Miracle-dz/UniFAR-FTUs huggingface dataset:
MGTag: a Multi-Dimensional Graph Labeling Scheme for Fast Reachability Queries

Reachability query asks whether a vertex can reach another vertex on large directed graphs. It is one of the most fundamental graph operators and has attracted many researchers to study it. Although there are many approaches solving this problem, it still remains a challenging problem when it comes to leverage the three main costs: the index construction time, the index size, and the query time on large and dense graphs. In this paper, we propose a High Dimension Graph Labeling approach to answer reachability queries. First, we recursively partition a graph into disjoint non-shared graphs, among which there are no common vertices, and cross edges. Second, we build a four dimensional label - one dimension of layer, one dimension of sub-graph and two dimensions of interval for each vertex. With the layer label and the sub-graph label, we can determine the positions (non-shared graphs) of any two vertices. Two dimensional interval label is used to assist to answer the reachability queries for vertex pair in those non-shared graphs. Finally, we design algorithms to answer researchability queries efficiently. In order to speed up query answering, we also build two directional labels: up and down labels to filter vertices quickly. The extensive experiments on 28 large/small and dense/sparse graphs show that building the high dimensional index is quickly and the index size is also competitive compared with most of the state of the art approaches. The results also show that our approach is more scalable and efficient than the state-of-the-art approaches in answering reachability queries.
Optimal design of a magnetically coupled filter for high efficiency, low cost and low volume dc-dc battery storage converter

The paper presents the optimal design of a magnetically coupled filter for a 500 kVA dc/dc converter. The converter is used for batteries storage applications in UPS systems. The high efficiency and low cost ;14% of converter total price saving; are obtained by using a magnetically coupled filter rather than classical chokes. The design of the filter is achieved using a multi-objective optimization algorithm. Several solutions are compared, including interleaved chokes, coupled monolithic interleg-transformer and coupled separated interleg- transformers. Two prototypes were built. Tests and validations were performed in an industrial environment.
Memory, Show the Way: Memory Based Few Shot Word Representation Learning

Distributional semantic models (DSMs) generally require sufficient examples for a word to learn a high quality representation. This is in stark contrast with human who can guess the meaning of a word from one or a few referents only. In this paper, we propose Mem2Vec, a memory based embedding learning method capable of acquiring high quality word representations from fairly limited context. Our method directly adapts the representations produced by a DSM with a longterm memory to guide its guess of a novel word. Based on a pre-trained embedding space, the proposed method delivers impressive performance on two challenging few-shot word similarity tasks. Embeddings learned with our method also lead to considerable improvements over strong baselines on NER and sentiment classification.
A Novel Partial Shading Detection Algorithm Utilizing Power Level Monitoring of Photovoltaic Panels

Maximum power point tracking (MPPT) under partial shading condition (PSC) is a challenging research topic in the PV array system. As the shaded PV module makes different peak patterns on the power versus voltage curve and misguides the MPPT algorithm, various kinds of global MPP (GMPP) detecting algorithms have been studied. Generally, too frequent execution of the GMPP tracking algorithm reduces the achievable power of PV module due to time spent on the scanning process. Thus, the partial shading detection algorithm is essential for efficient utilization of solar energy source. Based on the theoretical investigation of the characteristic curve patterns under various partial shading conditions, this paper presents a new detection algorithm utilizing power level monitoring. While conventional methods only focus on fast shading patterns, the proposed algorithm always shows superb performance regardless of the partial shading patterns.
Patients Reactions to Non-Invasive and Invasive Prenatal Tests: A Machine-Based Analysis from Reddit Posts

