Reviewerly/exHarmony huggingface dataset:
Unsupervised Learning of Landmarks by Descriptor Vector Exchange

Equivariance to random image transformations is an effective method to learn landmarks of object categories, such as the eyes and the nose in faces, without manual supervision. However, this method does not explicitly guarantee that the learned landmarks are consistent with changes between different instances of the same object, such as different facial identities. In this paper, we develop a new perspective on the equivariance approach by noting that dense landmark detectors can be interpreted as local image descriptors equipped with invariance to intra-category variations. We then propose a direct method to enforce such an invariance in the standard equivariant loss. We do so by exchanging descriptor vectors between images of different object instances prior to matching them geometrically. In this manner, the same vectors must work regardless of the specific object identity considered. We use this approach to learn vectors that can simultaneously be interpreted as local descriptors and dense landmarks, combining the advantages of both. Experiments on standard benchmarks show that this approach can match, and in some cases surpass state-of-the-art performance amongst existing methods that learn landmarks without supervision. Code is available at this http URL.
Smart Apps vs. Renovated Low-tech Devices with DIY Assistive Technology

In this research, the author analyzes the merits of smart device apps and low-tech solutions as examples of assistive technology with identification of banknotes by the visually impaired. In particular, the focus of this research is on the potential of low-tech devices that are often neglected in exploring emerging technology. Applying these devices to DIY assistive technology using ICT sometimes opens up new possibilities. In the latter part of this paper, the author attempts to illustrate such possibilities by developing a Web-based system that enables users to freely create 3D data of banknote identification plates according to individual needs.
Estimation of Rotation Gain Thresholds for Redirected Walking Considering FOV and Gender

Redirected walking techniques enable users to naturally locomote in virtual environments (VEs) that are larger than the tracked space. Redirected walking imperceptibly transforms the VE around the user with predefined estimated threshold gains. Previously estimated gains were evaluated with a 40° field of view (FOV). We conducted a within-participant user study to estimate and compare thresholds for rotation gains. Significant differences in detection thresholds were found between FOVs. When using a 110° FOV, rotations can be decreased 31% and increased 49% compared to decreased 18% and increased 47% with a 40° FOV. Significant differences were found between female and male gains with a 110° FOV.
Employing different program analysis methods to study bug evolution

The evolution of software bugs has been a well-studied topic in software engineering. We used three different program analysis tools to examine the different versions of two popular sets of programming tools (gnu Binary and Core utilities), and check if their bugs increase or decrease over time. Each tool is based on a different approach, namely: static analysis, symbolic execution, and fuzzing. In this way we can observe potential differences on the kinds of bugs that each tool detects and examine their effectiveness. To do so, we have performed a qualitative analysis on the results. Overall, our results indicate that we cannot say if bugs either decrease or increase over time and that the tools identify different bug types based on the method they follow.
NAISC: An Authoritative Linked Data Interlinking Approach for the Library Domain

By interlinking internal Linked Data (LD) entities to related LD entities published by authoritative creators and holders of data, libraries have the potential to expose their collections to a larger audience and to allow for richer user searches. While increasing numbers libraries are devoting time to publishing LD, the full potential of these datasets has not been explored due to limited LD interlinking. In 2018 we conducted a survey which explored the position of Information Professionals (IPs), such as librarians, archivists and cataloguers, with regards to LD. Results indicated that IPs find the process of data interlinking to be a particularly challenging step in the creation of Five Star LD. Consequently, we developed NAISC, an interlinking approach designed specifically for the library domain aimed at facilitating increased IP engagement in the LD interlinking process. Our paper provides an overview of the design and user-evaluation of NAISC. Results indicated that IPs found NAISC easy-to-use and useful for creating LD interlinks.
Computer aided detection of deep inferior epigastric perforators in computed tomography angiography scans

The deep inferior epigastric artery perforator (DIEAP) flap is the most common free flap used for breast reconstruction after a mastectomy. It makes use of the skin and fat of the lower abdomen to build a new breast mound either at the same time of the mastectomy or in a second surgery. This operation requires preoperative imaging studies to evaluate the branches - the perforators - that irrigate the tissue that will be used to reconstruct the breast mound. These branches will support tissue viability after the microsurgical ligation of the inferior epigastric vessels to the receptor vessels in the thorax. Usually through a computed tomography angiography (CTA), each perforator is manually identified and characterized by the imaging team, who will subsequently draw a map for the identification of the best vascular support for the reconstruction. In the current work we propose a semi-automatic methodology that aims at reducing the time and subjectivity inherent to the manual annotation. In 21 CTAs from patients proposed for breast reconstruction with DIEAP flaps, the subcutaneous region of each perforator was extracted, by means of a tracking procedure, whereas the intramuscular portion was detected through a minimum cost approach. Both were subsequently compared with the radiologist manual annotation. Results showed that the semi-automatic procedure was able to correctly detect the course of the DIEAPs with a minimum error (average error of 0.64 and 0.50 mm regarding the extraction of subcutaneous and intramuscular paths, respectively), taking little time to do so. The objective methodology is a promising tool in the automatic detection of perforators in CTA and can contribute to spare human resources and reduce subjectivity in the aforementioned task.
Multi-Vehicle Close Enough Orienteering Problem with Bézier Curves for Multi-Rotor Aerial Vehicles

