Reviewerly/exHarmony huggingface dataset:
Succinct BWT-Based Sequence Prediction

Sequences of symbols can be used to represent data in many domains such as text documents, activity logs, customer transactions and website click-streams. Sequence prediction is a popular task, which consists of predicting the next symbol of a sequence, given a set of training sequences. Although numerous prediction models have been proposed, many have a low accuracy because they are lossy models (they discard information from training sequences to build the model), while lossless models are often more accurate but typically consume a large amount of memory. This paper addresses these issues by proposing a novel sequence prediction model named SuBSeq that is lossless and utilizes the succinct Wavelet Tree data structure and the Burrows-Wheeler Transform to compactly store and efficiently access training sequences for prediction. An experimental evaluation shows that SuBSeq has a very low memory consumption and excellent accuracy when compared to eight state-of-the-art predictors on seven real datasets.
Personalized Personal Spaces for Virtual Reality

An important criterion for virtual reality experiences is that they are very immersive. The person inside the head-mounted display feels like really being in the virtual environment. While this can be a very pleasant experience, the opposite can happen as well. The concepts of personal spaces and people or unfriendly avatars entering them, can lead to the same discomfort as if it would happen in real life. In this work, we propose to define multi-level artificial barriers for other avatars and objects, respecting the personal spaces as defined by users. We apply this to both interactive rendered environments and as much as possible also to 360 degree photo and video content.
Development of Sensitive Glove Type Wearable Robot System

In this paper, a sensitive glove type wearable robot is designed, and its remote-control system is developed for operating a robot hand remotely. By sensing the motions of human fingers through pressure sensors in the glove and sending the information to the robot hand, the robot hand can accurately repeat the motions of users.
Design and implementation of book publishing topic selection system based on collaborative filtering algorithm

Abstract In view of the current book publishing topic planning may appear to rely on subjective experience of the problem. As well as book topic selection cannot be very good in line with the current hot vocabulary of this potential problem. Through the book sales market investigation, in line with the purpose of serving the book publishing industry. A collaborative filtering algorithm is used to design a business Intelligence Publishing topic selection system based on data mining. Follows are the process of processing data. First, we should put the crawl of online book information into the database. Second, filter to get the complete data we want. Finally, analysis the obtained data by collaborative filtering algorithm. This can be personalized to recommend the publishing house required keywords and the direction of the topic. Doing so can greatly reduce the burden on publishing house staff. It facilitates the development of the topic selection work of publishing house. Through the prediction of book publishing topic, we can understand the market law in a timely manner, cater to the customer’s consumption concept. In this way, we can greatly improve the efficiency of publishing house selection and reduce the consumption of human financial and material resources.
Live Stereoscopic 3D Image with Constant Capture Direction of 360° Cameras for High-Quality Visual Telepresence

To capture a remote 3D image, conventional stereo cameras attached to a robot head have been commonly used. However, when the head and cameras rotate, the captured image in buffers is degraded by latency and motion blur, which may cause VR sickness. In the present study, we propose a method named TwinCam in which we use two 360° cameras spaced at the standard interpupillary distance and keep the direction of the lens constant in the world coordinate even when the camera bodies are rotated to reflect the orientation of the observer's head and the position of the eyes. We consider that this method can suppress the image buffer size to send to the observer because each camera captures the omnidirectional image without lens rotation. This paper introduces the mechanical design of our camera system and its potential for visual telepresence through three experiments. Experiment 1 confirmed the requirement of a stereoscopic rather than monoscopic camera for highly accurate depth perception, and Experiments 2 and 3 proved that our mechanical camera setup can reduce motion blur and VR sickness.
Distributed Bidding-Based Detect-and-Avoid for Multiple Unmanned Aerial Vehicles in National Airspace

Unmanned Aerial Vehicles (UAVs) have great potential in civilian applications, such as package delivery, agriculture, and disaster management. The number of UAVs in these applications will continue to increase, and thus so does the importance of safely integrating them into the National Airspace System (NAS), with one key component being the management of UAV traffic. Agents may be given pre-planned paths from a mediator traffic management system, but a system must also be implemented to resolve conflicts arising from issues such as deviation from preplanned paths or uncooperative agents. Deviations can occur from any number of factors, and a proper traffic management framework must be capable of resolving these conflicts. Proposed here is a Detect-and-Avoid (DAA) system that will resolve conflicts between UAVs in the airspace in a distributed bidding based method. The results show this approach can be scaled to a system with a large number of UAVs, and will provide near-optimal solutions in a dynamic environment.
PURE: A Privacy Aware Rule-Based Framework over Knowledge Graphs