Machine (learning)-based techniques have made substantial advances recently, and there is a general suggestion that they will drive major changes in health care within a few years. Yet, we all suffer from the lack of precise comparative studies on the accuracy of machine-based interpretations of medical data. To fill this gap, in this paper we investigate on the efficacy of using an automated mood analysis methodology to understand how patients react to the prescription to take different kinds of prenatal diagnostic tests (invasive vs non-invasive) and to the corresponding outcomes, based on conversations developed on Reddit. Our study essentially provides answers to research questions concerning: i) the popularity of prenatal diagnosis, ii) the patients' sentiment about different prenatal tests, iii) the existence of a cause-effect relationship between prenatal testing and patients' mood, and iv) the type of dialogues held by patients and physicians on this topic. Nonetheless, a general result emerging from our research is that a machine-based decision loop for now still needs human involvement, at least to alleviate the tension between empirical data and their correct medical interpretation.
Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning

Automatic essay scoring (AES) is the task of assigning grades to essays without human interference. Existing systems for AES are typically trained to predict the score of each single essay at a time without considering the rating schema. In order to address this issue, we propose a reinforcement learning framework for essay scoring that incorporates quadratic weighted kappa as guidance to optimize the scoring system. Experiment results on benchmark datasets show the effectiveness of our framework.
Jupiter: A Blockchain Platform for Mobile Devices

With the success of Bitcoin, the technique behind it, Blockchain, is catching massive attention recently. Blockchain is a collection of several techniques like cryptology, P2P and distributed consensus protocol. The main idea of Blockchain is that nodes in the network keep the same distributed ledger. Because of this immutable ledger, a trusted bridge is built among parties without fully trust. Blockchain can be used in variety of areas, especially in financial fields, like supply chain management, cross-border payment and global bank settlement. Meanwhile, we can observe that mobile network is growing rapidly and nibbling the PC market. However, the current public Blockchain applications like Bitcoin or Ethereum require the nodes to store the whole ledger which exceeds the capacity of the mobile devices. Thus, we need to develop a blockchain platform to support mobile devices. In this demo, we introduce Jupiter, a mobile-based Blockchain platform which provides a novel concept called consensus unit (CU) to alleviate the storage problem of mobile. We present the system architecture and demonstrate several CU scenarios via Jupiter.
Artifact-Tolerant Opamp-Less Delta-Modulated Bidirectional Neuro-Interface

We present a 16-channel bidirectional wireless neural interface with arbitrary-waveform neurostimulators triggered by remote closed-loop analysis of simultaneously recorded neural activity. The delta-modulated neural ADC uses no input capacitors and no statically-biased circuits such as opamps, saving both channel area (0.0054mm2) and power (730nW). Delta modulation yields tolerance to input DC offsets of any value, up to the power rail voltage. The differential-difference comparator architecture offers super-GOhm input impedance ensuring that both of the differential inputs transfer functions are well matched, resulting in a superior 78dB rejection of common-mode (CM) signals and artifacts. The highly-oversampling nature of the ADC also renders it insensitive to stimulation artifacts with differential amplitudes of up to 10mVpp, maintaining an ENOB of 9.7 bits and 2. 6µVrms integrated input-referred noise. Experimental results validate the key features of the design and include in-vivo recordings in behaving guinea pigs.
Health Monitoring and Diagnosis of Equipment Based on Multi-sensor Fusion

As the information measured by a single sensor cannot reflect the real situation of mechanical devices completely, a multi-sensor data fusion based on evidence theory is introduced. Evidence theory has the advantage of dealing with uncertain information. However, it produces unreasonable conclusions when the evidence conflicts. An improved fusion method is proposed to solve this problem. Basic probability assignment of evidence is corrected according to evidence and sensor weights, and an optimal fusion algorithm is selected by comparing an introduced threshold and a conflict factor. The effectiveness and practicability of the algorithm are tested by simulating the monitoring and diagnosis of rolling bearings. The result shows that the method has better robustness.
The impact of failing, flaky, and high failure tests on the number of crash reports associated with Firefox builds

