Reviewerly/exHarmony huggingface dataset:
Implementation of 3D Object Models for Mobile Applications in UI/UX Design Using SceneView API

The improvement of mobile application has improved over the years with the number of libraries and APIs that are shared for the developers. Resources such as APIs and libraries will brings more items that developers can use and utilized in their software projects mainly mobile applications. Right up to this moment, finding a complete documentation of integrating a 3D object model to a mobile application is very difficult. This brings up the question on to "why developers are not using 3D models to be used in their mobile applications?" It is a valid question because developers will always try and use the newest and latest thing on the market to build their mobile applications, for a 3D object model not included in that category is surprising. For this paper, a deep dive into 3D visualizations API called SceneView will be conducted to see if the 3D model technologies for mobile application is relevant for the current style of mobile application design and understand on what a 3D model can do for a mobile android application when used correctly.
OPSUM: An Opinion Summary Generator Model for Customer Feedback on Restaurants

Recently, intense research and development have been conducted in the research area of Artificial Intelligence, to find various techniques to implement this and create more advanced versions of the technology. Emotion Artificial Intelligence or opinion mining happens to be one such technique that provides advancement to Artificial Intelligence. By definition, opinion mining refers to the collection of different opinions or reviews of people with respect to a particular subject. Classification and categorization of these reviews in a consolidated manner of broad categories are known as summarization of these opinions. The representation of this information thus obtained in the forms of models, charts, and graphs is known as visualization of this summary. In the past couple of years, online platforms have increased rapidly. These platforms advanced themselves to not just providing items that can be purchased but also other activities that can be done with the help of the internet such as reservations, financial transactions. The services also include purchasing/booking various kinds of tickets, for traveling or for entertainment, or ordering food using these online portals. In the following work, the various restaurants which deliver food via online portals and their services have been discussed with respect to the feedbacks or opinions received by customers. In this work, the various reviews left by the customers have been summarized and presented and categorized manner classifying them as positive, negative. These have also been visualized for a better understanding and help in comparison to two or more restaurants. The main goal of this work is to ease the difficulty faced by people to decide which place to go and will also increase the efficiency as the time consumed will be reduced as compared to visiting each website and reading the reviews.
Secured Ticketless Booking System to Monuments and Museums Using Cryptography

The background of our prototype—‘Safar’, is to develop a web-based application based on the effectiveness of QR codes for authenticating an E-ticket. The aim is to provide a simple and sophisticated system to generate an E-ticket, it will be able to increase ticket sales. The proposed system will allow the user to book a ticket for a particular monument on a particular day. After booking a ticket, the user is directed to the payment portal where the money transaction is carried out using the RazorPay testing API. If the payment is successful, an E-ticket containing the QR is generated which is sent to the email of the tourist. The tourist then scans this QR code at the monument to gain entry into the monument. The proposed website also contains an admin portal where the live count of visitors with their credentials and the history of tourists for that monument is stored. All the user data will be encrypted using a novel cryptographic approach before it is saved to the database, to provide added security. As tourism accounts for 7% of India’s GDP, the proposed system aims to attract tourists by giving them an all-round, complete experience. It not only reduces the time to wait for a long duration in queues but also makes the process of visiting a monument seamless and hassle free. The proposed system is developed using React, HTML, CSS, JavaScript, and Django. It will give a boost to the heritage and culture of our country and also ensure a greener and cleaner economy.
Review on Use of Agile Techniques in Software Development over Traditional Customs

Software development using agile techniques is gaining popularity nowadays just because of its benefits over traditional development. Many software practitioners and researchers are taking interest in agile development, but they need to be aware about all agile techniques currently available and about all the challenges they will face while migrating to the agile technologies. It has been seen that only big companies are getting benefits of agile, but not small or startup companies, while agile can be beneficial more for such type of organization. More awareness for agile development should be spread. This paper presents agile goals, principles, team ethics, and moral values with different agile methodologies which will help to create a truly agile team for developing software.
Logical Interpretation of Omissive Implicature

