Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-path Fusion Network For Semantic Image Segmentation

Recently, deep convolutional neural networks (CNNs) have led to significant improvement over semantic image segmentation and have also been the best choice. In this paper, we propose a deep neural network architecture, Multi-Path Fusion Network (MPFNet), for semantic image segmentation. In MPFNet, we add more convolution paths to every convolution layer. The depth of each convolutional path increases linearly, which provides a superior method for pixel level prediction. Using this method, we integrate contextual information and local information to produce good quality results on the semantic segmentation task. In addition, dense skip connections are added to repeatedly leverage previous features. The proposed approach improves strong baselines built upon VGG16 on two urban scene datasets, CamVid and Cityscapes, which demonstrate its effectiveness in modeling context information.
Anomaly-Based Insider Threat Detection Using Deep Autoencoders

In recent years, the malicious insider threat has become one of the most significant cyber security threats that an organisation can be subject to. Due to an insider's natural ability to evade deployed information security mechanisms such as firewalls and endpoint protections, the detection of an insider threat can be challenging. Moreover, compared to the volume of audit data that an organization collects for the purpose of intrusion/anomaly detection, the digital footprint left by a malicious insider's action can be minuscule. To detect insider threats from large and complex audit data, in this paper, we propose a detection system that implements anomaly detection using an ensemble of deep autoencoders. Each autoencoder in the ensemble is trained using a certain category of audit data, which represents a user's normal behaviour accurately. The reconstruction error obtained between the original and the decoded data is used to measure whether any behaviour is anomalous or not. After the data has been processed by the individually trained autoencoders and the respective reconstruction errors obtained, a joint decision-making mechanism is used to report a user's overall maliciousness score. Numerical experiments are conducted using a benchmark dataset for insider threat detection. Results indicate that the proposed detection system is able to detect all of the malicious insider actions with a reasonable false positive rate.
Integrated Waveguides in Nanoporous Alumina Membrane for Millimeter-Wave Interposer

The integration of rectangular waveguides inside a nanoporous alumina membrane is proposed in this letter. The horizontal copper plates are realized by a photolithography process while the vertical connections are made of copper nanowires selectively grown inside the nanopores. The lateral wall position can, therefore, be freely adjusted. Waveguides in the <inline-formula> <tex-math notation="LaTeX">$V$ </tex-math></inline-formula>-band (50–75 GHz) and the <inline-formula> <tex-math notation="LaTeX">$W$ </tex-math></inline-formula>-band (75–110 GHz) were manufactured in a 50-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>-thick membrane. For the measurement purpose, wideband transitions from a grounded coplanar waveguide to the waveguide were designed. The measured performance validates the propagation of guided waves with an attenuation constant of about 0.5–0.8 dB/mm in the operating bands, opening the way for the design of functionalized millimeter-wave alumina interposers.
Treatment of advanced nonsmall cell lung cancer: First line, maintenance and second line – Indian consensus statement update

Abstract The management of advanced nonsmall cell lung cancer (NSCLC) patients is becoming increasingly complex with the identification of driver mutations/rearrangements and development/availability of appropriate targeted therapies. In 2017, an expert group of medical oncologists with expertise in treating lung cancer used data from published literature and experience to arrive at practical consensus recommendations on treatment of advanced NSCLC for use by the community oncologists. This was published subsequently in the Indian Journal of Cancer with a plan to be updated annually. The present document is an update to the 2017 document.
Contextualized Word Representations for Self-Attention Network

Transfer learning is one approach that could be used to better train deep neural networks. It plays a key role in initializing a network in computer vision applications as opposed to implementing a network from scratch which could be time-consuming. Natural Language Processing (NLP) shares a similar concept of transferring from large-scale data. Recent studies demonstrated that pretrained language models can be used to achieve state-of-the-art results on a multitude of NLP tasks such as sentiment analysis, machine translation and text summarization. In this paper, we demonstrate that a free RNN/CNN self-attention model used for sentiment analysis can be improved with 2.53% by using contextualized word representation learned in a language modeling task.
Single relay cooperative transmission/reception techniques

