Reviewerly/exHarmony huggingface dataset:
BKW Meets Fourier: New Algorithms for LPN with Sparse Parities.

We consider the Learning Parity with Noise (LPN) problem with sparse secret, where the secret vector \(\mathbf {s}\) of dimension n has Hamming weight at most k. We are interested in algorithms with asymptotic improvement in the exponent beyond the state of the art. Prior work in this setting presented algorithms with runtime \(n^{c \cdot k}\) for constant \(c < 1\), obtaining a constant factor improvement over brute force search, which runs in time \({n \atopwithdelims ()k}\). We obtain the following results:We first consider the constant error rate setting, and in this case present a new algorithm that leverages a subroutine from the acclaimed BKW algorithm [Blum, Kalai, Wasserman, J. ACM ’03] as well as techniques from Fourier analysis for p-biased distributions. Our algorithm achieves asymptotic improvement in the exponent compared to prior work, when the sparsity \(k = k(n) = \frac{n}{\log ^{1+ 1/c}(n)}\), where \(c \in o(\log \log (n))\) and \(c \in \omega (1)\). The runtime and sample complexity of this algorithm are approximately the same.We next consider the low noise setting, where the error is subconstant. We present a new algorithm in this setting that requires only a polynomial number of samples and achieves asymptotic improvement in the exponent compared to prior work, when the sparsity \(k = \frac{1}{\eta } \cdot \frac{\log (n)}{\log (f(n))}\) and noise rate of \(\eta \ne 1/2\) and \(\eta ^2 = \left( \frac{\log (n)}{n} \cdot f(n)\right) \), for \(f(n) \in \omega (1) \cap n^{o(1)}\). To obtain the improvement in sample complexity, we create subsets of samples using the design of Nisan and Wigderson [J. Comput. Syst. Sci. ’94], so that any two subsets have a small intersection, while the number of subsets is large. Each of these subsets is used to generate a single p-biased sample for the Fourier analysis step. We then show that this allows us to bound the covariance of pairs of samples, which is sufficient for the Fourier analysis.Finally, we show that our first algorithm extends to the setting where the noise rate is very high \(1/2 - o(1)\), and in this case can be used as a subroutine to obtain new algorithms for learning DNFs and Juntas. Our algorithms achieve asymptotic improvement in the exponent for certain regimes. For DNFs of size s with approximation factor \(\epsilon \) this regime is when \(\log \frac{s}{\epsilon } \in \omega \left( \frac{c}{\log n \log \log c}\right) \), and \(\log \frac{s}{\epsilon } \in n^{1 - o(1)}\), for \(c \in n^{1 - o(1)}\). For Juntas of k the regime is when \(k \in \omega \left( \frac{c}{\log n \log \log c}\right) \), and \(k \in n^{1 - o(1)}\), for \(c \in n^{1 - o(1)}\).
UserBERT: Contrastive User Model Pre-training

User modeling is critical for personalized web applications. Existing user modeling methods usually train user models from user behaviors with task-specific labeled data. However, labeled data in a target task may be insufficient for training accurate user models. Fortunately, there are usually rich unlabeled user behavior data which encode rich information of user characteristics and interests. Thus, pre-training user models on unlabeled user behavior data has the potential to improve user modeling for many downstream tasks. In this paper, we propose a contrastive user model pre-training method named UserBERT. Two self-supervision tasks are incorporated in UserBERT for user model pre-training on unlabeled user behavior data to empower user modeling. The first one is masked behavior prediction, which aims to model the relatedness between user behaviors. The second one is behavior sequence matching, which aims to capture the inherent user interests that are consistent in different periods. In addition, we propose a medium-hard negative sampling framework to select informative negative samples for better contrastive pre-training. We maintain a synchronously updated candidate behavior pool and an asynchronously updated candidate behavior sequence pool to select the locally hardest negative behaviors and behavior sequences in an efficient way. Extensive experiments on two real-world datasets in different tasks show that UserBERT can effectively improve various user models.
Analisis Implementasi Teknologi Informasi terhadap Audit Internal dan Pengendalian Internal Bank SulutGo

All areas of human life have been entered by information technology, including the internal audit and internal control of a company. This research expects to decide the impact of data innovation on inner review and interior control at Bank SulutGo. This research uses a qualitative method with a literature study approach. The results obtained are that information technology has an effect on internal audit and internal control of Bank SulutGo. The audit process of Bank SulutGo has developed its information technology by creating an internal audit system based on the Auditing Through the Computer method. Bank SulutGo's internal control is also developed in accordance with the development of information technology as evidenced by manually or computer data archives and using the OLIBS (On Line Integrated Banking System) system. The practical impact that can be felt from this research is that it becomes an evaluation material for Bank SulutGo to continue to improve information technology in relation to internal audit and internal control.Â Keywords:Â Information Technology, Internal Audit, Internal Control
Analytical Study On The Importance Of Applying Advance Psychometric Testing Based On Marston’s Disc Theory And Evaluating Its Effect On Organisational Productivity And Behavioral Engagement Of Employees: Research Review On It Sector

