Reviewerly/exHarmony huggingface dataset:
Realtime Computer Vision-Based Accurate Vehicle Counting and Speed Estimation for Highways

In Australian urban roads, pneumatic tubes are temporarily installed over roads to determine the road usage by vehicles. This is a relatively expensive process and the data cannot be obtained for about two weeks until a manual retrieval of data. This data is collected very randomly in order to determine the road usage in Australia. However, since the manual labor of installing such a device is very expensive in Australia, such deployments are rare and do not provide adequate information to the Road Maritime Services in Australia for future design and management of roads. We have developed a highly accurate real-time computer vision-based system which relies on back ground subtraction, morphological operations and Gaussian filtering to track centroid of vehicles and accurately determine their speeds and count them. The system also sends speed alerts when congestion lowers the allowable speeds on highways. The real-time processing is achieved through optimization of our algorithms implemented using OpenCV. The code optimization has resulted in real-time operation of our system requiring no more processing power than that is available on a typical modern smartphone. The system is robust against typical lighting variations due to the movement of the sun and can maintain the accuracy in a drizzle or cloud movement making the system very practicable for deployment in Australian highways. In order to avoid the occlusion problem faced by many vision-based traffic monitoring systems, we have placed our cameras strategically above the highways with excellent results.
Replanning for Situated Robots

Planning enables intelligent agents, such as robots, to act so as to achieve their long term goals. To make the planning process tractable, a relatively low fidelity model of the world is often used, which sometimes leads to the need to replan. The typical view of replanning is that the robot is given the current state, the goal, and possibly some data from the previous planning process. However, for robots (or teams of robots) that exist in continuous physical space, act concurrently, have deadlines, or must otherwise consider durative actions, things are not so simple. In this paper, we address the problem of replanning for situated robots. Relying on previous work on situated temporal planning, we frame the replanning problem as a situated temporal planning problem, where currently executing actions are handled via Timed Initial Literals (TILs), under the assumption that actions cannot be interrupted. We then relax this assumption, and address situated replanning with interruptible actions. We bridge the gap between the low-level model of the robot and the high-level model used for planning by the novel notion of a bail out action generator, which relies on the low-level model to generate highlevel actions that describe possible ways to interrupt currently executing actions. Because actions can be interrupted at different times during their execution, we also propose a novel algorithm to handle temporal planning with time-dependent durations.
Learning Management System CATS

The use of information technologies and, in particular, information training systems increases capacity of both a teacher and a student to achieve their goals in an educational process, taking into account individual characteristics of each and providing opportunities for continuing education. The paper proposes and considers a new automated learning management system, called CATS (Care About the Students). The proposed system covers all components of the educational process, allows to conduct an electronic journal of visiting and defending laboratory works, distribute topics and monitor progress of course and diploma projects (creating automatically task sheets with subsequent export to MS Word editor). The CATS LMS makes it possible to test students’ knowledge in control and self-education modes, to form electronic educational and methodical complexes, to inform students about topics of lecture and practical classes with the possibility of downloading relevant materials, to provide educational content in SCORM format, developed by teachers around the world. Working in the CATS system, it is possible to exchange messages with students and teachers, to monitor progress and process of studying in respect of the proposed material by students, to check accomplished assignments of students for plagiarism. The system under consideration also takes into account specificity of training at a technical higher education institution, providing for work with an integrated project management system and ability to submit bugs. The CATS LMS has been implemented as a web application using modern technologies and it is available in the local network within the whole university at [172.16.11.72:2020].
Stereo Vision-based Autonomous Target Detection and Tracking on an Omnidirectional Mobile Robot

In this paper, a mobile robot equipped with an onboard computing unit and a stereo camera for autonomous target detection and tracking is introduced. This system can figure out an interesting target and track it robustly in real time. It is based on the ROS framework and can handle multi-resource information, such as RGB images, depth information, and IMU data. To balance the performance of the machine learning based object detection algorithm and the algorithm for object tracking, the Hamming distance and the intersection over union are selected as criteria. The performance of the system is verified in a hardware experiment in two typical scenarios.
Assessment of Combined Shape, Color and Textural Features for Video Duplication

