Reviewerly/exHarmony huggingface dataset:
Multiparty Computation in Practice: Increasing Security of Documents in Enterprise Content Management Systems

Multiparty computation is undoubtedly one of the most interesting research topic in information security field, developing dynamically over the last decade. In this work we analyze an application case in which multiparty computation protocols are used to increase the security level of digital resources stored encrypted in cloud environments that are constantly exposed to network threats. The distribution of the cryptographic secrets with the use of multiparty computation modules provides analyzed Enterprise Content Management system with the increased resistance to attacks on key confidentiality. With the proposed approach there is no need to choose between hardware-based cryptographic modules that are secure but expensive and traditional software-based centralized cryptographic modules that are affordable but vulnerable. Due to mass scale of application of Enterprise Content Management, the deployment of the solutions built similarly to the presented prototype may become a factor that significantly minimizes the security risks and business costs. The presented architecture can be used as a solid starting point for building secure enterprise systems that generate negligible costs in relation to their counterparts based on hardware-based cryptographic modules.
Parallel Implementation of Marathi Text News Categorization Using GPU

Nowadays, handling immense online-generated data has become the most challenging task for a data analyst. Like all other things, reading new articles online is a new trend. For India, there are a total of 22 major regional languages. So, the news publishers of regional language newspaper have hosted their websites for daily news. The manual categorization of the news articles is a difficult and time-consuming task. Hence, an automatic categorization of news articles using the latest machine learning techniques can be used. K-NN machine learning classification algorithm with the most popular term-weighting scheme TF-IDF for categorization of the Marathi news article in one of the pre-defined categories (sports, economy and entertainment) is used here. As this work is compute-intensive, a parallel algorithm is proposed and implemented on GPU Nvidia Tesla T4. This paper specifies the categorization of Marathi news into categories like sports, entertainment, and economy with the help of GPU.
What If Keys Are Leaked? towards Practical and Secure Re-Encryption in Deduplication-Based Cloud Storage

By only storing a unique copy of duplicate data possessed by different data owners, deduplication can significantly reduce storage cost, and hence is used broadly in public clouds. When combining with confidentiality, deduplication will become problematic as encryption performed by different data owners may differentiate identical data which may then become not deduplicable. The Message-Locked Encryption (MLE) is thus utilized to derive the same encryption key for the identical data, by which the encrypted data are still deduplicable after being encrypted by different data owners. As keys may be leaked over time, re-encrypting outsourced data is of paramount importance to ensure continuous confidentiality, which, however, has not been well addressed in the literature. In this paper, we design SEDER, a SEcure client-side Deduplication system enabling Efficient Re-encryption for cloud storage by (1) leveraging all-or-nothing transform (AONT), (2) designing a new delegated re-encryption (DRE), and (3) proposing a new proof of ownership scheme for encrypted cloud data (PoWC). Security analysis and experimental evaluation validate security and efficiency of SEDER, respectively.
A Fast Preprocessing Method for Micro-Expression Spotting via Perceptual Detection of Frozen Frames

This paper presents a preliminary study concerning a fast preprocessing method for facial microexpression (ME) spotting in video sequences. The rationale is to detect frames containing frozen expressions as a quick warning for the presence of MEs. In fact, those frames can either precede or follow (or both) MEs according to ME type and the subject’s reaction. To that end, inspired by the Adelson–Bergen motion energy model and the instinctive nature of the preattentive vision, global visual perception-based features were employed for the detection of frozen frames. Preliminary results achieved on both controlled and uncontrolled videos confirmed that the proposed method is able to correctly detect frozen frames and those revealing the presence of nearby MEs—independently of ME kind and facial region. This property can then contribute to speeding up and simplifying the ME spotting process, especially during long video acquisitions.
An Implementation of Blockchain Decentralized Application for Industrial IoT Scenarios

Blockchain Decentralized Applications (Blockchain Dapps or Dapps in short) have been implemented in many different industrial applications, including recent Industrial Internet-of-Things (IIoT) and Cyber Physical Systems (CPS). In this paper, we propose an architectural design that integrates both a permissioned blockchain network and an IIoT infrastructure for establishing a decentralized IIoT data marketplace. The decentralized IIoT data marketplace allows both data providers and data consumers, to share the available IIoT datasets without any centralized third-party user authorization. First, our proposed Dapp is enabled to connect with a time series database that is considered as a persistent storage of IIoT machine and sensor data. Second, by defining the written data via a smart contract, we ensure that all transactional records between data providers and data consumers are traceable and immutable. Third, we deploy a distributed file system in our proposed architecture to strengthen data privacy by converting the output results into cryptographic hashes. Besides, we demonstrate the designated role-based web user interfaces (UIs) for our proposed Dapp. Finally, we perform an experimental study by utilizing a time series database benchmark tool as an exemplary IIoT system to evaluate the effectiveness and efficiency of our proposed Dapp.
Learning Dynamic Convolutions for Multi-modal 3D MRI Brain Tumor Segmentation

