Reviewerly/exHarmony huggingface dataset:
Errors in ternary code vectors, their classification and detection using noise-resistant coding

vector errors classification, codes with error detection, codes with monotonous error detection
Designing Affirmative Action Policies under Uncertainty

We study university admissions under a centralized system that uses grades and standardized test scores to match applicants to university programs. In the context of this system, we explore affirmative action policies that seek to narrow the gap between the admission rates of different socio-demographic groups while still accepting students with high scores. Since there is uncertainty about the score distribution of the students who will apply to each program, it is unclear what policy would have the desired effect on the admission rates of different groups. We address this challenge by using a predictive model trained on historical data to help optimize the parameters of such policies. We find that a learned predictive model does significantly better than relying on the ideal parameters for the last year. At the same time, we also find that a large pool of historical data yields similar results as our predictive approach for our data. Due to the more complex nature of the predictive approach, we conclude that a simpler approach should be preferred if enough data is available (e.g., long-standing, traditional university programs), but not for newer programs and other cases in which our predictive strategy can prove helpful.
Scrum Training for Product Configuration Systems Development

Product configuring systems (PCS) enable companies to develop product alternatives to facilitate sales and production processes. Hence, PCS projects are kind of complex IT projects and have driven significant amount of research and development lately. This qualitative case study research aims to evaluate the Scrum training for PCS projects. This study, first, aims at assessing and understanding the training experiences at the case company. Secondly, through interview, we investigate about the quality of the current training at the case company for PCS projects.
A cognitive model to predict human interest in smart environments

Recently, the idea of smart cities has made several strides forward in literature. Work has hypothesize that the combination of Artificial Intelligence, Cloud Computing, and High powered computers will make technology more human-centric, even, the idea that smart cities will be able to understand the thought process of a human being seems very much likely today. This paper is along this line of thought. In particular, we try to present a method to model the cognitive state of human interest. This is done to take one more step towards the realization of a smart cognitive city. An approach which is Subjective–Objective in nature is presented to model the computation of activity inspired by interest. Based on activity, human latent state values are indirectly deduced. Inspiration is drawn from Physics and interest is modeled upon the Ornstein–Uhlenbeck (OU) process. Concepts of Adaptive filtering are used to formulate an evolving transformation function that automatically and adaptively models the conversion of interest into activity. Particle filter is employed to provide an elucidation which is computationally feasible. To validate the viability of the method, experimentation is performed with real datasets.
Causes and Countermeasures of English Culture Deficiency in College English Language Teaching

&lt;p&gt;Culture is the embodiment of national spirit, and language is one of the important carriers of culture, which is the most intuitive way to reflect culture. College English, which aims at cultivating students' comprehensive English application ability, is an important compulsory course in college. However, in the process of English application, due to the huge differences between China and the west in geographical conditions, climate and other conditions, the cultures formed by the two sides are also very different from each other. Therefore, misunderstanding of individual words and sentences may easily occur in the process of English application, leading to misunderstanding between the two sides. Some universities propose to introduce English culture into English teaching. Based on the importance of English culture in college English language teaching, this paper analyzes the reasons for the lack of English culture in college English language teaching and the corresponding countermeasures.&lt;/p&gt;
Towards a UX Assessment Method for AI-Enabled Domestic Devices

Artificial Intelligence is increasingly integrating into everyday life and is becoming an increasingly pervasive reality. Domestic AI-enhanced devices are aggressively conquering new markets, nevertheless such products seem to respond to the taste for novelty rather than having a significant utility for the user, remaining confined to the dimension of the gadget or toy. Interestingly, although AI has been indicated as a new material for designers, the design discipline has not yet fully tackled this issue. Moving from these premises, the MeEt-AI research program aims at developing a new UX assessment method specifically addressed to AI-enhanced domestic devices and environments. Accordingly, we frame the project within the vast and variegated field of UX assessment methods, focusing on three main aspects of UX assessment – methodology, UX dimensions and analyzed objects – by looking at what current methods propose from the standpoint of AI-enhanced domestic products and environments. What emerges are general considerations that are at the basis of the positioning of the MeEt-AI research program.
Formulas Free From Inconsistency: An Atom-Centric Characterization in Priest's Minimally Inconsistent LP (Extended Abstract)

