Reviewerly/exHarmony huggingface dataset:
Deblur-CycleGAN: A Generative Cyclic Approach for Image Blind Motion Deblurring

In this paper, we propose an end-to-end generative adversarial network (GAN) for single image blind motion deblur-ring, which we called Deblur-CycleGAN. Inspired by the cyclic nature of the original CycleGAN, we perform single image blind motion deblurring in similar fashion while presenting motion deblurring as a cycle-consistent approach. Our proposed method achieves the best qualitative and quantitative results in comparison with existing state-of-the-art methods on GoPro dataset. We also explore the industrial aspect of motion deblurring in wind turbines (WT) with surface cracks on turbine blades. We collect 700 high-resolution images of faulty WT blades via UAV, which we called Turbine Blade dataset. Finally, we compare the performance of our proposed method against existing methods on Turbine Blade dataset and show that our proposed approach achieves the best performance both qualitatively and quantitatively.
Novel Encryption Scheme Based on Continued Fraction and Permutation

An arbitrary positive rational number has a unique continued fraction expression. In this study, we propose a novel cryptosystem based on continued fraction and permutation, which is a two-step encryption operation. First, we can encode message or data stream into a set of quotient numbers, and then determine uniquely two large integers as the first step ciphertext accordingly. The operation of a continued fraction is equivalent to the execution of Euclid&#x2019;s algorithm. Euclid&#x2019;s algorithm belongs to the polynomial complexity; thus, both encryption and decryption of the proposed scheme are quick and efficient. The first step ciphertext is subject to permutation encryption to derive the final ciphertext. Any factorial function resulting from the execution time of solving a factorial function with input size n &#x003E; 30 is still infeasible for the current computing system. The proposed two-step encryption scheme can possess two merits: efficiency and confidentiality, and the scheme is considered a lightweight cryptosystem, which applies to the limited resources of internet of things devices.
Research on Multi-round Dialogue Tasks Based on Sequicity

Multi-round dialogue is one of the most practical techniques in natural language processing. The current multi-round dialogue systems generally suffer from contextual information loss and lack of diversity in generated answers. Therefore, we propose a model based on Sequicity. We use the gate recurrent unit (GRU) to encode the current question, the response of the previous sentence and the semantic slot information. Then, encoding results are fed into a context encoder to generate context information. During the training procedure, the results of the two encoders are input into the recognition network, and the latent variables are sampled from the recognition network; During the test procedure, the concatenating results are input into the prior network, and the latent variables are sampled from the prior network. The latent variables, the encoding results of the current question and the above semantic slot information are concatenated and input to the response decoder. Finally, the decoder employs the Softmax function for decoding. On both CamRest and KVRET public datasets, the proposed model achieves the best results. Compared with the baseline Sequicity, which had the best results before on CamRest, the model's Success F1 is relatively improved by 3.4%, BLEU by 9.6% and Entity match rate by 3.3%. On the KVRET dataset, Success F1 is relatively improved by 2.0%, BLEU by 1.5% and Entity Match Rate by 2.8%.
A real-time visual tracking and distance measuring algorithm based on SSD

Real-time visual tracking and distance measuring algorithms are of critical importance in industry areas. In this paper, we propose a novel method to meet the needs. This algorithm can measure the distance of the tracking object with high accuracy in real-time. First, we compare the ability of the traditional visual tracking algorithms with deep learning visual tracking methods. Furthermore, three deep learning visual tracking methods are compared and evaluated to find the most suitable one that can meet the real-time requirement of tracking targets. Then we create a real-time visual tracking and distance measuring algorithm based on SSD. We raise two methods for distance measuring measurement algorithms and both of the methods are tested. The experiments are implemented to validate the performance of the designed approach. From the experimental results, we confirmed the designed real-time visual tracking and distance measuring algorithm based on SSD performed successfully.
Incorporating Social Relationships for Multi-modality Person Re-identification

Person re-identification (ReID) which aims to retrieve particular persons across non-overlapped cameras is very important to intelligent security system. Most previous methods of re-id focused on learning individual appearance feature embedding, but it is hard for the models to deal with many difficult situations such as occlusions. There are also some works those have attempted to integrate multi-modal cues, e.g. clothes and social relations. While showing improvements, they still have some limitations, such as designing too many objective functions to guide the model and relying on simple heuristics to combine multi-modal cues. In this paper, we propose a new framework to solve the above problems. Specifically, our model consists of three branches to extract and fuse information under three modalities respectively. The model can effectively utilize the information of person appearances, background scenes and social relationships in images to infer the identity of the person. The experimental results show that our method outperforms the state-of-the-art on the public datasets.
Comparative Study on Image Detection using Variants of CNN and YOLO

