Miracle-dz/UniFAR-FTUs huggingface dataset:
Guideline for Drug Courts on Screening and Assessment

ii iii Acknowledgments The authors express sincere appreciation to the following persons who volunteered their valuable time and expertise to the development of this document: Foreword This guideline is written to help drug courts develop effective policies, procedures, and techniques for screening and assessing treatment needs of drug court participants. This document describes the principles and methods of screening and assessment of adult drug court participants, and gives drug courts specific tools and information to establish and sustain screening and assessment processes. While much of the information here will be helpful for juvenile drug courts, specific guidance for juveniles should be obtained from other sources. 3 Several key principles and strategies for conducting effective screening and assessment described in the guideline are derived from experiences of existing drug courts and other community-based substance abuse treatment programs for offenders. Several useful guidelines and monographs on screening and assessment for criminal justice and non-criminal justice populations are included as references in the back of this guideline. Readers interested in additional information on this subject are encouraged to use these resources. This publication is one of several technical assistance monographs for drug courts that the U.S. Department of Justice, Office of Justice Programs, has developed. This document presents general issues related to screening and assessing drug court clients, describes the processes and elements of screening and assessment in detail, summarizes key issues for drug courts to consider as they screen and assess participants , and provides resource materials for those seeking additional information.
ROBOTIC ARM IN CRISIS

An automated arm is a kind of mechanical arm, normally programmable, with comparative capacities to a human arm; the arm might be the entirety of the system or might be a piece of a progressively intricate robot. The connections of a controller are associated by joints shaping a chain and the end of the kinematic chain of the controller is known as the effecter which is practically equivalent to the human hand. Arm in Crisis has the objective to execute two arms, one inside another to expand the usage of automated such arm innovation by limiting the hazard. In need, on the off chance that one arm quits working, at that point another will get initiated to take upon the charge and complete the activity.
Multichannel Electroencephalography-based Emotion Recognition Using Machine Learning

In recent years, research in the field of human-computer interaction (HCI) has focused on strengthening machine functions in recognizing and understanding human emotions. Emotion recognition can be done in several ways, among others, through sounds, facial expressions, or a combination of both. The different sounds and facial expressions from different races and nations cause less accurate in the reading of emotions using these methods. Another method for recognizing emotions can be done by analysing the data from an electroencephalograph (EEG). The EEG signals from the human brain are the result of various activities carried out. One of them is emotion. The EEG signal used in this study came from the DEAP dataset. This dataset consists of 32 files, each of which contains 40 EEG recordings. The emotions from this dataset are classified based on the dimensions of arousal and valence. The signal was then decomposed into three different frequency groups (alpha, beta, and gamma) through band-pass filtering. After that, the principal component analysis (PCA) and resampling were carried out. The classification processes used a number of methods of machine learning. The result was known that the performance of K-star was the highest while naïve Bayes was the lowest. The accuracies of K-star in arousal and valence classification were 81.2, 82.6, respectively. The naive Bayes got 51.2 for the arousal, and 52.5 for the valence.
Expelling Information of Events from Critical Public Space using Social Sensor Big Data

Open foundation frameworks give a significant number of the administrations that are basic to the wellbeing, working, and security of society. A considerable lot of these frameworks, in any case, need persistent physical sensor checking to have the option to recognize disappointment occasions or harm that has struck these frameworks. We propose the utilization of social sensor enormous information to recognize these occasions. We center around two primary framework frameworks, transportation and vitality, and use information from Twitter streams to identify harm to spans, expressways, gas lines, and power foundation. Through a three-step filtering approach and assignment to geographical cells, we are able to filter out noise in this data to produce relevant geo-located tweets identifying failure events. Applying the strategy to real-world data, we demonstrate the ability of our approach to utilize social sensor big data to detect damage and failure events in these critical public infrastructures.
EMOTIONAL DISTRESS, JOB SATISFACTION AND JOB RELATED TENSION AMONG JUNIOR DOCTORS

