Reviewerly/exHarmony huggingface dataset:
Auto-view contrastive learning for few-shot image recognition

Few-shot learning aims to recognize new classes with few annotated instances within each category. Recently, metric-based meta-learning approaches have shown the superior performance in tackling few-shot learning problems. Despite their success, existing metric-based few-shot approaches often fail to push the fine-grained sub-categories apart in the embedding space given no fine-grained labels. This may result in poor generalization to fine-grained sub-categories, and thus affects model interpretation. To alleviate this problem, we introduce contrastive loss into few-shot classification for learning latent fine-grained structure in the embedding space. Furthermore, to overcome the drawbacks of random image transformation used in current contrastive learning in producing noisy and inaccurate image pairs (i.e., views), we develop a learning-to-learn algorithm to automatically generate different views of the same image. Extensive experiments on standard few-shot learning benchmarks and few-shot fine-grained image classification demonstrate the superiority of our method.
Neo4j Graph Database Implementation for LinkedIn

The presented article mainly circumspect the idea for the use and implementation of graph database in the most social media of today. Currently many companies are using neo4j graph database for their workouts for data management. We shall stick with social media for this particular paper. We have used neo4j graph database for maintaining data of LinkedIn user and pages in a very systematic manner. Facebook and Twitter do currently use graph databases so we thought of implementing the same for LinkedIn.
Token Sequence Labeling vs. Clause Classification for English Emotion Stimulus Detection

Emotion stimulus detection is the task of finding the cause of an emotion in a textual description, similar to target or aspect detection for sentiment analysis. Previous work approached this in three ways, namely (1) as text classification into an inventory of predefined possible stimuli (“Is the stimulus category A or B?”), (2) as sequence labeling of tokens (“Which tokens describe the stimulus?”), and (3) as clause classification (“Does this clause contain the emotion stimulus?”). So far, setting (3) has been evaluated broadly on Mandarin and (2) on English, but no comparison has been performed. Therefore, we analyze whether clause classification or token sequence labeling is better suited for emotion stimulus detection in English. We propose an integrated framework which enables us to evaluate the two different approaches comparably, implement models inspired by state-of-the-art approaches in Mandarin, and test them on four English data sets from different domains. Our results show that token sequence labeling is superior on three out of four datasets, in both clause-based and token sequence-based evaluation. The only case in which clause classification performs better is one data set with a high density of clause annotations. Our error analysis further confirms quantitatively and qualitatively that clauses are not the appropriate stimulus unit in English.
Teaching Speaking by Using Talking Stick Technique

The objective of this study is to figure out whether the use of Talking Stick Technique improves  the second year students of SMPN 14 Banda Aceh in speaking. The population of this study was all the second grade students in this school, and the sample of this study was the VIIIA class which consisted of 30 students. The writer applied quantitative method with one group pre-test and post-test design, in which the students were given oral tests before and after the teaching treatment took place as part of data collection. The writer adopted the procedure of Talking Stick technique by Lie (2008) in teaching speaking for the students. After the treatment and the tests were accomplished, it showed that the students had made better change in their speaking ability. It was proven by the increase of the fluency aspect which some students could reach the category of very good , where no one could reach this category in post-test .Where the mean score of the pre-test was 55.93 then increased in the post-test to become 69.76. Furthermore, the statistic also showed that t-test was 13.83 and t-table 2.045. As the t-test score was higher than t-table, it indicated that the students in the experimental class who were taught by using Talking Stick Technique got better improvement in their speaking skill.
Introduction to Keyboard Music Practice in the Early Music Era – Principles and Their Practical Implementation

This study focuses on the rich production from the late Renaissance to the Baroque period of early music for keyboard. During the performance of these works, because of the less than reliable sources, a number of problems arise, such as manuscript distribution, and the initial teething problems of printing. Sheet music and its interpretation add another trap for present-day performers for without knowledge of the interpretation practice of that era (articulation, phrasing, agogic, accentuation, ornamentation, fingering, pedalling) coupled with inadequate tools an incorrect style presentation is born. This study provides specific musical examples for the practical implementation of these principles. Keywords: early music, fingering, pedalling, articulation, ornamentation
Design of Parallel Sorting System Using Discrete-Time Neural Circuit Model

