Reviewerly/exHarmony huggingface dataset:
Opacity, obscurity, and the geometry of question-asking

Asking questions is a pervasive human activity, but little is understood about what makes them difficult to answer. An analysis of a pair of large databases, New York Times crosswords and questions from the quiz-show Jeopardy, establishes two orthogonal dimensions of question difficulty: obscurity (the rarity of the answer) and opacity (the indirectness of question cues, operationalized with word2vec). The importance of opacity, and the role of synergistic information in resolving it, suggests that accounts of difficulty in terms of prior expectations captures only a part of the question-asking process. A further regression analysis shows the presence of additional dimensions to question-asking: question complexity, the answer's local network density, cue intersection, and the presence of signal words. Our work shows how question-askers can help their interlocutors by using contextual cues, or, conversely, how a particular kind of unfamiliarity with the domain in question can make it harder for individuals to learn from others. Taken together, these results suggest how Bayesian models of question difficulty can be supplemented by process models and accounts of the heuristics individuals use to navigate conceptual spaces.
Modeling and Training of Neural Processing Systems

The field of deep learning has become more and more pervasive in the last years as we have seen varieties of problems being solved using neural processing techniques. Image analysis and detection, control, speech recognition, translation are only a few prominent examples tackled successfully by neural networks. Thereby, the discipline imposes a completely new problem solving paradigm requiring a rethinking of classical software development methods. The high demand for deep learning technology has led to a large amount of competing frameworks mostly having a Python interface - a quasi standard in the community. Although, existing tools often provide great flexibility and high performance, they still lack to deliver a completely domain oriented problem view. Furthermore, using neural networks as reusable building blocks with clear interfaces in productive systems is still a challenge. In this work we propose a domain specific modeling methodology tackling design, training, and integration of deep neural networks. Thereby, we distinguish between three main modeling concerns: architecture, training, and data. We integrate our methodology in a component-based modeling toolchain allowing one to employ and reuse neural networks in large software architectures.
Crossing Blind Intersections from a Full Stop Using Estimated Visibility of Approaching Vehicles

Due to the complexity of the environment and occlusions which often present in the scene, autonomous driving in an urban area is a challenging task. In some critical locations, e.g., intersections with low visibility, occlusions need to be taken into account as failing to do so might lead to a severe accident. In this paper, a method for crossing blind intersections with a mandatory stop using the estimated visibility of possible approaching vehicles is proposed. Speed profiles generated by the proposed method were compared with those of an expert driver. The results showed that the proposed method could produce a similar driving characteristic of an expert driver at low-visibility intersections with a mandatory stop.
Measureability of Functional Size in Agile Software Projects: Multiple Case Studies with COSMIC FSM

Functional size measurement (FSM) has been used in software engineering for decades as a main driver for estimation and significant input for other various project management activities throughout the project life span. To apply FSM accurately at the early stages of software development process, especially for estimation purposes, functional user requirements need to be available in detail as required by the adopted FSM method. However, in agile software development, requirement specifications, in general, are kept minimal. For this reason, the adjustment of the requirements to the necessary granularity level has been articulated as one of the barriers preventing the diffusion of FSM practices among agile teams. In this paper, we take a closer look at this problem in order to investigate the usability of FSM and to reveal FSM related challenges empirically through case studies on real agile projects from different software organizations. This study also provides a snapshot of agile organizations in terms of requirement specification and estimation related practices.
A Blockchain-based Payment and Validity Check System for Vehicle Services

Self-driving cars rely on several services to operate, some of which require financial interactions, such as paying for parking spaces or paying for battery charging in the case of the electric vehicles. Providing these services demands the cooperation of several parties and organizations that do not necessarily trust each other. Over the past few years, several blockchain-based services have been introduced to enable users with a safe and secure medium for conducting business in a trustless environment. In this work, we propose a blockchain-based solution to enable vehicles to consume and pay for services.
Mobile Augmented Reality for Learning Traditional Culture Using Marker Based Tracking

Abstract The purpose of this research is to design a mobile augmented reality for learning Batavia traditional culture on improving the knowledge of the citizen of Jakarta. Augmented reality is applied as an approach to enhance the interaction of users for improving their level of knowledge. The method used in this research is a marker-based method detection which resulted in a minimum response of less than 2 second, a minimum distance of 10 cm and a maximum distance of 50 cm, a minimum angle of 25 degrees and a maximum 80 degrees performed on the augmented reality marker. The result of this research is the marker-based tracking method applied on a mobile augmented reality has proven to be excellent and giving an increasing knowledge effect of the citizen of Jakarta for learning their traditional arts, architecture and weaponry.
Big Data Analysis in Film Production

