Reviewerly/exHarmony huggingface dataset:
Efficient Multi-Client Functional Encryption for Conjunctive Equality and Range Queries

In multi-client functional encryption (MC-FE) for predicate queries, clients generate ciphertexts of plaintexts x 1 , . . ., x n binding with a time period T and store them on a cloud server, and the cloud server receives a function key corresponding to a predicate f from a trusted center and learns whether f (x 1 , . . ., x n ) = 1 or not by running the decryption algorithm on the multiple ciphertexts of the same time period.MC-FE for predicates can be used for a network event or medical data monitoring system based on time series data gathered by multiple clients.In this paper, we propose efficient MC-FE schemes that support conjunctive equality or range queries on encrypted data in the multi-client settings.First, we propose an efficient multi-client hidden vector encryption (MC-HVE) scheme in bilinear groups and prove the selective security with static corruptions.Our MC-HVE scheme is very efficient since a function key is composed of four group elements, a ciphertext consists of O(ℓ) group elements where ℓ is the size of a plaintext, and the decryption algorithm only requires four pairing operations.Second, we propose an efficient multi-client range query encryption (MC-RQE) scheme and prove the selective weak security with static corruptions.Our MC-RQE scheme which uses a binary tree is efficient since a ciphertext consists of O(log D) group elements and a function key consists of O(n log D) group elements where D is the maximum value of a range.
Logic of Fusion

We pursue an extension of the Curry-Howard isomorphism of propositions and types by a correspondence of cut elimination and program fusion. In particular, we explore the repercussions of this extension in generic and transformational programming. It provides a logical interpretation of build fusion, or deforestation, in terms of the inductive and the coinductive datatypes. Viewed categorically, this interpretation leads to the novel structure of paranatural transformations. This is a modified version of functorial polymorphism, that played a prominent role in the work of Andre Scedrov.
Analysis of Vehicle Insurance Data to Detect Fraud using Machine Learning

There are more than thousands of insurance companies in the world which handles large amount of data. Insurance fraud has become the most common among the organizations across the industries. Insurance industry is one among the most growing industries hence fraud detection becomes very important in this current world. Fraud detection can be implemented in various fields like banking, insurance, financial sectors and information security systems. There are many approaches by which fraud can be detected. In this paper, we use various techniques of machine learning for the detection of vehicle insurance fraud. We make use of Random forest and KNN algorithm for the accuracy detection the insurance fraud. The performance calculation is done by calculation of confusion matrix.
MIRNet: Learning multiple identities representations in overlapped speech

Many approaches can derive information about a single speaker's identity from the speech by learning to recognize consistent characteristics of acoustic parameters. However, it is challenging to determine identity information when there are multiple concurrent speakers in a given signal. In this paper, we propose a novel deep speaker representation strategy that can reliably extract multiple speaker identities from an overlapped speech. We design a network that can extract a high-level embedding that contains information about each speaker's identity from a given mixture. Unlike conventional approaches that need reference acoustic features for training, our proposed algorithm only requires the speaker identity labels of the overlapped speech segments. We demonstrate the effectiveness and usefulness of our algorithm in a speaker verification task and a speech separation system conditioned on the target speaker embeddings obtained through the proposed method.
Ubicomp Digital 2020 -- Handwriting classification using a convolutional recurrent network

The Ubicomp Digital 2020 -- Time Series Classification Challenge from STABILO is a challenge about multi-variate time series classification. The data collected from 100 volunteer writers, and contains 15 features measured with multiple sensors on a pen. In this paper,we use a neural network to classify the data into 52 classes, that is lower and upper cases of Arabic letters. The proposed architecture of the neural network a is CNN-LSTM network. It combines convolutional neural network (CNN) for short term context with along short term memory layer (LSTM) for also long term dependencies. We reached an accuracy of 68% on our writer exclusive test set and64.6% on the blind challenge test set resulting in the second place.
Expertise of Projects as an Innovative Method for Evaluating Students’ Professional Competences

