Reviewerly/exHarmony huggingface dataset:
Spatial Dynamic Searchable Encryption with Forward Security

Nowadays, spatial search services bring unprecedented convenience in people's daily life (e.g., location-based services, social networking) and are becoming more and more popular. To protect the privacy of outsourcing data, several schemes have been proposed to achieve secure search over encrypted spatial databases. However, most existing schemes cannot support dynamic updates, which seriously hinders the practical application of spatial databases. To address this issue, in this paper, we propose two novel Spatial Dynamic Searchable Encryption (SDSE) constructions for outsourcing spatial databases, which achieve various security guarantees. First, we present a basic construction supporting dynamic update with sub-linear search complexity based on the order-revealing encryption and Quadtree. Then, to ensure that updates do not reveal any information underlying the prior modifications beyond some explicit leakage (i.e., forward security), we further give an improved construction according to constrained pseudo-random functions. Both the nearest neighbor search and geometric search are supported in our constructions, which meet almost all spatial search needs. The experiments using real-world dataset demonstrate that our constructions are efficient and feasible in practice.
A quantified approach of predicting suitability of using the Unscented Kalman Filter in a non-linear application

A mathematical framework to predict the Unscented Kalman Filter (UKF) performance improvement relative to the Extended Kalman Filter (EKF) using a quantitative measure of non-linearity is presented. It is also shown that the range of performance improvement the UKF can attain, for a given minimum probability depends on the Non-linearity Indices of the corresponding system and measurement models. Three distinct non-linear estimation problems are examined to verify these relations. A launch vehicle trajectory estimation problem, a satellite orbit estimation problem and a re-entry vehicle position estimation problem are examined to verify these relations. Using these relations, a procedure is suggested to predict the estimation performance improvement offered by the UKF relative to the EKF for a given non-linear system and measurement without designing, implementing and tuning the two Kalman Filters.
Education at a Cooperative University in the Digital Economy

The article discusses the need for innovative updating and modernization of education in a cooperative university in a digital economy. The relevance of studying the educational process in the university on the basis of its digitalization is due to the expansion of the information space and the idea of digital literacy with the development of technologies. The paper analyzes the key areas of digitalization in educational activities for producing competitive specialists for work in consumer cooperation organizations. The authors review the development of professional competencies of the 21st century. The study uses the SAMR model by Dr. R. Puentedura to analyze practical conditions for the use of digital technologies in the audience. They were developed for the effectiveness of training and improving digital literacy of students as a result of professional development in the university. The widespread use of information educational systems and elements of e-learning in the process of education creates additional conditions for the conscious choice of a profession and the sustainable motivation of students to receive university education. The results of scientific research presented in the article can be used to increase the effectiveness of educational activities in a cooperative university.
Signing Blockchain Transactions Using Qualified Certificates

Blockchain technology is increasingly being considered among both private enterprises and public services. However, it poses a challenge with regard to aligning its identity management scheme with the Public Key Infrastructure and the Qualified Digital Certificates issued by Qualified Trust Service Providers. To solve this challenge, we will present a solution in the form of an architecture reference model, which enables enterprises and public services to leverage blockchain technology by integrating Qualified Electronic Signatures with blockchain transactions. The evaluation of the architecture reference model is provided through the design of a Blockchain-based Trusted Public Service and a use-case scenario example. The proposed architecture reference model is based on the CEF building blocks EBSI, eSignature, and eID compliant with eIDAS.
BETA-Rec: Build, Evaluate and Tune Automated Recommender Systems

The field of recommender systems has rapidly evolved over the last few years, with significant advances made due to the in-flux of deep learning techniques. However, as a result of this rapid progress, escalating barriers-to-entry for new researchers is emerging. In particular, state-of-the-art approaches have fragmented into a large number of code-bases, often requiring different input formats, pre-processing stages and evaluating with different metric packages. Hence, it is time-consuming for new researchers to reach the point of having both an effective baseline set and a sound comparative environment. As a step towards elevating this problem, we have developed BETA-Rec, an open source project for Building, Evaluating and Tuning Automated Recommender Systems. BETA-Rec aims to provide a practical data toolkit for building end-to-end recommendation systems in a standardized way. It provides means for dataset preparation and splitting using common strategies, a generalized model engine for implementing recommender models using Pytorch with 9 models available out-of-the-box, as well as a unified training, validation, tuning and testing pipeline. Furthermore, BETA-Rec is designed to be both modular and extensible, enabling new models to be quickly added to the framework. It is deployable in a wide range of environments via pre-built docker containers and supports distributed parameter tuning using Ray. In this demo, we will illustrate the deployment and use of BETA-Rec for researchers and practitioners on a number of standard recommendation datasets. The source code of the project is available at github: https://github.com/beta-team/beta-recsys.
Facial expression recognition based on geometric features

