Reviewerly/exHarmony huggingface dataset:
Face Recognition in CCTV Systems

As the technology developed in its peak, human beings highly dependable on its applications. Closed-Circuit Television (CCTV) is one of such technological invention of human which assures the security of the firms and offices in a great extent. Even though CCTV systems are highly useful for the observational purposes, it also possess some requirements. In a circumstantial such as a robbery happened inside a bank the forensic analysis can be done through the footage from the CCTV systems itself. The parameters which influences the identification process are lot such as the camera quality, distance measurements from object to camera, the angle of the camera etc. Here two phases are including in the facial recognition technique. In the first phase we collect different faces of subjects to form the database. In the second phase we extract target face from the CCTV evidence footage and it undergoes image enhancement process to create a good quality output images for the comparison process with the database images.
Reflections on the IEA International Computer and Information Literacy Study 2018

IEA’s International Computer and Information Literacy Study (ICILS) has contributed to understanding the extent to which young people are able to use information and communication technology (ICT) productively for a range of purposes. Those purposes relate to what happens in school and in other environments such as home, society, and future workplaces. This chapter reflects on the contributions of ICILS through the provision of evidence-based descriptions of achievement progressions for each of computer and information literacy (CIL) and computational thinking (CT), and provides an overview of the main themes emerging from the analyses of the data collected by ICILS 2018. These include: a further challenge to the assertion that young people are “digital natives” and discussion the educational consequences accepting or rejecting this assertion, the relationship between CIL and CT including differences in their relationships with gender, evidence of the digital divide in ICILS data, and the ongoing prevalent uses of digital devices as “digital textbooks” in classrooms. The chapter further discusses the need for teachers to be supported to make the most effective use of digital technologies in the classroom and what ICILS data suggest about the conditions within schools that support teachers to do so. Each of these themes offers a perspective for reflecting on the existing ICILS 2018 report, a starting point for further secondary analyses of data released in the ICILS 2018 international database (IDB) and guides early planning for the development of ICILS 2023.
Design of a Credible Blockchain-Based E-Health Records (CB-EHRS) Platform

With the rapid development of electronic health care, the era of medical big data has already emerged. However, in the global electronic health industry environment, one of the significant challenges is that the various medical institutions are independent of one another. Patients, doctors and medical researchers have significant barriers in accessing medical data. As an intervention strategy using blockchain principle, this paper explores the characteristics of blockchain which are applicable to the management of electronic health records (EHRs), and presents a credible blockchain-based electronic health records (CB-EHRs) management platform. A CB-EHRs platform is characterized by decentralization, data tamper-proof, collective maintenance mechanisms, security and credibility. This platform cannot only realize data sharing between medical institutions, but also ensures the privacy of users. This paper introduces the components of the CB-EHRs platformmodel and the implementation principle of its related functions. In addition, this paper also reviews and selects the delegated Byzantine Fault Tolerance (dBFT) consensus mechanism as a viable option for the CB-EHRs platform. Finally, by comparing with the Practical Byzantine Fault Tolerance (PBFT) consensus mechanism and our research, we highlight the potential advantages of our proposed CB-EHRs platform in the medical domain.
Imbalance-Aware Scheduler for Fast and Secure Ring ORAM Data Retrieval

Data encryption can enhance data privacy but can not prevent privacy leakage completely. Side channels such as memory access pattern can also leak critical information in the program, while encryption cannot help when the access addresses are exposed to the attackers. Oblivious RAM (ORAM) was proposed to eliminate the access pattern leakage, and it can be incorporated with the memory controller to obfuscate and reshuffle memory accesses. Tree-based ORAM, such as Path ORAM and Ring ORAM, is a cost-effective ORAM organization, and it obfuscates the memory access pattern by reading and remapping data blocks along the path. Ring ORAM can achieve a better online data retrieval performance with a read protocol which only reads selective blocks along the path, compared to Path ORAM. However, the read path operation in Ring ORAM does not take the implementation of multi-channel memory system into consideration, which yields a potential bandwidth waste and a slower response time. Therefore, in this work, we investigate the root cause of the bandwidth waste and propose a fast and secure scheduler which balances the read path operation for Ring ORAM. Our scheduling schemes can improve the read operation response time as well as the overall system performance. The experimental results show that the read path latency for data retrieval can be reduced by 33% compared to the baseline Ring ORAM implementation.
Power, Area, Speed, and Security (PASS) Trade-Offs of NIST PQC Signature Candidates Using a C to ASIC Design Flow

