Miracle-dz/UniFAR-FTUs huggingface dataset:
Psychometric Characteristics of the Macedonian Version of Clinical Assessment Interview for Negative Symptoms (CAINS).

BACKGROUND Despite the importance of effective assessment and treatment of negative symptoms among patients with psychosis, no validated instruments are available in the Republic of North Macedonia. The aim of this paper was to explore psychometric properties, namely factorial structure, internal consistency, convergent and discriminant validity of the Clinical Assessment Interview for Negative Symptoms (CAINS). SUBJECTS AND METHODS In this cross-sectional study 82 outpatients diagnosed with psychosis (64 with schizophrenia and 18 with bipolar disorder; female=34, mean age=41.05±10.09) were assessed. RESULTS The exploratory factor analysis revealed two factorial structure of the negative symptoms as measured by the CAINS, i.e. 'expression and motivation' and 'pleasure'. Two items aimed to measure motivation for family relations and motivation for work/school activities loaded on the expression factor instead on motivation and pleasure factor which differs from the original version of the CAINS. Convergent validity was proven by positive relationship to negative symptoms as measured by the BPRS. Positive, but weak correlation with BPRS positive symptoms demonstrated its discriminant validity. Internal consistency of overall CAINS scale and its two subscales was very high. CONCLUSION The CAINS can be used to assess negative symptoms in individuals with psychosis in the Macedonian clinical context. Consequently, this work can provide a foundation for further clinical advancement and research of negative symptoms in Macedonian healthcare.
Cogment: Open Source Framework For Distributed Multi-actor Human-AI Collaborative Environment

—In this work, we introduce Cogment - an open-source framework that introduces an actor formalism to support a variety of humans / agents collaboration topologies and training approaches through an easily scalable distributed micro service architecture. Leveraging these human-agent collaboration typologies, we further demonstrate how social-intelligence based benchmarks can be developed using Cogment.
The Role of Data Pre-processing Techniques in Improving Machine Learning Accuracy for Predicting Coronary Heart Disease

These days, in light of the rapid developments, people work day and night to live at a good level. This often causes them to not pay much attention to a healthy lifestyle, such as what they eat or even what physical activities they do. These people are often the most likely to suffer from coronary heart disease. The heart is a small organ responsible for pumping oxygen-rich blood to the rest of the human body through the coronary arteries. Accordingly, any blockage or narrowing in one of these coronary arteries may cause blood not to be pumped to the heart and from it to the rest of the body, and thus cause what is known as heart attacks. From here, the importance of early prediction of coronary heart disease has emerged, as it can help these people change their lifestyle and eating habits to become healthier and thus prevent coronary heart disease and avoid death. This paper improve the accuracy of machine learning techniques in predicting coronary heart disease using data preprocessing techniques. Data preprocessing is a technique used to improve the efficiency of a machine learning model by improving the quality of the feature. The popular Framingham Heart Study dataset was used for validation purposes. The results of the research paper indicate that the use of data preprocessing techniques had a role in improving the predictive accuracy of poorly efficient classifiers, and shows satisfactory performance in determining the risk of coronary heart disease. For example, the Decision Tree classifier led to a predictive accuracy of coronary heart disease of 91.39% with an increase of 1.39% over the previous work, the Random Forest classifier led to a predictive accuracy of 92.80% with an increase of 2.7% over the previous work, the K-Nearest Neighbor classifier led to a predictive accuracy of 92.68% with an increase of 2.58% over the previous work, the Multilayer Perceptron Neural Network (MLP) classifier led to a predictive accuracy of 92.64% with an increase of 2.64% over the previous work, and the Naı̈ve Bayes classifier led to a predictive accuracy of 90.56% with an increase of 0.66% over the previous work. Keywords—Coronary heart disease; heart; machine learning; data preprocessing; classification technique
Microbial induced mineralization of calcium carbonate for self-healing concrete