Accurate automated brain tumor segmentation with 3D Magnetic Resonance Image (MRIs) liberates doctors from tedious annotation work and further monitors and provides prompt treatment of the disease. Many recent Deep Convolutional Neural Networks (DCNN) achieve tremendous success on medical image analysis, especially tumor segmentation, while they usually use static networks without considering the inherent diversity of multi-modal inputs. In this paper, we introduce a dynamic convolutional module into brain tumor segmentation and help to learn input-adaptive parameters for specific multi-modal images. To the best of our knowledge, this is the first work to adopt dynamic convolutional networks to segment brain tumor with 3D MRI data. In addition, we employ multiple branches to learn low-level features from multi-modal inputs in an end-to-end fashion. We further investigate boundary information and propose a boundary-aware module to enforce our model to pay more attention to important pixels. Experimental results on the testing dataset and cross-validation dataset split from the training dataset of BraTS 2020 Challenge demonstrate that our proposed framework obtains competitive Dice scores compared with state-of-the-art approaches.
Mobile App for Automation of Observational Instrument for Continuous Emotional Evaluation

Emotions are psychophysiological expressions that are shown before a stimulus or situation, and their research in user experience environments has been growing exponentially. EMODIANA is an instrument for subjective measurement of emotions and their intensity, validated in experimental environments for academic and physical and motor rehabilitation purposes. A mobile APP is developed to automate EMODIANA, applying the ICONIX heavy-agile development methodology. For the implementation as a cross-platform hybrid application, the Ionic framework is used, and for non-relational database cloud storage, Firebase. The architecture proposed for development is based on the proposal by the Microsoft Application Architecture Guide for mobile applications, with functionalities to manage users and participants, apply EMODIANA, generate graph with interaction results, generate reports and make the prediction of the emotional state of the participant.
Looking around in the neighbourhood: Location estimation of outdoor urban images

Visual geolocalisation has remained as a challenge in the research community: Given a query image, and a geo-tagged reference database, the goal is to derive a location estimate for the query image. We propose an approach to tackling the geolocalisation problem in a four-step manner. Essentially, our approach focuses on re-ranking the candidate images after image retrieval, by considering the visual similarity of the candidate and its neighbouring images, to the query image. By introducing the neighbouring images, the visual information of a candidate location has been enriched. The evaluation has been conducted on three street view datasets, where our approach outperforms three baseline approaches, in terms of location estimation accuracy on two datasets. We provide discussions related to, firstly, whether using deep features for image retrieval helps improve location estimation accuracy, and the effectiveness of geographical neighbourhoods; secondly, using different deep architectures for feature extraction, and its impact on estimation accuracy; thirdly, investigating if our approach consistently outperforms the classic 1-NN approach, on two datasets with significant difference in visual elements.
Aspect-Based Passage Retrieval with Contextualized Discourse Vectors

Passage retrieval is the task of retrieving only the portions of a document that are relevant to a particular information need. One application medical doctors and researchers face is the challenge of reading a large amount of novel literature. For example, since the outbreak of Coronavirus disease 2019 (COVID-19), tens of thousands of papers have been published each month about the disease. We demonstrate how we can support healthcare professionals in this exploratory research task with our neural passage retrieval system based on Contextualized Discourse Vectors (CDV). CDV captures the discourse of long documents on sentence level and allows to query a large corpus with medical entities and aspects. Our demonstration covers over 27,000 diseases and 14,000 clinical aspects including symptoms, diagnostics, treatments and medications. It returns passages and highlights sentences to effectively answer clinical queries with up to 65% Recall@1. We showcase our system on the COVID-19 Open Research Dataset (CORD-19), Orphanet and Wikipedia diseases corpora.
Leaking Secrets Through Compressed Caches

We offer the first security analysis of cache compression, a promising architectural technique that is likely to appear in future mainstream processors. We find that cache compression has novel security implications because the compressibility of a cache line reveals information about its contents. Compressed caches introduce a new side channel that is especially insidious, as simply storing data transmits information about the data. We present two techniques that make attacks on compressed caches practical. Pack+Probe allows an attacker to learn the compressibility of victim cache lines, and Safecracker leaks secret data efficiently by strategically changing the values of nearby data. Our evaluation on a proof-of-concept application shows that, on a representative compressed cache architecture, Safecracker lets an attacker compromise an 8-byte secret key in under 10 ms. Even worse, Safecracker can be combined with latent memory safety vulnerabilities to leak a large fraction of program memory.
The Design of Multi Channel P-L Band Digital TR module