the goal of facial expression Recognition is to detect human emotion through facial images. But the biggest challenge of recognizing facial expression is how to extract distinctive characteristics from images of the human face to differentiate diverse emotions. To tackle this challenge, we propose a FER algorithm using geometric features. In the first step, facial landmarks are detected from input sequence video using Dlib Library and geometric features are extracted, considering the spatial position between landmarks. These feature vectors are then implemented in Support Vector Machine (SVM) classifier to classify facial expressions. The Experimental results demonstrate that our proposed method applied on a fusion of two databases (personal database and BUHMAP) shows 94.5% accuracy.
Minimum class variance multiple kernel learning

The purpose of multiple kernel learning (MKL) is to learn an appropriate kernel from a set of predefined base kernels. Most of the MKL methods follow the basic idea of support vector machine (SVM) to learn the optimal weights of base kernels and build the used classifier. However, SVM is a local method and ignores the structure information of the data in that its solution is exclusively determined by the so-called support vectors. In the paper, we propose an improved SVM-based MKL method called minimum class variance multiple kernel learning (MCVMKL). The key characteristic of MCVMKL is that it exploits the ellipsoidal structure of the data during learning the optimal weights and building the classifier. Besides, its formulation is invariant to scalings of the weights of base kernels. We develop two optimization strategies to handle the optimization model of MCVMKL. Further, we derive a rough upper bound for the objective function of MCVMKL and propose a variant called trace-constrained multiple kernel learning (TCMKL) by using the trace of the within-class scatter matrix. TCMKL enlarges the margin between different classes and simultaneously shrinks the region covering the data as much as possible. Moreover, it can automatically tune the regularization parameter and so saves the training time due to avoiding using the time-consuming cross-validation technique to select an appropriate regularization parameter. Finally, the comprehensive experiments are conducted and the results demonstrate that the proposed methods are effective and can achieve better performance over the competing methods.
Approach to improving training of human workers in industrial applications through the use of Intelligence Augmentation and Human-in-the-Loop

Training employees for industrial processes is a major challenge in the industry. There are different approaches to meet this challenge. In practice, however, we often observe that experienced employees are seconded to train new employees in processes. This is very expensive and does not allow intensive training. Digital assistance systems can support the training process to a certain extent, but they do not have the detailed experience knowledge that the older employees have. In particular, the implicit knowledge of experienced employees cannot be digitally mapped with the classic solutions. At this point, the approach presented here is intended to support the training process by using Intelligence Augmentation to combine artificial and human intelligence in order to design an assistance system capable of learning, which should considerably improve the training of inexperienced employees. How this solution is designed in detail is described in this article.
Generating Positive and Negative Sentiment Word Clouds from E-Commerce Product Reviews

Most customers who prefer buying products online on E-Commerce websites tend to rely on the ratings given to a product by other customers or a summary of the already existing customer reviews. However, a plethora of meaningful data is stored in the review text which eludes representation through customer ratings or the summary of the reviews likewise. But it is inefficient to go through each and every review. Our model thus adopts two approaches to demonstrate and resolve the generated issue - General Approach where the data is sorted based on the ratings, and Specific Approach where the data is sorted based on the products. The subsequent result is the generation of two new corpora followed by the generation of two new Word Clouds consisting of positive and negative features respectively for each existing product. The purpose of these Word Clouds is to highlight the features of products that are mentioned in the reviews. Hence, such a model provides more accurate as well as an efficient analysis of the offered products.
Identification of IoT Devices for Forensic Investigation

As the Internet of Things (IoT) environment rapidly grows, so do the distribution and utilisation of IoT devices as well as Cyber-Enabled and Dependent Crime activities. This leads to challenges investigators face when forensically investigating the IoT to present evidence in the court of law. Current tools and frameworks demonstrate a variety of limitations which need to be improved to efficiently deal with IoT forensics. In this article, we introduce a novel approach to uniquely identify IoT devices to improve IoT Forensics while introducing new terms, such as the DNA and Genes of devices. These novel terms for a dynamic and complex environment, such as the IoT, will ease the understanding of unique device identifiers due to its well-known association with traditional crime scene investigation. To elaborate on the importance of unique device identification we establish the context of the IoT, its Forensic Investigation and challenges as well as the approach of Universal Identification Numbers utilising Set Theory. Additionally, we consider the implementation within our proposed IoT Server model environment to demonstrate the necessity in IoT Forensics.
Improving Person Re-Identification With Iterative Impression Aggregation