Elkhateeb WA, Elnahas MO, Daba GM. 2021. Review: Microbial induced mineralization of calcium carbonate for selfHealing concrete. Asian J Nat Prod Biochem 19: 1-9. Low-cost solutions achieving concrete self-healing are attracting researchers attention. Generally, concrete self-healing mechanism has been so far accomplished by three approaches: autogenous healing, encapsulation of polymeric material, and microbial induced mineralization of calcium carbonate. Microbial approach seems like an attractive potent, relatively cheap way to achieve concrete self-healing. Hence, this review elucidates the microbial concrete self-healing mechanisms, and compare the roles of fungal and bacterial mediated self-healing concrete.
AxLS: An Open-Source Framework for Netlist Transformation Approximate Logic Synthesis

—With the rise of Approximate Computing as an energy-efﬁcient design paradigm for error-tolerant applications, different Approximate Logic Synthesis (ALS) approaches have been proposed to generate approximate circuits from accurate implementations automatically. In this paper, we present AxLS, our open-source framework for ALS techniques based on structural netlist transformations. We describe our framework and provide an experimental evaluation for arithmetic circuits with our current implementation. AxLS enables the study and test of existing ALS techniques based on netlist transformations and the proposition of new ones. AxLS is available to download at https://github.com/ECASLab/AxLS.
Traffic Sign Recognition Evaluation for Senior Adults Using EEG Signals

Elderly people are not likely to recognize road signs due to low cognitive ability and presbyopia. In our study, three shapes of traffic symbols (circles, squares, and triangles) which are most commonly used in road driving were used to evaluate the elderly drivers’ recognition. When traffic signs are randomly shown in HUD (head-up display), subjects compare them with the symbol displayed outside of the vehicle. In this test, we conducted a Go/Nogo test and determined the differences in ERP (event-related potential) data between correct and incorrect answers of EEG signals. As a result, the wrong answer rate for the elderly was 1.5 times higher than for the youths. All generation groups had a delay of 20–30 ms of P300 with incorrect answers. In order to achieve clearer differentiation, ERP data were modeled with unsupervised machine learning and supervised deep learning. The young group’s correct/incorrect data were classified well using unsupervised machine learning with no pre-processing, but the elderly group’s data were not. On the other hand, the elderly group’s data were classified with a high accuracy of 75% using supervised deep learning with simple signal processing. Our results can be used as a basis for the implementation of a personalized safe driving system for the elderly.
Temporal Difference Rewards for End-to-end Vision-based Active Robot Tracking using Deep Reinforcement Learning

Object tracking allows for localizing moving objects in sequences of frames providing detailed information regarding the trajectory of objects that appear in a scene. In this paper, we study active object tracking, where a tracker receives an input visual observation and directly outputs the most appropriate control actions in order to follow and keep the target in its field of view, unifying in this way the task of visual tracking and control. This is in contrast with conventional tracking approaches, as typically developed by the computer vision community, where the problem of detecting the tracked object in a frame is decoupled from the problem of controlling the camera and/or the robot to follow the object. Deep Reinforcement Learning (DLR) methods hold the credentials for overcoming these issues, since they allow for tackling both problems, i.e., detecting the tracked object and providing control commands, at the same time. However, DRL algorithms require a significantly different methodology for training compared to traditional computer vision models, e.g., they rely on dynamic simulations for training instead of static datasets, while they are often notoriously difficult to converge, often requiring reward shaping approaches for increasing convergence speed and stability. The main contribution of this paper is a DRL, vision-based active tracking method, along with an appropriately designed reward shaping approach for active tracking problems. The developed methods are evaluated using a state-of-the-art robotics simulator, demonstrating good generalization on various dynamic trajectories of moving objects under a wide range of different setups.
AUDIT OF LAPAROSCOPIC APPENDICECTOMY IN A SURGICAL UNIT.