Open data initiatives and FAIR data principles have encouraged the publication of large volumes of data, encoding knowledge relevant for the advance of science and technology. However, to mine knowledge, it is usually required the processing of data collected from sources regulated by diverse access and privacy policies. We address the problem of enforcing data privacy and access regulations (EDPR) and propose PURE, a framework able to solve this problem during query processing. PURE relies on the local as view approach for defining the rules that represent the access control policies imposed over a federation of RDF knowledge graphs. Moreover, PURE maps the problem of checking if a query meets the privacy regulations to the problem of query rewriting (QRP) using views; it resorts to state-of-the-art QRP solutions for determining if a query violates or not the defined policies. We have evaluated the efficiency of PURE over the Berlin SPARQL Benchmark (BSBM). Observed results suggest that PURE is able to scale up to complex scenarios where a large number of rules represents diverse types of policies.
Color balancing and geometrical registration of high-resolution planetary imagery for improved orthographic image mosaicking

High-resolution orthographic image mosaics of planetary surfaces are an important prerequisite for a range of scientific research and applications. Because images are collected under different conditions, the individual images will inevitably present color differences and geometrical misalignments. Current color balancing methods generally adopt a linear model, including gain and offset values, based on a reference image. However, global color consistency may be severely influenced by the reference image, and in complex scenarios the linear model may not be able to remedy the color differences. This paper presents a novel color balancing approach that does not require a reference image. It incorporates a novel regularization term in the derivative of the color transferring model, which guaranties minimal contrast variation of the color model and removes the ambiguity of the nullspace of the optimization. In addition, to adapt to more complex color differences, a spline model in the color space is proposed in place of the linear model, and a special parametrization of the spline is exploited for least-squares optimization. Based on the two competing goals of minimizing color differences and preserving transfer regularities, the spline model is solved in a single global optimization for all images. Furthermore, an effective geometrical registration method is also used to reduce the misalignments in the object space. Experimental evaluations using two typical image datasets for Mars and the Moon reveal that the proposed approach achieves favorable geometrical consistency. Compared with two off-the-shelf solutions, it both achieves better color consistency and preserves better color contrast.
Factored Contextual Policy Search with Bayesian optimization

Scarce data is a major challenge to scaling robot learning to truly complex tasks, as we need to generalize locally learned policies over different task contexts. Contextual policy search offers data-efficient learning and generalization by explicitly conditioning the policy on a parametric context space. In this paper, we further structure the contextual policy representation. We propose to factor contexts into two components: target contexts that describe the task objectives, e.g. target position for throwing a ball; and environment contexts that characterize the environment, e.g. initial position or mass of the ball. Our key observation is that experience can be directly generalized over target contexts. We show that this can be easily exploited in contextual policy search algorithms. In particular, we apply factorization to a Bayesian optimization approach to contextual policy search both in sampling-based and active learning settings. Our simulation results show faster learning and better generalization in various robotic domains. See our supplementary video: https://youtu.be/IIJTbBAOufDY.
Privacy and Robust Hashes

Within a forensic examination of a computer for illegal image content, robust hashing can be used to detect images even after they have been altered. Here the perceptible properties of an image are used to create the hash values. Whether an image has the same content is determined by a distance function. Cryptographic hash functions, on the other hand, create a unique bit-sensitive value. With these, no similarity measurement is possible, since only with exact agreement a picture is found. A minimal change in the image results in a completely different cryptographic hash value. However, the robust hashes have an big disadvantage: hash values can reveal something about the structure of the picture. This results in a data protection leak. The advantage of a cryptographic hash function is in turn that its values do not allow any conclusions about the structure of an image. The aim of this work is to develop a procedure for which combines the advantages of both hashing functions.
Securing Online Bank's Big Data Through Block Chain Technology

Many sectors and fields are being computerized to make the work paperless, more transparent, and efficient. Banking is one such sector that has undergone enormous changes. Any amount from any part to any corner of the world is now possible around the clock. The dependency on technology for providing the services necessitates security, and the additional risks involved in cross-border nature of transactions of banks poses new challenges for banking regulators and supervisors. Many types of research are going in this area of banks big data processing, data analytics, and providing security for cross-border payments to mitigate the risks. Block chain is one such advancement for addressing the challenges in financial services. This chapter provides a brief overview of block chain usage, addressing the traditional issues and challenges for cross-border transactions.
Chapter 9. Attitudes to flat adverbs and English usage advice

