Reviewerly/exHarmony huggingface dataset:
Persepsi Mahasiswa Akuntansi Terhadap Independensi Akuntan Publik: Studi Empirik pada Mahasiswa Akuntansi di Bandung

Abstract&#x0D; This study aims to determine the perceptions of accounting students towards the independence of public accountants in the city of Bandung. Positive perceptions indicate that the accounting profession, especially public accountants, has a proper place in accounting students. Through a questionnaire distributed with a sample of accounting students in the city of Bandung, with a target sample size of 60 students. From 137 respondents gathered, it can be seen based on the results of the analysis that students majoring in accounting in Bandung perceive that public accountants in Indonesia are not independent. In addition, the results of this study are expected to be inference for accounting students in Indonesia. The results of this study also show that there are differences in the perceptions of students majoring in accounting in Bandung who have never and who have taken auditing courses on the independence of public accountants in Indonesia.&#x0D; Keywords: Perception, Independence, Accounting Profession, and Public Accountant
You Never Cluster Alone

Recent advances in self-supervised learning with instance-level contrastive objectives facilitate unsupervised clustering. However, a standalone datum is not perceiving the context of the holistic cluster, and may undergo sub-optimal assignment. In this paper, we extend the mainstream contrastive learning paradigm to a cluster-level scheme, where all the data subjected to the same cluster contribute to a unified representation that encodes the context of each data group. Contrastive learning with this representation then rewards the assignment of each datum. To implement this vision, we propose twin-contrast clustering (TCC). We define a set of categorical variables as clustering assignment confidence, which links the instance-level learning track with the cluster-level one. On one hand, with the corresponding assignment variables being the weight, a weighted aggregation along the data points implements the set representation of a cluster. We further propose heuristic cluster augmentation equivalents to enable cluster-level contrastive learning. On the other hand, we derive the evidence lower-bound of the instance-level contrastive objective with the assignments. By reparametrizing the assignment variables, TCC is trained end-to-end, requiring no alternating steps. Extensive experiments show that TCC outperforms the state-of-the-art on challenging benchmarks.
An Intelligent Security Model for Online Banking Authentication

Online banking services have become the most used services on the Internet, as the end-user can manage their bank transactions and accounts without having to physically go to a bank. Security issues’ regarding authentication need to be taken into account. Financial institutions should consider integrating multi-factor authentication with Fast Identity Online (FIDO) in order to eliminate the usage of passwords and use keys that support FIDO standards. This paper presents the use of FIDO protocol that enables multi-factor authentication.
Broadcast Secret-Sharing, Bounds and Applications

Consider a sender 𝒮 and a group of n recipients. 𝒮 holds a secret message 𝗆 of length l bits and the goal is to allow 𝒮 to create a secret sharing of 𝗆 with privacy threshold t among the recipients, by broadcasting a single message 𝖼 to the recipients. Our goal is to do this with information theoretic security in a model with a simple form of correlated randomness. Namely, for each subset 𝒜 of recipients of size q, 𝒮 may share a random key with all recipients in 𝒜. (The keys shared with different subsets 𝒜 must be independent.) We call this Broadcast Secret-Sharing (BSS) with parameters l, n, t and q. Our main question is: how large must 𝖼 be, as a function of the parameters? We show that (n-t)/q l is a lower bound, and we show an upper bound of ((n(t+1)/(q+t)) -t)l, matching the lower bound whenever t = 0, or when q = 1 or n-t. When q = n-t, the size of 𝖼 is exactly l which is clearly minimal. The protocol demonstrating the upper bound in this case requires 𝒮 to share a key with every subset of size n-t. We show that this overhead cannot be avoided when 𝖼 has minimal size.We also show that if access is additionally given to an idealized PRG, the lower bound on ciphertext size becomes (n-t)/q λ + l - negl(λ) (where λ is the length of the input to the PRG). The upper bound becomes ((n(t+1))/(q+t) -t)λ + l.BSS can be applied directly to secret-key threshold encryption. We can also consider a setting where the correlated randomness is generated using computationally secure and non-interactive key exchange, where we assume that each recipient has an (independently generated) public key for this purpose. In this model, any protocol for non-interactive secret sharing becomes an ad hoc threshold encryption (ATE) scheme, which is a threshold encryption scheme with no trusted setup beyond a PKI. Our upper bounds imply new ATE schemes, and our lower bound becomes a lower bound on the ciphertext size in any ATE scheme that uses a key exchange functionality and no other cryptographic primitives.
Sorting through the noise: Testing robustness of information processing in pre-trained language models

Pre-trained LMs have shown impressive performance on downstream NLP tasks, but we have yet to establish a clear understanding of their sophistication when it comes to processing, retaining, and applying information presented in their input. In this paper we tackle a component of this question by examining robustness of models’ ability to deploy relevant context information in the face of distracting content. We present models with cloze tasks requiring use of critical context information, and introduce distracting content to test how robustly the models retain and use that critical information for prediction. We also systematically manipulate the nature of these distractors, to shed light on dynamics of models’ use of contextual cues. We find that although models appear in simple contexts to make predictions based on understanding and applying relevant facts from prior context, the presence of distracting but irrelevant content has clear impact in confusing model predictions. In particular, models appear particularly susceptible to factors of semantic similarity and word position. The findings are consistent with the conclusion that LM predictions are driven in large part by superficial contextual cues, rather than by robust representations of context meaning.
Topic Transferable Table Question Answering.

