Miracle-dz/UniFAR-FTUs huggingface dataset:
Mental Health Literacy in Emerging Adults in a University Setting: Distinctions Between Symptom Awareness and Appraisal

Despite the high prevalence of mental health concerns in university populations, students are unlikely to seek formal help. The current study examined help-seeking behaviors among emerging adults in a university setting using a mental health literacy framework. Responses from 122 university undergraduates were examined. Students ranged in age from 17 to 25 years (M = 20.67 years, SD = 2.03 years). Quantitative data were collected to determine students’ knowledge and use of campus services and to compare students’ awareness and appraisal of common mental health symptoms. Narrative data were collected to identify the key symptoms that are appraised to be “early warning signs” versus “early action signs,” and to identify barriers and facilitators to help-seeking. Mental health symptoms were more likely to be assessed as warning signs than signs warranting action. Lack of knowledge and stigma were barriers to help-seeking, while urging from family and friends, increased knowledge, and confidentiality were identified as facilitators to help-seeking. Emerging adults in a university setting tend to make distinctions between warning and action signs. Although demonstrating good awareness of the signs of declining mental health, students may respond reactively rather than proactively to symptoms.
A Survey: Centralized, Decentralized, and Distributed Control Scheme in Smart Grid Systems

When electricity was fist made available in the late nineteenth century, it was through central stations serving a group of nearby customers. Generation and distribution were localized, and long-distance transmission was not yet necessary. As demand for electricity service increased, distribution networks expanded in capacity and geography. Systems once isolated from one another were becoming interconnected. Recently, the integration of the distributed renewable energy resources is evolving as an emerging and promising power scenario for the energy system. Smart grid, as a modernized electrical grid, uses information and communication technology to improve the efficiency, reliability, and economics of the production and distribution of electricity [9, 10, 11]. As the main building block of the smart grid, microgrid (MG) integrates a number of local DG units, energy storage systems, and local loads together to form a small-scale power system. In this paper, we will propose some distributed control schemes for the MG control.
Analysis of Impact of Electric Vehicles on Distribution Grid Using Survey Data

Electric vehicles are more environmentally friendly and lower fuel costs than conventional vehicles. Thanks to these advantages, their numbers have increased rapidly in recent years and it is thought that they will be used more in the future so the power demanded from the electricity grid will increase. If electricity distribution companies and energy producing companies do not take the necessary precautions, they will not be able to provide quality service to users in the following years. Therefore, the effects of electric vehicles on the electricity distribution system should be examined. In this study, a survey was applied to a working group of 50 people. This survey was analyzed and daily travel information was modeled. Statistical home arrival time, work arrival time and daily trip distance curves were formed. Charging loads were calculated by converting the obtained model to daily charging model of electric vehicles.
Smart Clothing Recommendation System with Deep Learning

Recommendation systems based on machine learning are very important both customers and sellers in our daily life. Many recommendation systems need user’s previous shopping activities and digital footprints to make best recommendation purpose for next item shopping. In this study, we develop a cloth recommendation system with using only single photo of user with scalable embedded system. This study lead to important results and give new opportunities for clothing companies and advertisements. In this study, we show that how our system recommends a cloth options without user’s previous shopping act data with embedded system and machine learning. In order to recommend a cloth, we develop two inception based convolutional neural networks as prediction part and one feed forward neural network as recommender. In this study, we reach to 98% accuracy on color prediction, 86% accuracy on gender and cloth’s pattern predictions and 75% accuracy on clothing recommendation.
A Model Based Approach for Android Design Patterns Detection

Design patterns in software development have shown great promise in improving software quality. Traditionally, software developers utilize a set of design patterns to foster reusability and better software design. Recently, mobile applications (apps) have become a mainstay of modern computing, as well as, a challenging domain for software engineers. This is because mobile apps now target more critical domains such as health, banking, m-payments, and even military to mention just a few. Android is a very popular mobile platform, and has managed to take over the majority of mobile market. It is true that there are comprehensive studies in the area of design patterns detection in several object-oriented languages such as Java, C# and C++. However, little studies target design patterns in Android apps. As a step toward helping to measure and explore the application of design patterns in Android apps, we introduce PatRoid, an automated framework for detecting design patterns. PatRoid is a model-based approach that is able to detect design patterns laying inside Android apps source code. The model is based on a graph isomorphism approach, where design patterns are divided into sub-patterns that can be aggregated to formulate design patterns. We have conducted a preliminary evaluation and the results show that PatRoid can detect all of the 23 GoF design patterns.
Pruning Blocks for CNN Compression and Acceleration via Online Ensemble Distillation

