Reviewerly/exHarmony huggingface dataset:
Video-based Person Re-identification without Bells and Whistles

Video-based person re-identification (Re-ID) aims at matching the video tracklets with cropped video frames for identifying the pedestrians under different cameras. However, there exists severe spatial and temporal misalignment for those cropped tracklets due to the imperfect detection and tracking results generated with obsolete methods. To address this issue, we present a simple re-Detect and Link (DL) module which can effectively reduce those unexpected noise through applying the deep learning-based detection and tracking on the cropped tracklets. Furthermore, we introduce an improved model called Coarse-to-Fine Axial-Attention Network (CF-AAN). Based on the typical Non-local Network, we replace the non-local module with three 1-D position-sensitive axial attentions, in addition to our proposed coarse-to-fine structure. With the developed CF-AAN, compared to the original non-local operation, we can not only significantly reduce the computation cost but also obtain the state-of-the-art performance (91.3% in rank-1 and 86.5% in mAP) on the large-scale MARS dataset. Meanwhile, by simply adopting our DL module for data alignment, to our surprise, several baseline models can achieve better or comparable results with the current state-of-the-arts. Besides, we discover the errors not only for the identity labels of tracklets but also for the evaluation protocol for the test data of MARS. We hope that our work can help the community for the further development of invariant representation without the hassle of the spatial and temporal alignment and dataset noise. The code, corrected labels, evaluation protocol, and the aligned data will be available at https://github.com/jackie840129/CF-AAN.
Results of the Second SIGMORPHON Shared Task on Multilingual Grapheme-to-Phoneme Conversion

Grapheme-to-phoneme conversion is an important component in many speech technologies, but until recently there were no multilingual benchmarks for this task.The second iteration of the SIGMORPHON shared task on multilingual grapheme-to-phoneme conversion features many improvements from the previous year's task (Gorman et al. 2020), including additional languages, a stronger baseline, three subtasks varying the amount of available resources, extensive quality assurance procedures, and automated error analyses.Four teams submitted a total of thirteen systems, at best achieving relative reductions of word error rate of 11% in the high-resource subtask and 4% in the low-resource subtask.
Bias Correction in Reinforcement Learning via the Deterministic Policy Gradient Method for MPC-Based Policies

In this paper, we discuss the implementation of the Deterministic Policy Gradient using the Actor-Critic technique based on linear compatible advantage function approximations in the context of constrained policies. We focus on MPC-based policies, though the discussion is general. We show that in that context, the classic linear compatible advantage function approximation fails to deliver a correct policy gradient due to the exploration becoming distorted by the constraints, and we propose a generalized linear compatible advantage function approximation that corrects the problem. We show that this correction requires an estimation of the mean and covariance of the constrained exploration. The validity of that generalization is formally established and demonstrated on a simple example.
CriminelBART

Learning language representations is a key component in many natural language processing tasks, and their usefulness is most often challenged by specialized target domains and vocabulary. We have witnessed several neural causal language models (CLM) that learn contextual representations such as ELMo [8]. More recently, the Transformer architecture [10] has tremendously improved language representation learning, giving birth to new architectures such as BERT [4], a masked language model, pushing the state-of-the-art of natural language understanding to an unprecedented level of performance on standard benchmarks. Moreover, it has been found that Transformer-based CLM, such as GPT [9], are excellent feature extractors as well as being impressive text generators. BART [7], an architecture combining the backbone of both BERT and GTP proved to be particularly effective at generating text while being competitive in comprehension tasks. BARThez, the French version of BART, was recently introduced as a pre-trained model on a very large monolingual French corpus [6]. In this paper, we introduce CriminelBART, a fine-tuned version of BARThez specialized for criminal law using a French Canadian corpus of legal judgments, and we evaluate its performance on different tasks.
Ethereum synchronicity, upside volatility and Bitcoin crash risk

The fundamental value of Bitcoin is difficult to evaluate because there is no underlying company or cashflow. Extant literature documented various macro-economic factors and technical factors to price Bitcoin. However, for a highly speculative instrument, investors’ behavior plays an important role in asset pricing. This paper incorporates Bitcoin Ethereum synchronicity conditional on upside volatility of Bitcoin as a proxy for the fear of high Bitcoin prices. Empirical results reveal that when upside volatility is high, Ethereum synchronicity exerts a significant positive influence on Bitcoin crash risk.
Detection of segmented uterine cancer images by Hotspot Detection method using deep learning models, Pigeon-Inspired Optimization, types-based dominant activation selection approaches

