Reviewerly/exHarmony huggingface dataset:
Different Techniques for Human Activity Recognition

Human activity recognition (HAR) in live videos has become an important research topic in computer vision. RHA is widely used in different fields such as healthcare, robot learning, intelligent surveillance system, human computer interactions (HCI), and many more. Recognition of activities in live or normal videos include a huge amount of data are required to be processed that is why it is a tough task. In the recent decades, researchers have developed various models using artificial intelligence and especially deep learning (DL) with multiple input sensor paradigm. This paper provides a comprehensive review of recent models of deep learning for HAR based on input sensor paradigm, dataset, feature extraction from dataset, preprocessing of data, classification of input data, and accuracy.
A Comprehensive Study of Optical Character Recognition

In recent decades recognition of characters has become a most important research topic for computer vision researchers or scientists. One of the major techniques for character recognition is optical character recognition (OCR) which plays a vital role in recent years in the development of various methodologies for the recognition of characters of several languages alphabets. Currently, OCR technology is utilized by most of the applications of scanning documents and makes them readable for the users such as google translate, which has been developed for translating the language from one language to the other language. But the rate of accuracy and timing to perform the specified task is still a problem. This paper presents a brief description of the OCR technology, the timeline of its development, some major applications of this technology, and its future perspective in our daily life. Moreover, this article provides an overview of this fascinating research topic for the early-stage researchers of computer vision.
Heavy Metals Source Apportionment and Human Health Risk Assessment of Contaminated Soils of Zamfara State, Nigeria

Progressive illegal artisanal mining activities threaten public health without functional law enforcement on pollution control and proper management practices. This is not an exception of Zamfara State, Nigeria, where a large portion of the populace participates in artisanal mining. The study was conducted to assess the level of health risk associated with heavy metals contaminated soils of Zamfara state, Nigeria. Soil samples were collected from five mining locations (Abare, Bagega, Dareta, Sunke, and Tungar Kudaku) and Anka-town (control site) with no record of mining activities. In each place, bulked soil samples were collected from three sites (mining site, processing site, and village), and the concentration of six heavy metals (Fe, Pb, Cd, Cr, Zn, and Ni) in all the samples were analyzed. The result of the principal component analysis and correlation analysis revealed that Pb, Zn, Cr, and Ni originated from the same source, i.e., anthropogenic/mining activities. While Fe and Cd originated from the geogenic processes because of their high abundance in the soil of the study area, as Anka-town (control site) also recorded high concentrations of Fe and Cd. Health risk assessments were carried out in two groups of population (adult and children) through three exposure pathways (i.e., ingestion, dermal contact, and inhalation). The results showed that ingestion dominated dermal contact and inhalation pathways, and Fe is the riskiest metal while Cd and Ni have the lowest risk of exposure for daily intakes. The non-cancer hazard quotient (HQ) values were all recorded below 1. For the total hazard index (THI), all the adult's exposure pathways were negligible, while for children, only Bagega has ingestion of heavy metals exceeding one (1.10), indicating that non-cancer health risks for children exist. The other four mining locations, Abare, Sunke, Tunga, and Dareta, have values approaching one (i.e., 0.71, 0.60, 0.50, and 0.74, respectively). While for Anka town, which is the control site, it has a value far less than one (0.16). These indicate that all the study locations have the potential for children's health risk through ingesting food produced from contaminated soils. Therefore, there is an urgent need to apply remediation measures immediately to combat complications raised due to heavy metal contaminations.
Putting the AR in (AR)chitecture - Integrating voice recognition and gesture control for Augmented Reality interaction to enhance design practice

The architectural design process involves the development of spatial explorable 3D models, but the computer screen is main medium to communicate information to clients.Yet, Augmented Reality (AR) and Virtual Reality (VR) are the closest way to replicate our world, create new ones and interact within them.AR and VR headsets offer different ways to allow multiple stakeholders to effectively immerse themselves in 3D representations of design projects.But, to interact within these spaces and to perform design modifications, the development of new workflows is required.This research presents a new method where AR is used to visualize and edit project models using both voice recognition and hand-gestures software.While numerous projects are addressing software interoperability issues, user-interaction in an AR space remains a developing area of crucial relevance in research.Although hand-gestures are the usual form of model-state control employed in such systems, voice-control is emerging as a highly desirable and everyday form of human-computer interaction.This paper presents a plugin for the Hololens that allows the user to use voice and hand gestures to enhance the ability to work with 3D models and discusses and evaluates the project.
Determinants of Sustainable Growth of SMEs in Developing Countries: The Case of Ethiopia

