Reviewerly/exHarmony huggingface dataset:
Low Altitude Georeferencing for Imaging Sensors in Maritime Tracking

This paper presents a method for georeferencing low-altitude camera sensors, both infrared and electro-optical, in a maritime context. Accurate georeferencing require very high precision for the object pixel coordinates due to sensor resolution. To achieve this we refine the bounding boxes provided by an SSD object detector using the Sobel operator and the Hough transform. Using real world data this method is applied in a maritime tracking system based on the Joint Integrated Probabilistic Data Association method and compared to radar tracking. The georeferenced cameras surpassed radar performance in several of the benchmarks and maintained tracks with greater reliability at the cost of reduced position accuracy.
Estimation of partially occluded 2D human joints with a Bayesian approach

Human pose estimation is an important problem which has found applications in different fields. One problem in this context is to estimate the human joints or keypoints under occlusion. Most of the methods in the literature use CNN structures to estimate keypoint positions. In this paper, a Bayesian Approach for occluded Keypoint Estimation, BAKE, is presented to complete the missing human pose elements. A state-of-the-art CNN-based pose estimation algorithm Openpose [1] is used for detecting the visible joints. Then a Bayesian pose estimation is applied for the missing 2D pose elements. A Gaussian model composed of the length and angle parameters of a 13-keypoint skeleton model to encode the 2D human body pose is presented. Model parameters are obtained from the global and local statistics. Global statistics are extracted from COCO database [2] whereas the local statistics are obtained from the non-occluded initial video frames under consideration. The local and global statistics are combined and updated by using the action-specific body relations. A new predictability score is proposed to determine the confidence of completing the missing human joints. This confidence score is used to develop a hybrid technique which combines the predictions of the Openpose and the proposed method, BAKE. The proposed approach can be seen as an alternative for a 3D CNN structure which is trying to model the time-dependent sequence of events. Several experiments are performed to compare the outputs of the Openpose, BAKE, and the hybrid approach. It is shown that BAKE outperforms the Openpose estimates in general and the hybrid method generates a slight improvement over the BAKE algorithm.
English to Nepali Sentence Translation Using Recurrent Neural Network with Attention

Machine Translation, an automated system that intakes the text from the source language as an input, applies some computation on that input and gives the equivalent text in the target language without any human involvement. This research work focuses on developing the models for English to Nepali sentence translation incorporating Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM) with attention. Bilingual Evaluation Understudy (BLEU) Score is calculated to evaluate the efficiency of the model. Different parameters has been used to test the model. The model has been tested with neural network layer 2 and 4 and with hidden units 128, 256 and 512. The GRU cells in encoder and decoder with attention with 2 layer of neural network and 512 hidden units appears to be better in translating the English sentences into Nepali sentences with highest BLEU score 12.3.
On the Relationships Between the Grammatical Genders of Inanimate Nouns and Their Co-Occurring Adjectives and Verbs

Abstract We use large-scale corpora in six different gendered languages, along with tools from NLP and information theory, to test whether there is a relationship between the grammatical genders of inanimate nouns and the adjectives used to describe those nouns. For all six languages, we find that there is a statistically significant relationship. We also find that there are statistically significant relationships between the grammatical genders of inanimate nouns and the verbs that take those nouns as direct objects, as indirect objects, and as subjects. We defer deeper investigation of these relationships for future work.
Fast-DENSER: Fast Deep Evolutionary Network Structured Representation

This paper introduces a grammar-based general purpose framework for the automatic search and deployment of potentially Deep Artificial Neural Networks (DANNs). The approach is known as Fast Deep Evolutionary Network Structured Representation (Fast-DENSER) and is capable of simultaneously optimising the topology, learning strategy and any other required hyper-parameters (e.g., data pre-processing or augmentation). Fast-DENSER has been successfully applied to numerous object recognition tasks, with the generation of Convolutional Neural Networks (CNNs). The code is developed and tested in Python3, and made available as a library. A simple and easy to follow example is described for the automatic search of CNNs for the Fashion-MNIST benchmark.
Adaptive live migration of virtual machines under limited network bandwidth