Implicature is a linguistic concept allowing inferences about what is said during interaction. However, it differs from an implication in that does not involve a definition or truth tables in a logic. In particular, an omissive implicature leads to inferences about what is omitted or not said. Omission in linguistic terms brings us to the intention of remaining silent about something by whatever reason. That is, omission is the word that is not uttered. In this research, a semantics was formulated to explain omission in testimonies, as well as in the context of dialogues, where its role is common. In testimonies, we achieved a logic-based knowledge representation, allowing reasoning through Answer Set Programming. These allowed to generate models illustrating the implications of silence in several logical-linguistic puzzles. Puzzles were taken as case study given that they state, in simple or everyday language, common situations, requiring the use of arithmetic, geometry, or logic, for its solution. In dialogues, a procedure was developed to make decisions, based on answers and a record (knowledge base) of the occurrences of omission, while maintaining the communication process. The procedure was oriented to psychotherapy interviews, where the Beck Inventory was extended to include silence, to assess the degree of depression of a person.
Low Communication Secure Computation From Semi-Trusted Hardware

In privacy-preserving machine learning and many other applications, the involved parties want to obtain the computation result without revealing their private inputs. Secure computation aims to solve this problem, but current secure computation protocols often fail to provide efficient solutions due to large communication, especially in a real-life Internet network where the bandwidth and the delay can be unsatisfying. Assuming the existence of a trusted hardware component that is resilient to side-channel attacks and will faithfully compute a pre-agreed program, secure computation can be realized by each party sending its input to the hardware and receiving the execution result back. However, a recent work of Lu <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al</i> . (ESORICS’21) points out that the hardware components can’t be fully trusted. In this work, we improve the semi-trusted hardware model of Lu <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al</i> ., and we propose secure computation protocols with low communication in the new model. We observe that the ESORICS’21 two-party computation protocol have some security flaws; in this work, we fix them and improve its online efficiency. Moreover, we propose an efficient constant-round secure multi-party computation protocol which has a communication cost of ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> – 1)λ + 2( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> – 1)ℓ bits, where <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</i> is the number of the parties, λ is the security parameter and ℓ is the input/output size. The computation cost of our multi-party protocol is also much smaller than current best-known constant-round protocols.
Efficient and Privacy-Preserving Arbitrary Polygon Range Query Scheme Over Dynamic and Time-Series Location Data

Location-based services (LBSs) provide enhanced functionality of mobile applications and convenience for mobile users, which plays a more and more remarkable role in people’s daily life. In LBSs, spatial range query is an essential tool for users to find interesting points in a specific region. However, during spatial range query, it is necessary for data owners and query users to exchange their location data, and the leakage of private location information has drawn significant attention in both governmental and social aspects. Meanwhile, most existing location privacy protection schemes only focus on achieving regular geometry range query over static location datasets. In this paper, we present an efficient and privacy-preserving arbitrary polygon range query scheme, named EPAPRQ. With EPAPRQ, the arbitrary and fine-grained polygon range query can be executed over a dynamic and time-series location dataset with privacy protection. Specifically, in EPAPRQ, an arbitrary polygon range query algorithm is first introduced with sub-range query technique. Then, to protect the private location information of the data owner and query users, a series privacy-preserving data computation protocols are constructed with a symmetric homomorphic encryption algorithm, and a ciphertext-based location dataset updating strategy is also designed. Finally, we propose a double filtration method through combining the quadtree index structure and minimum bounded rectangle, which is able to greatly improve the query efficiency over ciphertexts. Detailed security analysis shows that the sensitive location information in EPAPRQ can be well protected. Furthermore, we evaluate the performance of EPAPRQ in the real map, and the results demonstrate that EPAPRQ is indeed efficient.
DRAM Bender: An Extensible and Versatile FPGA-Based Infrastructure to Easily Test State-of-the-Art DRAM Chips

