Reviewerly/exHarmony huggingface dataset:
Statistics of the Optical Phase of a Gain-Switched Semiconductor Laser for Fast Quantum Randomness Generation

The statistics of the optical phase of the light emitted by a semiconductor laser diode when subject to periodic modulation of the applied bias current are theoretically analyzed. Numerical simulations of the stochastic rate equations describing the previous system are performed to describe the temporal dependence of the phase statistics. These simulations are performed by considering two cases corresponding to random and deterministic initial conditions. In contrast to the Gaussian character of the phase that has been assumed in previous works, we show that the phase is not distributed as a Gaussian during the initial stages of evolution. We characterize the time it takes the phase to become Gaussian by calculating the dynamical evolution of the kurtosis coefficient of the phase. We show that, under the typical gain-switching with square-wave modulation used for quantum random number generation, quantity is in the ns time scale; that corresponds to the time it takes the system to lose the memory of the distribution of the initial conditions. We compare the standard deviation of the phase obtained with random and deterministic initial conditions to show that their differences become more important as the modulation speed is increased.
An Efficient Labeled/Unlabeled Random Finite Set Algorithm for Multiobject Tracking

In this article, we propose an efficient random finite set (RFS)-based algorithm for multiobject tracking, in which the object states are modeled by a combination of a labeled multi-Bernoulli (LMB) RFS and a Poisson RFS. The less computationally demanding Poisson part of the algorithm is used to track potential objects whose existence is unlikely. Only if a quantity characterizing the plausibility of object existence is above a threshold, a new labeled Bernoulli component is created, and the object is tracked by the more accurate but more computationally demanding LMB part of the algorithm. Conversely, a labeled Bernoulli component is transferred back to the Poisson RFS if the corresponding existence probability falls below another threshold. Contrary to existing hybrid algorithms based on multi-Bernoulli and Poisson RFSs, the proposed method facilitates track continuity and implements complexity-reducing features. Simulation results demonstrate a large complexity reduction relative to other RFS-based algorithms with comparable performance.
Lane Intrusion Behaviors Dataset: Action Recognition in Real-world Highway Scenarios for Self-driving

It is necessary for the development of self-driving to fulfill the requirements for safety, stability, and intelligence, especially in high-speed conditions. Therefore, the detection of pedestrians that may occur in highway scenarios during driving and understanding the meaning of their behaviors in advance are significantly important for the self-driving vehicle to make correct decisions. However, no existing datasets are available for behavior recognition in self-driving scenarios. In order to advance the task of interactive cognition between the vehicle and pedestrians, in this paper, we present a new dataset, called THU-IntrudBehavior, that collects lane intrusion behaviors of pedestrians that can be applied in real world highway scenarios. The dataset contains diverse behaviors of single or multiple pedestrians/cyclists that are simulated in different urban roads under various weather conditions. We describe annotations of each video and report several experimental results of baseline methods on our self-collected dataset. Our THU-IntrudBehavior dataset provides new support for behavior recognition in high-speed conditions for self-driving.
Is YouTube Still a Radicalizer? An Exploratory Study on Autoplay and Recommendation

This work investigates the functioning of YouTube’s recommendation system with focus on the autoplay function. The autoplay function was often referred to as “radicalizer” in the past, as it was considered to lead towards more extremist content. By an automated data collection through browser remote control, we simulate different usage scenarios (allowing and disallowing autoplay) with personalized accounts as well as with anonymous users. This leads to multiple recommendation paths, which are analyzed. The presented analyses suggest that while YouTube continues to rely on familiar mechanisms for capturing users’ attention, ongoing public criticism with respect to the recommendation system has seemingly led to changes in YouTube’s algorithm parameterization and to more cautious recommendations.
DNSWeight: Quantifying Country-Wise Importance of Domain Name System

DNS (Domain Name System) is one fundamental Internet infrastructure related to most network activities. As a feasible tool to govern the Internet, DNS’s stability and interoperability will be impacted by the countries’ policies or actions along the path. Especially now that many countries have stricter control over the Internet and even sometimes "unplug" it. But there was no study to quantify the countries’ impact systematically. To fill this research gap, we present DNSWeight. This new data-driven approach utilizes a large-scale DNS dataset and BGP (Border Gateway Protocol) routing information to calculate the country-importance score so that a country’s impact on DNS can be gauged. By applying DNSWeight on large-scale DNS and BGP datasets jointly, our study shows the importance among different countries is divided. A handful of countries show dominant significance to the current DNS ecosystem. Some countries with a history of Internet shutdowns are too influential to be ignored if they choose to break themselves from the Internet. We also examine the impact of IPv6 (IP Version 6) and reveal the "loop" phenomenon that occurs in some DNS queries. In conjunction with our findings, some discussion and suggestions are given. In summary, our study shows that DNS reliability needs to be reconsidered at the country’s level.
Unsupervised Semantic Segmentation Method of User Interface Component of Games

