Reviewerly/exHarmony huggingface dataset:
Internal Control, AIS Quality and Accounting Information Quality

This research was conducted to analyze the influence of accounting information system (AIS) quality as an intervening variable which is influenced by internal control and affecting the quality of accounting information (AI). Rigorous competition especially in developing the resources owned and winning students, forcing higher education, in particular, private universities to prepare dynamically and planned so that universities can have the ability to innovate. This will in the end support their business continuity and can only be done when private universities are supported by the information of quality. The quality of information is useful in decision making, which determines the sustainability of an organization. Various researches state that information quality was obtained from the application of the quality of AIS. This research was executed in 16 private universities in West Java -- Indonesia and 81 data were collected. The analytical technique used was Structural Equation Modelling (SEM) using SmartPLS 3.0. application. The result of the hypothesis testing indicates a positive relationship between the effectiveness of internal control and the influence of the quality of the AIS. Eventually, the quality of AIS influences the quality of AI positively.
Design and Implementation of Public Opinion System in Colleges

With the development of the network, the campus network has become the main method of information dissemination between college teachers and students. Therefore, the public opinion of colleges and universities has rapidly developed. Teachers and students of major universities use the public opinion system to obtain the required information and express their various opinions, but there are also some negative public opinions, which will have a negative impact on the ideology of college students. Therefore, an applicable university public opinion system becomes increasingly important.
Attribute-Based User Revocable Data Integrity Audit for Internet-of-Things Devices in Cloud Storage

Mobile crowdsensing (MCS) is a sensing paradigm exploiting the capabilities of mobile devices (Internet-of-Things devices, smartphones, etc.) to gather large volume of data. MCS has been widely used in cloud storage environment. However, MCS often faces the challenge of data integrity and user revocation issues. To solve these challenges, this paper uses attribute-based revocable signature mechanisms to construct a data integrity auditing scheme for IoT devices in the cloud storage environment. Users use attribute private keys to generate attribute signatures, and limit the user’s permission to use shared data through access policy control. Only when the user attribute is included in the global attribute set, and the attribute threshold is not less than the specified number, the user can use the attribute key for the data to generate a valid signature that can be authenticated under the control of the signature strategy. At the same time, the group manager (GM) can send secret information to a third-party auditor (TPA) to track the creator of the signature, to withdraw the user’s access to data when the business changes, and realize the safe revocation of user group membership. Formal security analysis and experimental results show that the proposed data-auditing solution is suitable for IoT devices in the cloud storage environment with respect to security and performance.
Channel Correlation Based Robust Audio Watermarking Mechanism for Stereo Signals

The music industry, especially the Internet-based platforms, is confronting the challenge of protecting the copyright. The traditional watermarking algorithms have limited robustness against the desynchronization attacks, such as time-invariant-pitch-scaling (TIPS) and pitch-invariant-time-scaling (PITS). Furthermore, only a very small part of the existing methods is designed for stereo signals, which is one of the most widely used formats in web-based music platforms. In this paper, we propose a robust watermarking mechanism for stereo signals for web-based applications. We first transform both of the left channel signal and the right channel signal into the frequency domain. Then the watermark bits are embedded by adjusting the Pearson correlation coefficient (PCC) between the frequency coefficients of the left channel and the right channel in the selected frequency band. Since TIPS and PITS can be approximated as stretching the signal in the frequency domain, the proposed channel correlation based watermarking mechanism is resilient to these attacks. Besides, the proposed method is also robust to other common attacks. Theoretical analysis and simulation results show the validity of the proposed method.
An Efficient Sorting Algorithm for binary data

In this era of fast technological advancement, managing data is a major problem in the field of computer science. Many algorithms are developed to handle this task in which sorting of data is a basic requirement. Sorting of data is nothing but arranging a random set of data elements into sequential ascending/descending order. In this paper, we proposed an efficient sorting algorithm named bin_sort which is a short form of binary sorting. Bin_sort considers the binary version of the data elements to sort the data as its name indicates. Bin_sort performs an iterative recursive operation in time complexity of O(n log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> n) to perform the sort operation on the data, which clearly indicates bin_sort is more efficient for binary data and on large input dataset than most of the existing sorting algorithms.
Building a model for digital content management in agricultural university libraries in Bangladesh

