Reviewerly/exHarmony huggingface dataset:
Cyber security in New Space

Abstract Developments in technologies, attitudes and investment are transforming the space environment, achieving greater accessibility for an increasing number of parties. New and proposed constellations will increase the in-orbit satellite population by the order of thousands, expanding the threat landscape of the space industry. This article analyses past satellite security threats and incidents to assess the motivations and characteristics of adversarial threats to satellites. The ground and radio frequency communications were the most favoured targets; however, the boom of satellites constellations in the upcoming years may shift this focus towards the space segment which must be addressed. Key technology advancements and open issues in the satellite industry related to security and operational requirements are also discussed.
Multi-Aspect Aware Session-Based Recommendation for Intelligent Transportation Services

In the intelligent transportation system, the session data usually represents the users' demand. However, the traditional approaches only focus on the sequence information or the last item clicked by the user, which cannot fully represent user preferences. To address this issue, this paper proposes an Multi-aspect Aware Session-based Recommendation (MASR) model for intelligent transportation services, which comprehensively considers the user's personalized behavior from multiple aspects. In addition, it developed a concise and efficient transformer-style self-attention to analyze the sequence information of the current session, for accurately grasping the user's intention. Finally, the experimental results show that MASR is available to improve user satisfaction with more accurate and rapid recommendations, and reduce the number of user operations to decrease the safety risk during the transportation service.
Optimal Sensor Placement for Target Localization Using Hybrid RSS, AOA and TOA Measurements

This letter investigates the optimal sensor placement strategy for single static target localization using the hybrid received-signal-strength (RSS), angle-of-arrival (AOA) and time-of-arrival (TOA) measurements on the 2-dimensional (2D) plane. Firstly, the problem formulation is introduced. Secondly, the theoretical smallest trace of the Cramér-Rao lower bound (CRLB), which is equivalent to the smallest estimation mean-squared-error (MSE), is derived using the A-optimality criterion. Besides, we propose that the reachable smallest trace of the CRLB (tr(CRLB)) will be determined, once the number and characters of the sensors are selected. Thirdly, the corresponding optimal sensor placement strategies are proposed and concluded in two observations. Finally, the findings are demonstrated by simulation examples.
FitByte: Automatic Diet Monitoring in Unconstrained Situations Using Multimodal Sensing on Eyeglasses

In an attempt to help users reach their health goals and practitioners understand the relationship between diet and disease, researchers have proposed many wearable systems to automatically monitor food consumption. When a person consumes food, he/she brings the food close to their mouth, take a sip or bite and chew, and then swallow. Most diet monitoring approaches focus on one of these aspects of food intake, but this narrow reliance requires high precision and often fails in noisy and unconstrained situations common in a person's daily life. In this paper, we introduce FitByte, a multi-modal sensing approach on a pair of eyeglasses that tracks all phases of food intake. FitByte contains a set of inertial and optical sensors that allow it to reliably detect food intake events in noisy environments. It also has an on-board camera that opportunistically captures visuals of the food as the user consumes it. We evaluated the system in two studies with decreasing environmental constraints with 23 participants. On average, FitByte achieved 89% F1-score in detecting eating and drinking episodes.
Sequence-Aware Factorization Machines for Temporal Predictive Analytics

In various web applications like targeted advertising and recommender systems, the available categorical features (e.g., product type) are often of great importance but sparse. As a widely adopted solution, models based on Factorization Machines (FMs) are capable of modelling high-order interactions among features for effective sparse predictive analytics. As the volume of web-scale data grows exponentially over time, sparse predictive analytics inevitably involves dynamic and sequential features. However, existing FM-based models assume no temporal orders in the data, and are unable to capture the sequential dependencies or patterns within the dynamic features, impeding the performance and adaptivity of these methods. Hence, in this paper, we propose a novel Sequence-Aware Factorization Machine (SeqFM) for temporal predictive analytics, which models feature interactions by fully investigating the effect of sequential dependencies. As static features (e.g., user gender) and dynamic features (e.g., user interacted items) express different semantics, we innovatively devise a multi-view self-attention scheme that separately models the effect of static features, dynamic features and the mutual interactions between static and dynamic features in three different views. In SeqFM, we further map the learned representations of feature interactions to the desired output with a shared residual network. To showcase the versatility and generalizability of SeqFM, we test SeqFM in three popular application scenarios for FM-based models, namely ranking, classification and regression tasks. Extensive experimental results on six large-scale datasets demonstrate the superior effectiveness and efficiency of SeqFM.
Deep Learning-based Extraction of Algorithmic Metadata in Full-Text Scholarly Documents