Our impression about one person often updates after we see more aspects of him/her and this process keeps iterating given more meetings. We formulate such an intuition into the problem of person re-identification (re-ID), where the representation of a query (probe) image is iteratively updated with new information from the candidates in the gallery. Specifically, we propose a simple attentional aggregation formulation to instantiate this idea and showcase that such a pipeline achieves competitive performance on standard benchmarks including CUHK03, Market-1501 and DukeMTMC. Not only does such a simple method improve the performance of the baseline models, it also achieves comparable performance with latest advanced re-ranking methods. Another advantage of this proposal is its flexibility to incorporate different representations and similarity metrics. By utilizing stronger representations and metrics, we further demonstrate state-of-the-art person re-ID performance, which also validates the general applicability of the proposed method.
Place perception from the fusion of different image representation

Inspired by the human way of place understanding, we present a novel indoor place perception network to overcome: 1). the simplicity of existing methods that only use the image features of object regions to recognize the indoor place, 2). insufficient consideration of the semantic information about object attributes and states. By utilizing multi-modal information containing the image and natural language, the proposed method can comprehensively express the attributes, state, and relationships of objects which are beneficial for indoor place understanding and recognition. Specifically, we first present a natural language generation framework based on a Convolution Neural Network (CNN) and Long Short-Term Memory (LSTM) to imitate the process of place understanding. Next, a Convolutional Auto-Encoder (CAE) and a mixed CNN-LSTM are proposed to extract image features and semantic features, respectively. Then, two different fusion strategies, namely feature-level fusion and object-level fusion, are designed to integrate different types of features and features from different objects. The category of the indoor place is finally recognized based on fused information. Comprehensive experiments are conducted on public datasets, and the results verify the effectiveness of the proposed place perception method based on linguistic cues.
Accelerated implementation of FQSqueezer novel genomic compression method

Biological data contain very important information for genoma analysis. In last decades, the size of these data is constantly growing. So the Next Generation Sequence (NGS) data has been introduced. These kind of data are represented by different data formats, such as FASTQ, FASTA, SAM, etc. In order to allow a good analysis and storing of them, due to large dimension of these data, several compressors have been performed. FQSqueezer is a novel genomic compressor for FASTQ data files. But several issues are present due to multithread version that runs on multi-core hardware. It is wellknown that the number of cores in a CPU is limited and very minor with respect to GPUs' cores number. In order to increase the performance related to this compressor method, in this work we present a GPU-parallel implementation of cited compressor by exploiting CUDA framework. More precisely, a suitable domain decomposition is able to give an appreciable gain of performance in terms of time and reliability. Several execution tests confirm the gain of efficiency achieved by our parallel implementation.
AES/FPGA Encryption Module Integration for Satellite Remote Sensing Systems: LST-SW case

Satellite remote sensing embedded systems need to be secure to protect data transmission between satellites and the ground station for any threat can affects the hardware of satellite and interception of data, in addition to unauthorized access to satellite system. This research proposes an approach for a secure integration of FPGA Encryption module based on the iterative looping architecture for remote sensing algorithm and especially for the LST-SW algorithm. The target hardware used in this paper is Virtex-5 XC5VLX50T FPGA from Xilinx. Hardware Description Language was used to design the complete system. The analysis of the proposed designed shows that this implementation can achieved a throughput of 1634.71 Mbps and take 6 % of slice in area utilization with one block of RAM.
Comparison of image edge detection methods on potholes road images

Abstract Dinas pekerjaan Umum (DPU) in Surabaya in carrying out road repairs, especially in potholes, must know the position, area and depth. So it is important to do research to find out the surface area of potholes. To find out the surface area of the hole, the edge detection process is done first. Edge detection functions to get the edge of an object. Edge detection is obtained by utilizing a drastic change in the intensity value at the boundary of two areas. In this study, to determine the edge of an object (potholes road) by comparing three edge detection methods consisting of the Frei-Chen, Laplacian and Laplacian of Gaussian (LoG) methods. From the results of testing the Laplacian of Gaussian method has an average value of accuracy of 67%, sensitivity of 81.97% and specificity of 65.16%.The measurement results obtained by the Lapalacian of Gaussian method which is the best method for edge detection in potholes road images, because it produces the highest accuracy, sensitivity and specificity.
Bedtk: finding interval overlap with implicit interval tree

We present bedtk, a new toolkit for manipulating genomic intervals in the BED format. It supports sorting, merging, intersection, subtraction and the calculation of the breadth of coverage. Bedtk uses implicit interval tree, a data structure for fast interval overlap queries. It is several to tens of times faster than existing tools and tends to use less memory.The source code is available at https://github.com/lh3/bedtk.
Grapheme-to-Phoneme Transduction for Cross-Language ASR

