Miracle-dz/UniFAR-FTUs huggingface dataset:
Torque Characteristics Comparison and Analysis of Surface-Mounted Permanent Magnet Vernier Machines With Different Stator topologies

Permanent magnet vernier machines (PMVM) have gained considerable attention due to its inherently high torque density and simple mechanical structure. In this paper, output torque characteristics among several stator topologies with different tooth features are investigated both by theoretical analysis and finite element analysis (FEA) in split-pole PMVM, which can provide valuable instructions for PMVM design. For a fair comparison, all the models are fully optimized and compared under same external conditions. The results shows that the stator topologies with relative small number of main teeth and auxiliary teeth possess more competitive output torque capability and flux weakening capability. Meanwhile, the geometrical variation of the flux modulation pole can produce extra working harmonics to enhance the output torque capability. It is found that there always exists the optimal combination among the angle of auxiliary teeth and slots to generate the maximum torque. Consequently, a parameter $K$ is introduced to deliver the quantitative relationship among the angles and the range of $K$ is evaluated and given. Finally, the results of theoretical analysis are verified through FEA method.
The BQ Corpus: A Large-scale Domain-specific Chinese Corpus For Sentence Semantic Equivalence Identification

This paper introduces the Bank Question (BQ) corpus, a Chinese corpus for sentence semantic equivalence identification (SSEI). The BQ corpus contains 120,000 question pairs from 1-year online bank custom service logs. To efficiently process and annotate questions from such a large scale of logs, this paper proposes a clustering based annotation method to achieve questions with the same intent. First, the deduplicated questions with the same answer are clustered into stacks by the Word Mover’s Distance (WMD) based Affinity Propagation (AP) algorithm. Then, the annotators are asked to assign the clustered questions into different intent categories. Finally, the positive and negative question pairs for SSEI are selected in the same intent category and between different intent categories respectively. We also present six SSEI benchmark performance on our corpus, including state-of-the-art algorithms. As the largest manually annotated public Chinese SSEI corpus in the bank domain, the BQ corpus is not only useful for Chinese question semantic matching research, but also a significant resource for cross-lingual and cross-domain SSEI research. The corpus is available in public.
Influence Maximization by Link Activation in Social Networks

The propagation of innovations in social networks has been widely studied recently. Previous research mostly focuses on either maximizing the influence by identifying a set of initial adopters, or minimizing the influence by link blocking under a certain diffusion model. In our case, we address an influence maximization problem considering the link activation under the Independent Cascade model. For this problem, we propose an approximate solution based on the computation of a cost-degree coefficient for selecting links to be activated. Simulations performed on a real network show that our algorithm performs well.
Security Assessment of the Smart Grid: A Review focusing on the NAN Architecture

This paper presents a comprehensive review on the security aspect of the smart grid communication network. The paper focus on the Neighborhood Area Network (NAN) cybersecurity and it laid emphasis on how the NAN architecture is such an attractive target to intruders and attackers. The paper aims at summarizing recent research efforts on some of the attacks and the various techniques employed in tackling them as discussed in recent literatures and research works. Furthermore, the paper presents a detailed review on the smart grid communication layers, wireless technology standards, networks and the security challenges the grid is currently facing. The work concludes by explaining current and future directions NAN communication security could consider in terms of data privacy measures. The data privacy measures are discussed in terms of prevention and detection techniques.
A Usability Analysis of Blocks-based Programming Editors using Cognitive Dimensions

Blocks-based programming holds potential for end-user developers. Like all visual programming languages, blocks-based programming languages embody both a language design and a user interface design for the editing environment. For blocks-based languages, these designs are focused on learnability and low error rates, which makes them effective for education. For end-user developers who program as part of their professions, other characteristics of usability, like efficiency of use, will also be important. This paper presents a usability analysis, supported by a user study, of the editor design of current blocks-based programming systems, based on the Cognitive Dimensions of Notations framework, and we present design manoeuvres aimed at improving programming time and effort, program comprehension and programmer comfort.
A Unified Syntax-aware Framework for Semantic Role Labeling