The game user interface (UI) provides a large volume of information necessary to analyze the game screen. The availability of such information can be functional in vision-based machine learning algorithms. With this, there will be an enhancement in the application power of vision deep learning neural networks. Therefore, this paper proposes a game UI segmentation technique based on unsupervised learning. We developed synthetic labeling created on the game engine, image-to-image translation and segmented UI components in the game. The network learned in this manner can segment the target UI area in the target game regardless of the location of the corresponding component. The proposed method can help interpret game screens without applying data augmentation. Also, as this scheme is an unsupervised technique, it has the advantage of not requiring paring data. Our methodology can help researchers who need to extract semantic information from game image data. It can also be used for UI prototyping in the game industry.
Block chain-based access control protocol in Internet of Drones

The Internet of Drones (IoD) is an emerging domain due to its capability of being deployed in several domains. The drones in IoD are capable of exploring the platform and transfer commodities with embedded sensors. However, the data accumulated by drone sensors face privacy and security issues due to advanced technologies. This paper devises a blockchain-based access control scheme for initiating secure communication among the drones in IoD. The system has four different entities that involve the Drone, server, user, and control room. The proposed Blockchain-based access control scheme involves two phases: the authentication and access control phases. The authentication phase is performed considering sender drone credentials, and the access control phase is done using four methods. In the access control phase, four kinds of access control mechanisms are considered: Drone to drone access control, Drone to server access control, Emergency access control if drone failure and Control set up with blockchain technology. Both the communicating entities can establish the secret key for secure communication at the end of access control. The proposed Blockchain-based access control scheme provided superior performance concerning classical methods with a minimal end-to-end delay of 0.870sec, a minimal packet loss rate of 2%, and maximal throughput of 70.493bps.
More Interpretable Decision Trees

We present a new Decision Tree Classifier (DTC) induction algorithm that produces vastly more interpretable trees in many situations. These understandable trees are highly relevant for explainable artificial intelligence, fair automatic classification, and human-in-the-loop learning systems. Our method is an improvement over the Nested Cavities (NC) algorithm. That is, we profit from the parallel-coordinates visualisation of high dimensional datasets. However, we build a hybrid with other decision tree heuristics to generate node-expanding splits. The rules in the DTCs learnt using our algorithm have a straightforward representation and, thus, are readily understood by a human user, even though our algorithm constructs rules whose nodes can involve multiple attributes. We compare our algorithm to the well-known decision tree induction algorithm C4.5, and find that our methods produce similar accuracy with significantly smaller trees. When coupled with a human-in-the-loop-learning (HILL) system, our approach can be highly effective for inferring understandable patterns in datasets.
Not All Contexts are Important: The Impact of Effective Context in Conversational Neural Machine Translation

Multilingual chat systems are the need of the hour for organizations who render online conversational services to their customers. This can effectively be facilitated using the robust machine translation (MT) systems. Translating user-generated contents is regarded as one of the challenging tasks for MT. As for translating conversations, it is more challenging since the meaning of any particular utterance in a conversation usually depends on its context. Moreover, chats in conversational systems are usually informal, contain code-mixed (mix of more than one language), and other grammatical inconsistencies. In this paper, we use state-of-the-art Transformer models to build our MT systems and to translate conversations between the customer service agents and customers. We propose a novel method which effectively selects contextual information from the source text of conversation to be translated. We also employ a terminology-based pseudo in-domain corpus mining strategy for fine-tuning our translation model. We evaluate our methods on the German-English WMT20 Shared Task on Chat Translation dataset, and obtain 61.1 and 63.9 BLEU points on evaluation test sets for English-to-German and German-to-English, respectively, surpassing the present state-of-the-art MT systems by 0.7 BLEU and 1.5 BLEU points, respectively. In this paper, we use state-of-the-art Transformer models to build our MT systems and to translate conversations between the customer service agents and customers. We propose a novel method which effectively selects contextual information from the source text of conversation to be translated. We also employ a terminology-based pseudo in-domain corpus mining strategy for fine-tuning our translation model. We evaluate our methods on the German-English WMT20 Shared Task on Chat Translation dataset, and obtain 61.1 and 63.9 BLEU points on evaluation test sets for English-to-German and German-to-English, respectively, surpassing the present state-of-the-art MT systems by 0.7 BLEU and 1.5 BLEU points, respectively.
Node Embedding using Mutual Information and Self-Supervision based Bi-level Aggregation

