Reviewerly/exHarmony huggingface dataset:
Afgjort: A Partially Synchronous Finality Layer for Blockchains

Most existing blockchains either rely on a Nakamoto-style of consensus, where the chain can fork and produce rollbacks, or on a committee-based Byzantine fault tolerant (CBFT) consensus, where no rollbacks are possible. While the latter ones offer better consistency, the former tolerate more corruptions. To achieve the best of both worlds, we initiate the formal study of finality layers. Such a finality layer can be combined with a Nakamoto-style blockchain (NSB) and periodically declare blocks as final, preventing rollbacks beyond final blocks. As conceptual contributions, we formalize the concept of a finality layer and identify the following properties to be crucial for finality layers: finalized blocks form a chain (chain-forming), all parties agree on the finalized blocks (agreement), the last finalized block does not fall too far behind the last block in the underlying blockchain (updated), and all finalized blocks at some point have been on the chain adopted by honest parties holding at least k units of the resource on which consensus is based, e.g., stake or computing power (k-support). As our main technical contribution we propose the finality layer protocol Afgjort. We prove that it satisfies all of the aforementioned requirements in the setting with less than 1/3 corruption among the finalizers and a partially synchronous network. We further show that tolerating less than 1/3 corruption is optimal for partially synchronous finality layers. Finally, we provide data from experiments ran with an implementation of our protocol; the data confirms that finality is reached much faster than without our finality layer.
Rendering Ultrasound Pressure Distribution on Hand Surface in Real-Time

In this paper, we propose a method for rendering the pressure distribution on the skin surface of a hand in real-time using an ultrasonic phased array. Our method generates a polygon mesh model representing the hand shape by fitting a rigid template to the point cloud captured by depth sensors. Obtaining the entire hand shape as a mesh model enables to solve scattering problem to generate a precise distribution on the hand surface. Therefor, for example, our method can control the width of the distribution on the fingertip according to the size of the contact area with the virtual object. We have experimentally verified that considering the scattering on the mesh model contributes to accurate pressure pattern reproduction.
What is Game Balancing? - An Examination of Concepts

Balancing is commonly considered critical to any game’s success. However, there is no consensus on what “game balancing” actually means. This paper examines fourteen publications by experienced game designers and other renowned authors. The results of a formal semantic analysis of the respective authors’ reflections prove that no two authors share identical understandings of “game balancing.” Their differing concepts of the term are analyzed and discussed in order to identify key aspects, similarities, and differences. Contrary to what one might expect, concepts such as fairness, flow, or user satisfaction are identified to be important, but no central or fundamental concepts for the definition of game balancing.
The correlation strength of the most important cryptocurrencies in the bull and bear market

The article explores the correlation strength of the ten most important cryptocurrencies, emphasizing the examination of differences during the periods of rising and falling prices. The daily and weekly returns of selected cryptocurrencies are taken as the basis for calculating and determining the correlation strength using the Pearson correlation coefficient. The survey covers the period from the beginning of 2017 to Bitcoin’s last local bottom in mid-March 2020. Research findings are as follows: 1) the most important cryptocurrencies are mostly moderately positively correlated with each other over time; 2) correlation strength decreases slightly during the bull period, but mostly remain in the range of moderate correlation; 3) correlation strength increases significantly during the bear period, with most cryptocurrencies strongly correlated with each other. The results do not change significantly if the daily or weekly cryptocurrency returns are used as the basis. A strong correlation in the period of falling prices prevents the effective diversification of the cryptocurrency portfolio, which must be considered when investing funds in the cryptocurrency market.
Forensic document examination and algorithmic handwriting analysis of Judahite biblical period inscriptions reveal significant literacy level

Arad is a well preserved desert fort on the southern frontier of the biblical kingdom of Judah. Excavation of the site yielded over 100 Hebrew ostraca (ink inscriptions on potsherds) dated to ca. 600 BCE, the eve of Nebuchadnezzar's destruction of Jerusalem. Due to the site's isolation, small size and texts that were written in a short time span, the Arad corpus holds important keys to understanding dissemination of literacy in Judah. Here we present the handwriting analysis of 18 Arad inscriptions, including more than 150 pair-wise assessments of writer's identity. The examination was performed by two new algorithmic handwriting analysis methods and independently by a professional forensic document examiner. To the best of our knowledge, no such large-scale pair-wise assessments of ancient documents by a forensic expert has previously been published. Comparison of forensic examination with algorithmic analysis is also unique. Our study demonstrates substantial agreement between the results of these independent methods of investigation. Remarkably, the forensic examination reveals a high probability of at least 12 writers within the analyzed corpus. This is a major increment over the previously published algorithmic estimations, which revealed 4–7 writers for the same assemblage. The high literacy rate detected within the small Arad stronghold, estimated (using broadly-accepted paleo-demographic coefficients) to have accommodated 20–30 soldiers, demonstrates widespread literacy in the late 7th century BCE Judahite military and administration apparatuses, with the ability to compose biblical texts during this period a possible by-product.
Responsive Matrix Cells: A Focus+Context Approach for Exploring and Editing Multivariate Graphs

