Reviewerly/exHarmony huggingface dataset:
A Question answering System with a sequence to sequence grammatical correction

Dialogue or conversational systems including chatbots, and personal assistants are becoming ubiquitous in modern society. Here we present a question answering system that's help a customer to search a product in an ecommerce website and inquiring about it.
Trade Route: An Anonymous P2P Online Exchange Market Using Blockchain Technology

Due to the Internet rapid growth, e-commerce is becoming a new way for businesses to market their listings online and not be limited to local customers. Many buyers like to shop online because of the convenience. However, when clients shop online, many concerns emerge, such as worrying about how personal data will be handled, how secure/private the online site is, and whether or not the website should be trusted in the first place. In this paper, we propose a novel approach leveraging the blockchain technology to create a platform that helps peers exchange goods without the need to go through an intermediary or the use of any traditional currency. All peers in this platform will have to sell their goods in exchange for "Trade Coins" that are only usable in the "Trade Route" market.
Addressing Anonymous Abuses

Anonymous networks intended to enhance privacy and evade censorship are also being exploited for abusive activities. Technical schemes have been proposed to selectively revoke the anonymity of abusive users, or simply limit them from anonymously accessing online service providers. We designed an empirical survey study to assess the effects of deploying these schemes on 75 users of the Tor anonymous network. We evaluated proposed schemes based on examples of the intended or abusive use cases they may address, their technical implementation and the types of entities responsible for enforcing them. Our results show that revocable anonymity schemes would particularly deter the intended uses of anonymous networks. We found a lower reported decrease in usage for schemes addressing spam than those directly compromising free expression. However, participants were concerned that all technical mechanisms for addressing anonymous abuses could be exploited beyond their intended goals (51.7%) to harm users (43.8%). Participants were distrustful of the enforcing entities involved (43.8%) and concerned about being unable to verify (49.3%) how particular mechanisms were applied.
Kronos: Lightweight Knowledge-based Event Analysis in Cyber-Physical Data Streams

We demonstrate Kronos, a framework and system that automatically extracts highly dynamic knowledge for complex event analysis in Cyber-Physical systems. Kronos captures events with anomaly-based event model, and integrates various events by correlating with their temporal associations in realtime, from heterogeneous, continuous cyber-physical measurement data streams. It maintains a lightweight highly dynamic knowledge base, enabled by online, window-based ensemble learning and incremental association analysis for event detection and linkage, respectively. These algorithms incur time costs determined by available memory, independent of the size of streams. Exploiting the highly dynamic knowledge, Kronos supports a rich set of stream event analytical queries including event search (keywords and query-by-example), provenance queries ("which measurements or features are responsible for detected events?"), and root cause analysis. We demonstrate how the GUI of Kronos interacts with users to support both continuous and ad-hoc queries online and enables situational awareness in Cyber-power systems, communication, and traffic networks.
Tri-level optimization-based image rectification for polydioptric cameras

Recently, as the number of cameras built into polydioptric cameras has increased, image rectification of these cameras has become complicated. However, because conventional methods cannot compensate for the calibration errors or are limited by the camera arrangement, they cannot be widely applied to various kinds of polydioptric cameras. In this work, we adopted the idea of disparity-error-minimization to overcome these limitations. We introduced the following several improvements in the optimization-based rectification. (1) We modified the objective function to include both the x and y disparity errors. (2) We added a regularization term to perform robustly for mismatched pairs. (3) We employed tri-level optimization to determine the camera pose corresponding to the rectified images. For two representative polydioptric cameras, this method reduced the average disparity error up to 66.57% compared to conventional methods. The results showed that our method exhibited significant generalization capabilities, achieving significant improvements over the existing methods.
ITSM Analysis using ITIL V3 in Service Operation in PT.Inovasi Tjaraka Buana

Abstract PT. Inovasi Tjaraka Buana is a company in ISP (Internet Service Provider) sector that uses information technology to sustain activities the company. At this time the company is experiencing scaling-up, which is a drastic increase in the number of user of internet services, and coverage areas that make companies have to develop for various aspects of the company. The problem is that companies cannot at this time balancing service operations when handling an incident that occurs with drastic addition of the number of users and the amount of coverage area. To solve the problem, it is necessary to implement Information Technology Service Management (ITSM). The method used in this research is data collection by interview and observation techniques. This research will produce Incident Management Flow and Problem Management Flow. ITIL, which is a framework that illustrates best practices that focus on managing IT services, IT development and operations, which can help companies to overcome problems, so the company can apply it in order to balance service operations when handling incidents that occur.
Ant Colony Optimization for Time-Dependent Travelling Salesman Problem