Semantic role labeling (SRL) aims to recognize the predicate-argument structure of a sentence. Syntactic information has been paid a great attention over the role of enhancing SRL. However, the latest advance shows that syntax would not be so important for SRL with the emerging much smaller gap between syntax-aware and syntax-agnostic SRL. To comprehensively explore the role of syntax for SRL task, we extend existing models and propose a unified framework to investigate more effective and more diverse ways of incorporating syntax into sequential neural networks. Exploring the effect of syntactic input quality on SRL performance, we confirm that high-quality syntactic parse could still effectively enhance syntactically-driven SRL. Using empirically optimized integration strategy, we even enlarge the gap between syntax-aware and syntax-agnostic SRL. Our framework achieves state-of-the-art results on CoNLL-2009 benchmarks both for English and Chinese, substantially outperforming all previous models.
Picking Apart Story Salads

During natural disasters and conflicts, information about what happened is often confusing and messy, and distributed across many sources. We would like to be able to automatically identify relevant information and assemble it into coherent narratives of what happened. To make this task accessible to neural models, we introduce Story Salads, mixtures of multiple documents that can be generated at scale. By exploiting the Wikipedia hierarchy, we can generate salads that exhibit challenging inference problems. Story salads give rise to a novel, challenging clustering task, where the objective is to group sentences from the same narratives. We demonstrate that simple bag-of-words similarity clustering falls short on this task, and that it is necessary to take into account global context and coherence.
An Ultra-Compact 0.17mm2 2.4GHz Low-Voltage Class-E Power Amplifier in 28nm CMOS

This paper presents the design of a single ended class-E power amplifier in a low voltage28nm CMOS process using a stacked transistors technique. It is designed to amplify transmittedsignals in a Bluetooth Low Energy (BLE) transceiver for Internet of Things (IoT) applications at 2.4GHz. The power amplifier is fully integrated. In the on-wafer measurements, the amplifier achieves an output power in the range between 7.0dBm and 10.5dBm while continuously providing a drain efficiency of 40%. To the authors’ best knowledge, the active chip area of 0.17mm2 presents the smallest size ever published for power amplifiers in this specific scope of application.
Submodule Voltage Balancing and Loss Equalisation in Alternate Arm Converters Based on Virtual Voltages

Balancing of the submodule (SM) capacitor voltages in the alternate arm converter (AAC) exhibits a great amount of similarities with other modular topologies, especially the modular multilevel converter (MMC). However, the requirements of operation at overmodulation range and the alternate conduction of the phase current further complicate the voltage balancing task. In this paper, a voltage balancing and loss equalisation approach based on virtual SM voltages in combination with sorting algorithms is introduced. The developed approach uses the switching states of the SMs as feedback and provides reduction of the switching frequency of the SMs, further addressing excessive switching due to component parameter variation, tolerances and faults. The effectiveness of the algorithm is demonstrated with simulation results from an equivalent full switching model of an AAC for HVDC applications.
Network Formation by Contagion Averse Agents: Modeling Bounded Rationality with Logit Learning

Economic and convenience benefits of interconnectivity drive current explosive emergence and growth of networked systems. However, as recent catastrophic contagious failures in numerous large scale networked infrastructures demonstrated, these benefits of interconnectivity are inherently associated with various risks, including risk of undesirable contagion. Current research on network formation by contagion risk averse agents, which analyzes Nash or some other game-theoretic equilibrium notion of the corresponding game, suffers from interrelated problems of intractability and oversimplification. We argue that these problems can be alleviated with dynamic view, which assumes logit responses by strategic agents with utilities quantifying multiple competing incentives. While this approach naturally incorporates practically critical assumption of bounded rationality, it also allows for leveraging a vast body of results on network formation, e.g., preferential attachment in growing networks.
Refining Pretrained Word Embeddings Using Layer-wise Relevance Propagation

In this paper, we propose a simple method for refining pretrained word embeddings using layer-wise relevance propagation. Given a target semantic representation one would like word vectors to reflect, our method first trains the mapping between the original word vectors and the target representation using a neural network. Estimated target values are then propagated backward toward word vectors, and a relevance score is computed for each dimension of word vectors. Finally, the relevance score vectors are used to refine the original word vectors so that they are projected into the subspace that reflects the information relevant to the target representation. The evaluation experiment using binary classification of word pairs demonstrates that the refined vectors by our method achieve the higher performance than the original vectors.
Performance Evaluation of Lazy and Decision Tree Classifier: A Data Mining Approach for Global Celebrity's Death Analysis

