Reviewerly/exHarmony huggingface dataset:
Informal-to-Formal Word Conversion for Persian Language Using Natural Language Processing Techniques

A vast amount of text data is available today on the Internet due to the extensive use of social media. Valuable information can be extracted from this data through natural language processing. However, the process of information extraction can be difficult due to the informal forms of these texts. This paper aims to address this challenge by focusing on the conversion of Persian informal words to formal words by using the spell-checking approach. For this purpose, two datasets for formal and informal words were extracted from the four most visited news websites in Persian. Then Persian informal words were divided into multiple categories based on the level of changes required to build the formal equivalents. These were then converted to the formal forms according to their features. Statistical analyses combined with correction rules were used to produce a “candidate list” to find the best formal candidate equivalents. The performance of our conversion system was evaluated through people's comments extracted from the four most visited Persian (Farsi) news agencies. Results show that our proposed system can detect approximately 94% of the Persian informal words, with the ability to detect 85% of the best equivalent formal words. In addition, the comparison between the proposed system and two well-known Persian spell-checkers, Virastyar and Vafa, shows that in terms of detection and correction, the proposed system outperforms significantly. Further analysis shows that the time complexity of the proposed system is linear.
Preparing Students to Compose across Media for Various Audiences: Syllabus for an Upper-Level Professional Writing Course

Advanced Writing and Editing is an upper-level undergraduate course that gives students the theoretical knowledge to writing across media for a variety of audiences and contexts. Advanced Writing and Editing is an elective course within the English department’s Editing, Writing, and Media major. As digital technologies continue to expand the professional writing situations students find themselves in post-graduation, this class seeks to give students theoretical and technical knowledge needed to rhetorically approach digital and print writing and editing. The syllabus also uses contract grading and game theory to help students align their professional goals to the course’s learning outcomes.
Influence of post‐appreciation environment on the memory of visual art ‐toward an impressive exhibition planning at museums

Abstract In this study, we focus on the environment at the museum after the appreciation of art. We conducted an experiment, in which participants were instructed to watch short films and then perform one out of three activities (Activity ‐ A: Sitting, B: Walking, C: Talking quietly with the experimenter). According to the latent memory test following each activity, the participants recalled the content in elaborate detail after either sitting calmly (A) or walking (B) rather than after talking (C). This proved that even though the content is the same, the depth of the impression varies according to the activity performed after the appreciation.
Secure and Privacy-Respecting Documentation for Interactive Manufacturing and Quality Assurance

The automated documentation of work steps is a requirement of many modern manufacturing processes. Especially when it comes to important procedures such as safety critical screw connections or weld seams, the correct and complete execution of certain manufacturing steps needs to be properly supervised, e.g., by capturing video snippets of the worker to be checked in hindsight. Without proper technical and organizational safeguards, such documentation data carries the potential for covert performance monitoring to the disadvantage of employees. Naïve documentation architectures interfere with data protection requirements, and thus cannot expect acceptance of employees. In this paper we outline use cases for automated documentation and describe an exemplary system architecture of a workflow recognition and documentation system. We derive privacy protection goals that we address with a suitable security architecture based on hybrid encryption, secret-sharing among multiple parties and remote attestation of the system to prevent manipulation. We finally contribute an outlook towards problems and possible solutions with regards to information that can leak through accessible metadata and with regard to more modular system architectures, where more sophisticated remote attestation approaches are needed to ensure the integrity of distributed components.
Hacking Artificial Intelligence (AI) Engines for Organised Crime and Inciting Chaos

With the increased use of new technology in everyday applications, such as Artificial Intelligence, users rely on technology to guide them. What if a system that is trusted without a doubt can be manipulated and there are no warnings or detections? Even though users are being made aware of misinformation within social media and news outlets, we attempt to prove there are still vectors all users can be fooled by and never be aware. This also extends to manufacturers and technology companies that may incorporate specific application/APIs within their systems. As we test and prove that the popular Google Maps application can be affected by data manipulation attacks, and we are able to inject false traffic information that is displayed to other users. Injection of misinformation, combined with a 51% attack, traffic on any road could be controlled by an attacker causing chaos or used within organised crime. We also attempt to examine current AI security testing methodologies and attempt to contribute by creating a universal base AI security testing methodology, that is aimed at current and future Artificial Intelligence (AI) development. The further research provides a number of ideas and platforms that could also be affected by misinformation vulnerabilities, AI injection attacks and new concepts that could also be used to create chaos either by privacy-advocates, hacktivists or nation-states.
A Boosting Framework of Factorization Machine