The article analyzes the current trends of digitalization for large innovative industrial production, which are international, large-capacity, distributed in different geographical locations and having several production lines at each plant. Such trends of digitalization as predictive analytics and 6 sigma methodology, which includes Ishikawa diagram and DMAIC (definition, measure, analysis, improvement, control) cycle, are considered. The novelty of the work lies in the application of methods and technologies of intellectual analysis of large industrial data for production of polymeric films and in the application of mathematical models that allow online calculation of uncontrolled consumer characteristics of products (thickness, color of polymeric films) and integrate them into one single system of data mining. Developed software solution includes visualization unit, forecast unit, statistical data analysis unit. Software solution allows us: determine the types of films with the best yield; check the production data for normalcy; calculate process capability index; calculate key performance indicators. Application and testing of the big data analysis system on the example of large industrial Corporation Kloeckner Pentaplast proved its efficiency.
Efficient Vanishing Point Detection for Driving Assistance Based on Visual Saliency Map and Image Segmentation from a Vehicle Black-Box Camera

Techniques for detecting a vanishing point (VP) which estimates the direction of a vehicle by analyzing its relationship with surrounding objects have gained considerable attention recently. VPs can be used to support safe vehicle driving in areas such as for autonomous driving, lane-departure avoidance, distance estimation, and road-area detection, by detecting points in which parallel extension lines of objects are concentrated at a single point in a 3D space. In this paper, we proposed a method of detecting the VP in real time for applications to intelligent safe-driving support systems. In order to support safe driving of autonomous vehicles, it is necessary to drive the vehicle with the VP in center of the road image in order to prevent the vehicle from moving out of the road area while driving. Accordingly, in order to detect the VP in the road image, a method of detecting a point where straight lines intersect in an area where edge directional feature information is concentrated is required. The visual attention model and image segmentation process are applied to quickly identify candidate VPs in the area where the edge directional feature-information is concentrated and the intensity contrast difference is large. In the proposed method, VPs are detected by analyzing the edges, visual-attention regions, linear components using the Hough transform, and image segmentation results in an input image. Our experimental results have shown that the proposed method could be applied to safe-driving support systems.
Megapolis Tourism Development Strategic Planning with Cognitive Modelling Support

The strategic planning of the megapolis tourism development is the process that has to take into account hundreds of factors. Some of the factors cannot be calculated or do not have statistic history. Some of the factors are latent or incorrect. The long-term strategic planning usually includes a short-term action planning. In this case, experts are creating cognitive models that take into consideration non-formalized cognitive semantics. The modelling shows that small change in resource allocation or some mistakes in decision-making can be the reason not to achieve the goals and can replace the optimistic scenario of tourism development by a pessimistic one. The cognitive models could be improved by mapping on the relevant Big Data. The special author’s approach to make the process of tourism strategic decision-making convergent was applied. This paper addresses the issue of using convergent approach to megapolis tourism development strategic planning with a lot of focus groups and cognitive modelling. The inverse problem solving with genetic algorithm helped to find effective strategic decisions and reduce risks of decision-making. For taking into account non-quantitative factors, it is suggested using networked expertise. The convergent approach with cognitive modelling was applied for creating the megapolis tourism development strategic plan for the megapolis. It helps to find the multiplying events and prioritize strategic directions for tourism development.
Impact of Low-Bitwidth Quantization on the Adversarial Robustness for Embedded Neural Networks

As the will to deploy neural network models on embedded systems grows, and considering the related memory footprint and energy consumption requirements, finding lighter solutions to store neural networks such as parameter quantization and more efficient inference methods becomes major research topics. Parallel to that, adversarial machine learning has risen recently, unveiling some critical flaws of machine learning models, especially neural networks. In particular, perturbed inputs called adversarial examples have been shown to fool a model into making incorrect predictions. In this paper, we investigate the adversarial robustness of quantized neural networks under different attacks. We show that quantization is not a robust protection when considering advanced threats and may result in severe form of gradient masking which leads to a false impression of security. However, and interestingly, we experimentally observe poor transferability capacities between full-precision and quantized models and between models with different quantization levels which we explain by the quantization value shift phenomenon and gradient misalignment.
Features of the development of information systems for working with blockchain technology