Widespread as they are in non-standard and informal varieties of English, flat or suffixless adverbs are subject to prescriptive criticism when appearing in standard English. In the present study we repeated the survey by Mittins et al. (1970) in Attitudes to English Usage to investigate whether the acceptability of flat adverbs has changed since the late 1960s. Our findings suggest that acceptability has grown over the past fifty years, with flat adverbs losing their status of a usage problem. The analysis of the Hyper Usage Guide of English database suggests the same. Interestingly, we identified a new usage problem related to the usage of adverbs, one primarily associated with American English, the dual-pair adverb, thus/thusly.
Hierarchical Recursive Network for Single Image Super Resolution

Super Resolution (SR) technique aims to reconstruct the high resolution (HR) image from the observed low resolution (LR) one, which is a significant application in our daily life. In this paper, we propose a novel structure named hier-archical recursive network (HRN), which consists of several sub networks and will reconstruct the HR progressively. In each sub network, the LR feature map will be used as input, the contextual information will be explored and the predicted residuals together with the transposed convolutional outputs will be fused to the finer one. Besides, our network can generate multi-scale HR images with a single model and thus is potentially useful in practical applications. Extensive experi-mental results show that our proposed method can achieve the state-of-the-art performance.
Estimation of Detection Thresholds for Redirected Turning

Redirection makes it possible to walk around a vast virtual space in a limited real space while providing a natural walking sensation by applying a gain to the amount of movement in a real space. However, manipulating the walking path while keeping it and maintaining the naturalness of walking when turning at a corner cannot be achieved by the existing methods. To realize natural manipulation for turning at a corner, this study proposes novel “turning gains”, which refer to the increase in real and virtual turning degrees. The result of an experiment which aims to estimate the detection thresholds of turning gains indicated that when the turning radius is 0.5 m, discrimination is more difficult compared with the rotation gains (r = 0.0m).
Applying Memetic algorithm with Improved L-SHADE and Local Search Pool for the 100-digit challenge on Single Objective Numerical Optimization

In this paper, we have proposed a new optimization algorithm, Memetic improved L-SHADE with a local search pool, MiLSHADE-LSP, a memetic algorithm that combines an improved L-SHADE with a local search pool. Improved L-SHADE modifies several important parameters during the run to encourage exploration in initial stages and to focus later the search around the most promising solutions. The local search pool is responsible to continuously improve the best solutions. MiLSHADE-LSP uses a pool of two different local search, LS, methods, the Broyden-Fletcher-Goldfarb-Shanno method with limited memory, L-BFGS-B, and the Solis-Wets algorithms, with an adaptive mechanism to choose which one of them is applied in each iteration selecting which had obtained a greater improvement last time it was applied. In order to avoid waste LS applications, the proposed algorithm stores a list of individuals that were not previously improved by each LS method. It also includes a restart mechanism to explore new areas when the search is stuck, restarting the population but maintaining the best found solution, and resetting the LS Pool parameters. In the experimental section we have tested and analyzed MiLSHADE-LSP using the proposed benchmark for the competition 100-digit challenge on Single Objective Numerical Optimization, obtaining that the LS Pool improves the algorithm, both achieving more optima and with a better performance. Results obtained show that MiLSHADE-LSP is a very competitive algorithm.
On the impact of service-oriented patterns on software evolvability: a controlled experiment and metric-based analysis

Design patterns are supposed to improve various quality attributes of software systems. However, there is controversial quantitative evidence of this impact. Especially for younger paradigms such as service- and Microservice-based systems, there is a lack of empirical studies.In this study, we focused on the effect of four service-based patterns-namely Process Abstraction, Service Façade, Decomposed Capability, and Event-Driven Messaging-on the evolvability of a system from the viewpoint of inexperienced developers.We conducted a controlled experiment with Bachelor students (N = 69). Two functionally equivalent versions of a service-based web shop-one with patterns (treatment group), one without (control group)-had to be changed and extended in three tasks. We measured evolvability by the effectiveness and efficiency of the participants in these tasks. Additionally, we compared both system versions with nine structural maintainability metrics for size, granularity, complexity, cohesion, and coupling.Both experiment groups were able to complete a similar number of tasks within the allowed 90 min. Median effectiveness was 1/3. Mean efficiency was 12% higher in the treatment group, but this difference was not statistically significant. Only for the third task, we found statistical support for accepting the alternative hypothesis that the pattern version led to higher efficiency. In the metric analysis, the pattern version had worse measurements for size and granularity while simultaneously having slightly better values for coupling metrics. Complexity and cohesion were not impacted.For the experiment, our analysis suggests that the difference in efficiency is stronger with more experienced participants and increased from task to task. With respect to the metrics, the patterns introduce additional volume in the system, but also seem to decrease coupling in some areas.Overall, there was no clear evidence for a decisive positive effect of using service-based patterns, neither for the student experiment nor for the metric analysis. This effect might only be visible in an experiment setting with higher initial effort to understand the system or with more experienced developers.
A Unified Deep Learning Framework for Multi-Modal Multi-Dimensional Data