Small and medium-sized businesses (SMEs) played a significant role in domestic employment and inclusive GDP in developing countries such as Ethiopia. Accordingly, the purpose of this study is to investigate the determinants of sustainable growth of SMEs in developing countries, notably in Ethiopia. To achieve the objectives of this study, both descriptive and explanatory research designs were used. In addition to this, primary data was collected from SMEs engaged in service, manufacturing, trade, construction, and urban agriculture. The sampling techniques used for this study is the stratified simple random sampling technique in which 194 employees of SMEs were drawn as a sample size for this study. Moreover, multiple linear regression models were used with the Statistical Package for the Social Sciences (SPSS) version 23 software as the data analysis tool. According to the study’s findings, crucial issues such as the coronavirus and political uncertainty in Ethiopia, which have allowed for widespread corruption, are now harming the growth of SMEs. According to the findings, political instability, corruption, and COVID-19 are now having a substantial effect on the growth of SMEs in Ethiopia. These concerns have serious consequences for the long-term sustainability of Ethiopian SMEs. Hence, the study strongly advocates for sequential policy reform in the region, as well as a review of current policies aimed at ensuring effective corruption control in the region and bringing political stability to the region, particularly in Ambo town, Ethiopia.
Automatic grading for Arabic short answer questions using optimized deep learning model

Auto-grading of short answer questions is considered a challenging problem in the processing of natural language. It requires a system to comprehend the free text answers to automatically assign a grade for a student answer compared to one or more model answers. This paper suggests an optimized deep learning model for grading short-answer questions automatically by using various sizes of datasets collected in the Science subject for students in seventh grade in Egypt. The proposed system is a hybrid approach that optimizes a deep learning technique called LSTM (Long Short Term Memory) with a recent optimization algorithm called a Grey Wolf Optimizer (GWO). The GWO is employed to optimize the LSTM by selecting the best dropout and recurrent dropout rates of LSTM hyperparameters rather than manual choice. Using GWO makes the LSTM model more generalized and can also avoid the problem of overfitting in forecasting the students' scores to improve the learning process and save instructors' time and effort. The model's performance is measured in terms of the Root Mean Squared Error (RMSE), the Pearson correlation coefficient, and R-Square. According to the simulation results, the hybrid GWO with the LSTM model ensured the best performance and outperformed the classical LSTM model and other compared models such that it had the highest Pearson correlation coefficient value, the lowest RMSE value, and the best R square value in all experiments, but higher training time than the traditional deep learning model.
Attack Prediction to Enhance Attack Path Discovery Using Improved Attack Graph

Organisations and governments constantly face potential security attacks.However, the need for nextgeneration cyber defence has become even more urgent in a day and age when attack surfaces that hackers can exploit have grown at an alarming rate with an increase in the number of devices that are connected to the Internet.As such, next-generation cyber defence that relies on predictive analysis is more proactive than existing technologies that rely on intrusion detection.Many approaches with which to detect and predict attacks have been proposed in recent times.One such approach is attack graphs.The primary purpose of an attack graph is to not only predict an attack but its next steps within a network as well as.More specifically, an attack graph depicts the paths that an attacker may employ to circumvent network policies by exploiting interdependencies between the vulnerabilities.However, extant attack graphs are plagued with a few issues.Scalability is just one of the main issues that attack graph generation faces.This is because an increase in the number of devices used increases the number of vulnerabilities within a network.This, in turn, increases the complexity as well as the amount of time required to generate an attack graph.At present, existing studies that have used attack graphs to predict the subsequent steps during an attack have had to manually assigned the attack location for attack graph analysis.In order to overcome this limitation, this present study recommends the use of intelligent agents to reduce reachability time by calculating between the nodes as well as using the A* prune algorithm to remove useless edges and reduce attack graph complexity.For the attack graph analysis, the random forest (RF) algorithm was used to detect, predict, and dynamically ascertain the attack location in the network.The results of the attack graph generation experiment revealed that the A* prune attack graph produced better results than existing attack graphs.
Remote Moderator and Observer Experiences and Decision-making During Usability Testing of a Web-Based Empathy Training Portal: Content Analysis