Live migration is a crucial feature in existing virtualization platforms. Since memory is dirtied rapidly during the execution of a virtual machine (VM), boosting memory migration speed becomes a significant factor in guaranteeing a high-level success ratio and efficiency. However, the statically-configured migration strategy cannot cope with various workloads running in VMs, resulting in frequently aborted migration processes and low success ratio. This paper proposed a one-for-all migration architecture called Adaptive Live Migration (AdaMig) to address these issues. This QEMU-based solution dynamically switches migration methods and tunes related parameters by monitoring the run-time statistics from the migration process and the physical host. Once AdaMig detects the tendency that migration cannot converge, it will switch to another migration method to synchronize remaining dirty pages. During the whole process, AdaMig also dynamically tunes migration parameters according to current resources available in the physical host and migration efficiency. Experimental results reflect that AdaMig improves the success ratio from 26.7% to 93.3% over various workloads, and migration time is reduced by up to 45.5% in comparison with the original solution in QEMU.
Design and Evaluation of an Augmented Reality Application for Landing Training

While the use of Augmented Reality (AR) for pilot support in military and commercial aviation is already established, the easier access to high-tech AR devices offers new possibilities to explore the usefulness of these technologies in General Aviation. Research shows that landing a light aircraft is one of the most difficult parts of the ab initio flight training. This is also an area considered to benefit from AR. For this exploratory study, an AR application was developed to support the landing approach on a flight simulator with a Flight Path Vector (FPV), altitude, air speed and a feedback-tool. Training effects are descriptively analyzed by comparing learners’ self-evaluation and landing performance. This study provides first insights into the design, implementation, and evaluation of an AR application to support ab initio flight training. In addition, empirical data of the usefulness and limitations of the tested AR application in a flight simulator setting are discussed.
A Durable Biometric Authentication Scheme via Blockchain

In the digital era, one of the aspects is data security, where the combination of biometrics with blockchain is introduced as an advanced and durable technique to protect sensitive information. In modern society, biometric systems are widely employed instead of the password as an authentication process with high-level security, is complicated to hack the system by the hacker. At this juncture, the blockchain can provide high-level security as a fast-accessing process in an efficient manner. Any authentication process refers to some conventional hashing policy for the preparation of digital signatures. The same digital signature can process with the blockchain technique by referring to the hashing approach is discussed in this paper.
Consensus Algorithms in Blockchain-Based Cryptocurrencies

Blockchain has been a breakthrough in the technology since the last decade and is anticipated as the go-to thing due to features presented by it. The features such as secure, immutable, transparent, and anonymous makes Blockchain technology the frontrunner in the untrustworthy distributed environment. The driving force behind the Blockchain technology are the consensus algorithms that are at the heart of any Blockchain technology. These algorithms determine the security and performance of any Blockchain. The most popular consensus algorithm is the Proof-of-Work which was first used in the Bitcoin. Since then, a number of consensus algorithms have been proposed. This paper presents a brief overview of the Blockchain Technology and popular cryptocurrencies based on Blockchain. Next, the most popular consensus algorithms in the context of Blockchain and cryptocurrencies are discussed.
Optimization-based motion planning for multi-steered articulated vehicles

The task of maneuvering a multi-steered articulated vehicle in confined environments is difficult even for experienced drivers. In this work, we present an optimization-based trajectory planner targeting low-speed maneuvers in unstructured environments for multi-steered N-trailer vehicles, which are comprised of a car-like tractor and an arbitrary number of interconnected trailers with fixed or steerable wheels. The proposed trajectory planning framework is divided into two steps, where a lattice-based trajectory planner is used in a first step to compute a resolution optimal solution to a discretized version of the trajectory planning problem. The output from the lattice planner is then used in a second step to initialize an optimal control problem solver, which enables the framework to compute locally optimal trajectories that start at the vehicle's initial state and reaches the goal state exactly. The performance of the proposed optimization-based trajectory planner is evaluated in a set of practically relevant scenarios for a multi-steered 3-trailer vehicle with a car-like tractor where the last trailer is steerable.
BPA-GAN: Human motion transfer using body-part-aware generative adversarial networks

Human motion transfer has many applications in human behavior analysis, training data augmentation, and personalization in mixed reality. We propose a Body-Parts-Aware Generative Adversarial Network (BPA-GAN) for image-based human motion transfer. Our key idea is to take advantage of the human body with segmented parts instead of using the human skeleton like most of existing methods to encode the human motion information. As a result, we improve the reconstruction quality, the training efficiency, and the temporal consistency via training multiple GANs in a local-to-global manner and adding regularization on the source motion. Extensive experiments show that our method outperforms the baseline and the state-of-the-art techniques in preserving the details of body parts.
Cascade Attribute Network: Decomposing Reinforcement Learning Control Policies using Hierarchical Neural Networks