The aim of the work is to identify the duplication in a video database with the aid of feature extraction techniques.The process includes extraction of image features (shape, color, and texture) for duplicate identification.The color contains 256 features, shape contains 200 features, the texture contains two different features namely gray-level co-occurrence matrix (GLCM) (22 features in 4 degrees) and grey-level run length matrix (GLRLM) (11 features) are extracted.In this paper, the preliminary work is to convert video into frames and then each frame into blocks subsequently including feature extraction.A query video is then considered for the same process of feature extraction and compared with the normal video.The distance between query video and normal video if found to be similar then the video identified as duplicate video.The results are performed for various evaluation matrix and plotted graphs are shown.The sensitivity value for whole feature extractions is 0.88, the specificity value for whole feature extractions is 0.83 and the accuracy value for whole extractions is 0.86.The entire process implemented in the working platform of MATLAB.
CBA-Detector: An Accurate Detector Against Cache-Based Attacks Using HPCs and Pintools

Cloud computing is convenient to provide adequate resources for tenants, but it suffers from information disclosure risks because hardware resources are shared among multiple tenants. For example, secret information in the shared cache can be inferred by other malicious processes, which is called cache-based attacks. To defeat against such attacks, many detection methods have been proposed. However, most of the existing detection mechanisms completely rely on the hardware performance counters (HPCs) and induce high false positives in detecting attacks. This paper proposes an accurate detector named CBA-Detector to detect cache-based side-channel attacks in real time. CBA-Detector is composed of an offline analysis phase and an online detection phase. The former analyzes the hardware events generated by sample programs. Then it extracts features from these events to train machine learning models. Based on the models, the latter monitors active processes in real time to discover suspicious processes. These suspicious processes will be checked again at the instruction level by customized Pintools, which effectively eliminates false positives. As shown in our experiments, CBA-Detector can accurately identify attacks in real time and introduces 4.4% overhead on PARSEC and about 10% overhead on web server.
Smooth Path planning for Fixed-Wing Aircraft in 3D Environment Using a Layered Essential Visibility Graph

This paper deals with the problem of flight path planning for unmanned fixed-wing air vehicles (UAVs) in complex 3D environments. Flight paths must be compliant with both mission constraints defined in terms of no-fly zones, obstacles and destination points and aircraft performance constraints such as maximum flight path angle and minimum turn radius.Trajectory generation is addressed as a minimum cost path search using a novel layered Essential Visibility Graph whose arcs and corresponding weights are obtained via an efficient branching algorithm to reduce computational time. The resulting path is a piecewise trajectory composed by only circular arcs and straight segments, according to Dubins paradigm. To prove the effectiveness of the proposed method, operational scenarios derived from real terrain morphology have been used.
Two classes of differentially 4-uniform permutations over $ \mathbb{F}_{2^{n}} $ with $ n $ even

A construction of differentially 4-uniform permutations by modifying the values of the inverse function on a union of some cosets of a multiplication subgroup of $ \mathbb{F}_{2^n}^* $ was given by Peng et al. in [15]. In this paper, we extend their results to differentially 4-uniform permutations whose values are different from the values of the inverse function on some subsets of the unit circle of $ \mathbb{F}_{2^n} $ or on the multiplication group of some subfield of $ \mathbb{F}_{2^n} $. Moreover, it has been checked by the Magma software that some permutations in the resulted differentially 4-uniform permutations are CCZ-inequivalent to the known functions for small $ n $.
Relaxed Oriented Image Foresting Transform for Seeded Image Segmentation

In this work, we propose a hybrid method for seeded image segmentation, named Relaxed OIFT, which extends a method by Malmberg et al. to directed graphs, to properly incorporate the boundary polarity constraint. Relaxed OIFT, lies between the pure Oriented Image Foresting Transform (OIFT) at one end and the extension of Random Walks (RW) to directed graphs as proposed by Singaraju et al. Relaxed OIFT is evaluated in MR and CT medical images, producing more intuitively correct segmentation results than both OIFT and RW, and being easy to be extended to multi-dimensional images.
Extension of HMM-Based ADL Recognition With Markov Chains of Activities and Activity Transition Cost