In data science applications, multi-modal and multidimensional nature of data leads to various types of manual features and tailored classifiers to specific problems. Traditional feature engineering demands expertise and the performance from integrated classifiers has reached a plateau after years of development. Emerging technologies such as deep learning make a great performance leap after transition from feature engineering to architecture engineering. However, putting this technology to practical use faces numerous challenges. First, it is known that the success of deep learning is built upon large amount of training data, but most data science problems do not have sufficient training data. In addition, designing the architecture of deep models needs substantial expertise. Moreover, existing deep neural networks have limitations on the type of inputs they can work on, and their output is limited in the type of problems they can solve. To address these challenges, we propose a unified deep learning framework that suits data in multiple modalities and dimensions, and prove that using the proposed framework yields better performance than traditional machine learning approaches. Besides the examples used for validation, the proposed framework demonstrates potential in becoming a foundation, upon which more wide-spread data science problems can benefit from using deep learning.
A Sparse Fireworks Algorithm for Gene Regulatory Network Reconstruction based on Fuzzy Cognitive Maps

Gene regulatory networks (GRNs) denote the interrelation among genes in the genomic level. In reality, gene regulatory networks are presented as sparse networks, so using sparse models to represent GRNs is a meaningful task. Fuzzy cognitive maps (FCMs) have been used to reconstruct GRNs. However, the networks learned by automated derivate-free methods are much denser than those in practical applications. Moreover, the performance of current sparse FCM learning algorithms is worse than what we expect. The fireworks algorithm is an efficient and simple optimization algorithm. However, there are few fireworks algorithms currently used to solve the sparse optimization problem. To utilize the powerful learning ability of fireworks algorithms to learn sparse FCMs, we propose a sparse fireworks algorithm (SFWA-FCM). Compared with existing FCM learning algorithms, SFWA-FCM's excellent numerical fitting ability and sparse modeling ability are illustrated. In addition, SFWA-FCM is used to solve the problem of GRN reconstruction. On the GRN reconstruction benchmark DREAM4, SFWA shows the high accuracy. The good performance in learning sparse FCMs illustrates the effectiveness of SFWA-FCM, and the simplicity and scalability of the framework ensure that it can be adapted to a wide range of needs.
Change Analysis of #if-def Blocks with <i>FeatureCloud</i>

FeatureCloud is a git client for the visualization of evolution in annotated SPL, i.e. those resorting to ifdefs for variability. Specifically, FeatureCloud (1) mines git repositories; (2) extracts ifdefs; (3) works out differences between two versions of the same ifdefs; (4) abstracts ifdefs in terms of their code churn, tangling and scattering; and finally (5), aggregates and visualizes these properties through "feature clouds". Feature clouds aim to play the same role for SPLs that "word clouds" for textual content: provide an abstract view of the occurrence of features along evolving code, where "repetition" account for scattering and tangling of features. Here, we introduce the analysis goals, the perspective (i.e. the object of analysis) and visualization strategies that underpin FeatureCloud.
Deriving Probabilistic SVM Kernels from Exponential Family Approximations to Multivariate Distributions for Count Data

This work aims to propose a robust hybrid probabilistic learning approach that combines appropriately the advantages of both the generative and discriminative models for modeling count data. We build new probabilistic kernels based on information divergences and Fisher score from efficient approximations to multivariate distributions for support vector machines (SVMs). More precisely, we drive probabilistic kernels from the mixture of exponential family approximation to two powerful generative models for count data, namely the multinomial compound Dirichlet (DCM) and the generalized Dirichlet multinomial (GDM). The developed hybrid models are introduced as effective SVM kernels able to incorporate prior knowledge about the nature of data involved in the problem at hand and, therefore, permits a good data discrimination. We demonstrate the flexibility and the merits of the proposed frameworks for the problem of analyzing activities in surveillance scenes.