Reinforcement learning methods have been developed to achieve great success in training control policies in various automation tasks. However, a main challenge of the wider application of reinforcement learning in practical automation is that the training process is hard and the pretrained policy networks are hardly reusable in other similar cases. To address this problem, we propose the cascade attribute network (CAN), which utilizes its hierarchical structure to decompose a complicated control policy in terms of the requirement constraints, which we call attributes, encoded in the control tasks. We validated the effectiveness of our proposed method on two robot control scenarios with various add-on attributes. For some control tasks with more than one add-on attribute attribute, by directly assembling the attribute modules in cascade, the CAN can provide ideal control policies in a zero-shot manner.
Situation and Behavior Understanding by Trope Detection on Films

The human ability of deep cognitive skills is crucial for the development of various real-world applications that process diverse and abundant user generated input. While recent progress of deep learning and natural language processing have enabled learning system to reach human performance on some benchmarks requiring shallow semantics, such human ability still remains challenging for even modern contextual embedding models, as pointed out by many recent studies [9, 10, 22, 24, 32]. Existing machine comprehension datasets assume sentence-level input, lack of casual or motivational inferences, or can be answered with question-answer bias. Here, we present a challenging novel task, trope detection on films, in an effort to create a situation and behavior understanding for machines. Tropes are frequently used storytelling devices for creative works. Comparing to existing movie tag prediction tasks, tropes are more sophisticated as they can vary widely, from a moral concept to a series of circumstances, and embedded with motivations and cause-and-effects. We introduce a new dataset, Tropes in Movie Synopses (TiMoS), with 5623 movie synopses and 95 different tropes collecting from a Wikipedia-style database, TVTropes. We present a multi-stream comprehension network (MulCom) leveraging multi-level attention of words, sentences, and role relations. Experimental result demonstrates that modern models including BERT contextual embedding, movie tag prediction systems, and relational networks, perform at most 37% of human performance (23.97/64.87) in terms of F1 score. Our MulCom outperforms all modern baselines, by 1.5 to 5.0 F1 score and 1.5 to 3.0 mean of average precision (mAP) score. We also provide a detailed analysis and human evaluation to pave ways for future research.
Building a central repository landmarks a new era for artificial intelligence–assisted digital pathology development in Europe

The emergence of digital pathology has swiftly been followed by breakthroughs in the use of artificial intelligence (AI) for decision support in pathology, especially in cancer, aiming at enhanced and improved diagnostic pipelines. Histopathology, forming the cornerstone of many diagnoses, still relies heavily on the subjective interpretation of a histology sample by a qualified pathologist. The recent developments of high-throughput whole slide image (WSI) slide scanners and the rise of deep learning have enabled unprecedented possibilities to develop tools for automated detection, classification, and quantification of tissue pathology. Accordingly, computational pathology and computer-aided diagnostics start to sound familiar to more and more clinical pathologists.
Outlier Robust State Estimation Through Smoothing on a Sliding Window

Measurement outliers can severely impact on the performance of conventional state estimators. The design of state estimators exhibiting enhanced robustness to measurement outliers is of interest in many areas of systems and control engineering. In marine robotics applications the issue is particularly relevant for navigation and model identification tasks exploiting acoustic based positioning and velocity sensors that are subject to relatively high rates of outliers. A sliding window state estimator is designed by minimizing the Least Median of Squares cost function evaluated by running a Rauch-Tung-Striebel smoother on the current window. The resulting estimator is tested on Doppler Velocity Log navigation data acquired on an underwater robot. Although these are only preliminary results, they confirm that the approach can be successfully used online.
Trade Law in a Data-Driven Economy: The Need for Modesty and Resilience

Abstract This article examines the social challenges posed by the data-driven economy, their implications for international trade law, the current trade negotiating context involving distinct models advanced by the United States, European Union, and China, and a way forward that can both enhance trade and regulatory efficacy. It examines seven interrelated risks. They are the rise of ‘winner-take-all’ companies; social control through public and private surveillance; social polarization; premature deindustrialization; national security threats; cybersecurity risks; and threats to personal privacy. In response to these risks, the article contends that trade agreements should be deferential to national regulation, while supporting mechanisms for regulatory learning and adaptation. In this spirit, the article advances a governance framework that goes beyond ‘liberalization' and that foregrounds the importance of building resilience and engaging in regulatory problem solving.
Verifying System-Level Security of a Smart Ballot Box

Event-B, a refinement-based formal modelling language, has traditionally focused on safety, but now increasingly finds a new role in developing secure systems. In this paper we take a fresh look at security and focus on what security means for the system rather than looking at detailed protocols. We use Event-B for proving security from an abstract view and refining it towards design details, focusing on the refinement of the availability property of the system. We define a general approach to guarantee the availability of events by ensuring the non-strengthening of their guards, taking into consideration their parameter types. We illustrate our approach using a smart ballot system, an integral part of modern voting systems.
CrowdGP: a Gaussian Process Model for Inferring Relevance from Crowd Annotations