One of primary objectives of future wireless systems is to provide uniform high rate coverage. Essentially, a wireless system must take multipath fading, path loss and shadowing effect into consideration. In the pursuit of schemes that will provide a solution to these problems, various granular and distributed network architectures based on relaying techniques are emerging. In particular, three basic relaying schemes have been proposed including amplify-and-forward (AF), decode-and-forward (DF) and compress-and-forward (CF). This thesis has investigated the performance of various single-relaying schemes from the theoretical point of view and established a fundamental understanding of relay-assisted communication systems. An improved version of DF - Soft DF - has been evaluated through the derived upper bounds of Bit-Error-Rate (BER) and Block-Error-Rate (BLER). For the most complicated relaying scheme - CF, we proposed some novel techniques to efficiently improve its performance. Moreover, both theoretic and simulation results have shown that one single-relaying scheme cannot always outperform the others over all channel conditions. In this regard, the relaying schemes need to be deployed in a hybrid fashion to adapt to the current channel realization. We have provided two hybrid relaying schemes in this thesis and investigated the integration of the hybrid relaying schemes with hybrid retransmission protocols. The proposed integrated technique is named as H2-ARQ-Relaying schemes. Moreover, we have proposed two H2-ARQ-Relaying strategies that efficiently exploit a hybrid CF/DF forwarding scheme, where the relay, based on its decoding status, could dynamically switch between CF and DF and analyzed the performance analysis has been performed for both strategies mainly in terms of the obtained outage probabilities and throughputs. Considering the availability of Channel State Information (CSI) at the relay in practical implementations, we have proposed modified CF/DF-based H2-ARQ-Relaying strategies with limited CSI feedback accordingly.
Balancing Stragglers Against Staleness in Distributed Deep Learning

Synchronous SGD is frequently the algorithm of choice for training deep learning models on compute clusters within reasonable time frames. However, even if a large number of workers (CPUs or GPUs) are at disposal for training, hetero-geneity of compute nodes and unreliability of the interconnecting network frequently pose a bottleneck to the training speed. Since the workers have to wait for each other at every model update step, even a single straggler/slow worker can derail the whole training performance. In this paper, we propose a novel approach to mitigate the straggler problem in large compute clusters. We cluster the compute nodes into multiple groups where each group updates the model synchronously stored in its own parameter server. The parameter servers of the different groups update the model in a central parameter server in an asynchronous manner. Few stragglers in the same group (or even separate groups) have little effect on the computational performance. The staleness of the asynchronous updates can be controlled by limiting the number of groups. Our method, in essence, provides a mechanism to move seamlessly between a pure synchronous and a pure asynchronous setting, thereby balancing between the computational overhead of synchronous SGD and the accuracy degradation of a pure asynchronous SGD. We empirically show that with increasing delay from straggler nodes (more than 300% delay in a node), progressive grouping of available workers still finishes the training within 20% of the no-delay case, with the limit to the number of groups governed by the permissible degradation in accuracy (≤ 2.5% compared to the no-delay case).
A Novel Multi-Attractor Period Multi-Scroll Chaotic Integrated Circuit Based on CMOS Wide Adjustable CCCII

This paper proposes a multi-attractor period multi-scroll chaotic system and a second-generation current-controlled current conveyor (CCCII) with a wide tunable range of intrinsic resistance. The newly proposed multi-attractor period multi-scroll chaotic system is constructed by this CCCII. The proposed chaotic system can generate single-attractor period, double-attractor period, three-attractor period, and even more attractor period multi-scroll. The four-dimensional state equation of the chaotic system contains four nonlinear functions for generating multi-attractor period multi-scroll chaos. Since the intrinsic resistance of the CCCII can be adjusted by external voltages or currents, the multi-attractor period multi-scroll chaotic integrated circuit based on the proposed CCCII have a tunable characteristic. It is worth noting that a chaotic integrated circuit has been implemented completely using current signals instead of voltage signals, and more interestingly, it does not contain any passive resistor. The dynamic characteristics of the multi-attractor period multi-scroll chaotic system are given. The circuit designs of the CMOS CCCII and the corresponding chaotic circuit are made in detail. We conduct the numerical and circuit simulations of the proposed chaotic system and the integrated circuit implementation of the chaotic circuit based on the CMOS CCCII. The correctness of the CCCII circuit and the chaotic system are proved through hardware experiments. The comparison between the implementation scheme of the chaotic circuit in this paper and several reported chaotic circuits is conducted.
The effect of block and traditional periodization training models on jump and sprint performance in collegiate basketball players