In present world data is a valuable asset. The best utilization of this asset by means of technology gives an upper hand to an organization. Technologies like machine learning, data mining, and artificial intelligence are no exception to this either. Celebrities influence common people behaviors through biological, psychological and social processes to a great extent. Simultaneously there is vast disparity amongst their demise over the cause, region, and age. Therefore, it is a challenging and interesting endorsement to work upon. The objective of this work is to come up with the comprehensive result to understand the celebrity deaths by investigating the incidence happened over the decade. The database for training is created from the public and open access databases for years 2006–2016 comprising of 11, 200 reported deaths over the globe. Findings of the work are year by year extraction of death, the cause behind it, age, gender, and place. Lazy and decision tree classifier model of data mining is being used for the analysis based on the profession as evaluation class.
Burstiness in Query Log: Web Search Analysis by Combining Global and Local Evidences

Web search analysis plays a critical role in improving the performance of cutting-edge search engines. Most of the existing models, such as the click graph and its variants, focus on utilizing the wisdom of the crowd. However, how to design a model supporting both the collective wisdom as well as the unique characteristic of individuals is rarely studied. In this paper, our goal is to solve the new problem of user-specific web search analysis. We go beyond click graph and propose two probabilistic topic models, Topic Independence Model(TIM) and Topic Dependence Model (TDM). TIM adopts an assumption that the generation of query terms and URLs are topically independent; TDM captures the coupling between search queries and URLs. We also capture the temporal burstiness of topics by utilizing the continuous Beta distribution. Through a large-scale analysis of a real-life search query log, we observe that each user's web search trail enjoys multiple kinds of user-based unique characteristics. On a massive search query log, the new models achieve a better held-out likelihood than standard LDA, DCMLDA and TOT, and they can also effectively reveal the latent evolutions of topics on the corpus level and user-based level.
Annotating Claims in the Vaccination Debate

In this paper we present annotation experiments with three different annotation schemes for the identification of argument components in texts related to the vaccination debate. Identifying claims about vaccinations made by participants in the debate is of great societal interest, as the decision to vaccinate or not has impact in public health and safety. Since most corpora that have been annotated with argumentation information contain texts that belong to a specific genre and have a well defined argumentation structure, we needed to adjust the annotation schemes to our corpus, which contains heterogeneous texts from the Web. We started with a complex annotation scheme that had to be simplified due to low IAA. In our final experiment, which focused on annotating claims, annotators reached 57.3% IAA.
Jaccard Affiliation Graph (JAG) Model For Explaining Overlapping Community Behaviors

Understanding overlapping community structures is crucial for network analysis and prediction. AGM (Affiliation Graph Model) is one of the favorite models for explaining the densely overlapped community structures. In this paper, we thoroughly re-investigate the assumptions made by the AGM model on real datasets. We find that the AGM model is not sufficient to explain several empirical behaviors observed in popular real-world networks. To our surprise, all our experimental results can be explained by a parameter-free hypothesis, leading to more straightforward modeling than AGM which has many parameters. Based on these findings, we propose a parameter-free Jaccard-based Affiliation Graph (JAG) model which models the probability of edge as a network specific constant times the Jaccard similarity between community sets associated with the individuals. Our modeling is significantly simpler than AGM, and it eliminates the need of associating a parameter, the probability value, with each community. Furthermore, JAG model naturally explains why (and in fact when) overlapping communities are densely connected. Based on these observations, we propose a new community-driven friendship formation process, which mathematically recovers the JAG model. JAG is the first model that points towards a direct causal relationship between tight connections in the given community with the number of overlapping communities inside it. Thus, the most effective way to bring a community together is to form more sub-communities within it. The community detection algorithm based on our modeling demonstrates a significantly simple algorithm with state-of-the-art accuracy on six real-world network datasets compared to the existing link analysis based methods.
Energy Efficient Adversarial Routing in Shared Channels