Deep learning has enabled a variety of detection methods, such as text identification and recognition, picture extraction, visual-action detection, and object movement detection, to be implemented in a single algorithm. Machine Learning, a popular contemporary trend, employs the concept of Artificial Neural Networks (ANN). Because of biological computational models, the performance and processing speed of many different types of Artificial Intelligence have increased. Convolutional Neural Networks (CNNs) are a form of Artificial Neural Network design (ANN). CNN's major goal is to reduce the complexity of image-based pattern recognition to a design that can always support artificial neural networks. Some CNN versions that illustrate how objects are discovered. This paper gives a quick introduction of how CNN works and the main components that help with image refining and feature extraction.
Customized Authorization Process for Cloud Computing and IoT Using Attribute-Based Encryption

Maintaining a complex access control over the encrypted data represents one of the most challenging processes of cloud computing and IoT (Internet-of-Things). In this paper we will present and discuss a policy approach based on cipher-text policy and attribute-based encryption in such way that we are able to guarantee a high level of complexity for controlling the access over encrypted data, and by providing a personalized verifiable authorization process. This process and scheme will be noted as CCIoT -CP-ABE (Cloud Computing Internet-of-Things Cipher Policy-Attribute-Based Encryption). The main advantage of the proposed scheme is to provide data confidentiality for the encrypted data in case if the server has been compromised. The second advantage of the proposed scheme is that guarantees a high level of security against collusion attacks. The evaluation of the performance is presented as well.
A novel approach on transformation and analysis of data linked to distributed databases: A case study

In general, transformation of data in several forms nowadays is quite possible and feasible due to easy accumulation as well as storage linked with large volume databases. In fact, the complexity of tasks associated with the queries in the databases can be optimized and resolved prioritizing the activities involved in the applications. Usually the traditional approaches linked with the conventional databases may not be adequate towards satisfying the requirements in the current situations. It is essential to focus on the emerging applications of databases being suitable towards obtaining optimality on large scaled data. In this manuscript, it is intended to prioritize and optimize the data linked to distributed relational database implementing soft computing approach. Also it may keep the information regarding the structure and association of database queries. The optimality in this case can be obtained based on the volume of stored data along with the execution time of database queries.
Real-Time Data Generation and Anomaly Detection for Security User Profiles

Mostly, security user profiles are being generated from real-time sources of users’ data. User profiles generation process involves complicated and comprehensive data analysis mechanisms. Machine learning is, and become the most popular, technique among the researchers and the practitioners for this real-time data analysis. The scope of this paper is twofold: 1) to implement the anomaly detection module for real-time source of users’ data, and 2) to build real-time data generation engine to train and test this model and to provide the researchers an alternative or simulated methods for real-time data source. The data generation engine is powered by the Synthetic Data Vault (SDV) python library and is relaying on both the historical (past) and real-time (fresh) data. The purpose of using historical data generation is to increase the accuracy of anomaly detection model by expanding the user’s activity which will give more insights of the user behavior. The data generation model simulates the real-world data environments to animate the realtime based analytic systems. Anomaly detection model, in other words, is a real-time flagging system utilizing Kafka platform to generate security user profile. Therefore, the security user profiles are generated based on integrated and collaborated tools to enhance the performance of knowledge-based user authentication systems. Finally, this work generates new knowledge which will allow the researchers to implement and train various machine learning techniques with real-time data generation engine.
AI-enabled IoT based multimodal authentication system for securing the hardware and software clients

Security is one of most essential needs in today’s era. With the advent of today’s technology, the threats have also increased, and it is now challenging to safeguard one’s belongings. It is desired to reduce human involvement and effort as much as possible, and here comes the need for a multimodal authentication system. In order to standardize the development of authentication agents without compromising the response time, security, and robustness, an AI-enabled IOT based authentication system is presented in this paper. Different authentication modules such as real-time face recognition using Artificial Intelligence (AI) based algorithms, fingerprint recognition, RFID authentication, and manual keypad entry are used in this authentication system. All these modules are then interconnected to take a global decision about the person authentications. Experimental outcomes reveal that the presented multimodal authentication system works well and provides good results.
Using smart devices for fall detection: algorithms, systems and applications