This paper introduces the Close Enough Orienteering Problem (CEOP) for planning missions with multi-rotor aerial vehicles considering their maximal velocity and acceleration limits. The addressed problem stands to select the most rewarding target locations and sequence to visit them in the given limited travel budget. The reward is collected within a non-zero range from a particular target location that allows saving the travel cost, and thus collect more rewards. Hence, we are searching for the fastest trajectories to collect the most valuable rewards such that the motion constraints are not violated, and the travel budget is satisfied. We leverage on existing trajectory parametrization based on Bezier curves recently deployed in surveillance planning using unsupervised learning, and we propose to employ the learning in a solution of the introduced multi-vehicle CEOP. Feasibility of the proposed approach is supported by empirical evaluation and experimental deployment using multi-rotor vehicles.
Privacy and security issues in cloud computing using idyllic approach

Cloud provides the collection of virtual machine which have both computational and storage facility. The important aspect of cloud computing is to present effective process to hyper distributed resources. Now days, cloud system is developing and facing many challenges, two of them is scheduling process and other main challenge is a security issue. In existing approaches did not concentrate on both security and scheduling issues in the cloud computing. Scheduling refers to the scheduler adapts its scheduling strategy, according to the changing to a set of morals to control the order of work to be implemented by a computer system. In this research paper, we have implemented a scheduling algorithm of collocate FCFS of supremacy elements. Here, we improve the system efficiency by using FCFS in a parallel manner. To address this security issue, we have introduced a novel concept of Crisscross Advance Encryption Standard (CAES). In this approach, crisscross AES has implemented by increasing the security to the cloud through the grid manner. Aggregate of this proposed work is to enhance the system efficiency as well as the security by using the both crisscross AES and Collocate FCFS of supremacy elements.
A Navigation Scheme for a Random Maze using Reinforcement Learning with Quadrotor Vision

In this paper, the maze navigation problem for the unmanned ground vehicle (UGV) is considered. A new maze navigation scheme with Reinforcement Learning (RL) is proposed to find the optimal path from the entrance to the exit for the UGV. First, the quadrotor with a camera at its bottom is used to capture the image data of a random maze in a 3D view, and an image processing approach is implemented to reconstruct the maze in a virtual platform. Then, a novel exploration algorithm, Q-Learning (λ) with improved e-greedy (iε-greedy), is proposed to find the shortest path in the reconstructed maze. The advantages of this paper are that the navigation scheme can provide an optimal path to the UGV without experiencing the maze cell by cell, and the proposed exploration algorithm can greatly reduce the randomness comparing to the traditional RL method. The effectiveness of both the proposed navigation scheme and the proposed exploration algorithm has been verified by simulations.
RelivelnVR: Capturing and Reliving Virtual Reality Experiences Together

We present a new type of sharing VR experience over distance which allows people to relive their recorded experience in VR together. We describe a pilot study examining the user experience when people share their VR experience together remotely. Finally, we discuss the implications for sharing VR experiences over time and space.
Predicting the Attractiveness of Real-Estate Images by Pairwise Comparison using Deep Learning

Even images of the same object, people's impression can vary greatly with the image capture method and angle. In this study, we focused on the exterior images of real-estate to predict which of two images of the same property is more attractive. In general, using a model that regresses the attractiveness score of the property image is common; however, it is difficult to stably evaluate a property image attractiveness using an exact attractiveness value. Therefore, instead of using exact values to evaluate the more attractive image, generating a model correctly predicting which is the more attractive one from the two property images of the same property is better. We created a dataset and developed a model that ensures similar output values for image pairs with similar attractiveness and different output values for pairs with different attractiveness. The proposed method predicted the more attractive image with higher accuracy than conventional models.
Optimizing DNN Architectures for High Speed Autonomous Navigation in GPS Denied Environments on Edge Devices