Testing is an integral part of release engineering and continuous integration. In theory, a failed test on a build indicates a problem that should be fixed and the build should not be released. In practice, tests decay and developers often release builds, ignoring failing tests. In this paper, we studying the link between builds with failing tests and the number of crash reports on the Firefox webbrowser. Builds with all tests passing have a median of only two crash reports. In contrast, builds with one or more failing tests are associated with a median of 508 and 291 crash reports for Beta and Production builds, respectively. We further investigate the impact of ``flaky'' tests, which can both pass and fail on the same build, and find that they have a median of 514 and 234 crash reports for Beta and Production builds. Finally, building on previous research that has shown that tests that have failed frequently in the past will fail frequently in the future, we find that Builds with HighFailureTests have a median of 585 and 780 crash reports for Beta and Production builds. Unlike other types of test failures, HighFailureTests have a larger impact on Production releases than on Beta builds, and they have a median of 2.7 times more crashes than builds with normal test failures. We conclude that ignoring test failures is related to a dramatic increase in the number of crashes reported by users.
CloudRaid: hunting concurrency bugs in the cloud via log-mining

Cloud systems suffer from distributed concurrency bugs, which are notoriously difficult to detect and often lead to data loss and service outage. This paper presents CloudRaid, a new effective tool to battle distributed concurrency bugs. CloudRaid automatically detects concurrency bugs in cloud systems, by analyzing and testing those message orderings that are likely to expose errors. We observe that large-scale online cloud applications process millions of user requests per second, exercising many permutations of message orderings extensively. Those already sufficiently-tested message orderings are unlikely to expose errors. Hence, CloudRaid mines logs from previous executions to uncover those message orderings which are feasible, but not sufficiently tested. Specifically, CloudRaid tries to flip the order of a pair of messages if they may happen in parallel, but S always arrives before P from existing logs, i.e., excercising the order P ↣ S. The log-based approach makes it suitable to live systems. We have applied CloudRaid to automatically test four representative distributed systems: Apache Hadoop2/Yarn, HBase, HDFS and Cassandra. CloudRaid can automatically test 40 different versions of the 4 systems (10 versions per system) in 35 hours, and can successfully trigger 28 concurrency bugs, including 8 new bugs that have never been found before. The 8 new bugs have all been confirmed by their original developers, and 3 of them are considered as critical bugs that have already been fixed.
Design of Dual-Band Metasurface Antenna Array Using Characteristic Mode Analysis (CMA) for 5G Millimeter-Wave Applications

A metasurface antenna array is proposed (for the dual-band operation. The metasurface includes 2h\textbf{2 square patches with a right-angled slot etched in the corner of each patch.A substrate integrated waveguide (SIW) cavity-fed slot is employed to excite the proposed metasurface at the bottom. The modal behaviors of the metasurface are investigated based on the theory of characteristic modes and two resonant modes are optimized at 29 GHz and 38 GHz for the dual-band operation of the coming 5G. To verify the proposed metasurface antenna, a 1htextbf{{4 array is designed, fabricated and tested. The measurements show that the -10 dB impedancebandwidth is 22.3-30.6 GHz and 36.5-41 GHz with simulated gains of 16.37 dBi and 16.71 dBi, respectively. The proposed dual-band metasurface antenna array features the advantages of low-profile and wideband, suitable for the incoming dualband 5G millimeter-wave applications.
SAP: Improving Continuous Top-K Queries over Streaming Data

Continuous top-k query over streaming data is a fundamental problem in database. In this paper, we focus on sliding window scenario, where a continuous top-k query returns the top-k objects within each query window on the data stream. Existing algorithms support this type of queries via incrementally maintaining a subset of objects in the window and try to retrieve the answer from this subset as much as possible whenever the window slides. However, since all the existing algorithms are sensitive to query parameters and data distribution, they all suffer from expensive incremental maintenance cost. In this paper, we propose a self-adaptive partition framework to support continuous top-k query. It partitions the window into subwindows and only maintains a small number of candidates with highest scores in each sub-window. Based on this framework, we have developed several partition algorithms to cater for different object distributions and query parameters. It is the first algorithm that achieves logarithmic complexity w.r. t. k for incremental maintaining the candidate set even in the worst case.
Long-Range Surface Plasmon Resonance Sensor Based on Side-Polished Fiber for Biosensing Applications