Graph Neural Networks (GNNs) learn low dimensional representations of nodes by aggregating information from their neighborhood in graphs. However, traditional GNNs suffer from two fundamental shortcomings due to their local (l-hop neighborhood) aggregation scheme. First, not all nodes in the neighborhood carry relevant information for the target node. Since GNNs do not exclude noisy nodes in their neighborhood, irrelevant information gets aggregated, which reduces the quality of the representation. Second, traditional GNNs also fail to capture long-range non-local dependencies between nodes. To address these limitations, we exploit mutual information (MI) to define two types of neighborhood, 1) Local Neighborhood where nodes are densely connected within a community and each node would share higher MI with its neighbors, and 2) Non-Local Neighborhood where MI-based node clustering is introduced to assemble informative but graphically distant nodes in the same cluster. To generate node presentations, we combine the embeddings generated by bi-level aggregation - local aggregation to aggregate features from local neighborhoods to avoid noisy information and non-local aggregation to aggregate features from non-local neighborhoods. Furthermore, we leverage self-supervision learning to estimate MI with few labeled data. Finally, we show that our model significantly outperforms the state-of-the-art methods in a wide range of assortative and disassortative graphs <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Source Code at: https://github.com/forkkr/LnL-GNN.
Security Oriented Design Framework for EM Side-Channel Protection in RTL Implementations

Electromagnetic (EM) side-channel analysis is a powerful attack for extracting secret information from cryptographic hardware implementations. Countermeasures have been proposed at the register-transfer level (RTL), layout level, and device level. However, existing EM radiation modeling and side-channel vulnerability mitigation methods do not consider the structural resilience of original designs, nor do they provide fine-grained security enhancements to those vulnerable submodules/components. These universal solutions may introduce unnecessary overheads on the circuit under protection and may not be optimized for individual designs. In this article, we propose a design/synthesis for side-channel security evaluation and optimization framework based on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${t}$ </tex-math></inline-formula> -test evaluation results derived from RTL hardware implementations. While the framework apply to different side-channel leakage, we focus more on EM side channels. Supported by this framework, different RTL implementations of the same cryptographic algorithm will be evaluated for their side-channel resistance. In vulnerable implementations, submodules with the most significant side-channel leakages will be identified. Security design/synthesis rules will then be applied to these vulnerable submodules for security enhancements against side-channel attacks (SCAs). Experiments, including simulations and FPGA implementations on different AES designs, are performed to validate the effectiveness of the proposed framework as well as the security design/synthesis rules.
Wendy Grows Up: More Order Fairness

Transaction order related issues such as MEV (Miner Extractable Value) and frontrunning in blockchain networks stemming from the ability of validators to reorder transactions. This can not only extract a tax on honest users, but also skew the validator economy by making it harder to control financial inactivation of validators, and might even lead to regulatory issues. We present a pre-protocol that allows a blockchain application to combine different forms of order fairness with causal order to prevent such issues on the level of the blockchain, and that can be combined with various blockchain implementations.
A Transformer based Multi-task Model for Domain Classification, Intent Detection and Slot-Filling

With the ever increasing complexity of the user queries in a multi-domain based task-oriented dialogue system, it is imperative to facilitate robust Spoken Language Understanding (SLU) modules that perform multiple tasks in an unified way. In this paper, we present a novel multi-task approach for the joint modelling of three tasks together, namely, Domain Classification, Intent Detection and Slot-Filling. We hypothesize with the intuition that the cross dependencies of all these three tasks mutually help each other towards their representations and classifications which further simplify the SLU module in a multi-domain scenario. Towards this end, we propose a BERT language model based multi-task framework utilizing capsule networks and conditional random fields for addressing the classification and sequence labeling problems, respectively, for different tasks. Experimental results indicate that the proposed multi-task model outperformed several strong baselines and its single task counterparts on three benchmark datasets of different domains and attained state-of-the-art results on different tasks.
Research on Copyright Protection of Sports Events Based on Blockchain Technology