The advancements of search engines for traditional text documents have enabled the effective retrieval of massive textual information in a resource-efficient manner. However, such conventional search methodologies often suffer from poor retrieval accuracy especially when documents exhibit unique properties that behoove specialized and deeper semantic extraction. Recently, AlgorithmSeer, a search engine for algorithms has been proposed, that extracts pseudo-codes and shallow textual metadata from scientific publications and treats them as traditional documents so that the conventional search engine methodology could be applied. However, such a system fails to facilitate user search queries that seek to identify algorithm-specific information, such as the datasets on which algorithms operate, the performance of algorithms, and runtime complexity, etc. In this paper, a set of enhancements to the previously proposed algorithm search engine are presented. Specifically, we propose a set of methods to automatically identify and extract algorithmic pseudo-codes and the sentences that convey related algorithmic metadata using a set of machine-learning techniques. In an experiment with over 93,000 text lines, we introduce 60 novel features, comprising content-based, font style based and structure-based feature groups, to extract algorithmic pseudo-codes. Our proposed pseudo-code extraction method achieves 93.32% F1-score, outperforming the state-of-the-art techniques by 28%. Additionally, we propose a method to extract algorithmic-related sentences using deep neural networks and achieve an accuracy of 78.5%, outperforming a Rule-based model and a support vector machine model by 28% and 16%, respectively.
Human posture recognition based on multiple features and rule learning

Abstract The use of skeleton data for human posture recognition is a key research topic in the human-computer interaction field. To improve the accuracy of human posture recognition, a new algorithm based on multiple features and rule learning is proposed in this paper. Firstly, a 219-dimensional vector that includes angle features and distance features is defined. Specifically, the angle and distance features are defined in terms of the local relationship between joints and the global spatial location of joints. Then, during human posture classification, the rule learning method is used together with the Bagging and random subspace methods to create different samples and features for improved classification performance of sub-classifiers for different samples. Finally, the performance of our proposed algorithm is evaluated on four human posture datasets. The experimental results show that our algorithm can recognize many kinds of human postures effectively, and the results obtained by the rule-based learning method are of higher interpretability than those by traditional machine learning methods and CNNs.
Fast Authentication and Progressive Authorization in Large-Scale IoT: How to Leverage AI for Security Enhancement

Security provisioning has become the most important design consideration for large-scale Internet of Things (IoT) systems due to their critical roles in supporting diverse vertical applications by connecting heterogenous devices, machines, and industry processes. Conventional authentication and authorization schemes are insufficient to overcome the emerging IoT security challenges due to their reliance on both static digital mechanisms and computational complexity for improving security levels. Furthermore, the isolated security designs for different layers and link segments while ignoring the overall protection leads to cascaded security risks as well as growing communication latency and overhead. In this article, we envision new artificial intelligence (AI)-enabled security provisioning approaches to overcome these issues while achieving fast authentication and progressive authorization. To be more specific, a lightweight intelligent authentication approach is developed by exploring machine learning at the base station to identify the prearranged access time sequences or frequency bands or codes used in IoT devices. Then we propose a holistic authentication and authorization approach, where online machine learning and trust management are adopted for achieving adaptive access control. These new AI-enabled approaches establish the connections between transceivers quickly and enhance security progressively so that communication latency can be reduced and security risks are well controlled in large-scale IoT systems. Finally, we outline several areas for AI-enabled security provisioning for future research.
Introduction to the Third Annual Lifelog Search Challenge (LSC'20)

The Lifelog Search Challenge (LSC) is an annual comparative benchmarking activity for comparing approaches to interactive retrieval from multi-modal lifelogs. LSC'20, the third such challenge, attracts fourteen participants with their interactive lifelog retrieval systems. These systems are comparatively evaluated in front of a live-audience at the LSC workshop at ACM ICMR'20 in Dublin, Ireland. This overview motivates the challenge, presents the dataset and system configuration used in the challenge, and briefly presents the participating teams.
Implementation of Machine Learning Technique for Identification of Yoga Poses