National Institute of Standards and Technology (NIST) is standardizing post-quantum cryptographic (PQC) algorithms. Most of the PQC algorithms are complex; rendering their hardware modeling, evaluation, and benchmarking challenging. We developed a High-Level Synthesis (HLS) → ASIC flow for fast evaluation of Power, Area, Speed, and Security (PASS) trade-offs of the NIST round 2 PQC algorithms using an industry-standard design flow. In this paper, we discuss this flow and the preliminary results on some of the PQC signature algorithms.
Design of a Flexible Schönhage-Strassen FFT Polynomial Multiplier with High- Level Synthesis to Accelerate HE in the Cloud

Homomorphic Encryption (HE) allows for encrypted data to be sent to, stored, and operated on by untrusted parties without the risk of privacy compromise. The benefits and applications of HE are far reaching, especially in regard to cloud computing. However, current HE solutions require a large number of resource intensive arithmetic operations such as high precision, high degree polynomial multiplication. This work aims to accelerate the multi-precision arithmetic operations used in HE with specific focus on an implementation of the Schönhage-Strassen Fast Fourier Transform (FFT)-based multiplication algorithm. It is planned to be incorporated into a larger HE library of arithmetic functions tuned for High-Level Synthesis (HLS) that enables flexible solutions for hardware/software systems on reconfigurable cloud resources. The developed FFT based polynomial multiplier exhibits flexibility in the selection of HE security parameters facilitating its use in a wide range of schemes and applications. The design yields substantial speedup over the polynomial multiplication functions implemented in the Number Theory Library (NTL) utilized by software based HE solutions.
Cases without Borders: Automating Knowledge Acquisition Approach using Deep Autoencoders and Siamese Networks in Case-Based Reasoning

Finding an ideal text case-base representation for a new domain is important for a CBR [1] system. To do this, the choice of an ideal representation is guided by the domain characteristics and the complexity of its cases. Recently, the explosion of deep learning techniques and other forms of vectorised representations, has provided a new source for case insights. Richer text features can be extracted and used for each case if required. In this paper, we build on recent work in this area and generate richer case representation by automatically acquiring domain knowledge from unstructured sentences. We describe how the Deep Knowledge Acquisition Framework obtains its representation vectors from stemmed words and improving these vectors iteratively, suggesting high quality outputs and relevance to domain experts based on either explicit queries or their past intentions. We evaluate these ideas using two, not related, datasets from the automotive and legal domains respectively. The results show the benefits of combining Autoencoders and Siamese Networks in CBR while achieving better textual data dimensionality reduction, data de-noising and similarity measures.
The Means of Engagement (MOE) Model of the Agreement towards the Enterprise Resource Planning (ERP) Implementation

Faster economic development led to increasingly tight business competition. With the development of increasingly sophisticated technology, these companies compete in leveraging technology to enhance competitive advantage and efficiency of their company's performance compared to its competitors. ERP systems are already commonly used in large companies and companies that develop because it is considered can improve performance and can help the process efficiency of data each process in system performance for flow can be connected. ERP system implementation is not always running smoothly. When the company failed to perform some of the success factors of ERP implementation, then these companies will experience failure in implementation. Companies must have a good strategy to achieve the desired success. Business strategy is done by optimizing the company's internal resources and innovate to face competition with other companies. By applying an ERP system can help companies to keep up with changes in the business that would later show up to the new business requirements. By using the Means of Engagement (MOE) model concept, the company can know the things to be aware of and consider implementing ERP software, so that it can minimize the failures that will occur. There are three levels in the MOE model, namely Adoption, Approval, Acceptance, and Agreement. At the level of Agreement that can be used by the analysis of clustering.
Research Proposal: Reliability Evaluation of the Apache Kafka Streaming System

Apache Kafka is a distributed messaging system with high throughput, high scalability and low latency. It has been widely adopted in enterprise and due to its widespread integration into enterprise-level infrastructures, the research on the reliability of Kafka consumers has become an increasingly important issue. The application scenarios vary from tracking user profiles on a website, server log monitoring, to online bank transfer and online reservation. The main purpose of this research is to evaluate the reliability of Kafka in different application scenarios. Kafka is highly configurable and provides many options to manage reliability strategies. In this research we test the impacts of all kinds of configuration parameters on the reliability of Kafka, including retry strategies and replications of partitions for fault tolerance. The tradeoffs between performance and reliability is another portion of our research, which help users of Kafka using it in an appropriate way.
Usability Testing of Augmented Reality For Food Advertisement Based On Mobile Phone Using System Usability Scale

