Reviewerly/exHarmony huggingface dataset:
There is Limited Correlation between Coverage and Robustness for Deep  Neural Networks

Deep neural networks (DNN) are increasingly applied in safety-critical systems, e.g., for face recognition, autonomous car control and malware detection. It is also shown that DNNs are subject to attacks such as adversarial perturbation and thus must be properly tested. Many coverage criteria for DNN since have been proposed, inspired by the success of code coverage criteria for software programs. The expectation is that if a DNN is a well tested (and retrained) according to such coverage criteria, it is more likely to be robust. In this work, we conduct an empirical study to evaluate the relationship between coverage, robustness and attack/defense metrics for DNN. Our study is the largest to date and systematically done based on 100 DNN models and 25 metrics. One of our findings is that there is limited correlation between coverage and robustness, i.e., improving coverage does not help improve the robustness. Our dataset and implementation have been made available to serve as a benchmark for future studies on testing DNN.
Automatic System for Detecting Student Attendance in College Classroom Based on Template Matching Method and Cellphone Storage Hanging Pocket

Because teachers in colleges and universities generally do not know the number of students in their classes, they spend considerable time monitoring student attendance, reducing the teaching time.In this study, we investigated a new method for effectively monitoring the attendance of students in classes of colleges and universities.When students enter a classroom at our campus, they must store their cellphones in a pouch containing multiple pockets that is hung from a wall of the classroom.These cellphone storage hanging pockets (abbreviated to cellphone pockets) have become a necessary tool for students to store cellphones and not only improve the efficiency of studying in the classroom, but are also convenient for teachers to check student attendance at the start of classes.We investigated a template matching method for efficiently finding the attendance of students using cellphone pockets.We used images of cellphone pockets as the input images for template matching to find the grids of the cellphone pockets with no cellphones.This enabled teachers to use the serial numbers of the cellphone pocket grids with no cellphones to identify the absent students.
Cryptographic security concerns on timestamp sharing via a public channel in quantum-key-distribution systems

Quantum key distribution protocols are known to be vulnerable against a side channel attack that exploits the time difference in detector responses used to obtain key bits. The recommended solution against this timing side channel attack is to use a large time bin width instead of high resolution timing information. Common notion is that using a large bin width reduces the resolution of detector responses, hence supposedly minimizes the information leakage to an eavesdropper. We challenge this conventional wisdom, and demonstrate that increasing the bin width does not monotonically reduce the mutual information between the key bits and the eavesdropper's observation of detector responses. Instead of randomly increasing the bin width, it should be carefully chosen because the mutual information fluctuates with respect to the bin width. We also examined the effect of full width half maximums (FWHMs) of the detectors responses on the mutual information and showed that decreasing the FWHM increases the mutual information. Lastly, the start time of binning is also shown to be important in binning process and the mutual information fluctuates periodically with respect to it.
Implementation of K-Medoids Clustering Method for Indihome Service Package Market Segmentation

IndiHome (Indonesia Digital Home) is a leading digital fibre optic service product consisting of fibre optic internet services, landline telephones, and interactive TV services. Although the coverage of Indihome products is extensive in the city of Medan, in marketing, Indihome products have not reached the planned target. Based on data from Indihome service package users that have been received, Indihome product users only numbered 6419 customers in all STOs in Medan City. At the same time, the target was planned by PT. Telkom Access Medan, namely Marketing Indihome products, must reach 5,000 customers per month in all STOs in Medan City. Indihome product marketing is an obstacle for PT. Telkom Access Medan, because the Indihome product is a new product, the people of Medan City do not fully know what Indihome is and what facilities they get from using the Indihome service package. Therefore PT. Telkom Access Medan needs to make a plan to make a marketing strategy. The first step that needs to be done is to segment the market for the Indihome service package. This study aimed to determine the application of Data Mining using the K-Medoids Clustering method in the Indihome service package market segmentation at PT. Telkom Access Medan. With this research, it is hoped that it can provide a reference for the results of the decision so that it can help related parties to make it easier to classify the market segmentation of the Indihome service package at PT. Telkom Access Medan. Because the value of S &gt; 0, then the calculation is stopped and ends in the 3rd iteration. Indihome service package data processing uses the k-medoids clustering method in the form of potential, potential, and not potential STO (Sentral Telephone Automated) cluster members.
Variable Selection in High Dimensional Data with Interactions