We address the challenge of high speed autonomous navigation of micro aerial vehicles (MAVs) using DNNs in GPS-denied environments with limited computational resources; specifically, we use the ODROID XU4 and the Raspberry Pi 3. The high computation costs of using DNNs for inference, particularly in the absence of powerful GPUs, necessitates negotiating a tradeoff between accuracy and inference. We address this tradeoff by employing sparsified neural networks. To obtain such architectures, we propose a novel algorithm to find sparse “sub networks” of existing pre trained models. Contrary to existing pruning-only strategies, our proposal includes a novel exploration step that efficiently searches for a different, but identically sparse, architecture with better generalization abilities. We derive learning theoretic bounds that reinforce our empirical findings that the optimized network achieves comparable generalization to the original network. We show that using our algorithm it is possible to discover models which, on average, have upto 19x fewer parameters than those obtained using existing state of the art pruning methods on autonomous navigation datasets, and achieve upto 6x improvements on inference time compared to existing state of the art shallow models on the ODROID XU4 and Raspberry Pi 3. Last, we demonstrate that our sparsified models can complete autonomous navigation missions with speeds upto 4 m/s using the ODROID XU4, which existing state of the art methods fail to do.
A Real-Time Processing Stand-Alone Multiple Object Visual Tracking System

Detection and tracking of multiple objects in real applications requires real-time performance, the management of tens of simultaneous objects, and handling frequent partial and total occlusions. Moreover, due to the software and hardware requirements of the different algorithms, this kind of systems require a distributed architecture to run in real-time. In this paper, we propose a vision based tracking system with three components: detection, tracking and data association. Tracking is based on a Discriminative Correlation Filter combined with a Kalman filter for occlusions handling. Also, our data association uses deep features to improve robustness. The complete system runs in real-time with tens of simultaneous objects, taking into account the runtimes of the Convolutional Neural Network detector, the tracking and the data association.
Class-Conditional Data Augmentation Applied to Image Classification

Image classification is widely researched in the literature, where models based on Convolutional Neural Networks (CNNs) have provided better results. When data is not enough, CNN models tend to be overfitted. To deal with this, often, traditional techniques of data augmentation are applied, such as: affine transformations, adjusting the color balance, among others. However, we argue that some techniques of data augmentation may be more appropriate for some of the classes. In order to select the techniques that work best for particular class, we propose to explore the epistemic uncertainty for the samples within each class. From our experiments, we can observe that when the data augmentation is applied class-conditionally, we improve the results in terms of accuracy and also reduce the overall epistemic uncertainty. To summarize, in this paper we propose a class-conditional data augmentation procedure that allows us to obtain better results and improve robustness of the classification in the face of model uncertainty.
Hand Component Decomposition for the Hand Gesture Recognition Based on FingerPaint Dataset

In the human-machine interaction system, hand component decomposing is important to recognize the human gesture. This paper proposes a method to decompose the hand component for the hand gesture recognition from human body image of FingerPaint dataset which is Microsoft research open data. We choose 36,750 randomly images for training and choose the remaining 15,750 images for the testing from the FingerPaint dataset. We conducted the PFACA(Proportion of frames with average classification accuracy) for the accuracy of the hand component(thumb, index finger, middle finger, ring finger, pinky, palm, wrist). In the results of five times repeated experiments that we showed maximum of 0.9849042 and minimum of 0.949042 at a frame of more than 0.2 of Epsilon.
A Study on Prediction of Housing Price Using Deep Learning

The purpose of this study is to estimate housing prices using deep running. The simple RNN, LSTM, and GRU models, which are evaluated to be suitable for time series forecasting, are based on the time series data of apartment real price index, interest rate, household loan, building permit area and consumer price index. As a result of the empirical analysis, it is confirmed that the prediction power of the GRU model is superior to that of the learning data by evaluating the performance of forecasting power on apartment real price index based on the RMSE value. On the other hand, in the verification data, it is confirmed that the prediction power of the RNN model is excellent. Also, if the performance of the deep running model is evaluated with accuracy, the accuracy of the RNN model and the GRU model is the highest. As a result of this study, the government needs to build and develop a system that can predict and diagnose the housing market by using the deep learning technique that combines artificial neural network and big data to advance the housing market.
On the Null-Space of the Shape-Color Moment Invariants

