Reviewerly/exHarmony huggingface dataset:
Data-Free Knowledge Distillation with Soft Targeted Transfer Set Synthesis

Knowledge distillation (KD) has proved to be an effective approach for deep neural network compression, which learns a compact network (student) by transferring the knowledge from a pre-trained, over-parameterized network (teacher). In traditional KD, the transferred knowledge is usually obtained by feeding training samples to the teacher network to obtain the class probabilities. However, the original training dataset is not always available due to storage costs or privacy issues. In this study, we propose a novel data-free KD approach by modeling the intermediate feature space of the teacher with a multivariate normal distribution and leveraging the soft targeted labels generated by the distribution to synthesize pseudo samples as the transfer set. Several student networks trained with these synthesized transfer sets present competitive performance compared to the networks trained with the original training set and other data-free KD approaches.
Efficient Quantum State Sample Tomography with Basis-Dependent Neural Networks

We use a metalearning neural-network approach to analyze data from a measured quantum state. Once our neural network has been trained, it can be used to efficiently sample measurements of the state in measurement bases not contained in the training data. These samples can be used to calculate expectation values and other useful quantities. We refer to this process as "state sample tomography." We encode the state's measurement outcome distributions using an efficiently parameterized generative neural network. This allows each stage in the tomography process to be performed efficiently even for large systems. Our scheme is demonstrated on recent IBM Quantum devices, producing a model for a six-qubit state's measurement outcomes with a predictive accuracy (classical fidelity) greater than 95% for all test cases using only 100 random measurement settings as opposed to the 729 settings required for standard full tomography using local measurements. This reduction in the required number of measurements scales favorably, with training data in 200 measurement settings, yielding a predictive accuracy greater than 92% for a ten-qubit state where 59 049 settings are typically required for full local measurement-based quantum state tomography. A reduction in the number of measurements by a factor, in this case, of almost 600 could allow for estimations of expectation values and state fidelities in practicable times on current quantum devices.Received 18 August 2020Accepted 1 June 2021DOI:https://doi.org/10.1103/PRXQuantum.2.020348Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasMachine learningQuantum benchmarkingQuantum computationQuantum information processingQuantum metrologyQuantum simulationQuantum tomographyQuantum Information
Mask-Align: Self-Supervised Neural Word Alignment

Word alignment, which aims to align translationally equivalent words between source and target sentences, plays an important role in many natural language processing tasks.Current unsupervised neural alignment methods focus on inducing alignments from neural machine translation models, which does not leverage the full context in the target sequence.In this paper, we propose MASK-ALIGN, a selfsupervised word alignment model that takes advantage of the full context on the target side.Our model parallelly masks out each target token and predicts it conditioned on both source and the remaining target tokens.This two-step process is based on the assumption that the source token contributing most to recovering the masked target token should be aligned.We also introduce an attention variant called leaky attention, which alleviates the problem of high cross-attention weights on specific tokens such as periods.Experiments on four language pairs show that our model outperforms previous unsupervised neural aligners and obtains new state-of-the-art results.
Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering

Knowledge retrieval and reasoning are two key stages in multi-hop question answering (QA) at web scale.Existing approaches suffer from low confidence when retrieving evidence facts to fill the knowledge gap and lack transparent reasoning process.In this paper, we propose a new framework to exploit more valid facts while obtaining explainability for multi-hop QA by dynamically constructing a semantic graph and reasoning over it.We employ Abstract Meaning Representation (AMR) as semantic graph representation.Our framework contains three new ideas: (a) AMR-SG, an AMR-based Semantic Graph, constructed by candidate fact AMRs to uncover any hop relations among question, answer and multiple facts.(b) A novel path-based fact analytics approach exploiting AMR-SG to extract active facts from a large fact pool to answer questions.(c) A fact-level relation modeling leveraging graph convolution network (GCN) to guide the reasoning process.Results on two scientific multi-hop QA datasets show that we can surpass recent approaches including those using additional knowledge graphs while maintaining high explainability on OpenBookQA and achieve a new state-ofthe-art result on ARC-Challenge in a computationally practicable setting.
Learning an Effective Context-Response Matching Model with Self-Supervised Tasks for Retrieval-based Dialogues