Ambient assisted living in smart home environments is becoming an important goal in an aging society with challenges in elderly care. A key component in such environments is the accurate recognition of activities of daily living from various sensor data. Recent research directions explored several classification methods, including hidden Markov models. This research presents a hidden Markov model-based system for activity recognition, and extends it with a second-order Markov chain model of activity sequences to achieve long-term dependency in the model. We also introduce an activity transition cost to counteract the tendency of hidden Markov models to make a large number of transitions. The proposed models are used for activity recognition, with their scores being combined using heuristically determined weights for optimal performance. We also present a modified Viterbi algorithm, which incorporates both models and the activity transition cost. We used a dataset from the CASAS project to test and evaluate the proposed models. A comparison of the results shows the potential of introducing long term dependencies and the managing the number of activity transitions. We show results regarding the modeling ability to predict activity sequences, a comparison of predicted and actual activity transitions, and final recognition accuracy results. The results show an increase of total activity recognition accuracy from 93.9 % to 94.52 % on individual activities, and from 68.89 % to 70.95 % over the combination of all concurrent activities. The results also show a reduction of predicted activity transitions from 741 to 236, whereas the number of actual activity transitions in the evaluation set is 141.
NeurVPS: Neural Vanishing Point Scanning via Conic Convolution

We present a simple yet effective end-to-end trainable deep network with geometry-inspired convolutional operators for detecting vanishing points in images. Traditional convolutional neural networks rely on aggregating edge features and do not have mechanisms to directly exploit the geometric properties of vanishing points as the intersections of parallel lines. In this work, we identify a canonical conic space in which the neural network can effectively compute the global geometric information of vanishing points locally, and we propose a novel operator named conic convolution that can be implemented as regular convolutions in this space. This new operator explicitly enforces feature extractions and aggregations along the structural lines and yet has the same number of parameters as the regular 2D convolution. Our extensive experiments on both synthetic and real-world datasets show that the proposed operator significantly improves the performance of vanishing point detection over traditional methods. The code and dataset have been made publicly available at https://github.com/zhou13/neurvps.
Learning Cascaded Siamese Networks for High Performance Visual Tracking

Visual tracking is one of the most challenging computer vision problems. In order to achieve high performance visual tracking in various negative scenarios, a novel cascaded Siamese network is proposed and developed based on two different deep learning networks: a matching subnetwork and a classification subnetwork. The matching subnetwork is a fully convolutional Siamese network. According to the similarity score between the exemplar image and the candidate image, it aims to search possible object positions and crop scaled candidate patches. The classification subnet-work is designed to further evaluate the cropped candidate patches and determine the optimal tracking results based on the classification score. The matching subnetwork is trained offline and fixed online, while the classification subnetwork performs stochastic gradient descent online to learn more target-specific information. To improve the tracking performance further, an effective classification subnetwork update method based on both similarity and classification scores is utilized for updating the classification subnetwork. Extensive experimental results demonstrate that our proposed approach achieves state-of-the-art performance in recent benchmarks.
Regression Planning Networks

Recent learning-to-plan methods have shown promising results on planning directly from observation space. Yet, their ability to plan for long-horizon tasks is limited by the accuracy of the prediction model. On the other hand, classical symbolic planners show remarkable capabilities in solving long-horizon tasks, but they require predefined symbolic rules and symbolic states, restricting their real-world applicability. In this work, we combine the benefits of these two paradigms and propose a learning-to-plan method that can directly generate a long-term symbolic plan conditioned on high-dimensional observations. We borrow the idea of regression (backward) planning from classical planning literature and introduce Regression Planning Networks (RPN), a neural network architecture that plans backward starting at a task goal and generates a sequence of intermediate goals that reaches the current observation. We show that our model not only inherits many favorable traits from symbolic planning --including the ability to solve previously unseen tasks-- but also can learn from visual inputs in an end-to-end manner. We evaluate the capabilities of RPN in a grid world environment and a simulated 3D kitchen environment featuring complex visual scenes and long task horizon, and show that it achieves near-optimal performance in completely new task instances.
Convergence Guarantees for Adaptive Bayesian Quadrature Methods

Adaptive Bayesian quadrature (ABQ) is a powerful approach to numerical integration that empirically compares favorably with Monte Carlo integration on problems of medium dimensionality (where non-adaptive quadrature is not competitive). Its key ingredient is an acquisition function that changes as a function of previously collected values of the integrand. While this adaptivity appears to be empirically powerful, it complicates analysis. Consequently, there are no theoretical guarantees so far for this class of methods. In this work, for a broad class of adaptive Bayesian quadrature methods, we prove consistency, deriving non-tight but informative convergence rates. To do so we introduce a new concept we call \emph{weak adaptivity}. Our results identify a large and flexible class of adaptive Bayesian quadrature rules as consistent, within which practitioners can develop empirically efficient methods.
Differentially Private Anonymized Histograms

