Reviewerly/exHarmony huggingface dataset:
Differential Machine Learning

Differential machine learning combines automatic adjoint differentiation (AAD) with modern machine learning (ML) in the context of risk management of financial Derivatives. We introduce novel algorithms for training fast, accurate pricing and risk approximations, online, in real-time, with convergence guarantees. Our machinery is applicable to arbitrary Derivatives instruments or trading books, under arbitrary stochastic models of the underlying market variables. It effectively resolves computational bottlenecks of Derivatives risk reports and capital calculations.Differential ML is a general extension of supervised learning, where ML models are trained on examples of not only inputs and labels but also differentials of labels wrt inputs. It is also applicable in many situations outside finance, where high-quality first-order derivatives wrt training inputs are available. Applications in Physics, for example, may leverage differentials known from first principles to learn function approximations more effectively. In finance, AAD computes pathwise differentials with remarkable efficacy so differential ML algorithms provide extremely effective pricing and risk approximations. We can produce fast analytics in models too complex for closed-form solutions, extract the risk factors of complex transactions and trading books, and effectively compute risk management metrics like reports across a large number of scenarios, backtesting and simulation of hedge strategies, or regulations like XVA, CCR, FRTB or SIMM-MVA. TensorFlow implementation is available on https://github.com/differential-machine-learning
Objects detection toward complicated high remote basketball sports by leveraging deep CNN architecture

The analysis of high-difficulty action recognition technology in basketball is mainly to identify and analyze the physical behavior of basketball players in the video to complete the technical action. The purpose of video recognition is to provide an important guarantee for improving the level of basketball training. The current target recognition technology has achieved some results. It shows that the application of target detection technology in basketball sports scene is of great significance and can improve the effect of sports training. However, traditional sports target recognition is limited by technology and injury, and the analysis of difficult sports skills is limited by the scene, dynamic background and technology, and cannot achieve the desired effect. This is not conducive to the improvement of athletes' skills. Therefore, this article aims to develop a big data motion target detection system based on deep convolutional neural network for sports difficult motion image recognition. More specifically, we use the high discriminative power of the convolutional neural network to extract images to perform computational preprocessing for the recognition of each human motion image in the video stream. Then, the skeleton recognition algorithm based on LSTM is used to detect the key points of the human body, which is of great significance for modeling different movements. Finally, we developed an object detection system to reconstruct each movement. By selecting five groups of highly difficult actions that are likely to cause sports injuries to conduct experimental research, the results prove the effectiveness of the target detection system we proposed.
An efficient simulation for quantum secure multiparty computation

The quantum secure multiparty computation is one of the important properties of secure quantum communication. In this paper, we propose a quantum secure multiparty summation (QSMS) protocol based on (t, n) threshold approach, which can be used in many complex quantum operations. To make this protocol secure and realistic, we combine both the classical and quantum phenomena. The existing protocols have some security and efficiency issues because they use (n, n) threshold approach, where all the honest players need to perform the quantum multiparty summation protocol. We however use a (t, n) threshold approach, where only t honest players need to compute the quantum summation protocol. Compared to other protocols our proposed protocol is more cost-effective, realistic, and secure. We also simulate it using the IBM corporation's online quantum computer, or quantum experience.
What Do Practitioners Discuss about IoT and Industry 4.0 Related Technologies? Characterization and Identification of IoT and Industry 4.0 Categories in Stack Overflow Discussions

Internet of Things and Industry 4.0–related discussions have become increasingly prevalent on several online Q&A websites in recent years. Analyzing and understanding such discussions could provide practitioners with more insights into various trends in the evolution of topics of interest and help different research communities, including software development and industrial sectors, better understand the needs and challenges facing practitioners as they operate in these fields. We conduct an exploratory study to capture and compare the popularity as well as the effect of the discussion topics on the domain across such communities. We use extracted data from popular online Q&A websites, such as Stack Exchange, and investigate 176,819 posts to investigate what practitioners are asking and looking for. We also use Machine Learning for Language Toolkit (MALLET) based–Latent Dirichlet Allocation (LDA) topic models to help summarize Internet of Things/Industry 4.0–related questions, identify the most challenging popular related issues, explore specific issues of the Internet of Things/Industry 4.0, and investigate the types of questions that practitioners ask about. We summarize all the topics into various categories and evaluate the popularity and difficulty of various topics as well. Our findings help to highlight the challenges facing the Internet of Things/Industry 4.0 practitioners which in the future would require more attention from various research and software development communities and develop new approaches to analyzing questions posed on various Q&A forums.
Chiral Single-Photon Generators