Background COVID-19 restrictions severely curtailed empirical endeavors that involved in-person interaction, such as usability testing sessions for technology development. Researchers and developers found themselves using web-based moderation for usability testing. Skilled remote moderators and observers are fundamental in this approach. However, to date, more empirical work is needed that captures the perceptions and support needs of moderators and observers in testing situations. Objective The aim of this paper was to identify remote moderator and observer participant experiences and their use of certain tools to capture feedback of users as they interact with the web browser application. Methods This research is part of a broader study on an educational web browser application for nursing students to learn perspective taking and enhance their perceptual understanding of a dialogue partner’s thoughts and feelings. The broader study used a quantitative and think-aloud qualitative problem-discovery usability study design. This case study explored written accounts of the remote moderator and observer participants regarding their roles, experiences, and reactions to the testing protocol and their suggestions for improved techniques and strategies for conducting remote usability testing. Content analysis was used to analyze participants’ experiences in the usability testing sessions. Results We collected data from 1 remote moderator and 2 remote observers. Five themes were identified: dealing with personal stressors, dealing with user anxiety, maintaining social presence, ethical response to the study protocol, and communication during sessions. The participants offered recommendations for the design of future remote testing activities as well as evidence-informed training materials for usability project personnel. Conclusions This study’s findings contribute to a growing body of endeavors to understand human-computer interaction and its impact on remote moderator and observer roles. As technology rapidly advances, more remote usability testing will occur where the knowledge gleaned in this study can have an impact. Recommendations based on moderator and observer participant perspectives identify the need for more evidence-informed training materials for their roles that focus on web-based interpersonal communication skills, execution of user testing protocols, troubleshooting technology and test user issues, proficiency in web conferencing platforms, behavior analysis and feedback technologies, and time management.
Estimating Efforts for Various Activities in Agile Software Development: An Empirical Study

Effort estimation is an important practice in agile software development.The agile community believes that developers' estimates get more accurate over time due to the cumulative effect of learning from short and frequent feedback.However, there is no empirical evidence of an improvement in estimation accuracy over time, nor have prior studies examined effort estimation in different development activities, which are associated with substantial costs.This study fills the knowledge gap in the field of software estimation in agile software development by investigating estimations across time and different development activities based on data collected from a large agile project.This study investigated effort estimation in various development activities, including feature development, bug fixing, and refactoring in agile software development.The results indicate that estimation of agile development does not improve over time, as claimed in the literature.Our data also indicate that no difference exists in the magnitude of estimation errors between feature tasks and bug-fixing/refactoring tasks, while bug-fixing and refactoring tasks are overestimated more frequently than feature tasks.This study also contributes to our knowledge about overestimation and underestimation patterns in agile software development.
Future Activities Prediction Framework in Smart Homes Environment

Smart homes have been recently important sources for providing Activity of Daily Living (ADL) data about their residents. ADL data can be a great asset while analyzing residents&#x2019; behavior to provide residents with better and optimized services. A popular example is to analyze residents&#x2019; behavior to predict their future activities and optimize smart homes performance accordingly. This paper proposes a forecasting framework that utilizes ADL data to predict residents&#x2019; next activities in a smart home environment. Forecasting is performed via the conjunction of embedding algorithm to encode the data and Bidirectional Long Short-Term Memory (BiLSTM) deep neural networks to process the data. The proposed framework is evaluated over five real ADL datasets where the experiments show the outperformance of the proposed framework with accuracy scores ranging from 98.7% to 93.8%.
A Hierarchy Byzantine Fault Tolerance Consensus Protocol Based on Node Reputation

A blockchain has been applied in many areas, such as cryptocurrency, smart cities and digital finance. The consensus protocol is the core part of the blockchain network, which addresses the problem of transaction consistency among the involved participants. However, the scalability, efficiency and security of the consensus protocol are greatly restricted with the increasing number of nodes. A Hierarchy Byzantine Fault Tolerance consensus protocol (HBFT) based on node reputation has been proposed. The two-layer hierarchy structure is designed to improve the scalability by assigning nodes to different layers. Each node only needs to exchange messages within its group, which deducts the communication complexity between nodes. Specifically, a reputation model is proposed to distinguish normal nodes from malicious ones by a punish and reward mechanism. It is applied to ensure that the malicious node merely existing in the bottom layer and the communication complexity in the high layer can be further lowered. Finally, a random selection mechanism is applied in the selection of the leader node. The mechanism can ensure the security of the blockchain network with the characteristics of unpredictability and randomicity. Some experimental results demonstrated that the proposed consensus protocol has excellent performance in comparison to some state-of-the-art models.
Monitoring-induced entanglement entropy and sampling complexity