Recently, Factorization Machine (FM) has become more and more popular for recommendation systems due to its effectiveness in finding informative interactions between features. Usually, the weights for the interactions are learned as a low rank weight matrix, which is formulated as an inner product of two low rank matrices. This low rank matrix can help improve the generalization ability of Factorization Machine. However, to choose the rank properly, it usually needs to run the algorithm for many times using different ranks, which clearly is inefficient for some large-scale datasets. To alleviate this issue, we propose an Adaptive Boosting framework of Factorization Machine (AdaFM), which can adaptively search for proper ranks for different datasets without re-training. Instead of using a fixed rank for FM, the proposed algorithm will gradually increase its rank according to its performance until the performance does not grow. Extensive experiments are conducted to validate the proposed method on multiple large-scale datasets. The experimental results demonstrate that the proposed method can be more effective than the state-of-the-art Factorization Machines.
Robot arm navigation using deep deterministic policy gradient algorithms

In this paper, the Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithm is employed to enable a double-jointed robot arm to reach continuously changing target locations. The experimentation of the algorithm is carried out by training an agent to control the movement of this double-jointed robot arm. The architectures of the actor and cretic networks are meticulously designed and the DDPG hyperparameters are carefully tuned. An enhanced version of the DDPG is also presented to handle multiple robot arms simultaneously. The trained agents are successfully tested in the Unity Machine Learning Agents environment for controlling both a single robot arm as well as multiple simultaneous robot arms. The testing shows the robust performance of the DDPG algorithm for empowering robot arm manoeuvring in complex environments.
THE ROLE OF INFORMATION AND COMMUNICATION TECHNOLOGY (ICT) TO ALLEVIATE POVERTY IN BANGLADESH

Poverty is one of the problems in third-world countries like Bangladesh. There has been an ongoing battle against challenging living conditions-overcrowding, floods, deforestation, erosion, soil depletion, and natural calamities. Quite a lot of programs have been tried since the independence of Bangladesh in 1971, and to tell the truth, most have failed. Each time an anti-poverty program fails, it gives more fuel to the richer class to argue that there's no point in trying to help the poor. However, this is an age of information technology. Almost all of the spheres of human life are contributed by Information technology. Information Technology can change the fate of the poor by helping them to be with the current time and happenings by ensuring their capabilities to access information. The Government of Bangladesh also realizes the importance of the issue, and recently they concentrated on transforming the government services to digitize. As one of the initiatives to empower the poor with information, GOB has launched Union Information and Service Centre (UISC) in all of the Union councils of the country. The private sector started digitization of operations earlier than the government. Now, as the government has also initiated the process, the journey will get a smooth and parallel speed to develop the socio-economic condition of the poor of the country. This paper attempts to explore the capabilities of ICT to reduce poverty in Bangladesh. The study will also try to suggest how ICT can be better used to eradicate poverty. ICT is a rapidly growing field in Bangladesh in recent times. The contribution of ICT in every sector of the country is trying to be addressed to improve the situation. This study is conducted by reviewing the studies in this line to suggest better policies to be formulated. The results show that ICT has excellent capabilities to alleviate poverty despite many challenges to be faced.
Encryption of Optically Compressed Medical Images Using Phase Matrix

Nowadays computer network is a blessing to modern society due to its various features which are especially suitable for cloud computing, teleconferencing etc. A lot of information transmission is the basic criteria of these applications. Unfortunately, cyber attackers are active everywhere and they try to misuse this facility. Therefore, this information should be transmitted securely. Moreover, this information should be compressed for proper optimization of bandwidth of the transmitted medium. In this communication, we propose a novel method for compression and encryption of medical images using optical process which is suitable for secure teleconferencing. For our research work, we have used sinusoidal amplitude grating. Medical images are modulated with the help of high frequency grating frequency and variable orientation angle. As a result of modulation, three spectrums have been generated for each image. One spectrum is the FT of the image (center spectrum) and other two are sidebands. Out of these two sidebands, only one sideband of each image is taken for filtering as the sidebands of the image are symmetrical to each other. Inverse Fourier Transform is applied at specific zones to filter the output images. As only one sideband is used for image recovering, therefore this method is effective for teleconferencing in rural medical system using optimum bandwidth. We have used chosen ciphertext attack to prove the effectiveness of our method and we have proved that our system is secure.
A modulation recognition method based on enhanced data representation and convolutional neural network