This study investigated the effect of block periodization (BP) and traditional periodization (TP) approaches on jumping and sprinting performance in collegiate basketball players during an 8-week pre-season period. Ten collegiate male basketball players (mean±SD; age: 21.5±1.7 years; body mass: 83.5±8.9 kg; stature: 192.5±5.4 cm) from the same team were equally assigned to a training group (BP or TP). BP and TP were designed with different numbers of power sessions (BP=8; TP=16) and recovery days (BP=14; TP=8). Counter-movement jump (CMJ) and 20-m sprint performance was measured prior to training commencement (baseline) and every 2 weeks thereafter (week 2, week 4, week 6 and week 8). Within-group, between-group and individual changes were assessed using magnitude-based statistics. Substantially higher (likely positive) CMJ scores were evident in week 8 compared to baseline, week 2 and week 4 with BP training. Substantially higher CMJ values were only observed in week 2 (likely positive) compared to baseline, with TP training. Sprint data showed likely negative differences in week 6 compared to baseline in both TP and BP, with no substantial differences in week 8. The only performance difference between TP and BP training was in CMJ in week 8 (very likely negative). Individual analysis showed that only three athletes demonstrated a negative predicted score (i.e. lower sprinting time) in BP, while all players following the TP model demonstrated positive predicted scores. BP training showed substantially higher jumping performance compared to TP, while no improvement in sprinting performance was observed in either training approach. Basketball coaches should consider using BP training rather than TP to train players’ jumping abilities.
Clothing Extremity Identification Using Convolutional Neural Network Regressor

Understanding and manipulating a textile objects with a high-dimensional configuration space in relation to its context poses a considerable challenge in the area of Robotics. One of the first step for manipulating textiles is to identify key grasping points on extremities such as collars and hem in order to have a context-aware robotic grasping system. In this study, we proposed a method for identifying clothing extremity using a Convolutional Neural Network as a bounding box regression approach. Results indicate that the said method was able to identify and discriminate features of the collar while providing a high accuracy on identifying collar keypoints through a bounding box approach.
Role of CD138, CD56, and light chain immunohistochemistry in suspected and diagnosed plasma cell myeloma: A prospective study

Abstract Introduction: Plasma cells (PCs) have conventionally been counted on the bone marrow aspirate, and small focal involvement may be missed even on bone marrow biopsy sections. Material and Methods: We aimed to study the role of CD138, CD56, anti-κ, and anti-λ immunohistochemistry (IHC) to separate PC myeloma from reactive plasmacytosis and to study the utility of these in cases suspected as myelomas and lacking >10% PCs on bone marrow aspirate. The study comprised 35 diagnosed myelomas, 20 reactive plasmacytosis, and 19 M-band positive suspected myelomas. CD138 IHC was performed on all cases along with CD56, anti-κ, and anti-λ IHC. PCs were counted on CD138-immunostained sections by manual count and by image analysis. In addition, CD56 expression was correlated with clinical features in diagnosed myeloma group. Results: In all cases, both manual counts and image analysis, PC counts were significantly higher on the CD138 stained sections than bone marrow aspirates. It was seen that the manual PC counts and image analysis counts were equivalent in diagnosed myeloma cases. CD56 expression was seen in ~62.85% diagnosed myeloma cases while it was negative in cases of reactive plasmacytosis. CD56 expression was significantly higher in patients with lytic lesions (78.26% vs. 21.74%). CD138, anti-κ, and anti-λ IHC also helped classify 11/19 (57.8%) cases correctly. Conclusion: The use of CD138 along with the light chain and CD56 IHC adds a high diagnostic value in myeloma patients and suspected myeloma cases. The PCs can be counted manually on the CD138-immunostained sections and correlate well with the counts obtained by image analysis.
A Novel Method for Online Coupling Factor Determination in Inductive Power Transfer Systems

The transient behaviour of serially compensated inductive power transfer systems contains overshoots in the current, which have to be taken into account for the overall system dimensioning. In this contribution, a relation between the coupling factor and the transient response of a system is discovered. The overshoot duration and the envelope of the current show a dependence on design frequency and coupling factor only. With this discovery, the coupling factor can be estimated during operation without any additional communication channel or short-circuit measurements. This novel method for online (i. e., during operation) parameter identification is especially of interest for dynamic energy transfer and for systems in which the coupling factor changes over time.
Parallel Read Partitioning for Concurrent Assembly of Metagenomic Data

We present MetaPartMin and MetaPart, two new lightweight parallel metagenomic read partitioning strategies. Metagenomic data partitioning can aid the concurrent de novo assembly of partitions. Prior read partitioning methods tend to create a giant component of reads. We avoid this problem with new heuristics amenable to statically load-balanced parallelization. Our strategies require enumerating and sorting k-mers and minimizers from the input read sequences, and traversing an implicit graph to identify components. MetaPartMin uses minimizers to significantly lower aggregate main memory use, thereby enabling the processing of massive datasets on a modest number of compute nodes. All steps in our strategies exploit hybrid multicore and distributed-memory parallelism. We demonstrate scaling and efficiency on a collection of large-scale datasets. MetaPartMin can process a 1.25 terabase soil metagenome in 6 minutes on just 32 Intel Skylake nodes (48 cores each) of the Stampede2 supercomputer, and a 252 gigabase soil metagenome in 54 seconds on 16 Stampede2 Skylake nodes. The source code is available at https://github.com/vasupsu/MetaPart.
Development of a Mobile Game Application to Boost Students’ Motivation in Learning English Vocabulary

