Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficiently Reinforcing Social Networks over User Engagement and Tie Strength

User engagement and tie strength are fundamental and important components in social networks. The model of k-truss not only captures actively engaged users, but also ensures strong tie strength among these users. It motivates us to utilize the model of k-truss in preventing network unraveling, which simultaneously considers both of the basic components. In this paper, we propose and investigate the anchored k-truss problem to reinforce a network by anchoring critical users who can significantly stop the unraveling. We prove the problem is NP-hard for k ≥ 4. A fast edge deletion order based algorithm, named AKT, is proposed with efficient candidate exploration and pruning techniques based on the order. Comprehensive experiments on 10 real-life graphs demonstrate the effectiveness of our model and the efficiency of our methods.
Ceramic Embedding as Packaging Solution for Future Power Electronic Applications

This paper proposes a novel packaging concept for power electronic applications on basis of embedding power devices in ceramic circuit carriers, such as direct bonded copper (DBC) substrates. The semiconductor devices are assembled into laser structured DBC substrates and then sealed with a copper cover afterwards. This proposed method is an alternative solution to printed circuit board (PCB) embedding and low-temperature co-fired ceramic (LTCC) based multilayer technologies, which are insufficient for high power applications due to the limited temperature resistance and current carrying capacity. The feasibility study confirmed that the DBC embedding approach was successfully implemented by using laser technology combined with conductive gluing, solder, and silver sintering processes.
Optimization of maintenance by failure prediction considering instantaneous and cumulative effects of external environments

We propose a method of determining power equipment renewal by using a method of failure prediction. Renewal is conventionally determined from equipment age by using a bathtub curve. However, it is impossible to make a renewal assessment in accordance with the deterioration level of individual units that are in different environment with the conventional method. It may be possible to renew equipment whose deterioration has not progressed more than its actual aging. Therefore, we propose a method of individually predicting the deterioration level of each unit by considering the instantaneous and cumulative effects of external environment simultaneously. We tested and verified the accuracy of this proposed method using real maintenance data of power equipment and reported the results.
Video-based Person Re-identification Using Spatial-Temporal Attention Networks

We consider the problem of video-based person re-identification. The goal is to identify a person from videos captured under different cameras. In this paper, we propose an efficient spatial-temporal attention based model for person re-identification from videos. Our method generates an attention score for each frame based on frame-level features. The attention scores of all frames in a video are used to produce a weighted feature vector for the input video. Unlike most existing deep learning methods that use global representation, our approach focuses on attention scores. Extensive experiments on two benchmark datasets demonstrate that our method achieves the state-of-the-art performance. This is a technical report.
Heuristically Informed Unsupervised Idiom Usage Recognition

Many idiomatic expressions can be interpreted figuratively or literally depending on their contexts. This paper proposes an unsupervised learning method for recognizing the intended usages of idioms. We treat the usages as a latent variable in probabilistic models and train them in a linguistically motivated feature space. Crucially, we show that distributional semantics is a helpful heuristic for distinguishing the literal usage of idioms, giving us a way to formulate a literal usage metric to estimate the likelihood that the idiom is intended literally. This information then serves as a form of distant supervision to guide the unsupervised training process for the probabilistic models. Experiments show that our overall model performs competitively against supervised methods.
Hierarchical Dirichlet Gaussian Marked Hawkes Process for Narrative Reconstruction in Continuous Time Domain

In news and discussions, many articles and posts are provided without their related previous articles or posts. Hence, it is difficult to understand the context from which the articles and posts have occurred. In this paper, we propose the Hierarchical Dirichlet Gaussian Marked Hawkes process (HD-GMHP) for reconstructing the narratives and thread structures of news articles and discussion posts. HD-GMHP unifies three modeling strategies in previous research: temporal characteristics, triggering event relations, and meta information of text in news articles and discussion threads. To show the effectiveness of the model, we perform experiments in narrative reconstruction and thread reconstruction with real world datasets: articles from the New York Times and a corpus of Wikipedia conversations. The experimental results show that HD-GMHP outperforms the baselines of LDA, HDP, and HDHP for both tasks.
KHz–MHz Rate Laser-Based Tracking of Particles and Product Gases for Multiphase Blast Fields