A common research area in statistical machine learning has been variable selection in high dimensional settings. In recent years, numerous effective approaches have been created to deal with these challenges. In order to improve the prediction accuracy of the model for the given dataset, this study sought to present a double approach variable selection method when pairwise interactions between the explanatory variables exist and to choose the smallest explanatory variable set (considering interactions among them). In this study, a double step method consolidating Random Forest and Adaptive Elastic Net was further examined to mimic potential health effects of environmental contamination. When there were existing interactions in the data or none at all, the double step approach was compared to the single-step adaptive elastic net method and two-step CART paired with the adaptive elastic net method. Using significant statistical tests like RMSE, R2 , and the quantity of the variable chosen for the final model, the success of the strategies was measured. The double step RF+AENET approach produces a simple, constrained model. Despite the complex association between exposure variables, it has the lowest false detection rate for null interactions. A set of variables that have correlation with the result are effectively retained by the screening and variable reduction processes in the RF step of the RF+AENET approach. The double step RF+AENET performs prediction better than a single technique and chooses a sparse model that is close to the true model. Thus, it can be said that when there are pairwise interactions between variables in the simulated biological dataset, the double step technique is a better method for model prediction and parameter estimation. Keywords: Adaptive Elastic Net, Random Forest, Variable Selection, CART.
Finite Entailment of UCRPQs over ALC Ontologies

We investigate the problem of finite entailment of ontology-mediated queries. We consider the expressive query language, unions of conjunctive regular path queries (UCRPQs), extending the well-known class of union of conjunctive queries, with regular expressions over roles. We look at ontologies formulated using the description logic ALC, and show a tight 2ExpTime upper bound for entailment of UCRPQs. At the core of our decision procedure, there is a novel automata-based technique introducing a stratification of interpretations induced by the deterministic finite automaton underlying the input UCRPQ.
Research on the visual attribute of Ambiguity image in advertisement

The content of advertising design often gives people a sense of confusion. What is the reason for this characteristic of blurred image? From the point of view of notation, there are many reasons for Ambiguity, and the specific core elements of Ambiguity are based on 'inconsistency'. In advertising, visual information becomes the central element, so ambiguity of meaning is portrayed. In his opinion, when advertising is centered on linguistic information, the meaning is conveyed more clearly, but visual information becomes an important component of advertising, thus increasing the room for polysemy interpretation. Therefore, based on the method of case study, this study tries to find out what the visual characteristics of fuzzy images are and what formative factors affect these characteristics. From the perspectives of visual condensability, information deficiency, semantic complexity and concept divergence, 15 specific methods and various forming factors of image fuzziness are found. This study provides a practical classification item for the visualization method of Ambiguity image in advertisement.
[Nursing Digital Transformation: An Example of an Electronic Whiteboard in a Regional Hospital in Central Taiwan].

The electronic whiteboard system is an important part of smart medical care. This system has been digitized and upgraded gradually over time, and now functions as a dashboard, incorporating sound effects, touch control, image display, face recognition, and other functions that maximize usage efficiency. In hospitals, electronic whiteboards are specialized for dedicated use in one of two areas: nursing stations and wards. Those used in nursing stations may upload data into the medical information system based on departmental and institutional requirements. Systems are built to the specific needs of different clinical departments and thus differ widely in terms of settings and functionality. Therefore, hospitals should promote regular communication among doctors, nurses, and patients.以中部區域教學醫院護理數位作業的轉型—以電子白板為例.電子白板系統（electronic white board system）是智慧醫療中重要的一環，其功能逐漸提升成儀表板（dashboard）形式，並升級為具音效、觸控、圖像顯示及人臉辨識等功能，使得電子白板的效能更上層樓。電子白板在醫院的應用可分為兩大區域：護理站區域及病房區域。護理站電子白板可以依各科室、部門、院所的需求載入醫療資訊系統，為因應各科室的不同，所建置的系統也大相徑庭。因此醫院在建置護理站電子白板系統時，應提供適當的教育訓練，讓護理師了解電子白板與護理工作的關係及助益，以提高護理師對電子白板的使用意願及滿意度。醫療電子白板未來須強化病人區域電子白板功能，提升精準醫療照護及病人安全，並將知的權利回到病人身上，以提升醫、護、病溝通效率及滿意度。.
JPEG Codec Based on ASIMD Parallel Acceleration

Abstract The system is based on JPEG image encoding and decoding. The frame skipping algorithm is used to quickly obtain the pictures collected by the camera in r eal time. Through the neural network code, the collected low illumination pictures are enhanced. Through the bottom assembly language, the collected original image is compressed into JPEG and saved. Then the JPEG image is read and decompressed into the original image. The underlying assembly statements are vectorized through ASIMD instructions and accelerated in parallel through multithreading. Finally, the number of video frames displayed is equal to the number of video frames collected by the camera. The acceleration effect is very good.
Graph Enhanced Representation and Reasoning Model for Tabular Fact Verification