This paper implements P-L band digital TR module. The direct generation and sampling of any waveform signal in the DC-2000Mhz range are completed. Through the design of JESD204B protocol and synchronous clock driver, the transceiver synchronization between multiple digital channels is realized. Finally, real-time control of waveform, frequency, phase and other parameters of multi-digital transceiver channel is realized by upper computer software.
Intelligent Improvement Measures for the Broadcasting and Hosting Major based on Multimedia and Virtual Reality

Intelligent improvement measures and solutions for the broadcasting and hosting major based on multimedia and virtual reality is proposed in this paper. With the rapid development of digital video technology and network technology, the application of the multiple media information tends to be integrated and interactive. This paper has the novelties as follows. First, the interference items will seriously affect the negative information data detection results of the multimedia network, and will lead to high false positive rates and also missed detection rates which will enhance the overall performance. Second, the compression coding and decoding technology of the video image data is one of the most important technologies in multimedia information processing, we applied it to the application. In the experiment, we compare the proposed model with the state-of-the-art approaches, and obtained the nice results.
Human Pose Recognition under Cloth-like Objects from Depth Images using a Synthetic Image Dataset with Cloth Simulation

This paper proposes a method of human pose recognition when the body is largely covered by cloth-like objects such as blankets. Such a recognition is useful for robotic monitoring of the elderly and the disabled. Human pose recognition under cloth-like object is challenging due to a large variety of the shape of covering objects. Since we would like to use depth images for addressing privacy and illumination issues, it further makes the problem difficult. In this paper, we utilize computer graphics tools including cloth simulation for generating a synthetic dataset, which is then used for training a deep neural network for body parts segmentation. We achieved around 90% accuracy in synthetic data and show the effectiveness of simulating cloth-like objects in data generation. We also applied it to real data and examined the results for identifying remaining issues.
Stock Price Movement Cross-Predictability in Supply Chain Networks

This paper leverages the structure and connections of firms in a supply chain network and develops a model to predict firm performance. Using large-scale real-world supply-chain data, we build the business network among firms in S&P500 and utilize it to predict a firm’s stock movements by leveraging the performance of its network neighbors. Our approach significantly improves the performance of stock movement prediction compared to traditional features proposed in the finance literature. We are also the first to show that focal firms’ performance can be better predicted from the performance of similar firms located farther away in the network.
On two-weight (linear and nonlinear) codes

We consider q-ary (linear and nonlinear) block codes with exactly two distances: d and d+δ. We obtain, as a necessary condition, a quadratic equation which relates the length n of the code to the remaining parameters. We also obtain upper bounds for the maximum size of such codes by utilizing a useful relation with two-distance spherical codes. Three tables of lower and upper bounds for alphabet sizes q =2, 3 and 4, respectively, are presented as examples.
Deep Learning Algorithms for Human Activity Recognition: A Comparative Analysis

Human activity recognition is considered one of the most promising fields of inquiry in computer science research. The process of interpreting human body gestures to determine human activities has been widely applied in health care and human–computer interaction. Different researchers have employed various methods in this domain, like wearable, device-free, and object tags, to successfully recognize human activities. In this paper, a detailed analysis is made on the processing of HAR sensor data by using two deep neural network models, convolutional and recurrent neural networks, and compared their respective accuracies. The data is utilized to create the appropriate DNN model provided by the wireless sensor data mining (WSDM) lab. The data was collected from 36 people carrying wearable sensors and performing six different activities: (1) walking, (2) sitting, (3) upstairs, (4) downstairs, (5) jogging, (6) standing and performing each activity several times. In the end, we discuss the two approaches (CNN and RNN) based on their activity recognition performance.
THE PROBLEM OF THE EDUCATIONAL ENVIRONMENT DIGITALIZATION IN TERMS OF INFORMATION SOCIETY CONCEPT