In this paper, the time-dependent travelling salesman problem (TDTSP) is reviewed and the heuristic based on ant colony optimization for solving the TDTSP is proposed. The TDTSP is an extension of the classical travelling salesman problem in which the edge costs depend on the order in which the edges are visited. This extension is even more computationally complex than the original problem and therefore a heuristic must be used in order to get a solution close to the optimal one for larger-scale problems. We combine the ant colony optimization algorithm with a modified local search and apply the heuristic to a simplified version of the flying tourist problem.
On implementation of an approximating k-ary GCD algorithm

Abstract In our paper, we investigate some implementations of an approximating k -ary GCD algorithm (AKA) for calculating the greatest common divisor of natural numbers for given A and B . This task has a great importance in the development of parameters for cryptographic encryption methods. A post-publication change was made to this article on 9 Jun 2020 to correct the author names.
Illuminating the Dark or how to recover what should not be seen in FE-based classifiers

Abstract Classification algorithms/tools become more and more powerful and pervasive. Yet, for some use cases, it is necessary to be able to protect data privacy while benefiting from the functionalities they provide. Among the tools that may be used to ensure such privacy, we are focusing in this paper on functional encryption . These relatively new cryptographic primitives enable the evaluation of functions over encrypted inputs, outputting cleartext results. Theoretically, this property makes them well-suited to process classification over encrypted data in a privacy by design ’ rationale, enabling to perform the classification algorithm over encrypted inputs (i.e. without knowing the inputs) while only getting the input classes as a result in the clear. In this paper, we study the security and privacy issues of classifiers using today practical functional encryption schemes. We provide an analysis of the information leakage about the input data that are processed in the encrypted domain with state-of-the-art functional encryption schemes. This study, based on experiments ran on MNIST and Census Income datasets, shows that neural networks are able to partially recover information that should have been kept secret. Hence, great care should be taken when using the currently available functional encryption schemes to build privacy-preserving classification services. It should be emphasized that this work does not attack the cryptographic security of functional encryption schemes, it rather warns the community against the fact that they should be used with caution for some use cases and that the current state-ofthe-art may lead to some operational weaknesses that could be mitigated in the future once more powerful functional encryption schemes are available.
Research On Mybatis Mapper Model Based On SQL Template

This article researches the implementation of Mybatis and the idea of SQL template design, analyzes the design idea of the persistence layer framework to solve the “impedance mismatch”, and analyzes the question of low development efficiency caused by the imperfection of mapper model in the current popular Mybatis persistence layer framework. Based on the framework Mybatis, this article combines operators to design a more complete mapper model, and designs a two-way synchronization mechanism for the SQL template for the model, which avoids problems such as the difficulty of late optimization caused by the strong coupling of operators and SQL. This model avoids redundant configuration files, while reducing optimization costs and improving flexibility and applicability through a twoway synchronization mechanism.
The foldinterface editor

Designing foldable user interfaces ("foldinterfaces") is complex and time-consuming because of the multiple technologies involved in the process, from the hardware details for folding pixels to design requirements regarding efficiency and ease of use. To assist this process, we introduce a visual editor for designers to specify foldinterfaces by implementing an extension of the Yoshizawa-Randlett diagramming system for origami and Event-Condition-Action rules from event-driven software architecture. The outcome is rendered as a 3-D foldable surface in a virtual environment.
Grounded Sequence to Sequence Transduction

Speech recognition and machine translation have made major progress over the past decades, providing practical systems to map one language sequence to another. Although multiple modalities such as sound and video are becoming increasingly available, the state-of-the-art systems are inherently unimodal, in the sense that they take a single modality - either speech or text - as input. Evidence from human learning suggests that additional modalities can provide disambiguating signals crucial for many language tasks. In this article, we describe the How2 dataset , a large, open-domain collection of videos with transcriptions and their translations. We then show how this single dataset can be used to develop systems for a variety of language tasks and present a number of models meant as starting points. Across tasks, we find that building multimodal architectures that perform better than their unimodal counterpart remains a challenge. This leaves plenty of room for the exploration of more advanced solutions that fully exploit the multimodal nature of the How2 dataset , and the general direction of multimodal learning with other datasets as well.
Sparse Grid Regression for Performance Prediction Using High-Dimensional Run Time Data