The purpose of this study is to build a model for Digital Content Management (DCM) in agricultural university libraries in Bangladesh. The model focuses on identifying the necessary tools and techniques required for DCM and finding out the problems and prospects of DCM for enhancing resource sharing among the agricultural universities in Bangladesh. To investigate these issues, surveys were used to collect data from 245 participants, who were students and teachers from seven public agricultural universities in Bangladesh. The findings reveal some hindrances to the establishment of DCM, such as lack of constant power supply, limited bandwidth speed, some users have lacking fundamental IT knowledge and shortage of digital resources, in addition to some suggestions for improving DCM in these libraries. Based on the survey results and related literature, the DCM model for agricultural university libraries of Bangladesh has been proposed and discussed.
Exploring Unknown States with Action Balance

Exploration is a key problem in reinforcement learning. Recently bonus-based methods have achieved considerable successes in environments where exploration is difficult such as Montezuma's Revenge, which assign additional bonuses (e.g., intrinsic rewards) to guide the agent to rarely visited states. Since the bonus is calculated according to the novelty of the next state after performing an action, we call such methods as the next-state bonus methods. However, the next-state bonus methods force the agent to pay overmuch attention in exploring known states and ignore finding unknown states since the exploration is driven by the next state already visited, which may slow the pace of finding reward in some environments. In this paper, we focus on improving the effectiveness of finding unknown states and propose action balance exploration, which balances the frequency of selecting each action at a given state and can be treated as an extension of upper confidence bound (UCB) to deep reinforcement learning. Moreover, we propose action balance RND that combines the next-state bonus methods (e.g., random network distillation exploration, RND) and our action balance exploration to take advantage of both sides. The experiments on the grid world and Atari games demonstrate action balance exploration has a better capability in finding unknown states and can improve the performance of RND in some hard exploration environments respectively.
Improving the Performance of MCTS-Based µRTS Agents Through Move Pruning

The following topics are dealt with: computer games; learning (artificial intelligence); virtual reality; computer aided instruction; Monte Carlo methods; neural nets; game theory; tree searching; artificial intelligence; and serious games (computing).
Scalable and Efficient Spatial-Aware Parallelization Strategies for Multimedia Retrieval

Similarity search is a key operation in several multimedia applications, including online Content-Based Multimedia Retrieval (CBMR) services. These applications have to deal with very large databases and are submitted to high query rates. In this context, scalability in distributed memory system is critical to assemble the required computing power and memory space. However, we have identified that the Data Equal Split (DES) parallelization and associated data partition strategy employed by the related works on the domain have limitations in terms of efficiency and scalability. Therefore, in this paper, we developed and implemented a framework for similarity search execution on distributed memory machines and proposed a novel class of data partition strategies that takes into account the data spatial organization in its distribution. This approach leads to a reduction in communication traffic and in costs associated with processing each task in local searches carried out in the distributed machine. Our approach attained a speedup of 2.4× on top of DES in the baseline case (5 nodes) and also achieves higher scalability efficiency and is 14.5× faster when 160 nodes are used. In fact, our novel data organization led to superlinear scalability in all configurations evaluated.
Algorithm based on Byzantine agreement among decentralized agents (BADA)

Distributed consensus requires the consent of more than half of the congress to produce irreversible results, and the performance of the consensus algorithm deteriorates with the increase in the number of nodes. This problem can be addressed by delegating the agreement to a few selected nodes. Since the selected nodes must comply with the Byzantine node ratio criteria required by the algorithm, the result selected by any decentralized node cannot be trusted. However, some trusted nodes monopolize the consensus node selection process, thereby breaking decentralization and causing a trilemma. Therefore, a consensus node selection algorithm is required that can construct a congress that can withstand Byzantine faults with the decentralized method. In this paper, an algorithm based on the Byzantine agreement among decentralized agents to facilitate agreement between decentralization nodes is proposed. It selects a group of random consensus nodes per block by applying the proposed proof of nonce algorithm. By controlling the percentage of Byzantine included in the selected nodes, it solves the trilemma when an arbitrary node selects the consensus nodes.
Using Augmented Reality Technology to Learn Cube Expansion Diagram in Spatial Geometry of Elementary Mathematics

Spatial geometry has always been the key and difficult part in the curriculum of elementary mathematics. Compared with traditional teaching methods using, Augmented Reality (AR) technology shows its potential to teach spatial geometry through visualization, interaction and situation. This study focused on cube expansion diagram in spatial geometry and developed an AR learning tool based on inquiry for the course. The research aimed to examine the effectiveness of AR-assisted math lesson by designing and implementing two lessons in an elementary school. Two classes including 92 students in grade 5 participated in this study, and they were assigned to an experimental and a control group. This study adopted mixed methods methodology and utilized pre/post-tests, questionnaires and interviews. Results showed that students can general accept of using AR to learn spatial geometry, and AR-assisted teaching methods significantly improved students' learning outcome. It is also noticed that students' learning level affected their performance in AR-assisted math lessons.
A Proficient Model to Classify Bangladeshi Bank Notes for Automatic Vending Machine Using a Tıny Dataset with One-Shot Learning &amp; Siamese Networks

