Miracle-dz/UniFAR-FTUs huggingface dataset:
Corona Resistance of Direct Flourinated Epoxy/Al2O3 Nanocomposites

Pure epoxy resin and epoxy/Al2O3nanocomposite sheets were prepared and directly fluorinated. The ATR-IR spectrum proves the existence of C-Fx groups in fluorinated samples. Dielectric permittivity increases because of Al2O3nanoparticles yet fluorination leads to decreasing of permittivity. Results of surface potential decay and surface morphology measurement indicate that combination of fluorination and nano filling significantly improves corona resistance of epoxy resin. Charge injection to sample matrix tends to be restrained by high surface conductivity of fluorinated layer, while nanoparticles may attenuate the mean kinetic energy of negative charge carriers that arriving sample surface, and increase the probability of charge being captured in matrix. In addition, the high bond energy of C-Fx groups and Al2O3nanoparticles may contributed to the improved resistance of epoxy resin to corona discharge.
Statistical Translation of English Texts to API Code Templates

We develop T2API, a context-sensitive, graph-based statistical translation approach that takes as input an English description of a programming task and synthesizes the corresponding API code template for the task. We train T2API to statistically learn the alignments between English and API elements and determine the relevant API elements. The training is done on StackOverflow, a bilingual corpus on which developers discuss programming problems in two types of language: English and programming language. T2API considers both the context of the words in the input query and the context of API elements that often go together in the corpus. The derived API elements with their relevance scores are assembled into an API usage by GraSyn, a novel graph-based API synthesis algorithm that generates a graph representing an API usage from a large code corpus. Importantly, it is capable of generating new API usages from previously seen sub-usages. We curate a test benchmark of 250 real-world StackOverflow posts. Across the benchmark, T2API's synthesized snippets have the correct API elements with a median top-1 precision and recall of 67% and 100%, respectively. Four professional developers and five graduate students judged that 77% of our top synthesized API code templates are useful to solve the problem presented in the StackOverflow posts.
T1, T1 contrast, and Ernst‐angle images of four rat‐lung pathologies

To initiate the archive of relaxation‐weighted images that may help discriminate between pulmonary pathologies relevant to acute respiratory distress syndrome. MRI has the ability to distinguish pathologies by providing a variety of different contrast mechanisms. Lungs have historically been difficult to image with MRI but image quality is sufficient to begin cataloging the appearance of pathologies in T1‐ and T2‐weighted images. This study documents T1 and the use of T1 contrast with four experimental rat lung pathologies.
Reassessing Hierarchical Representation for Action Recognition in Still Images

Typical still action recognition methods rely on human body part detection and object detection. However, current human body part detectors and object detectors are far from perfect, leading to a negative impact on subsequent spatial relation learning of human-object interactions (HOIs). Bag-of-features (BoF)-based methods go beyond such modeling paradigms, but they do not achieve the state-of-the-art accuracies. In this paper, we propose two still action recognition methods that model HOI layouts by image hierarchical representation, rather than explicitly constructing HOI relations. The first method encodes a dense set of SIFT features using Fisher vectors, where an image is divided into increasingly fine regions with the spatial pyramid. The second method takes recent pretrained deep networks as feature execrators, where an image is divided into overlapped regions. The improvement effect of the hierarchical representation is proven by extensive comparison experiments. Our methods are very simple and easy-to-use, which remarkably outperform those BoF-based methods and complicated human-centric methods. To the best of our knowledge, our methods achieve the highest accuracies to date on the Sports, PPMI, and extended PPMI data sets.
Implementation of an Efficient Prosthetic Limb Controlled by Electro Myo Graphic(EMG) Signal

In this paper, we are discussing the concept, design, modeling, and prototype of a Myoelectric Prosthetic Arm, which uses the EMG signals from the patient for controlling the movements of the Prosthetic Arm. The electromyography sensor allows the user to measure the electrical activity of muscles. It is also used for controlling the prosthetic device. The signal acquired from the patient's body and after suitable processing, it is transmitted to Arduino, which uses the received muscle signal as an input to drive motors which are in turn coupled to the prosthetic arm. So, the amputee wore the arm initiate the control mechanism control. Though an amputee is dependent on the prosthetic arm, it is his/her myoelectric signals which run the arm, which is a confidence-boosting aspect of the myoelectric prosthetic arm.
Ionic‐Liquid Doping Enables High Transconductance, Fast Response Time, and High Ion Sensitivity in Organic Electrochemical Transistors