Uterine cancer consists of cells of a layer that forms the inside of the uterus. Sometimes, as a result of abnormal growth of normal cells, it can damage the surrounding tissues and cause the formation of cancerous cells. In the USA, according to the projections for 2021, approximately 66 thousand new cases of uterine cancer will be detected and approximately 13 thousand of these cancer patients are expected to die from uterine cancer. Early diagnosis of cancer is important. Recently, artificial intelligence-based technologies have been used in the diagnosis and treatment processes of various diseases. In this study, five categories of datasets including normal, abnormal, and benign cells were used. The dataset consists of cellular images and is publicly available. The proposed approach consists of three steps. In the first step, the Hotspot method was used to detect the tumor cells in the images. In the second step, tumor cells that were brought to the fore by segmentation were trained by deep learning models, and activation sets of five types from each deep learning model were created. In the last step, the best activation sets were selected among the activation sets obtained by deep learning models of each type (for five dataset types). Pigeon-Inspired Optimization was used for this selection. Thus, the activation sets with the best performance of the five types were classified by the Softmax method. The overall accuracy success achieved with the approach suggested as a result of the classification was 99.65%.
Towards a Multi-Label Dataset of Internet Traffic for Digital Behavior Classification

With the digital transformation of model society, the deep understanding of digital behavior is critical for both users and service providers. Nevertheless this work is challenging due to the lack of an extensive model and the corresponding dataset to support digital behavior classification. In response to this, we presented in this work a complete process of modelling, data collection and classification of user digital behaviors over Internet: firstly the fundamental digital context model is introduced to provide a thorough understanding of digital behavior and digital environment properties. Based on the model, the data collection process is presented and a multi-label dataset of Internet traffic (MLDIT) has been collected with all model properties, finally a first series of classification experiments with MLDIT has been conducted showing promising results to identify user interaction state, applications and actions. Aiming at providing a thorough model of digital behavior and a reference process for data collection and classification, we expect likewise to attract community efforts to collaborate on the MLDIT enrichment.
Measurement-based Quantum Repeater Network Coding

Quantum network coding can effectively improve the aggregate throughput of quantum networks and alleviate bottlenecks caused by topological constraints.Most of previous schemes are dedicated to the efficient teleportation of unknown quantum states in a quantum network.Herein a proposal for transmission of deterministic known states over quantum repeater network based on quantum measurements.We show that the new protocol offers advantages over three aspects.Firstly, the senders in our protocol obtain the knowledge of the quantum state to be transmitted, which enables the autonomy of quantum network transmission.Secondly, we study the quantum repeater network coding for longdistance deterministic quantum state communication.Quantum repeater network initialization requires entanglement distribution only among neighboring nodes, greatly saving entanglement resources, channel overhead and storage costs.Thirdly, based on Pauli measurements and local complementation, new protocol realizes parallel coding operations to mitigate latency issues sufficiently.Combining quantum network coding and quantum remote state preparation technology, our protocol provides an important solution for deterministic known states transmission over large-scale quantum network in the future.
An Incremental Learning of YOLOv3 Without Catastrophic Forgetting for Smart City Applications

Deep learning models have revealed outstanding performance on image classification and object detection tasks. However, there is a crucial drop in performance when they are subject to learn new data incrementally in the absence of previous training data. They suffer from catastrophic forgetting—abrupt drop in performance. This phenomenon affects the implementation of artificial intelligence in practical scenarios. To overcome catastrophic forgetting, the previous method has either saved previous data in memory or generated the previous data. However, these methods are computationally complex and infeasible for real-time applications. In this article, we proposed the YOLOv3 as an object detection framework for incremental learning. A knowledge distillation loss is introduced for the prediction of previously learned knowledge without utilizing previous training data. Consequently, these predictions are updated while learning the current model. Experimental results on the Pascal VOC2007 indicate that the proposed method significantly improved the mean average precision up to 74% for two classes in comparison to the state-of-the-art methods.
Identifying Rhythmic Patterns for Face Forgery Detection and Categorization

With the emergence of GAN, face forgery technologies have been heavily abused. Achieving accurate face forgery detection is imminent. Inspired by remote photoplethysmography (rPPG) that PPG signal corresponds to the periodic change of skin color caused by heartbeat in face videos, we observe that despite the inevitable loss of PPG signal during the forgery process, there is still a mixture of PPG signals in the forgery video with a unique rhythmic pattern depending on its generation method. Motivated by this key observation, we propose a two-stage network for face forgery detection and categorization consisting of: 1) a Spatial-Temporal Filter Module (STFM) for PPG signals filtering, and 2) an Adjacency Interaction Module (AIM) for constraint and interaction of PPG signals. Moreover, with insight into the generation of forgery methods, we further propose Spatial-Temporal Mixup (ST-Mixup) to boost the performance of the network. Overall, extensive experiments have proved the superiority of our method.
Examining User Preference for Agreeableness in Chatbots

