Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimizing Machine Learning on Apache Spark in HPC Environments

Machine learning has established itself as a powerful tool for the construction of decision making models and algorithms through the use of statistical techniques on training data. However, a significant impediment to its progress is the time spent training and improving the accuracy of these models - this is a data and compute intensive process, which can often take days, weeks or even months to complete. A common approach to accelerate this process is to employ the use of multiple machines simultaneously, a trait shared with the field of High Performance Computing (HPC) and its clusters. However, existing distributed frameworks for data analytics and machine learning are designed for commodity servers, which do not realize the full potential of a HPC cluster, and thus denies the effective use of a readily available and potentially useful resource. In this work we adapt the application of Apache Spark, a distributed data-flow framework, to support the use of machine learning in HPC environments for the purposes of machine learning. There are inherent challenges to using Spark in this context; memory management, communication costs and synchronization overheads all pose challenges to its efficiency. To this end we introduce: (i) the application of MapRDD, a fine grained distributed data representation; (ii) a task-based all-reduce implementation; and (iii) a new asynchronous Stochastic Gradient Descent (SGD) algorithm using non-blocking all-reduce. We demonstrate up to a 2.6x overall speedup (or a 11.2x theoretical speedup with a Nvidia K80 graphics card), a 82–91 % compute ratio, and a 80% reduction in the memory usage, when training the GoogLeNet model to classify 10% of the ImageNet dataset on a 32-node cluster. We also demonstrate a comparable convergence rate using the new asynchronous SGD with respect to the synchronous method. With increasing use of accelerator cards, larger cluster computers and deeper neural network models, we predict a 2x further speedup (i.e. 22.4x accumulated speedup) is obtainable with the new asynchronous SGD algorithm on heterogeneous clusters.
Hybrid Beamforming Design for Full-Duplex Millimeter Wave Communication

Harnessing the abundant availability of spectral resources at millimeter wave (mmWave) frequencies is an attractive solution to meet the escalating data rate demands. Additionally, it has been shown that full-duplex (FD) communication has the potential of doubling the bandwidth efficiency. However, the presence of significant residual self-interference (SI), which is especially more pronounced at mmWave frequencies because of the non-linearities in the hardware components, erodes the full potential of FD in practice. Conventionally, the residual SI is canceled in the baseband using digital processing with the aid of a transmit precoder. In this work, we propose a hybrid beamforming design for FD mmWave communications, where the SI is canceled by the joint design of beamformer weights at the radio-frequency and the precoder as well as combiner in the baseband. Our proposed design preserves the dimensions of the transmit signal, while suppressing the SI. We demonstrate that our joint design is capable of reducing the SI by upto 30 dB, hence performing similarly to the interference-free FD system while being computationally efficient. Our simulation results show that the proposed design significantly outperforms eigen-beamforming.
Automatic Gender and Identity Recognition in Annotated Multimodal Face-to-face Conversations

This paper addresses the automatic recognition of the gender and identity of speakers in spontaneous dyadic conversations using information about the multimodal communicative behavior of the participants. Identifying gender or individual specific behaviors in face to face communication is relevant for constructing advanced and robust interactive systems. This information also contributes to understanding how humans communicate face-to-face. In the present work, classifiers have been trained on features extracted from an annotated multimodal corpus of twelve first encounters in order to distinguish the gender and the identity of the participants. The training features comprise speech duration and shape annotations of co-speech communicative head movements, facial expressions, body postures and hand gestures of six female and six male participants. Information about the emotions shown by the participants’ facial expressions was also added to the training set. Differing from other studies addressing recognition of individuals for security systems using databases built for the purpose, the multimodal training features in this study are exclusively related to communication and the data are spontaneous occurring conversations since we study multimodal communication. A number of classifiers were trained on the data and the best results were obtained by a multilayer perceptron for gender recognition with a weighed F-score of 0.65 (accuracy 64%) and by multinomial logistic regression for the classification of 12 participants with an F-score of 0.31 (accuracy 30%). The most useful features for gender recognition were information about the emotions shown by the participants, the type of head movements and handedness, while the features which were most useful for the identification of individuals are emotions, head movements, handedness and body direction. The results on both tasks are significantly better than by chance accuracy and the results obtained by a majority classifier. This is promising since this is a first pilot study on a corpus of limited size. The features addressed in this study could in the future be combined to other biometric patterns such as those used in multimedia security systems.
Scene Adaptation for Semantic Segmentation using Adversarial Learning