An imaging system for analysis of particle fields and product gases in a multiphase blast is proposed. The approach combines a kHz-MHz-rate burst-mode laser with particle image velocimetry (PIV) or tomographic feature tracking velocimetry (FTV) to resolve flowfields over a wide range of time scales.
Write Disturb in Ferroelectric FETs and Its Implication for 1T-FeFET AND Memory Arrays

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WECA: A WordNet-Encoded Collocation-Attention Network for Homographic Pun Recognition

Homographic puns have a long history in human writing, widely used in written and spoken literature, which usually occur in a certain syntactic or stylistic structure. How to recognize homographic puns is an important research. However, homographic pun recognition does not solve very well in existing work. In this work, we first use WordNet to understand and expand word embedding for settling the polysemy of homographic puns, and then propose a WordNet-Encoded Collocation-Attention network model (WECA) which combined with the context weights for recognizing the puns. Our experiments on the SemEval2017 Task7 and Pun of the Day demonstrate that the proposed model is able to distinguish between homographic pun and non-homographic pun texts. We show the effectiveness of the model to present the capability of choosing qualitatively informative words. The results show that our model achieves the state-of-the-art performance on homographic puns recognition.
Efficient Computing of Radius-Bounded k-Cores

Driven by real-life applications in geo-social networks, in this paper, we investigate the problem of computing the radius-bounded k-cores (RB-k-cores) that aims to find cohesive subgraphs satisfying both social and spatial constraints on large geo-social networks. In particular, we use k-core to ensure the social cohesiveness and we use a radius-bounded circle to restrict the locations of users in a RB-k-core. We explore several algorithmic paradigms to compute RB-k-cores, including a triple vertex-based paradigm, a binary-vertex-based paradigm, and a paradigm utilizing the concept of rotating circles. The rotating circle-based paradigm is further enhanced with several pruning techniques to achieve better efficiency. The experimental studies conducted on both real and synthetic datasets demonstrate that our proposed rotating-circle-based algorithms can compute all RB-k-cores very efficiently. Moreover, it can also be used to compute the minimum-circle-bounded k-core and significantly outperforms the existing techniques for computing the minimum circle-bounded k-core.
An End-to-End Scalable Copyright Detection System for Online Video Sharing Platforms

Combating copyright infringing multimedia content has arisen as a critical undertaking in online video sharing platforms, such as YouTube and Twitch. In contrast to the traditional copyright detection problem that studies the static content (e.g., music, films, digital documents), the proposed system focuses on a much more challenging problem: detecting copyright infringements in live video streams. This is motivated by the observation that a large amount of copyright-infringing videos bypass the detector while many legal videos are taken down by mistake. In this paper, we present an end-to-end system that is dedicated to combating the copyright infringements in live video streams. The system to be demonstrated consists of 1) a web front-end for user interaction and customized video query, 2) a scalable and real-time video crawling system that can collect video metadata, live chat messages, and visual content of the live video streams on video sharing platforms, and 3) a novel supervised copyright detection engine that leverages the live chat messages of the audience to detect the copyright infringement of live videos.
Schema-Agnostic Progressive Entity Resolution

Entity Resolution (ER) is the task of finding entity profiles that correspond to the same real-world entity. Progressive ER aims to efficiently resolve large datasets when limited time and/or computational resources are available. In practice, its goal is to provide the best possible partial solution by approximating the optimal comparison order of the entity profiles. So far, Progressive ER has only been examined in the context of structured (relational) data sources, as the existing methods rely on schema knowledge to save unnecessary comparisons: they restrict their search space to similar entities with the help of schema-based blocking keys (i.e., signatures that represent the entity profiles). As a result, these solutions are not applicable in Big Data integration applications, which involve large and heterogeneous datasets, such as relational and RDF databases, JSON files, Web corpus etc. To cover this gap, we propose a family of schema-agnostic Progressive ER methods, which do not require schema information, thus applying to heterogeneous data sources of any schema variety. First, we introduce two naïve schema-agnostic methods, showing that straightforward solutions exhibit a poor performance that does not scale well to large volumes of data. Then, we propose four different advanced methods. Through an extensive experimental evaluation over 7 real-world, established datasets, we show that all the advanced methods outperform to a significant extent both the naïve and the state-of-the-art schema-based ones. We also investigate the relative performance of the advanced methods, providing guidelines on the method selection.
Operation Strategy and Efficiency Map Calculation of a Synchronous Reluctance Motor