Abstract This article is devoted to the development of an information system for conducting financial transactions on a cryptocurrency exchange based on blockchain technology. The paper provides a brief overview of the underlying technology, the advantages and disadvantages are identified, the design of the information system and user interface is carried out.
Weighted triple-sequence loss for video-based person re-identification

Person re-identification (re-id) task has attracted a lot of attention because of the excellent performance powered by the Convolutional Neural Network (CNN). However, video-based person re-id is still challenging and far to be solved. On the one hand, the sequence contains complementary information but also more noise information. On the other hand, the training for video is still mainly based on classifying a single frame via its person identity. The representation of the video is generated by aggregating each frame feature. All these will cause that the robustness of video feature is not adequate at the training stage, and the model is easy to be misled by noise information existing in frames. In order to alleviate the difficulty of training video-based re-id, we propose a novel loss named Weighted Triple-Sequence Loss (WTSL) to optimize the video-based feature and reduce the impact of outliers. Further more, we design a Spatial Transformed Partial Network (STPN) coordinated with jointly optimizing image-level and video-level features to generate more robust representation. Extensive experiments show that our algorithm outperforms the state-of-the-art results and achieves 82.2%, 95.2%, and 85.9% rank-1 accuracy on three popular video-based benchmarks: iLIDS-VID, PRID2011, and MARS, respectively.
Distributed Text Feature Selection Based On Bat Algorithm Optimization

The feature selection effect directly affects the classification accuracy of the text. This paper introduces a new text feature selection method based on bat optimization. This method uses the traditional feature selection method to pre-select the original features, and then uses the bat group algorithm to optimize the pre-selected features in binary code form, and uses the classification accuracy as the individual fitness. However, when the amount of text information is large, the execution time of the single machine is long. According to this shortcoming, combining the Bat Algorithm and the Spark parallel computing framework, the text feature selection algorithm SBATFS is proposed. The algorithm combines the good search performance of the bat algorithm with the distributed and efficient calculation speed to realize the efficient solution of the text feature selection optimization model. The results show that compared with the traditional feature selection method, after SBATFS is used for feature optimization, the classification accuracy is effectively improved.
Performance analysis of continuous-variable measurementdevice-independent quantum key distribution under diverse weather conditions*

The effects of weather conditions are ubiquitous in practical wireless quantum communication links. Here in this work, the performances of atmospheric continuous-variable measurement-device-independent quantum key distribution (CV-MDI-QKD) under diverse weather conditions are analyzed quantitatively. According to the Mie scattering theory and atmospheric CV-MDI-QKD model, we numerically simulate the relationship between performance of CV-MDI-QKD and the rainy and foggy conditions, aiming to get close to the actual combat environment in the future. The results show that both rain and fog will degrade the performance of the CV-MDI-QKD protocol. Under the rainy condition, the larger the raindrop diameter, the more obvious the extinction effect is and the lower the secret key rate accordingly. In addition, we find that the secret key rate decreases with the increase of spot deflection distance and the fluctuation of deflection. Under the foggy condition, the results illustrate that the transmittance decreases with the increase of droplet radius or deflection distance, which eventually yields the decrease in the secret key rate. Besides, in both weather conditions, the increase of transmission distance also leads the secret key rate to deteriorate. Our work can provide a foundation for evaluating the performance evaluation and successfully implementing the atmospheric CV-MDI-QKD in the future field operation environment under different weather conditions.
Towards Generating Transformation Rules without Examples for Android API Replacement

Deprecation of APIs in software libraries is common when library maintainers make changes to a library and will no longer support certain APIs in the future. When deprecation occurs, developers whose programs depend on the APIs need to replace the usages of the deprecated APIs sooner or later. Often times, software documentation specifies which new APIs the developers should use in place of a deprecated API. However, replacing the usages of a deprecated API remains a challenge since developers may not know exactly how to use the new APIs. The developers would first need to understand the API changes before they can replace the deprecated API correctly. Previous work has proposed an approach to assist developers in deprecated Android API replacement by learning from examples. In this work, we also focus on Android APIs and propose an approach named No Example API Transformation (NEAT) to generate transformation rules that can assist developers in deprecated API replacement even when code examples are not available (e.g., when the deprecation has happened recently). We have validated the effectiveness of NEATon generating transformation rules for deprecated Android APIs. Using NEAT, we can generate 37 transformation rules for 37 out of a selection of 100 deprecated APIs and have validated these rules to be correct.
Image Corpus Representative Summarization