When it comes to assessing the influence of psychometric testing on the productivity of employees and teams, it is clear that most of the studies assess are concerning how psychometric testing elements is applied to team performance of an organisation. The purpose of this research review is to study the previous literature articles that have highlighted the importance of adopting advance psychometric test based on Marston’s Disc Theory on enhancing organisational productivity and employee behavioural engagement in IT sector.
A Study on the Development of a Template Model for Linking Service Learning and School Curriculum

Service learning is not just an activity.It needs to be conducted in a way that students develop their own problem-solving skills.Teachers also should understand the meaning of service learning and recognize the need for educational cooperation.However, practical cooperation is difficult.In particular, because the college entrance exam is important in schools, there are many restrictions on the realization of service learning.Considering this reality, service learning tasks should be developed into educational contents.This study was initiated by necessity in this context.The purpose of this study is to develop a highly useful template by dividing the service learning program into subject, goal, content, method, and composition.For this purpose, this study analyzed the programs conducted at the autonomous district volunteer center and practice school.As a result, this study presents templates that are easy to link course content, core competencies and activities.
Modular Framework for Visuomotor Language Grounding

Natural language instruction following tasks serve as a valuable test-bed for grounded language and robotics research. However, data collection for these tasks is expensive and end-to-end approaches suffer from data inefficiency. We propose the structuring of language, acting, and visual tasks into separate modules that can be trained independently. Using a Language, Action, and Vision (LAV) framework removes the dependence of action and vision modules on instruction following datasets, making them more efficient to train. We also present a preliminary evaluation of LAV on the ALFRED task for visual and interactive instruction following.
Analisis Penurunan Daya Pada Sistem Propulsi Lokomotif Transmisi Diesel Elektrik CC 203 Di Depo Lokomotif

Depo Lokomotif is a work unit that is responsible for locomotive reliability in running the railroad transportation operational business. According to internal monthly report data from February 2019 to February 2020, there were 6 cases of weak locomotive power disruptions that caused operational delays. Analysis was carried out on the CC203 locomotive propulsion system in order to obtain the value of the power reduction in one case of disturbance by comparing the output value against the set standard value. Using mathematical calculation methods from the applicable equations and processing the data on MS.Excell. From the results of the study, the cause of the decrease in locomotive power is due to a decrease in the excitation value so that it affects the output value of the Main Generator at each Notch and has an impact on the motor traction power supply also decreases. The Main Generator can only issue a maximum Horse Power at Notch 8 of 1389.96 HP from 1985.31 HP with a decrease of 595 HP. The electric motion of the generator exciter has the largest decrease at Notch 7 of 6.02 volts from 30.48 volts to 24.46 volts. Motor traction can only output a maximum terminal power of 985.067 Watt from 1407 KWatt. The maximum electromagnetic torque of the Notch 8 motor traction is 2855.48 N.m in the series transition and 3070.21 N.m in the parallel transition. Losses on a series transition of 36886 Watts and a parallel transition of 9122 Watts.
Swami Vivekananda’s Educational Thoughts and its Implications in Present Scenario

Swami Vivekananda was a well versed, spiritually enlightened monk. He was a great Vedantist who preaches the ideas of Vedas. In his very short life he affected the whole world and tried to modernize India while keeping its culture intact. His ideas encourage the spirit of humanity and they are reliable for all times.Swami Vivekananda’s idea of education is harmonious development. He believes in all kind of physical, intellectual, social, moral and spiritual development. He opines that education must develop ability to reason along with character building and man making. His thoughts are for all times and culture. His views are the best foundation for reconstruction of modern education in India. He practiced the idea of Vedas throughout his life such as self knowledge, self reliance, fearlessness and concentration. He preaches the central idea of Veda the universal oneness throughout his life. He was true when he said that world must learn Indian philosophical thoughts along with western sciences.
Mapping sub-districts-level and predicting spatial spread of COVID-19 death case in Jakarta