Abstract Tabular fact verification is a challenging task that requires obtaining relevant evidence from the table and utilizing them to verify a given claim. The main difficulty in tabular fact verification is that traditional language models cannot capture the underlying information carried in tabular data. To solve this problem, we propose GraERR, a Graph Enhanced Representation and Reasoning Model for Tabular Fact Verification. It consists of two modules: a data initial representation module based on the DeBERTa model and a graph-augmented representation and inference module. The former improves the inference ability of the model with a DeBERTa model pre-trained on the natural language inference dataset. The graph enhancement module enhances the model’s ability to learn table representations by effectively integrating textual and structural information in tables. The best results obtained experimentally on the InfoTabs dataset demonstrate the effectiveness of the GECMT model on the tabular fact verification task.
PENGARUH MEDIA LECTORA TERHADAP HASIL BELAJAR PESERTA DIDIK PADA MATERI PERANGKAT LUNAK MPEMBUAT GRAFIS DI SMA NEGERI 1 RAO

This research was conducted because of SMA 1 Rao was already have adequate facilities to support learning process like library and computer room. However, in fact, the facility had not been used maximally in the learning process, especially in learning process of Information Communication and Technology (ICT) lesson. Some teachers also still used speech method and conversional media. In line with the problems, this research aimed to find out the effect of Lectora media as a learning media towards the students’ learning achievement in graphics software material at SMA Negeri 1 Rao. This research used a quantitative approach, and the subject of this research was SMA Negeri 1 Rao, the experimental class was XII.IPA-3 and XII.IPA-4 as control class. In data collection techniques, the researcher used questionnaires and tests. Questionnaires were distributed in the experimental class which used lectora media, and control class with no questionnaire because they did not use lectora media.The result of research showed that the effect of lectora media towards the learning achievement was the magnitude of effect of 5.1%, while the percentage between experiment class and control class were increasement of percentage of 42.5%, thus it was proven that the effect of lectora media to students' learning achievement was better than who did not use the lactor media.
Sistem Informasi Pembayaran SPP Menggunakan Whatsapp Gateway di SMK Tamansiswa Padang

Currently, paying tuition fees at SMK Tamansiswa Padang is still done manually, which is still recording payment reports in a financial book. In addition, the treasurer has other obstacles, such as busy teaching and additional duties from the principal. So it takes a long time to record payments and calculate the number of payments from paying students. This information system uses the System Development Life Cycle (SDLC) method with an iterative development model. This model has several stages: design, analysis, implementation, testing, and evaluation. The research results on the spp payment information system using the WhatsApp gateway at SMK Tamansiswa Padang are from the tests carried out from black-box testing, white box testing, and beta testing to get valid results and the system can run well.
The Value of Character Education in the Novel Shoes Dahlan and Its Relevance to Indonesian Language Learning in Elementary Schools

The purpose of this study was to determine the value of character education in the Novel Shoes Dahlan by Khrisna Pabichara. The research method uses a descriptive qualitative approach. The results of this study are that there are character education values in the form of (1) hard work in the form of parents having a hard work spirit to meet the needs of their children, as well as a child having a hard work spirit to achieve goals with their sweat, (2) independent in the form of not being too dependent on others, (3) discipline in the form of parents training their children about discipline. The child instills discipline in his daily life, (4) nationalist and (5) religious in the form of always praying before doing something. The relevance of character education in Indonesian is that creative literary works are born from the art of language and are likened to a description of human sociocultural life. Literary works can describe human life in various forms. The values possessed by literary works are accepted and understood by the reader, which will indirectly provide a picture of the reader's attitude and personality. Literature not only has a role in inculcating noble character but also has a role in character building since childhood.
Preface: The International Conference on Mathematics and Learning Research (ICOMER) 2021

First Page
Ablation Analysis of Seq2Seq Models and Vanilla Transformers for Spanish to English Translation

Machine translation, a widely known field in computer linguistics, is a challenging problem which deals with translating between different languages. We undertake the task of translating from Spanish to French in this paper. We provide a comparative analysis of various sequential and non-sequential models such as Seq2Seq, Seq2Seq with teacher forcing, Seq2Seq with attention, and transformers with encoder–decoder architectures for the proposed task. Our experiments show that transformer architecture along with label smoothing attains the best BLEU score on the UN dataset, and it is the most efficient choice. Furthermore, we compare various decoding methods like nucleus sampling, top-k sampling, and greedy approach and also conduct an ablation analysis of each of these until we find the best performing method. Our proposed architecture combination is easily extensible and could be used for other translation tasks as well, with minor changes in the pre-processing steps depending upon the languages under consideration. This method is also computationally economic and might help under constrained environments.
Comparative Research of Neuron Circuits