In recent years, yoga has become part of life for many people across the world. Due to this there is the need of scientific analysis of y postures. It has been observed that pose detection techniques can be used to identify the postures and also to assist the people to perform yoga more accurately. Recognition of posture is a challenging task due to the lack availability of dataset and also to detect posture on real-time bases. To overcome this problem a large dataset has been created which contain at least 5500 images of ten different yoga pose and used a tf-pose estimation Algorithm which draws a skeleton of a human body on the real-time bases. Angles of the joints in the human body are extracted using the tf-pose skeleton and used them as a feature to implement various machine learning models. 80% of the dataset has been used for training purpose and 20% of the dataset has been used for testing. This dataset is tested on different Machine learning classification models and achieves an accuracy of 99.04% by using a Random Forest Classifier.
Diverse Image Generation via Self-Conditioned GANs

We introduce a simple but effective unsupervised method for generating diverse images. We train a class-conditional GAN model without using manually annotated class labels. Instead, our model is conditional on labels automatically derived from clustering in the discriminator’s feature space. Our clustering step automatically discovers diverse modes, and explicitly requires the generator to cover them. Experiments on standard mode collapse benchmarks show that our method outperforms several competing methods when addressing mode collapse. Our method also performs well on large-scale datasets such as ImageNet and Places365, improving both diversity and standard metrics (e.g., Fréchet Inception Distance), compared to previous methods.
Unsupervised Reinforcement Learning of Transferable Meta-Skills for Embodied Navigation

Visual navigation is a task of training an embodied agent by intelligently navigating to a target object (e.g., television) using only visual observations. A key challenge for current deep reinforcement learning models lies in the requirements for a large amount of training data. It is exceedingly expensive to construct sufficient 3D synthetic environments annotated with the target object information. In this paper, we focus on visual navigation in the low-resource setting, where we have only a few training environments annotated with object information. We propose a novel unsupervised reinforcement learning approach to learn transferable meta-skills (e.g., bypass obstacles, go straight) from unannotated environments without any supervisory signals. The agent can then fast adapt to visual navigation through learning a high-level master policy to combine these meta-skills, when the visual-navigation-specified reward is provided. Experimental results show that our method significantly outperforms the baseline by 53.34% relatively on SPL, and further qualitative analysis demonstrates that our method learns transferable motor primitives for visual navigation.
Knowledge Transfer Dehazing Network for NonHomogeneous Dehazing

Single image dehazing is an ill-posed problem that has recently drawn important attention. It is a challenging image process task, especially in nonhomogeneous scene. However, the existing dehazing methods are commonly designed to handle homogeneous haze which is easily violated in practice, due to the unknown haze distribution of real world. In this paper, we propose a knowledge transfer method that utilizes abundant clear images to train a teacher network to provide strong and robust image prior. The derived architecture is referred to as the Knowledge Transform Dehaze Network (KTDN), which consists of the teacher network and the dehazing network with identical architecture. Through the supervision between intermediate features, the dehazing network is encouraged to imitate the teacher network. In addition, we use attention mechanism to combine channel attention with pixel attention to capture effective information, and employ an enhancing module to refine detail textures. Extensive experimental results on synthetic and real scene datasets demonstrates that the proposed method outperforms the state-of-the-arts in both quantitative and qualitative evaluations. The KTDN ranks 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> in NTIRE-2020 NonHomogeneous Dehazing Challenge [4], [5].
OrigamiNet: Weakly-Supervised, Segmentation-Free, One-Step, Full Page Text Recognition by learning to unfold

Text recognition is a major computer vision task with a big set of associated challenges. One of those traditional challenges is the coupled nature of text recognition and segmentation. This problem has been progressively solved over the past decades, going from segmentation based recognition to segmentation free approaches, which proved more accurate and much cheaper to annotate data for. We take a step from segmentation-free single line recognition towards segmentation-free multi-line / full page recognition. We propose a novel and simple neural network module, termed OrigamiNet, that can augment any CTC-trained, fully convolutional single line text recognizer, to convert it into a multi-line version by providing the model with enough spatial capacity to be able to properly collapse a 2D input signal into 1D without losing information. Such modified networks can be trained using exactly their same simple original procedure, and using only unsegmented image and text pairs. We carry out a set of interpretability experiments that show that our trained models learn an accurate implicit line segmentation. We achieve state-of-the-art character error rate on both IAM & ICDAR 2017 HTR benchmarks for handwriting recognition, surpassing all other methods in the literature. On IAM we even surpass single line methods that use accurate localization information during training. Our code is available online at https://github.com/IntuitionMachines/OrigamiNet .
Guidelines for the search strategy to update systematic literature reviews in software engineering