Signal modulation recognition is a key technique in the domain of spectrum monitoring and management. However, due to the complexity of signal modulation types and the lack of signal prior information, the performance of modulation recognition might degrade dramatically. In this paper, a modulation recognition approach based on enhanced data representation and neural network is proposed. Firstly, a deep convolutional neural network structure is designed for learning the latent features and identifies the modulation type of input signal. Then, in order to alleviate the information scarcity of received signal, the enhanced representation matrix of received spectrum data is constructed as networks inputs. Finally, the convolutional neural network is adjusted to adopt and combined with the enhanced data representation to form the presented modulation recognition scheme. The experiments results demonstrate that the proposed neural network is effective for modulation recognition and the enhanced data representation can effectively improve the performance of the neural network.
Picasso the Drawing Robot: An Application of Inverse Kinematics

With the recent advancement in the field of robotics, Robots have become versatile in size and form and can be programmed to do the most mundane as well as the most sophisticated and specialized tasks. Hence, with the advancement in this ever-demanding field comes the need to have a strong understanding of various concepts of automation and robotics. Inverse and forward kinematics are some of the key techniques in the automation and the working of robots, and a deep under-standing of the same is required for designing effective algorithms for strenuous tasks. Hence, in this manuscript, we have designed and implemented a robotic system that uses algorithms based on the concept of Inverse-Kinematics, travelling salesman/shortest path, etc, for achieving the basic task of drawing. The proposed simulated robot (Picasso) can draw basic shapes like: circles, squares, pentagons, and complex figures like: cat, flower, etc. As a part of our experimentation, we have analyzed the efficiency of our system over various images and the experimental results proved the efficiency of the system over basic shapes, but for complex figures, the proposed system fails to perform efficiently. Further, we discuss the use of the proposed system in extending fields of applications in robotics and automation.
Development of Blended Learning Media Using Character-Based Flipbook Smartphone

This study aims to determine the design stages of developing blended learning smartphone media learning products and to determine the feasibility of integral learning media using the developed flipbook. This study uses a small-scale trial on FHIP UHAMKA while for large-scale trials conducted by Malaysian UTP with 29 respondents including Physics Education FKIP UHAMKA, Mathematics Education FKIP UHAMKA and also Malaysian UTP. The method used is Research and Development (R&D) using the Brog and Gall development procedure. The results of the media interface test obtained a percentage of 77.18%. This shows that the products developed included in the category are very feasible to use while for the effectiveness of students as teaching material in the learning process, the average of the effectiveness of students is 83.26%, the value obtained can be said to be very well seen from the Likert scale index used, then from the assessment of the effectiveness of media students it is feasible to be used in integral learning. Blended learning media with character given to one class in FKIP UHAMKA, obtained an average post-test score of 48.04. The data obtained, the character development of the character questionnaire was 75.04.
Online Supplementary Material: On the Equilibrium Strategies for Time-Inconsistent Problems in Continuous Time

This document contains supplementary materials to "On the Equilibrium Strategies forTime-Inconsistent Problems in Continuous Time" by X.D. He and Z.L. Jiang. The paper to which these supplementary materials apply will appear in the SIAM Journal on Control and Optimization and is available at the following URL: https://papers.ssrn.com/abstract=3308274.
On the Problem of Visualization of Big Graphs for Infrastructure Engineering

The work is devoted to the development of an approach allowing us to visualize complex infrastructure networks. We offer the way of construction of the graph based on the formal description of the infrastructure on macro- and microtopology. The results of testing various layout algorithms on the plane of the graph representing the complex infrastructure network are presented. The method of automatic graph layout is offered, which allows to efficiently perform the layout of a graph of a large-scale infrastructure network. The method of clustering the graph of the infrastructure network, taking into account the strength of the branches and the number of clusters unknown in advance, is proposed. The method is implemented in R language with help of igraph package for visualization. Different layout schemes of nodes are compared: circle, star, tree, grid, force-directed. Using in hierarchical clustering, we illustrate the arrangement of the clusters produced by the corresponding analysis. An example of graph layout for the topology of the Western States Power Grid of the United States for 4990 nodes is considered.
Rethinking Print Journal Subscriptions at a Large Research University