In this paper, we extend the theory of the combined Shape-Color Affine Moment Invariants (SCAMIs) for recognition of color images, proposed originally by Gong et al. in [3]. Since in the real pictures the shape deformation is always accompanied by the color deformation, it is not sufficient to use the shape invariant or color invariant descriptors only and the use of combined invariants is needed. However, the SCAMIs are not able to recognize images, the color channels of which are linearly dependent or highly correlated. This situation is not rare in practice and is of particular importance in hyper-spectral image analysis, where the spectral bands are highly correlated. We analyze why the SCAMIs fail in such situations, correct the theory and propose a solution to overcome such drawback. Unlike the SCAMIs, the new invariants have in the null-space the constant-zero images only, which leads to a better discrimination power, as demonstrated also on various pictures.
Named entity recognition for Polish

Abstract In this article we discuss the current state-of-the-art for named entity recognition for Polish. We present publicly available resources and open-source tools for named entity recognition. The overview includes various kind of resources, i.e. guidelines, annotated corpora (NKJP, KPWr, CEN, PST) and lexicons (NELexiconS, PNET, Gazetteer). We present the major NER tools for Polish (Sprout, NERF, Liner2, Parallel LSTM-CRFs and PolDeepNer) and discuss their performance on the reference datasets. In the article we cover identification of named entity mentions in the running text, local and global entity categorization, fine- and coarse-grained categorization and lemmatization of proper names.
AID Shuffling Mechanism Based on Group-Buying Auction for Identifier Network Security

Identifier network is one of promising network architectures to solve IP dual-property issues by separating network into access network and core network. The key design is the identifier/location separation mechanism which introduces user accessing address space and network routing address space to represent the identifier and location of terminal, respectively. The core network can prevent the cyber attacks by controlling the mapping relationship between Accessing IDentifier (AID) and Routing IDentifier (RID) via the mapping system. While the access network is facing serious security problems which makes it easily to be attacked. Therefore, this work focuses on access network security and studies AID shuffling mechanism by randomizing identifier through virtual AID. However, how the mapping servers allocate virtual AID to terminals in a reasonable way is quite difficult. To solve this problem, we propose an AID shuffling mechanism based on Group-buying Auction for Identifier Network (GAIN) among mapping servers, accessing switching routers and terminals. First, GAIN decides a group bid for each group leader and the winning virtual AID range for each group. Second, the mapping servers allocate the identifier for each terminal. Third, GAIN determines how much each winning group leaders should pay for virtual AID range to each mapping server in the winning group. We evaluate the identifier assignment of GAIN in terms of rationality, budget balance, computational tractability and truthfulness. The large-scale simulations are performed to evaluate GAIN, and the results are presented to verify the effectiveness and efficiency by comparing with other state-of-the-art approaches.
Sentiment analysis for Polish

Abstract This article is a comprehensive review of freely available tools and software for sentiment analysis of texts written in Polish. It covers solutions which deal with all levels of linguistic analysis: starting from word-level, through phrase-level and up to sentence-level sentiment analysis. Technically, the tools include dictionaries, rule-based systems as well as deep neural networks. The text also describes a solution for finding opinion targets. The article also contains remarks that compare the landscape of available tools in Polish with that for English language. It is useful from the standpoint of multiple disciplines, not only information technology and computer science, but applied linguistics and social sciences.
Color Visual Cryptography with Completely Randomly Coded Colors

The concept of visual cryptography with two completely random shares is applied to color images. In the classic coding the shares are not correlated with the coded image, but are not totally random. We apply a completely random coding, proposed previously for the case of black and white images, to color images. This improves the security of the process. The proposed method has similar properties to those of the majority of purely visual cryptographic methods, including reduced contrast and loss of minor details. In the decoding process no calculations are necessary so the decoding can be done by an unarmed human eye. Using the completely random coding of colors introduces some noise into the results, but its level is moderate. The RGB color model is used and the (2, 2) cryptography scheme is considered.
Decision Trees Based Software Development Effort Estimation: A Systematic Mapping Study

The decision tree (DT) represents a nonparametric estimation method that has been mostly used for both classification and regression problems. DTs were adopted for software development effort estimation (SDEE) generally for their simplicity of use and interpretation contrary to other learning methods. Nevertheless, to our self-knowledge, no systematic mapping has been devoted especially to decision trees. The aim of this study is to elaborate a systematic mapping study that classifies DTs papers in conformity with the succeeding criteria: research approach, contribution type, techniques employed in combination with DT methods besides identifying publication channels and trends. An automated search of five digital libraries was made to carry out a systematic mapping of DT studies mainly devoted to SDEE that were published in the period 1985-2017. We identify 46 relevant studies. Basically, the results revealed that most researchers focus on technique contribution type. In addition, the majority of papers deal with improving the existing DT models while few studies have proposed novel models to improve the reliability of SDEE. Furthermore, solution proposal and case study are the most frequently used approaches.