Reviewerly/exHarmony huggingface dataset:
A Novel Privacy Preservation Scheme for Internet of Things Using Blockchain Strategy

Internet of Things (IoT), the modern approach of science and technology, is currently experiencing a rapid evolution in the area of research and industry, but it still suffers from various security issues [1]. In an IoT environment, privacy is the prime concern, and the information is being handled mainly through the decentralized topology [2]. The commonly utilized methods for communicating and fetching data are carried out through the Internet by utilizing different types of smart devices that have the ability to go around the Internet into an unsafe platform. It is due to the receptiveness and heterogeneousness humor of IoT terminals, where they are uncovered to attackers by making its user’s identity easily traceable and for the most part, is not secured-by-design [3]. It is essential for an upgradation, to fix their vulnerabilities and to anticipate attackers from enlisting them into botnets. This paper is based on the relevant security issues and confidential challenges upon various vulnerabilities of Internet services for IoT devices by presenting some typical attack cases with an outlook on possible solutions that are carried out in a systematic literature review. We have focused on the privacy issue that prevails in the IoT environment and thereby proposed an appropriate security model through Blockchain strategy for establishing a secure and reliable communication among peers.
VEP Detection for Read, Extempore and Conversation Speech

In this paper, we propose a novel approach for accurate detection of the vowel end points (VEPs) in any mode of speech. VEP is the instant at which the vowel ends in the speech signal. In this study, we have considered three broad modes of speech, namely; conversation, extempore, and read. The existing methods were explored the VEP detection for read mode of speech, and it may not be appropriate for the VEP detection in extempore and conversation modes. This is due to the acoustic characteristic of read mode is very different from the modes as mentioned earlier. To handle this problem, we proposed a two-stage method for accurately detecting the VEPs, irrespective of modes. At the first stage, vowel onset points (VOPs) are detected in a speech signal using our recent method based on continuous wavelet transform and phone boundary. VOP represents the start of the vowel in the speech signal. At the second stage, phone boundaries are detected using spectral transition measure approach, and then the closest succeeding phone boundary for each detected VOP is considered as detected VEP. Experiments involve TIMIT and Bengali speech corpora. Performance of the proposed VEP detection method is compared with two state-of-the-art signal processing methods. The significance of the proposed method is shown by automatically detecting vowel regions from the TIMIT and Bengali speech corpora. The evaluation results report that the performance of the proposed method is significantly better than the existing methods.
New Data Embedding and Detecting Method for Printed Image

To associate a printed medium with information on the Internet, several data embedding and detecting methods using the camera of a mobile device have been proposed. This paper proposes a new data embedding method to obtain a higher detection rate. A new data embedding positions and a new calculation method of correlations for detecting the data are introduced. We also focus on the capture of versions of the same image simultaneously using a mobile device. We propose a method of selecting the blocks for which a high detection rate can be expected, decoding the embedded data from the blocks, and combining the decoded data. From an experiment, it is shown that the proposed method is effective for improving the detection rate.
DaST

Over the last few years data science has emerged both as a new research field and as an educational domain that attracted a large number of researchers and data practitioners. Although data science research is developing at a high pace, the educational process in the field has been left behind in terms of educational tools and practices, despite the high number of data science courses offered and the number of involved stakeholders (professors, tutors, and students). The present work aims to cover the gap of educational data science tools by proposing a novel platform; the platform, coined Data Science Tutor (DaST), is a free online tool that offers automated step-by-step exercise solving in a variety of data science algorithms/techniques aiming at giving insight to the particularities of each algorithm. The solutions of the exercises are accompanied with in-context explanations that refer to the operation of the respective algorithm/technique, and are compatible with the terminology and the methodology in popular textbooks. The tool aims at students, lecturers, and data practitioners in many diverse fields (ranging from data analysts to transport engineers to logistics managers) that want to learn the particularities of data science algorithms in a stepwise, interactive manner.
Evaluation of Oversampling Strategies in Machine Learning for Space Debris Detection

In recent years, the number of resident space objects has increased dramatically. The chances of space objects colliding with each other are increasing, thus posing a threat to active satellites and future space missions. Identifying and detecting space debris is essential in ensuring the security of space assets. In this contribution, we investigate the effectiveness of several feature extraction and oversampling techniques by attempting classification of real-world light curves of space objects using eight machine learning methods. Three feature extraction tools are utilized to represent the light curves as sets of features, i.e., FATS (Feature Analysis for Time Series), feets (feATURE eXTRACTOR FOR tIME sERIES) and UPSILoN (AUtomated Classification for Periodic Variable Stars using MachIne LearNing) public tools. To address the problem of class imbalance, four oversampling techniques are applied, i.e., ADaptive SYNthetic Sampling Approach (ADASYN), Synthetic Minority Oversampling TEchnique (SMOTE), and two modifications of SMOTE, specifically, Borderline-SMOTE and Support Vector Machine (SVM)-SMOTE. Results show that the features extracted using the FATS tool lead to a better performance, and therefore, they appear to represent light curves in a more informative manner, compared to feets and UPSILoN. Moreover, the use of SVM-SMOTE technique improves the performance of the utilized classifiers more than other oversampling techniques.
Contextual multimodal sentiment analysis with information enhancement