Chiral photons have the potential to advance information technologies due to their robustness in carrying binary data against noisy backgrounds as well as their capacity for constructing single-photon isolators and circulators through nonreciprocal photon propagation. In this Perspective, we highlight recent efforts to generate chiral single photons using circularly polarized light sources. We delve into possible future technologies that integrate these light sources with other active optical elements as a versatile platform for information processing.
Neural Networks with Emotion Associations, Topic Modeling and Supervised Term Weighting for Sentiment Analysis

Automated sentiment analysis is becoming increasingly recognized due to the growing importance of social media and e-commerce platform review websites. Deep neural networks outperform traditional lexicon-based and machine learning methods by effectively exploiting contextual word embeddings to generate dense document representation. However, this representation model is not fully adequate to capture topical semantics and the sentiment polarity of words. To overcome these problems, a novel sentiment analysis model is proposed that utilizes richer document representations of word-emotion associations and topic models, which is the main computational novelty of this study. The sentiment analysis model integrates word embeddings with lexicon-based sentiment and emotion indicators, including negations and emoticons, and to further improve its performance, a topic modeling component is utilized together with a bag-of-words model based on a supervised term weighting scheme. The effectiveness of the proposed model is evaluated using large datasets of Amazon product reviews and hotel reviews. Experimental results prove that the proposed document representation is valid for the sentiment analysis of product and hotel reviews, irrespective of their class imbalance. The results also show that the proposed model improves on existing machine learning methods.
Timelines: A World-Building Activity for Values Advocacy

This paper presents Timelines, a design activity to assist values advocates: people who help others recognize values and ethical concerns as relevant to technical practice. Rather than integrate seamlessly into existing design processes, Timelines aims to create a space for critical reflection and contestation among expert participants (such as technology researchers, practitioners, or students) and a values advocate facilitator to surface the importance and relevance of values and ethical concerns. The activity's design is motivated by theoretical perspectives from design fiction, scenario planning, and value sensitive design. The activity helps participants surface discussion of broad societal-level changes related to a technology by creating stories from news headlines, and recognize a diversity of experiences situated in the everyday by creating social media posts from different viewpoints. We reflect on how decisions on the activity's design and facilitation enables it to assist in values advocacy practices.
Beyond Trivial Counterfactual Explanations with Diverse Valuable Explanations

Explainability for machine learning models has gained considerable attention within the research community given the importance of deploying more reliable machine-learning systems. In computer vision applications, generative counterfactual methods indicate how to perturb a model’s input to change its prediction, providing details about the model’s decision-making. Current methods tend to generate trivial counterfactuals about a model’s decisions, as they often suggest to exaggerate or remove the presence of the attribute being classified. For the machine learning practitioner, these types of counterfactuals offer little value, since they provide no new information about undesired model or data biases. In this work, we identify the problem of trivial counterfactual generation and we propose DiVE to alleviate it. DiVE learns a perturbation in a disentangled latent space that is constrained using a diversity-enforcing loss to uncover multiple valuable explanations about the model’s prediction. Further, we introduce a mechanism to prevent the model from producing trivial explanations. Experiments on CelebA and Synbols demonstrate that our model improves the success rate of producing high-quality valuable explanations when compared to previous state-of-the-art methods. Code is available at https://github.com/ElementAI/beyond-trivial-explanations.
AANE: Anomaly Aware Network Embedding for Anomalous Link Detection

Existing network embedding models regard all the links in a network as normal and model them without distinction. In real networks, there may be anomalous links like noise or adversarial links. We explicitly consider the existence of anomalous links in a network and propose anomaly aware network embedding (AANE) model. The key of AANE is the design of a new loss, which consists of anomaly aware loss and adjusted fitting loss. We adopt an anomaly indicator to iteratively select significant anomalous links from the network during model training, and removal loss and deviation loss are designed to model the reconstruction errors of selected anomalous and normal links respectively. To instantiate AANE, AAGAE and AAGCN are implemented on graph auto-encoder (GAE) and graph convolution based auto-encoder (GCNAE) respectively. For the purpose of evaluation, a heuristic anomalous link generation algorithm is proposed and by using the algorithm we generate anomalous links into six real world network datasets. Experimental results show that AANE outperforms both basic and competitive network embedding models in terms of anomalous link detection performance in most cases.
Real-Time Multiple Object Visual Tracking for Embedded GPU Systems