In 2020–2021, the University of Connecticut (UConn) Library ceased more than 90% of its print journal subscriptions for the general collections. This decision was the outcome of a data-based review and cost-benefit analysis. Staff considered factors such as online availability, number of WorldCat holdings, scan-on-demand and interlibrary lending and borrowing activity, and hidden costs of storage and staff time for processing. Recorded usage proved all but nonexistent, with an annual average use of one per title. Additionally, print journals were inaccessible to patrons because of the closure of library stacks during the COVID-19 pandemic. We did not conduct an extensive qualitative review. UConn Library’s decisions broke new ground among U.S. research universities, which have winnowed their print subscriptions over the decades but rarely made a decisive break from print. This article will discuss the project, process, decisions, and data and will share UConn’s assessment strategies and criteria for reinstituting subscriptions. At stake was the principle that libraries must use their scarce resources effectively—at any scale.
The Story of a Re-Operating Windmill

Abstract Research on the history of the development of grain milling structures is a priority topic of the Department of Technical Sciences of the Transylvanian Museum Association. Quite a few publications and study volumes contain the results of the research. The present dissertation presents another grinding structure, the windmill, its reconstruction, creating a connection between the theoretical foundations and the practical implementation. Although it was built as a museum and tourist object, it is also a significant work from the point of view of technical history. The Felsőszentiván windmill is powered by a 14-meter-diameter windmill. The mill has a two-stage accelerator gear that increases the low, non-grinding speed of the wind turbine for the rotation typical of grindstone mills. The upper stones of a pair of grindstones rest on a vertical axis, and by raising this the gap between the stones can be adjusted. This determines the particle size of the grind. The windmill consists of three functional parts: a windmill, an accelerator gear and a grain mill made up of a pair of grindstones.
Value-Based Fuzzy Approach for Non-functional Requirements Prioritization

Non-functional requirements (NFRs) are often addressed late in a project and, in turn, can get less attention in the requirements prioritization (RP) process. For various reasons, RP may happen based on functional requirements (FRs) only. While many approaches for prioritizing NFRs have been published, these are known also for some limitations, e.g. not being scalable, being domain-specific and not able to cope with changing requirements. In this paper, we proposes a value-based fuzzy approach for prioritizing NFRs together with FRs. Our proposed approach takes into account (1) the relationships of NFRs with FRs using experts’ evaluations and fuzzy logic, and (2) the dependencies among both types of requirements and also the interdependencies that particularly exist among the NFRs themselves. We evaluated our proposal by conducting a real-world case study of an ATM system. We also compared the list of prioritized NFRs with the list of NFRs prioritized by different stakeholders on the basis of classification factors. The results of applying the proposed approach on NFRs of ATM system show that the approach produces a conflict-free and consistent list of prioritized NFRs.
B++: A High-Throughput Proof-of-Work based Blockchain with Eventual Consistency

Blockchain plays an important role in a variety of fields because it enables peers to cooperate with each other in a decentralized database. In Bitcoin-like protocols, each block is always mined by all miners, which implies a design philosophy that all transactions should be protected with the highest security. As a result, the throughput is bounded in these protocols. In real-world applications, the security requirement of transactions differs. Therefore, we present B++, a scalable blockchain structure, which breaks the limits of blockchain security so that B++ can distribute system resources efficiently to confirm transactions with different requirements. In our analysis, we show that B++ inherits eventual consistency in Bitcoin-like protocols. Moreover, our open-source simulation indicates that the throughput of B++ can be more than 100 times greater than that of baseline approaches; besides, the number of timeout transactions in B++ can decrease by up to 70% than that in a Bitcoin-like blockchain.
Multiparameter Stochastic Control Model Based on the Random Sequence Canonical Expansion

A stochastic mathematical model of the control system for an arbitrary number of coordinates of the object under study and control parameters is obtained. The developed mathematical model allows one to fully take into account the features of random sequences of changes of coordinates and control parameters, as well as to fully use all known a posteriori and a priori data about the control object. To obtain the model, the apparatus of nonlinear vector canonical expansions is used. The work presents diagrams that reflect the specifics of determining the parameters of a multiparameter stochastic control model and the regularities of its functioning. The obtained mathematical model has wide possibilities of practical application for solving problems of controlling objects of various nature in conditions of uncertainty.
Persona: Real-Time Neural 3D Face Reconstruction for Visual Effects on Mobile Devices

We present Persona, a real-time human face-oriented visual effect solution on mobile devices. Persona consists of a 3D face tracker with multi-scale reconstruction models for different-level of mobile devices and a visual effect authoring tool. Our face tracker is able to reliably predict a sequence of facial and illumination parameters from a monocular video in real-time. Those parameters can then be used to develop many interesting applications. We demonstrate that our method outperforms existing state-of-the-art work about 3D face reconstruction on mobile devices and showcase results generated by our tool.