Weakly-supervised table question-answering (TableQA) models have achieved state-of-art performance by using pre-trained BERT transformer to jointly encoding a question and a table to produce structured query for the question. However, in practical settings TableQA systems are deployed over table corpora having topic and word distributions quite distinct from BERT’s pretraining corpus. In this work we simulate the practical topic shift scenario by designing novel challenge benchmarks WikiSQL-TS and WikiTable-TS, consisting of train-dev-test splits in five distinct topic groups, based on the popular WikiSQL and WikiTable-Questions datasets. We empirically show that, despite pre-training on large open-domain text, performance of models degrades significantly when they are evaluated on unseen topics. In response, we propose T3QA (Topic Transferable Table Question Answering) a pragmatic adaptation framework for TableQA comprising of: (1) topic-specific vocabulary injection into BERT, (2) a novel text-to-text transformer generator (such as T5, GPT2) based natural language question generation pipeline focused on generating topic-specific training data, and (3) a logical form re-ranker. We show that T3QA provides a reasonably good baseline for our topic shift benchmarks. We believe our topic split benchmarks will lead to robust TableQA solutions that are better suited for practical deployment
BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining

Neural network robustness has become a central topic in machine learning in recent years. Most training algorithms that improve the model's robustness to adversarial and common corruptions also introduce a large computational overhead, requiring as many as ten times the number of forward and backward passes in order to converge. To combat this inefficiency, we propose BulletTrain $-$ a boundary example mining technique to drastically reduce the computational cost of robust training. Our key observation is that only a small fraction of examples are beneficial for improving robustness. BulletTrain dynamically predicts these important examples and optimizes robust training algorithms to focus on the important examples. We apply our technique to several existing robust training algorithms and achieve a 2.1$\times$ speed-up for TRADES and MART on CIFAR-10 and a 1.7$\times$ speed-up for AugMix on CIFAR-10-C and CIFAR-100-C without any reduction in clean and robust accuracy.
The alternative Method to Finish Modular Exponentiation and Point Multiplication Processes

The aim of this paper is to propose the alternative algorithm to finish the process in public key cryptography.In general, the proposed method can be selected to finish both of modular exponentiation and point multiplication.Although this method is not the best method in all cases, it may be the most efficient method when the condition responds well to this approach.Assuming that the binary system of the exponent or the multiplier is considered and it is divided into groups, the binary system is in excellent condition when the number of groups is small.Each group is generated from a number of 0 that is adjacent to each other.The main idea behind the proposed method is to convert the exponent or the multiplier as the subtraction between two integers.For these integers, it is impossible that the bit which is equal to 1 will be assigned in the same position.The experiment is split into two sections.The first section is an experiment to examine the modular exponentiation.The results demonstrate that the cost of completing the modular multiplication is decreased if the number of groups is very small.In tables 7 -9, four modular multiplications are required when there is one group, although number of bits which are equal to 0 in each table is different.The second component is the experiment to examine the point multiplication process in Elliptic Curves Cryptography.The findings demonstrate that if the number of groups is small, the costs to compute point additions are low.In tables 10 -12, assigning one group is appeared, number of point addition is one when the multiplier of a point is an even number.However, three-point additions are required when the multiplier is an odd number.As a result, the proposed method is an alternative way that should be used when the number of groups is minimal in order to save the costs.
Flashcard Aksara Sunda: Sebuah Inovasi Media Pembelajaran Untuk Sekolah Dasar

This study aims to determine the effectiveness of the Sundanese alphabet flashcard media. The method used in this study is the literature review method, the type of descriptive analysis research, data collection techniques by collecting various scientific sources, and data analysis techniques using interactive models. The results of this study indicate that 1) Sundanese alphabets flashcard media is a form of learning media innovation that can be used in learning activities, especially for elementary school, 2) flashcard media has advantages including attractive for children, relatively cheap, and easy used, and 3) effective flashcard media to be used in Sundanese alphabets learning activities. Thus, it can be concluded that flashcard media is one form of effective learning media innovation so that it can be used as an alternative solution for learning so that children are enthusiastic about learning Sundanese alphabets.
ANALISIS KUALITAS SUMBER AIR DI DESA PALAAN KECAMATAN NGAJUM KABUPATEN MALANG