To understand and improve DRAM performance, reliability, security, and energy efficiency, prior works study characteristics of commodity DRAM chips. Unfortunately, state-of-the-art open source infrastructures capable of conducting such studies are obsolete, poorly supported, or difficult to use, or their inflexibility limits the types of studies they can conduct. We propose DRAM Bender, a new FPGA-based infrastructure that enables experimental studies on state-of-the-art DRAM chips. DRAM Bender offers three key features at the same time. First, DRAM Bender enables directly interfacing with a DRAM chip through its low-level interface. This allows users to issue DRAM commands in arbitrary order and with finer-grained time intervals compared to other open source infrastructures. Second, DRAM Bender exposes easy-to-use C++ and Python programm ing interfaces, allowing users to quickly and easily develop different types of DRAM experiments. Third, DRAM Bender is easily extensible. The modular design of DRAM Bender allows extending it to: 1) support existing and emerging DRAM interfaces and 2) run on new commercial or custom FPGA boards with little effort. To demonstrate that DRAM Bender is a versatile infrastructure, we conduct three case studies, two of which lead to new observations about the DRAM RowHammer vulnerability. In particular, we show that data patterns supported by DRAM Bender uncover a larger set of bit-flips on a victim row than those commonly used by prior work. We demonstrate the extensibility of DRAM Bender by implementing it on five different FPGAs with DDR4 and DDR3 support. DRAM Bender is freely and openly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/CMU-SAFARI/DRAM-Bender</uri> .
Generalized Transfer Extreme Learning Machine for Unsupervised Cross-Domain Fault Diagnosis With Small and Imbalanced Samples

In most engineering scenarios, the collected datasets typically have small and imbalanced characteristics and usually possess different data distributions between variable working conditions. Thus, several existing diagnosis models trained by small and imbalanced samples under one working condition may suffer from low accuracy for fault data under another working condition. To effectively alleviate such a problem, a novel domain adaptation method, referred to as a generalized transfer extreme learning machine (G-TELM), is proposed in this study. First, in a random feature space, marginal and conditional distribution discrepancy between source and target domains is minimized based on projected maximum mean discrepancy (MMD). Second, in order to give varied attention to different faulty samples, a cost-sensitive source domain classifier is utilized to reduce the difference between types of misclassification loss for small and imbalanced source data. Third, misclassification loss of target data is used to minimize the difference between the network’s outputs and prediction labels of target data. Finally, L2-norm regularization is leveraged to constrain network output weights, and they are iteratively updated to optimize the transferability of model G-TELM. Experimental results of 168 transfer tasks with varying difficulty verify that the proposed model G-TELM can achieve an accurate diagnosis and outperform several state-of-the-art domain adaptation methods.
An Intelligent Framework for Movie Recommendation Through Online Social Media

The production, promotion, and distribution of content in the electronic media and entertainment sector must adapt to new strategies. The reason for this is that current customers can search for and access content on any device, at any time, from anywhere. The world has entered a media-rich information period due to the proliferation of online services and flexible innovations. An efficient recommender system always makes sure to record the users’ actual preferences and only makes recommendations for items that the user genuinely wants. Recommender systems have been used for two decades to recommend goods, contents, and services to online users in a variety of applications. Although recommender systems have been successful in many application domains, there are still a number of problems that limit their effectiveness. This paper suggests a hybridized algorithm for a movie recommendation system that uses a particle swarm optimization-based crow search algorithm. The suggested model considered both the most recent and previous user ratings and conducted statistical analysis on a real dataset. Additionally, when performing particle swarm optimization, the items’ contents are taken into consideration. As a result, the recommender system’s data sparsity issue is greatly diminished. More individualized movie track recommendations are made possible by the concept of hybridization.
Handwritten Character Recognition from Image Using CNN

Handwritten character identification has always been an intriguing area of study in the realm of pattern recognition in image processing. Because of its numerous applications, the requirement for identifying handwritten characters is growing every day. Many scholars have defined their work in this field, and additional research is being carried out to obtain high precision. In compared to other major languages such as Bangla, there are numerous works in handwritten character recognition available for English. The goal is to present a comprehensive, effective, and efficient method for classifying and recognizing both Bangla and English letters. An extended convolution neural network (CNN) model has been suggested to recognize Bangla and English characters. Character recognition is achieved through segmentation, feature extraction, and classification. CNNs were recently discovered to be adept at English text detection. A CNN-based Bangla handwritten character recognition system is also being researched. A total of 23,040-character samples have been used, with 25% of the data having served as a test set and the remaining 75% having been used to train the recognition model.
Artificial Intelligence-Based React Application (Powered by Conversational ALAN-AI Voice Assistance)