Organic electrochemical transistors (OECTs) are highly attractive for applications ranging from circuit elements and neuromorphic devices to transducers for biological sensing, and the archetypal channel material is poly(3,4‐ethylenedioxythiophene):poly(styrenesulfonate), PEDOT:PSS. The operation of OECTs involves the doping and dedoping of a conjugated polymer due to ion intercalation under the application of a gate voltage. However, the challenge is the trade‐off in morphology for mixed conduction since good electronic charge transport requires a high degree of ordering among PEDOT chains, while efficient ion uptake and volumetric doping necessitates open and loose packing of the polymer chains. Ionic‐liquid‐doped PEDOT:PSS that overcomes this limitation is demonstrated. Ionic‐liquid‐doped OECTs show high transconductance, fast transient response, and high device stability over 3600 switching cycles. The OECTs are further capable of having good ion sensitivity and robust toward physical deformation. These findings pave the way for higher performance bioelectronics and flexible/wearable electronics.
The Comparison of MOOC (Massive Open Online Course) Platforms of edX and Coursera (Study Case: Student of Programming Courses)

The recent development of e-learning platform has provided the learning environment with exciting experiences to teach a various and massive learners population using MOOC (Massive Open Online Courses) platforms such as edX, Coursera, etc. A large user uses these platforms to enhance their competency and improve their skills. Numerous platform of MOOC has been implemented in higher education institution, and the public debate about MOOC is growing; is the platform relevant to be formal education or not. Therefore, this research objective is to explore the MOOC's features, particularly, to identify the difference platform of edX and Coursera. Those platforms have the most significant number of learners; in learner's perspectives through the Resource Activity Support Evaluation (RASE) pedagogical method and learning activity components, which consists of Absorb, Do, Connect activity. The study conducted at a private university in Indonesia with 60 students as respondents and they were also involved in experiments activity to use MOOC's platform. This study used quantitative approach to identify the difference platform between edX and Coursera. The significant results from this study can open mind the researcher to understand the uniqueness of MOOC's platform particularly in edX and Coursera, and it also can be considered as a starting finding toward advanced research in education technology area for the higher education institution.
Detecting Indonesian Spammer on Twitter

Nowadays, Twitter is one of the most popular social media today. However, Twitter has several problems that have negative impacts to the users, one of which is spam. We introduce a different approach compared to previous research are the scope of Indonesian-language Twitter, crawling automatically for user and tweets data, as well as the addition of new features. We use two features dimension, i.e., user-based and tweet-based. In this paper, we detect Indonesian spammers on Twitter using four classification algorithms, namely Naïve Bayes (NB), Support Vector Machine (SVM), Logistic Regression (Logit), and J48. The results are confirmed for having better accuracy that of the existing. The highest accuracy of 93,67% is achieved using Logistic Regression (Logit).
A Fully Observable Supply Chain Management System Using Block Chain and IOT

The more traditional approach to supply chain has always been a web site that has been hosted on a server the company has to set up or get hosted from a provider. The supply chain data is centralized here, leaving the website the only gateway to the data, thus reducing the availability of data, increasing the risk factor, leading to an inflexible system where every update had to route to the hosted system. The orders, updates, changes, tracking details, and the details of customers, sellers, manufacturers of raw material, assemblers, and others have to be decentralized, that's the purpose of introducing traditional supply chain to the block chain. The decentralization of this data across different levels ranging from raw material manufacturer to the retailer will lead to a more flexible and transparent system. These traits would in turn result into a system where data flows faster than traditional centralized approach to supply chain management. Use of IOT ensures that there are no inconsistencies in the processes that are in-transit, automating the updating process that would other require human intervention.
A Micromodel to Predict Message Propagation for Twitter Users

Twitter is the most popular social platform for broadcasting opinions but the reach of tweets is often non-deterministic. While people believe the world is taking cognizance of their tweets, this seldom is the truth. This paper presents a micro-prediction model for determining message propagation for a user, especially for the non-influential majority. Our framework uses Ego network in predicting message propagation. The work focuses on determining the possible users who would interact and their immediate reach. This is achieved by using Twitter API in a limited manner. We attempt to make a responsive prediction model; simple, stateless and scalable, capable of catering to parallel requests. The simulation results shows a promising 86.4% accuracy for data constituting of 336768 connected users.
Fifteen hub genes associated with progression and prognosis of clear cell renal cell carcinoma identified by coexpression analysis

