Reviewerly/exHarmony huggingface dataset:
Critical Analysis of Data Storage Approaches

The requirement of applications to change their structure and schema, in recent times has been more than it has been in the past. With data being in big volumes, high level of sparseness and different varieties, the uncertainty of having a predefined schema calls for inventing and coming up with an effective way to handle data. Not only is the problem restricted to modification of the schema, it also penetrates the data stored within the schema, and the queries posed on the schema. This necessitates the need for having a crystal-clear understanding of how and where to store data depending on its kind. This study aims at critically analyzing data storage approaches by considering various parameters such as schema evolution, sparseness and query execution time since state of the art applications that are up and coming demand data schemas that are populated sparsely, evolving continuously and are rapid in responding to queries. For this, we have considered both Schema-based and Schema-less Techniques.
Classification of Zingiber Plants Based on Stomate Microscopic Images Using Probabilistic Neural Network (PNN) Algorithm

Abstract Herbal plants are medicinal plants included in the first choice for medicinal ingredients for rural residents. Herbal plants such as ginger and lime are plants that are often used and have plants that are similar in level. One way to identify these types of plants is through stomata microscopic imagery. As for the inspection that is still done manually by the pharmacists, the manual inspection takes a long time and misidentification may occur because there are some similarities with direct viewing, so a method is needed to identify the types of herbal plants based on stomata microscopic images automatically and to improve accuracy in the identification process. The method proposed in this study is Probabilistic Neural Network for the identification of herbal plant types based on stomata microscopic images and the algorithm used to identify these plant types Probabilistic Neural Network (PNN). Before the identification stage, the image will go through three stages, namely preprocessing, segmentation and feature extraction using the Gray level co-occuration matrix method. After testing using 20 microscopic image data of ginger and bitter ginger stomata. It was concluded that the proposed method has the ability to identify types of herbal plants with an accuracy percentage of 90 %.
THE USE OF FIVES STRATEGY TO TEACH READING COMPREHENSION FOR ELEVENTH GRADERS

One of the objectives that should be developed in teaching is reading comprehension skills. In fact, many students still have a lot of problems with reading. Therefore, teachers should use specific strategies in teaching reading comprehension to resolve students’ difficulties. The purpose of the research was to find out whether or not it is significantly effective to teach reading comprehension by using the FIVES strategy for eleventh-grade students of SMA Negeri 2 Lubuklinggau. This research applied the pre-experimental method with one group pre- and post-test only. The population of the study was all of the eleventh-grade students of SMA Negeri 2 Lubuklinggau. Cluster random sampling was taken to take the sample class. Class XI MIA 4 was chosen as the sample class. The data was collected through a reading test consisted of 20 items of multiple-choice form. The results of this research were: 1) the average score in the pre-test was 61.81, and the post-test was 78.06, 2) the result of the Paired t-test was 10.167, which higher than t-table (1.689) at the significant level of 5% with df 35. Therefore, the alternative hypotheses (Ha) was accepted. It means that it was significantly effective to teach reading comprehension by using the FIVES strategy for eleventh-grade students of SMA Negeri 2 Lubuklinggau.
The Financial Derivative Ecosystem is Old - Decentralized Ledger Technology is its Fountain of Youth

Financial derivatives are a crucial part of our global financial infrastructure. They serve as effective hedging tools and improve economic development, but conversely are complicated and subject to heavy regulation. Decentralized Ledger Technology (DLT) enables a distributed, trustworthy ledger that stores encrypted information. It could enable frictionless, transparent, financial derivative processing by creating an inherently trustworthy ecosystem that eliminates the need for transaction verification. This paper discusses how different DLT protocols could facilitate financial derivatives. We then present a protocol for facilitating financial derivatives using Stellar's DLT protocol. Assessing our protocol shows that it processes derivatives efficiently with complete transparency. Comparing our DLT based derivatives to traditional derivatives affirms our belief that DLT enables an increase in transparency and reduction of market friction. However, we also found that DLT based derivatives have increased security and counterparty risk. Overall, if the developer mitigates the risks associated with DLT, our protocol could enable a level 3 blockchain application to improve the financial derivatives ecosystem significantly.
Design of Beidou timing module

Abstract The Beidou timing module receives the Beidou Satellite Information, calculates the time and positioning information, transmits the time and timing information through various external interfaces, and has the function of short message forwarding. This paper presents a system design scheme for the Beidou time-keeping module, which uses adaptive filter switching technology to achieve accurate and stable time-keeping results, by automatically switching synchronous clock source (Beidou, IRIG-B (DC), internal high-stability crystal Oscillator) to complete time service, Beidou module with seamless switching, short-circuit protection, fault detection and diagnosis, health management and other functions.
QUIXO is EXPTIME-complete