Abstract A parallel sorting system based on discrete-time K- Winners-Take-All (KWTA) neural circuit (NC) model is designed. The system is described by a set of difference equations and by step functions. The KWTA NC model of discrete-time is given by difference equation and by step functions. Corresponding functional block-diagrams of the system are presented. The upper limit on the number of iterations required to achieve a convergence of the sorting process to the steady state is defined. The system does not need a knowledge of range of input data change. In order to use the system, the minimal difference between the inputs should be known. The system is suitable for processing unknown inputs of finite values located in arbitrary unknown finite range. The system is characterized by arbitrary finite resolution of inputs, high speed, moderate computational complexity and complexity of software implementation. The results of computer simulations illustrating efficiency of the system are provided.
A modified algorithm of the latent semantic analysis for text processing in the Russian language

Abstract The paper presents a methodology for analyzing texts in the Russian language. The methodology is based on the Latent Semantic Analysis (LSA) algorithm. A number of disadvantages of the classical method are considered, and modification methods of extracting N-grams from the text are proposed. The modified method allows one to reduce a number of extracted N-grams and an increasing the meaningfulness of the retrieved collection in comparison with a standard method. The reduction of the collection size leads to a reduced dimension of the TF-IDF matrix and accelerated the execution of the SVD method. The advantages of the developed machine learning algorithm are demonstrated on simple sentences. Owing to discussed ideas it becomes possible to effectively parallelize the text processing at the lemmatization step.
Dynamic Backdoor Attacks Against Deep Neural Networks

Current Deep Neural Network (DNN) backdooring attacks rely on adding static triggers (with fixed patterns and locations) on model inputs that are prone to detection. In this paper, we propose the first class of dynamic backdooring techniques: Random Backdoor, Backdoor Generating Network (BaN), and conditional Backdoor Generating Network (c-BaN). Triggers generated by our techniques have random patterns and locations. In particular, BaN and c-BaN based on a novel generative network are the first two schemes that algorithmically generate triggers. Moreover, c-BaN is the first conditional backdooring technique that given a target label, it can generate a target-specific trigger. Both BaN and c-BaN are essentially a general framework which renders the adversary the flexibility for further customizing backdoor attacks. We extensively evaluate our techniques on three benchmark datasets and show that our techniques achieve almost perfect attack performance on backdoored data with a negligible utility loss. More importantly, our techniques can bypass state-of-the-art defense mechanisms.
EDUCATIONAL DISCIPLINE "PHILOLOGY IN THE SYSTEM OF CONTEMPORARY HUMANITARIAN KNOWLEDGE": SPECIFICITY OF TEACHING IN A FOREIGN AUDIENCE

THIS ARTICLE IS ABOUT EDUCATIONAL DISCIPLINE PHILOLOGY IN THE SYSTEM OF CONTEMPORARY HUMANITARIAN KNOWLEDGE: SPECIFICITY OF TEACHING IN A FOREIGN AUDIENCE
Factors Affecting Computer Science Research Productivity and Impact in Nigeria: A Bibliometric Evidence

Computer science is a burgeoning research field and has the potential to accelerate the rate of industrialisation and subsequently, economic development. Using bibliometric data obtained from Scopus, this study employed a 15-year bibliometric analysis to highlight Nigeria’s productivity and impact trends in the computer science research landscape. Our findings are summarised as follows: First, Nigeria’s computer science research contribution and citations are meager in comparison to the global output. Secondly, international collaboration is generally weak as most collaborations are national in scope. Third, Nigeria’s computer science-related research is published in low-quality outlets, as Scopus has discontinued the indexing of most of the outlets. Although, the publication growth trend is encouraging, the volume and impact of computer science-related research can improve significantly with the conduct of more quality researches that facilitated by strong international collaborations, and published in very high-quality outlets.
Development of the Student’s Educational Independence in the Course of Designing His Individual Educational Route

The authors of the article substantiate the relevance of expanding the term “educational independence” to the concept of “educational&#x0D; independence”, which is proposed for use in primary school didactics. Educational independence, in contrast to educational independence, is not limited to educational situations, but also manifests itself in arbitrary cognitive activity, which meets modern requirements for learning outcomes, regardless of its period. However, when characterizing educational independence, it is necessary to take into account the individual capabilities of children of primary school age. As an effective means of developing educational independence, the article considers the construction of an individual educational route for a younger student, analyzes the process of its development using the Moodle distance learning system, which includes four stages: diagnostic, design, technological and evaluation. The sequence of joint actions of a teacher and a student to draw up a map of his individual educational route within the framework of a separate academic subject is given.
Coverless Information Hiding Algorithm Based on Image Classification

