Reviewerly/exHarmony huggingface dataset:
Structure from Articulated Motion: Accurate and Stable Monocular 3D Reconstruction without Training Data

Recovery of articulated 3D structure from 2D observations is a challenging computer vision problem with many applications. Current learning-based approaches achieve state-of-the-art accuracy on public benchmarks but are restricted to specific types of objects and motions covered by the training datasets. Model-based approaches do not rely on training data but show lower accuracy on these datasets. In this paper, we introduce a model-based method called Structure from Articulated Motion (SfAM), which can recover multiple object and motion types without training on extensive data collections. At the same time, it performs on par with learning-based state-of-the-art approaches on public benchmarks and outperforms previous non-rigid structure from motion (NRSfM) methods. SfAM is built upon a general-purpose NRSfM technique while integrating a soft spatio-temporal constraint on the bone lengths. We use alternating optimization strategy to recover optimal geometry (i.e., bone proportions) together with 3D joint positions by enforcing the bone lengths consistency over a series of frames. SfAM is highly robust to noisy 2D annotations, generalizes to arbitrary objects and does not rely on training data, which is shown in extensive experiments on public benchmarks and real video sequences. We believe that it brings a new perspective on the domain of monocular 3D recovery of articulated structures, including human motion capture.
Entropy as a Proxy for Gap Complexity in Open Cloze Tests

This paper presents a pilot study of entropy as a measure of gap complexity in open cloze tests aimed at learners of English.Entropy is used to quantify the information content in each gap, which can be used to estimate complexity.Our study shows that average gap entropy correlates positively with proficiency levels while individual gap entropy can capture contextual complexity.To the best of our knowledge, this is the first unsupervised information-theoretical approach to evaluating the quality of cloze tests.
Learning to Detect Opinion Snippet for Aspect-Based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is to predict the sentiment polarity towards a particular aspect in a sentence.Recently, this task has been widely addressed by the neural attention mechanism, which computes attention weights to softly select words for generating aspect-specific sentence representations.The attention is expected to concentrate on opinion words for accurate sentiment prediction.However, attention is prone to be distracted by noisy or misleading words, or opinion words from other aspects.In this paper, we propose an alternative hard-selection approach, which determines the start and end positions of the opinion snippet, and selects the words between these two positions for sentiment prediction.Specifically, we learn deep associations between the sentence and aspect, and the long-term dependencies within the sentence by leveraging the pre-trained BERT model.We further detect the opinion snippet by selfcritical reinforcement learning.Especially, experimental results demonstrate the effectiveness of our method and prove that our hardselection approach outperforms soft-selection approaches when handling multi-aspect sentences.
Learning to Create Sentence Semantic Relation Graphs for Multi-Document Summarization

Linking facts across documents is a challenging task, as the language used to express the same information in a sentence can vary significantly, which complicates the task of multidocument summarization.Consequently, existing approaches heavily rely on hand-crafted features, which are domain-dependent and hard to craft, or additional annotated data, which is costly to gather.To overcome these limitations, we present a novel method, which makes use of two types of sentence embeddings : universal embeddings, which are trained on a large unrelated corpus, and domainspecific embeddings, which are learned during training.To this end, we develop Sem-SentSum, a fully data-driven model able to leverage both types of sentence embeddings by building a sentence semantic relation graph.SemSentSum achieves competitive results on two types of summary, consisting of 665 bytes and 100 words.Unlike other state-of-the-art models, neither hand-crafted features nor additional annotated data are necessary, and the method is easily adaptable for other tasks.To our knowledge, we are the first to use multiple sentence embeddings for the task of multidocument summarization.
Team GPLSI. Approach for automated fact checking

Fever Shared 2.0 Task is a challenge meant for developing automated fact checking systems. Our approach for the Fever 2.0 is based on a previous proposal developed by Team Athene UKP TU Darmstadt. Our proposal modifies the sentence retrieval phase, using statement extraction and representation in the form of triplets (subject, object, action). Triplets are extracted from the claim and compare to triplets extracted from Wikipedia articles using semantic similarity. Our results are satisfactory but there is room for improvement.
EAPA

