Miracle-dz/UniFAR-FTUs huggingface dataset:
Friend Recommendation in Location-Based Social Networks via Deep Pairwise Learning

Generating friend recommendations in location-based social networks is a challenging task, as we have to learn how different contextual factors influence users' behavior to form social relationships. For example, the contextual information of users' check-in behavior at common locations and users' activities at close regions may impact users' relationships. In this paper we propose a deep pairwise learning model, namely FDPL. Our model first learns the low dimensional latent embeddings of users' social relationships by jointly factorizing them with the available contextual information based on a multi-view learning strategy. In addition, to account for the fact that the contextual information is non-linearly correlated with users' social relationships we design a deep pairwise learning architecture based on a Bayesian personalized ranking strategy. We learn the non-linear deep representations of the computed low dimensional latent embeddings by formulating the top-$k$ friend recommendation task at location-based social networks as a ranking task in our deep pairwise learning strategy. Our experiments on three real world location-based social networks from Brightkite, Gowalla and Foursquare show that the proposed FDPL model significantly outperforms other state-of-the-art methods. Finally, we evaluate the impact of contextual information on our model and we experimentally show that it is a key factor to boost the friend recommendation accuracy at location-based social networks.
A Nonnegative Matrix Factorization Approach for Multiple Local Community Detection

Existing works on local community detection in social networks focus on finding one single community a few seed members are most likely to be in. In this work, we address a much harder problem of multiple local community detection and propose a Nonnegative Matrix Factorization algorithm for finding multiple local communities for a single seed chosen randomly in multiple ground truth communities. The number of detected communities for the seed is determined automatically by the algorithm. We first apply a Breadth-First Search to sample the input graph up to several levels depending on the network density. We then use Nonnegative Matrix Factorization on the adjacency matrix of the sampled subgraph to estimate the number of communities, and then cluster the nodes of the subgraph into communities. Our proposed method differs from the existing NMF-based community detection methods as it does not use“ argmax ” function to assign nodes to communities. Our method has been evaluated on real-world networks and shows good accuracy as evaluated by the F1 score when comparing with the state-of-the-art local community detection algorithm.
To Protect and to Preserve: Novel Preservation Strategies for Extracellular Vesicles

Extracellular vesicles (EVs)-based therapeutics are based on the premise that EVs shed by stem cells exert similar therapeutic effects and these have been proposed as an alternative to cell therapies. EV-mediated delivery is an effective and efficient system of cell-to-cell communication which can confer therapeutic benefits to their target cells. EVs have been shown to promote tissue repair and regeneration in various animal models such as, wound healing, cardiac ischemia, diabetes, lung fibrosis, kidney injury, and many others. Given the unique attributes of EVs, considerable thought must be given to the preservation, formulation and cold chain strategies in order to effectively translate exciting preclinical observations to clinical and commercial success. This review summarizes current understanding around EV preservation, challenges in maintaining EV quality, and also bioengineering advances aimed at enhancing the long-term stability of EVs.
CloudTP: A Cloud-Based Flexible Trajectory Preprocessing Framework

Trajectory data preprocessing is to convert raw GPS logs into organized trajectories, which is a common, necessary but tedious task in many urban applications. This paper proposes CloudTP, a cloud-based flexible trajectory data preprocessing framework, to provide an efficient online service, easing the burdens of urban application builders. The proposed system is designed and implemented based on the cloud storage and parallel computing framework (i.e. Spark). Its features consist of 1) noise filtering, 2) trajectory segmentation, 3) map matching, and 4) index building. CloudTP is useful for both normal users and advanced users. By simply uploading trajectory datasets and setting corresponding parameters, normal users can get organized trajectories, statistics and visualizations on the cloud, while advanced users can also customize their own algorithms in any preprocessing module. Finally, usage scenarios are demonstrated to show the capability and flexibility of CloudTP.
Local Detection of Critical Nodes in Active Graphs

The identification of critical nodes in a graph is a fundamental task in network analysis. Centrality measures are commonly used for this purpose. These methods rely on two assumptions that restrict their applicability. First, they only depend on the topology of the network and do not consider the activity over the network. Second, they assume the entire network is available. However, in many applications, it is the underlying activity of the network such as interactions and communications that makes a node critical, and it is hard to collect the entire network topology, when the network is vast and autonomous. We propose a new measure, Active Betweenness Cardinality, where the importance of the nodes are based not on the static structure, but the active utilization of the network. We show how this metric can be computed efficiently by only local information for a given node and how we can locate the critical nodes by using only a few nodes. We also show how this metric can be used to monitor a network and identify node failures. We evaluate our metric and algorithms on real-world networks and show the effectiveness of the proposed methods.
Investigation of Possible Role of Hydrometeor Arrays on Subsequent Stroke Initiation Using System of Artificial Thunderstorm Cells of Negative Polarity