In order to improve the data embedding capacity and the communication efficiency of coverless information hiding algorithm, addressing the advantages of semi-structured coverless information hiding algorithm，this paper introduces a semi-structured coverless information hiding algorithm based on the behavioral habits of social platforms. The specific idea of the algorithm is to build a one-to-one mapping relationship between icons and secret messages in a small icon library. According to certain principles，some small icons are montaged a picture, the secret information can be expressed by the splicing picture, and the transmission of secret messages is realized by delivering the spliced pictures. In order to improve the recognition rate of small icons and the anti-interference ability of the whole hidden communication system, convolutional neural network is also introduced to train and classify the icons in the icon library, and the interference samples are introduced as training samples set. The experimental results show that the algorithm has good anti-attack ability and the hiding capacity can be improved, and therefore, the algorithm can be used in covert communication. Keywords:  deep learning,  image classification,  behavioral habits,  steganography,  coverless information hiding
Design of an IoT-Based Mountaineering Team Management Device Using Kalman Filter Algorithm

The heart rate and body temperature for the mountaineering user is very import to detect their life risk factor. Amidst the emergence of wearable and Internet of Things (IoT) devices, exercise- and leisure-related wearable devices have exhibited the most growth in popularity. While many exercise-related wearable devices are available, few have been designed for group interactions. This paper proposes an IoT-based mountaineering team management device to effectively assist mountaineering guides in leading mountaineering teams. The device can monitor the real-time physiological status and coordinate of each team member, and uploads the information to the cloud service platform via the fourth-generation (4G) mobile Internet. We used an unscented Kalman filter (UKF) to reduce the data influence of motion since the user’s heartbeat and temperature are unstable during the motion of mountaineering. When an abnormal event occurs, the device allows the guide to immediately acquire the real-time information of each member. If accidents occur or team members become lost, the device enables quickly locating the lost members. Additionally, the device can be used to make announcements to all team members. The proposed device can immediately and effectively assist mountaineering guides managing mountaineering teams, thereby improving mountaineering safety.
A Hybrid Image Processing Approach to Enhance Signal to Noise Ratio of Carbonate Micro Ct-Images

Summary Pore network extraction is mostly used for constructing digital cores and computing complex rock properties. However, this method strongly depends on the quality of CT-scanning, which can vary due to several factors (e.g., low X-Ray radiation exposure time). The segmentation of these images needs several processing steps to obtain a segmented image. In this study, de-noising filters such as Non-local means, Gaussian, and Median filters are applied to the acquired CT-images to reduce noise. The wave transformation equation is used to extract a homogeneous image. To remove the shadow effect from images, we used the Sobel algorithm to detect edge pixels first and assign them to pore or grain phases based on a threshold value. All codes are implemented in Python. Applying the above filters to a micro CT-image of a carbonate rock sample shows that the proposed method successfully decreased the influence of low X-Ray radiation time. Among the applied filters, the Gaussian filter provides acceptable results during the noise removal process. Implementing the wave transform equation makes the CT images homogeneous enough that they could be segmented by the simple Otsu thresholding. The porosity of the extracted network is also validated with that of the conventional methods.
COMPUTER ASSISTED DIAGNOSIS SYSTEM FOR MAMMOGRAM IMAGE ANALYSIS- A SYSTEMATIC REVIEW

Cancer is the mainly cruel disease and it is the prime target to cure the disease based on new scientific invention. The early detection system is very useful to prevent and cure the cancer disease completely. Too many techniques and methods are available for the detection of cancer in world wide. Breast cancer is the most affected cancer disease among women. Screening through mammography is the best methodologies for early detection breast cancer and easy way to cure the disease based on the better treatment implementation. Many scientific investigators and researchers are presented their research analysis in the area of early detection of breast cancer. In this paper, mainly discussed about the existing breast cancer detection techniques and swarm intelligence based implementation. Several concepts and domains are discussed in the early detection of breast cancer. The main topics include soft Computing, neural networks, swarm intelligence based optimization and Image processing etc.
Querent Intent in Multi-Sentence Questions

Multi-sentence questions (MSQs) are sequences of questions connected by relations which, unlike sequences of standalone questions, need to be answered as a unit. Following Rhetorical Structure Theory (RST), we recognise that different “question discourse relations” between the subparts of MSQs reflect different speaker intents, and consequently elicit different answering strategies. Correctly identifying these relations is therefore a crucial step in automatically answering MSQs. We identify five different types of MSQs in English, and define five novel relations to describe them. We extract over 162,000 MSQs from Stack Exchange to enable future research. Finally, we implement a high-precision baseline classifier based on surface features.
FAOD: Fast Automatic Option Discovery in Hierarchical Reinforcement Learning