Internet users globally are kept increasing every year. This phenomenon affects the increasing number of digital advertising because traditional advertising cost much more. One of the digital advertising technology that often used today is Augmented Reality (AR). By using AR customers can interact more interactively with ads. AR can be used to market a variety of products, one of them is a food product. Our research uses Vuforia to make the AR for food advertising. System Usability Scale (SUS) is used to find out whether our system can be used by users. Referring to the results, the average score from SUS is 51 which means our system was not easy to use. Because some user needs to find the marker to make 3D objects appear on the mobile phone.
Cross-Age Face Recognition using Deep Residual Networks

In the area of Artificial Intelligence and Deep Learning, face recognition is one of the demanding areas. Though a majority of research is going on, there is still a scope of improvement in the existing techniques. Our faces change with time when age increases, while the images in our dataset remain the same. We intend to study the accuracy of Residual Network (ResNet) [1] for the purpose of cross-age face recognition. The performance is compared to cross-age reference coding (CARC), Amazon Web Services (AWS) Rekognition and other techniques on the various data set viz., cross-age celebrity dataset (CACD) and a verification subset CACD-VS. ResNet and AWS Rekognition achieved 98.40% and 99.45% accuracy, respectively on the CACD-VS dataset.
A Design of FPGA Acceleration System for Myers bit-vector based on OpenCL

A FPGA-based acceleration system has been designed based on OpenCL framework. The acceleration of Myers bit-vector algorithm in system is achieved by a dynamic programming way, and it has been applied to the high-throughput sequencing technology. The heterogeneous computing between X86 platform and FPGA platform, similar to heterogeneous computing between between X86 platform and FPGA platform, eliminates the bandwidth limitations of data transmission between two platforms with the third-generation PCI Express X8. As the internal data transmission bus in the FPGA, the Advanced Extensible Interface(AXI) bus can make full use of the bandwidth of dynamic random access memory(DRAM), because its clock frequency can reach 240Mhz after optimization and the data-width increases to 512bit. Furthermore, the acceleration system completes a global alignment in one clock cycle in pipeline working mode. These advantages allow this design to achieve 70 million global alignments of short gene sequence per second with only one MBV module.
Summarization of Odia Text Document Using Cosine Similarity and Clustering

Automatic text summarization a subfield of Natural Language Processing (NLP) aims at producing precise and non redundant text aided by machine learning techniques. Using varied methods, especially machine learning techniques has enhanced its performance from different perspectives. The proposed work efficiently utilizes hierarchical clustering by using cosine similarity measure for segregating sentences. The model adopts an extractive method for summarizing Odia text document. It focuses on minimizing redundancy and achieves it through cosine similarity matrix. Though the methods employed are primitive for European languages like English, Odia language which is computationally in a passive state and has complex morphological structure, is a novel work. The results obtained can modestly be considered satisfactory.
Failures of interest

What kind of failure is it to lose interest and detach from a project, empirical scene, or cause? Should we always stay interested in our work and attempt to interest others? Through discussion of two ongoing but perhaps failed projects alongside reflection on various failures over my career, I explore the relationship between affects of failure and the event and relation of interest. Staying with the non-evental dynamics of the ordinary failures that accumulate in the background to all academic lives, I explore how losing interest or not holding interest relates to and expresses the attachments and detachments we have to academic life. Via these reflections, I argue that we should pay attention to those ordinary failures – like losing interest - that don't quite fit with institutionally named and collectively recognised events of failure – a rejected paper or grant, for example.
A Fuzzy Matching based Image Classification System for Printed and Handwritten Text Documents

This article proposes a bi-leveled image classification system to classify printed and handwritten English documents into mutually exclusive predefined categories. The proposed system follows the steps of preprocessing, segmentation, feature extraction, and SVM based character classification at level 1, and word association and fuzzy matching based document classification at level 2. The system architecture and its modular structure discuss various task stages and their functionalities. Further, a case study on document classification is discussed to show the internal score computations of words and keywords with fuzzy matching. The experiments on proposed system illustrate that the system achieves promising results in the time-efficient manner and achieves better accuracy with less computation time for printed documents than handwritten ones. Finally, the performance of the proposed system is compared with the existing systems and it is observed that proposed system performs better than many other systems.
AI for AI: What NLP Techniques Help Researchers Find the Right Articles on NLP