The measured flux-linkage characteristic is shown for a synchronous reluctance motor (SynRM), designed as 11 kW totally enclosed, fan-cooled, four-pole motor. The impact of iron saturation and cross-coupling between $d-$ and $q$ - axis on the maximum torque per ampere strategy is discussed with additional consideration of the fundamental power factor. An enhanced operation strategy is then used for the measurement of the efficiency and the power factor of the SynRM in different operating points in the base speed range as well as for overload and the constant power speed range. Furthermore different methods for the loss interpolation (IEC 60034-2-3 vs. curve-fitting) are compared to obtain a reliable efficiency map of the SynRM and to figure out the limitations of these interpolation methods.
Improvements in the Hysteresis and Cogging Evaluation with an Innovative Methodology

The paper presents an innovative methodology to detect the cogging and hysteresis torque in radial flux machines. The study moves from the traditional way adopted to measure them, that is by conventional no load tests; the experimental procedure here proposed is described in details, highlighting the advantages with respect to the previous method. A cheaper test bench is needed, as the gearbox is avoided, and a stepper motor in place of a DC motor is adopted to move the system. The procedure has been tested on two identical machine structures, but with different stator materials (traditional laminated steel and Soft Magnetic Composite material); a comparison with the results obtained with traditional no load tests has been performed to validate the proposed procedure. The novel method is more accurate in the detection of the cogging torque, even if requires a measurement time slightly longer (about 30 minutes of duration).
Local and Global Information Preserved Network Embedding

Networks such as social networks, airplane networks, and citation networks are ubiquitous. To apply advanced machine learning algorithms to network data, low-dimensional and continuous representations are desired. To achieve this goal, many network embedding methods have been proposed recently. The majority of existing methods facilitate the local information i.e. local connections between nodes, to learn the representations, while neglecting global information (or node status), which has been proven to boost numerous network mining tasks such as link prediction and social recommendation. In this paper, we study the problem of preserving local and global information for network embedding. In particular, we introduce an approach to capture global information and propose a network embedding framework LOG, which can coherently model LOcal and Global information. Experiments demonstrate the effectiveness of the proposed framework.
Rule-Based Graph Repairing: Semantic and Efficient Repairing Methods

Real-life graph datasets extracted from Web are inevitably full of incompleteness, conflicts, and redundancies, so graph data cleaning shows its necessity. One of the main issues is to automatically repair the graph with some repairing rules. Although rules like data dependencies have been widely studied in relational data repairing, very few works exist to repair the graph data. In this paper, we introduce an automatic repairing semantic for graphs, called Graph-Repairing Rules (GRRs). This semantic can capture the incompleteness, conflicts, and redundancies in the graphs and indicate how to correct these errors. We study three fundamental problems associated with GRRs, implication, consistency and termination, which show whether a given set of GRRs make sense. Repairing the graph data using GRRs involves a problem of finding isomorphic subgraphs of the graph data for each GRR, which is NP-complete. To efficiently circumvent the complex calculation of subgraph isomorphism, we design a decomposition-and-join strategy to solve this problem. Extensive experiments on real datasets show that our GRR semantic and corresponding repairing algorithms can effectively and efficiently repair real-life graph data.
NAR-miner: discovering negative association rules from code for bug detection

Inferring programming rules from source code based on data mining techniques has been proven to be effective to detect software bugs. Existing studies focus on discovering positive rules in the form of A ⇒ B, indicating that when operation A appears, operation B should also be here. Unfortunately, the negative rules (A ⇒ ¬ B), indicating the mutual suppression or conflict relationships among program elements, have not gotten the attention they deserve. In fact, violating such negative rules can also result in serious bugs. In this paper, we propose a novel method called NAR-Miner to automatically extract negative association programming rules from large-scale systems, and detect their violations to find bugs. However, mining negative rules faces a more serious rule explosion problem than mining positive ones. Most of the obtained negative rules are uninteresting and can lead to unacceptable false alarms. To address the issue, we design a semantics-constrained mining algorithm to focus rule mining on the elements with strong semantic relationships. Furthermore, we introduce information entropy to rank candidate negative rules and highlight the interesting ones. Consequently, we effectively mitigate the rule explosion problem. We implement NAR-Miner and apply it to a Linux kernel (v4.12-rc6). The experiments show that the uninteresting rules are dramatically reduced and 17 detected violations have been confirmed as real bugs and patched by kernel community. We also apply NAR-Miner to PostgreSQL, OpenSSL and FFmpeg and discover six real bugs.
M2TD: Multi-Task Tensor Decomposition for Sparse Ensemble Simulations

