Reviewerly/exHarmony huggingface dataset:
QKD terminal for Canada’s Quantum Encryption and Science Satellite (QEYSSat)

Honeywell Aerospace is implementing on behalf of the Canadian Space Agency the Quantum Encryption and Science Satellite (QEYSSat), a Canadian-owned and operated scientific and technology demonstration mission aimed at developing the next-generation of secure communications infrastructure backed by quantum physics. The mission management is led by the Canadian Space Agency and the science is led by the Institute for Quantum Computing at the University of Waterloo. Quantum key distribution (QKD) is a method for issuing, via single-photon transmission, verifiably-confidential encryption keys between two parties. This capability is a powerful tool for the transfer of sensitive data (e.g. financial transactions, health records, etc.), however current terrestrial QKD networks are limited to a few hundred kilometres in geographic reach between nodes. The QEYSSat mission will use a satellite receiver as a trusted node to demonstrate the distribution of secure keys between ground stations separated by at least 400 km. In addition, Honeywell intends to fly an optical intersatellite link (OISL) terminal as a hosted payload on this mission. The QEYSSat mission will utilize both weak coherent pulse (WCP) sources and entangled photon sources in an uplink configuration to study the performance of QKD, and to perform Bell tests of long-range quantum entanglement. Honeywell is building the QKD receiver terminal consisting of a front-end telescope, a precision pointing and tracking system and single-photon detectors. Major technical challenges include polarization management throughout the optical chain, accurate pointing and tracking, and suppression of background and stray light sources. To address these challenges, Honeywell is leveraging its existing commercial optical communications solutions to meet the more stringent performance requirements for space-based QKD. The QKD terminal architecture consists of an afocal front-end telescope, a wide FOV high-precision pointing and tracking assembly, a polarization analyzer and single-photon photodetector system. A large-diameter on-axis telescope for geostationary optical communications forms the basis for the terminal's front-end optics, and Honeywell's commercial Optical Pointing and Tracking Relay Assembly for intersatellite Communications (OPTRAC) has been adapted as a quantum-ready pointing and tracking unit (QTRAC). For each element, substantial effort has been made to develop an optical system that preserves single-photon states with high fidelity despite the large number of optical surfaces in the chain. The optical assembly for the QKD terminal was developed and tested at the breadboard level in 2020; this paper will highlight the development and testing of these units as well as the overall architecture and concept of the QEYSSat mission.
Universal Adversarial Perturbation of SAR Images for Deep Learning Based Target Classification

Deep learning-based SAR target classification has gained great success recently. However, the machine learning models are vulnerable to adversarial attacks which may cause severe security issue. In this paper, a convolution neural network (CNN)-based SAR target classification model is trained and used to perform the universal perturbation attack. We find out that the SAR target classification model is also vulnerable to universal adversarial attacks. By adding an image-agnostic and very small perturbation, the classification accuracy deteriorates a lot.
Box-Level Tube Tracking and Refinement for Vehicles Anomaly Detection

Traffic Anomaly detection is an essential computer vision task and plays a critical role in video structure analysis and urban traffic analysis. In this paper, we propose a box-level tracking and refinement algorithm to identify anomaly detection in road scenes. We first link the detection results to construct candidate spatio-temporal tubes via greedy search. Then the box-level refinement scheme is introduced to employ auxiliary detection cues to promote the abnormal predictions, which consists of spatial fusion, still-thing filter, temporal fusion, and feedforward optimization. Still-thing filter and feedforward optimization employ complementary detection concepts to promote the abnormal predictions, which helps determine an accurate abnormal period. The experimental results show that our approach is superior in the Traffic Anomaly Detection Track test set of the NVIDIA AI CITY 2021 CHALLENGE, which ranked second in this competition, with a 93.18% F1-score and 3.1623 root mean square error It reveals that the proposed approach contributes to fine-grained anomaly detection in actual traffic accident scenarios and promoting the development of intelligent transportation.
Self-representation and matrix factorization based multi-view clustering