A long-range surface plasmon resonance (LRSPR) sensor with enhanced performance has been theoretically calculated and experimentally investigated for biosensing applications. The LRSPR-based sensor is fabricated by employing a side-polished multimode fiber as a waveguide and MgF2 as a buffer layer beneath a thin silver film for supporting LRSPR excitation. Theoretical optimization based on transfer matrix method and the corresponding validation experiments were carried out in detail. The theoretical and experimental results consistently indicate that the sensing performance parameters of the LRSPR-based sensor are improved with varying degrees in comparison to conventional SPR (CSPR) sensors. It was experimentally achieved that the maximal figure of merit of the LRSPR-based sensor exceeds that of the CSPR sensor by a factor of 1.14. At last the bovine serum albumin (BSA) concentration detection was performed to further confirm the sensitivity enhancement of the optimized sensor in biosensing. This SPR sensor design and development is expected to exert a positive influence on real-time and label-free.
Towards Mining of Player Intent for Targeted Gaming Services

Mining and profiling intent of players is an important facet in today's online games for the gaming companies to take targeted actions and provide personalized services. While analyzing and predicting player behavior have been studied in the online gaming domain, investigating intent behind those behaviors from the data has not been studied. We introduce the problem of player intent mining. With the example of online Rummy, a popular card game in India, we show how specific behavior aspects of player over time plays a key role in identifying their intent. Through both supervised and unsupervised techniques we achieve reasonable accuracy in identifying the intent.
Modeling Influence on Posting Engagement: The Gaza Great Return March Analyzed on Twitter

Exposure to online participation in an online social network (OSN) is often modeled as a neighbor-to-neighbor flow. Exposure to participation shift of a neighbor in an OSN, can influence a user to shift participation and engage in posting. Since many postings on OSNs are public, exposure to participation shifts of non-neighbors can also influence one's posting engagement. Modeling local and global influence on the posting engagement of a user, we demonstrate the model on Twitter interactions related to Gaza's Great Return March (GRM) and show the effects of both local and global influence.
Analog to Digital Converter with FPGA Interface and Measurements for Custom Base Station Transceiver

In this paper, we present a solution to improve signal reception in multichannel radio base station receivers. In the system, where there are many simultaneous narrowband reception channels, we face challenges typical for near-far problem-signal blocking, when large signal blocks a small one. This implies need of high dynamic range and resolution. Proposed solution to this problem is to use discrete radio receiver, with Analog to Digital Converter (ADC) to sample baseband. We test our solution by creating a testbench for an high-speed ADC, along with FPGA interface. We have evaluated the use of Texas Instruments ADS5562 for custom base station receiver. Among with analog characteristics, we show the advantages of using oversampling in reducing noise thus Increasing dynamic range.
Stepovers and Signal Detection: Response Sensitivity and Bias in the Differentiation of Genuine and Deceptive Football Actions

The ability to differentiate genuine and deceptive actions was examined using a combination of spatial and temporal occlusion to examine sensitivity to lower body, upper body, and full body sources of visual information. High-skilled and low-skilled association football players judged whether a player genuinely intended to take the ball to the participant’s left or right or intended to step over the ball then take it in the other direction. Signal detection analysis was used to calculate measures of sensitivity (d′) in differentiating genuine and deceptive actions and bias (c) toward judging an action to be genuine or deceptive. Analysis revealed that high-skilled players had higher sensitivity than low-skilled players and this was consistent across all spatial occlusion conditions. Low-skilled players were more biased toward judging actions to be genuine. Receiver Operating Characteristic (ROC) curves revealed that accuracy on deceptive trials in the lower body and full body conditions most accurately classified participants as high-skilled or low-skilled. The results highlight the value of using signal detection analysis in studies of deceptive actions. They suggest that information from the lower body or upper body was sufficient for differentiating genuine and deceptive actions and that global information concurrently derived from these sources was not necessary to support the expert advantage.
Efficient Computation of a Near-Maximum Independent Set over Evolving Graphs