As one of fundamental properties to characterize inconsistency measures for knowledge bases, the property of free formula independence captures well the intuition that free formulas are independent of the amount of inconsistency in a knowledge base for cases where inconsistency is characterized in terms of minimal inconsistent subsets. But it has been argued that not all the free formulas are independent of inconsistency in some other contexts of inconsistency characterization. In this paper, we propose a notion of Bi-free formula to describe formulas that are free from inconsistency in both syntactic characterization and paraconsistent models in the framework of Priest's minimally inconsistent LP. Then we propose the property of Bi-free formula independence, which is more suitable for characterizing the role of formulas free from inconsistency in measuring inconsistency from both syntactic and semantic perspectives.
MAP Estimation With Bernoulli Randomness, and Its Application to Text Analysis and Recommender Systems

MAP estimation plays an important role in many probabilistic models. However, in many cases, the MAP problem is non-convex and intractable. In this work, we propose a novel algorithm, called BOPE, which uses Bernoulli randomness for Online Maximum a Posteriori Estimation. We show that BOPE has a fast convergence rate. In particular, BOPE implicitly employs a prior which plays as regularization. Such a prior is different from the one of the MAP problem and will be vanishing as BOPE does more iterations. This property of BOPE is significant and enables to reduce severe overfitting for probabilistic models in ill-posed cases, including short text, sparse data, and noisy data. We validate the practical efficiency of BOPE in two contexts: text analysis and recommender systems. Both contexts show the superior of BOPE over the baselines.
A Study on the Influence of Quality of Educational Service on Perceived Value, Job Satisfaction and Organizational Commitment for Hotel Employees

In this study, it is meaningful to suggest elements of educational service quality that should be improved or developed by the hotel"s management and education personnel by identifying the perceived value, job satisfaction, and impact on the organization"s commitment by dividing the quality of the hotel"s education into five categories. The data collection was conducted on employees working at five-star hotels in Seoul, and the statistical processing of the collected data was analyzed using the SPSS v. 21.0 statistical package program. The study found that the "understanding of the staff" of the quality of educational services and the "reliability of the educational program" affect the perceived value of the staff. Secondly, the higher the perception of the quality of educational services by the hotel staff, the more satisfied they were with their duties, and the fact that the employee satisfied with the job was also immersed in their organization. It is hoped that the results of this study will be used as a reference to improve employees" job satisfaction and organizational commitment through training programs optimized for hotel staff amid changing hotel management trends.
Research on Performance Evaluation of Knowledge Management Innovation in Colleges and Universities

In the context of the rapid development of the knowledge economy, knowledge in human resources and science and technology is an important capital to win in the fierce market competition.In universities, the knowledge is the source of life that the universities survive from the world.Currently most scholars who research on knowledge management innovation performance put emphasis on mostly enterprise knowledge management performance, it involves the research of university knowledge management innovation performance rarely.Therefore, the purpose of this paper is exploratory constructing knowledge management innovation performance evaluation model from the related theory of knowledge management in university, and then evaluating the innovation performance of knowledge management in universities.
Research and Application of Hybrid Teaching Model of Landscape Architecture Engineering based on Cloud Course

&lt;p&gt;Blended teaching, as an important part of current teaching reform, is a combination of information-based teaching and traditional classroom teaching, which is conducive to students' independent learning. Based on the connotation of cloud course, the cloud course teaching platform and traditional teaching methods are integrated into a new teaching system, aiming at making students become the main body of the class and letting teachers play a more guiding role. It has been proved by practice that this mixed teaching mode has a good teaching effect in the teaching of landscape architecture engineering, which can fully mobilize students' learning enthusiasm, increase the interaction between teachers and students, make the teaching content more diversified, more practical, and make the teaching of landscape architecture engineering more perfect.&lt;/p&gt;
Spatio-Temporal Alignment and Trajectory Matching for Netted Radar Without Prior Spatial Information and Time Delay

For netted radars, a new alignment algorithm without prior spatial information (i.e., locations and attitudes of radars) and time delay is proposed to keep the spatio-temporal alignment. The unknown parameters to be estimated include the rotation matrix, the translation vector and the delay between radar stations in this paper. The minimum error function in unified space and time coordinate system is established based on the target trajectory measured by each radar. Firstly, the alternating spatio-temporal alignment method is used to estimate the spatial and temporal parameters, and its statistical performance is compared to the Cramer-Rao bound. Even under severe conditions in which each radar in the network can only observe part of the trajectory, the proposed algorithm can still be adopted to estimate the alignment parameters and complete the trajectory. Then, a trajectory matching algorithm based on random sample consensus (RANSAC) is proposed for multitarget. The corresponding relationship between trajectories is established through minimizing sum of paired trajectory error, and multitarget trajectories from each radar are matched. Finally, the spatio-temporal parameters are refined by all matched trajectory pairs. Simulation results show that the tracking information from different radars is transformed into a unified coordinate after spatio-temporal alignment. Moreover, the registration error is reduced and the tracking accuracy is improved.
Map-Reduce based Frequent Sub-Graph Extraction

