Reviewerly/exHarmony huggingface dataset:
Data Security Model Using Deep Learning and Edge Computing for Internet of Things (IoT) in Smart City

In the current ongoing world of IoT (Internet of things) devices, it is vital to have a safe, secure and reliable cyberspace. Cyberspace or network is free from all sorts of unethical activities like hacked systems, data breaches and stolen data. For that goal to be accomplished, we need to have a modern, solid and rigid cybersecurity system to stay safe from harm's way. Our research includes recent IoT data security issues and a robust security model that ensures the IoT devices' data security based on a smart city's perspective. According to the recent research data on the vulnerable IoT devices we propose an intelligent and effective security approach to tackle the modern IoT security issues because our data needs to be protected as the IoT devices or “things” as we call it needs to have proper security. A few areas of future research indicate the use of blockchain to resolve the cybersecurity issues of IoT devices are the most promising and exciting.
Ontology Development Based on Quality Service of Coffee Shop Management at Kunming, China

This research study ontology development by analyzing 6 months of customer comments on Dianping.com. The text analysis results are to get 1028 customer comments about independent coffee shops in Kunming. Among these, there are 183 customer comments about service quality. Based on these comments about service quality, there are 45 keywords from the analysis. Then the sentiment analysis is performed on the customer comments. The calculated sentiment scores of customer comments are 64 and −162, and the number of negative customer comments is 103, with negative sentiment accounting for 56.28% of the total 183 comments. It defines whether the customer comments are positive or negative. Furthermore, it shows that customers are dissatisfied with the service of independent coffee shops in Kunming. Therefore, this research finds that independent coffee shops in Kunming lack good service quality. In order to improve this situation, this research extends the ontology related to service quality using an ontological model to obtain an ontological model combining domain ontology and affective orientation related to service quality information in coffee shops based on the keywords and affective orientation in the comments. It allows managers to understand more intuitively and the relationship between each knowledge related to service quality in independent coffee shops from the models and find the critical points of service quality problems and improve them.
A Scalable Self-Learning Chatbot for Classroom

Classroom Assistant is a publicly distributable Slack application that intelligently answers student questions in place of an instructor. The application creates a personalized knowledge base per Slack workspace and learns based on information and responses provided by an instructor. This provides an automated solution enabling instructors to only answer a question once. Classroom Assistant utilizes Dialogflow, a Natural Language Processing (NLP) service on Google Cloud Platform (GCP), to detect question intent and store workspace-specific knowledge. Dialogflow provides a basic solution for integrating its service with Slack, however, it is not scalable nor flexible as any changes to the knowledge base have to go through the Dialogflow web interface on GCP. We address this limitation by creating a custom integration that is highly scalable and also enables the public distribution of the app. Via a single click installation, our Classroom Assistant allows instructors to deploy the chatbot app to different Slack works paces and auto-update the knowledge base via a direct answer to students' questions inside Slack.
TOTEM SDK: an open toolset for token controlled computation managed by blockchain

As a part of using the Internet, surging over the past decade, a wide range of data has been generated at a breakneck pace from various sources such as social media, banking sectors and governments. Many organizations have changed their work culture and adopted Big Data analytic to gain various benefits from the data being produced. Nevertheless, sharing this big data was always a tremendous challenge for scientists and engineers as it involves a large volume and sensitive data, which cannot be handled by the conventional way of data analysis. TOTEM: Token for controlled computation, accounts for an innovative concept that integrates both blockchain technologies and big data systems and uses their advantages to present a better, secure and more cost-effective solution for both data owners and data consumers. The TOTEM architecture (US Patent No.: US11,121,874 B2) aims to overcome security and privacy breaches and prevent moving large data sets across the network for analysis. Totem is an entity used in TOTEM architecture for putting constraints on computational operations. Authorised users in the network are allowed to write their own code in a specific format through a TOTEM defined SDK for analysing the data provided by the data owner. The SDK, along with the deployed smart contracts in the network, form a pre monitoring system that keeps track of totem entity value associated with each users&#x2019; submitted codes using an estimator table. In this article, we will focus on the layers of this TOTEM defined SDK and further explain how the SDK interacts with the code, analyze it within the layers and finally how it responds to it.
CRAR: Accelerating Stereo Matching with Cascaded Residual Regression and Adaptive Refinement