Application of the system of two negatively charged artificial thunderstorm cells with a model hydrometeor array between them could help in the investigation of the problem of the subsequent lightning stroke initiation and propagation inside a thundercloud. The article presents the results of an experimental investigation of the influence of the large model hydrometeor arrays on the possibility of switching of upper negatively charged cell to a discharge channel between the bottom cell and the ground. It also compares the peculiarities of the first and subsequent return stroke formation in the systems “negative artificial cell – negative artificial cell – ground” in cases with and without the model hydrometeor array between the cells. Application of the obtained results to a natural thunderstorm including a possible role of the large hail arrays in the multi stroke lightning formation is discussed.
A Self-Attentive Model with Gate Mechanism for Spoken Language Understanding

Spoken Language Understanding (SLU), which typically involves intent determination and slot filling, is a core component of spoken dialogue systems. Joint learning has shown to be effective in SLU given that slot tags and intents are supposed to share knowledge with each other. However, most existing joint learning methods only consider joint learning by sharing parameters on surface level rather than semantic level. In this work, we propose a novel self-attentive model with gate mechanism to fully utilize the semantic correlation between slot and intent. Our model first obtains intent-augmented embeddings based on neural network with self-attention mechanism. And then the intent semantic representation is utilized as the gate for labelling slot tags. The objectives of both tasks are optimized simultaneously via joint learning in an end-to-end way. We conduct experiment on popular benchmark ATIS. The results show that our model achieves state-of-the-art and outperforms other popular methods by a large margin in terms of both intent detection error rate and slot filling F1-score. This paper gives a new perspective for research on SLU.
Classification of Short-Texts Generated During Disasters: A Deep Neural Network Based Approach

Micro-blogging sites provide a wealth of resources during disaster events in the form of short texts. Correct classification of these text data into various actionable classes can be of great help in shaping the means to rescue people in disaster-affected places. The process of classification of these text data poses a challenging problem because the texts are usually short and very noisy and finding good features that can distinguish these texts into different classes is time consuming, tedious and often requires a lot of domain knowledge. We propose a deep learning based model to classify tweets into different actionable classes such as resource need and availability, activities of various NGO etc. Our model requires no domain knowledge and can be used in any disaster scenario with little to no modification.
GulliVR: A Walking-Oriented Technique for Navigation in Virtual Reality Games Based on Virtual Body Resizing

Virtual reality games are often centered around our feeling of 'being there'. That presence can be significantly enhanced by supporting physical walking. Although modern virtual reality systems enable room-scale motions, the size of our living rooms is not enough to explore vast virtual environments. Developers bypass that limitation by adding virtual navigation such as teleportation. Although such techniques are intended (or designed) to extend but not replace natural walking, what we often observe are nonmoving players beaming to a location that is one real step ahead. Our navigation metaphor emphasizes physical walking by promoting players into giants on demand to cover large distances. In contrast to flying, our technique proportionally increases the modeled eye distance, preventing cybersickness and creating the feeling of being in a miniature world. Our evaluations underpin a significantly increased presence and walking distance compared to the teleportation approach. Finally, we derive a set of game design implications related to the integration of our technique.
Acknowledging iJOE 2017 Reviewers

Educational tool is one of the prominent solutions for aiding students to learn course material in Information Technology (IT) domain. However, most of them are not used in practice since they do not properly fit student necessity. This paper evaluates the impact of an educational tool, namely PythonTutor, for completing programming laboratory task regarding data structure materials. Such evaluation will be conducted in one semester by implementing a quasi-experimental design. As a result, six findings can be deducted which are: 1) PythonTutor might positively affect student performance when the students have used such tool before; 2) Sometimes, student perspective regarding the impact of educational tool is not always in-sync with actual laboratory result; 3) the impact of PythonTutor might be improved when similar data representation is used consequently for several weeks; 4) the correlation between the use of PythonTutor and student performance might not be significant when the control and intervened group share completely different characteristics; 5) the students might experience some difficulties when they are asked to handle a big task for the first time; and 6) the students might be able to complete a particular weekly task with a promising result if the students have understood the material well. Keywords—quasi-experimental design, empirical evaluation, program visualization, educational tool, laboratory session
Learning Neural Representation for CLIR with Adversarial Framework