Hatta SidiT. ManiamABSTRACTThis is a descriptive, cross-sectional study on a sample of 40 junior doctors in a hospital in KualaLumpur. The aim of this study was to look at the prevalence of emotional distress, the level of jobsatisfaction and job related tension among junior doctors. The differences in relation to gender andmarital status were studied.Out of40, 31 (77.5%) doctors were found to be positive for emotional distress on the General HealthQuestionnaire (GHQ) which was used as a screening test. They were compared with teachersteaching in Kuala Lumpur who were matched for age, sex, marital status, race and level ofqualification. Level of satisfaction was measured using the Job Satisfaction Questionnaire and levelof job tension studied using the Job Related Tension Questionnaire.Junior doctors were found to be generally dissatisfied (t = -6.82, p < 0.005) and had more job relatedtension (t = 4.88, p < 0.005) than teachers. Junior doctors were dissatisfied compared to teachers inrelation to physical condition, freedom to choose method of working, recognition one gets for goodwork, amount of responsibility that was given, rate of pay, opportunity to use one's abilities,relationship between administrator and Junior doctors in the department, chance of promotion, theway how the department was managed, the attention paid to suggestion one makes and hours ofwork. However there were no differences in relation to the fellow workers, the immediate boss,variety in the job and job security. There were no significant differences between doctors who hademotional distress and those who did not in terms of gender and marital status.Key words: Emotional distress, job satisfaction, junior doctorsINTRODUCTIONMalaysian medical and health services have expanded tremendously in the past few years. In thedelivery of services, the role of manpower is crucial, especially that of young doctors who are thefirst to attend to patients and who serve the frontline in emergencies.Recently, the issue of shortage of doctors in the public sector has drawn the attention of manyquarters. The shortage has caused more workload to junior doctors who cannot resign before thethree years of compulsory service, and any emotional distress that they previously experienced maybe intensified. Many doctors report feeling angry, bored, easily irritable at times and have frequentepisodes of cognitive impairment due to sleep deprivation (1). The issues related to stress in theworkplace, presence of affective symptoms (2) and ambivalence in further career choice have beenthe serious concern of officialdom (3). 16
Dynamic optimisation and parameter estimation for biochemical process systems

A vast number of contemporary production processes rely on living cells or biomolecules preforming chemical transformations as a vital step in the manufacture of valuable products. Providing sustainable supplies of food and drink relies on such biochemical processes which are essentially unchanged for millennia, and will remain quintessential for future communities and societies. Likewise many pharmaceutical products and produced from similar systems, and have formed an essential component of modern civilisation. To ensure high productivity without wasting resources (agricultural feedstock, equipment, time), it is critical to determine optimal dynamic operating profiles toward prescribing implementable control methodologies. Mathematical models have been developed for many important food and drink manufacturing processes operated using suboptimal recipes: these journal publications are quite rigorous and extensive (often describing not only composition, but also how flavour can be tuned as desired), but they frequently require consistent kinetic parameter estimation on the basis of industrial data, which is itself a dynamic optimisation problem (multi-parametric error minimisation). Dynamic optimisation of biochemical processes is of extreme technoeconomic interest and importance in industrial control practice, particularly for biochemical process systems which display steady-state and/or operating regime multiplicity, and require sizeable vectors of time-dependent concentrations and temperature-dependent kinetic parameters. Alcohol fermentation is undergoing continuous development for several millennia via concurrent advances in chemistry and chemical engineering (which greatly affected the art of bringing yeast, barley and hops together); at the same time, the biological evolution of yeast strands by natural selection as well as empirical recipes and procedures
Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees

A spatial point process can be characterized by an intensity function which predicts the number of events that occur across space. In this paper, we develop a method to infer predictive intensity intervals by learning a spatial model using a regularized criterion. We prove that the proposed method exhibits out-of-sample prediction performance guarantees which, unlike standard estimators, are valid even when the spatial model is misspecified. The method is demonstrated using synthetic as well as real spatial data.
Substrate Dependent Analysis of Printed Sensors for Detection of Volatile Organic Compounds

This paper investigates all-printed gas sensors developed on different unconventional substrates and their effect of the detection of various volatile organic compounds (VOCs). An inkjet printing system is used to deposit the functional materials on PET (polyethylene terephthalate), paper and cotton fabric substrates. The different surface morphology and texture of the substrates are explored and used for developing the VOC sensors. Silver (Ag) nanoparticles based ink is used for printing the interdigital electrodes and the interconnect lines, whereas carbon black paste is used as the sensing layer. Sensors are tested against two different concentrations i.e. 4 and 22 ppm (parts per million) of three types of VOCs, i.e. acetone, ethanol and isopropanol. The comparative studies show a strong dependence of the VOCs detection on the type of substrates. Pros and cons of all the substrates, based on the processability, wearability, sensitivity and response times etc. are evaluated. This research presents significant contribution towards developing large area, low-cost and wearable VOC sensors, which are highly demanding in industrial and environmental monitoring as well as for potential analytes in biosensors to detect various chronic diseases.
Periodontitis severa, edentulismo y neuropatía en pacientes con diabetes mellitus tipo 2