The wide deployment of devices in Internet of Things (IoT) not only brings many benefits, but also incurs some security challenges. Remote attestation becomes an attractive method to guarantee the security of IoT devices. Unfortunately, most current attestation schemes only focus on the software attacks, but cannot detect the physical attacks. Several remote attestation schemes resilient to physical attacks still have some drawbacks in energy consumption, runtime, and security. In this paper, we propose an Efficient Attestation scheme resilient to Physical Attacks (EAPA) for IoT devices. We exploit a distributed attestation mode to make the protocol be executed in parallel, which reduces the total runtime to $O(1)$. Besides, we introduce an accusation mechanism to report compromised devices and design a new key update method, ensuring the efficiency and the security of our scheme. Furthermore, we present the security analysis and the performance evaluation of EAPA. The results indicate that EAPA has the lowest energy and runtime consumption compared with related works. Particularly, it shows a constant value in terms of runtime consumption.
Creation of Greenberger-Horne-Zeilinger states with thousands of atoms by entanglement amplification

Abstract We propose an entanglement-creation scheme in a multi-atom ensemble trapped in an optical cavity, named entanglement amplification, converting unentangled states into entangled states and amplifying less-entangled ones to maximally entangled Greenberger-Horne-Zeilinger (GHZ) states whose fidelity is logarithmically dependent on the atom number and robust against common experimental noises. The scheme starts with a multi-atom ensemble initialized in a coherent spin state. By shifting the energy of a particular Dicke state, we break the Hilbert space of the ensemble into two isolated subspaces to tear the coherent spin state into two components so that entanglement is introduced. After that, we utilize the isolated subspaces to further enhance the entanglement by coherently separating the two components. By single-particle Rabi drivings on atoms in a high-finesse optical cavity illuminated by a single-frequency light, 2000-atom GHZ states can be created with a fidelity above 80% in an experimentally achievable system, making resources of ensembles at Heisenberg limit practically available for quantum metrology.
Industry 4.0: Smart Contract-based Industrial Internet of Things Process Management

Through the Internet of Industrial Things, significant investments in the industry are expected. In this new environment, machine-to-machine communication showed immediate potential. However, most communication implementations require a trusted intermediary. The introduction of smart contracts can enable communication without the need for a trusted intermediary. To provide security and decentralization in industrial communication processes, smart contract-based middleware is proposed. This proposal is evaluated for impacts against the stringent communication requirements required by industrial applications. Experimental results show that while this approach offers greater security and decentralization than traditional proposals, blockchain-based smart contracts cannot yet be applied to industrial systems due to blocking time.
Continuous-variable entanglement distillation by cascaded photon replacement

We study the entanglement distillation in continuous-variable systems when a photon replacement protocol is employed. A cascaded protocol is studied and we find that the resultant entanglement increases by increasing the number of repetitions. Interestingly, the entanglement enhancement is not sensitive to the asymmetry of the protocol and gives the same result for any arrangement in the absence of loss. The non-Gaussianity of the outcome state is also studied and it is found that the non-Gaussianity of the state dramatically depends on the experimental arrangements. We also consider the case of imperfect detectors and show that the main results are valid provided high-efficiency detectors are employed. By providing practical information on photon replacement operation, this work is one step towards the realization of universal quantum computation; in particular, in setups where de-Gaussifying protocols are only applicable to one of the parties.
A Complete Framework of Radar Pulse Detection and Modulation Classification for Cognitive EW

In this paper, we consider automatic radar pulse detection and intra-pulse modulation classification for cognitive electronic warfare applications.In this manner, we introduce an end-to-end framework for detection and classification of radar pulses.Our approach is complete, i.e., we provide raw radar signal at the input side and produce categorical output at the output.We use short time Fourier transform to obtain timefrequency image of the signal.Hough transform is used to detect pulses in time-frequency images and pulses are represented with a single line.Then, convolutional neural networks are used for pulse classification.In experiments, we provide classification results at different SNR levels.
An Energy and SLA-Aware Resource Management Strategy in Cloud Data Centers

Reducing energy consumption of data centers is an important way for cloud providers to improve their investment yield, but they must also ensure that the services delivered meet the various requirements of consumers. In this paper, we propose a resource management strategy to reduce both energy consumption and Service Level Agreement (SLA) violations in cloud data centers. It contains three improved methods for subproblems in dynamic virtual machine (VM) consolidation. For making hosts detection more effective and improving the VM selection results, first, the overloaded hosts detecting method sets a dynamic independent saturation threshold for each host, respectively, which takes the CPU utilization trend into consideration; second, the underutilized hosts detecting method uses multiple factors besides CPU utilization and the Naive Bayesian classifier to calculate the combined weights of hosts in prioritization step; and third, the VM selection method considers both current CPU usage and future growth space of CPU demand of VMs. To evaluate the performance of the proposed strategy, it is simulated in CloudSim and compared with five existing energy–saving strategies using real-world workload traces. The experimental results show that our strategy outperforms others with minimum energy consumption and SLA violation.
I-SAFE