Falling is often an unexpected event that can affect the health and safety of persons and objects. Fall detection algorithms have been designed to take advantage of the multitude of smart devices that incorporate sensors capable of detecting a fall event. This is an overview paper that investigates the main components of a fall detection system implemented in the current literature and presents a test Android application for sensor data collection with the purpose of further implementing and testing fall detection algorithms.
New Normal Modes of Education During Pandemic

Online learning is nothing new, it was already prevalent. With the COVID-19 pandemic, schools and colleges had to shift from physical classrooms to remote classrooms. Powered through the use of the internet, video conferencing software(s), and other suit(s) of online tools, online teaching and learning is the new normal mode of education. This paper aims to discuss the benefits, the disadvantages, the classroom environment, and factors affecting evaluation in the online mode of education.
Contemporary present-tense fiction: Crossing boundaries in narrative

The use of the present tense as the primary narrative tense has become a commonly encountered phenomenon in contemporary fiction. The textual effects of the use of the present narrative tense, however, have not yet been fully explored. This paper first reviews how the use of tenses contributes to constructing narrative worlds, focusing on three facets of narrative: the relationship between the narrator and the narrated, time frames within the narrative and characters’ discourse embedded in narrative. Then, using corpus data which includes both present- and past-tense fiction, I will show that the boundaries and distinctions which are consistently taken for granted in past-tense narrative can be blurred, crossed within narratorial structures and partly expanded at a meta-textual level from written discourse to spoken discourse.
Calculation of User-Awareness by Text Features to Improve Specification Quality

Many defects after the release of software packages are caused due to omissions of sufficient test items in test specifications. Poor test specifications are detected by manual review, which imposes a high human load. The prevention of omissions depends on the end-user-awareness of test specification writers. If test specifications were written while envisioning the context of end-users, the number of omissions in test items would be greatly reduced. The paper notes that specifications that achieve this are different from specifications that do not achieve this in terms of richness of description. It propose a method for estimating the degree of user-awareness of a specification writer using text features between software specifications and test specifications. We conduct an experiment to obtain the text data from specifications with and without user-awareness. The test specification is automatically identified by text features. We construct a 2D graph and a random forest model. We are able to confirm a positive and significant correlation between the text features of interest and the quality of the test specifications. The classification of specifications with and without user-awareness by graphs and models is highly accurate. As a result, it is confirmed that “degree of intersection” and “degree of word embodiment” are important variables as text features in calculating user-awareness. The proposed method enables us to automatically classify test specifications. It also prevents test item omissions, because it reveals writing features that test specifications of high quality should satisfy.
Smart Approach Based on CNN and Shearlet Transform for Age Prediction

The age and gender, which are basic features of the face, play a very fundamental role in social interactions, which makes age and gender estimation from a single photo of the face an important process in smart applications. In our last research work, we proposed an approach based on convolutional neural network (CNN) combined with shearlet transform (ST), in order to design a gender recognition system. In this work, we will exploit this approach, by proposing an age recognition system. Based on a CNN for classification and a shearlet transformation to extract the essential features of the face, our approach guarantees a better recognition rate compared to using a simple CNN. This improvement in the recognition rate is the result of extracting multiple frequency views from a face photo which increases the input sample in the CNN learning phase.
A Socio-Technological Analysis of the Use of Blockchain in Financial Systems

The key paradigm of blockchain is the assertion that two parties that do not trust each other can transact directly in a secure manner. No intermediaries who are capable of taking advantage of their position are needed. A number of cases of overpricing or, in some extreme cases, abusive practices of irresponsible intermediaries, show that this argument against intermediaries is not entirely unfounded. On the other hand, a number of issues with blockchain use have been identified in practice—some of the most prominent being processing efficiency, cryptocurrency heists, and smart contracts abuse. They clearly show that both blockchain technology and the social context in which it is used for financial transactions processing need to be better understood and further developed. In this paper, we analyze key components of blockchain technology and its supporting business processes to better understand the requirements that blockchain has to meet to be used as a technologically reliable and business trustworthy financial platform. Economic, behavioral, technological, and security aspects are considered. The objective is to identify the strengths and weaknesses of blockchain-based financial systems and suggest some topics for further research, and to find solutions for issues that are not being currently addressed.
Identifying Guidelines for Test-Driven Development in Software Engineering—A Literature Review