The dynamics of open quantum systems is generally described by a master equation, which describes the loss of information into the environment. By using a simple model of uncoupled emitters, we illustrate how the recovery of this information depends on the monitoring scheme applied to register the decay clicks. The dissipative dynamics, in this case, is described by pure-state stochastic trajectories, and we examine different unravelings of the same master equation. More precisely, we demonstrate how registering the sequence of clicks from spontaneously emitted photons through a linear optical interferometer induces entanglement in the trajectory states. Since this model consists of an array of single-photon emitters, we show a direct equivalence with Fock-state boson sampling and link the hardness of sampling the outcomes of the quantum jumps with the scaling of trajectory entanglement.Received 1 February 2022Accepted 12 July 2022DOI:https://doi.org/10.1103/PhysRevResearch.4.L032021Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasEntanglement entropyPhoton statisticsQuantum opticsAtomic, Molecular & Optical
Possibilistic fuzzy c-means with partial supervision

Clustering is a data analysis method that creates groups of objects according to a similarity notion. Amongst the existing clustering algorithms, the possibilistic fuzzy c-means (PFCM) is a well-known algorithm since it generates a possibilistic partition. Such possibilistic partition is helpful in the presence of a noisy environment and allows to express various types of uncertainty and imprecision. In recent years, the performance of clustering methods has been improved by incorporating partial information. The approach, called semi-supervised clustering, introduces instance-level information such as labeled patterns in the clustering process. In this work, we propose to extend PFCM to combine labeled patterns with the possibilistic framework. To provide more flexibility to the new method, in addition to the Euclidean distance, an adaptive distance measure is considered. Experimental results show the interest of our new semi-supervised possibilistic fuzzy c-means algorithm on various data sets.
New Quantum Codes from CSS Codes

We present a new propagation rule for CSS codes. Starting with a CSS code $[\![n,k,d]\!]_q$, we construct a CSS code with parameters $[\![n-2,k,d-1]\!]_q$. In general, one would only obtain a code with parameters $[\![n-2,k,d-2]\!]_q$. The construction applies to asymmetric quantum codes from the CSS construction as well.
Named Entity Recognition and Knowledge Extraction from Pharmaceutical Texts using Transfer Learning

The challenge of recognizing named entities in a given text has been a very dynamic field in recent years. This task is generally focused on tagging common entities, such as Person, Organization, Date, etc. However, many domain-specific use-cases exist which require tagging custom entities that are not part of the pre-trained models. This can be solved by fine-tuning the pre-trained models or training custom models. The main challenge lies in obtaining reliable labeled training and test datasets, and manual labeling would be a highly tedious task. This paper presents a text analysis platform focused on the pharmaceutical domain. We perform text classification using state-of-the-art transfer learning models based on spaCy, AllenNLP, BERT, and BioBERT. We developed methodology that is used to create accurately labeled training and test datasets used for custom entity labeling model fine-tuning. Finally, this methodology is applied in the process of detecting Pharmaceutical Organizations and Drugs in texts from the pharmaceutical domain. The obtained F1 scores are 96.14% for the entities occuring in the training set, and 95.14% for the unseen entities, which is noteworthy compared to other state-of-the-art methods. The proposed approach implemented in the platform could be applied in mobile and pervasive systems since it can provide more relevant and understandable information to patients by allowing them to scan the medication guides of their drugs. Furthermore, the proposed methodology has a potential application in verifying whether another drug from another vendor is compatible with the patient's prescription medicine. Such approaches are the future of patient empowerment.
Techniques and Paradigms in Modern Game AI Systems

Games have long been benchmarks and test-beds for AI algorithms. With the development of AI techniques and the boost of computational power, modern game AI systems have achieved superhuman performance in many games played by humans. These games have various features and present different challenges to AI research, so the algorithms used in each of these AI systems vary. This survey aims to give a systematic review of the techniques and paradigms used in modern game AI systems. By decomposing each of the recent milestones into basic components and comparing them based on the features of games, we summarize the common paradigms to build game AI systems and their scope and limitations. We claim that deep reinforcement learning is the most general methodology to become a mainstream method for games with higher complexity. We hope this survey can both provide a review of game AI algorithms and bring inspiration to the game AI community for future directions.