The existing studies in cross-language information retrieval (CLIR) mostly rely on general text representation models (e.g., vector space model or latent semantic analysis). These models are not optimized for the target retrieval task. In this paper, we follow the success of neural representation in natural language processing (NLP) and develop a novel text representation model based on adversarial learning, which seeks a task-specific embedding space for CLIR. Adversarial learning is implemented as an interplay between the generator process and the discriminator process. In order to adapt adversarial learning to CLIR, we design three constraints to direct representation learning, which are (1) a matching constraint capturing essential characteristics of cross-language ranking, (2) a translation constraint bridging language gaps, and (3) an adversarial constraint forcing both language and media invariant to be reached more efficiently and effectively. Through the joint exploitation of these constraints in an adversarial manner, the underlying cross-language semantics relevant to retrieval tasks are better preserved in the embedding space. Standard CLIR experiments show that our model significantly outperforms state-of-the-art continuous space models and is better than the strong machine translation baseline.
Introducing a Reliability Analysis Framework for High Performance Computing Environments

Supercomputing environments are becoming the norm for daily use. However, their complex infrastructure makes troubleshooting and monitoring failures extremely difficult. This is because these infrastructures contain thousands of nodes representing various applications and processors. To address these concerns, we propose a real-time reliability analysis framework for high performance computing (HPC) environments where the contributions are three-fold. First, an improved data network extrapolation (DNE) methodology is proposed as a pre-processing module. This component incorporates the system failure information (i.e. job, fault, and error log files) and performs robust job-based failure accounting for sequential and parallel jobs. This element also performs cross-referencing to compute task-based failure accounting, where the assumption is made that tasks are comprised of either one or more jobs. Next, a reliability characterization and analysis (RCA) schema is proposed that takes the failure information from the DNE process to perform survival analyses on each individual node in addition to the entire reliability infrastructure. This is coupled with a failure metrics characterization (FMC) schema that estimates the failure metrics such as the mean time to failure (MTTF) as well as the hazard rate. Additionally, a comparative analysis is made between the Log-Normal or Weibull distributions in terms of modeling job and task-based failure activity. Empirical analysis using the Structural Simulation Toolkit (SST) illustrate the promise of this approach in terms of characterizing, monitoring, and troubleshooting failure behavior. The results of this work can aide systems administrators the dynamic tools to pinpoint and monitor failure behavior; its impacts; and alternative job-scheduling policies without interrupting production processes.
Potential Therapeutic Drugs for Parkinson's Disease Based on Data Mining and Bioinformatics Analysis

The objective is to search potential therapeutic drugs for Parkinson's disease based on data mining and bioinformatics analysis and providing new ideas for research studies on “new application of conventional drugs. ” Method differential gene candidates were obtained based on data mining of genes of PD brain tissue, original gene data analysis, differential gene crossover, pathway enrichment analysis, and protein interaction, and potential therapeutic drugs for Parkinson's disease were obtained through drug-gene relationship. Result. 250 common differential genes were obtained from 3 research studies, and 31 differential gene candidates were obtained through gene enrichment analysis and protein interaction. 10 drugs such as metformin hydrochloride were directly or indirectly correlated to differential gene candidates. Conclusion. Potential therapeutic drugs that may be used for prevention and treatment of Parkinson's disease were discovered through data mining and bioinformatics analysis, which provided new ideas for research and development of drugs. Results showed that metformin hydrochloride and other drugs had certain therapeutical effect on Parkinson's disease, and melbine (DMBG) can be used for treatment of Parkinson's disease and type 2 diabetes patients.
Model to Generate Benchmark Graphs Based on Evolution Dynamics

The past decade has seen tremendous growth in the field of Complex Social Networks. Several network generation models have been extensively studied to develop an understanding of how real world networks evolve over time. Two important applications of these models are to study the evolution dynamics and processes that shape a network, and to generate benchmark networks with known community structures. Research has been conducted in both these directions, relatively independent of the other. This creates a disjunct between real world networks and the networks generated as benchmarks to study community detection algorithms. In this paper, we propose to study both these application areas together. We introduce a network generation model which is based on evolution dynamics of real world networks and, it can generate networks with community structures that can be used as benchmark graphs. We study the behaviour of different community detection algorithms based on the proposed model and compare it with other models to generate benchmark graphs. Results suggest that the proposed model can generate networks which are not only structurally similar to real world networks but can be used to generate networks with varying community sizes and topologies.
Themis: automatically testing software for discrimination