Abstract The results of sentiment analysis research can be applied to various social situations, including human communication understanding and dialogue system. Multimodal sentiment analysis involves the identification of sentiment in videos. This paper proposes a multimodal sentiment analysis framework, in which the Attention-based Bi-directional Gated Recurrent Unit (AT-BiGRU) model is used to obtain the contextual relationship among utterances, and the Information Enhancement Fusion (IE-Fusion) model is used to fuse the multimodal features of each utterance. Experimental results show that our framework achieves 82.85% and 65.77% accuracies on two different benchmark datasets, CMU-MOSI and IEMOCAP, respectively. Our method outperforms the state-of-the-art models on the datasets.
Application of IoT Using Neuro-fuzzy Based on Thai Speech Classification to Control Model Hospital Bed with Arduino

Nowadays, the number of chronic patients who had to stay in the hospital is high. The bedridden patients need carers to help them in many activities such as giving medicines, eating food, flipping their body. The development of prototype in this research, including software and equipment relieves the burden for carers or helps the bedridden patients. The software is a mobile application controlling hardware by touch screen or using Thai voice. The hardware is a model hospital bed controlling by an Arduino. The hardware and software are connected via Wi-Fi. To implement Thai voice command in the mobile application, the Neuro-fuzzy with Mel frequency Cepstral coefficients is selected to create the Thai speech classification model. There are several experiments to find the best structure. The average accuracy of 5-fold cross-validation of the best model in testing data is 71.50%.
A New Task Scheduling for Minimizing Completion Time and Execution Cost in Smart Grid Cloud

Cloud computing technology has become the most popular network technology. Therefore, it has been combined into grid, establishing smart grid cloud for independent data management of power supply stations. Smart grid cloud task scheduling is one of the key technologies that affect resource allocation efficiency in cloud computing environment. An improved particle swarm optimization-genetic algorithm cloud task scheduling strategy is proposed in this paper. The algorithm aims at minimizing the task completion time and execution cost, and carries out mathematical modeling on the cloud task scheduling problem. Coding the Particle in Particle Swarm Optimization Algorithm, and integrating the crossover and mutation operations of the genetic algorithm into the particle swarm algorithm so that the particle swarm algorithm can better solve the discrete problem. Finally, adding the precocious judgment and chaotic disturbance mechanism to the algorithm to help the algorithm jump out of the local optimum. The experimental results have shown the proposed strategy behaves well.
Multilayer WFRFT and chaotic encryption wireless communication system based on MIMO

Abstract In order to improve the concealment and anti interception performance of satellite and other wireless communication.Based on the characteristics of multilayer weighted fractional-order Fourier transform and chaotic mapping, a multilayer weighted fractional-order Fourier transform and chaotic mapping wireless communication system is designed. The number of modulation layers of multilayer WFRFT is consistent with the number of transmitting antennas, and different modulation orders are adopted in each layer, which makes it more difficult to crack the information by chaotic mapping scrambling codes on the modulated signals. The simulation results show that the designed system has better concealment performance by comparing the bit error rate of the receiver with that of the eavesdropper. The bit error rate of the eavesdropper is about 0.5 when the eavesdropper does not know the specific parameters.
Modeling of the development of the fetus cognitive map from the sensorimotor system

The human cognitive map formation is still an open question. Based on biological facts, the cognitive map origin goes back to the age of the fetus human. In this paper, our aim is to provide a possible answer to that question. Accordingly, we present a theoretical model of the development of the cognitive map of a fetus human using its sensorimotor data. We define positions of the cognitive map as associations between high-level perceptions created from different sensory sources. We use a proposed method referred to as Frequency-based-means clustering algorithm to develop the perceptions that form the association map. Our proposed theoretical model is tested on simulated data. Results show that our model is a possible candidate for demonstrating how the cognitive map is formed. In addition, comparison with k-means clustering is presented and results show that the frequency-based-means clustering has a better performance than k-means clustering and is more suitable for this application.
Company Industry Classification with Neural and Attention-Based Learning Models