Dense stereo matching estimates the depth for each pixel of the referenced images. Recently, deep learning algorithms have dramatically promoted the development of stereo matching. The state-of-the-art result is achieved by models adopting deep convolutional neural networks. However, a considerable computational burden is also introduced, which slows the inference. To solve this problem, previous works down-sampled the input images to decrease the spatial size. However, down-sampling increases the error rate and its lower bound. In this article, we accelerate stereo matching algorithms through the improvement of network structure. Inspired by network compression, we conduct decomposition and sparsification to squeeze the computationally expensive cost optimization network. It is sparsified and then decomposed into smaller networks, which are designed and trained in a cascaded manner to reach the nearest possible performance of the larger network. Previous methods have utilized numerous refinement methods to adjust the coarse disparity. We integrate refinement methods to create an unified algorithm to utilize parallelism for running devices to further accelerate the inference. The extensive experiments on Kitti2015, Kitti2012, and Middlebury datasets demonstrate the efficiency of our method.
A Time-varying Mutation Operator for Balancing the Exploration and Exploitation Behaviours of Genetic Algorithm

Many real-world combinatorial optimization problems (COPs) are NP-hard and challenging to find the optimal solution using classical linear and convex optimization methods. In addition, the computational complexity of these optimization tasks increases exponentially with the increasing number of decision variables. A further difficulty can be also caused by the search space being intrinsically multimodal and non-convex. In such a case, an effective optimization method is required that can cope better with these problem characteristics. Genetic algorithm (GA) is a widely used method for COPs. The original GA and its variants have been used to solve a number of classic discrete optimization problems. Literature shows that the static mutation probability is commonly used for the GA and its variants which cause the imbalance between exploration and exploitation, limiting the performance of GA. To overcome this problem, this research proposes a time-varying mutation operator for GA. In this paper, the balance between exploration and exploitation of the proposed GA has been verified using the benchmark instances of a well-known combinatorial optimization problem i.e., the 0&#x2013;1 knapsack problem. The numerical results show that the proposed GA can obtain better results with on average a significant number of function evaluations compared to the well-known metaheuristic methods.
Comparative Analysis of CNN-based Deep Learning Approaches on Complex Activity Recognition

For those with Parkinson's disease, freezing of gait (FOG) could be a distressing condition. In individuals with diabetes, FOG could have a significant influence on their quality of life and cause falls, which can be dangerous. A questionnaire has traditionally been used to measure the severity of FOG symptoms; nevertheless, this technique is subjective and can not accurately reflect the intensity of this illness. The adoption of sensor-based devices may give reliable and objective information to follow the progression of symptoms, allowing for better Parkinson's disease management and therapy. A compact deep convolutional neural network comprising squeeze and excitation components, which we called SE-DeepConV model, was designed to identify FOG utilizing wearable data from the sensors. The SE-DeepConV model was constructed and evaluated using a publicly accessible standard FOG dataset, particularly the Daphnet dataset. The SE-DeepConV model outperforms other benchmark deep learning models in terms of accuracy, with the most outstanding accuracy of 95.65%, according to research observations.
Real-Time Recognition of Bangla Sign Language Characters: A Computer Vision Based Approach Using Convolutional Neural Network

Sign Language is the elementary communication media for Deaf &amp; Mute (D&amp;M) people. On the other hand, it seems too tenacious for the general people to understand this language. In order to tear out this communication barrier, a real-time automated translator is essential. Through this research, a computer vision-based approach has been developed for the recognition of Bangla Sign Language (BdSL) characters. In this work, a deep learning-based recognition model has been developed. Adaptive thresholding has been integrated with 2D Convolutional Neural Network (CNN) to construct this model. Proposed model has been trained to build this real-time automated translator through our own created dataset (dataset containing 3600 different images for 36 distinct characters). The proposed model has been trained and tested with 2880 (80%) training images and 720 (20%) testing images respectively. Thirty-six unique characters of Bangla Sign Language can be recognized through this model with significant accuracy. The model delivers validation accuracy of 99.72% and validation loss of 0.73%. A significant result has been achieved for the recognition and translation of Bangla Sign Language characters with this dataset over other existing Bangla Sign Language Recognition model.
Pre-planning Process Model in Agile Global Software Development

To produce working software deliverables, geographically distributed teams are increasingly adopting Global Software Development (GSD). This enables companies to decrease software development cost and time, access to skillful resources, increase product quality, and the benefit of the 24-hour development model. However, due to geographical, temporal, socio-cultural, and organizational distances, GSD projects face multiple challenges that impede attaining the intended goals and severely impact project planning. Agile planning is an approach, traditionally conducted in a collocated environment, that allows fast-reacting, project progress review, detecting development bottlenecks, and decreasing the misunderstanding between developers and customers. In a distributed setting, agile planning remains severely impacted by the shortage of face-to-face communication. Therefore it’s necessary to consider the team distribution and resulted challenges to adopt a beneficial agile planning process for GSD. Based on different research papers related to Agile Planning of Global Software Development, this paper focuses on the pre-planning phase. It describes the application of this phase in the GSD context, and defines the various activities included as well as all the used repositories.
Multi-label learning via minimax probability machine