Introduction: Periodontitis is a frequent pathologic condition in diabetic patient, and has been associated with chronic complications like nephropathy, cardiovascular disease, peripheral artery disease or death. Objective: To document the association between severe periodontitis and edentulism with the presence of sensory-motor neuropathy in diabetic patients. Material and method: Cross-sectional study in type 2 diabetic patients from the family medicine unit no. 27 of the IMSS in Tijuana, Mexico. Patients were evaluated to identify periodontitis and sensory-motor neuropathy. Information was also obtained about sex, age, duration of diabetes, glycemic control, smoking and alcohol use. Results: Four hundred and thirty-six patients completed all measurements. In 180 (41.3%) neuropathy was identified, and associated with age (p < 0.001); duration of diabetes (p < 0.001); fasting glucose (p < 0.001); severe periodontitis (OR: 2.7; IC 95%: 1.5-4.8); and with edentulism (OR: 4.4; IC 95%: 2.0-9.4). Logistic regression multivariable analysis kept as significative the association between severe periodontitis and edentulism with neuropathy (adjusted OR: 1.7; IC 95%: 1.1-2.6). Conclusions: Periodontitis and edentulism are associated with the presence of neuropathy in diabetic patients.
Text Emotion Distribution Learning from Small Sample: A Meta-Learning Approach

Text emotion distribution learning (EDL) aims to develop models that can predict the intensity values of a sentence across a set of emotion categories. Existing methods based on supervised learning require a large amount of well-labelled training data, which is difficult to obtain due to inconsistent perception of fine-grained emotion intensity. In this paper, we propose a meta-learning approach to learn text emotion distributions from a small sample. Specifically, we propose to learn low-rank sentence embeddings by tensor decomposition to capture their contextual semantic similarity, and use K-nearest neighbors (KNNs) of each sentence in the embedding space to generate sample clusters. We then train a meta-learner that can adapt to new data with only a few training samples on the clusters, and further fit the meta-learner on KNNs of a testing sample for EDL. In this way, we effectively augment the learning ability of a model on the small sample. To demonstrate the performance, we compare the proposed approach with state-of-the-art EDL methods on a widely used EDL dataset: SemEval 2007 Task 14 (Strapparava and Mihalcea, 2007). Results show the superiority of our method on small-sample emotion distribution learning.
Original Semantics-Oriented Attention and Deep Fusion Network for Sentence Matching

Sentence matching is a key issue in natural language inference and paraphrase identification. Despite the recent progress on multi-layered neural network with cross sentence attention, one sentence learns attention to the intermediate representations of another sentence, which are propagated from preceding layers and therefore are uncertain and unstable for matching, particularly at the risk of error propagation. In this paper, we present an original semantics-oriented attention and deep fusion network (OSOA-DFN) for sentence matching. Unlike existing models, each attention layer of OSOA-DFN is oriented to the original semantic representation of another sentence, which captures the relevant information from a fixed matching target. The multiple attention layers allow one sentence to repeatedly read the important information of another sentence for better matching. We then additionally design deep fusion to propagate the attention information at each matching layer. At last, we introduce a self-attention mechanism to capture global context to enhance attention-aware representation within each sentence. Experiment results on three sentence matching benchmark datasets SNLI, SciTail and Quora show that OSOA-DFN has the ability to model sentence matching more precisely.
Simulation Framework for a Visual-Inertial Navigation System

The ability to test methods based on simulated data is an important component in the development of intelligent localization systems at the present time. This includes the replication of realistic environments and sensors on the one hand. On the other hand, an implementation of a correct movement profile of the target mobile platform is required. This is complicated by the large variety of possible platform variants. We propose a method to transfer movement profiles that have been recorded in real world into a simulation environment. The associated modular simulation framework was designed especially, but not limiting, for the development of visual-inertial odometry of a handheld navigation system. We demonstrate the application of our pipeline through an experiment to determine the influence of motion blur on the visual odometry component.
Platform to Effectively Disseminate Location Information in 5G for Supporting Mission Critical Services

In today’s world, 5G has been considered as a pivotal technology to provide wide range of services belonging to use cases like enhanced Mobile Broadband (eMBB), massive Machine Type Communication (mMTC) and ultra Reliable Low Latency Communication (uRLLC). Sharing critical information such as location is a key requirement in mission critical services like telemedicine, surveillance, autonomous cars, etc. In legacy network, the location data are provided by the Over-the-Top (OTT) providers with Global Positioning System (GPS). By the time 5G technology rolls out, the number of location based services for mission critical services using the drones, robots, autonomous car etc. will increase and network topology will evolve to dynamic beam-based cells. Beam-based cells collect and maintain precise location of users at the edge of the network. In this paper, platform based approach to cater the needs of 5G hyper location services have been proposed. It is envisaged that such precision mapping requires effective super-resolution source estimation algorithms. One example candidate super resolution algorithm, well known in the literature, has been analyzed to understand the suitability for hyper-location estimation framework. The overall conceptual design of the platform with the underlying work-flow has been addressed to shed light on the future trends in the Service Provider (SP) platforms.
Modelling the disaggregated demand for electricity at the level of residential buildings with the use of artificial neural networks (deep learning approach)