Automatic vending machine is a necessity at this technological era. It's a step to go toward the vendor-less shop management, which supports the commandment of the 4th Industrial Revolution. But image recognition system needs a lot of data to get the pattern. Deep learning applications is very computationally extravagant for getting good features and find in many cases that little data cannot give good learning features. We have used and reworked the architecture of Siamese Neural Network for One-shot learning to recognize the Bangladeshi bank notes with a tiny dataset. In this article we analyzed 20 images only for 5 different Bangladeshi bank notes what people used regularly. This research will help general people to get better experience with vending machines which can recognize notes with one data example only. We used 5 notes (5,10,20,50,100 TAKA) and get excellent result with 97.38% accuracy using help of convolutional architecture.
The impact of the virtual learning environment in managing the knowledge transmitted to students

This research follows the evolution, role and interactions between e-learning and specific social/educational activities, under crisis conditions too. The impossibility of the physical presence of teachers and students in educational institutions has automatically led to the use of online platforms. Taking account of the changes in learning activities in the context of the effects of the pandemic facing all mankind, we wanted to highlight the current state of research in the Web of Science for keywords "e-learning and University".
Fine-Grained Relevance Annotations for Multi-Task Document Ranking and Question Answering

There are many existing retrieval and question answering datasets. However, most of them either focus on ranked list evaluation or single-candidate question answering. This divide makes it challenging to properly evaluate approaches concerned with ranking documents and providing snippets or answers for a given query. In this work, we present FiRA: a novel dataset of Fine-Grained Relevance Annotations. We extend the ranked retrieval annotations of the Deep Learning track of TREC 2019 with passage and word level graded relevance annotations for all relevant documents. We use our newly created data to study the distribution of relevance in long documents, as well as the attention of annotators to specific positions of the text. As an example, we evaluate the recently introduced TKL document ranking model. We find that although TKL exhibits state-of-the-art retrieval results for long documents, it misses many relevant passages.
Towards an Extended Requirements Problem Formulation for Superintelligence Safety

Under the headline "AI safety", a wide-reaching issue is being discussed, whether in the future some "superhuman artificial intelligence" / "superintelligence" could pose a threat to humanity. In addition, the late Steven Hawking warned that the rise of robots may be disastrous for mankind. A major concern is that even benevolent superhuman artificial intelligence (AI) may become seriously harmful if its given goals are not exactly aligned with ours, or if we cannot specify precisely its objective function. Metaphorically, this is compared to king Midas in Greek mythology, who expressed the wish that everything he touched should turn to gold, but obviously this wish was not specified precisely enough. In our view, this sounds like requirements problems and the challenge of their precise formulation. Hence, we take a new perspective on the problem by exploring it using insights from requirements engineering (RE). In addition, the overall issue calls for a major RE endeavor, figuring out the wishes and the needs with regard to a superintelligence, which will in our opinion most likely be a very complex softwareintensive system based on AI. In this paper, we introduce the idea of developing a new theoretical formulation of an extended requirements problem applicable to it, since it involves goals of both stakeholders and of the AI-based system-to-be-built.
BOOKiiIT - Designing a Venue Booking System (Technical Demo)

Every academic institution has its system of managing the bookings of spaces. In an academic institution, rooms in different buildings may be handled by separate departments for booking purposes and most are carried out using the mail system. People using this system face numerous challenges like the absence of knowledge of available spaces, tediousness, improper management of bookings, clashes in bookings, communication failures, etc. These indicated a need for a well-defined, efficient, visualizable, and user-friendly space management system. BOOKiiIT, a Flutter app was proposed and designed using design thinking and PACT framework to make it user friendly and efficient in the different contexts of use. This app was implemented for the Indraprastha Institute of Information Technology (IIITD), but it can be easily replicated for other institutions.
Hidden Markov Linear Regression Model and its Parameter Estimation