In this paper, we propose Minimax Probability Machine for Multi-label data classification and is termed as Multi-Label Minimax Probability Machine (MLMPM). Based on data mean and covariance information, MLMPM builds a classifier that minimizes an upper bound on the mis-classification probability of unseen future data. For capturing label correlation we have considered asymmetric co-occurrency matrix into the model. The proposed model has also been extended to non-linear settings using the Mercer Kernel trick. To accelerate the training procedure, iterative weighted least squares is used to train the underlying optimization model efficiently. Extensive experimental comparisons of our proposed method with related multi-label algorithms on synthetic as well as real world multi-label datasets, along with Amazon rainforest satellite images dataset, prove its efficacy.
A Brief Review of Image Dehazing Algorithms Based on Deep Learning

Single image dehazing is a challenging problem, and it is far from solved. The application of deep learning in dehazing is only in the initial stage of exploration since the structure of deep learning is not designed for it. It occurs frequently that outdoor image quality is seriously affected when capturing image outside with dense haze, the contrast of the picture drops, and the information is lost due to the particles in the atmosphere. It seems indispensable to work on images without dehazing. A great number of methods have been proposed over the past dozen years. Those methods can be divided into traditional and deep learning methods. This paper mainly summarizes and uses traditional algorithms to compare, explains the classic algorithms of deep learning and introduces recent new efficient algorithms. The deep learning method architecture in the paper has been classified into the following two categories, (a) Convolution neural network (CNN) and (b) Generative adversarial network (GAN).
Verifying Opacity Properties in Security Systems

We delineate a methodology for the specification and verification of flow security properties expressible in the opacity framework. We propose a logic, <inline-formula><tex-math notation="LaTeX">${{\sf OpacTL}}$</tex-math></inline-formula> , for straightforwardly expressing such properties in systems that can be modelled as partially observable labelled transition systems. We develop verification techniques for analysing property opacity with respect to observation notions. Adding a probabilistic operator to the specification language enables quantitative analysis and verification. This analysis is implemented as an extension to the PRISM model checker and illustrated via a number of examples. Finally, an alternative approach to quantifying the opacity property based on entropy is sketched.
Ternary Optical Computer: An Overview and Recent Developments

The ternary optical computer (TOC) is a new computer system whose first prototype came out in 2017. Using a dark state and two bright states with perpendicular polarization directions to express information, a ternary optical processor (TOP) consists of rotators and polarizers. Compared with commonly used electronic computers, this new type of optical computer has five advantages. Firstly, it is possible to build a TOP with millions of processor bits. Secondly, the many processor bits can be arbitrarily parted or spliced such that TOP can perform parallel operations on massive data. Thirdly, the hardware's computing function of each processor bit can be dynamically configured at runtime. Fourthly, a many-bit parallel adder without continuous carry delay can be built on TOP. Fifthly, TOP will have a faster computing speed and lower energy consumption than current electronic processors. In this paper, we firstly give an overview of the TOC system, including basic structure, construction method, reconfigurable processor, parallel adder, memory system, and programming method. Then, we report its most recent developments including latest prototypes, application studies, and multi-valued electronic processor emulators.
Image Feature Detectors for Deepfake Image Detection Using Transfer Learning

Deepfake is heavily based on artificial intelligence and machine learning to generate audio content and visuals with an extremely high potential to deceive. In this paper, a new detection system to identify any deepfake for audio or images. In other words, transfer learning techniques are used to present an intelligent detection system of deepfake videos. In this work, a support vector machine is employed for the detection process. Outstanding results are obtained from the train and test this system with various types of images that are real or fake.
Theme-Aware Aesthetic Distribution Prediction With Full-Resolution Photographs

Aesthetic quality assessment (AQA) is a challenging task due to complex aesthetic factors. Currently, it is common to conduct AQA using deep neural networks that require fixed-size inputs. Existing methods mainly transform images by resizing, cropping, and padding or employ adaptive pooling to alternately capture the aesthetic features from fixed-size inputs. However, these transformations potentially damage aesthetic features. To address this issue, we propose a simple but effective method to accomplish full-resolution image AQA by combining image padding with region of image (RoM) pooling. Padding turns inputs into the same size. RoM pooling pools image features and discards extra padded features to eliminate the side effects of padding. In addition, the image aspect ratios are encoded and fused with visual features to remedy the shape information loss of RoM pooling. Furthermore, we observe that the same image may receive different aesthetic evaluations under different themes, which we call theme criterion bias. Hence, a theme-aware model that uses theme information to guide model predictions is proposed. Finally, we design an attention-based feature fusion module to effectively utilize both the shape and theme information. Extensive experiments prove the effectiveness of the proposed method over state-of-the-art methods.
Conceptual Model for Crowd-Sourcing Digital Forensic Evidence