The problem of innovative development of the university has become really urgent recently. The purpose of the study is to consider the project expertise as one of the methods for assessing students’ professional competencies. Relevance is due to the need for positive changes in the education system and the search for effective ways to evaluate learning outcomes. Modern approaches to updating education in higher educational institutions are described and considered as competency-based, modular and project-based. The requirements for projects that are used in assessing the professional competencies of students are presented. The procedures for evaluating various types of projects performed by students are described. The results of teaching students the basics of project activities that are subject to examination are indicated. A conclusion that is relevant for developers of evaluation technologies is made about the need to take into account the skills and abilities of project activities in the process of forming professional competencies of students at the University.
Memory Efficient Class-Incremental Learning for Image Classification

With the memory-resource-limited constraints, class-incremental learning (CIL) usually suffers from the "catastrophic forgetting" problem when updating the joint classification model on the arrival of newly added classes. To cope with the forgetting problem, many CIL methods transfer the knowledge of old classes by preserving some exemplar samples into the size-constrained memory buffer. To utilize the memory buffer more efficiently, we propose to keep more auxiliary low-fidelity exemplar samples rather than the original real high-fidelity exemplar samples. Such a memory-efficient exemplar preserving scheme makes the old-class knowledge transfer more effective. However, the low-fidelity exemplar samples are often distributed in a different domain away from that of the original exemplar samples, that is, a domain shift. To alleviate this problem, we propose a duplet learning scheme that seeks to construct domain-compatible feature extractors and classifiers, which greatly narrows down the above domain gap. As a result, these low-fidelity auxiliary exemplar samples have the ability to moderately replace the original exemplar samples with a lower memory cost. In addition, we present a robust classifier adaptation scheme, which further refines the biased classifier (learned with the samples containing distillation label knowledge about old classes) with the help of the samples of pure true class labels. Experimental results demonstrate the effectiveness of this work against the state-of-the-art approaches.
Research on Blended Teaching Mode of College English Listening Class

With the rapid development of educational informatization, blended teaching integrates the advantages of traditional teaching and online teaching, which is more and more widely used in education.English listening has always been a weak link in the process of college English teaching.This paper starts with the current situation of college English listening teaching, analyzes the features and advantages of blended teaching mode, and discusses the new mode and method of college English listening teaching.The blended teaching mode of college English listening is established in order to further improve the quality and effect of college English listening teaching, optimize the learning environment for students, and achieve the best learning effect.
Influence of Students’ Learning Style and Performance in the Comprehensive English Class

The purpose of this research was to improve Chinese university students’ performance in four aspects, namely sense of responsibility, academics in students’ grades, engagement and self-esteem by using student-centered learning approach at a University in China and quantitative and qualitative measurements. Changing students’ learning style was implemented with careful consideration. With that in mind, new English language learning process and teaching model were designed in order to improve the teaching and learning quality. Quantitative measurements was used for data analysis. The research instruments included a set of ten-item questionnaire as well as one open-ended question. In order to obtain the qualitative data, focus group interviews was done in team basis.
Challenges and Considerations in Developing and Architecting Large-Scale Distributed Systems

This paper investigates large-scale distributed system design.It looks at features, main design considerations and provides the Netflix API, Cassandra and Oracle as examples of such systems.Moreover, the paper investigates the challenges of designing, developing, deploying, and maintaining such systems, in regard to the features presented.Finally, the paper discusses aspects of available solutions and current practices to challenges that large-scale distributed systems face.
Exploring Eye-Gaze Behaviors on Emotional States for Human-Computer Interaction (HCI) Using Eye Tracking Technique

Human computer interaction (HCI) is becoming an essential area of study these days. However, most contemporary HCI systems are unable to identify human emotional states and use this information in deciding upon proper actions to execute. To solve this problem, eye tracking has been introduced to record the eye gaze behaviours which can signify the insight of emotions. This manuscript will look into this study by empirically attempt to apply the eye tracking device and method to investigate the relationship of human emotions and eye-gaze behaviour. We explored the pupil size and fixation duration stimulated by film clips of different arousal content using the eye tracking technology. Fifteen students from Malaysian-Japan International Institute of Technology (MJIIT) are chosen. Emotions are analysed by studying the eye gaze behaviours using five emotional video stimuli e.g. Amusement, Joy, Neutral, Sad and Fear. These stimuli are displayed to the subjects to obtain and record their gaze behaviour using the Tobii TX300 eye tracker. The results which are obtained are analysed using statistical ANOVA analysis. The ANOVA analysis shows that the significance, p value less than 0.05 for both fixation duration and pupil dilation which indicates there is significant relationship between eye-gaze behaviour and human emotions.
On Different Types of Negative Concord Items and Their Interpretation in Korean