Currently, the number of deaths caused by COVID-19 continues to increase significantly, especially in areas with high population density and mobility such as Jakarta, Indonesia. Spread of infectious diseases has a spatial closeness which results in cases of COVID-19 deaths also have a spatial dependency which is influenced by cases of deaths in the surrounding area. This study aims to map and predict the number of COVID-19 deaths using the Bayes Linear Mixed Model (LMM) method involving spatial random effects. The response variable is number of deaths and the explanatory variable are number of positive cases of COVID-19 and population density with sub-district area units in Jakarta. Response variable is divided into 6 schemes (PSBB 1, PSBB Transition 1, PSBB 2, PSBB Transition 2, PPKM 1 and PPKM 2) which is adjusted to the policies and social distancing activities from Jakarta provincial government, and assumed to have a normal distribution with INLA (Integrated Nested Laplace Approximation) inference approach. Some important results from this study are: in all 6 social distancing schemes, the number of positive cases of Covid-19 has a significant effect on the increase in number of deaths, while population density has a significant effect along with the increasing variance value of response data. The Bayes LMM has successfully mapped the spread of COVID-19 cases with the best RMSE value of 3.31. The mapping results show that several sub-districts with high population density and sub-districts located on Jakarta border have a high risk of death. Furthermore, the PSBB and PSBB Transition social distancing schemes are considered to be quite effective in suppressing the diversity number of deaths. However, it is different from the PPKM scheme where it is predicted that there will be an increase in the number of high-risk districts for COVID-19 up to 51% per day.
Modification of Standardized Agility 505 Test by Using Modern Technology

The upright body position, the movements of the body segments, and any human activity require a certain level of motor skills.The quality of motor skills and the pace of their development (under the influence of training) directly affect human daily activities, as well as human health status.In sports, they are directly related to the result, whereas, in team sports, speed and agility are perhaps the most important ones.However, finding an acceptable definition of agility is most likely the result of the influence of various factors from different disciplines of sports science, which affect the manifestation of agility.The most accepted definition of agility is the rapid movement of the whole body with a change in speed or direction of movement in response to a certain stimulus, whereas examples of agility assessment protocols are 505, T-test, Step aside, Zigzag test.Of all the agility tests mentioned, the 505 test is the most commonly used for testing both junior and elite athletes.The test itself has the most possibilities for modification since only small corrections can take into account the specifics of the sports.In sports, there is an increasing tendency to save athletes from long testing procedures and to test as many motor skills as possible in the shortest possible time, thus the new tests need to be as effective as possible.Therefore, this paper aims to propose a future modification of the 505 test to test other motor skills in addition to agility by using modern technology.
Surveillance Society: Its Nature And Characteristics

21st century has seen a growth in digital surveillance across the world. Countries like China, Singapore, the U.S strictly adheres to technology for monitoring their citizens. While supporters of surveillance has said it is important to monitor citizens’ movements to reduce crime and mitigate danger, critiques have raised questions concerning privacy and human rights violation due to increased surveillance. The current Government in India has been currently on the news after the alleged spying done through the Israeli Pegasus malware.This article will discuss the nature and characteristics of a surveillance society and the risks it possesses accordingly.
DIETEXPERT – ANDROID APPLICATION FOR PERSONAL DIET CONSULTANT

This application provides the user with a complex algorithm which can provide the user with a diet plan based on his/her characteristics like height, weight, BMI, gender etc. Everyone today dreams of healthy life cycle. In Today’s busy life healthy body is dream for everyone to have a proper balanced diet. A balanced diet is important because your organs and tissues need proper nutrition to work effectively. Without good nutrition, your body is more prone to disease, infection, fatigue, and poor performance. Children with a poor diet run the risk of growth and developmental problems and poor academic performance, and bad eating habits can persist for the rest of their lives. At the core of a balanced diet are foods that are low in unnecessary fats and sugars and high in vitamins, minerals, and other nutrients. The following food groups are essential parts of a balanced diet. Calories play a vital role in our growth and energy. A good diet can help you manipulate calorie intake based on your requirements. The proposed application will provide the user with a user-friendly User-Interface where they can create an account, manage their account and get the diet by the click of just one button. If the user is allergic to some kind of food, it also has the feature to contact an actual dietitian to consult. And there’s also a page where the user can just read some interesting facts on health and human body. This application will save a lot of user’s time by not actually visiting a dietitian and getting everything done on their phone.
RESEARCH ON THE IMPLEMENTATION SCHEME OF MULTIDIMENSIONAL BLENDED TEACHING MODE OF MATHEMATICS COURSE ORIENTED BY COMPETENCE CULTIVATION

In order to meet the demand of talent training in the new era, this paper puts forward a multi-dimensional blended teaching mode of mathematics course which is oriented by competence cultivation. With the help of the intelligent teaching platform and tools, this paper gives the implementation scheme of the mixed teaching in three dimensions of pre-class, in-class and after-class in mathematics teaching, which provides theoretical and practical exploration for the course's competence cultivation objectives.
Fine-Grained Chemical Entity Typing with Multimodal Knowledge Representation