Although the promising clustering performance, existing self-representation based multi-view subspace clustering methods directly minimize the divergence between affinity matrices to learn the consensus affinity matrix. This does not make sense for multi-view clustering due to the facts that multi-view data are often a collection of distinct attributes of the objects, and each view includes some contents of the objects that other views do not. Thus, the learned affinity representation is sub-optimal and cannot well characterize the cluster structure. To handle this problem, drawing the inspiration from matrix factorization, which lends embedding representation to clustering interpretation, we propose a novel multi-view subspace clustering method. Our method learns affinity representation between data by joint self-representation and matrix factorization with weighted tensor Schatten p-norm constraint. Moreover, auto-weighted strategy is introduced to adaptively characterize the difference between singular values to improve the stableness of the algorithm. To further characterize class-specificity distribution, which well encodes cluster structure, we employ the ℓ1,2-norm regularization on affinity representation. Experimental results on several data sets indicate that our method outperforms state-of-the-art self-representation based multi-view subspace clustering methods.
RePReL: Integrating Relational Planning and Reinforcement Learning for Effective Abstraction

State abstraction is necessary for better task transfer in complex reinforcement learning environments. Inspired by the benefit of state abstraction in MAXQ and building upon hybrid planner-RL architectures, we propose RePReL, a hierarchical framework that leverages a relational planner to provide useful state abstractions. Our experiments demonstrate that the abstractions enable faster learning and efficient transfer across tasks. More importantly, our framework enables the application of standard RL approaches for learning in structured domains. The benefit of using the state abstractions is critical in relational settings, where the number and/or types of objects are not fixed apriori. Our experiments clearly show that RePReL framework not only achieves better performance and efficient learning on the task at hand but also demonstrates better generalization to unseen tasks.
A smartly simple way for joint crowd counting and localization

A growing number of state-of-the-art crowd counting methods employ the regression model. Such a model learns a person-density map first and its integral is further calculated to obtain the final count. However, this learned density map is uninterpretable and could deviate largely from the true person distribution even when the final count is accurate. In comparison, we present a conceptually interpretable and technically simple classification model for crowd counting, which consists of three novel modules: Deep Integrated Module (DIM), Scale Adaptive Module (SAM), and Interval Aware Module (IAM). Different from the traditional density map, the proposed pedestrian-aware density map (PADM) in our model can reveal the true people density, and meanwhile tackle the rarely-explored crowd localization task simultaneously. The proposed joint crowd counting and localization method does not require extra pretraining or fine-tuning for individual components of the network, and we train our model end-to-end in a single step. Without bells and whistles but a few lines of code, our simple yet effective method achieves better performances on both crowd counting and localization tasks when compared with state-of-the-art methods. The code is available online.
Emotion detection in Twitter posts: a rule-based algorithm for annotated data acquisition

Social media analysis plays a key role to the understanding of the public's opinion regarding recent events and decisions, to the design and management of advertising campaigns as well as to the planning of next steps and mitigation actions for public relationship initiatives. Significant effort has been dedicated recently to the development of data analysis algorithms that will perform, in an automated way, sentiment analysis over publicly available text. Most of the available research work, focuses on binary categorizing text as positive or negative without further investigating the emotions leading to that categorization. The current needs, however, for in-depth analysis of the available content combined with the complexity and multidimensional aspects of the human emotions and opinions have rendered such solutions obsolete. Due to these needs, currently, research is focusing on specifying the emotions and not only the sentiment expressed in a given text. This is, however, a very challenging effort due to not only the lack of annotated datasets that can be used for emotion detection in text but also the subjectivity infused in datasets that have been created based on manual annotations. A hybrid rule-based algorithm is presented in this paper, that supports the creation of a fully annotated dataset over the Plutchik's eight basic emotions. The presented algorithm takes into consideration the available emoji in the text and utilized them as objective indicators of the expressed emotion thus efficiently tackling both identified challenges. This is a full regular paper submitted to the CSCI-ISNA Symposium.
Deep Denoising of Flash and No-Flash Pairs for Photography in Low-Light Environments

We introduce a neural network-based method to denoise pairs of images taken in quick succession, with and without a flash, in low-light environments. Our goal is to produce a high-quality rendering of the scene that preserves the color and mood from the ambient illumination of the noisy no-flash image, while recovering surface texture and detail revealed by the flash. Our network outputs a gain map and a field of kernels, the latter obtained by linearly mixing elements of a per-image low-rank kernel basis. We first apply the kernel field to the no-flash image, and then multiply the result with the gain map to create the final output. We show our network effectively learns to produce high-quality images by combining a smoothed out estimate of the scene’s ambient appearance from the no-flash image, with high-frequency albedo details extracted from the flash input. Our experiments show significant improvements over alternative captures without a flash, and baseline denoisers that use flash no-flash pairs. In particular, our method produces images that are both noise-free and contain accurate ambient colors without the sharp shadows or strong specular highlights visible in the flash image.
Multi-Facet Recommender Networks with Spherical Optimization