Recent research suggests that deliberately manipulating a chatbot’s personality and matching it to the user’s personality can positively impact the user experience. Yet, little is known about whether this similarity attraction effect also applies to the personality dimension agreeableness. In a lab experiment, 30 participants interacted with three versions of an agreeable chatbot (agreeable, neutral, and disagreeable). Whilst our results corroborate a similarity attraction effect between user agreeableness and their preference for the agreeable chatbot, we did not find a reversed relationship with a disagreeable chatbot. Our findings point to a need for moderate instead of extreme chatbot personalities.
On Tight Quantum Security of HMAC and NMAC in the Quantum Random Oracle Model

HMAC and NMAC are the most basic and important constructions to convert Merkle-Damgård hash functions into message authentication codes (MACs) or pseudorandom functions (PRFs). In the quantum setting, at CRYPTO 2017, Song and Yun showed that HMAC and NMAC are quantum pseudorandom functions (qPRFs) under the standard assumption that the underlying compression function is a qPRF. Their proof guarantees security up to $$O(2^{n/5})$$ or $$O(2^{n/8})$$ quantum queries when the output length of HMAC and NMAC is n bits. However, there is a gap between the provable security bound and a simple distinguishing attack that uses $$O(2^{n/3})$$ quantum queries. This paper settles the problem of closing the gap. We show that the tight bound of the number of quantum queries to distinguish HMAC or NMAC from a random function is $$\Theta (2^{n/3})$$ in the quantum random oracle model, where compression functions are modeled as quantum random oracles. To give the tight quantum bound, based on an alternative formalization of Zhandry's compressed oracle technique, we introduce a new proof technique focusing on the symmetry of quantum query records.
Blockchain and GDPR – A Study on Compatibility Issues of the Distributed Ledger Technology with GDPR Data Processing

Abstract This research work aims to investigate Blockchain technology and GDPR compliance studies. This will analyze the data privacy perspective with respect to distributed ledger technology. Blockchain has become one of the most frequently discussed technologies for its ability to allow for peer-to-peer transactions without a centralized intermediary. The GDPR was implemented in May 2018 for EU member states to maintain data privacy. DLT, the underlying technology of blockchain as is a decentralized system without any monetary authority. This research conducted a thorough literature review on prior conducted research to investigate the problems and determine the gaps of GDPR compliance with blockchain technologies and discuss the technical, use-case designs or solutions that make blockchain more compliant GDPR in terms of privacy. This systematic literature review addresses the gaps, feasibility, efficiency, and data privacy issues on compatibility problems that are primarily concerned with how a distributed ledger technology system in which recorded data or transaction cannot be changed or erased is challenging the GDPR data subject access rights (DSAR), where every data subject’s personal data which is compliant to GDPR has a right to exercise their Rights to rectify, delete or limit the processing of your personal data at any time if necessary.
Thermal Image Super-Resolution Challenge - PBVS 2021

This paper presents results from the second Thermal Image Super-Resolution (TISR) challenge organized in the framework of the Perception Beyond the Visible Spectrum (PBVS) 2021 workshop. For this second edition, the same thermal image dataset considered during the first challenge has been used; only mid-resolution (MR) and high-resolution (HR) sets have been considered. The dataset consists of 951 training images and 50 testing images for each resolution. A set of 20 images for each resolution is kept aside for evaluation. The two evaluation methodologies proposed for the first challenge are also considered in this opportunity. The first evaluation task consists of measuring the PSNR and SSIM between the obtained SR image and the corresponding ground truth (i.e., the HR thermal image downsampled by four). The second evaluation also consists of measuring the PSNR and SSIM, but in this case, considers the ×2 SR obtained from the given MR thermal image; this evaluation is performed between the SR image with respect to the semi-registered HR image, which has been acquired with another camera. The results out-performed those from the first challenge, thus showing an improvement in both evaluation metrics.
Arrival time from the general theory of quantum time distributions

We further develop the general theory of quantum time distributions introduced in [D. Jurman and H. Nikolić, Phys. Lett. A 396, 127247 (2021)] and apply it to find the distribution of arrival times at the detector. Even though the Hamiltonian in the absence of detector is hermitian, the time evolution of the system before detection involves dealing with a non-hermitian operator obtained from the projection of the hermitian Hamiltonian onto the region in front of the detector. Such a formalism eventually gives rise to a simple and physically sensible analytical expression for the arrival time distribution, for arbitrary wave packet moving in one spatial dimension with negligible distortion.
Robust fringe projection measurement based on reference phase reconstruction