At the 18th collective study session of the Political Bureau of the Communist Party of China Central Committee on 24 October 2019, General Secretary Xi Jinping identified blockchain as "a crucial breakthrough point for the indigenous innovation of core technologies". The deals of Sports event copyright are part of digital asset deals, Sports event copyright also is the focus of copyright protection and the sports industry development, which is a product of sports industry with internet attribute. Actually, protecting the copyright of sports events is equal to protecting the development of sports industry. Due to the deficiency of current copyright protection system, it is difficult to adapt the development of sports copyright. The blockchain is emerging technology which provides a way to solve the problem of copyright protection of sports events. This paper analyzed the situation of digital rights management of sports events, pointed out the shortcomings of digital rights management technology in dealing with Internet piracy and the bewilderment on the copyright protection of new media of sports events, and put forward the blockchain technology to build a good ecological sports copyright dealing mechanism.
Perspectives and limitations of visible-thermal image pair synthesis via generative adversarial networks

Many applications rely on thermal imagers to complement or replace visible light sensors in difficult imaging conditions. Recent advances in machine learning have opened the possibility of analyzing or enhancing images, yet these methods require large annotated databases. Training approaches that leverage data augmentation via simulated and synthetically-generated images could offer promising prospects. Here, we report on a method that uses generative adversarial nets (GANs) to synthesize images of a complementary contrast. Starting from a dual-modality dataset of co-registered visible and thermal images, we trained a GAN to generate synthetic thermal images from visible images and vice versa. Our results show that the procedure yields sharp synthesized images that might be used to augment dual-modality datasets or assist in visual interpretation, yet are also subject to the limitations imposed by contrast independence between thermal and visible images.
Audio DistilBERT: A Distilled Audio BERT for Speech Representation Learning

Self-supervised speech representation learning has been considered as an outstanding manner to improve the performance of downstream tasks. However, those models are often too cumbersome, which sets a barrier to deploy them on the edge and improves the threshold of the pre-training process. In this paper, we propose Audio DistilBERT, a distilled BERT-style speech representation learning method. It learns dark knowledge from a larger teacher model through one new designed loss which combines soft and hard targets. By doing this, it can achieve competitive performance with fewer parameters and faster inference time. The experimental results among two downstream tasks show that the proposed method can retain above 98% performance of the large model with about 1.8× smaller model size and over 1.6× faster inference speed. In a low-resource environment with very few labeled data and pretraining steps, our model also exhibits similar or even better performance compared to the large model. Furthermore, we explore the knowledge transfer competence between the teacher and student model.
HelaNER: A Novel Approach for Nested Named Entity Boundary Detection

Named entity recognition (NER) is a prominent task in identifying text spans to specific types. Named entity boundary detection can be mentioned as a rising research area under NER. Although a limited work has been conducted for nested NE boundary detection, flat NE boundary detection can be considered as at a pinnacle stage. Nested NE boundary detection is an important aspect in information extraction, information retrieval, event extraction, sentiment analysis etc. On the other hand, spreading religious unhealthy statements through social media has become a burden for the wellbeing of the society. The prime objective of this research is to implement a novel system for nested NE boundary detection for Sinhala language considering religious unhealthy statements in social media. A constructive literature survey has been conducted for analyzing the already developed NE type and boundary detection approaches and systems. Along with that, identifying the linguistic structures and patterns of Sinhala hate speech detection has been conducted. A corpus of more than 100,000 Sinhala hates speech contents have been extracted, preprocessed, and annotated by an expert panel. Then, a deep neural approach has been applied for capturing the complexity indexes, matrices, and other related elements of the corpus. Next, a novel approach called "boundary bubbles" has been conducted for capturing word representation, head word detection, entity mention nuggets identification and region classification for NE boundary detection. Experiments reveal that our scientific novel approach has achieved the state-of-art performance over the existing baselines.
Design and Image Research of Tennis Line Examination Based on Machine Vision Analysis

In view of the intelligent demand of tennis line examination, this paper performs a systematic analysis on the intelligent recognition of tennis line examination. Then, a tennis line recognition method based on machine vision is proposed. In this paper, the color region of the image recognition region is divided based on the region growth, and the rough estimation of the court boundary is realized. In order to achieve the effect of camera calibration, a fast camera calibration method which can be used for a variety of court types is proposed. On the basis of camera calibration, a tennis line examination and segmentation system based on machine vision analysis is constructed, and the experimental results are verified by design experiments. The results show that the machine vision analysis-based intelligent segmentation system of tennis line examination has high recognition accuracy and can meet the actual needs of tennis line examination.