ABSTRACTThe objectives of this study are: 1) to determine the quality of water sources in Microbiological, Physical, and Chemical terms at Campus 2 Unira Palaan Village, Ngajum District, Malang Regency, 2) determine the feasibility of water sources used for drinking water needs. There were four samples taken, namely I. water source in the pipe, II. water source outside pipe one, III. water source outside pipe two, IV. source of water flowing in the ditch. This research is a quantitative descriptive, while the data were analyzed using the Scoring Strunges method. Based on the results of laboratory scale tests, the results obtained include three parameters. Microbiological parameters of the physical parameters of the chemical parameters. The results of the water quality test on the water sample can be said to meet the requirements as clean water and dringking water. The things that affect the quality of drinking water include: color content, kesadahan total, mangan, pH, and organic substances.
WhyAct: Identifying Action Reasons in Lifestyle Vlogs

We aim to automatically identify human action reasons in online videos. We focus on the widespread genre of lifestyle vlogs, in which people perform actions while verbally describing them. We introduce and make publicly available the WhyAct dataset, consisting of 1,077 visual actions manually annotated with their reasons. We describe a multimodal model that leverages visual and textual information to automatically infer the reasons corresponding to an action presented in the video.
Analisis Sistem Informasi Akuntansi Penerimaan Dan Pengeluaran Dana BOS

This research was conducted on the school operational assistance fund (BOS) at SD Negeri Cigalasar. This study aims to determine how the accounting information system for the receipt and expenditure of BOS funds is. The object of this research is the receipt and expenditure of BOS funds. Testing is done by looking at the facts in the field regarding the acceptance of the system and also the BOS fund contest. This study uses a descriptive method with a qualitative approach. The type of data collection is by observing, documenting and interviewing the treasurer, principal, and school committee. The results of the study indicate that the accounting information system has been running well, this can be seen from the reports and evidence of each transaction, and in accordance with the applicable BOS technical guidelines.
RETRACTED ARTICLE: A comparative study of different acceleration sensors in measuring energy consumption of human martial arts

Abstract At present, there are many acceleration sensors for measuring human martial arts in the market. However, due to the inaccurate measurement of some acceleration sensors, people who love martial arts are deeply troubled and unable to find an excellent acceleration sensor specifically for energy consumption detection of human martial arts. The development of this sensor is imminent, which is of great significance for the comparative study of energy consumption measurement of human martial arts in our country. In this study, 160 students aged 11–14 years were selected, and the subjects were divided into normal body mass group and abnormal body mass group. Of the 96 male adolescents, 32 were obese body mass, which was determined as male abnormal body mass Group; 64 male adolescents were normal body weight and male normal body weight group; female 64 adolescents were normal body weight and set as female normal body mass group. Using a built-in accelerometer and a mobile phone three-dimensional accelerometer, the subjects were subjected to a 3–8 km/h human martial arts exercise load test (each speed is continuously performed for 5 min). The two acceleration sensors collectively assess the accuracy of the prediction of the use of force in human martial arts experiments. The average power consumption of human art exercises uses a frequency of 60 times/min, 90 times/min and 120 times/min compared to two acceleration sensors. Test results show that the data points for the mobile accelerator eraser are scattered, and the distance between the data varies. The data points of the three-dimensional acceleration sensor are more concentrated and present a certain trend. The use of three-dimensional acceleration sensors to measure martial arts can fully reflect the energy consumption of human activities, and achieve an energy consumption measurement accuracy of more than 94%.
Automatic Detection of Social Distance Violation from Real Time Web Camera Using Open CV with Deep Learning

In current days there is a great demand for social distancing due to covid19 pandemic. As we all know lot of survey reports tell that by maintaining proper social distancing one can able to reduce the spread of covid 19.In this current article, we presents a methodology for social distancing detection using OpenCV with deep learning  to identify the distance between people to mitigate the impact of this coronavirus pandemic. The proposed model was designed to alert people to maintain a safe distance with each other by evaluating a video feed. In order to test the model, we try to collect video sequences collected from CCTV cameras and then try to apply some pre-trained CNN models on that input video. Here we try to apply the YOLOv3 algorithm  for identifying the persons who are walking on the road.Once the pedestrian detection is completed, now the video file is converted into a top-down view for distance measurement from the 2D plane. If the distance between any pair of people is less than expected length then it is marked in red color frame and identified as social distance violation is clearly seen. If the distance between any pair of people seems more than the expected distance, than those pair of pedestrians are marked with blue or green color. In order to test the accuracy of our current model,we try to test the model on a pre-recorded video of pedestrians walking on the street. The result shows that the proposed method is able to determine the social distancing measures between multiple people in the video. This same model can be deployed as a tool in future for real time applications to detect the social distance violations.
Information and awareness as a means to develop water supply systems in rural and peri-urban Bangladesh: Designing the Knowledge module of the TAPP App