Phase measurement profilometry plays an important role in industrial applications. Traditional fringe projection profilometry (FPP) requires a stable reference plane to record the reference phase for the 3D reconstruction, which makes it sensitive to the motion-induced error caused by the reference plane. Thus, it is challenging for the traditional FPP to implement a robust measurement in industrial scenarios though it exhibits high accuracy. To solve this problem, a phase reconstruction method is proposed where the reference phase is denoised and reconstructed from the background of the modulated phase by minimizing the total variation (TV) via the theory of Sylvester equation. Since this strategy achieves synchronous extraction of the reference phase and the object one, it can correct the reference phase in real-time and reduce the measurement error caused by deviation of the reference plane. The proposed method has been applied to measure the step-shape workpiece with different deviations of the reference plane and the gear workpieces on conveyer. The experimental results show competitive performance with traditional FPP in accuracy and more robustness in complex industrial scenes.
System-Level Simulation for Integrated Phase-Change Photonics

Conventional computing systems are limited in performance by the well-known von Neumann bottleneck, arising from the physical separation of processor and memory units. The use of electrical signals in such systems also limits computing speeds and introduces significant energy losses. There is thus a pressing need for unconventional computing approaches, ones that can exploit the high bandwidths/speeds and low losses intrinsic to photonics. A promising platform for such a purpose is that offered by integrated phase-change photonics. Here, chalcogenide phase-change materials are incorporated into standard integrated photonics devices to deliver wide-ranging computational functionality, including non-volatile memory and fast, low-energy arithmetic and neuromorphic processing. We report the development of a compact behavioral model for integrated phase-change photonic devices, one which is fast enough to allow system level simulations to be run in a reasonable timescale with basic computing resources, while also being accurate enough to capture the key operating characteristics of real devices. Moreover, our model is readily incorporated with commercially available simulation software for photonic integrated circuits, thereby enabling the design, simulation and optimization of large-scale phase-change photonics systems. We demonstrate such capabilities by exploring the optimization and simulation of the operating characteristics of two important phase-change photonic systems recently reported, namely a spiking neural network system and a matrix-vector photonic crossbar array (photonic tensor core). Results show that use of our behavioral model can significantly facilitate the design and optimization at the system level, as well as expediting exploration of the capabilities of novel phase-change computing architectures.
GSTChain: A Blockchain Network Application for the Goods and Services Tax

In 2017, the Government of India launched the goods and services tax (GST), referred to as “one tax, one nation, one market”. This tax all Indian businesses are subject to this tax. GST was framed with the objective of bringing tax handling for all businesses onto a single platform and developing a transparent and effective system in which all businesses will pay taxes. This paper identifies and addresses GST implementation challenges and proposes a solution, GSTChain, using blockchain network technology. Currently, GST is collected at the sellers end and bifurcated between the Indian state and central governments. GSTChain is a blockchain system based on trust and autonomy with the objective of making taxpayers’ lives easy and tax collection efficient and transparent for the government.
ObjectGraphs: Using Objects and a Graph Convolutional Network for the Bottom-up Recognition and Explanation of Events in Video

In this paper a novel bottom-up video event recognition approach is proposed, ObjectGraphs, which utilizes a rich frame representation and the relations between objects within each frame. Following the application of an object detector (OD) on the frames, graphs are used to model the object relations and a graph convolutional network (GCN) is utilized to perform reasoning on the graphs. The resulting object-based frame-level features are then forwarded to a long short-term memory (LSTM) network for video event recognition. Moreover, the weighted in-degrees (WiDs) derived from the graph's adjacency matrix at frame level are used for identifying the objects that were considered most (or least) salient for event recognition and contributed the most (or least) to the final event recognition decision, thus providing an explanation for the latter. The experimental results show that the proposed method achieves state-of-the-art performance on the publicly available FCVID and YLI-MED datasets <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> .
Hey Google, Can You Help Me Learn?

In this provocation, we discuss the capacities and limitations of using commercially available smart speakers like Amazon Echo and Google Home to support informal learning. Bridging theories in self-regulated learning (SRL) and metacognition, we discuss whether smart speakers can (1) help learners to learn on their own and (2) help learners to learn better. We evaluate the current applications of smart speakers on learning and suggest future directions to harness smart speakers as learning partners in informal learning.