With the significance, ubiquity, and complexity of information technology continuously rising, the corresponding quality assurance becomes increasingly important and challenging. Consequently, numerous tools, techniques, paradigms, and strategies that facilitate the quality assurance of the developed applications have emerged. Test-driven development (TDD) is one of those strategies. In contrast to the traditional approach, it places testing ahead of the actual implementation, which, in turn, also heavily influences the system’s design. With the approach being rather complex, naturally, guidance on its correct application is one of the most relevant aspects for many practitioners. To provide assistance to them, but also to researchers concerned with the domain, the publication at hand identifies twenty guidelines for the practical application of TDD in software engineering, by conducting a structured literature review. In doing so, it provides a solid foundation for the implementation of corresponding endeavors as well as a starting point for further discussion.
Clutch &amp; Grasp: Activation Gestures and Grip Styles for Device-Based Interaction in Medical Spatial Augmented Reality

Presenting medical volume data using augmented reality (AR) can facilitate the identification of anatomical structures, the perception of their spatial relations and the development of mental maps compared to more commonly used monitors. However, interaction methods explored in these conventional settings may not be applicable in AR environments, or perform differently. In terms of mode activation, gestural interaction was shown to be a viable, touchless alternative to traditional input devices, which is desirable in sterile medical use cases. Therefore, we present a user study (n=21) comparing hand and foot gestures with voice commands for the activation of interaction modes within a projectorbased, spatial AR prototype to visualize medical volume data. Interaction itself was performed via hand movements captured by a data glove. Consistent, statistically significant results across measured variables suggest advantages of voice commands. In addition, a second experiment (n=17) compared the hand-based interaction with two motion-sensitive devices held in power and in precision grip respectively. All modes were activated using voice commands. No considerable differences between tested grip styles could be determined. The findings suggest that the choice of preferable interaction devices is user and use case dependent.
How Inflation and Unemployment Can Affect the Cryptocurrencies' Performance: The Case of Bitcoin

This paper evaluates the impact of worldwide inflation and unemployment on the cryptocurrencies' performance. We are using Bitcoin as the main point of reference. The main objective is to measure the damage of inflation and unemployment on Bitcoin in the short and long run. This research is willing to present a single formula to measure the influence of inflation and unemployment simultaneously, in the performance of Bitcoin value. The Nobel formula is entitled “The Cryptocurrencies Value Desgrowth Rate under Inflation and Unemployment (-ðĊ).” The main objective of this research is to evaluate the impact of inflation and unemployment on the Bitcoin trend rapidly. We try to study the performance of Bitcoin, inflation, and unemployment rates in the same graphical space. We analyze all these three variables' behavior daily together in the same graphical space, we can evaluate two experiments with an experimental database. Now, we have a new formula to make a pre-evaluation and take precautions faster for investors, private banks, and central banks. This formula is easy to apply by any investor. Hence, this research discloses this formula for different readers interested in cryptocurrencies. From now we are calling all Cryptocurrencies under the classification of M3 because it can be a new form of currency and money worldwide.
Training Needs Analysis of Professional Translators Working in Translation Agencies in Riyadh, Saudi Arabia

The imminent thrust of globalization is equally challenging but promising. The role of translators in various walks of life – law, medicine, and education, among others – has become indispensable and crucial both socially and economically. The Saudi translation industry is self-supporting because it is not much governed by prevailing regulations. Hence, translation agencies thrive based on the fundamental principles and practices of the market. Competence defines the game, and training improves the performance of each player. It is the main objective of this study to identify the training needs of professional Saudi translators. Improving translation competency can be catered through effective training where needs analysis comes as its preliminary and integral requirement. This study has involved 166 professional translators and 20 owner-managers from the biggest translation agencies in Riyadh, Saudi Arabia. Leaning on quantitative and qualitative data gathered from them, the essential factors for translator training were derived, to wit: translation fundamentals, extra-linguistic abilities, and bilingualism. Having arrived at these findings, this study hopes to meaningfully contribute to the design and development of effective translator trainings.