Renal cell carcinoma (RCC) is the most common type of renal tumor, and the clear cell renal cell carcinoma (ccRCC) is the most frequent subtype. In this study, our aim is to identify potential biomarkers that could effectively predict the prognosis and progression of ccRCC. First, we used The Cancer Genome Atlas (TCGA) RNA‐sequencing (RNA‐seq) data of ccRCC to identify 2370 differentially expressed genes (DEGs). Second, the DEGs were used to construct a coexpression network by weighted gene coexpression network analysis (WGCNA). Moreover, we identified the yellow module, which was strongly related to the histologic grade and pathological stage of ccRCC. Then, the functional annotation of the yellow module and single‐samples gene‐set enrichment analysis of DEGs were performed and mainly enriched in cell cycle. Subsequently, 18 candidate hub genes were screened through WGCNA and protein–protein interaction (PPI) network analysis. After verification of TCGA’s ccRCC data set, Gene Expression Omnibus (GEO) data set (GSE73731) and tissue validation, we finally identified 15 hub genes that can actually predict the progression of ccRCC. In addition, by using survival analysis, we found that patients of ccRCC with high expression of each hub gene were more likely to have poor prognosis than those with low expression. The receiver operating characteristic curve showed that each hub gene could effectively distinguish between localized and advanced ccRCC. In summary, our study indicates that 15 hub genes have great predictive value for the prognosis and progression of ccRCC, and may contribute to the exploration of the pathogenesis of ccRCC.
Broadband Transition of Substrate-Integrated Waveguide-to-Air-Filled Rectangular Waveguide

A single-layer right-angle broadband transition between the substrate-integrated waveguide and air-filled standard rectangular waveguide is proposed and demonstrated at the V-band. An aperture coupled patch with two pairs of inductive posts is employed to form the transition. An impedance bandwidth wider than 48% for return loss less than −10 dB is achieved. The proposed back-to-back transition is fabricated on a single layer substrate using the printed circuit board technology. The measured results of back-to-back prototype show that the insertion loss of a single transition is less than 0.5 dB. The measured results show a good agreement with HFSS simulation results. This transition with compact size, wide bandwidth, and low cost can be used for a variety of millimeter-wave circuits and systems.
Towards a User and Role-Based Behavior Analysis Method for Insider Threat Detection

Organizations are experiencing an ever-growing concern of how to identify and defend against insider threats. Existing methods have distinguished the minority of users who show suspicious behavior from the majority of users. However, these methods failed to apply the features reflecting the deviation between the behaviors of users and those of their user groups within the similar job roles. This paper focuses on insider threat detection by conducting both user and role behaviors analysis. It extracts multiple features that represent the details of activities conducted by each user and their deviations from the behaviors of their user groups. The malicious users are then detected by using an unsupervised algorithm, Isolation Forest Algorithm, which evaluates the variance that each user exhibits across multiple attributes, compared against the other users. To evaluate the performance of the proposed models comprehensively, we implement a series of experiments with the data lasting 17 months. We compare the proposed method with an existing state-of-the-art method and the results demonstrate the robust performance of the proposed detection method.
Blind Source Separation and Equalization for High Order QAM Signals in MIMO System

In this paper, we first introduce cross-correlation term to a new multimode based algorithm, then propose two dual-mode blind source separation and equalization schemes to mitigate interuser (IUI) and intersymbol interferences (ISI) for high order QAM signals in multiple-input multiple-output (MIMO) system. In the first mode, β-multimodulus algorithm (βMMA) and cross-correlation function are applied for the purpose of fast convergence and lower IUI and ISI. In the second mode, decision-directed (DD) algorithm and alphabet matched algorithm (AMA) are applied respectively to reduce the total interference (TI) further. Under the condition of 2-input/3-output linear channel, computer simulation experiments verified the feasibility of the two dual-mode schemes and prove its better performance than the single multimode-based blind algorithms for high order QAM signals.
Enhancing 802.11ac Dynamic Access method for Increasing the Throughput of Low Priority Access Categories BK and BE

Wireless Local Area Networks (WLANs) are becoming an essential form of communication in our life, and they have known a strong history of development. The current IEEE 802.11ac technology aims at achieving a Very High Throughput (VHT), by utilizing wider channels 80 and 160 MHz. The wide channels are obtained by bonding adjacent 20 MHz sub-channels. This is why, Static and Dynamic Multichannel Access (SMA and DMA) methods are proposed for controlling such multichannel transmissions. SMA and DMA guarantee the requirements of Quality of Service (QoS) for VHT WLANs, by following the existing 802.11e Enhanced Distributed Channel Access (EDCA). However, EDCA still suffers from the starvation among low priority ACs. Although several enhancements have been proposed since the emergence of the starvation issue, none of the proposed research works have addressed the critical state of low priority ACs BestEffort (BE) and BacKground (BK) under wide channels. In this paper, we propose to enhance the DMA method, in order to improve the multichannel access efficience and hence increase the throughputs of BK and BE. The obtained simulation results demonstrate the contribution of the Starvation resolution within the DMA method (SDMA) for increasing the throughputs of low priority ACs while maintaining the throughputs of high priority ACs.
Personalize Vison-based Human Following for Mobile Robots by Learning from Human-Driven Demonstrations