Abstract: Spiking neurons can be implemented in hardware, for example, to model large neural systems, simulate real-time behaviour, and interface bi-directionally between brains and machines. Circuit solutions used to implement silicon neuron circuits depend on the application requirements. Various neuron circuits are presented in this thesis, including spike-event generators (Axon Hillock neuron circuits), above-threshold neuron circuits (Quadratic Integrate and Fire neuron circuits), and differential pair integrator circuits. Cadence's tool simulates these circuits using 180nm technology. Comparing these circuits is based on their working properties and simulation results, and their features are demonstrated with experiments.
Paperless Enrollment System: Functionality and Credibility as an Online Platform

It is a fact that dozens of Universities and Colleges already have the online platform long before the pandemic. They both have an online enrollment system and electronic learning for online classes however this way of learning is in least priorities since the usual way is by going to school premises and process the enrollment physically. Face to face classes as well is the focus of every school organization since it promotes personal interaction which is essential to develop and boost confidence among students. This usual way of learning needs an immediate change and require an effective solution during the pandemic. It puts everybody on their individual houses and stops us to almost everything we are doing outside our home. A rapid fall to every country’s economy and a widespread scarcity for food is one of the main problem we face. Online processes is the key in order not to violate all safety protocols being implemented. Online Enrollment System is needed to be prioritize and improve as students can’t afford to discontinue and skip taking their remaining semester and subjects in school. Creating the Online System is a huge advantage for parents, students and to the school itself. Since it is an online platform, it can cater all incoming students, current students and returnees which will ease the hustle as it is a comfortable way in terms of filing documents for enrollment.
Pemanfaatan Aplikasi Mitigasi Bencana Berbasis Android dalam Pembelajaran IPS

Geological location of Indonesia is at the confluence of active plates and 2 active mountain lines and is in a tropical climate, so most of Indonesia is prone to natural disasters. There are various kinds of natural disasters in Indonesia so that Indonesia is known as a Natural Disaster Laboratory. The Indonesian people, especially the young generation of the nation's heirs, really need to get disaster mitigation education as an effort to create a disaster-resilient generation. Disaster mitigation education for millennial generation students must be adjusted, according to Ki Hajar Dewantara's message "educate children in a way that is in accordance with the demands of nature and the times". 21st century learning is marked by advances in information technology for critical, creative, collaborative and communication thinking. Literature study to examine the development of information technology media about android-based disaster mitigation applications available with the aim that students have awareness of their own existence and are able to interact with their immediate environment, able to analyze the relationship between regional geographical conditions and their relation to disaster mitigation.
Research on Information Extraction of the Dongting Lake Ecological Wetland Based on Genetic Algorithm Optimized Convolutional Neural Network

Dongting Lake is an important lake wetland in China. How to quickly and accurately obtain the basic information of the Dongting Lake ecological wetland is of great + significance for the dynamic monitoring, protection, and sustainable utilization of the wetland. Therefore, this article proposes the information extraction of the Dongting Lake ecological wetland based on genetic algorithm optimized convolutional neural network (GA-CNN), an analysis model combining genetic algorithm (GA) and convolutional neural network (CNN). Firstly, we know the environmental information of Dongting Lake, take Gaofen-1 image as the data source, and use normalized vegetation index and normalized water body index as auxiliary data to preprocess the change detection of remote sensing images to obtain high-precision fitting images. GA-CNN is constructed to efficiently extract the information of the Dongting Lake ecological wetland, and the Relu excitation function is used to improve the phenomenon of gradient disappearance and convergence fluctuation so as to reduce the operation time. Logistic regression is used for feature extraction, and finally the automatic identification and information extraction of the Dongting Lake ecological wetland are realized. The research results show that the method proposed in this article can more deeply dig the information of ground objects, express depth features, and has high accuracy and credibility.
An Automated Method of Causal Inference of the Underlying Cause of Death of Citizens

It is of great significance to correctly infer the underlying cause of death for citizens, especially under the current worldwide situation. The medical resources of all countries are overwhelmed under the impact of coronavirus disease 2019 (COVID-19) and countries need to allocate limited resources to the most suitable place. Traditionally, the cause-of-death inference relies on manual methods, which require a large resource cost and are not so efficient. To address the challenges, in this work, we present a mixed inference method named Sink-CF. The Sink-CF algorithm is based on confidence measurement and is used to automatically infer the underlying cause of death of citizens. The method proposed in this paper combines a mathematical statistics method and a collaborative filtering and analysis algorithm in machine learning. Thus, our method can not only effectively achieve a certain accuracy, but also does not rely on a large quantity of manually labeled data to continuously optimize the model, which can save computer computing power and time, and has the characteristics of being simple, easy and efficient. The experimental results show that our method generates a reasonable precision (93.82%) and recall (90.11%) and outperforms other state-of-the-art machine learning algorithms.