Background With the advent of laparoscopy, surgeons need new skills and must relearn the procedures that had been familiar in open surgery. For some procedures, like appendicectomy, the learning curve can be steep, owing to the diversity of clinical presentations and the different locations of the appendix. Laparoscopic appendicectomy is gaining momentum worldwide because of the purported benefits of minimal access. The implication is that surgical training needs to adjust accordingly to provide adequate skills to prospective surgeons. In this manuscript, we endeavor to review the practice of laparoscopic appendicectomy in our unit. Objective A two-year review of laparoscopic appendicectomy in a surgical unit to reflect on the stages of appendicitis at presentation, the percentage of procedures done laparoscopically, and the trainees’ contributions. Method Retrospective analysis of data prospectively collected between January 2013 and December 2014 in a surgical unit at the Chris Hani Baragwanath Academic Hospital (CHBAH), Johannesburg, South Africa. The parameters analyzed included patient demographics, surgical approach, intraoperative findings, and histopathological reports. Results The male to female ratio was 2:1, with a mean age of 25.7 years. The percentages of complicated, uncomplicated, and normal appendices, based on histopathological reports, were 53%, 20%, and 12%, respectively. Conclusion The laparoscopic approach represented 63% of all appendicectomies. More than half of the cases of appendicitis were complicated. Specialist surgeons performed most of the laparoscopic cases.
Accelerating Encrypted Deduplication via SGX

Encrypted deduplication preserves the deduplication effectiveness on encrypted data and is attractive for outsourced storage. However, existing encrypted deduplication approaches build on expensive cryptographic primitives that incur substantial performance slowdown. We present SGXDedup , which leverages Intel SGX to speed up encrypted deduplication based on server-aided message-locked encryption (MLE), while preserving security via SGX. SGXDedup implements a suite of secure interfaces to execute MLE key generation and proof-of-ownership operations in SGX enclaves. It also proposes various designs to support secure and efﬁcient enclave operations. Evaluation on synthetic and real-world workloads shows that SGXDedup achieves signiﬁcant speedups and maintains high bandwidth and storage savings.
A Framework of Servant Leadership Impact on Job Performance: The Mediation Role of Employee Happiness in UAE Healthcare Sector

Servant leadership influences the behaviors of human resources by increasing the happiness of employees and fostering their performance. Moreover, this leadership style focuses on the needs of individuals, and serves followers to fulfill these needs. People who accept servant leadership experience higher levels of satisfaction, trust, creativity, recognition and independence in their work. Today, many organizations in the healthcare sector are facing various challenges associated with human resources. One of these challenges is the absence of a strategy to make employees happy at the workplace. Another challenge is finding the appropriate leadership style, which enhance job performance. Hence, the issue of happiness at the workplace needs to be properly conceptualized so that to understand the role of leadership from different perspective. Accordingly, this study developed a conceptual framework that constructs employee happiness as a mediator between servant leadership and job performance. The methodology adopted in this study is quantitative, whereas the survey has been conducted in Abu Dhabi Health Services Company (SEHA). The Population represents the employees and administrative staff. The result reveals that the empirical data has a satisfactory degree of fit with the conceptual framework. The findings of this study contributed to the body of knowledge in servant leadership and happiness at the workplace. Moreover, it shows that the performance of human resources in healthcare institutions can be improved if the organizations adopted servant leadership style.
Ge’ez POS Tagger Using Hybrid Approach HAGOS GEBREMEDHIN GEBREMESKEL