This article first defines a hidden Markov linear regression model for the purpose of further studying the mutual transformation between different states in the linear regression model, and the regression relationship between the dependent variable and the independent variable in each state. And then, K-means clustering analysis methods are used to identify the hidden states of observed data, and the maximum likelihood estimation of the hidden state transition probability matrix elements is obtained by using the maximum likelihood estimation method, and parameter estimation of unknown parameters in linear regression model is also presented by using the least squares method. Finally, the observation vector set is generated according to the defined model, and the empirical simulation demonstrates that the parameter estimation method shown in this work is reliable.
Affecting Mood, Motivation and Productivity in Requirements Engineering Meetings and Beyond: A Research Vision

In Requirements Engineering, a lot of communication takes place in conversations and meetings, such as workshops, focus groups, interviews, and review sessions. Research has shown that interactions in meetings influence the group affect after the meeting - and hence the participants' motivation for (further) contributing to the project. However, it remains unclear, how positive affect can be achieved. In this vision paper, we propose to analyze interactions in meetings to find relations between the participants' behavior and affect afterwards. This allows to identify "good" behavior in meetings to smooth the way to satisfied project team members and successful projects.
Sentiment Analysis of Product Reviews Based on JST Model

Product reviews are information that users comment after purchasing products online, and it contains user's sentiment information about the product. Considering that the e-commerce platform implements personal recommendation of products based on browsing information of product. We propose a sentiment analysis method of product reviews based on the Joint Sentiment/Topic model, which can implement the personal recommendation of products based on the sentiment orientation of product reviews. Firstly, we build a sentiment dictionary for analyzing product reviews by integrating multiple external sentiment dictionaries. Secondly, we give a method to mark the sentiment polarity of the product reviews text. It can tag the sentiment polarity of the product reviews text to generate prior knowledge for the Joint Sentiment/Topic model. Finally, we give the formula for calculating the value of sentiment orientation on product reviews based on the Joint Sentiment/Topic model. Experiments show that the proposed method can effectively obtain the sentiment orientation of product reviews, making the product recommendation of the e-commerce platform more scientific and reasonable.
Index Obfuscation for Oblivious Document Retrieval in a Trusted Execution Environment

This paper studies privacy-aware inverted index design and document retrieval for multi-keyword document search in a trusted hardware execution environment such as Intel SGX. The previous work uses time-consuming oblivious computing techniques to avoid the leakage of memory access patterns for privacy preservations in such an environment. This paper proposes an efficiency-enhanced design that obfuscates the inverted index structure with posting bucketing and document ID masking, which aims to hide document-term association and avoid the access pattern leakage. This paper describes privacy-aware oblivious document retrieval during online query processing based on such an index. Both privacy and efficiency analyses are provided, followed by evaluation results comparing proposed designs with multiple baselines.
PP-TAN: a Privacy Preserving Multi-party Tree Augmented Naive Bayes Classifier

The rapid growth of Information and Communication Technologies emerges deep concerns on how data mining techniques and intelligent systems parse, analyze and manage enormous amount of data. Due to sensitive information contained within, data can be exploited by potential aggressors. Previous research has shown the most accurate approach to acquire knowledge from data while simultaneously preserving privacy is the exploitation of cryptography. In this paper we introduce an extension of a privacy preserving data mining algorithm designed and developed for both horizontally and vertically partitioned databases. The proposed algorithm exploits the multi-candidate election schema and its capabilities to build a privacy preserving Tree Augmented Naive Bayesian classifier. Security analysis and experimental results ensure the preservation of private data throughout mining processes.
A Novel Expert Finding System for Community Question Answering

With the popularity of community question answering (CQA) sites, the research on identifying the expert users in online communities attracted increasing attention. We present a novel expert ranking algorithm based on the quality of user posts and the authority of user in community, and the similarity between the knowledge tags of users and questions in CQA sites is adopted in our scheme. Experimental results show that our scheme has better performance and accuracy under the same background with an amount of data samples.
Personalised Human Device Interaction through Context aware Augmented Reality

Human-device interactions in smart environments are shifting prominently towards naturalistic user interactions such as gaze and gesture. However, ambiguities arise when users have to switch interactions as contexts change. This could confuse users who are accustomed to a set of conventional controls leading to system inefficiencies. My research explores how to reduce interaction ambiguity by semantically modelling user specific interactions with context, enabling personalised interactions through AR. Sensory data captured from an AR device is utilised to interpret user interactions and context which is then modeled in an extendable knowledge graph along with user's interaction preference using semantic web standards. These representations are utilized to bring semantics to AR applications about user's intent to interact with a particular device affordance. Therefore, this research aims to bring semantical modeling of personalised gesture interactions in AR/VR applications for smart/immersive environments.