Frequent subgraph extraction from a substantial number of small graphs is a crude activity for some, information mining applications.To extricate frequent subgraphs, existing systems need to identify countless which is super straight with the cardinality of the dataset.Given the huge developing volume of graph information, it is hard to play out the regular subgraph extraction on a unified machine proficiently.Along these lines, there is a need to explore how to effectively play out this extraction over expansive datasets utilizing MapReduce.Parallelizing existing strategies straightforwardly utilizing MapReduce does not yield great execution as it is hard to adjust the remaining task at hand among the figure hubs.This structure receives the MRFSE procedure to iteratively remove Frequent subgraphs, i.e., all incessant size-( i+1) subgraphs are created dependent on continuous size-I subgraphs at the ith emphasis utilizing a solitary MapReduce work.To productively separate successive subgraphs, arrangement and mining stage are utilized which incorporates isomorphism testing to wipe out copy designs.Frequent subgraphs extraction should be possible productively and effectively by utilizing a disseminated domain named Hadoop MapReduce structure.
Cohesion and Coherence in College English Writing Teaching

&lt;p&gt;Due to the effect of many years of traditional teaching methods, College English writing teaching pays too much attention to grammar and equivalence at word or clause level, but ignores cohesion and coherence of the text as a unified whole. This paper mainly studies the problems concerning cohesion and coherence that most students in Ordos Institute of Technology(OIT) face in writing, reasons, and ways to help students exploit cohesive devices properly to ensure the coherence of the text and improve their writings.&lt;/p&gt;
Research on the progress of key technology of cotton harvester

Abstract: The research and development process and main models of cotton pickers at home and abroad are expounded, and the gap between China and developed countries is analyzed. The characteristics and research progress of general techniques and devices such as picking, conveying and removing impurities are expounded, as well as the research and exploration of domestic cotton pickers in new technologies such as image detection and hydraulic profiling. The main factors restricting the current cotton picker harvesting machinery and intelligence in China and the harvesting characteristics of cotton production areas in China are analyzed.
Implementasi Algoritma Apriori Untuk Menentukan Stok Obat

The supply of drugs in a pharmacy is very important to maintain the fulfillment of consumer needs based on a doctor's prescription. Problems arise due to limitations on the expiry date of each drug, this needs to be overcome so that there is no buildup of drug stocks at the pharmacy so that it causes losses because there are types of drugs that have expired in sufficient quantities, therefore we need data mining that can determine which pattern of drug type works best, using a priori algorithm. The association method is needed to see the correlation between a number of attributes for example if a consumer buys drug A then he will buy drug B as well. A priori analysis to determine the minimum conditions for support and confidence. The conclusion of this research is that if you buy amlodipine 5 mg, you will buy sanmol, this is obtained from 33.33% support and 66.66% confidence, if you buy 500 mg amoxan, you will buy sanmol with a support value of 41.66% and confidence 71, 42% and if you buy sanmol, you will buy amoxan 500 mg with a support value of 41.66% and confidence 62.50%.
Classification of Twitter Data by Sentiment Analysis in the Malay Language

A set of tweets are characterized manually using human clarification with their sentiments and contemplate as training data.Then an additional set of tweets that is live streaming, are composed hinge on the text mining on Twitter Streaming Application Programming Interface (API).The tweets are fetched and retain as a text data and later will be utilized as a testing set.Testing data will grasp from training data calculation to forecast sentiment significance.The scenario of this research is no Twitter dataset collection available in the Malay language with characterizing sentiment significance.Then, findings are filtered to search tweets in the Malay language that is from Malaysia.The crucial dispute about this research is to cumulate a characterize entity as a training set.Considering there is no characterize Twitter entity available in the Malay language, a database of sentences is manually characterized with sentiments utilizing human explanation and utilize tweet's geo-position to search for tweets display within Malaysia.The outcome of this research, Twitter entity utilizing Twitter Streaming API capable to be collected and tweets from Malaysia collected by utilizing tweet's geo-position capable to be acquired.
Analysis of the Difference Between the Production of Computer Music and the Production of Traditional Music