Data-and model-driven computer simulations are increasingly critical in many application domains. These simulations may track 10s or 100s of parameters, affected by complex inter-dependent dynamic processes. Moreover, decision makers usually need to run large simulation ensembles, containing 1000s of simulations. In this paper, we rely on a tensor-based framework to represent and analyze patterns in large simulation ensemble data sets to obtain a high-level understanding of the dynamic processes implied by a given ensemble of simulations.We, further, note that the inherent sparsity of the simulation ensembles (relative to the space of potential simulations one can run) constitutes a significant problem in discovering these underlying patterns. To address this challenge, we propose a partition-stitch sampling scheme, which divides the parameter space into subspaces to collect several lower modal ensembles, and complement this with a novel Multi-Task Tensor Decomposition (M2TD), technique which helps effectively and efficiently stitch these subensembles back. Experiments showed that, for a given budget of simulations, the proposed structured sampling scheme leads to significantly better overall accuracy relative to traditional sampling approaches, even when the user does not have a perfect information to help guide the structured partitioning process.
Mining Individual Features to Enhance Link Prediction Efficiency in Location Based Social Networks

One of the most attractive problems of social network analysis is the link prediction. Social networks' user growth is mostly supported with data driven friend recommendations which are provided by link predictors. Previously, we had studied new features to improve prediction accuracy in Location Based Social Networks (LBSNs) where users share temporal location information with check-in interactions. In this paper, we focused on the efficiency of link predictors as the speed of prediction is as critical as its accuracy in LBSNs. Extraction time costs and prediction accuracy of individual LBSN features are mined to pick a feature subset that is achieving faster link prediction while not losing from accuracy.
Data Block Partitioning Methods to Mitigate Stuck-At Faults in Limited Endurance Memories

Deep scaling in conjunction with increased process variation has resulted in increasingly faulty memories. Emerging memories, particularly phase-change and resistive memories, can experience stuck-at faults due to limited endurance. Partition and flip (PAF) schemes partition data into blocks and invert these blocks as needed to ensure data that is written matches the stuck-at cells. In this paper, we propose two novel correction schemes that substantially enhance the fault-tolerance capabilities of existing PAF techniques. First, dynamic partitioning increases the number of possible configurations with equivalent auxiliary bits. At high fixed error rates, the increase in configurations results in improved write error rates for flip-N-write and Aegis partitioning by 7%–72% and 5– $53\times $ , respectively. Our second novel partitioning method, relaxed partitioning, dramatically and effectively increases the partitioning search space by specifying minimally overlapping configurations. Through Monte Carlo simulations, data-aware dynamic partitioning tolerates 25% and 27% more faults over its lifetime than Aegis with 36 and 43 auxiliary bits per 512-bit data block, respectively, while relaxed partitioning achieves an extra 15% and 24% additional improvement while requiring two fewer overhead bits per data block.
Realtime Traffic Speed Estimation with Sparse Crowdsourced Data

Realtime traffic speed estimation is an important issue in urban computation. Existing approaches usually focus on exploiting the periodicity properties of the traffic speed and utilize crowdsourcing techniques to facilitate real-time estimation. The quality of such estimation is limited in real world: 1) the accuracy of existing estimation over-relies on the probed data; 2) the accidental traffic variance is ignored; 3) existing strategies incur exhaustive usage of human workers to get fine-grained estimation results. Thus, a more intelligent RTSE approach is desired. In this paper, we propose the framework of CrowdRTSE (Crowdsourcing-based Real-time Traffic Speed Estimation), which adopts a hybrid offline-online process to collaboratively exploit the historical and real-time data to produce high-quality RTSE. To accomplish such a framework, we devise effective algorithms to judiciously select the best group of human workers with a constant approximation ratio, and effectively propagate the crowdsourced data with high efficiency. Comprehensive evaluations have been conducted on both synthetic and real world datasets. The experimental results verify the effectiveness and efficiency of our proposed methods.