Real-time visual object tracking provides every object of interest with a unique identity and a trajectory across video frames. This is a fundamental task of many video analytics applications, such as traffic monitoring or video surveillance in general. The development of real-time multiple object tracking systems on low-power edge devices as IoT nodes, without compromising accuracy, is a challenge due to the limited computing capacity of said devices. This might rule out the best in-class computer vision solutions, which, nowadays, are based on deep learning, and thus, they are very hardware demanding. This article meets this challenge with a multiple object detection and tracking system that employs cutting-edge deep learning architectures on an embedded GPU while operating in real time. For this purpose, a system has been designed that extends a joint architecture of tracking and detection by adding a module comprised of appearance-based and movement-based trackers that allow to maintain the identity of the objects of interest for longer periods of time while alleviating the burden of the detector. Our system is mapped onto an embedded GPU platform, cutting down power consumption significantly with respect to a server GPU. Tracking performance metrics show a 51.1% in multiple object tracking accuracy (MOTA) on the MOT16 data set. This, in conjunction with a real-time processing speed of 25.2 FPS for up to 45 simultaneous objects and low-power consumption of 15 W, make our system an ideal solution for a wide range of video analytics applications.
Chinese Pre-service Music Teachers’ Perceptions of Augmented Reality-Assisted Musical Instrument Learning

Given the rapid growth of music technology, this study reports Chinese pre-service music teachers’ perceptions of musical instrument learning assisted by augmented reality (AR). In this study, we conducted a small-scale case study with six pre-service teachers enrolled in a music teacher training programme at a comprehensive university in China. Participants engaged in semi-structured, face-to-face interviews after hands-on experiences with an AR-based piano learning app. Thematic analysis revealed that the participants were generally aware of the potential of this instructional approach but doubted its efficacy and exhibited weak intention to adopt it in their future classrooms. Implications of the findings for music teacher training are discussed.
Hierarchical distillation learning for scalable person search

Existing person search methods typically focus on improving person detection accuracy. This ignores the model inference efficiency, which however is fundamentally significant for real-world applications. In this work, we address this limitation by investigating the scalability problem of person search involving both model accuracy and inference efficiency simultaneously. Specifically, we formulate a Hierarchical Distillation Learning (HDL) approach. With HDL, we aim to comprehensively distil the knowledge of a strong teacher model with strong learning capability to a lightweight student model with weak learning capability. To facilitate the HDL process, we design a simple and powerful teacher model for joint learning of person detection and person re-identification matching in unconstrained scene images. Extensive experiments show the modelling advantages and cost-effectiveness superiority of HDL over the state-of-the-art person search methods on three large person search benchmarks: CUHK-SYSU, PRW, and DukeMTMC-PS.
A UAV Path Planning Method Based on Deep Reinforcement Learning

The path planning of Unmanned Aerial Vehicle (UAV) is a critical component of rescue operation. As impacted by the continuity of the task space and the high dynamics of the aircraft, conventional approaches cannot find the optimal control strategy. Accordingly, in this study, a deep reinforcement learning (DRL)-based UAV path planning method is proposed, enabling the UAV to complete the path planning in a 3D continuous environment. The deep deterministic policy gradient (DDPG) algorithm is employed to enable UAV to autonomously make decisions. Besides, to avoid obstacles, the concepts of connected area and threat function are proposed and adopted in the reward shaping. Lastly, an environment with static obstacles is built, and the agent is trained using the proposed method. As has been proved by the experiments, the proposed algorithm can fit a range of scenarios.
Comparative Analysis of Different Transformer Based Architectures Used in Sentiment Analysis

In the era of research where topics like Sentiment Analysis, NLP (natural language processing), Artificial Intelli- gence, Transfer learning are buzz words. Sentiment Analysis is a field which have proved its importance in day to day activities which could be related to monetary analysis, person's emotional count etc. With the introduction of Transformer based language models the field of NLP has got a huge attraction of researchers as well as practitioners. [16] [9] [6]. Using transfer learning with these models, have proved to give exceptional accuracy. Some of the State-of-the-art model includes BERT [16], DistilBERT, XLNet and T5. In this paper we have investigated the classification power of sentiments from pre-trained language models, e.g., BERT, XLnet. Specifically, we have simulated and presented number of experimental results which shows what amount of accuracy could be attained with different models. We have used most popular imdb-reviews dataset for doing analysis of the models.
Correlated tuple data release via differential privacy