Most existing algorithms computing the maximum independent set (MIS) or independent set (IS) are designed for handling static graphs, which may not be practicable as many networks are dynamically evolving over time. In this paper, we study the MIS/IS problem in evolving graphs by considering graph update operations: vertex/edge addition and vertex/edge deletion. Instead of computing the MIS/IS of the updated graph from scratch, we propose a baseline algorithm that finds the MIS/IS at time t i+1 based on the MIS/IS at time ti. Due to the hardness of computing an exact MIS, we develop an efficient constant-time algorithm LSTwo to return a high-quality (large-size) independent set. Then we design a lazy search algorithm which produces higher-quality independent sets. To improve the time efficiency further, we devise the conditional besieging and k-petal based methods to reduce the search space. Extensive experimental studies over large-scale graphs confirm the effectiveness and efficiency of our proposed algorithms.
Communication Efficient Parallel Algorithms for Optimization on Manifolds

The last decade has witnessed an explosion in the development of models, theory and computational algorithms for "big data" analysis. In particular, distributed computing has served as a natural and dominating paradigm for statistical inference. However, the existing literature on parallel inference almost exclusively focuses on Euclidean data and parameters. While this assumption is valid for many applications, it is increasingly more common to encounter problems where the data or the parameters lie on a non-Euclidean space, like a manifold for example. Our work aims to fill a critical gap in the literature by generalizing parallel inference algorithms to optimization on manifolds. We show that our proposed algorithm is both communication efficient and carries theoretical convergence guarantees. In addition, we demonstrate the performance of our algorithm to the estimation of Fr\'echet means on simulated spherical data and the low-rank matrix completion problem over Grassmann manifolds applied to the Netflix prize data set.
A 0.76MM2 0.22NJ/Pixel DL-Assisted 4K Video Encoder LSI for Quality-of-Experience Over Smart-Phones

This paper proposes the world's first deep learning (DL)-assisted video encoder LSI fabricated in a 10nm process with a core area of 0.76mm2 to integrate quad-core DL accelerators and 4K×2K H.264/H.265 video standards. A visual-contact-field network (VCFNet) DL model is newly designed to predict human focus information for extraordinary reducing the encoding complexity, leading to 82.3% of power reduction. Moreover, input channel reduction and layer merging approaches reduce VCFNet complexity by 69%. Operated at 0.9V and 504MHz, the proposed DL-assisted 4K video encoder LSI consumes 56.54mW to achieve 0.22nJ/pixel of energy efficiency, cutting 0.1-14nJ/pixel compared to conventional designs [1]–[3].
A Deep Multi-Modal Pairwise Ranking Model for User Generated Food Data

Due to the emergence of several nutrition-related mobile applications and websites in recent years, as well as the massive amount of crowd-sourced nutrition data, searching and finding relevant results has become increasingly difficult for users. This problem becomes even more challenging when dealing with crowd-sourced food names that are noisy and not well-structured. Because food names are short in length, it is difficult to incorporate existing methods to achieve an optimal matching quality. Despite several recent studies on nutrition data, these challenges remain. In this paper, we propose a novel learning-to-rank framework for crowd-sourced food names that has significant real-world applications, including food search and food recommendations. In particular, we propose a deep learning based, multi-modal learning-to-rank model that leverages the text describing a food name and the numerical values that represent its nutritional information. To this end, we also introduce a novel type of loss-function, which extends standard triplets hinge loss function into a multi-modal scenario. The proposed model is flexible and supports various data types as well as an arbitrary number of modalities. The effectiveness of our proposed model is demonstrated through several experiments on real-data, consisting of more than six million instances.