Semantic Segmentation algorithms based on the deep learning paradigm have reached outstanding performances. However, in order to achieve good results in a new domain, it is generally demanded to fine-tune a pre-trained deep architecture using new labeled data coming from the target application domain. The fine-tuning procedure is also required when the domain application settings change, e. g. , when a camera is moved, or a new camera is installed. This implies the collection and pixel-wise la-beling of images to be used for training, which slows down the deployment of semantic segmentation systems in real industrial scenarios and increases the industrial costs. Taking into account the aforementioned issues, in this paper we propose an approach based on Adversarial Learning to perform scene adaptation for semantic segmentation. We frame scene adaptation as the task of predicting semantic segmentation masks for images belonging to a Target Scene Context given labeled images coming from a Source Scene Context and unlabeled images coming from the Target Scene Context. Experiments highlight that the proposed method achieves promising performances both when the two scenes contain similar content (i.e., they are related to two different points of view of the same scene) and when the observed scenes contain unrelated content (i.e., they account to completely different scenes).
Imaging Lithospheric Discontinuities Beneath the Northern East African Rift Using S‐to‐P Receiver Functions

Imaging the lithosphere is key to understand mechanisms of extension as rifting progresses. Continental rifting results in a combination of mechanical stretching and thinning of the lithosphere, decompression upwelling, heating, sometimes partial melting of the asthenosphere, and potentially partial melting of the mantle lithosphere. The northern East African Rift system is an ideal locale to study these processes as it exposes the transition from tectonically active continental rifting to incipient seafloor spreading. Here we use S‐to‐P receiver functions to image the lithospheric structure beneath the northernmost East African Rift system where it forms a triple junction between the Main Ethiopian rift, the Red Sea rift, and the Gulf of Aden rift. We image the Moho at 31 ± 6 km beneath the Ethiopian plateau. The crust is 28 ± 3 km thick beneath the Main Ethiopian rift and thins to 23 ± 2 km in northern Afar. We identify a negative phase, a velocity decrease with depth, at 67 ± 3 km depth beneath the Ethiopian plateau, likely associated with the lithosphere‐asthenosphere boundary (LAB), and a lack of a LAB phase beneath the rift. Using observations and waveform modeling, we show that the LAB phase beneath the plateau is likely defined by a small amount of partial melt. The lack of a LAB phase beneath the rift suggests melt percolation through the base of the lithosphere beneath the northernmost East African Rift system.
Evaluating on-device ASR on Field Recordings from an Interactive Reading Companion

Many applications designed to assess and improve oral reading fluency use automated speech recognition (ASR) to provide feedback to students, teachers, and parents. Most such applications rely on a distributed architecture with the speech recognition component located in the cloud. For interactive applications, this approach requires a reliable Internet connection that may not always be available. We investigate whether on-device ASR can be used for a virtual reading companion using recordings obtained from children both in a controlled environment and in the field. Our limited evaluation makes us cautiously optimistic about the feasibility of using on-device ASR for our application.
Combatting Fibrosis: Exosome‐Based Therapies in the Regression of Liver Fibrosis

Hepatic fibrosis results from chronic injury and inflammation in the liver and leads to cirrhosis, liver failure, and portal hypertension. Understanding the molecular mechanisms underlying hepatic fibrosis has advanced the prospect of developing therapies for regression of the disease. Resolution of fibrosis requires a reduction of proinflammatory and fibrogenic cytokines, a decrease in extracellular matrix (ECM) protein production, an increase in collagenase activity, and finally, a disappearance of activated myofibroblasts. Exosomes are nanovesicles of endocytic origin secreted by most cell types. They epigenetically reprogram and alter the phenotype of their recipient cells and hold great promise for the reversal of fibrosis. Recent studies have shown that exosomes function as conduits for intercellular transfer and contain all the necessary components to induce resolution of fibrosis, including the ability to (1) inhibit macrophage activation and cytokine secretion, (2) remodel ECM production and decrease fibrous scars, and (3) inactivate hepatic stellate cells, a major myofibroblast population. Here, we discuss the research involving the regression of hepatic fibrosis. We focus on the newly discovered roles of exosomes during fibrogenesis and as a therapy for fibrosis reversal. We also emphasize the novel discoveries of exosome‐based antifibrotic treatments in vitro and in vivo.
Power Quality Indices for Electrical Power Systems under Non-Stationary Disturbances

This paper presents a methodology to evaluate power quality indices using Wavelet Packet Transform in electrical power systems, in the presence of harmonics, under stationary, non-stationary and short-circuit occurrence conditions, in order to achieve efficient monitoring of power systems. Results on several test systems and various disturbances simulated by Matlab/Simulink demonstrate the effectiveness and robustness of the proposed method.
Decentralized Voltage and Power Control of Multi-Machine Power Systems With Global Asymptotic Stability