QUIXO is a two-player strategic board game played on a 5×5 game board. Players alternately move cubes on the game board, and the player who first made a line of five cubes with the player's symbol wins the game. In this paper, we prove EXPTIME-completeness of deciding the winner on generalized QUIXO played on an n×n game board.
The Teaching Model Reform of Advanced Financial Accounting Course Based on Quality Model of Computer Aided Technology under OBE Teaching Concept

Abstract Achievement education (OBE) is a hot topic in college education research.It is an educational concept that organizes, implements and evaluates education based on students’ expected learning outcomes.It is of great practical significance to study the teaching mode reform of higher financial accounting course under the concept of OBE.This paper first introduces the teaching connotation of "advanced financial accounting" under the OBE teaching mode, then analyzes the reconstruction of "advanced financial accounting" under the OBE teaching mode, and finally puts forward the reform strategy of "advanced financial accounting" teaching mode based on the OBE teaching concept.
Entity perception of Two-Step-Matching framework for public opinions

Entity perception of ambiguous user comments is a critical problem of target identification for huge amount of public opinions. In this paper, a Two-Step-Matching method is proposed to identify the precise target entity from multiple entities mentioned. Firstly, potential entities are extracted by BiLSTM-CRF model and characteristic words by TF-IDF model from public comments. Secondly, the first matching is implemented between potential entities and an official business directory by Jaro–Winkler distance algorithm. Then, in order to find the precise one, an industry-characteristic dictionary is developed into the second matching process. The precise entity is identified according to the count of characteristic words matching to industry-characteristic dictionary. In addition, associated rate (global indicator) and accuracy rate (sample indicator) are defined for evaluation of matching accuracy. The results for three data sets of public opinions about major public health events show that the highest associated rate and accuracy rate arrive at 0.93 and 0.95, averagely enhanced by 32% and 30% above the case of using the first matching process alone. This framework provides the method to find the true target entity of really wanted expression from public opinions.
Filtering Distributed Information to Build a Plausible Scene for Autonomous and Connected Vehicles

To make their decisions, autonomous vehicles need to build a reliable representation of their environment. In the presence of sensors that are redundant, but not necessarily equivalent, that may get unreliable, unavailable or faulty, or that may get attacked, it is of fundamental importance to assess the plausibility of each information at hand. To this end, we propose a model that combines four criteria (relevance, trust, freshness and consistency) in order to assess the confidence in the value of a feature, and to select the values that are most plausible. We show that it enables to handle various difficult situations (attacks, failures, etc.), by maintaining a coherent scene at any time despite possibly major defects.
Literacy Culture of Islamic Education Teachers in Senior High School of Bengkulu City

The present article is aimed at revealing and analyzing the literacy culture implemented by Islamic education (PAI) teachers in Bengkulu city. This study explores and analyzes the problem qualitatively. The method used in collecting data is deep observation of 37 SMA(s) in Bengkulu city. In addition, interviews and surveys were also used by distributing questionnaires to representatives of PAI teachers from 37 SMA/SMK when they were invited to attend a workshop on enhancing literacy culture for PAI teachers. After collecting the data, it was analyzed with the Miles and Huberman style flow model, namely data collection, data reduction: the presentation of data in the form of a percentage of indicators of low reading and percentage of written papers made by PAI teachers; and conclusions. The results showed that literacy culture of PAI teachers, in the context of the habit of reading scientific books and writing scientific papers, was a relatively low. This is influenced by the lack of practice and habit of writing scientific papers such as PTK. The use of digital media such as e-book applications, e-learning, and google shcolar for learning is also low. However, teachers always tell students to visit the library to read books when assignments are given. It was followed that the literacy culture of PAI teachers in Bengkulu city is quite good, but the utilization of digital literacy is still low.
A Recommendation Approach Based on Bayesian Networks for Clone Refactor

Reusing code fragments by copying and pasting them with or without minor adaptation is a common activity in software development. As a result, software systems often contain sections of code that are very similar, called code clones. Code clones are beneficial in reducing software development costs and development risks. However, recent studies have indicated some negative impacts as a result. In order to effectively manage and utilize the clones, we design an approach for recommending refactoring clones based on a Bayesian network. Firstly, clone codes are detected from the source code. Secondly, the clones that need to be refactored are identified, and the static and evolutions features are extracted to build the feature database. Finally, the Bayesian network classifier is used for training and evaluating the classification results. Based on more than 640 refactor examples of five open source software developed in C, we observe a considerable enhancement. The results show that the accuracy of the approach is larger than 90%. We believe our approach will provide a more accurate and reasonable code refactoring and maintenance advice for software developers.
Exploring the usability and UX toolkit