Implicit feedback is widely explored by modern recommender systems. Since the feedback is often sparse and imbalanced, it poses great challenges to the learning of complex interactions among users and items. Metric learning has been proposed to capture user-item interactions from implicit feedback, but existing methods only represent users and items in a single metric space, ignoring the fact that users can have multiple preferences and items can have multiple properties, which leads to potential conflicts limiting their performance in recommendation. To capture the multiple facets of user preferences and item properties while resolving their potential conflicts, we propose the novel framework of Multi-fAcet Recommender networks with Spherical optimization (MARS). By designing a cross-facet similarity measurement, we project users and items into multiple metric spaces for fine-grained representation learning, and compare them only in the proper spaces. Furthermore, we devise a spherical optimization strategy to enhance the effectiveness and robustness of the multi-facet recommendation framework. Extensive experiments on six real-world benchmark datasets show drastic performance gains brought by MARS, which constantly achieves up to 40% improvements over the state-of-the-art baselines regarding both HR and nDCG metrics.
Fast and Exact Outlier Detection in Metric Spaces

Distance-based outlier detection is widely adopted in many fields, e.g., data mining and machine learning, because it is unsupervised, can be employed in a generic metric space, and does not have any assumptions of data distributions. Data mining and machine learning applications face a challenge of dealing with large datasets, which requires efficient distance-based outlier detection algorithms. Due to the popularization of computational environments with large memory, it is possible to build a main-memory index and detect outliers based on it, which is a promising solution for fast distance-based outlier detection.
Characterization and Prediction of Questions without Accepted Answers on Stack Overflow

A fast and effective approach to obtain information regarding software development problems is to search them to find similar solved problems or post questions on community question answering (CQA) websites. Solving coding problems in a short time is important, so these CQAs have a considerable impact on the software development process. However, if developers do not get their expected answers, the websites will not be useful, and software development time will increase. Stack Overflow is the most popular CQA concerning programming problems. According to its rules, the only sign that shows a question poser has achieved the desired answer is the user's acceptance. In this paper, we investigate unresolved questions, without accepted answers, on Stack Overflow. The number of unresolved questions is increasing. As of August 2019, 47% of Stack Overflow questions were unresolved. In this study, we analyze the effectiveness of various features, including some novel features, to resolve a question. We do not use the features that contain information not present at the time of asking a question, such as answers. To evaluate our features, we deploy several predictive models trained on the features of 18 million questions to predict whether a question will get an accepted answer or not. The results of this study show a significant relationship between our proposed features and getting accepted answers. Finally, we introduce an online tool that predicts whether a question will get an accepted answer or not. Currently, Stack Overflow's users do not receive any feedback on their questions before asking them, so they could carelessly ask unclear, unreadable, or inappropriately tagged questions. By using this tool, they can modify their questions and tags to check the different results of the tool and deliberately improve their questions to get accepted answers.
An effective and elastic blockchain-based provenance preserving solution for the open data

Purpose In the open data context, the shared data could come through many transformation processes, originating from many sources, which exposes the risk of non-authentic data. Moreover, each data set has different properties, shared under various licenses, which means the updated data could change its characteristics and related policies. This paper aims to introduce an effective and elastic solution to keep track of data changes and manage their characteristics within the open data platform. These changes have to be immutable to avoid violated modification and could be used as the certified provenance to improve the quality of data. Design/methodology/approach This paper will propose a pragmatic solution that focuses on the combination of comprehensive knowledge archive network – the broadest used open data platform and hyperledger fabric blockchain to ensure all the changes are immutable and transparent. As using smart contracts plus a standard provenance data format, all processes are running automatically and could be extended to integrate with other provenance systems and so the introduced solution is quite flexible to be used in different open data ecosystems and real-world application domains. Findings The research involves some related studies about the provenance system. This study finds out that most of the studies are focused on the commercial sector or applicable to a specific domain and not relevant for the open-data section. To show that the proposed solution is a logical and feasible direction, this paper conducts an experimental sample to validate the result. The testing model is running successfully with an elastic system architect and promising overall performance. Originality/value Open data is the future of many businesses but still does not receive enough attention from the research community. The paper contributes a novel approach to protect the provenance of open data.
Application of Data Mining in Predicting College Graduates Employment