Maintaining power system stability is becoming urgent due to the large-scale interconnection of power grids and the high penetration of uncertain renewable energy sources. The excitation control and governor control of synchronous generators have been considered as two crucial measures for enhancing the power system stability. However, a major challenge is to simultaneously achieve global asymptotic stability (GAS), voltage regulation (VR), and power regulation (PR) in the excitation and governor control. In this paper, a Lyapunov-based decentralized control (LBC) is proposed to address this challenge. The time-derivative of the Lyapunov function is designed by the feedback control of synchronous generators in order to guarantee GAS. VR and PR are ensured by considering voltage and power deviations as the feedback variables. The simulation results on the New-England ten-machine power system validate the effectiveness of the proposed LBC in improving power system transient stability and simultaneously achieving VR and PR. Although the proportional–integral- and power system stabilizer-based control can also perform VR and PR, the proposed control has much better dynamic performance and can more significantly improve the system transient stability.
Utilizing Hybrid Information Sources to Learn Representations of Cards in Collectible Card Video Games

We investigate the problem of learning representations of cards in collectible card video games. Our goal is to utilize such representations in modeling contextual similarity between cards. When constructed appropriately, such similarity models can offer many benefits to players. In particular, one can employ them to recommend cheaper or more available card replacements in popular decks. To this end, we utilize some known NLP methods, such as word2vec and Latent Semantic Analysis, to extract card embeddings from their base characteristics and textual descriptions. We also propose two new approaches that make use of information regarding multiple decks constructed by the community of players and attempt to capture the notion of card interchangeability. We empirically validate the described methods and compare their performance using data obtained for two popular games, Hearthstone: Heroes of Warcraft and Clash Royale. In the experiments, we consider various representations of cards and then, we derive the corresponding similarities. To validate the compared methods, we check how consistent are the similarity measurements, which they produce with the assessments made by experienced players. Results of our analysis show that combining outcomes of methods that work with different sources of information, i.e., textual descriptions of individual cards and deck-specific card co-occurrences, can improve performance in the task of similarity assessment. Moreover, a clustering of cards in the constructed vector space can provide some interesting insights for the community of players. As already mentioned, it can be used to suggest replacements of cards that players lack in their collections or to indicate cards that are likely to deteriorate win chances of particular decks.
Low Power On/Off Control Method for Active-Diode Applied in Wireless Power Transmission

In this paper, multiple-pulse (MP) problem of comparator based active rectifier is theoretically analyzed by establishing control loop model. Two methods to eliminate the MP problem are demonstrated. Then a delay time based high precision on/off control method is proposed. The new controller senses the current passing through MOSFET and generates the gate driving signals to control the next operating cycle of the active rectifier. Under a 13.65 MHz wireless power transmission test platform, with the same rectification parameters, the experimental results show that the quiescent power consumption of the proposed controller is reduced by 2/3 compared with that of the comparator-based structure. The reverse current is almost eliminated because the on/off control precision reachesps level. Thus, the conversion efficiency of the rectifier is improved significantly. The good performance of the proposed design shows its well practicality for energy harvesting systems.
A Terahertz Tunable Waveguide Bandpass Filter Based on Bimorph Microactuators

Terahertz (THz) tunable filter plays a significant role for miniaturization and multifunction in application systems. The frequency of existing schemes for tunable waveguide filter can only reach up to the $W$ -band. The selection of tuning and assembly solution for THz band is significant and challenging. In this letter, we investigate the design of bimorph microactuator which uses microelectromechanical system (MEMS) technology. Based on this microactuator, we propose a THz tunable waveguide bandpass filter, whose center frequency can be tuned from 247 to 260 GHz. It is the first time to extend the center frequency of tunable waveguide bandpass filter to over 220 GHz while using MEMS bimorph microactuators to tune. The tuning ratio of the filter is 5.26%, and the measured insertion loss is better than 6 dB in the passband.
A Cache-Level Quality of Experience Metric to Characterize ICNs for Adaptive Streaming

Adaptive streaming has motivated information-centric network designs to improve end-user quality of experience (QoE). However, their management and evaluation rely either on conventional cache-level metrics that are poor representations of QoE, or consumer-side indicators that are opaque to network services. This letter proposes a measure to bridge the gap between cache performance and consumer QoE. We introduce maximal sustainable bitrate (MSB), defined as the highest bitrate deliverable in time to be in time to meet a given request without buffering. Based on our observations, we posit that QoE is maximal when requested bitrates match a cache’s MSB for that content. We design a cache-level reward function as a benchmark metric that measures the difference between requested bitrates and MSB. We hypothesize that aggregated rewards are an indicator of overall system performance. Performance evaluations show high correlation between the sum of cache rewards and consumer QoE.
Human Upper Body Detection in Still Images Based on Extended PCA