The field of conversational AI is growing at a fast pace. It basically includes the methods for developing software agents that are capable of involving in a conversation with humans just like other humans. This research paper is an effort of presenting a glimpse of how a conversational React news application controlled by voice can be developed by making use of ALAN-AI. ALAN-AI is an innovative technology that allows the inclusion of voice capabilities in any application. It enables an application to be controlled completely by the voice, and it is very easy to integrate it. The paper describes several npm commands and discusses the functionality of ALAN-AI in the proposed react application along with the ALAN Studio itself. A brief differentiation of react, Angular, and Vue is also presented to give an idea of the certain advantages that react has over other libraries and frameworks. The paper elaborates how voice scripts can be created via the Web portal of ALAN Studio which provides a set of tools for convenient scriptwriting and testing, including a source code editor, testing and debugging tools, and analytics tool. The paper ends up by explaining the ALAN-AI’s ability to mimic human look-like communication between the user and the app. They work together to allow ALAN to understand human speech, create answers, and complete the app’s tasks.
APPLIED AND ECONOMIC ASPECTS OF EFFICIENT MEASUREMENT OF DISTANCE BETWEEN NON-CONVEX OBJECTS

In this article, an effective algorithm for measuring the distance between geometric objects is studied. Effective distance measurement between non-convex objects is of great economic importance in various fields. It can be used in many fields. Currently, measuring the distance between objects allows you to bypass obstacles and find optimal paths. The diversity of the field of application results in an increase in the demand for the software, as well as the saved funds. The algorithm developed in this article divides the object into four parts using rectangles and measures the distance using a circle. The segmentation continues until the part of the object inside those rectangles is not convex. A hierarch is built, a tree whose leaves are convex polygons covering the surface of the object, and whose root is the original object. The advantage of our algorithm is accuracy, and the disadvantage is speed.
An Open CV-Based Social Distance Violation Identification Using a Deep Learning Technique

Due to the Covid-19 epidemic, there is now a high need for social seclusion. As several studies have shown, it is possible to limit the transmission of Covid-19 by keeping appropriate social distances. Open CV with deep learning is used to detect the distance between individuals to lessen the effect of the coronavirus epidemic, as we describe in this paper. Individuals should be warned to stay a safe distance from one other using a video broadcast, it was suggested. To put the model to the test, we use CCTV camera footage to gather video clips, which we then feed into CNN models that have already been trained. We are attempting to use YOLOv3 algorithm to recognize pedestrians on the road. Video file now transformed top-down perspective distance measurement from the 2D plane once pedestrian recognition is complete. Social distance violation is the visible distance between any two persons less than the intended length. For every person, the distance between them is highlighted in blue or green if it seems to be more than the predicted distance. Our current model is being tested with prerecorded videos of people strolling the street to verify its correctness. According to the finding, the suggested system is capable of detecting various degrees of social distance between different characters given movie. The same approach may be used future to identify social distance violations in real-time applications.
Investigating Graph-Based Recommendations Systems and Graph Traversal Algorithms

A recommendation system is used by most if the industries nowadays as it helps in increasing the sales in all. This is due to the personal touch a customer gets because of the recommendations. A recommendation system is a technique that helps in recommending products and other services to consumers using information filtration. Recommendation systems can be of many types a few of them being, collaborative filtering-based recommendation systems, content-based recommendation systems, utility-based recommendation systems and more. During this study we will be using collaborative filtering-based recommendation system along with graph-based learning techniques like Gensim Word2vec and GraphSage. For the collaborative filtering part we will take help of a correlation matrix and a pivot table to get the relationship between the users and products and eventually finding the relationship between different users. And in the graph learning models we will be using the word embeddings created using the graphs made using the gathered data and for the traversal of graphs we will be using deep walk and random walk algorithms, and then using this information we have recommended the products to the users. The assessment of each model is done on the basis of Personalization method.
Teaching eco-translation

Abstract The coronavirus pandemic has kept us individually as well as publicly in a state of preoccupation that prohibited us from staying with the trouble of an ecologically damaged planet. The authors of this contribution are sharing their experience and reflections upon teaching, as an active intervention to this absent presence in everyday Covid-19 lives, an eco-translation course. For this we will first offer a short discussion of our scholarly backgrounds and biases. Second, we will describe our efforts in translating these biases into concrete teaching. The course that serves here as a case study was taught in a project format in the 2021 winter term 2 at the Faculty for Translation Studies, Linguistics and Cultural Studies at Mainz University. It was a five-day course which blended localisation and nature writing framed by a holistic approach to scholarship and teaching. Third, we will discuss the teaching experience in terms of the presences and absences it made apparent.