In this paper, we propose an online ensemble distillation (OED) method to automatically prune blocks/layers of a target network by transferring the knowledge from a strong teacher in an end-to-end manner. To accomplish this, we first introduce a soft mask to scale the output of each block in the target network and enforce the sparsity of the mask by sparsity regularization. Then, a strong teacher network is constructed online by replicating the same target networks and ensembling the discriminative features from each target as its new features. Cooperative learning between multiple target networks and the teacher network is further conducted in a closed-loop form, which improves their performance. To solve the optimization problem in an end-to-end manner, we employ the fast iterative shrinkage-thresholding algorithm to fast and reliably remove the redundant blocks, in which the corresponding soft masks are equal to zero. Compared to other structured pruning methods with iterative fine-tuning, the proposed OED is trained more efficiently in one training cycle. Extensive experiments demonstrate the effectiveness of OED, which can not only simultaneously compress and accelerate a variety of CNN architectures but also enhance the robustness of the pruned networks.
Dental Restorations Packing Based on the Contour Similar Features Matching and Collision Strategy

Dental restoration is a complex process to recover patients’ teeth using artificial materials. It is important to improve the utilization of the expensive material for cost saving. The tooth side surface is projected as a two-dimensional (2D) contour in the actual processing, which forms a packing problem of irregular contours. The 2D irregular packing has received a lot of attention in research to solve this NP-hard problem. Contours of the dental restoration are more complicated than those in traditional 2D irregular packing problems. The existing packing methods cannot obtain good results for these complex contours. In order to solve the contour packing problem of restoration models using the NC machining technology, a dental restoration contour packing algorithm is proposed based on the contour similar feature matching and collision. The algorithm uses dynamic programming for the longest common subsequence to search the solution of 2D free contour packing. Comparing with the existing packing methods, our proposed method can significantly reduce the processing time and improve the filling rate.
Material progress toward recyclable insulation of power cables part 2: Polypropylene-based thermoplastic materials

Progress in the development of polyethylene (PE) based thermoplastic materials for power cable insulation has been reviewed in the first article on this subject [1]. New catalysis technique, copolymerization, blending, nanotechnology and additives have been used to tailor the electrical property of PE in the past few decades. However, these methods have brought about limited improvement in the thermomechanical property of PE. For instance, crosslinking reactions can improve the thermomechanical property of PE so that the cable operation temperature can be increased from 70°C to 90°C of low density polyethylene (LDPE) cables (Table 1); however, as discussed in the first article, crosslinking brings in undesirable effects. Apart from the ultra-low recyclability, the crosslinking and degassing also lead to potential safety concerns during cable installation and operation, in addition to long duration and high energy consumption needed for cable production. Since the beginning of the 21st century, polypropylene (PP) based thermoplastic insulation has attracted tremendous interests because of its high temperature stability and excellent recyclability. Compared with XLPE, PP possesses advantages of not only increased operating temperature higher than 110°C but also facile manufacturing processing with reduced cost, increased production efficiency and unlimited cable length, which are highly desirable in the power cable industry. As a promising future solution, PP based thermoplastic cable insulation starts to receive significant interest and attention from academia and industry [2]. However, conventional PP cannot be used directly as power cable insulation because of its high hardness, high crystallinity and low flexibility. In recent years, PP has been modified as thermoplastic cable insulation. This second article discusses the structure-property relationship of PP and reviews the progress on the development of PP based thermoplastic materials for power cable insulation.
Throughput Maximization for Wireless Powered Multimedia Communication Systems Under Statistical Latency Constraint

This paper investigates the relationship among the traffic throughput, statistical data latency requirement and system configurations in a wireless powered multimedia communication system. Firstly, a system model is built up with considerations of two operation modes and practical non-linear energy harvesting process. Based on the system model, the effective capacity of the considered system is derived based on the wireless charging parameter and fix data transmission rate. Then, the latency violation provability is studied by jointly taking effective capacity, traffic arrival and maximum tolerable data latency into account. Thereafter, the traffic maximization problem is transformed to the effective capacity maximization problem. Furthermore, We propose an optimal solution and a near-optimal solution to find out the system configurations of wireless charging time and transmission rate. Simulation results verify the accuracy of the near-optimal solution and also shed light on the operation mode selection in the considered system.
A Replicable Comparison Study of NER Software: StanfordNLP, NLTK, OpenNLP, SpaCy, Gate