Objective of this article is to determine the relationship between the digitalization of education and general process of forming the information society, connected with the technicalization of human life space. The globalization, informatization and virtualization of modern social life are the result of fundamental economic, political and cultural changes. The relationship between man and technics is being actively transformed during of the 19th - 20th centuries. These changes become especially significant in the 21st century. Applied methods include descriptive characteristics of the most significant works, related to the considered problem, as well as hermeneutic and comparative approaches in the analysis of literature. Results of the article reveal that the appearance of cyberspace, reflecting all spheres of social life, brings the formation of the new type of “third wave” man: constantly immersed to informational space person, who is able to perceive the streams of compressed and “clipped” information. The digitalization of education process is the natural consequence of the networked society's development. Conclusion the article concerns the digitalization of education changes the focus of the socialization: access to information and educational communication are not limited to the space of the school, high involvement in the learning process is achieved due to the independent interest and initiative of the student. At the same time, digitalization is accompanied by globalization, standardization and unification of education. The indicated problem is considered by cultural and social philosophical positions.
The use of augmented reality to improve mathematics conceptual understanding of pre-service elementary education teachers

Abstract The purpose of this study was to determine the understanding of mathematics concepts of pre-service elementary education teachers at Universitas Sultan Ageng Tirtayasa (Sultan Ageng Tirtayasa University) through augmented reality learning media. The method used in this study was a quasi-experiment with the design of one group pre-test and post-test. The instrument used in this study was a test of understanding of mathematics concepts on geometry topic in the form of 10 description tests and tested on 20 pre-service teachers (higher education students). The average result of N-gain in pre-service elementary education teachers was 0.48 in the medium category. Based on the results obtained, the use of augmented reality can be used as a learning medium for mathematical concepts. In the learning process, students can easily understand the material taught. The suggestion in this research is that further development of augmented reality media is needed at all levels of education and it needs to be tested in a larger scale in order to know its effectiveness to be used in all levels of education.
Evaluation Method of Teaching Effect Based on Visual Calculation in Classroom Environment

In order to improve the shortcomings of traditional classroom evaluation methods, a teaching effectiveness evaluation method based on computer vision technology is proposed. Attendance is obtained by face detection. The distance measurement function is obtained by using the fitting method to calculate the distance between students in the classroom and the camera. The seat selection distribution is obtained according to the distance and direction analysis and quantitatively evaluated by using the curve fitting method. By using the head posture estimation technology and facial expression recognition technology to identify students' up-or-down state and expressions respectively, the ratios of students' raising their heads and positive emotions are obtained. Finally, a geometric mean function based on attendance, seat selection distribution, head up rate and the proportion of positive emotions, is suggested to evaluate the teaching effect. The experimental results show that the evaluation results of this method are highly consistent with those of teachers and students.
Predicting People Flow for Supporting Facility Management

This paper proposes a novel method to predict people flow in a facility such as a shopping mall for supporting facility management including air-conditioning, cleaning, maintenance, and support for tenant shops or restaurants. Existing studies have attempted to estimate or predict people flow several minutes or hours in the future or only occupancy level. However, facility managers need to know people flow including the number of visitors, their direction of movement, and their average speed up until several days before because they must decide details of air-conditioning, cleaning, and maintenance several days in advance. To support their daily facility management, we propose a novel method to predict the number of moving people and their average speed in each defined direction in a facility the next day or later on the basis of characteristics of past people flow on weekends and weekdays. We evaluated the effectiveness of our proposal using measured data in the shopping mall.
The Application of Cloud Class in the Teaching of Biochemistry

Abstract Interactive cloud class platform assisted instruction is an important supplement of classroom teaching, and it is becoming one of the important forms of new teaching methods. Biochemistry is the most important specialized basic course for biotechnology students, and it is also considered one of the hardest subjects among the curriculum. The current work explores the application of the cloud class in the teaching of the theoretical biochemistry courses. Practical research shows that the application of the cloud class teaching platform can effectively motivate students’ learning potential to the greatest extent. With this tool, teachers and students have come to get keenly a sense of the kind of creativity and advancement of information-based teaching, and the blended learning integrated face-to-face instruction and online teaching provides a new way and method in higher education teaching reforming.
DarkGAN: Night Image Enhancement Using Generative Adversarial Networks

Low light image enhancement is one of the challenging tasks in computer vision, and it becomes more difficult when images are very dark. Recently, most of low light image enhancement work is done either on synthetic data or on the images which are considerably visible. In this paper, we propose a method to enhance real-world night time images, which are dark and noisy. The proposed DarkGAN consists of two pairs of Generator - Discriminator. Moreover, the proposed network enhances dark shades and removes noise up to a much extent, with natural-looking colors in the output image. Experimental results evaluation of the proposed method on the “See In the Dark” dataset demonstrates the effectiveness of the proposed model compared with other state-of-the-art methods. The proposed method yields comparable better results on qualitative and quantitative assessments when compared with the existing methods.