We employ sparse grid regression to predict the run time in three types of numerical simulation: molecular dynamics (MD), weather and climate simulation. The impact of algorithmic, OpenMP/MPI and hardware-aware optimization parameters on performance is studied. We show that normalization of run time data via algorithmic complexity arguments significantly improves prediction accuracy. Mean relative prediction errors are in the range of few percent; in MD, a five-dimensional parameter space exploration results in mean relative prediction errors of ca. 15% using ca. 178 run time samples.
A distributed collaborative trust service recommender system for secure cloud computing

Abstract A model for providing solutions to the security issue of the cloud computing environment is addressed in this article by proposing a distributed collaborative trust service recommender (DTSR) system. DTSRs are kept in a distributed manner spread over the clouds, and they are used to extract raw trust from different sources. This trust information is based on the feedback by cloud service user's observation about the adherence of a cloud service provider (CSP) to a service level agreement for the cloud‐based service provided. Using this trust data, the overall trust of CSPs is computed by using collaborative computation. The trust values are propagated among the distributed DTSRs in the network so that the trust values are available to everyone in the cloud environment. Experiments were conducted, which clearly shows that our proposed system is useful in providing a secure multicloud environment.
Environmental, Social and Governance Risk in Public and Private Financial Systems

Environmental, social, and governance (ESG) risk is relevant for the public and private financial sectors and it has an impact on financial performance in both sectors. The aim of this study was to diagnose the impact of ESG risk on public and private financial systems and to determine the sector-specific significance of ESG risk attitudes. Fuzzy cognitive maps were used to diagnose ESG risk in the Polish public and private financial systems. Eight independent experts were asked to evaluate a total of 62 factors, of which 21 were environmental related, 25 were social related, and 16 were governance related. The results of the study revealed that the number of ESG factors and the sustainability pillar they represented differed between the financial systems. As a result, different ESG risk attitudes were observed in public and private financial systems due to the sector-specific influence. The public financial system was found to be more responsive to environmental factors, while the private sector is more likely to take into account factors representing social sustainability. These findings have practical importance and should be considered when designing ESG risk policy and management systems in public and private financial systems.
Image Recoloring of Art Paintings for the Color Blind Guided by Semantic Segmentation

This paper introduces a semantic-segmentation guided image recoloring approach of digitized art paintings to enhance the color perception of color- blind people that suffer from protanopia and deuteranopia. Semantic segmentation using transfer learning between natural images and art paintings is applied to extract annotated color information. By using a standard technique, the annotated colors are transformed to simulate the effects of protanopia and deuteranopia. Then, a specialized objective function is minimized to recolor only the colors that are significantly different from the respective simulated ones, because these colors are perceived as confusing by the color blind. The effectiveness of the proposed method is demonstrated through its comparison with other algorithms in several experimental cases.
Algebraic decoding of the (41, 21, 9) quadratic residue code without determining the unknown syndromes

Quadratic residue (QR) codes have a high prospect on error correction for reliable data conveyance over channel with noise. This paper presents a fast algebraic scheme for decoding the binary (41, 21, 9) QR code for correcting up to 4 errors on an one-case-by-one-case basis, in which the calculation of unknown syndromes is eliminated and the conditions for checking for the various numbers of errors that exist in the received word are also simplified compared to those from Lin T. C. et al.'s algorithm, which is a traditional algebraic decoding algorithm (ADA). The computational complexity of the decoder performing the binary (41, 21, 9) QR code is analyzed thoroughly, which demonstrates that the proposed decoding scheme is faster, simpler, and more suitable for implementation than Lin T. C. et al.'s algorithm. Numerical emulation results demonstrate that the proposed decoding scheme achieves the same error-rate performance as Lin T. C. et al.'s algorithm, but it has a significantly decreased the complexity of this decoder in terms of the CPU time.
CoreBigBench

Significant effort was put into big data benchmarking with focus on end-to-end applications. While covering basic functionalities implicitly, the details of the individual contributions to the overall performance are hidden. As a result, end-to-end benchmarks could be biased toward certain basic functions. Micro-benchmarks are more explicit at covering basic functionalities but they are usually targeted at some highly specialized functions. In this paper we present CoreBigBench, a benchmark that focuses on the most common big data engines/platforms functionalities like scans, two way joins, common UDF execution and more. These common functionalities are benchmarked over relational and key-value data models which covers majority of data models. The benchmark consists of 22 queries applied to sales data and key-value web logs covering the basic functionalities. We ran CoreBigBench on Hive as a proof of concept and verified that the benchmark is easy to deploy and collected performance data. Finally, we believe that CoreBigBench is a good fit for commercial big data engines performance testing focused on basic engine functionalities not covered in end-to-end benchmarks.
Smartphone learning for Kids edutainment