Systematic Literature Reviews (SLRs) have been adopted within Software Engineering (SE) for more than a decade to provide meaningful summaries of evidence on several topics. Many of these SLRs are now potentially not fully up-to-date, and there are no standard proposals on how to update SLRs in SE. The objective of this paper is to propose guidelines on how to best search for evidence when updating SLRs in SE, and to evaluate these guidelines using an SLR that was not employed during the formulation of the guidelines. To propose our guidelines, we compare and discuss outcomes from applying different search strategies to identify primary studies in a published SLR, an SLR update, and two replications in the area of effort estimation. These guidelines are then evaluated using an SLR in the area of software ecosystems, its update and a replication. The use of a single iteration forward snowballing with Google Scholar, and employing as a seed set the original SLR and its primary studies is the most cost-effective way to search for new evidence when updating SLRs. Furthermore, the importance of having more than one researcher involved in the selection of papers when applying the inclusion and exclusion criteria is highlighted through the results. Our proposed guidelines formulated based upon an effort estimation SLR, its update and two replications, were supported when using an SLR in the area of software ecosystems, its update and a replication. Therefore, we put forward that our guidelines ought to be adopted for updating SLRs in SE.
Global-Local GCN: Large-Scale Label Noise Cleansing for Face Recognition

In the field of face recognition, large-scale web-collected datasets are essential for learning discriminative representations, but they suffer from noisy identity labels, such as outliers and label flips. It is beneficial to automatically cleanse their label noise for improving recognition accuracy. Unfortunately, existing cleansing methods cannot accurately identify noise in the wild. To solve this problem, we propose an effective automatic label noise cleansing framework for face recognition datasets, FaceGraph. Using two cascaded graph convolutional networks, FaceGraph performs global-to-local discrimination to select useful data in a noisy environment. Extensive experiments show that cleansing widely used datasets, such as CASIA-WebFace, VGGFace2, MegaFace2, and MS-Celeb-1M, using the proposed method can improve the recognition performance of state-of-the-art representation learning methods like Arcface. Further, we cleanse massive self-collected celebrity data, namely MillionCelebs, to provide 18.8M images of 636K identities. Training with the new data, Arcface surpasses state-of-the-art performance by a notable margin to reach 95.62% TPR at 1e-5 FPR on the IJB-C benchmark.
On Vocabulary Reliance in Scene Text Recognition

The pursuit of high performance on public benchmarks has been the driving force for research in scene text recognition, and notable progresses have been achieved. However, a close investigation reveals a startling fact that the state-of-the-art methods perform well on images with words within vocabulary but generalize poorly to images with words outside vocabulary. We call this phenomenon ``vocabulary reliance''. In this paper, we establish an analytical framework, in which different datasets, metrics and module combinations for quantitative comparisons are devised, to conduct an in-depth study on the problem of vocabulary reliance in scene text recognition. Key findings include: (1) Vocabulary reliance is ubiquitous, i.e., all existing algorithms more or less exhibit such characteristic; (2) Attention-based decoders prove weak in generalizing to words outside vocabulary and segmentation-based decoders perform well in utilizing visual features; (3) Context modeling is highly coupled with the prediction layers. These findings provide new insights and can benefit future research in scene text recognition. Furthermore, we propose a simple yet effective mutual learning strategy to allow models of two families (attention-based and segmentation-based) to learn collaboratively. This remedy alleviates the problem of vocabulary reliance and significantly improves the overall scene text recognition performance.
Machine learning techniques

Abstract Machine learning uses the theory of statistics to build mathematical models, as the main objective is to yield inferences from a sample. Once a model is built, its representation and algorithmic solution for interpretation needs to be competent as well. In some applications, the competence of the machine learning algorithm might be as important as its classification accuracy. Machine learning is used in several fields, including forecasting, anomaly detection, and biomedical data analysis as a decision support element. The purpose of this chapter is to help scientists select an appropriate machine learning technique and guide them using optimal strategies by employing real-time databases, in addition to familiarizing readers with the basics of machine learning before taking a deep dive into solving real-world problems with machine learning techniques. Basic concepts are covered in areas such as artificial intelligence, data mining, computer science, data science, natural language processing, deep learning, mathematics, and statistics. Topics related to the various machine learning techniques will be explored, including supervised, unsupervised, and reinforcement learning, hence important machine learning algorithms are discussed in this chapter. Toward the end of every section, suitable Python functions will be illustrated as examples. Most of the examples are taken from Python–scikit-learn library ( https://scikit-learn.org/stable/ ) and TensorFlow, and then adapted.