Automated knowledge discovery from trending chemical literature is essential for more efficient biomedical research. How to extract detailed knowledge about chemical reactions from the core chemistry literature is a new emerging challenge that has not been well studied. In this paper, we study the new problem of fine-grained chemical entity typing, which poses interesting new challenges especially because of the complex name mentions frequently occurring in chemistry literature and graphic representation of entities. We introduce a new benchmark data set (CHEMET) to facilitate the study of the new task and propose a novel multi-modal representation learning framework to solve the problem of fine-grained chemical entity typing by leveraging external resources with chemical structures and using cross-modal attention to learn effective representation of text in the chemistry domain. Experiment results show that the proposed framework outperforms multiple state-of-the-art methods.
Cost Optimization Methods for Computational Time and Storage Space using Compression and Crypto-Graphical Techniques for a File in the Cloud

All the transactions are digitalized today and there is an extreme need for information security to be available in efficient and effective way. Security plays a vital role in this digital world. A secure connection must be established between the sender and receiver, this paper describes through Diffie-Hellman key exchange algorithm. Valuable data must be kept as safe as possible in order to stay away from attacks. This can be achieved through cryptography. Reducing the amount of space to store data we can enhance security in best and cheap way. Using various symmetric and asymmetric algorithms, data security can be enhanced. There are proven results that compared to symmetric algorithms, asymmetric algorithms are the best ones due to its advanced feature like private (secret) key. This paper narrated about how effectively the space and computational time can be obtained in a cloud. This is applicable in Business Organizations, Research centers, nuclear power plants, satellites, etc.
Under-bagging Nearest Neighbors for Imbalanced Classification

In this paper, we propose an ensemble learning algorithm called \textit{under-bagging $k$-nearest neighbors} (\textit{under-bagging $k$-NN}) for imbalanced classification problems. On the theoretical side, by developing a new learning theory analysis, we show that with properly chosen parameters, i.e., the number of nearest neighbors $k$, the expected sub-sample size $s$, and the bagging rounds $B$, optimal convergence rates for under-bagging $k$-NN can be achieved under mild assumptions w.r.t.~the arithmetic mean (AM) of recalls. Moreover, we show that with a relatively small $B$, the expected sub-sample size $s$ can be much smaller than the number of training data $n$ at each bagging round, and the number of nearest neighbors $k$ can be reduced simultaneously, especially when the data are highly imbalanced, which leads to substantially lower time complexity and roughly the same space complexity. On the practical side, we conduct numerical experiments to verify the theoretical results on the benefits of the under-bagging technique by the promising AM performance and efficiency of our proposed algorithm.
Bitcoin by the beach: Miami eyes paying workers and collecting taxes in cryptocurrency

Miami Mayor Francis Suarez praised the city's governing commission for passing a resolution Thursday that will study the feasibility of paying employees in bitcoin, as well as allowing residents to pay fees and taxes in bitcoin.
Pengelompokan Jurnal Ilmiah Berdasarkan Judul Menggunakan LDA

Scientific journals develop very rapidly along with the development of science. Reporting from labs.semanticscholar.org/corpus, the number of scientific journals has reached over 39 million. The large number of scientific journals makes it challenging to grouping scientific journals. Grouping become more difficult because each scientific journal can have more than one topic. Therefore, special methods are needed to group the scientific journals.One of the well-known topic modeling methods is Latent Dirichlet Allocation (LDA). This research is an implementation of the LDA algorithm to do topic modeling in scientific journals. The topic modeling in this study uses the title as a corpus. Various titles are processed into bag of words in the pre-processing process so that they can be used to distribute. The results of the distribution stage are used for sampling with the Gibbs Sampling method. Through the sampling process, testing can also be done to determine the optimal parameters. The testing in this study used perplexity to find the most optimal number of iterations and topics. The result from this research are that LDA Algorithm successfully performs topic modeling in scientific journals by generating a list of keywords for each topic and grouping documents on each topic. The optimal parameters based on the results of perplexity comparison are 3 topics and 500 iterations.
Copyright Policies and Workflows in Libraries: A Concise Handbook

"Copyright Policies and Workflows in Libraries: A Concise Handbook." Journal of Electronic Resources in Medical Libraries, 18(2-3), pp. 169–170
Ontology Reasoning for Explanatory Feedback Generation to Teach How Algorithms Work

Developing algorithms using control structures and understanding their building blocks are essential skills in mastering programming. Ontologies and software reasoning is a promising method for developing intelligent tutoring systems in well-defined domains (like programming languages and algorithms); it can be used for many kinds of teaching tasks. In this work, we used a formal model consisting of production rules for Apache Jena reasoner as a basis for developing a constraint-based tutor for introductory programming domain. The tutor can determine fault reasons for any incorrect answer that a student can enter. The problem the student should solve is building an execution trace for the given algorithm. The problem is a closed-ended question that requires arranging given actions in the (unique) correct order; some actions can be used several times, while others can be omitted. Using formal reasoning to check domain constraints allowed us to provide explanatory feedback for all kinds of errors students can make.