COVID-19 scourge has made it challenging to combat digital crimes due to the complexity of attributing potential security incidents to perpetrators. Existing literature does not accurately pinpoint relevant models/frameworks that can be leveraged for crowd-sourcing digital forensic evidence. This paper suggests using feature engineering approaches for crowd-sourcing digital evidence to profile potential security incidents, for example, in a COVID-19 scenario. The authors have proposed a conceptual Crowd-sourcing (CRWD) model with three main components: Forensic data collection, feature engineering and the application of machine learning approaches, and also assessment with standardized reporting. This contribution is significantly poised to solve future investigative capabilities for forensic practitioners and computer security researchers.
Semi-automatic Detection of Persian Stopwords using FastText Library

A stopword is a word that does not add much semantic information to the text that despite of its very high frequency. Stopwords include prepositions, conjunctions, and pronouns. One of the steps in natural language processing is to remove stopwords to reduce dataset size and process faster. In this study, a semi-automatic method for collecting the Persian language’s stopwords is proposed. The proposed method lists the stopwords of each text depending on its subject. For this purpose, based on a corpus of news texts, the Inverse Document Frequency (IDF) weight in the text is calculated for each word and the stopwords candidates are determined. Then, using the fastText library, the vector of each word is obtained. In the next step, five neighbors are found for each vector. Next, by removing duplicate words, the final list of stopwords (1014 stopwords) is collected. The result of simulations show the accuracy of detecting stopwords by the k-nearest neighbor method is 94.6%.
A Dynamic Collaborative Filtering Algorithm based on Convolutional Neural Networks and Multi-layer Perceptron

In the current information era of enormous amount of data generation, recommendation systems are increasingly used as an efficient way to mitigate information overload in networks. The recommendation systems are used to provide more focused and personalized recommendation of relevant items based on user's historical preferences and constraints. An efficient recommendation system should enrich and improve end user experience. At present, recommendation systems commonly use traditional collaborative filtering algorithms to learn the hidden vectors from the user's commodity behavior matrix. Most of the traditional algorithms characterize data sparseness and cold start problems. Whilst deep learning is being utilized to accurately learn the auxiliary information to enhance the recommendation provided by collaborative filtering techniques, the implementation of deep learning should be fine-tuned in such a way that is satisfies specific application contexts. This paper intends to build a deep collaborative filtering model that integrates Convolutional Neural Networks and Multi-layer Perceptron, addressing the specific requirements of movie recommendation systems. Our proposed model can accurately capture the characteristics of local correlation and explicit and implicit feedback information. In the explicit recommendation task, implicit feedback search is used to embed similar groups, and multi-layer perceptron is used to model user projects, user similar project groups and project similar user groups. Experiments conducted based on the MovieLens dataset for predicting the movie scores demonstrate that our proposed algorithm improves the accuracy of movie score prediction, and outperforms notable traditional collaborative filtering algorithms in terms of alleviating the data sparseness and cold start problems.
Analysis of Transfer Learning Models for Face Mask Detection

The covid-19 outbreak caused a global health crisis, and it still continues to spread rapidly today. Considering that it is very difficult to manually control the use of masks in public places, it is inevitable to turn the process into an automatic detection system. In this study, various Transfer Learning networks in detecting the usage of face masks are analyzed using a large dataset. As a consequence, it can be determined whether the face images recorded in-the-wild and low resolution from various angles are masked / unmasked and whether they are wearing the mask correctly or not. The architecture depths and execution times are also examined to see whether they have a positive effect on the accuracy or not. The results indicate that ResNet50 has a significant success over the rest of the models and the depth of the used model does not guarantee the success nor does it determine the time it takes for training.
Estimation of the average of arbitrary unknown phase delays with Heisenberg-scaling precision

We show an estimation scheme which reaches the Heisenberg-scaling sensitivity in the estimation of the average of the optical phases along the two arms of a Mach-Zehnder interferometer, by using a single squeezed vacuum state and homodyne detection at a single output port. We show that, in order to achieve this quantum advantage, it is required only a classical prior knowledge about the two phases, namely obtainable with a classical estimation strategy with shot-noise limited precision.