate designs, assist users’ interactions with computers, and substitute users in simulations (Ritter et al (2000) ACM Trans Comput-Hum Interact 7(2):141–173. https://doi.org/10.1145/353485.353486).
A Wavelet Threshold Function for Treatment of Partial Discharge Measurements

Based on the wavelet transform filtering theory, the chapter will describe the elaboration of a wavelet threshold function intended for the denoising of the partial discharge phenomenon measurements. This new function, conveniently named Fleming threshold, is based on the logistic function, which is well known for its utility in several important areas. In the development is shown some variations in the application of the Fleming function, in an attempt to identify the decomposition levels where the thresholding process must be more stringent and those where it can be more lenient, which increases its effectiveness in the removal of noisy coefficients. The proposed function and its variants demonstrate excellent results compared to other wavelet thresholding methods already described in the literature, including the famous Hard and Soft functions.
Design and Cognitive Considerations for Industrial Mixed Reality Systems

In this paper we expound our theoretical hypothesis covering Design and Cognitive factors to be considered while designing an effective Augmented/Mixed Reality system solution for industrial use cases. For demonstration purposes, the targeted scenario chosen is an offshore oil-rig maintenance scenario: this scenario involves critical tasks in the areas of equipment maintenance, quick error recognition, safety, and effectiveness. With the goal towards enhanced effective Situational Awareness of the Oil Rig industrial system, the scenario involves oil-rig stakeholder collaboration/participation and remote assistance. An additional aim of the paper is introducing new perspectives for effective operations in an industrial maintenance scenario using mixed reality systems: the perspectives extends towards the following principles of study - Experience Design, Usability, Cognitive Science and Situational Awareness. Using Cognitive Science principles and Design Thinking approach, we try to bridge the existing white gap in the research space and intend to spearhead research towards surfacing the points to be considered in effectively designing Mixed Reality systems in such Industrial scenarios. The use of multimodal sensory augmentations such as sound and haptics to aid a better experience in performing a particular task, is also given attention. Elucidation of a functional prototype built using Microsoft Hololens1 is shared and next steps and targets are showcased as well.
Chinese Dialect Recognition Using X-vector with Multi-task Learning

Abstract Speech recognition is often hard for language in countries with diverse accents, like Mandarin. However, the recognition of different dialects may help to improve the later speech recognition. In this paper, we conduct some experiments on a ten-dialect recognition task. We improve the system with different methods, like x-vector and multi-task learning with phone recognition. Finally, with the combination of the methods, we improve the accuracy of baseline model from 76.48% to 88.25%, where the relative improvement is 15%.
The Modified Method of Statistical Differentiation for Matching of an Aerial Photograph and a Satellite Image

The article is devoted to the modification of the method of statistical differentiation, which is used to match the image from the camera of an unmanned aerial vehicle and a portion of a satellite image in order to obtain the parameters of their mutual shift, rotation and scale. The modification is aimed at increasing the reliability of the method in the case when the matching images differ in the conditions and the time of shooting. The proposed method differs from the original one by adding texture features to triangle descriptors, changing the structure of the stored data, and the way to select the best reference segment, which is used to calculate resulting shift, rotation and scale. The article presents the results of an experiment for comparing the modified and original methods, as well as the findings of comparison using the SIFT, SURF, KAZE, ORB, BRISK, and AKAZE methods.
A Memory-Based Sentence Split and Rephrase Model with Multi-task Training

The task of sentence split and rephrase refers to breaking down a complex sentence into some simple sentences with the same semantic information, which is a basic preprocess method for simplification in many natural language processing (NLP) fields. Previous works mainly focus on applying conventional sequence-to-sequence models into this task, which fails to capture relations between entities and lacks memory of the decoded parts, and thus causes duplication of generated subsequences and confuses the relationship between subjects and objects. In this paper, we introduce a memory-based Transformer model with multi-task training to improve the accuracy of the sentence information obtained by the encoder. To enrich the semantic representation of the model, we further incorporated a conditional Variational Autoencoder (VAE) component to our model. Through experiments on the WebSplit-v1.0 benchmark dataset, results show that our proposed model outperforms other state-of-the-art baselines from both BLEU and human evaluations.
Asymmetric Pairwise Preference Learning for Heterogeneous One-Class Collaborative Filtering

Heterogeneous one-class collaborative filtering (HOCCF) is a recent and important recommendation problem which involves two different types of one-class feedback such as purchases and examinations. In this paper, we propose a generic asymmetric pairwise preference assumption and a novel like-minded user-group construction strategy for the HOCCF problem. Specifically, our generic assumption contains six different pairwise preference relations derived from the heterogeneous feedback, where we introduce a series of weighting strategies to make our assumption more reasonable. Our group construction strategy introduces richer interactions within user-groups, which is expected to learn the users' preference more accurately. We then design a novel recommendation model called asymmetric pairwise preference learning (APPLE). Extensive empirical studies show that our APPLE can recommend items significantly more accurately than the closely related state-of-the-art methods on three real-world datasets.
<b>Techniques For Electronic Resource Management: TERMS And The Transition To Open</b>

Although most library materials budgets have remained stagnant in recent years, many academic libraries continue to reallocate funds to support a higher prioritization of electronic content. Whethe...
Framework Optimization for Face Recognition

In recent times, with the increasing interest in face recognition for smart homes. However, most of these studies are focused on the individual modules of such a system, and there is an evident lack of research on a face recognition system framework that can integrate and manage the entire face recognition system. Therefore, in this study, we propose a framework that enables the user to effectively develop an face recognition system in different data volume applications. This paper designs an edge computing architecture and a cloud architecture. The edge computing architecture is designed with a Centralized-Edge and Peer-to-Peer Edge. At the same time, the face screening, face disguise, training timing and dynamic adjustment of training samples in face recognition are analyzed to give a feasible solution. In particular, the face screening rules are designed to reduce unnecessary training and repeated training. This paper has important application value for the intelligentization of the Internet of Things.
An Improved Method of Cross-Lingual Image Captioning Based on Fluency-Guided

Generating image descriptions in different language is essential to satisfy users in the whole world. However, most researches of image captioning focused on English, as most available datasets are in this language. Aiming at solving the problem, somebody proposed cross-lingual captioning model which is with the help of machine translation. Nevertheless, this method will cause an imbalance of positive and negative samples when classified the fluency of sentences. Our approach proposed a new classifier and two improved strategies to conquer the disadvantage. We rebuild the fluent classifier with Focal Loss, which can address the class imbalance, and improved two strategies of training by segmented processing. As experiments on two bilingual (English-Chinese) datasets show, the proposed approach can significantly improved in all evaluation indicators.
An experimental approach to compare various deep learning architectures for sentiment analysis

This paper aims to study the efficiency of various seq2seq deep learning architectures for the solution of toxic speech classification and performing efficient sentiment analysis using unilingual publicly available dataset. Numerical examples are presented along with various validation metrics and graphs to indicate the efficiency of the various NLP techniques and confirm the experimental findings of the paper. We also compare and contrast between traditionally used natural language processing models and state of the art model like Bidirectional Encoder Representations from Transformers or BERT.
Study on the dynamic behavior of truncation errors in non-hyperbolic fixed points chaotic systems

It is always thought that discrete dynamical systems were collapsed from complicated long period to simple short period by computer truncation errors. In this paper, a system induced to complication completely by computer truncation is offered. The theoretical solution is a convergent sequence and the corresponding computer solution is a diverging system. Analysis shows that it's the computer truncation error that makes the system performance changes. This anti-degradation mechanism can be applied to chaotic encryption, which will solve the weak key problem caused by the collapse of chaotic system.