This chapter places usability testing into the big picture of user-centered design. It presents the UX toolkit according to the use of tools before, during, and after product launch. The phases of research move through analysis, design, implementation, and deployment, with UX tools to support each phase. A brief discussion of some of the tools, including site visits, card sorting, diary studies, and field testing, follows. A full discussion of heuristic evaluation and expert review is included. The chapter ends with a comparison of the two most common tools—usability testing and heuristic evaluation.
Future Trends in Voice User Interfaces

With the use of voice user interfaces (VUIs) only expected to grow and dominate in the future, this paper will explore the future trends and predictions for voice user interfaces, as well as the changing demands for users. Trends that will be discussed include the increase in personalized voice interface experiences and greater integration of voice user interfaces into our everyday devices. The predicted shift toward voice search in the future from text search will also be discussed. Finally, the use of voice device channel notifications, advertising through VUIs, and the need for better security of voice user interfaces to protect our personal information will be discussed.
WhiteNet: Zero-Day Phishing Website Detection by Visual Whitelists

Phishing websites are still a major threat in today's Internet ecosystem. Despite numerous previous efforts, black and whitelisting methods do not offer sufficient protection, in particular against zero-day phishing attacks. This paper contributes WhiteNet, a new similarity-based phishing detection framework, based on a triplet network with three shared Convolutional Neural Networks (CNNs). WhiteNet learns profiles for websites in order to detect zero-day phishing websites by a whitelist. We furthermore present WhitePhish, the largest dataset to date that facilitates visual phishing detection in an ecologically valid manner. We show that our method detects zero-day pages and outperforms previous visual similarity phishing detection approaches by a large margin while being robust against a range of evasion attacks.
A Genesis of an Effective Clustering-Based Fusion Descriptor for an Image Retrieval System

The tremendous advancements in digital technology pertaining to diverse application areas like medical diagnostics, crime detection, defense, etc., have led to an exceptional increase in the multimedia image content. This bears an acute requirement of an efficacious recovery system to cope up with human demands. The content-based is among the renowned retrieval systems which uses color, texture, shape, edge and other spatial information to extract basic image features. Here, in this paper to enhance the performance of the image retrieval system, a unique and unexcelled color fusion descriptor which combines color moment and color histogram is being proposed. A hybrid feature vector (HFV) is formed after combining features of these two color techniques, and this HFV is given as input to the clustering algorithm. This clustering algorithm performs an efficient class prediction of the given query image by the user. This clustering-based system is also very effective in solving issues related to retrieval time of desired images. Various benchmark datasets like Corel-1K, Corel-5K, Corel-10K and COIL-100 have been tested on the proposed system, and average precision of 91.35%, 77.6%, 71.5% and 97.7% has been obtained, respectively, which is comparatively higher than many state-of-the-art-related techniques.
DEVELOPING STUDENTS� READING SKILL THROUGH MAKING MULTIMODAL INFERENCES

This present study investigated the development of students� reading skills through making multimodal inferences. Moreover, the students� difficulties in making multimodal inferences were explored. This study applied a classroom action research by involving twenty students studying at English education program in West Java. Three instruments covering reading tests of making multimodal inference, classroom observations, and questionnaire were conducted as data collections of this study. The results of this study indicate that the students� reading skills improved significantly after making multimodal inferences. In the first cycle, sixty three percent of all students achieved the score more than seventy. Meanwhile, in the second cycle, eighty five percent of all students reached the score more than eighty-seven. It is reflected on the data gained from reading tests, classroom observations, and questionnaire. The findings show that almost all students achieved meaningful progress of reading skills through making multimodal inferences that they were able tomake inferences visually and verbally by using the clues of the texts and integrating it with their background knowledge, creating mental images in their minds, distinguishing between literal and implied meanings, implementing some reading strategies before, during, and after reading the text, and manifesting their inferences visually and verbally on Canva application. Thus, they were able to master micro and macro skills of reading comprehension and comprehend the multimodal texts completely.However, in making multimodal inference, some students got troubles in integrating visual and verbal aspects because of incomplete background knowledge in their minds. Therefore, the manifestation of their multimodal inferences was presented partially.
On Certain Classes of Irregular Languages