: This paper proposes a series of carefully designed a Ge’ez POS tagging using Hybrid approach. Trigram N tag tagger combined with the human written rule, regular expression and morphological pattern analysis based tagger of Ge’ez part of speech tagger. Ge’ez literature on syntax, morphology and grammar are reviewed to understand the nature of the language and also to identify possible tag sets. Experiments aiming at evaluating the influence of automatic pre-annotated on the manual part-of-speech annotation of a corpus, both from the efficiency and the accuracy points of view, with a specific attention drawn to biases. As a result, 26 broad tag sets were identified and 15,154 words from around 1,305 sentences collected from one genre i.e., Holy bible. Then, those words ware manually tagged by Ge’ez language professionals for training and testing purpose. The hybrid of TnT with human annotated rule, regex and morphological pattern analysis of Ge’ez language is assumed to perform better than the TnT taggers taken alone. Individual and hybrid experiments have conducted for the three types of taggers namely the TnT tagger, TnT with Regex tagger and Hybrid tagger. The results are 77.87%, 82.23% and 94.32% performances are obtained for TnT tagger, TnT with Regex tagger and Hybrid taggers respectively. Therefore, the performance of Hybrid approach have the best than individual performance. Finally, this paper concludes Hybrid approach have permissive result for Semitic languages.
Recent advancement in adipose stem cell-based bone tissue engineering

Adipose stem cells (ASCs), a variety of mesenchymal stem cells (MSCs) are an essential element within bone tissue engineering (BTE). ASCs offer several advantages in isolation, culturing, maintenance, differentiation, repair etc. Because of all these advantages of ASCs along with high paracrine activity, lacking major histocompatibility complex class II expression autologous ASCs are attracted towards treatment for organs damaged due to disease or injury and also in regenerative medicine. Bone is very compound tissue and it takes part in various processes which includes endocrine functions, haematopoiesis, mineral homeostasis and storage, body movement. Bone tissue engineering approaches involves combining cells and engineering materials with signalling molecules which is crucial for effective bone repair. ASCs are vital element in BTE. BTE requires 3 important elements: (1) osteoprogenitor cells, (2) bioactive factors, (3) scaffold. In this review, we will be focusing on the recent discoveries which have employed the use of scaffolds, ASCs, bioactive factors in BTE. This review first outlined the advantages of ADCs in terms of isolation, culture, differentiation etc. The different Growth factors involved in osteogenesis, which are the key segments in BTE. The available and recent innovations in the biological, mechanical and structural necessities in the design of a profitable scaffold and their effects, which is succeeded by summary of regular materials intended for scaffold fabrication
On-Die Capacitance Measurements in the Frequency and Time Domains

The quality of on-die power, jitter, and timing performance of silicon circuits are highly dependent upon on-die capacitance (ODC). Fast current transients (100pSec) are associated with the clock edge and must draw current from the ODC. The ODC forms a parallel LC circuit with the package inductance and resonates near 50 MHz, forming a peak in the power distribution network (PDN). ODC has been measured with a vector network analyzer (VNA) in the frequency domain and by charge conservation concepts in the time domain. The amount of ODC truly available for power supply ballast is measured and compared with software extraction techniques.
Forest fire prediction using ML and AI

Forest fire forecasting is crucial for resource allocation, mitigation, and recovery. The risk of forest wildfires is rising all around the planet. Large wildfire frequency and area damaged have increased by more than four and six times in the last five decades, respectively. Oxygen, humidity, and temperature are all essential factors in wildfire risk evaluations. A new forest fire risk prediction method is described, which is based on Support Vector Machines, Logistic Regression, KNN, decision trees, and Random Forest. The findings show that forest fire danger can be predicted with reasonable accuracy. So, using machine learning techniques and algorithms such as Logistic Regression, KNN, SVC, Random Forest, and Decision Tree, we proposed a system to predict the percentage of fire occurrence based on various parameters such as temperature, humidity, and oxygen data entered by the user in the front end
Google Loon: Balloon Powered Internet for All

The world has acknowledged the opportunities and possibilities the Internet offers. The Internet has been considered as a global community that is not only convenient, but genuinely helps to improve many different and important aspects of life including education, health, and economy. Sadly, only around 1/3 rd of the global population at present has access to it. Providing cost-effective and efficient connectivity to rural areas is a very challenging task that has been given many different solution models over the years. One solution model that seems to offer a substantial business case is based on the Google Loon project, which provides many advantages. In this paper, we’ve tried to give an overview of the Loon project and also highlighted some key finds and observations.
Evaluation of Parallel Diagnostic Test Combining BRAF V600E Mutation with TI-RADS on Thyroid Nodules