Urban imagery usually serves as forensic analysis and by design is available for incident mitigation. As more imagery collected, it is harder to narrow down to certain frames among thousands of video clips to a specific incident. A real-time, proactive surveillance system is desirable, which could instantly detect dubious personnel, identify suspicious activities, or raise momentous alerts. The recent proliferation of the edge computing paradigm allows more data-intensive tasks to be accomplished by smart edge devices with lightweight but powerful algorithms. This paper presents a forensic surveillance strategy by introducing an Instant Suspicious Activity identiFication at the Edge (I-SAFE) using fuzzy decision making. A fuzzy control system is proposed to mimic the decision-making process of a security officer. Decisions are made based on video features extracted by a lightweight Deep Machine Learning (DML) model. Based on the requirements from the first-line law enforcement officers, several features are selected and fuzzified to cope with the state of uncertainty that exists in the officers' decision-making process. Using features in the edge hierarchy minimizes the communication delay such that instant alerting is achieved. Additionally, leveraging the Microservices architecture, the I-SAFE scheme possesses good scalability given the increasing complexities at the network edge. Implemented as an edge-based application and tested using exemplary and various labeled dataset surveillance videos, the I-SAFE scheme raises alerts by identifying the suspicious activity in an average of 0.002 seconds. Compared to four other state-of-the-art methods over two other data sets, the experimental study verified the superiority of the I-SAFE decentralized method.
Fixed window aggregation AD-census algorithm for phase-based stereo matching

Phase-based stereo matching (PSM) is a vital step in binocular structured light. PSM is hard to strike a balance between efficiency and accuracy, especially because of the absolute phase matrix’s (APM) double data type. It means that PSM needs more run time and memory than conventional intensity images’ stereo matching. In this paper, we propose a modified absolute difference (AD)-Census algorithm called the fixed window aggregation AD-Census (FWA-AD-Census) to balance the contradiction between efficiency and accuracy in PSM. The FWA-AD-Census aggregates matching cost in a fixed support window instead of an adaptive support window in AD-Census. We analyze the reason why PSM is more suitable to aggregate the matching cost in a fixed support window. Simulations and experiments are conducted to verify the FWA-AD-Census’s performances by comparing the FWA-AD-Census with two other local stereo matching algorithms. One is the AD-Census, which is more accurate but less efficient. Its matching cost is aggregated in an adaptive support window. Another is the sum of absolute difference (SAD), which is more efficient but less accurate, and its matching cost is aggregated in a fixed support window. Theoretical analysis and experimental results both indicate that the proposed algorithm can achieve similar accuracy to the AD-Census with less run time.
Personalizing Grammatical Error Correction: Adaptation to Proficiency Level and L1

Grammar error correction (GEC) systems have become ubiquitous in a variety of software applications, and have started to approach human-level performance for some datasets. However, very little is known about how to efficiently personalize these systems to the user’s characteristics, such as their proficiency level and first language, or to emerging domains of text. We present the first results on adapting a general purpose neural GEC system to both the proficiency level and the first language of a writer, using only a few thousand annotated sentences. Our study is the broadest of its kind, covering five proficiency levels and twelve different languages, and comparing three different adaptation scenarios: adapting to the proficiency level only, to the first language only, or to both aspects simultaneously. We show that tailoring to both scenarios achieves the largest performance improvement (3.6 F0.5) relative to a strong baseline.
NearCount: Selecting critical instances based on the cited counts of nearest neighbors

Traditional instance selection algorithms are not good at addressing imbalanced problems. Moreover, most of them are sensitive to noise instances and suffer from complex selection rules. To solve these problems, in this paper, we propose a concise learning framework named NearCount to determine the importance of the instance without editing noise. In NearCount, the importance of an instance corresponds to the cited counts. The count is determined by the number of times that one instance is selected as a nearest neighbor of instances in different classes. For the instances with nonzero cited counts, the importance of the instance is inversely proportional to the cited count. To handle classification problems with different data distributions, two detailed NearCount-based algorithms – NearCount-IM and NearCount-IS – are introduced. For imbalanced problems, NearCount-IM selects the important majority instances with an equal number of minority instances, thus balancing the data distribution. For balanced scenarios, NearCount-IS selects the instances whose cited counts are greater than zero and equal or less than the number of nearest neighbors as critical instances in every class. The proposed NearCount-IM and NearCount-IS algorithms are evaluated by comparing them with classical instance selection algorithms on the benchmark data sets. Experiments validate the effectiveness of the proposed algorithms.
Robust Detection of Abandoned Object for Smart Video Surveillance in Illumination Changes