Named Entity Recognition (NER) is a key building block of any Natural Language Processing (NLP) system, making possible the detection and classification of entities (e.g., Person, Location) in any given text. While a large number of NER software exist today, it remains difficult for NLP and NER practitioners to clearly and objectively identify what software perform(s) the best. One of the reasons is the difference in results across the literature and the lack of information needed to be able to fully reproduce the experiment. To overcome this problem, this paper presents a comprehensive and replicable study to assess the performance of NER software, thus laying the groundwork for future benchmarking and meaningful comparison studies. As part of our experiments, the latest version of five well-known NER software were selected, along with two distinct corpora. We observe a discrepancy between the result we get and the result found in the literature being around 50% in certain cases. We also found that StanfordNLP usually performs the best.
A Generalized Deep Learning Model for Glaucoma Detection

Glaucoma is an ocular condition that may lead to blindness at its advanced stages. It results due to damaged optic nerve and does not have many early symptoms. Ophthalmologists may use perimetry, tonometry, and ophthalmoscopy to diagnose a glaucomatous eye. Computer-aided detection of glaucoma has also been possible using deep learning. This paper presents a generalized deep learning model for glaucoma detection using fundus images. Unlike previous studies, the model is trained and tested on multiple datasets and architectures. The results show that the model is comparable or better than earlier work in the literature 80% of the time.
Application-Oriented Network Scheduling With Metaflow

Distributed applications usually feature a set of correlated flows between two consecutive computation stages. The scheduling of these flows has a crucial influence on job completion time. Coflow improves performance by optimizing the finish time of the entire set of flows. However, the flows and computing tasks in one application have more complex relationships that exceed the coflow’s barrier assumption. In this context, scheduling via coflow abstraction may hurt application performance. Accordingly, we propose metaflow, a traffic abstraction derived from the computation graph of the application. Metaflow reveals the detailed flow requirements of the application and makes it easier to reduce the job completion time. Based on the metaflow, we first develop a mathematical model and formulate the scheduling problem as an integer linear programming (ILP) problem. We further prove that it has an equivalent linear programming (LP) problem through rigorous theoretical analysis in order to solve this ILP problem efficiently. To demonstrate the effectiveness of scheduling with metaflow, we have conducted extensive simulations with both synthetic single jobs and production traces containing multiple jobs. The simulation results verify that our new scheduler adapts well to different jobs and can achieve a significant increase in an average speed of $2.87\times $ on a real-life workload, compared to the state-of-the-art coflow scheduler.
On the Application of Automated Machine Vision for Leather Defect Inspection and Grading: A Survey

Reliably and effectively detecting and classifying leather surface defects is of great importance to tanneries and industries that use leather as a major raw material such as leather footwear and handbag manufacturers. This paper presents a detailed and methodical review of the leather surface defects, their effects on leather quality grading and automated visual inspection methods for leather defect inspection. A detailed review of inspection methods based on leather defect detection using image analysis methods is presented, which are usually classified as heuristic or basic machine learning based methods. Due to the recent success of deep learning methods in various related fields, various architectures of deep learning are discussed that are tailored to image classification, detection, and segmentation. In general, visual inspection applications, where recent CNN architectures are classified, compared, and a detailed review is subsequently presented on the role of deep learning methods in leather defect detection. Finally, research guidelines are presented to fellow researchers regarding data augmentation, leather quality quantification, and simultaneous defect inspection methods, which need to be investigated in the future to make progress in this crucial area of research.
An Instrument for Accurate and Non-Invasive Screening of Skin Cancer Based on Multimodal Imaging

We present an instrument based on commodity embedded hardware, that implements an automatic procedure for early skin-cancer screening using dynamic thermal imaging. The procedure leverages image segmentation in the visible range and real-time multimodal registration to compute the temperature recovery curve (TRC) of suspicious skin lesions using thermal infrared video. The instrument implements two algorithms that infer the malignancy of the lesion from the computed TRCs. The first algorithm assumes that the TRCs are deterministic and infers the malignancy from the distance between the TRC of the suspicious lesion and its surrounding skin, which is assumed to be healthy tissue. The second algorithm models the TRC of the lesion as a random process and uses detection theory to statistically infer its malignancy from the eigenfunctions and corresponding eigenvalues of its covariance function. We built a prototype of the instrument using a Raspberry Pi 3 model B+ board, which acquires a visible-range image of the lesion at the beginning of the procedure and performs image segmentation in 62ms. Operating on a $400 \times 400$ -pixel region-of-interest within the infrared video, the board performs frame-by-frame multimodal image registration and generates the TRCs in real time at more than 37 frames per second, thus eliminating the need to store video data for off-line processing. The statistical detection algorithm, which yields the best results, runs in 1.07s at the end of the procedure, and achieves a sensitivity of 98% and a specificity of 95% on a dataset of 116 volunteer subjects.
Network Solutions for CoMP Coordinated Scheduling