For a dataset of label-count pairs, an anonymized histogram is the multiset of counts. Anonymized histograms appear in various potentially sensitive contexts such as password-frequency lists, degree distribution in social networks, and estimation of symmetric properties of discrete distributions. Motivated by these applications, we propose the first differentially private mechanism to release anonymized histograms that achieves near-optimal privacy utility trade-off both in terms of number of items and the privacy parameter. Further, if the underlying histogram is given in a compact format, the proposed algorithm runs in time sub-linear in the number of items. For anonymized histograms generated from unknown discrete distributions, we show that the released histogram can be directly used for estimating symmetric properties of the underlying distribution.
Elderly Supportive Intelligent Wheelchair

Difficulty of the mobility and independent life have become a huge debilitate to the elderly people. They need the assistance of the care givers to carry out their daily routines. At present, with the busy livelihoods of the people, they may have the chance of neglecting their responsibilities towards the elderly people. Friends and relatives cannot provide the assistance for them every time. Also, the huge gap between the supply and the demand of the caregivers is widening day by day. Due to these facts many reliable innovations are coming in to operation. The intelligent wheelchair system has been developed based on a manual wheelchair with the integration of relevant electrical and mechanical modifications. The system is employed with joystick control interface and human interactive facilities to achieve the daily tasks of the user easily. In addition to that, safety precaution and human interactive facilities such as emotion detection, voice recognition, taking calls, checking the availability of meals and washroom from the point where the user stayed are deployed in to the system. The functionality and the preference of the user towards the developed wheelchair has been verified by conducting a user study and the results are presented.
The Universitat d’Alacant Submissions to the English-to-Kazakh News Translation Task at WMT 2019

This paper describes the two submissions of Universitat d'Alacant to the English-to-Kazakh news translation task at WMT 2019.Our submissions take advantage of monolingual data and parallel data from other language pairs by means of iterative backtranslation, pivot backtranslation and transfer learning.They also use linguistic information in two ways: morphological segmentation of Kazakh text, and integration of the output of a rule-based machine translation system.Our systems were ranked 2 nd in terms of chrF++ despite being built from an ensemble of only 2 independent training runs.
A Generic Framework for Learning Explicit and Implicit User-Item Couplings in Recommendation

The nature of recommendation is Non-IID, which has potential in improving recommendation quality and addressing issues such as sparsity and cold start. However, existing many state-of-the-art methods assume users and items are independent and same distributed while ignoring complex coupling relationships within and between users and items, resulting in limited performance improvement. To solve this issue, this paper proposes a novel neural user-item coupling learning model, short for CoupledCF, based on non-IID learning for collaborative filtering. CoupledCF joint learns explicit coupling with CNN and implicit coupling with deepCF within/between users and items accompanying user/item side information for recommendation tasks. User/item side information contains of attribute-based and feature-based. For different user/item side information, we use different embedding methods to learn embedding representation. We conduct comparative experiments on (1) two datasets from MovieLens1M and Tafeng with attribute-based user/item information for Top-K recommendation. (2) two datasets from MovieLens1M and BookCrossing with attribute-based user/item information for rating prediction. (3) two datasets from Amazon Movies and TV (AMT) and Yelp for feature-based user/item information for Top-K item recommendation and rating prediction tasks. Empirical results on five available real-world large datasets prove our proposed CoupledCF model is able to obtain better recommendation accuracy compared with several mainstream approaches for recommendation: BMF, neural matrix factorization, Google's Wide&Deep network, DeepFM, convMF, and A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> NCF model.
The Challenges and Trends of Deploying Blockchain in the Real World for the Users’ Need

Blockchain technology is a decentralized and open database maintained by a peer-to-peer network, offering a “trustless trust” for untrusted parties. Despite the fact that some researchers consider blockchain as a bubble, blockchain technology has the genuine potential to solve problems across industries. In this article, we provide an overview of the development that Blockchain technology has had in 2018 and point out the challenges of deploying blockchain-based applications in the real world from a Human-Computer Interaction view. We propose that blockchain practitioners should design blockchain applications from users’ perspective, think about who the users are, and what they need. Furthermore, we also lay out possible future trends for blockchain based systems.