Human body detection become one of the most attractive research field nowadays. The most difficult challenge with this research field is current approaches still cannot satisfy requirement of real life problem. In this paper, we propose a new method method for human body detection using extended PCA namely bidirectional PCA. There are two advantages of bidirectional Principal component analysis. The first one is preserving the shape structure of objects and the second one is effective computation mechanism. The method was tested on INRA PennFudanPed database and the experimental results show high accuracy of the proposed method.1
Repeater Insertion to Reduce Delay and Power in Copper and Carbon Nanotube-Based Nanointerconnects

Optimal repeater designs are performed for Cu and carbon nanotube (CNT)-based nanointerconnects to reduce the delay and power dissipation. The effects of inductance and metal-CNT contact resistance are treated appropriately. In this paper, the circuit parameters are calculated analytically, while they can be extracted experimentally for a specific foundry at a specific technology node. The particle swarm optimization (PSO) technique is employed to numerically calculate the optimal repeater size and the optimal number of repeaters in the Cu and CNT-based nanointerconnects. The results are verified against the analytical and genetic algorithm results. To facilitate CAD design, the machine-learning neural network (NN) is adopted. The data obtained using the PSO algorithm are used to train the NN and the feasibility of the NN is investigated and validated.
An Agile Multi-Node Multi-Antenna Wireless Channel Sounding System

The upcoming fifth-generation wireless technology application areas bring new communication performance requirements, mainly in terms of reliability and latency, but also in terms of radio planning, where the further detailed characterization of the wireless channel is needed. To address these demands, we developed an agile multi-node multi-antenna wireless channel sounding system, using multiple software-defined radio devices. The system consists of 12 testbed nodes which are controlled from a centralized testbed server. Each node features a control host computer and two multi-antenna universal software radio peripheral boards. By managing the transmission and reception of reference signals among all the distributed testbed nodes, the system can measure the channel conditions of all multiple independent radio links. At the same time, the distributed architecture of the testbed allows a large number of spatially distributed locations to be covered with only a few redeployments of the testbed nodes. As a consequence of this, the system favors the collection of a large number of distributed channel samples with limited effort within a short dedicated measurement time. In this paper, we detail the general testbed design considerations, along with the specific sounding signal processing implementations. As further support to the system design, we also include the results from different verification and calibration tests, as well as a real measurement application example.
Evaluating the Impact of Proposed OpenMP 5.0 Features on Performance, Portability and Productivity

We investigate how specialization mechanisms proposed for OpenMP 5.0 -- specifically, the metadirective and declare variant directives -- may be deployed in a real-life code, using the miniMD benchmark from the Mantevo suite. Additionally, we develop an OpenMP 4.5 implementation of miniMD that achieves a performance portability of 59.35% across contemporary CPU and GPU hardware, discuss the processes of porting and enabling this code, and show that the use of specialization would enable our code to be expressed in a significantly more compact form, with implications for productivity.
Phishing and Spam: A Pilot Experiment

In an exploratory pilot study 61 participants recruited during a university orientation week were exposed to social engineering directives in the form of fake email attacks that attempted to elicit personal information. Participants in a ‘Hunter’ condition were asked to remain to remain vigilant and report any suspicious content to researchers in the ANU Cybercrime Observatory. Participants in the ‘Passive’ condition received no such instruction. The first two attacks replicated mass generic phishing attempts in which use of names, personal greetings, and personal information were absent. The first generic attack required participants to respond to the email with personal information, and the second attack provided a ‘compromised’ link for participants to click. The third attack that was launched simulated a spear phishing attempt. In this attempt, the fake emails were tailored with participants’ personal information obtained from social media in an attempt to entice participants to click links and enter personal details. The tailored emails impersonated trusted companies or contexts. As hypothesised participants were more susceptible to the specially targeted or spear phishing attacks than generic phishing attacks. ‘Hunter’ participants were more susceptible to all attacks compared to ‘Passive’ participants. This finding was contrary to our second hypothesis that alert participants would be more circumspect than those not so primed. “Hunter” participants however, were under-prepared and limited by a single instruction a few months prior to the dispatch of fake emails. Gender differences were also observed with females more likely to be susceptible in all conditions. The ease with which personal information was collected and potentially used against participants, combined with participants’ higher susceptibility to targeted attacks, makes spear phishing a continuing threat. Crime prevention awareness combined with basic knowledge about spear phishing methods can reduce the risk of deception.