Privacy preserving methods supporting for tuple data release have attracted the attention of researchers in multidisciplinary fields. Among the advanced methods, differential privacy (DP), introducing independent Laplace noise, has become an influential privacy mechanism owing to its provable and rigorous privacy guarantee. Nonetheless, in practice, tuple data to be protected are always correlated while independent noise may cause undesirable information disclosure than expected. Recent researches attempt to optimize the sensitivity function of DP with consideration of the correlation strength between data – but have a drawback in a substantial growth of noise level. Therefore, for correlated tuple data release, how to decrease the noise level incurred by correlation strength is yet to be explored. To remedy this problem, this paper exploits the degradation of DP in expected privacy levels for correlated tuple data and proposes a solution to mitigate it. We first demonstrate a filtering attack, presenting a possibility of using the different dependence between original outputs and perturbations to sanitize a certain level of noise to extract individual’s sensitive information. Secondly, we introduce the notion of correlated tuple differential privacy (CTDP) to preserve expected privacy for correlated tuple data and further propose a generalized Laplace mechanism (GLM) to achieve privacy guarantees in CTDP. Then we design a practical iteration mechanism, including an update function, to conduct GLM when facing large scale queries. Finally, experimental evaluation on real-world datasets over multiple fields show that our solution consistently outperforms state-of-the-art mechanisms in data utility while providing the same privacy guarantee as other approaches for correlated tuple data.
Adversarial Shape Perturbations on 3D Point Clouds

The importance of training robust neural network grows as 3D data is increasingly utilized in deep learning for vision tasks in robotics, drone control, and autonomous driving. One commonly used 3D data type is 3D point clouds, which describe shape information. We examine the problem of creating robust models from the perspective of the attacker, which is necessary in understanding how neural networks can be exploited. We explore two categories of attacks: distributional attacks that involve imperceptible perturbations to the distribution of points, and shape attacks that involve deforming the shape represented by a point cloud. We explore three possible shape attacks for attacking 3D point cloud classification and show that some of them are able to be effective even against preprocessing steps, like the previously proposed point-removal defenses. (Source code available at https://github.com/Daniel-Liu-c0deb0t/Adversarial-point-perturbations-on-3D-objects ).
Enabling Proxy-Free Privacy-Preserving and Federated Crowdsourcing by Using Blockchain

With the rapid development and widespread application of crowdsourcing, the limitations of traditional systems are gradually exposed. First, traditional systems fail to protect the privacy of task requesters and workers. They typically rely on a centralized server to aggregate the task content and workers' interests, while these data contain sensitive information. Second, crowdsourcing resources in each system are isolated. The tasks in one system cannot reach potential workers in other systems. Thus, there is a great need to build a new privacy-preserving and federated crowdsourcing system. However, the existing privacy-preserving solutions rely on a trusted third party to perform key management, which is not applicable in a federated setting. To this end, we propose the first proxy-free privacy-preserving and federated crowdsourcing system. It interconnects the existing crowdsourcing systems and can perform encrypted task matching across various systems without relying on a trusted third-party authority. Our main idea is to achieve federated crowdsourcing by moving secure task matching to the trusted smart contract. To get rid of the dependence on the trusted authority, we combine the rewritable deterministic hashing technique with searchable encryption schemes to achieve secure on-chain task-matching authorization. Moreover, we utilize the puncturable encryption technique to implement secure authorization revocation. We formally analyze the security of our design and implement a prototype on Ethereum. Evaluation results demonstrate that our design is secure and efficient for blockchain-based crowdsourcing.
End-to-End Video Question-Answer Generation With Generator-Pretester Network

We study a novel task, Video Question-Answer Generation (VQAG), for challenging Video Question Answering (Video QA) task in multimedia. Due to expensive data annotation costs, many widely used, large-scale Video QA datasets such as Video-QA, MSVD-QA and MSRVTT-QA are automatically annotated using Caption Question Generation (CapQG) which inputs captions instead of the video itself. As captions neither fully represent a video, nor are they always practically available, it is crucial to generate question-answer pairs based on a video via Video Question-Answer Generation (VQAG). Existing video-to-text (V2T) approaches, despite taking a video as the input, only generate a question alone. In this work, we propose a novel model Generator-Pretester Network that focuses on two components: (1) The Joint Question-Answer Generator (JQAG) which generates a question with its corresponding answer to allow Video Question “Answering” training. (2) The Pretester (PT) verifies a generated question by trying to answer it and checks the pretested answer with both the model’s proposed answer and the ground truth answer. We evaluate our system with the only two available large-scale human-annotated Video QA datasets and achieves state-of-the-art question generation performances. Furthermore, using our generated QA pairs only on the Video QA task, we can surpass some supervised baselines. As a pre-training strategy, we outperform both CapQG and transfer learning approaches when employing semi-supervised (20%) or fully supervised learning with annotated data. These experimental results suggest the novel perspectives for Video QA training.