&lt;p&gt;With the development of economy, all kinds of new technologies keep emerging, so does the field of music. The new computer music production technology has greatly improved the efficiency of music production and made up for the defects of traditional music production in which it is still difficult to guarantee the quality of music even if adequate preparation is made before recording. This paper discusses the advantages and disadvantages of computer music production compared with traditional music production.&lt;/p&gt;
A Hybrid Caesar-Polybius Cipher with XOR Operation for Enhanced Cryptography

Cryptography is a technique that deals with securing data and is closely affiliated with information theory, computer security, and engineering.However, with the presence of adversaries that uses powerful computers, the need to increase the complexity of cryptographic techniques arises.This paper employed and hybridized the two commonly used ciphers in the literature, namely the Polybius square and the Caesar cipher, to ensure more secure data.To increase the strength of the hybrid cipher, the ciphertext generated by the Polybius square is XORed.Simulation results revealed that the proposed method generates a unique ciphertext that shows no trace of any pattern from the plain text, thus, devoid of being attacked by frequency analysis or by brute force.
Bringing Reality Back to Virtual Reality - A Collaborative Tool for Multidisciplinary Teams

The use of Virtual Reality (VR) supports larger strategic goals in the industry, including the geographically diverse working markets, large-scale projects and increased collaboration, both locally and globally.At the same time, VR is becoming increasingly popular as the technology becomes cheaper, more accessible, and better in terms of processing power and ease-of-use.The technology offers new possibilities for play, work and collaboration in various areas, from ordinary consumers to the industry.However, there are still many challenges before VR becomes a seamless experience for the user and can even cause restrictions in collaborative and multidisciplinary projects in some scenarios.One of the challenges with VR is the lack of a natural interface for interacting with the digital environment together with multiple users.In this paper we propose a new system combining a physical interface and a virtual environment that can be accessed through VR.Our aim is making collaboration in multidisciplinary teams in large-scale projects more productive through the joint effects of immersion and presence through VR and the tactility and intuitiveness of a physical interface.Our proof-of-concept prototype is presented, consisting of a physical 2D map and 3D printed objects that are being tracked with object detection and computer vision, which are linked to a digital twin.The digital twin can be accessed in VR in real time.We also explore solutions that can improve upon the prototype, such as producing an accurate physical 3D model of a real landscape to increase the resolution and authenticity of the physical model.A pilot experiment with 14 participants using our prototype has been conducted.Results of the study shows potential benefits of the system, such as the simplicity of a physical model promoting engagement, and the in-depth exploratory capabilities of the VR application enabling more solutions to be found.We also present limitations with potential solutions.
An Efficient Method for the Detection of Missing Person from Crowd

Numerous cases of missing are reported in Kerala.Our local department has the mechanism to watch every point on city.But it's not effectively helpful to seek out the missing person.It is found difficult to seek out an individual from crowd. During this paper, we present a mechanism to seek out the missing person or object from crowd by using opencv and image processing. The image of the person is subjected to image processing, which is taken as input for tracking the person or object from crowd using opencv. When the person or object is detected from video of crowd, an alert system is employed as a reminder. When an alert signal is produced, the authority can take necessary action within a short time. The objects are detected using one shot learning in real time scenarios. Main objective of the one shot learning is to detect various objects in real time video sequences. Once the network is trained by employing a single image of an individual, then the trained model can recognize that person in any angle. This real time analysis of the environment enables security and surveillance. This model can be implemented in different surveillance devices to detect attacks on many places like schools, government offices and hospitalswhere arms are completely restricted.
Efficient and Secure Implementation of NTRUEncrypt Using Signed Sliding Window Method

NTRUEncrypt is a public key cryptosystem based on hard problems over lattices. The dominant operation in NTRUEncrypt is convolution, i.e., multiplication over a quotient ring of polynomials. Based on the fact that a convolution has a highly regular structure, Lee et al. proposed the sliding window method for fast convolution of binary polynomials in 2013, which was then extended to ternary polynomials for ideal lattices by Akleylek, Alkim, and Tok in 2016. These sliding window methods reduce the cost of a convolution operation using look-up tables that store partial computation results related to repeated coefficient patterns. In this paper, we propose a signed sliding window method with side-channel resistance for NTRUEncrypt. The proposed method considers both positive and negative nonzero coefficients when constructing look-up tables. The new method not only accelerates convolution but also enables the application of power analysis countermeasures effectively. According to the experimental results, the constant-time implementation of the proposed method with timing and power analysis countermeasures accelerates the previously developed secure convolution method by up to 20%.