Learning should be the preparation of young students (kids) for new ways of thinking: ways that involve creativity, critical analysis, problem solving and decision-making. We propose in this article, a smartphone learning approach using a designed robot car for children. It is a package that could been introduced for kids edutainment in Algeria. In order to be exploited outside Algeria, a software part could been modified according to the educational need of other kids around the world. A designed package includes an android application and a robot car. This last is remotes controlled via a smartphone application. This software part consists into three edutainment games quiz related to three levels respectively. Therefore, the further kid moves into the game, the more control he has over the robot car. A child is enjoying his educational toy and learning in attractive context at the same time.
Demonstration of Chestnut: An In-memory Data Layout Designer for Database Applications

This demonstration showcases Chestnut, a data layout generator for in-memory object-oriented database applications. Given an application and a memory budget, Chestnut generates a customized in-memory data layout and the corresponding query plans that are specialized for the application queries. Our demo will let users design and improve simple web applications using Chestnut. Users can view the Chestnut-generated data layouts using a custom visualization system, which will allow users to see how the application parameters affect Chestnut's design. Finally, users will be able to run queries generated by the application via the customized query plans generated by Chestnut or traditional relational query engines, and can compare the results and observe the speedup achieved by the Chestnut-generated query plans.
Everyday Proxy Objects for Virtual Reality

Immersive virtual experiences are becoming ubiquitous in our daily lives. Besides visual and auditory feedback, other senses like haptics, smell and taste can enhance immersion in virtual environments. Most solutions presented in the past require specialized hardware to provide appropriate feedback. To mitigate this need, researchers conceptualized approaches leveraging everyday physical objects as proxies instead. Transferring these approaches to varying physical environments and conditions, however, poses significant challenges to a variety of disciplines such as HCI, VR, haptics, tracking, perceptual science, design, etc. This workshop will explore the integration of everyday items for multi-sensory feedback in virtual experiences and sets course for respective future research endeavors. Since the community still seems to lack a cohesive agenda for advancing this domain, the goal of this workshop is to bring together individuals interested in everyday proxy objects to review past work, build a unifying research agenda, share ongoing work, and encourage collaboration.
Sagittal alignment in an MR-TRUS fusion biopsy using only the prostate contour in the axial image

Abstract Purpose This paper examines the feasibility of automated alignment in sagittal direction in MR-TRUS fusion biopsy of the prostate by comparing the prostate contour in axial images between different modalities. In the treatment of prostate cancer, an important factor affecting the prognosis of patients is focal therapy of cancer within the prostate. Therefore, MR-TRUS fusion biopsy of the prostate is attracting attention as one of the most effective localization techniques. Because the accuracy of this biopsy is highly dependent on the doctor performing it, automation should reduce variability in diagnostic performance. Method The MR image is scaled to the same scale as the TRUS image, and the contours of the prostate on the MR and TRUS images are compared in polar coordinates. In addition, this method makes it possible to perform a robust comparison against deformation by comparing specific angle ranges. It is also possible to improve the accuracy of error calculation by accumulating contour data. Result The axial image selected by the proposed method using the prostate contour obtained from the doctor-labeled segmentation image has an error of about 4 mm in the sagittal direction on average compared to the axial image selected by the doctor did. Furthermore, using the inaccurate prostate contours obtained by performing segmentation with U-Net only slightly reduced the accuracy. In addition, it was found that alignment accuracy is improved by using the angler weight. Conclusion It has been shown that sagittal alignment can be performed with some degree of accuracy using only axial images. Also, the angular weight values used indicate that when comparing axial images, it may be an important factor in determining the same axial cross section to compare the parts that deform due to probe pressure.
The Recognition Method of Express Logistics Restricted Goods Based on Deep Convolution Neural Network

With the continuous increase of China's express logistics business volume and the emergence of various security risks, social concern to the issue of express safety has been raised. Based on deep convolutional neural network, this study introduced an automatic identification method for express logistics restriction products. This method establishes a deep neural network structure model including convolutional blocks and fully connected layers by using the express package X-ray image dataset. It achieves 93.1% recognition accuracy, and 853ms of single image recognition time, which is far superior to the traditional manual detection speed. Compared with other traditional identification methods, for example, random forest, decision tree, Bayesian network and so on, this method not only guarantees real-time identification, but also significantly improves the recognition accuracy by about 20%.
Transformation based on likelihood ratio

We respond here on a recent letter in this journal, on the transformation based on likelihood ratio.