This paper compares different solutions for the task of classifying companies with an industry classification scheme. Recent advances in deep learning methods show better performance in the text classification task. The dataset consists of short textual descriptions of companies and their economic activities. Target classification schemes are built by mapping related open data in a semi-controlled manner. Target classes are built from the bottom up by DBpedia. For the experiments are used modifications of methods BERT, XLNet, Glove and ULMfit with pre-trained models for English. Two simple models with perceptron architecture are used as the baseline. The results show that the best performance for multi-label classification of DBpedia companies abstracts is achieved by BERT and XLnet models, even for unbalanced classes.
An Improved Model of Multi-attention LSTM for Multimodal Sentiment Analysis

Multimodal sentiment analysis is the task of detecting emotions in videos using multimodal information such as text, visual and audio. One difficulty that is often faced is the complexity associated with different modes in the fusion of feature layers. In this paper, we present a novel feature-level fusion method for analyzing emotions called Multi-attention LSTM (MALM). The proposed approach uses LSTM to capture context information of contexts in the same video. At the same time, we use the attention mechanism before and after multimodal information fusion in order to focus attention on relatively important sequences and modalities. We evaluate our proposed approach on two multi-modal sentiment analysis benchmark datasets and compare to various proposed approaches on the same datasets. Evaluation results show approximately 5-10% performance improvement over the state-of-the-art models for the benchmark datasets.
Automated Systems for Detecting Volcano-Seismic Events Using Different Labeling Techniques

Several systems have been developed in the last years to automatically detect volcanic events based on their seismic signals. Many of those systems use supervised machine learning algorithms in order to create the detection models. However, the supervised training of these machine learning techniques requires labeled-signal catalogs (i.e., training, validation and test data-sets) that in many cases are difficult to obtain. In fact, existing labeling schemes can consume a lot of time and resources without guarantying that the final detection model is accurate enough. Moreover, every labeling technique can produce a different set of events, without being defined so far which technique is the best for volcanic-event detection. Hence, this work proves that the labeling scheme used to create training sets definitely impacts the performance of seismic-event detectors. This is demonstrated by comparing two techniques for labeling seismic signals before to train a system for automated detection of volcanic events. The first technique is automatic and computationally efficient, while the second one is a handmade and time-consuming process carried out by expert analysts. Results show that none of the labeling techniques is completely trustworthy. As a matter of fact, our main result reveals that an improved detection accuracy is obtained when machine learning classifiers are trained with the conjunction of diverse labeling techniques.
The Enabling Role of Optics and Photonics in the National Quantum Initiative

Optics and photonics play key roles in integrating university, industry and government research to move quantum information science and technology from theory into practice, including the central areas of quantum sensors, communication systems and computers.
Should Data Structures Look Flat for End Users?

Recent developments in business intelligence have not only led to the availability of more and more heterogeneous data but also to more and more heterogeneous user groups accessing them. Consequently, software vendors offer products with various views on data, wishing to give all users an easy access to the data. We conduct an online experiment among end users with varying analytical expertise for an empirical examination on how data should be structured for presentation.
An Identity-based Aggregate Signature Scheme for Wireless Sensor Network Environmental Monitoring

Aggregation signature technology can save transmission bandwidth and computational cost while providing message authentication for multiple users. It is suitable for message aggregation and batch signature verification in wireless sensor network environment monitoring. Based on the characteristics of wireless sensor network environment monitoring, this paper designs an identity-based aggregate signature scheme, which can aggregate the signatures of multiple sensor nodes into one signature and send it to the base station. The base station only needs to verify this signature to authenticate the messages sent by multiple sensor nodes. Based on the discrete logarithm problem of elliptic curves, it is proved that it is anti-existence forgery under adaptive selection message attack in stochastic prediction model. The scheme provides secure authentication for messages at low computational cost and is nonrepudiation.
A Model for Real-Time Biometric Authentication Using Facial and Hand Gesture Recognition

Facial recognition is the process of identifying and/or verifying distinct facial characteristics of an individual in an image or a video stream, pre-recorded or in real time. Hand gesture recognition refers to the process of identifying the configuration of an individual’s hand posture in an image and can be used to verify concurrency of it with respect to a pre-set randomized prompt example to use it in a way similar to CAPTCHA. This paper aims to unify these two similar, yet varying image processing technologies in order to be able to provide a multi-factor authentication system for web applications which cannot be tricked using pre-recorded footage. The face would be identified in the image using convolutional neural networks and then be masked from the image for the hand to be isolated by identifying them using the skin tone. Once the hand has been isolated, SIFT points identified would determine the gesture being performed by the subject, followed by matching the face with the requested credentials if approved. The facial recognition would act like a password and the hand gesture recognition as a means to verify the authenticity of the facial credentials provided.
In Your Face: Person Identification Through Ratios of Distances Between Facial Features