The hierarchical reinforcement learning framework breaks down the reinforcement learning problem into subtasks or extended actions called options in order to facilitate its resolution. Different models have been proposed where options were manually predefined or semi-automatically discovered. However, the automatic discovery of options has become a real challenge for research in hierarchical reinforcement learning and the new proposed approaches are very greedy in learning time or space. Thus we opt for a faster and less consuming approach. In this paper we propose an automatic option discovery method for hierarchical reinforcement learning, that we call FAOD (Fast Automatic Option Discovery). We take inspiration from robot learning methods to categorize the sensorimotor flow during navigation. Here, the agent moves along the walls to discover the rooms’ contour, closed spaces, doors and bottleneck regions to define terminate states for options. The FAOD method is evaluated on different classical maze problems, demonstrating fast and promising results.
A novel approach to data integrity auditing in PCS: Minimising any Trust on Third Parties (DIA-MTTP)

Data Integrity Auditing (DIA) is a security service for verifying the integrity of outsourced data in Public Cloud Storage (PCS) by users or by Third-Party Auditors (TPAs) on behalf of the users. This paper proposes a novel DIA framework, called DIA-MTTP. The major novelty of the framework lies in that, while providing the DIA service in a PCS environment, it supports the use of third parties, but does not require full trust in the third parties. In achieving this property, a number of ideas also have been embedded in the design. These ideas include the use of multiple third parties and a hierarchical approach to their communication structure making the service more suited to resource-constrained user devices, the provision of two integrity assurance levels to balance the trade-off between security protection levels and the costs incurred, the application of a data deduplication measure to both new data and existing data updates to minimise the number of tags (re-)generated. In supporting the dynamic data and deduplication measure, a distributed data structure, called Multiple Mapping Tables (M2T), is proposed. Security analysis indicates that our framework is secure with the use of untrusted third parties. Performance evaluation indicates that our framework imposes less computational, communication and storage overheads than related works.
An Efficient Identity Based Encryption in Cloud Computing with Outsourced Revocation

Identity-Based Encryption (IBE) which simplifies the public key and certificate management at Public Key Infrastructure (PKI) is an important alternative to public key encryption. However, one of the main efficiency drawbacks of IBE is the overhead computation at Private Key Generator (PKG) during user revocation. Efficient revocation has been well studied in traditional PKI setting, but the cumbersome management of certificates is precisely the burden that IBE strives to alleviate [2]. It aiming at tackling the critical issue of identity revocation, we introduce outsourcing computation into IBE for the first time and propose a revocable IBE scheme in the server-aided setting. Our scheme offloads most of the key generation related operations during key-issuing and key-update processes to a Key Update Cloud Service Provider, leaving only a constant number of simple operations for PKG and users to perform locally [3]. This goal is achieved by utilizing a novel collusion-resistant technique: we employ a hybrid private key for each user, in which an AND gate is involved to connect and bound the identity component and the time component [4]. Furthermore, we propose another construction which is provable secure under the recently formulized Refereed Delegation of Computation model. Finally, we provide extensive experimental results to demonstrate the efficiency of our proposed construction. In public key encryption every user must have a pair of keys, public key and private key, for encrypting and decrypting messages. An Identity-based encryption (IBE) eliminates the need for a Public Key Infrastructure (PKI). IBE uses the human intelligible identities (e.g., unique name, email address, IP address, etc) as public keys [5]. The sender using IBE encrypts message with the receivers’ identity rather than looking for receivers’ public key and corresponding certificate. Accordingly, receiver decrypts ciphertext using private key associated with the corresponding identity [6]. The private keys of users are obtained from a trusted third party called as Private Key Generator (PKG). The motivation of this paper is to study and review an efficient and secure Identity based encryption scheme with outsourced revocation for cloud computing [7].
APLIKASI TERDISTRIBUSI MERUPAKAN SALAH SATU SOLUSI PENYAMPAIAN INFORMASI

Abstract Internet as a global computer network has proved easier for users to obtain information and to communicate. Users can send and retrieve files, send messages, chat directly or distribute or retrieve information. To disseminate information through the Internet, there are some things that need to be addressed: the dissemination of information is up to date, visualize good data, the ease of accessing data and information. Thus required the selection of appropriate technologies for the results efficiently and effectively.