Matrix visualizations are a useful tool to provide a general overview of a graph's structure. For multivariate graphs, a remaining challenge is to cope with the attributes that are associated with nodes and edges. Addressing this challenge, we propose responsive matrix cells as a focus+context approach for embedding additional interactive views into a matrix. Responsive matrix cells are local zoomable regions of interest that provide auxiliary data exploration and editing facilities for multivariate graphs. They behave responsively by adapting their visual contents to the cell location, the available display space, and the user task. Responsive matrix cells enable users to reveal details about the graph, compare node and edge attributes, and edit data values directly in a matrix without resorting to external views or tools. We report the general design considerations for responsive matrix cells covering the visual and interactive means necessary to support a seamless data exploration and editing. Responsive matrix cells have been implemented in a web-based prototype based on which we demonstrate the utility of our approach. We describe a walk-through for the use case of analyzing a graph of soccer players and report on insights from a preliminary user feedback session.
Real-Time Systems Implications in the Blockchain-Based Vertical Integration of Industry 4.0

The Industrial Internet of Things (IIoT) is expected to attract significant investments for industry. In this new environment, blockchain presents immediate potential in applications of the IIoT, offering several benefits to industrial cyberphysical systems.
SENTRAC: A Novel Real Time Sentiment Analysis Approach Through Twitter Cloud Environment

In internet based life ‘Sentiment’ is a popular expression of the present patterns as there are heaps of work is being finished closing information or data mining. Utilizing sentiment examination different envision of events can be found, for example; human trafficking, burglary, ambiguous feelings and so on. Rapid miner, a social media mining stage which checks in the middle of sentiment relations through cloud mining techniques with a view to indicating the yield of a gross aftereffect of thought states. In this work, the authors have utilized rapid miner programming for investigating the continuous twitter cloud information mining methodology. Aftermath of this process, separation of human contemplation can be anticipated all through the varieties of their thinking sorts on each time based tweeting. The principle intention of this work is to demonstrate the performance execution between KNN and LR algorithms. Moreover, KNN performs superior to LR with 98% precision of genuine positivity upon individuals thinking examples or changes from constant data.
DrsNet: Dual-resolution semantic segmentation with rare class-oriented superpixel prior

Rare-class objects in natural scene images that are usually small and less frequent often convey more important information for scene understanding than the common ones. However, they are often overlooked in scene labeling studies due to two main reasons, low occurrence frequency and limited spatial coverage. Many methods have been proposed to enhance overall semantic labeling performance, but only a few consider rare-class objects. In this work, we present a deep semantic labeling framework with special consideration of rare classes via three techniques. First, a novel dual-resolution coarse-to-fine superpixel representation is developed, where fine and coarse superpixels are applied to rare classes and background areas respectively. This unique dual representation allows seamless incorporation of shape features into integrated global and local convolutional neural network (CNN) models. Second, shape information is directly involved during the CNN feature learning for both frequent and rare classes from the re-balanced training data, and also explicitly involved in data inference. Third, the proposed framework incorporates both shape information and the CNN architecture into semantic labeling through a fusion of probabilistic multi-class likelihood. Experimental results demonstrate competitive semantic labeling performance on two standard datasets both qualitatively and quantitatively, especially for rare-class objects.
A Deep Convolutional Network for Multitype Signal Detection and Classification in Spectrogram

Wideband signal detection is an important problem in wireless communication. With the rapid development of deep learning (DL) technology, some DL-based methods are applied to wireless communication and have shown great potential. In this paper, we present a novel neural network for detecting signals and classifying signal types in wideband spectrograms. Our network utilizes the key point estimation to locate the rough centerline of the signal region and recognize its class. Then, several regressions are carried out to obtain properties, including the local offset and the border offsets of a bounding box, which are further synthesized for a more fine location. Experimental results demonstrate that our method performs more accurate than other DL-based object detection methods previously employed for the same task. In addition, our method runs obviously faster than existing methods, and it abandons the candidate anchors, which make it more favorable for real-time applications.
AGTR: Adversarial Generation of Target Review for Rating Prediction