These days identification of a person is an integral part of many computer-based solutions. It is a key characteristic for access control, customized services, and a proof of identity. Over the last couple of decades, many new techniques were introduced for how to identify human faces. The purpose of this paper is to introduce yet another innovative approach for face recognition. The human face consists of multiple features that when considered together produces a unique signature that identifies a single person. Building upon this premise, we are studying the identification of faces by producing ratios from the distances between the different features on the face and their locations in an explainable algorithm with the possibility of future inclusion of multiple spectrum and 3D images for data processing and analysis.
Analysis of the Functionalities of a Shared ICS Security Operations Center

The basic step in the design of a security operations center (SOC) is identifying the necessary functions it needs to perform. The article offers an analysis of the ICS SOC functionalities and is focused to create a part of the concept of operations before the real design of Shared ICS SOC. We offer a complex of functionalities of Shared ICS SOC and analyze their effectiveness. The survey is based on a review of the legal framework, the ICS security incidents, research on the gaps between cybersecurity products and real needs for the ICS and SCADA community. Shared SOC performs role of community service hub with integrated experience, supplying security services for multiple ICS. By outsourcing these services, a company can reduce security staff and focus on its core business.
Where Do Our Problems Lie?: Comparing Rates of E-Access Problems Across Three Research Institutions

In order to build upon the research of Brett, the author categorized over three hundred e-resources troubleshooting tickets at The University of Alabama Libraries into a coding scheme originally created by Goldfinger and Hemhauser with the goal of cross-comparing the results with that of two other research libraries. The results of the analysis shed light upon the most commonly reported types of problems among the three institutions, and patterns emerged that could be indicative of industry-wide concerns. The author also developed a more specific, local classification schema in order to identify areas in which local solutions could be implemented to improve user experience.
Evaluation of the Hiding-Countermeasure PRINCE Using Differential Power Analysis

Lightweight block ciphers have been proposed as encryption processing for small devices. For a typical lightweight cryptographic PRINCE, hiding countermeasure for power analysis attacks has been reported. However, this countermeasure method evaluates the tamper resistance of only one kind of power analysis attacks. Therefore, this study applies other power analysis attacks to the countermeasure to evaluate the tamper resistance. From evaluation experiments, this study clarifies that WDDL-PRINCE has high tamper resistance not only to CPA but also to DPA.
Multi-Keyword Search Guaranteeing Forward and Backward Privacy over Large-Scale Cloud Data

Using searchable encryption (SE), users' data can be outsourced to an untrusted server while ensuring privacy of both the queries and the data. Meanwhile, to efficiently support data updating, dynamic SE (DSE) has also been proposed and applied to a variety of scenarios. However, recent work shows that even with little information leakage on updated keywords, most of existing DSE schemes are also vulnerable to adaptative attacks breaking the privacy of the queries. To address this problem, several privacy-preserving DSE have been exploited to mitigate the two major privacy issues in the data update process: i.e., Forward privacy and Backward privacy. Nevertheless, it is still an open problem to support clients multi-keyword-based searching over dynamic cloud data. In reality, as a promising query requirement, it is assurance that the cost of all participants can be fundamentally reduced by implementing multi-keyword-based querying. To combat that, in this paper, we design the first multi-keyword based search proposals ensuring forward and backward privacy over dynamic cloud data. Specifically, we utilize Symmetric Hidden Vector Encryption (SHVE) as the underlying structure to build multi-keyword search protocol. Then, Bloom filter integrating with pseudo-random function will be further adopted to enhance query efficiency. The security analysis proves the high security of our model, and extensive experiments conducted on real-world data also demonstrate the practical performance of our proposed scheme.
Indoor Mobile Robot Path Planning Using QR Code

Mobile robots that addresses the automation needs are extensively used in service-based environments like warehouses, hotels, hospitals, restaurants etc. Path planning to reach destination from a source is a crucial task for any mobile robot. An idle path planning algorithm given a source and destination, should plan a shortest path with less computation time autonomously. In this work Quick Response (QR) code is used as via point/landmark in the environment. QR code is a 2D barcode which stores more data than a traditional barcode system. Path matrix which contains navigation information to reach nearby via points or destinations is stored in QR code. These QR codes are placed on the floor strategically, such that given a destination, a mobile robot can navigate in a shortest route possible without any overall environment information. The proposed system is implemented and tested using an in-house built omnidirectional mobile robot powered by BeagleBone Black. The obtained results proves that the mobile robot is able to reach its destination point accurately.