*Correspondence: Guoqiang Su, Department of Surgical Oncology, The First Affiliated Hospital, Xiamen University, No.55 Zhenhai Road, Xaimen 361003, China, Tel: 865922137310; Fax: 865922137189; E-mail: suguoqiang66@163.com Zhibin Li, Epidemiology Research Unit, The First Affiliated Hospital, Xiamen University, No.55 Zhenhai Road, Xiamen 361003, China, Tel: +86-5922137364; Fax: +86-592-2137557; E-mail: zhibinli33@hotmail.com Received Date: 25 Oct 2018 Accepted Date: 26 Nov 2018 Published Date: 30 Nov 2018 Citation: Su J, Zeng C, Fan C, Li Z, Su G. Evaluation of Parallel Diagnostic Test Combining BRAF V600E Mutation with TI-RADS on Thyroid Nodules. Clin Oncol. 2018; 3: 1553. Copyright © 2018 Zhibin Li and Guoqiang Su. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Research Article Published: 30 Nov, 2018
Multivariate Time Series Data Clustering Method Based on Dynamic Time Warping and Affinity Propagation

In view of the importance of various components and asynchronous shapes of multivariate time series, a clustering method based on dynamic time warping and affinity propagation is proposed. From the two perspectives of the global and local properties information of multivariate time series, the relationship between the data objects is described. It uses dynamic time warping to measure the similarity between original time series data and obtain the similarity between the corresponding components. Moreover, it also uses the affinity propagation to cluster based on the similarity matrices and, respectively, establishes the correlation matrices for various components and the whole information of multivariate time series. In addition, we further put forward the synthetical correlation matrix to better reflect the relationship between multivariate time series data. Again the affinity propagation algorithm is applied to clustering the synthetical correlation matrix, which realizes the clustering analysis of the original multivariate time series data. Numerical experimental results demonstrate that the efficiency of the proposed method is superior to the traditional ones.
INFLUENCE OF PROCESS PARAMETER ON THE HEIGHT DEVIATION OF WELD BEAD IN WIRE ARC ADDITIVE MANUFACTURING

Significant attention towards Wire and Arc Additive Manufacturing (WAAM) has gradually increased. WAAM technology has been proven in building three-dimensional (3D) metal parts efficiently and economically. The combination of wire and arc welding is promising, especially in depositing metals with higher deposition rate, and low cost of raw materials, as well as manufacturing of a large-scale product. However, there are several process parameters should be optimized to ensure no internal defect, acceptable surface finish and consequently to have a good quality of final parts when using WAAM. Therefore, an experimental design using the Taguchi method was used to determine the effect of welding current, welding voltage, and travel speed on the responses, including the deviation in height were investigated. The results revealed that travel speed was the dominant factor affecting the waviness surface structure on top of the 3D metal parts. Besides, the contribution rate for each factor to the deviation in height was also determined.
Hierarchical Pooling in Graph Neural Networks to Enhance Classification Performance in Large Datasets

Deep learning methods predicated on convolutional neural networks and graph neural networks have enabled significant improvement in node classification and prediction when applied to graph representation with learning node embedding to effectively represent the hierarchical properties of graphs. An interesting approach (DiffPool) utilises a differentiable graph pooling technique which learns ‘differentiable soft cluster assignment’ for nodes at each layer of a deep graph neural network with nodes mapped on sets of clusters. However, effective control of the learning process is difficult given the inherent complexity in an ‘end-to-end’ model with the potential for a large number parameters (including the potential for redundant parameters). In this paper, we propose an approach termed FPool, which is a development of the basic method adopted in DiffPool (where pooling is applied directly to node representations). Techniques designed to enhance data classification have been created and evaluated using a number of popular and publicly available sensor datasets. Experimental results for FPool demonstrate improved classification and prediction performance when compared to alternative methods considered. Moreover, FPool shows a significant reduction in the training time over the basic DiffPool framework.