Abstract Recent years have witnessed a growing trend of utilizing reviews to improve the performance and interpretability of recommender systems. Almost all existing methods learn the latent representations from the user’s and the item’s historical reviews and then combine these two representations for rating prediction. The fatal limitation in these methods is that they are unable to utilize the most predictive review of the target user for the target item since such a review is not available at test time. In this paper, we propose a novel recommendation model, called AGTR, which can generate the unseen target review with adversarial training for rating prediction . To this end, we develop a unified framework to combine the rating tailored generative adversarial nets for synthetic review generation and the neural latent factor module using the generated target review along with historical reviews for rating prediction. Extensive experiments on four real-world datasets demonstrate that our model achieves the state-of-the-art performance in both rating prediction and review generation tasks.
Applying cheating identifiable secret sharing scheme in multimedia security

Abstract In ( k , n ) secret sharing scheme, one secret is encrypted into n shares in such a way that only k or more shares can decrypt the secret. Secret sharing scheme can be extended into the field of multimedia that provides an efficient way to protect confidential information on multimedia. Secret image sharing is just the most important extension of secret sharing that can safely guard the secrecy of images among multiple participants. On the other hand, cheating detection is an important issue in traditional secret sharing schemes that have been discussed for many years. However, the issue of cheating detection in secret image sharing has not been discussed sufficiently. In this paper, we consider the cheating problem in the application of secret image sharing schemes and construct a ( k , n ) secret image sharing scheme with the ability of cheating detection and identification. Our scheme is capable of identifying cheaters when k participants involve in reconstruction. The cheating identification ability and size of shadow in the proposed scheme are improved from the previous cheating identifiable secret image sharing scheme.
The Effect Of The Augmented Reality Applications In Science Class On Students' Cognitive And Emotional Learning

This study aims to determine how effective science teaching with augmented reality-based teaching material (science cards) is compared to the science curriculum prescribed methods on students' academic achievement and their views on augmented reality application. As a study group, 63 students who were studying in a 7th grade in a secondary school in Antalya in the 2019-2020 academic years were selected. Mixed research design was used in the research. In the quantitative side of the research, the control group and the experimental group who are taught using the augmented reality (science cards) were compared in terms of achievement. In the qualitative side on the other hand, interview and observation data were used. The results of the research have shown that while Augmented Reality (AR) applications increase students' achievement, contribute to the meaningful learning of abstract subjects, AR applications also increase the students' interest and motivation towards science lesson.
76‐2: <i>Distinguished Paper:</i> A High‐Image‐Quality OLED Display with Motion Blur Reduction for Ultra‐High Resolution and Premium TVs

We have developed a high image quality OLED Display with motion blur reduction technology. Our latest work includes driving method which reduces motion blur using an adaptive black data insertion, brightness compensation technology, the simple structure pixel with low capacitance coupling for horizontal noise, and the multi‐function integrated gate driver. The MPRT value of the OLED display panel with a fast response time was significantly affected by the frame frequency and the compensation driving method. The decrease in the MPRT value originated from the turning of the emitting pixels off in advance resulting from providing black data. The integrated gate drivers were designed to achieve the normal display, the black data insertion, and the compensation mode. The MPRT value of the 65‐inch UHD OLED panels was decreased to 3.4ms by using an integrated gate driver circuit. The motion blur of large‐size OLED display panels was significantly reduced due to a decrease in the MPRT value.
Impact analysis of cross-project bugs on software ecosystems

Software projects are increasingly forming social-technical ecosystems within which individual projects rely on the infrastructures or functional components provided by other projects, leading to complex inter-dependencies. Through inter-project dependencies, a bug in an upstream project may have profound impact on a large number of downstream projects, resulting in cross-project bugs. This emerging type of bugs has brought new challenges in bug fixing due to their unclear influence on downstream projects. In this paper, we present an approach to estimating the impact of a cross-project bug within its ecosystem by identifying the affected downstream modules (classes/methods). Note that a downstream project that uses a buggy upstream function may not be affected as the usage does not satisfy the failure inducing preconditions. For a reported bug with the known root cause function and failure inducing preconditions, we first collect the candidate downstream modules that call the upstream function through an ecosystem-wide dependence analysis. Then, the paths to the call sites of the buggy upstream function are encoded as symbolic constraints. Solving the constraints, together with the failure inducing preconditions, identifies the affected downstream modules. Our evaluation of 31 existing upstream bugs on the scientific Python ecosystem containing 121 versions of 22 popular projects (with a total of 16 millions LOC) shows that the approach is highly effective: from the 25490 candidate downstream modules that invoke the buggy upstream functions, it identifies 1132 modules where the upstream bugs can be triggered, pruning 95.6% of the candidates. The technique has no false negatives and an average false positive rate of 7.9%. Only 49 downstream modules (out of the 1132 we found) were reported before to be affected.
Implementation of Adaptive Learning Systems: Current State and Potential