Test collection has been a crucial factor for developing information retrieval systems. Constructing a test collection requires annotators to assess the relevance of massive query-document pairs. Relevance annotations acquired through crowdsourcing platforms alleviate the enormous cost of this process but they are often noisy. Existing models to denoise crowd annotations mostly assume that annotations are generated independently, based on which a probabilistic graphical model is designed to model the annotation generation process. However, tasks are often correlated with each other in reality. It is an understudied problem whether and how task correlation helps in denoising crowd annotations.
Perception of Fairness in Group Music Recommender Systems

Fairness is an important aspect in group recommender systems (GRSs). They must ensure that potentially diverse preferences of all group members are taken into consideration when providing recommendations. Previous work has proposed a number of conflict elicitation and merging techniques to produce preferable recommendations for group members. However, we have yet to understand the influence of user personality on the perception of fairness in GRSs. To examine this gap, we use music recommendation as an example domain. We have developed a web-based group music recommender system using the Spotify API and two simple ranking algorithms: one based on the time the songs were voted by users (time-based) and the other based on a dissimilarity score (dissimilarity-based). A within-subjects experiment was conducted with 45 participants divided into groups of 3 (15 groups). Results showed that openness personality has a negative correlation with the perception that fairness is important in groups.
How do Online Learning to Rank Methods Adapt to Changes of Intent?

Online learning to rank (OLTR) uses interaction data, such as clicks, to dynamically update rankers. OLTR has been thought to capture user intent change overtime - a task that is impossible for rankers trained on statistic datasets such as in offline and counterfactual learning to rank. However, this feature has never been demonstrated and empirically studied, as previous work only considered simulated online data with single user intent or real online data with no explicit notion of intents and how they change over interactions. In this paper, we address this gap by study the capability of OLTR algorithms to adapt to user intent change. Our empirical experiments show that the adaptation to intent change does vary across OLTR methods, and is also dependent on the amount of noise in the implicit feedback signal. This is an important result, as it highlights that intent change adaptation should be studied alongside online and offline performance. Investigating how OLTR algorithms adapt to intent change is challenging as current LTR datasets do not explicitly contain the required intent data. Along with the main findings reported in this paper related to intent change, we also contribute a methodology to investigate this aspect of OLTR methods. Specifically, we create a collection for OLTR with explicit intent change by adapting an existing TREC collection to this task. We further introduce methods to model and simulate click behaviour related to intent change. We further propose novel evaluation metrics tailored to study different aspects of how OLTR methods adapt to intent change.
A New Method for Detecting Compensation Hole Parameters of Automobile Brake Master Cylinder Based on Machine Vision

The machining accuracy of the compensation hole of the automobile brake master cylinder directly determines the safety of the automobile and the reliability of parking. How to detect the parameters of the compensation hole with high precision becomes a crucial issue. In this paper, by analyzing the principle of Hough transform detection technology and several optimization algorithms, a new method combining Zernike moment and improved gradient Hough transform is proposed to detect the circular hole parameters. The simulation experiment shows that the proposed algorithm satisfies 0.1 pixels in the coordinate detection of the center position, and the radius detection accuracy is 0.05 pixels, with fast detection speed and good robustness. Compared with the random Hough transform algorithm and the gradient Hough transform algorithm, the algorithm proposed in this paper has higher detection accuracy, faster detection speed, and better robustness, which meets the online detection accuracy requirements of the brake master cylinder compensation hole.
Detecting Telephone-based Social Engineering Attacks using Scam Signatures

As social engineering attacks have become prevalent, people are increasingly convinced to give their important personal or financial information to attackers. Telephone scams are common and less well-studied than phishing emails. We have found that social engineering attacks can be characterized by a set of speech acts which are performed as part of the scam. A speech act is statements or utterances expressed by an individual that not only conveys information but also performs an action. Although attackers adjust their delivery and wording on the phone to match the victim, scams can be grouped into classes that all share common speech acts. Each scam type is identified by a set of speech acts that are collectively referred to as a scam signature. We present a social engineering detection approach called the Anti-Social Engineering Tool ASsET, which detects attacks based on the semantic content of the conversation. Our approach uses word embedding techniques from natural language processing to determine if the meaning of a scam signature is contained in a conversation. In order to evaluate our approach, a dataset of telephone scams has been gathered which are written by volunteers based on examples of real scams from official websites. This dataset is the first telephone-based scam dataset, to the best of our knowledge. Our detection method was able to distinguish scam and non-scam calls with high accuracy.