The human progress of the coming years is largely associated with success in the field of artificial intelligence methods. The growth of knowledge in this area is in the nature of an information explosion. Researchers have to spend too much time monitoring a constantly evolving field, filtering out the flow of complex documents and texts that require a deep understanding of machine learning methods and their application in specific areas. In solving this problem, the very methods of machine learning and natural language processing are highly effective. The ability to take into account complex non-linear dependencies allows modern language models to effectively solve the problems of information retrieval, monitoring, facts extraction and further analysis. The article provides an overview of modern natural language processing methods that form the basis of modern text information retrieval systems and semi-automatic compilation of reviews and roadmaps. A review of approaches of text vectorization methods for semantic classification of documents. Known limitations of these techniques are also discussed.
Axial primary halo characterization and deep orebody prediction in the Ashele copper-zinc deposit, Xinjiang, NW China

In this investigation, a total of 404 rock and ore samples were systematically collected from 5 boreholes in two exploration lines of the Ashele copper-zinc polymetallic mining area in Xinjiang. Twelve elements were analyzed to evaluate the potential for deep and periphery prospecting. Subsequently, the concentration zoning and the vertical element zoning sequence of the primary halo were graphically generated. Based on both previous studies and the result of the actual analysis, it was determined that, for Ashele mining area, As, Sb, Ba, and Au are supra-ore halo elements, Pb, Zn, Cu, and Ag (Au) are near-ore halo elements, and Bi, Co, Mo, and Sn are sub-ore halo elements. Factor analysis revealed exhalative-sedimentation mineralization as the first metallogenic stage followed by metamorphic-deformation and hydrothermal superimposed mineralization as the second one. The superposition area of the sub-ore halo and the supra-ore halo and multivariate prospecting information (indicated by the primary halo zoning index, the geochemical parameter, the trilinear diagram of the primary halo, and the factor score map) suggest that there may be blind orebodies in the deep parts of four boreholes. Some geochemical parameters revealed the multiple-periodic and multiple staged overlapping the signatures of the mineralization.
A Convex Variational Approach for Image Deblurring With Multiplicative Structured Noise

The restoration of images corrupted by blurring and structured noise has attracted growing attention in the domains of image processing and computer vision. However, many works only focus on the restoration of the images degraded by blurring and additive structured noise or multiplicative structured noise separately. It is still a challenge and an open problem to restore degraded images with blurring and multiplicative structured noise, simultaneously. In this paper, based on the total variation (TV), the statistical property of the Gamma noise and the maximum a posteriori (MAP) estimator, we obtain a convex variational model to recover blurred images with multiplicative structured noise. Especially, to get this convex model, we reformulate the prior assumption of the images degradation model by division instead of multiplication. For solving this convex model, an effective alternating direction method of multipliers (ADMM) is employed. Numerical experiments are presented to illustrate the effectiveness and efficiency of the proposed model.
A New Forged Handwriting Detection Method Based on Fourier Spectral Density and Variation

Use of handwriting words for person identification in contrast to biometric features is gaining importance in the field of forensic applications. As a result, forging handwriting is a part of crime applications and hence is challenging for the researchers. This paper presents a new work for detecting forged handwriting words because width and amplitude of spectral distributions have the ability to exhibit unique properties for forged handwriting words compared to blurred, noisy and normal handwriting words. The proposed method studies spectral density and variation of input handwriting images through clustering of high and low frequency coefficients. The extracted features, which are invariant to rotation and scaling, are passed to a neural network classifier for the classification for forged handwriting words from other types of handwriting words (like blurred, noisy and normal handwriting words). Experimental results on our own dataset, which consists of four handwriting word classes, and two benchmark datasets, namely, caption and scene text classification and forged IMEI number dataset, show that the proposed method outperforms the existing methods in terms of classification rate.
Learning Deep Features for Giant Panda Gender Classification using Face Images

Giant panda (panda) has lived on earth for at least eight million years and is known as the living fossil. It is also a vulnerable species which requires urgent protection. It is essential to conduct population survey collecting information of their population, density, age structure, and gender ratio so as to design protection schemes and measure their effectiveness. However, it is challenging to accurately and timely obtain gender ratio of pandas because their pelage lacks distinguishable gender patterns and panda is sparsely distributed population in large habitats. All current approaches rely heavily on manual collection of samples in the wild, which are time consuming, costly, or even dangerous. With the widely deployed camera traps, if the gender of pandas can be determined from images, it is possible to monitor panda gender ratio in different regions in real-time. However, no such study was done. In this paper, a deep learning method is developed to study the distinctiveness of panda face for gender classification, in which the largest panda image dataset with 6,549 panda face images collected from 100 male and 121 female pandas is established. The experimental results show that panda faces contain some gender information, although they look very similar to human vision.