Automatic speech recognition (ASR) can be deployed in a previously unknown language, in less than 24 h, given just three resources: an acoustic model trained on other languages, a set of language-model training data, and a grapheme-to-phoneme (G2P) transducer to connect them. The LanguageNet G2Ps were created with the goal of being small, fast, and easy to port to a previously unseen language. Data come from pronunciation lexicons if available, but if there are no pronunciation lexicons in the target language, then data are generated from minimal resources: from a Wikipedia description of the target language, or from a one-hour interview with a native speaker of the language. Using such methods, the LanguageNet G2Ps now include simple models in nearly 150 languages, with trained finite state transducers in 122 languages, 59 of which are sufficiently well-resourced to permit measurement of their phone error rates. This paper proposes a measure of the distance between the G2Ps in different languages, and demonstrates that agglomerative clustering of the LanguageNet languages bears some resemblance to a phylogeographic language family tree. The LanguageNet G2Ps proposed in this paper have already been applied in three cross-language ASRs, using both hybrid and end-to-end neural architectures, and further experiments are ongoing.
A User Interface for Personalized Web Service Selection in Business Processes

Nowadays, due to the huge volume of information available on the web, the need for personalization is more than necessary. Choosing the right information for each user is as important as the way this information is presented to him or her. Currently, user-triggered recommendation requests for web services are implemented as an automatic recommendation based on parametric computation. This work reports on a specialized user interface for business processes, where writing code entails invocation of business process information. The paper presents the user interface design for Personalized Web Service Selection in Business Process scenario execution and the user evaluation by business process engineers.
Man-in-the-Middle Attacks to Detect and Identify Services in Encrypted Network Flows using Machine Learning

Wireless access points are deployed rapidly as users demand to grow massively to meet sufficient quality of services. Recently, the need for internet utility is enormously rising as the era of streaming social media services highly entailed which are the utmost treasured for the users. However, these applications are classified as encrypted network traffic mainly to protect and to adhere to users' privacy. On the other hand, Machine Learning (ML) adopted in a wide range of data classification as an efficient approach to organize indistinct data. The ML algorithms are accommodating any type of information which can be structured or unstructured data to distinguish rational patterns that can result in an understanding of superior decisions and forecasts. Therefore, in this work, we elaborate on a lab experiment of the Man-in-the-Middle (MITM) attack which sniffs the encrypted network traffic and analyzes it in rich details through a supervised ML approach to classify the social media applications.
A Heterogeneous Implementation of the Sobel Edge Detection Filter Using OpenCL

Today, edge detection is a cornerstone technique as edges are essential in many applications, such as image processing and biometric imaging. One popular algorithm for edge detection is the Sobel. Many researchers have focused on accelerating the Sobel filtering, but to the best of our knowledge we are the first to propose a 5×5 convolution kernel implementation using OpenCL. In this work, we implement the Sobel filter, one of the most effective and popular edge detection algorithms in image processing, in the OpenCL programming language. From the implementation of the Sobel algorithm we compare the performance of the CPU and GPU through OpenCL, in typical images ranging from 64×64 to 4096×4096 pixels. The Sobel operator uses a pair of 3×3 horizontal and vertical convolution kernels for edge detection functions. We apply 3×3 and 5×5 convolution kernels using OpenCL and compare them. The results have shown that for all image sizes, the GPU speed up ranges from 11,18 to 15,46 times with 3×3 convolution kernels, while speed up is from 10,05 to 13,46 times for the 5×5 convolution kernels. Finally, the results of our implementation are compared to other existing implementations and found to achieve better performance.
HTR-Flor++

Offline Handwritten Text Recognition (HTR) is a task that offers a challenge in computer vision, where images are the only source of information. In fact, several approaches to optical models have been developed, such as through of Hidden Markov Model (HMM) or recurrent Bidirectional/Multidimensional layers. The current state-of-the-art consists of combined deep learning techniques, the Convolutional Recurrent Neural Networks (CRNN), in which recurrent layers still suffer from vanishing gradient problem when processing very long texts. In a way, high-performance models generally have millions of trainable parameters and a high computational cost. However, recently a new optical model architecture, Gated-CNN, demonstrated improvements to complement CRNN modeling. Thus, in this work, we present a new small architecture for HTR (based on Gated-CNN) integrated with two steps of language model at the character and word levels, respectively. Therefore, we used 9 state-of-the-art approaches and validated the results using the IAM public dataset. Finally, the proposed model surpasses the results obtained by different approaches in the literature, reaching recognition rates of CER 2.7% and WER 5.6%, which means an improvement of 13% over the best results on IAM dataset.