Building an intelligent dialogue system with the ability to select a proper response according to a multi-turn context is a great challenging task. Existing studies focus on building a context-response matching model with various neural architectures or pretrained language models (PLMs) and typically learning with a single response prediction task. These approaches overlook many potential training signals contained in dialogue data, which might be beneficial for context understanding and produce better features for response prediction. Besides, the response retrieved from existing dialogue systems supervised by the conventional way still faces some critical challenges, including incoherence and inconsistency. To address these issues, in this paper, we propose learning a context-response matching model with auxiliary self-supervised tasks designed for the dialogue data based on pre-trained language models. Specifically, we introduce four self-supervised tasks including next session prediction, utterance restoration, incoherence detection and consistency discrimination, and jointly train the PLM-based response selection model with these auxiliary tasks in a multi-task manner. By this means, the auxiliary tasks can guide the learning of the matching model to achieve a better local optimum and select a more proper response. Experiment results on two benchmarks indicate that the proposed auxiliary self-supervised tasks bring significant improvement for multi-turn response selection in retrieval-based dialogues, and our model achieves new state-of-the-art results on both datasets.
Robustness Testing of Language Understanding in Task-Oriented Dialog

Most language understanding models in taskoriented dialog systems are trained on a small amount of annotated training data, and evaluated in a small set from the same distribution.However, these models can lead to system failure or undesirable output when being exposed to natural language perturbation or variation in practice.In this paper, we conduct comprehensive evaluation and analysis with respect to the robustness of natural language understanding models, and introduce three important aspects related to language understanding in realworld dialog systems, namely, language variety, speech characteristics, and noise perturbation.We propose a model-agnostic toolkit LAUG to approximate natural language perturbations for testing the robustness issues in taskoriented dialog.Four data augmentation approaches covering the three aspects are assembled in LAUG, which reveals critical robustness issues in state-of-the-art models.The augmented dataset through LAUG can be used to facilitate future research on the robustness testing of language understanding in task-oriented dialog.Recently task-oriented dialog systems have been attracting more and more research efforts (Gao et al., 2019; Zhang et al., 2020b), where understanding user utterances is a critical precursor to the success of such dialog systems.While modern neural networks have achieved state-of-the-art results on language understanding (LU) (Wang et al., 2018;
Reliability Testing for Natural Language Processing Systems

Questions of fairness, robustness, and transparency are paramount to address before deploying NLP systems.Central to these concerns is the question of reliability: Can NLP systems reliably treat different demographics fairly and function correctly in diverse and noisy environments?To address this, we argue for the need for reliability testing and contextualize it among existing work on improving accountability.We show how adversarial attacks can be reframed for this goal, via a framework for developing reliability tests.We argue that reliability testing -with an emphasis on interdisciplinary collaboration -will enable rigorous and targeted testing, and aid in the enactment and enforcement of industry standards.
A Framework for Anomaly Classification Using Deep Transfer Learning Approach

Over the last few years, surveillance CCTV cameras have rapidly grown to monitor human activities. Suspicious activities like assault, gun violence, kidnapping need to be observed in public places like malls, public roads, colleges, etc. There is a need for such a surveillance system that automatically recognizes human behavior, such as violent and non-violent actions. Action recognition has become an active research topic for researchers within the computer vision field. However, the human behavior recognition community has mainly focused only on regular actions like walking, running, jogging, etc. Though, detecting behavior in anomaly subjects like assault violence, gun violence, or general aggressive behavior has been comparatively less research in these specific events due to a lack of datasets and algorithms. Thus, there is an increasing demand for datasets to develop abnormal behavior algorithms that can classify anomaly actions. In this paper, the novel dataset is proposed named Human Behavior Dataset 2021 (HBD21). There are four categories of videos available in this dataset: Assault violence, Gun violence, Sabotage violence, and Normal events. This proposed dataset contains a total of 456 videos. Each video has the same length of each category. This paper aims to make a robust surveillance system framework with the help of a deep transfer learning approach and proposed a novel hybrid model. In this view, the current research work is categorized into three phases. Firstly, the preprocessing technique is applied to enhance the brightness of videos, and for resizing then, frames are extracted from each video. Secondly, the transfer learning-based Xception model is used to extract relevant features from frames. The third phase is a classification of behaviors in which a modified LSTM technique is applied. The model is trained using LSTM on the HBD21 dataset. Moreover, using proposed methods on the HBD21 dataset, the accuracy is obtained 97.25% overall.
Frontal Face Generation Algorithm from Multi-view Images Based on Generative Adversarial Network

In a face, there is much information of person’s identity. Because of this property, various tasks such as expression recognition, identity recognition and deepfake have been actively conducted. Most of them use the exact frontal view of the given face. However, various directions of the face can be observed rather than the exact frontal image in real situation. The profile (side view) lacks information when comparing with the frontal view image. Therefore, if we can generate the frontal face from other directions, we can obtain more information on the given face. In this paper, we propose a combined style model based the conditional generative adversarial network (cGAN) for generating the frontal face from multi-view images that consist of characteristics that not only includes the style around the face (hair and beard) but also detailed areas (eye, nose, and mouth).
Performance evaluation of feature selection and tree-based algorithms for traffic classification