This paper aims to investigate an idea, proposed by Park (2013) and Tieu & Kang (2014), that Korean has different types of NCIs in terms of feature specification, exemplified into amwu-(N)-to and etten-(N)-to. Based on the fact that amwu-(N)-to, but not etten-(N)-to, appears as a fragment form, the previous studies argue that NCI licensing is an instance of Agree where an interpretable [NEG] feature of an NCI checks off an uninterpretable [NEG] feature of negation. This paper, however, argues that the ability to appear as a fragment answer cannot be a crucial diagnostics to differentiate one from the other, because the wh-based etten-(N)-to also appears as an elided form when associated with the existence of a presupposition. This paper also casts a doubt on whether the Identity Condition in ellipsis is always guaranteed, since a fragment NCI can be interpreted as positive, which is not be restored under the Identity Condition. Finally, this paper brings up a question of whether an uninterpretable [NEG] feature of negation is conceptually plausible, which would stipulate the existence of other kinds of negation.
Safety design concepts for statistical machine learning components toward accordance with functional safety standards

In recent years, curial incidents and accidents have been reported due to un-intended control caused by misjudgment of statistical machine learning (SML), which include deep learning. The international functional safety standards for Electric/Electronic/Programmable (E/E/P) systems have been widely spread to improve safety. However, most of them do not recom-mended to use SML in safety critical systems so far. In practical the new concepts and methods are urgently required to enable SML to be safely used in safety critical systems. In this paper, we organize five kinds of technical safety concepts (TSCs) for SML components toward accordance with functional safety standards. We discuss not only quantitative evaluation criteria, but also development process based on XAI (eXplainable Artificial Intelligence) and Automotive SPICE to improve explainability and reliability in development phase. Fi-nally, we briefly compare the TSCs in cost and difficulty, and expect to en-courage further discussion in many communities and domain.
Penerapan Pembelajaran dengan E-Modul Berbasis Kvisoft Flipbook Maker untuk Mata Kualiah Program Linier

&lt;div&gt;&lt;table cellspacing="0" cellpadding="0" align="right"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td align="left" valign="top"&gt;&lt;p&gt;This study aims to improve the independence of students in learning Linear Program by using e-module learning media linear program based on Kvisoft Flipbook Maker. This research is a Classroom Action Research (CAR). This research was conducted in the mathematics education study program FKIP UAD. The results of this study indicate an increase in student learning independence, this is evidenced by the results of interviews with students who showed positive responses. In addition from the observations also obtained a positive response that is the first cycle the average score of learning independence of 52.52% in the second cycle increased by 62.21% and in the third cycle of 72.21%. As well as an increase in completeness of learning outcomes in the first cycle by 50%, then the second cycle increased by 61.53% and in the third cycle of 76.9%.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;
A native enhanced elastic extension tables multi-tenant database

A fundamental factor of digital image compression is the conversion processes. The intention of this process is to understand the shape of an image and to modify the digital image to a grayscale configuration where the encoding of the compression technique is operational. This article focuses on an investigation of compression algorithms for images with artistic effects. A key component in image compression is how to effectively preserve the original quality of images. Image compression is to condense by lessening the redundant data of images in order that they are transformed cost-effectively. The common techniques include discrete cosine transform (DCT), fast Fourier transform (FFT), and shifted FFT (SFFT). Experimental results point out compression ratio between original RGB images and grayscale images, as well as comparison. The superior algorithm improving a shape comprehension for images with grahic effect is SFFT technique.
Enhancing Disease Prediction on Imbalanced Metagenomic Dataset by Cost-Sensitive

Imbalanced datasets usually appear popularly to many real-world applications and studies.For metagenomic data, we also face the same issue where the number of patients is greater than the number of healthy individuals or vice versa.In this study, we propose a method to handle the imbalanced datasets issues by Cost-sensitive approach.The proposed method is evaluated on an imbalanced metagenomic dataset related to Inflammatory bowel disease to do prediction tasks.Our method reaches a noteworthy improvement on prediction performance with deep learning algorithms including a MultiLayer Perceptron and a Convolutional Neural Neural Network with the proposed cost-sensitive for Metagenome-based Disease Prediction tasks.
Statistical Approach for Text and Non-text Classifier in Off-line Handwritten Document