We investigate routing on networks modeled as multiple access channels, when packets are injected continually. An energy cap is a component of the system, understood as a bound on the number of stations that can be switched on simultaneously. Each packet is injected into some station and needs to be delivered to its destination station via the channel. A station has to be switched on in order to receive a packet when it is heard on the channel. Each station manages when it is switched on and off by way of a programmable wake-up mechanism, which is scheduled by a routing algorithm. Packet injection is governed by adversarial models that determine upper bounds on injection rates and burstiness. We develop deterministic distributed routing algorithms and assess their performance in the worst-case sense. An algorithm knows the number of stations but does not know the adversary. One of the algorithms maintains bounded queues for the maximum injection rate 1 subject only to the energy cap 3. This energy cap is provably optimal, in that obtaining the same throughput with the energy cap 2 is impossible. We give algorithms subject to the minimum energy cap 2 that have latency polynomial in the total number of stations~n for each fixed adversary of injection rate less than 1. An algorithm is k-energy-oblivious if at most k stations are switched on in a round and for each station the rounds when it will be switched on are determined in advance. We give a k-energy-oblivious algorithm that has packet delay O(n) for adversaries of injection rates less than (k-1)/(n-1), and show that there is no k-energy-oblivious stable algorithm against adversaries with injection rates greater than k/n. An algorithm routes directly when each packet makes only one hop from the station into which it is injected straight to its destination. We give a k-energy-oblivious algorithm routing directly, which has latency O(n^2/k) for adversaries of sufficiently small injection rates that are O(k^2/n^2). We develop a k-energy-oblivious algorithm routing directly, which is stable for injection rate k(k-1)/n(n-1), and show that no k-energy-oblivious algorithm routing directly can be stable against adversaries with injection rates greater than k(k-1)/n(n-1).
Harnessing Popularity in Social Media for Extractive Summarization of Online Conversations

We leverage a popularity measure in social media as a distant label for extractive summarization of online conversations. In social media, users can vote, share, or bookmark a post they prefer. The number of these actions is regarded as a measure of popularity. However, popularity is not determined solely by content of a post, e.g., a text or an image it contains, but is highly based on its contexts, e.g., timing, and authority. We propose Disjunctive model that computes the contribution of content and context separately. For evaluation, we build a dataset where the informativeness of comments is annotated. We evaluate the results with ranking metrics, and show that our model outperforms the baseline models which directly use popularity as a measure of informativeness.
Implementation and Performance Evaluation of the QUIC Protocol in Linux Kernel

QUIC is a new transport layer protocol proposed by Google that is rapidly increasing its share from Internet traffic. It is designed to improve performance for HTTPS connections and partly replace TCP, the dominant standard of Internet for decades, in application scenarios where new requirements such as packet encryption, stream multiplexing and connection migration are emerging and which have proven to be challenging for the TCP service model. QUIC has been massively deployed to serve some of the most popular Internet services, including YouTube. To enable easy deployment and rapid evolution to the protocol, the current deployment of QUIC runs in user-space, usually as part of the Chrome/Chromium browser. This potentially reduces the achievable performance of the protocol, as each message, including control messages, triggers a context switch between kernel and user spaces. To investigate the potential performance of QUIC in kernel mode and to achieve a fair comparison between QUIC and TCP, we implement QUIC in the Linux kernel where TCP and other transport layer protocols are running. We have conducted extensive measurements in both virtual machines and in a custom-built WIFI testbed to compare the two protocols. The empirical results indicate that QUIC outperforms TCP in major application scenarios such as network with low latency and high packet loss rate, while QUIC also shows a TCP-friendly rate control when the two protocols are running concurrently.
Omnidirectional View Synthesis and Test Images

In this paper we introduce an algorithm for omnidirectional virtual view synthesis based on Omnidirectional Video plus Depth (OVD) format. The implementation is done on the basis of View Synthesis Reference Software (VSRS) developed by MPEG of ISO/IEC. Also, we address the problem of lack of benchmark data disallowing objective quality assessment. We present a method for generating test images in OVD representation along with example omnidirectional images and omnidirectional depths, called “Poznan Hall 360 ”.
WikiConv: A Corpus of the Complete Conversational History of a Large Online Collaborative Community

We present a corpus that encompasses the complete history of conversations between contributors to Wikipedia, one of the largest online collaborative communities. By recording the intermediate states of conversations - including not only comments and replies, but also their modifications, deletions and restorations - this data offers an unprecedented view of online conversation. Our framework is designed to be language agnostic, and we show that it extracts high quality data in both Chinese and English. This level of detail supports new research questions pertaining to the process (and challenges) of large-scale online collaboration. We illustrate the corpus’ potential with two case studies on English Wikipedia that highlight new perspectives on earlier work. First, we explore how a person’s conversational behavior depends on how they relate to the discussion’s venue. Second, we show that community moderation of toxic behavior happens at a higher rate than previously estimated.
A Recommendation Service for Programming Study Based on Stack Overflow

This paper proposes a recommendation service for programming study based on Stack Overflow from the learners' point of view. The service consists of two parts: one is to preprocess and classify the questions in Stack Overflow, and it divides all the questions into six categories according to the learners' perspective; the other part is a learning recommendation mobile application based on the data of the existing classification, historical learning data of learners, etc. We evaluate the real performance of the recommendation service, and verify the feasibility and effectiveness of the service.