The rapid development of smart devices triggers a surge in new traffic and applications. Thus, network traffic classification has become a challenge in modern communications and may be applied to a various range of applications ranging from QoS provisioning to security-related applications. Developing Machine Learning (ML) methods, which can successfully distinguish network applications from each other, is one of the most important tasks. Since ML algorithms are as good as the quality of data, feature selection has become a crucial step in the ML process. Therefore, selecting effective and relevant features for traffic analysis is also another essential issue. In this paper, we are interested in identifying the most relevant features to characterize network traffic. Empirical results indicate that significant input feature selection is important to classify network traffic. Then, a comparative analysis of various Decision Tree-based models (both traditional and recent algorithms) has been conducted with feature selection methods in terms of accuracy, training, and classification time.
Searching for scientific evidence in a pandemic: An overview of TREC-COVID

We present an overview of the TREC-COVID Challenge, an information retrieval (IR) shared task to evaluate search on scientific literature related to COVID-19. The goals of TREC-COVID include the construction of a pandemic search test collection and the evaluation of IR methods for COVID-19. The challenge was conducted over five rounds from April to July 2020, with participation from 92 unique teams and 556 individual submissions. A total of 50 topics (sets of related queries) were used in the evaluation, starting at 30 topics for Round 1 and adding 5 new topics per round to target emerging topics at that state of the still-emerging pandemic. This paper provides a comprehensive overview of the structure and results of TREC-COVID. Specifically, the paper provides details on the background, task structure, topic structure, corpus, participation, pooling, assessment, judgments, results, top-performing systems, lessons learned, and benchmark datasets.
Tracking more than 100 arbitrary objects at 25 FPS through deep learning

Most video analytics applications rely on object detectors to localize objects in frames. However, when real-time is a requirement, running the detector at all the frames is usually not possible. This is somewhat circumvented by instantiating visual object trackers between detector calls, but this does not scale with the number of objects. To tackle this problem, we present SiamMT, a new deep learning multiple visual object tracking solution that applies single-object tracking principles to multiple arbitrary objects in real-time. To achieve this, SiamMT reuses feature computations, implements a novel crop-and-resize operator, and defines a new and efficient pairwise similarity operator. SiamMT naturally scales up to several dozens of targets, reaching 25 fps with 122 simultaneous objects for VGA videos, or up to 100 simultaneous objects in HD720 video. SiamMT has been validated on five large real-time benchmarks, achieving leading performance against current state-of-the-art trackers.
Tracing CVE Vulnerability Information to CAPEC Attack Patterns Using Natural Language Processing Techniques

For effective vulnerability management, vulnerability and attack information must be collected quickly and efficiently. A security knowledge repository can collect such information. The Common Vulnerabilities and Exposures (CVE) provides known vulnerabilities of products, while the Common Attack Pattern Enumeration and Classification (CAPEC) stores attack patterns, which are descriptions of common attributes and approaches employed by adversaries to exploit known weaknesses. Due to the fact that the information in these two repositories are not linked, identifying related CAPEC attack information from CVE vulnerability information is challenging. Currently, the related CAPEC-ID can be traced from the CVE-ID using Common Weakness Enumeration (CWE) in some but not all cases. Here, we propose a method to automatically trace the related CAPEC-IDs from CVE-ID using three similarity measures: TF–IDF, Universal Sentence Encoder (USE), and Sentence-BERT (SBERT). We prepared and used 58 CVE-IDs as test input data. Then, we tested whether we could trace CAPEC-IDs related to each of the 58 CVE-IDs. Additionally, we experimentally confirm that TF–IDF is the best similarity measure, as it traced 48 of the 58 CVE-IDs to the related CAPEC-ID.
Down-conversion processes in <i>ab initio</i> nonrelativistic quantum electrodynamics

The availability of efficient photon sources with specific properties is important for quantum-technological applications. However, the realization of such photon sources is often challenging and hence alternative perspectives that suggest new means to enhance desired properties while suppressing detrimental processes are valuable. In this work we highlight that ab-initio simulations of coupled light-matter systems can provide such new avenues. We show for a simple model of a quantum ring that by treating light and matter on equal footing we can create and enhance novel pathways for down-conversion processes. By changing the matter subsystem as well as the photonic environment in experimentally feasible ways, we can engineer hybrid light-matter states that enhance at the same time the efficiency of the down-conversion process and the non-classicality of the created photons. Furthermore we show that this also leads to a faster down-conversion, potentially avoiding detrimental decoherence effects.
Recent Advances in Heterogeneous Relation Learning for Recommendation