Hand writing and hand drawing are natural ways to take note. A pen and papers are used to make a note for a long time. In digital era, the notes are often converted into a durable and formal format for further use. Therefore, the conversion application was developed in many fields with many skill such as handwritten recognition, object recognition, object classification, and others. In this paper, we demonstrate a method to classify connected components as flowchart and text. We use the Online Handwritten Flowchart Dataset (OHFD) which contained 419 handwritten flowcharts to benchmark our methodology. The result shown our classification technique get F1-score 77.6%.
A comprehensive study of a similarity criterion in cardiac computerized tomography images enhancement

This research focuses on the study of a particular filter based on a similarity criterion that has been applied to improve the information contained in images acquired using different cardiac imaging modalities. The primary attention of this study is to examine which component of the similarity criterion generates more relevant information useful to increase the medical image quality. In this sense, four case studies are established, first a complete formulation of the similarity criterion is considered, and then three additional cases, representing each component of the criterion; such cases are referred to as full, main, residual1, and residual2, respectively. A score function is used for quantifying and then assessing the impact of each component of the similarity criterion. Such measure is a relation between some full–reference and blind–reference image enhancement measures. A computer generated phantom and a representative clinical dataset (1270 three–dimensional images from 126 patients) are used in a thorough evaluation of the similarity criterion. In general terms of performance of the image enhancement technique, the results of the study reveal that the component residual1 outperforms than the other two components of similarity criterion or its complete formulation.
PENENTUAN KANDUNGAN LOGAM BERAT PADA AIR TANAH DI KECAMATAN KUBU BABUSSALAM, ROKAN HILIR, RIAU

&lt;p class="abstract"&gt;&lt;em&gt;Water is a material that can not be separated from the life activities of living things on earth. One of the water resources that gets the most attention and the most needed is ground water. Ground water is a hydrological cycle that involves several aspects such as bio-geo-physical and socio-cultural.&lt;/em&gt; &lt;em&gt;Heavy metals are pollutants that are most often found in water. This heavy metal has a negative impact on humans who use the water and organisms that are in the water. The purpose of this study was to analyze the content of heavy metals contained in ground water in the District of Kubu Babussalam, Rokan Hilir, Riau. Sampling using random sampling technique. Ground water in Kubu Babussalam Subdistrict has been contaminated by heavy metals such as lead (Pb), copper (Cu), iron (Fe), cadmium (Cd) and chromium (Cr). The Pb content contained in ground water ranges from 0,01-0,06 mg/L, Fe ranges from 1,3-2,2 mg/L while the content of the other three metals still very little and were carried under the LOQ value of the measuring instrument used.&lt;/em&gt; &lt;em&gt;Based on Permenkes 416/MEN.KES/PER/IX/1990, there was one sample point that had Pb metal content above the quality standard, and there were seven sample points that had Fe metal content above the quality standard.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt; &lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;
Education on Research Master and Postgraduate Programs in the Context of the Development of the National Framework of Qualifications in the Field of “Science”

The article analyzes the methodological principles of organizing educational master’s and post-graduate programs for the preparation of teachers and researchers taking into account the requirements of new professional standards. The article analyzes the programs of training scientific and pedagogical personnel for universities that implement scientific and personnel policies in the 4: 0 University model (research / entrepreneurial university) in the logic of the state Strategy for scientific and technological development, in the logic of ensuring the development of global world-class research and educational centers. The organizational principles of educational programs of the new generation are described, implemented in the form of scientific and production cooperation, integration of research, teaching and management activities to accelerate the cycle of knowledge update and to create on the basis of new knowledge as a result of research projects — entrepreneurial projects of universities. The article analyzes the possibility of organizing a multi-disciplinary research master’s and post-graduate programs. Organizational and methodological principles for achieving competency-based results in the organization of research master’s and postgraduate programs are characterized. The article reveals how the formation of a new system of legal norms, social relations and organizational mechanisms initiates the implementation of new approaches to the development of scientific and scientific-pedagogical personnel and the development of a new type of knowledge society culture.