Currently, the power to master the English language has become prominent in academia, research, and business. Therefore, many non-English speaking countries, including Arab countries, are striving to improve their education systems in teaching English as a foreign language, and English vocabulary is an important factor to boost English proficiency among Arab students. Moreover, in Arab countries, there is a lack of motivation among learners of English as a foreign language which affects the learning process of students. As such, digital gaming technologies, especially mobile games, are emerging as the best way to create enthusiasm for learning new languages. Therefore, a mobile application named VocabGame was developed based on a set of persuasive guidelines, and it was launched in the Google Play Store. This paper investigated whether the developed VocabGame can motivate native Arab students learning the English language to achieve better performance. Sixty-four students were divided equally into two groups: the control group (high-performance group) and the experimental group (low-performance group). Students in the experimental group improved their motivation level significantly after the mobile learning intervention. Our findings showed that mobile game application is helpful for those students who had poor performance initially while studying English and improves their confidence. There was also an association between the pre-test and the post-test scores according to the motivation to learn based on the analysis of the covariate analysis with $\eta $ _p^2 being 0.148. A mobile application game was successfully developed to motivate Arab native students to learn English as a secondary language.
Design and Assessment of an Electric Vehicle Powertrain Model Based on Real-World Driving and Charging Cycles

In this paper, an advanced analytical model for an electric vehicle (EV) powertrain has been developed to illustrate the vehicular dynamics by combining electrical and mechanical models in the analysis. This study is based on a Nissan Leaf EV. In the electrical system, the powertrain has various components including a battery pack, a battery management system, a dc/dc converter, a dc/ac inverter, a permanent magnet synchronous motor, and a control system. In the mechanical system, it consists of power transmissions, axial shaft, and vehicle wheels. Furthermore, the driving performance of the Nissan Leaf is studied through the real-world driving tests and simulation tests in MATLAB/Simulink. In the analytical model, the vehicular dynamics is evaluated against changes in the vehicle velocity and acceleration, state of charge of the battery, and the motor power. Finally, a number of EVs involved in the power dispatch is studied. The greenhouse gas emissions of the EV are analyzed according to various energy power and driving features, and compared with the conventional internal combustion engine vehicle. In this case, Nissan Leaf is a pure EV. For a given drive cycle, Nissan Leaf can reduce CO2 emissions by 70%, depending on the way electricity is generated and duty cycles.
Neurophysiologic and Cognitive Changes Arising from Cognitive Training Interventions in Persons with Mild Cognitive Impairment: A Systematic Review

Background Previous reviews have generally reported cognitive//behavioral improvements after cognitively oriented treatments (COTs) in persons with MCI. However, comparatively little is known about the neural mechanisms associated with such cognitive improvement. Objective The primary aim of the current review was to examine neurophysiological changes measured by functional magnetic resonance imaging (fMRI) and possible cognitive changes following COTs in those with MCI. Methods. An extensive literature search was conducted up to August 2018. Inclusion criteria were (1) studies that evaluated the effects of the COTs in patients with amnestic single- or multiple-domain MCI using fMRI, (2) the MCI patient sample having met Petersen's or Jack/Bond's criteria, (3) randomized and/or controlled trials, (4) fMRI and cognitive assessments completed pre- and post-intervention, and (5) articles available in English. Results Amongst the 26 articles found, 7 studies were included according to the above inclusion criteria. A total of 3 studies applied rehearsal-based strategies as the primary intervention, all of which used computerized cognitive training. Four studies used fMRI to investigate the neurophysiologic and cognitive changes associated with memory strategy training. The majority of the studies included in this review showed evidence of improved objective cognitive performance associated with COTs, even in tasks similar to everyday life activities. In addition, there were significant changes in brain activation associated with interventions, in both typical and atypical brain areas and networks related to memory. Conclusions Although additional studies are needed given the small sample size, these initial findings suggest that cognitive improvement after COTs is generally associated with both compensatory (i.e., engaging alternative brain regions or networks not “typically” engaged) and restorative (i.e., reengaging the “typical” brain regions or networks) mechanisms.