Adaptive learning systems have been on the rise ever since the beginnings of e-learning. Their ability to provide instructions, guidance and content tailored towards learners’ individual needs bears a lot of potential for optimising learning processes. Due to recent technological advances, adaptive systems have become more sophisticated and prevalent. This chapter serves the purpose of characterising research in the field of adaptive learning by referring to six questions central to the adaptive process. We hereby provide an overview over various options on how adaptive systems can be designed and implemented in terms of core models, reasons for and goals of adaptation, learner characteristics, contexts and instructional techniques and methods. This chapter also highlights the current state of the research in that area, focussing on the effectiveness and efficiency of adaptive learning systems, as well as learners’ satisfaction with them and their actual implementation in various contexts. We end the chapter by presenting practical implications and future potential adaptive learning holds.
Quantum state discrimination on reconfigurable noise-robust quantum networks

A fundamental problem in quantum information processing is the discrimination among a set of quantum states of a system. In this paper, we address this problem on an open quantum system described by a graph, whose evolution is defined by a quantum stochastic walk. In particular, the structure of the graph mimics those of neural networks, with the quantum states to discriminate encoded on input nodes and with the discrimination obtained on the output nodes. We optimize the parameters of the network to obtain the highest probability of correct discrimination. Numerical simulations show that after a transient time the probability of correct decision approaches the theoretical optimal quantum limit. These results are confirmed analytically for small graphs. Finally, we analyze the robustness and reconfigurability of the network for different set of quantum states and show that this architecture can pave the way to experimental realizations of our protocol as well as novel quantum generalizations of deep learning.4 MoreReceived 25 March 2020Accepted 12 August 2020DOI:https://doi.org/10.1103/PhysRevResearch.2.043011Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasQuantum computationQuantum networksQuantum walksQuantum Information
Generating End-to-End Adversarial Examples for Malware Classifiers Using Explainability

In recent years, the topic of explainable machine learning (ML) has been extensively researched. Up until now, this research focused on regular ML users use-cases such as debugging a ML model. This paper takes a different posture and show that adversaries can leverage explainable ML to bypass multi-feature types malware classifiers. Previous adversarial attacks against such classifiers only add new features and not modify existing ones to avoid harming the modified malware executable's functionality. Current attacks use a single algorithm that both selects which features to modify and modifies them blindly, treating all features the same. In this paper, we present a different approach. We split the adversarial example generation task into two parts: First we find the importance of all features for a specific sample using explainability algorithms, and then we conduct a feature-specific modification, feature-by-feature. In order to apply our attack in black-box scenarios, we introduce the concept of transferability of explainability, that is, applying explainability algorithms to different classifiers using different features subsets and trained on different datasets still result in a similar subset of important features. We conclude that explainability algorithms can be leveraged by adversaries and thus the advocates of training more interpretable classifiers should consider the trade-off of higher vulnerability of those classifiers to adversarial attacks.
CNN Inference acceleration using low-power devices for human monitoring and security scenarios

Security is currently one of the top concerns in our society. From governmental installations to private companies and medical institutions, they all have to address directly with security issues as: access to restricted information quarantine control, or criminal tracking. As an example, identifying patients is critical in hospitals or geriatrics in order to isolate infected people, which has proven to be a non- trivial issue with the COVID-19 pandemic that is currently affecting all countries, or to locate fled patients. Face recognition is then a non-intrusive alternative for performing these tasks. Although FaceNet from Google has proved to be almost perfect, in a multi-face scenario its performance decays rapidly. In order to mitigate this loss of performance, in this paper a cluster based on the Neural Computer Stick version 2 and OpenVINO by Intel is proposed. A detailed power and runtime study is shown for two programming models, namely: multithreading and multiprocessing. Furthermore, 3 different hosts have been considered. In the most efficient configuration, an average of 6 frames per second has been achieved using the Raspberry Pi 4 as host and with a power consumption of just 11.2W, increasing by a factor of 3.3X the energy efficiency with respect to a PC-based solution in a multi-face scenario.