The paper addresses the issue of modelling the demand for electricity at the level of residential buildings with the use of artificial intelligence tools, namely artificial neural networks (ANN). The real data for six buildings acquired by measurement meters installed in them was used in the research. Their original frequency of 1 Hz has been resampled to a frequency of 1/600 Hz which corresponds to a period of 10 minutes. Out-of-sample forecasts verified the ability of ANN to disaggregate electricity usage for its specific applications. Four categories were distinguished, which were electricity consumption by: (i) fridge, (ii) washing machine, (iii) personal computer and (iv) freezer. Both standard ANNs with multilayer perceptron architecture and newer types of networks based on deep learning approach were used. The simulations included a total of over 10,000 ANNs differing, e.g. architecture, input variables, activation functions, their parameters, and training algorithms. The research confirmed the possibility of using ANN in modelling the disaggregation of electricity consumption and indicated the way of building a highly optimized model.
Deep Learning For Inter-Observer Congruency Prediction

According to the literature regarding visual saliency, observers may exhibit considerable variations in their gaze behaviors. These variations are influenced by aspects such as cultural background, age or prior experiences, but also by features in the observed images. The dispersion between the gaze of different observers looking at the same image is commonly referred as inter-observer congruency (IOC). Predicting this congruence can be of great interest when it comes to study the visual perception of an image. In this paper, we introduce a new method based on deep learning techniques to predict the IOC of an image. This is achieved by first extracting features from an image through a deep convolutional network. We then show that using such features to train a model with a shallow network regression technique significantly improves the precision of the prediction over existing approaches.
Implementation and analysis of MIMO system based on Alamouti coding

Use of multiple antennas at the receiver and transmitter in a wireless network is a rapidly emerging technology that promises higher data rates at longer ranges without consuming extra bandwidth or transmit power. These systems can be with single-input multiple-output (SIMO), multiple-input single-output (MISO), or with multiple-input multiple-output (MIMO) architectures to improve the signal quality at the receiver using multiple data pipes over a link. MIMO communication system considers multiple antennas used at the transmitting end as well as the receiving end. In addition, the MIMO system has the ability to spread in the spatial domain which is combined in such a way that they either create effective multiple parallel spatial data pipes and diversity to improve the quality (for example decrease the bit error rate). The benefit from the multiple antennas arise from the new dimension space. Hence, the spatial dimension comes as a complement to time. MIMO technology also known as space-time ‘wirelesses’. Space–time block coding (STBC) is a technique used in wireless communications to transmit multiple copies of a data stream across a number of antennas and to exploit the various received versions of the data to improve the reliability of data transfer. The Alamouti coding is a STBC coding technique that is widely use in wireless communication. Alamouti coding can be used in different models like using 2×1 MISO mode or a 2×2 MIMO mode and it can use OFDM system. The purpose of this project is to test a performance of wireless communication system under different type of noise, and the channel model. Also, implementation and analysis of MIMO system based on Alamouti STBC coding.
Deep Learning Model for Motion Video Processing

: In the film industry for motion picture processing, it requires different rate (fps) of motion capture. Utilizing single sequence compromises on quality. Different frames are required to be inputted to video editing tools as per motioning requirements. The goal of the paper is to construct a model to utilise the captured frame sequence and generate slow motioning using clustering and Convolutional Neural Networks.
YouMakeup: A Large-Scale Domain-Specific Multimodal Dataset for Fine-Grained Semantic Comprehension

Multimodal semantic comprehension has attracted increasing research interests recently such as visual question answering and caption generation. However, due to the data limitation, fine-grained semantic comprehension has not been well investigated, which requires to capture semantic details of multimodal contents. In this work, we introduce “YouMakeup”, a large-scale multimodal instructional video dataset to support fine-grained semantic comprehension research in specific domain. YouMakeup contains 2,800 videos from YouTube, spanning more than 420 hours in total. Each video is annotated with a sequence of natural language descriptions for instructional steps, grounded in temporal video range and spatial facial areas. The annotated steps in a video involve subtle difference in actions, products and regions, which requires fine-grained understanding and reasoning both temporally and spatially. In order to evaluate models’ ability for fined-grained comprehension, we further propose two groups of tasks including generation tasks and visual question answering from different aspects. We also establish a baseline of step caption generation for future comparison. The dataset will be publicly available at https://github. com/AIM3-RUC/YouMakeup to support research investigation in fine-grained semantic comprehension.