Human following is an important feature in various human-mobile-robot collaboration applications. Vision-based human following approaches employing visual servoing controls to achieve human following are commonly adopted. Such approaches, however, require to pre-define the desired human-following parameters and need to online extract features from the acquired images to calculate the human-following parameters which serve as the feedback of visual servoing controls. This paper proposes a novel visual servoing control using the non-vector space control theory, which makes the robot be able to personalize its desired human-following parameters as a desired image learnt from human-driven demonstrations. The approach provides an easy and intuitive way for humans to personalize mobile robots to complete human-following tasks in the manners that humans prefer. Experimental results demonstrate the effectiveness and advantage of the proposed approach.
Thermal-Aware Task Mapping on Dynamically Reconfigurable Network-on-Chip Based Multiprocessor System-on-Chip

Dark silicon is the phenomenon that a fraction of many-core chip has to be turned off or run in a low-power state in order to maintain the safe chip temperature. System-level thermal management techniques normally map application on non-adjacent cores, while communication efficiency among these cores will be oppositely affected over conventional network-on-chip (NoC). Recently, SMART NoC architecture is proposed, enabling single-cycle multi-hop bypass channels to be built between distant cores at runtime, to reduce communication latency. However, communication efficiency of SMART NoC will be diminished by communication contention, which will in turn decrease system performance. In this paper, we first propose an Integer-Linear Programming (ILP) model to properly address communication problem, which generates the optimal solutions with the consideration of inter-processor communication. We further present a novel heuristic algorithm for task mapping in dark silicon many-core systems, called TopoMap, on top of SMART architecture, which can effectively solve communication contention problem in polynomial time. With fine-grained consideration of chip thermal reliability and inter-processor communication, presented approaches are able to control the reconfigurability of NoC communication topology in task mapping and scheduling. Thermal-safe system is guaranteed by physically decentralized active cores, and communication overhead is reduced by the minimized communication contention and maximized bypass routing. Performance evaluation on PARSEC shows the applicability and effectiveness of the proposed techniques, which achieve on average 42.5 and 32.4 percent improvement in communication and application performance, and 32.3 percent reduction in system energy consumption, compared with state-of-the-art techniques. TopoMap only introduces 1.8 percent performance difference compared to ILP model and is more scalable to large-size NoCs.
Classification of Lobar Pneumonia by Two Different Classifiers in Lung CT Images

Pneumonia is a lung disorder caused due to microbial infections. The study is an attempt to identify and classify pneumonia into several stages as mild, moderate and severe using Gray Level occurrence Matrix (GLCM) texture features. The study involves 30 subjects (10 mild, 10 moderate and 10 severe) suffering from mild, moderate and severe lobar pneumonia. The CT examination of lung was performed using Philips MX8000 IDT 16 slice CT scanner for all subjects and saved in DICOM format. The obtained images were subjected to median filtering for noise removal and normalisation was done using contrast stretching. Subsequently GLCM was applied to extract various texture features namely contrast, energy, maximum probability, variance, mean, skewness, entropy, standard deviation, autocorrelation, median, mode, cluster prominence, cluster shade, homogeneity and kurtosis. A neural network classification scheme was adopted to identify the various diseased groups. The results of post hoc test (tukey HSD) revealed that there exist statistically significant differences for the texture features namely contrast, energy, mean, standard deviation, autocorrelation and median at the level $\mathbf{p} < 0.05$. The results of the study infer that the proposed method could be effectively used in diagnosis of pneumonia.
A Convolutional Neural Network for Smoking Activity Recognition

Smoking is linked to more than two million preventable deaths yearly. The widespread use of sensors embedded in everyday devices provides novel means for research on smoking. Smartphones and smartwatches can monitor smoking behavior, which could lead to the development of new methods for smoking reduction or cessation. However, smoking often co-occurs with other activities, such as drinking and eating, which makes the recognition of concurrent and overlapping smoking activities from wearable sensors challenging. In this paper, we proposed for the first time to use deep learning for the automatic detection of smoking activities. A Convolutional Neural Network (CNN) architecture was proposed, and this improved on previously reported performance results. We investigated the impact of various data preprocessing approaches that influence the CNN classification results with statistical features and raw sensor data. We also considered the individual performance of the smartwatch vs. the smartphone and the gyroscope vs. accelerometer sensors for smoking activity recognition. Considering a dataset of concurrent activities such as drinking, eating, smoking while sitting, standing, walking, and partaking in a group conversation, our CNN approach obtained an F1-score of 92-96% in person-independent evaluation.
Multiple Team Formation Using an Evolutionary Approach

Multiple team formation problem is an interesting problem in the area of social network analysis which asks how experts can be assigned among multiple projects so that the effectiveness of allocation can be maximized. In this paper, we consider an approach based on genetic algorithm and sociometry for calculating the social relationship between experts in each project. Due to unavailability of data set, experimentation has been performed on synthetic data set given by J. H. Gutiérrez et al. [1] and results show that the allocation of experts for different projects is effective compared to the algorithm proposed in the literature.