Demanding throughput, latency and scalability requirements of 5G networks may be addressed by relying on dense deployments of small cells. Coordinated Multipoint (CoMP) Coordinated Scheduling (CS) techniques are introduced to reduce inter-cell interference in case of dense deployment, given that local CoMP-CS information from the evolved NodeBs (eNodeBs) in the cluster are timely collected at the scheduling decision entity. This work studies how the distribution of CoMP-CS cell information is affected by the backhaul infrastructure in terms of both physical and logical topology. The differentiation between physical and logical infrastructure is justified in the context of new approaches like Software Defined Networking and Network Function Virtualization that enable the dynamic configuration of the network. We consider either a Packet Switched Network with three possible topologies (namely, ring, mesh and star) or a Time Division Multiplexing Passive Optical Network (TDM-PON), both carrying heterogeneous traffic. To improve the convergence time in the TDM-PON, we propose a novel bandwidth allocation scheme to prioritize the signaling traffic with respect to data traffic. Performance of both distributed and centralized CoMP-CS are compared in terms of convergence delay and traffic overhead. Finally, we analyze the impact of the periodicity of CS operations on mobile performance, in terms of average UEs throughput, in the presence of different cell loads.
A Robot Programming by Demonstration Method for Precise Manipulation in 3C Assembly

Industrial robots have been widely used in various production line but not in the 3C assembly line which is characterized by highly frequent change-over and still relies on labor heavily. To deal with this challenge, the Programming by Demonstration (PbD) method, which provides a simple and intuitive way for workers to transfer skills to the robot, will greatly reduces professional knowledge requirements for robot users. However, some practical factors in 3C assembly line, such as the mechanical diversities between the human body and the robot, make it difficult to apply PbD methods. Motivated by that, with a subtly designed demonstration data extraction platform and a posture mapping strategy, this article proposes a PbD method that transforms human assembly skill to the robot for 3C assembly tasks. Further more, by an iterative optimization method, the demonstration path is simplified to boost the assembly efficiency which counts for much in practice. A simulation and a 3C assembly experiment has demonstrated the effectiveness of the proposed method.
Neural Paraphrasing by Automatically Crawled and Aligned Sentence Pairs

Paraphrasing is the task of re-writing an input text using other words, without altering the meaning of the original content. Conversational systems can exploit automatic paraphrasing to make the conversation more natural, e.g., talking about a certain topic using different paraphrases in different time instants. Recently, the task of automatically generating paraphrases has been approached in the context of Natural Language Generation (NLG). While many existing systems simply consist in rule-based models, the recent success of the Deep Neural Networks in several NLG tasks naturally suggests the possibility of exploiting such networks for generating paraphrases. However, the main obstacle toward neural-network-based paraphrasing is the lack of large datasets with aligned pairs of sentences and paraphrases, that are needed to efficiently train the neural models. In this paper we present a method for the automatic generation of large aligned corpora, that is based on the assumption that news and blog websites talk about the same events using different narrative styles. We propose a similarity search procedure with linguistic constraints that, given a reference sentence, is able to locate the most similar candidate paraphrases out from millions of indexed sentences. The data generation process is evaluated in the case of the Italian language, performing experiments using pointer-based deep neural architectures.
Using Topical Networks to Detect Editor Communities in Wikipedias

Ahstract- The collaboration of Wikipedia editors is well researched, covered by scientific works of many different fields. There is a growing interest to implement recommender systems that guide inexperienced editors to projects which fit their interests in certain topical domains. Although there have been numerous studies focusing on editing behavior in Wikipedia the role of topical domains in this regard is still unclear. In particular, topical aspects of co-authorship are generally neglected. In this paper, we want to determine by which criteria editors usually choose articles they want to contribute to. We analyzed three different language editions of Wikipedia (Vietnamese, Hebrew, and Serbo-Croatian) by building social networks and running community detection algorithms on them, i.e. editors are grouped based on their shared involvement in Wikipedia articles using social network analysis techniques. Then, we related this to the topical domains of these articles based on Wikipedia's user defined category network. Our results demonstrated that communities in Wikipedia tend to edit articles with a higher than average topical relatedness. But the significance and quality of these results vary considerably in the different language versions of Wikipedia. Topical relations between contributors and articles are a complex matter and influenced by a number of different factors, e.g. by culture.