We propose a novel approach for image corpus representative summarization using GAN. Our technique can be used to automatically provide a condensed set of representatives for the given image collection. The generated summary can be used for rapid prototyping as models can be trained using the summarized set instead of the larger original dataset. The problem is challenging because a good summary must cover various aspects of an image set such as relevance and diversity. Additionally, lack of sufficient ground truth data makes the problem hard to solve using classical supervised machine learning approaches. In our algorithm, we use CNN and an MLP score layer to compute the priority of each image towards the summary. Our network is trained in an unsupervised manner using a generator for reconstructing the original dataset, and a discriminator, for classifying between original and summary. We show the efficacy of the algorithm using rigorous experiments.
A Study on the Current Status of Functional Idioms in Java

Over the past recent decades, numerous programming languages have expanded to embrace multi-paradigms such as the fusion of object-oriented and functional programming. For example, Java, one of the most famous object-oriented programming languages, introduced a number of functional idioms in 2014. This evolution enables developers to achieve various benefits from both paradigms. However, we do not know how Java developers use functional idioms actually. Additionally, the extent to which, while there are several criticisms against the idioms, the developers actually accept and/or use the idioms currently remains unclear. In this paper, we investigate the actual use status of three functional idioms (Lambda Expression, Stream, and Optional) in Java projects by mining 100 projects containing approximately 130,000 revisions. From the mining results, we determined that Lambda Expression is utilized in 16% of all the examined projects, whereas Stream and Optional are only utilized in 2% to 3% of those projects. It appears that most Java developers avoid using functional idioms just because of keeping compatibility Java versions, while a number of developers accept these idioms for reasons of readability and runtime performance improvements. Besides, when they adopt the idioms, Lambda Expression frequently consists of a single statement, and Stream is used to operate the elements of a collection. On the other hand, some developers implement Optional using deprecated methods. We can say that good usage of the idioms should be widely known among developers.
An interval type-2 fuzzy logic controller design method for hydraulic actuators of a human-like robot by using improved drone squadron optimization

Hydraulic actuator becomes an increasingly concerned driver for human-like robots. However, its dynamic performance under the control should be still further improved because hydraulic system is a typical nonlinearity system. Interval type-2 fuzzy logic controller is an advanced control method featured with high performance to deal with uncertain and nonlinear dynamics, so designing an interval type-2 fuzzy logic controller for the control of hydraulic is a feasible method. In this article, an improved drone squadron optimization-based approach is proposed to optimize interval type-2 fuzzy logic controller parameters. To verify the feasibility and priority of improved drone squadron optimization, a comparison on three different typical plants including proportional-derivative (PD) system, proportional-integral (PI) system, and PI nonlinear system between improved drone squadron optimization and other meta-heuristic algorithms is carried out. Simulation results demonstrate that improved drone squadron optimization not only gets an appropriate interval type-2 fuzzy logic controller for system control but also outperforms other popular algorithms in accuracy of performance.
A Generic Model of the Pseudo-Random Generator Based on Permutations Suitable for Security Solutions in Computationally-Constrained Environments

Symmetric cryptography methods have an important role in security solutions design in data protection. In that context, symmetric cryptography algorithms and pseudo-random generators connected with them have strong influence on designed security solutions. In the computationally constrained environment, security efficiency is also important. In this paper we proposed the design of a new efficient pseudo-random generator parameterized by two pseudo-random sequences. By the probabilistic, information-theoretic and number theory methods we analyze characteristics of the generator. Analysis produced several results. We derived sufficient conditions, regarding parameterizing sequences, so that the output sequence has uniform distribution. Sufficient conditions under which there is no correlation between parameterizing sequences and output sequence are also derived. Moreover, it is shown that mutual information between the output sequence and parameterizing sequences tends to zero when the generated output sequence length tends to infinity. Regarding periodicity, it is shown that, with appropriately selected parameterizing sequences, the period of the generated sequence is significantly longer than the periods of the parameterizing sequences. All this characteristics are desirable regarding security applications. The efficiency of the proposed construction can be achieved by selection parameterizing sequences from the set of efficient pseudo-random number generators, for example, multiple linear feedback shift registers.