Recommender systems have played a critical role in many web applications to meet user's personalized interests and alleviate the information overload. In this survey, we review the development of recommendation frameworks with the focus on heterogeneous relational learning, which consists of different types of dependencies among users and items. The objective of this task is to map heterogeneous relational data into latent representation space, such that the structural and relational properties from both user and item domain can be well preserved. To address this problem, recent research developments can fall into three major categories: social recommendation, knowledge graph-enhanced recommender system, and multi-behavior recommendation. We discuss the learning approaches in each category, such as matrix factorization, attention mechanism and graph neural networks, for effectively distilling heterogeneous contextual information. Finally, we present exploratory outlook to highlight several promising directions and opportunities in heterogeneous relational learning frameworks for recommendation.
Blockchain Technology Projects to Provide Telemedical Services: Systematic Review

One of the most promising health care development areas is introducing telemedicine services and creating solutions based on blockchain technology. The study of systems combining both these domains indicates the ongoing expansion of digital technologies in this market segment.This paper aims to review the feasibility of blockchain technology for telemedicine.The authors identified relevant studies via systematic searches of databases including PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar. The suitability of each for inclusion in this review was assessed independently. Owing to the lack of publications, available blockchain-based tokens were discovered via conventional web search engines (Google, Yahoo, and Yandex).Of the 40 discovered projects, only 18 met the selection criteria. The 5 most prevalent features of the available solutions (N=18) were medical data access (14/18, 78%), medical service processing (14/18, 78%), diagnostic support (10/18, 56%), payment transactions (10/18, 56%), and fundraising for telemedical instrument development (5/18, 28%).These different features (eg, medical data access, medical service processing, epidemiology reporting, diagnostic support, and treatment support) allow us to discuss the possibilities for integration of blockchain technology into telemedicine and health care on different levels. In this area, a wide range of tasks can be identified that could be accomplished based on digital technologies using blockchains.
Quantum advantages for Pauli channel estimation

We show that entangled measurements provide an exponential advantage in sample complexity for Pauli channel estimation, which is both a fundamental problem and a practically important subroutine for benchmarking near-term quantum devices. The specific task we consider is to simultaneously learn all the eigenvalues of an $n$-qubit Pauli channel to $\pm\varepsilon$ precision. We give an estimation protocol with an $n$-qubit ancilla that succeeds with high probability using only $O(n/\varepsilon^{2})$ copies of the Pauli channel, while prove that any ancilla-free protocol (possibly with adaptive control and channel concatenation) would need at least $\Omega(2^{n/3})$ rounds of measurement. We further study the advantages provided by a small number of ancillas. For the case that a $k$-qubit ancilla ($k\le n$) is available, we obtain a sample complexity lower bound of $\Omega(2^{(n-k)/3})$ for any non-concatenating protocol, and a stronger lower bound of $\Omega(n2^{n-k})$ for any non-adaptive, non-concatenating protocol, which is shown to be tight. We also show how to apply the ancilla-assisted estimation protocol to a practical quantum benchmarking task in a noise-resilient and sample-efficient manner, given reasonable noise assumptions. Our results provide a practically-interesting example for quantum advantages in learning and also bring new insight for quantum benchmarking.
Analysis of the Wavelet Domain Filtering Approach for Video Super-Resolution

The wavelet domain-centered algorithms for the super-resolution research area give better visual quality and have been explored by different researchers. The visual quality is achieved with increased complexity and cost as most of the systems embed different pre- and post-processing techniques. The frequency and spatial domain-based methods are the usual approaches for super-resolution with some benefits and limitations. Considering the benefits of wavelet domain processing, this paper deals with a new algorithm that depends on wavelet residues. The methodology opts for wavelet domain filtering and residue extraction to get super-resolved frames for better visuals without embedding other techniques. The avoidance of noisy high-frequency components from low-quality videos and the consideration of edge information in the frames are the main targets of the super-resolution process. This inverse process is carried with a proper combination of information present in low-frequency bands and residual information in the high-frequency components. The efficient known algorithms always have to sacrifice simplicity to achieve accuracy, but in the proposed algorithm efficiency is achieved with simplicity. The robustness of the algorithm is tested by analyzing different wavelet functions and at different noise levels. The proposed algorithm performs well in comparison to other techniques from the same domain.