When we think about the fact that 70% of the earth is covered by water, it may be easy to assume that water is abundant and that it would never have to be an issue in terms of having a constant supply to drink. In reality, it is a very complex matter for everyone to be able to access and drink safe water. Water is essential to maintain life on earth but as a resource it is not always located and in the state that is ideal for consumption. Often what is accessible is either contaminated, in dispute or not appropriate for its intended use. With regards to water safety, Bangladesh is the country with the largest proportion of people exposed to arsenic contamination around the world, the situation of which has been described by the World Health Organisation (2010) as the “largest mass poisoning of a population in history”. From the various technologies available, it has been established that Piped Water Supply Systems show a lot of promise in providing safe water to Rural and Peri -Urban communities of Bangladesh. Issues with this new system include lack of trust in the system, high costs, infrastructural and technical issues and operation and maintenance issues. These along with a lack of knowledge on water safety and varied literacy rates are some of the factors that contribute to low acceptance of the system. Awareness and educational campaigns have had a positive influence on communities regarding their choice of safe water sources. New and effective methods of building awareness through different channels is needed for long term impact and behaviour change. Approximately 50% of the rural households own and use smart phones with Internet access, and this number is rapidly increasing. This shows scope for monitoring water supply systems and building knowledge with smart phones in order to reach and engage communities. Uncertainties within the system were identified and information topics under the themes of Water quality, Health, Water practices and Water sources was designed to be communicated to the people of Bangladesh through the TAPP smart phone application, in order to build their knowledge and understanding of water safety. The long term goals of this project includes acceptance and engagement of Bangladeshi communities towards Piped Water Supply(PWS) systems and to affect the behaviour of people with regards to their choices on consuming safe drinking water. The use of ICT as the communication channel is relatively low cost and has a broad range of possibility to reach the citizens. The impact can be great if the knowledge penetrates within the families, and thus help in changing people’s decisions around drinking water.
Multilingual and Multilabel Emotion Recognition using Virtual Adversarial Training

Virtual Adversarial Training (VAT) has been effective in learning robust models under supervised and semi-supervised settings for both computer vision and NLP tasks. However, the efficacy of VAT for multilingual and multilabel emotion recognition has not been explored before. In this work, we explore VAT for multilabel emotion recognition with a focus on leveraging unlabelled data from different languages to improve the model performance. We perform extensive semi-supervised experiments on SemEval2018 multilabel and multilingual emotion recognition dataset and show performance gains of 6.2% (Arabic), 3.8% (Spanish) and 1.8% (English) over supervised learning with same amount of labelled data (10% of training data). We also improve the existing state-of-the-art by 7%, 4.5% and 1% (Jaccard Index) for Spanish, Arabic and English respectively and perform probing experiments for understanding the impact of different layers of the contextual models.
Highly Parallel Autoregressive Entity Linking with Discriminative Correction.

Generative approaches have been recently shown to be effective for both Entity Disambiguation and Entity Linking (i.e., joint mention detection and disambiguation). However, the previously proposed autoregressive formulation for EL suffers from i) high computational cost due to a complex (deep) decoder, ii) non-parallelizable decoding that scales with the source sequence length, and iii) the need for training on a large amount of data. In this work, we propose a very efficient approach that parallelizes autoregressive linking across all potential mentions and relies on a shallow and efficient decoder. Moreover, we augment the generative objective with an extra discriminative component, i.e., a correction term which lets us directly optimize the generator’s ranking. When taken together, these techniques tackle all the above issues: our model is >70 times faster and more accurate than the previous generative method, outperforming state-of-the-art approaches on the standard English dataset AIDA-CoNLL. Source code available at https://github.com/nicola-decao/efficient-autoregressive-EL
Near-Optimal Lower Bounds For Convex Optimization For All Orders of Smoothness

We study the complexity of optimizing highly smooth convex functions. For a positive integer $p$, we want to find an $\epsilon$-approximate minimum of a convex function $f$, given oracle access to the function and its first $p$ derivatives, assuming that the $p$th derivative of $f$ is Lipschitz. Recently, three independent research groups (Jiang et al., PLMR 2019; Gasnikov et al., PLMR 2019; Bubeck et al., PLMR 2019) developed a new algorithm that solves this problem with $\tilde{O}(1/\epsilon^{\frac{2}{3p+1}})$ oracle calls for constant $p$. This is known to be optimal (up to log factors) for deterministic algorithms, but known lower bounds for randomized algorithms do not match this bound. We prove a new lower bound that matches this bound (up to log factors), and holds not only for randomized algorithms, but also for quantum algorithms.