Most existing abandoned object detection algorithms use foreground information generated from background models. Detection using the background subtraction technique performs well under normal circumstances. However, it has a significant problem where the foreground information is gradually absorbed into the background as time passes and disappears, making it very vulnerable to sudden illumination changes that increase the false alarm rate. This paper presents an algorithm for detecting abandoned objects using a dual background model, which is robust even in illumination changes as well as other complex circumstances like occlusion, long-term abandonment, and owner re-attendance. The proposed algorithm can adapt quickly to various illumination changes. And also, it can precisely track the target objects to determine whether it is abandoned regardless of the existence of foreground information and the effect from the illumination changes, thanks to the largest-contour-based presence authentication mechanism proposed in this paper. For performance evaluation, we trialed the algorithm with the PETS2006, ABODA datasets as well as our dataset, especially to demonstrate its robustness in various illumination changes.
A Multi-Task Hardwired Accelerator for Face Detection and Alignment

Face detection and alignment are two fundamental tasks for facial applications and the corresponding accelerators have been designed to enable energy-efficient acceleration. However, these dedicated accelerators are always designed separately, thereby ignoring the inherent correlation between face detection and alignment and causing additional communication and area overhead. Based on this motivation, a multi-task cascaded convolutional networks (MTCNN) algorithm-based accelerator is presented in this work to support both face detection and alignment for multiple faces. First, multiply-accumulate (MAC) operations and memory access of the magnification process in the resize module are reduced by 22.8&#x0025; and 24.8&#x0025; on average, respectively, when compared with those of similar methods. Second, clustering non-maximum suppression (C-NMS) is proposed to significantly reduce the intersection over union computation and eliminate the hardware-inference sorting process in NMS, yielding a 16.0&#x0025; speedup in the overall process. Third, an efficient pipeline architecture is proposed to implement a complexity- and memory-intensive proposal network of MTCNN in a more computationally efficient manner, with 38.3&#x0025; less memory capacity than a similar solution. Meanwhile, only approximately half of the multipliers are needed to achieve the same throughput with high pipeline utilization. Fourth, considering the variable number of faces in each input, a batch schedule mechanism is proposed to improve the fully-connected layer hardware utilization by 16.7&#x0025; on average in the batch process. Based on a simulation with the TSMC 28 nm CMOS process, this accelerator consumes only 10.9ms at 400 MHz to simultaneously process 5 faces. The power efficiency reaches 4.80 TOPS/W, which is <inline-formula> <tex-math notation="LaTeX">$227.4\times $ </tex-math></inline-formula> higher than that of the state-of-the-art solution.
Development of Interactive E-Module Chemistry Magazine Based on Kvisoft Flipbook Maker for Thermochemistry Materials at Second Grade Senior High School

This investigation aims to develop an interactive e-module chemistry magazine-based teaching material based on Kvisoft Flipbook Maker on thermochemistry for Senior High School grade XI/MA level. The research design is a development research (Research and Development) with Plomp’s model. The object of the research is the e-module inter-addictive chemistry magazine based on Kvisoft Flipbook Maker. The data instrument collection is a validation sheet form that is given to four validators and user response sheets to two chemistry subject teachers and 30 students of Senior High School grade XI focusing on Science Class. The data analysis technique is by calculated the percentage score of the validation assessment and user response. The results revealed that the developed e-module interactive chemistry magazine based on Kvisoft Flipbook Maker filled out the valid criteria by the material validators based on content substance assessment aspect with a percentage 88.89% and learning design 97.22% and by the media validators based on display (visual communication) with a percentage96.88% and software utilization 100%. While the User responses based on the teachers' and students’ responses were in very good criteria with each score 92.61% and 86.80%.
A Note on Incomplete Information Modeling in the Evidence Theory

Incomplete information exists widely in practical engineering. How to model the incomplete information in the Dempster-Shafer evidence theory (DST) framework is still an open issue. In this paper, we present a framework on incomplete information processing in DST. We extend the model of uncertain information from the closed world assumption to the open world assumption where the incomplete information is a new type of uncertain information excluded in the closed world assumption. After that, we give a framework of incomplete information modelling and fusion in the DST framework. Finally, an example of decision-making process in air combat situation assessment with incomplete situation is introduced to show the rationality of the proposed method.