With the popularization and enrollment expansion of higher education in China, the employment of college students has been the focus of public attentions. In this study, taking the graduates of Grade 2016 in Guilin University of Technology as example, data mining techniques are applied in predicting employment by using five influencing factors. Based on the CART algorithm, the Gini indexes were calculated and decision tree was constructed. Furthermore, the random forest algorithm is used to improve accuracy rate of employment prediction. After data collection, cleaning and conversion, 496 employment records were obtained, 70% of them were taken as the training samples. The constructed model was tested by the remaining samples and the accuracy reaches 81%. Finally, the features academic achievement and graduation qualification are identified as the significant factors of students' employability. The combination model of decision tree and random forest provides a new method for employment forecasting, which is feasible and adaptable to employment guidance of schools.
Detection of Hate Speech in Videos Using Machine Learning

With the progression of the Internet and social media, people are given multiple platforms to share their thoughts and opinions about various subject matters freely. However, this freedom of speech is misused to direct hate towards individuals or group of people due to their race, religion, gender etc. The rise of hate speech has led to conflicts and cases of cyber bullying, causing many organizations to look for optimal solutions to solve this problem. Developments in the field of machine learning and deep learning have piqued the interest of researchers, leading them to research and implement solutions to solve the problem of hate speech. Currently, machine learning techniques are applied to textual data to detect hate speech. With the ample use of video sharing sites, there is a need to find a way to detect hate speech in videos. This research deals with classification of videos into normal or hateful categories based on the spoken content of the videos. The video dataset is built using a crawler to search and download videos based on offensive words that are specified as keywords. The audio is extracted from the videos and is converted into textual format using a Speech-to-Text converter to obtain a transcript of the videos. Experiments are conducted by training four models with three different feature sets extracted from the dataset. The models are evaluated by computing the specified evaluation metrics. The evaluated metrics indicate that Random Forrest Classifier model delivers the best results in classifying videos.
Adversarial Attack on Graph Neural Networks as An Influence Maximization Problem

Graph neural networks (GNNs) have attracted increasing interests. With broad deployments of GNNs in real-world applications, there is an urgent need for understanding the robustness of GNNs under adversarial attacks, especially in realistic setups. In this work, we study the problem of attacking GNNs in a restricted and realistic setup, by perturbing the features of a small set of nodes, with no access to model parameters and model predictions. Our formal analysis draws a connection between this type of attacks and an influence maximization problem on the graph. This connection not only enhances our understanding on the problem of adversarial attack on GNNs, but also allows us to propose a group of effective and practical attack strategies. Our experiments verify that the proposed attack strategies significantly degrade the performance of three popular GNN models and outperform baseline adversarial attack strategies.
Influence of Narrative Elements on User Behaviour in Photorealistic Social VR

Social Virtual Reality (VR) applications represent a big step forward in the field of remote communication. Social VR provides the possibility for participants to explore and interact with virtual environments and objects, feelings of a full sense of immersion, and being together. Understanding how user behaviour is influenced by the shared virtual space and its elements becomes the key to design and optimize novel immersive experiences. This paper presents a behavioural analysis of user navigating in 6 degrees of freedom social VR movie. Specifically, we analyse 48 user trajectories from a photorealistic telepresence experiment, in which subjects watch a crime movie together in VR. We investigate how users are affected by salient agents (i.e., virtual characters) and by narrative elements of the VR movie (i.e., dialogues versus interactive part). We complete our assessment by conducting a statistical analysis of the collected data. Results indicate that user behaviour is affected by different narrative and interactive elements. We conclude by presenting our observations and drawing conclusions on future paths for social VR experiences.
Enabling Machine Learning on the Edge Using SRAM Conserving Efficient Neural Networks Execution Approach

Edge analytics refers to the application of data analytics and Machine Learning (ML) algorithms on IoT devices. The concept of edge analytics is gaining popularity due to its ability to perform AI-based analytics at the device level, enabling autonomous decision-making, without depending on the cloud. However, the majority of Internet of Things (IoT) devices are embedded systems with a low-cost microcontroller unit (MCU) or a small CPU as its brain, which often are incapable of handling complex ML algorithms.