Objective of this paper is to prove certain regularity and irregularity conditions in languages determined by a set of integer vectors called distribution vectors of the number of letters in words over a finite alphabet. Each language over the finite alphabet uniquely determines its proprietary set of distribution vectors and vice versa, i.e., each set of vectors is associated with a language having this set of distribution vectors. A single necessary condition for the language regularity was considered associated with the concept of Z+-plane (sets of points with non-negative integer coordinates lying on a plane in the affine space). The condition is that a set of distribution vectors determined by any regular language could be represented as a finite union of the Z+-planes. Certain sufficient irregularity conditions associated with the distribution vector properties were proven. Based on this, classes of irregular languages could be identified. These classes are determined by a set of vectors (points) that could not be represented as a finite union of the Z+-planes; by a set of vectors containing vectors with arbitrarily high values of each coordinate and having certain restrictions on the difference between maximum and minimum values of the coordinates; by a set of vectors called the sparse sets. A method is proposed for building such sets using strictly convex and strictly increasing numerical sequences. These sufficient irregularity conditions are based on the Myhill --- Nerode theorem, which is known in the formal languages' theory. Examples of applying the proved theorems to the analysis of languages' regularity/irregularity are presented
On Dropout, Overfitting, and Interaction Effects in Deep Neural Networks

We examine Dropout through the perspective of interactions. Given N variables, there are O(N2) possible pairwise interactions, O(N3) possible 3-way interactions, i.e. O(Nk) possible interactions of k variables. Conversely, the probability of an interaction of k variables surviving Dropout at rate p is O((1−p)k). In this paper, we show that these rates cancel, and as a result, Dropout selectively regularizes against learning higher-order interactions. We prove this new perspective analytically for Input Dropout and empirically for Activation Dropout. This perspective on Dropout has several practical implications: (1) higher Dropout rates should be used when we need stronger regularization against spurious high-order interactions, (2) caution must be used when interpreting Dropout-based feature saliency measures, and (3) networks trained with Input Dropout are biased estimators, even with infinite data. We also compare Dropout to regularization via weight decay and early stopping and find that it is difficult to obtain the same regularization against high-order interactions with these methods.
Coded Distributed Computing with Partial Recovery.

Coded computation techniques provide robustness against straggling workers in distributed computing. However, most of the existing schemes require exact provisioning of the straggling behaviour and ignore the computations carried out by straggling workers. Moreover, these schemes are typically designed to recover the desired computation results accurately, while in many machine learning and iterative optimization algorithms, faster approximate solutions are known to result in an improvement in the overall convergence time. In this paper, we first introduce a novel coded matrix-vector multiplication scheme, called coded computation with partial recovery (CCPR), which benefits from the advantages of both coded and uncoded computation schemes, and reduces both the computation time and the decoding complexity by allowing a trade-off between the accuracy and the speed of computation. We then extend this approach to distributed implementation of more general computation tasks by proposing a coded communication scheme with partial recovery, where the results of subtasks computed by the workers are coded before being communicated. Numerical simulations on a large linear regression task confirm the benefits of the proposed distributed computation scheme with partial recovery in terms of the trade-off between the computation accuracy and latency.
Interaction in the Field of Design and Scientific Research Activities of School and University

Today, one of the requirements of the educational standard and the professional standard «Teacher» for the future teacher is his willingness to organize and manage the project and educational research activities of schoolchildren. This requires the acquisition of experience in the organization of such activities by students, which is noted in the educational standards of higher education in the area of preparation 44.03.01 Pedagogical education (undergraduate level). The article discusses the approaches and substantiates the involvement of undergraduate students in the field of preparation 44.03.01 Pedagogical Education in organizing project and educational research activities of schoolchildren. The experience gained at the faculty of history of Pskov State University is disclosed. An analysis is made of the level of competence formation among future teachers of the history of experience in organizing project and educational research activities of schoolchildren.
Measurement of Brain Activity during Facial Self-touching by Near-infrared Spectroscopy

Using near-infrared spectroscopy, the effect of facial self-touching on the prefrontal cortex activity was investigated by comparing the activations produced by the following conditions: 1) pressure applied to the cheeks (cheek-pressing) with bare hands, 2) cheek-pressing with hands wearing rubber groves, and 3) no touching. The results indicated that regions of the frontopolar cortex (FPC) and orbitofrontal cortex (OFC) were significantly activated only under condition 1 (cheek-pressing with bare hands). In addition, the standard scores (z-scores) for cerebral blood flow in the FPC and OFC were significantly correlated with the subjective scores for pleasant emotions. These results indicate that the FPC and OFC are related to pleasant emotions during self-touching. These findings are expected to be applied to the quantitative assessment of pleasant emotions during facial self-touching.