Bias in decisions made by modern software is becoming a common and serious problem. We present Themis, an automated test suite generator to measure two types of discrimination, including causal relationships between sensitive inputs and program behavior. We explain how Themis can measure discrimination and aid its debugging, describe a set of optimizations Themis uses to reduce test suite size, and demonstrate Themis' effectiveness on open-source software. Themis is open-source and all our evaluation data are available at http://fairness.cs. umass.edu/. See a video of Themis in action: https://youtu.be/brB8wkaUesY
Design and Testing of 1 kV H-bridge Power Electronics Building Block Based on 1.7 kV SiC MOSFET Module

This paper presents a power electronics building block (PEBB) design based on 1.7 kV SiC MOSFET power modules. The PEBB power stage is an H-bridge circuit that can be cascaded to build modular converters. The evolution of PEBB architectures is introduced at the beginning and the specifications are given, followed by the design considerations and solutions of critical PEBB components. In particular, laminated DC bus, differential-mode inductor, smart gate driver, high noise-immunity digital control system, and DC-fed auxiliary power supply have been accomplished and demonstrated. Finally, a novel hybrid-current-mode switching-cycle control approach has been proposed and validated on a SiC-PEBB-based modular multilevel Buck converter (MMBC).
An Energy Measurement System for Characterization of Energy Harvesting Systems

Energy neutral computing is seen as a design approach for wireless sensor nodes (WSN) which are powered by energy harvesters. The approach relies on keeping an equilibrium between consumption and harvesting of energy and typically uses models for energy prediction and management. To adapt models to specific systems and environments, often characterization measurements are used. Measurements strongly influence the model quality and with this the usability of such WSN. In this paper a novel measurement system (PiEMS) is presented that outperforms existing approaches and helps to optimize typical characterization tasks. The increased accuracy of characterization and modeling are exemplified for different WSN applications.
Cut to the Chase: A Context Zoom-in Network for Reading Comprehension

In recent years many deep neural networks have been proposed to solve Reading Comprehension (RC) tasks. Most of these models suffer from reasoning over long documents and do not trivially generalize to cases where the answer is not present as a span in a given document. We present a novel neural-based architecture that is capable of extracting relevant regions based on a given question-document pair and generating a well-formed answer. To show the effectiveness of our architecture, we conducted several experiments on the recently proposed and challenging RC dataset ‘NarrativeQA’. The proposed architecture outperforms state-of-the-art results by 12.62% (ROUGE-L) relative improvement.
Cognitive Involvement in Balance, Gait and Dual-Tasking in Aging: A Focused Review From a Neuroscience of Aging Perspective

A substantial corpus of evidence suggests that the cognitive involvement in postural control and gait increases with aging. A large portion of such studies were based on dual-task experimental designs, which typically use the simultaneous performance of a motor task (e.g., static or dynamic balancing, walking) and a continuous cognitive task (e.g., mental arithmetic, tone detection). This focused review takes a cognitive neuroscience of aging perspective in interpreting cognitive motor dual-task findings. Specifically, we consider the importance of identifying the neural circuits that are engaged by the cognitive task in relation to those that are engaged during motor task performance. Following the principle of neural overlap, dual-task interference should be greatest when the cognitive and motor tasks engage the same neural circuits. Moreover, the literature on brain aging in general, and models of dedifferentiation and compensation, in particular, suggest that in cognitive motor dual-task performance, the cognitive task engages different neural substrates in young as compared to older adults. Also considered is the concept of multisensory aging, and the degree to which the age-related decline of other systems (e.g., vision, hearing) contribute to cognitive load. Finally, we discuss recent work on focused cognitive training, exercise and multimodal training of older adults and their effects on postural and gait outcomes. In keeping with the principle of neural overlap, the available cognitive training research suggests that targeting processes such as dividing attention and inhibition lead to improved balance and gait in older adults. However, more studies are needed that include functional neuroimaging during actual, upright performance of gait and balance tasks, in order to directly test the principle of neural overlap, and to better optimize the design of intervention studies to improve gait and posture.