Reviewerly/exHarmony huggingface dataset:
An Overview on Blockchain Technology and Its Applications

Blockchain, the establishment of Bit coin, has gotten broad considerations as of late. Block chain fills in as a changeless record which permits exchanges occur in a decentralized way. Blockchain-based applications are jumping up, covering various fields including money related administrations, notoriety framework and Internet of Things IoT, etc. Be that as it may, there are as yet numerous difficulties of Blockchain innovation such as adaptability and security issues holding on to be survived. This paper gives an Overview on BlockChain Technology and its Architecture.
User Modeling from Review Browsing History for Personal Values-Based Recommendation

This paper proposes a personal values-based user modeling method from user’s browsing history of reviews. Personal values-based user modeling and its application to recommender systems have been studied. This approach models users’ personal values as the effect of item’s attributes on their decision making. While existing method obtains a user model from reviews posted by a user, this paper proposes to obtain it from reviews a user consulted for his/her decision making. Methods for determining reviews to present for obtaining user feedback, as well as for selecting items to recommend are proposed, of which effectiveness are shown with user experiments.
A novel approach of cursive signature generation for personal identity

Signatures are used in many situations of daily life. In many non-English-speaking countries like Japan, people are generally not familiar with English and they do not have English signatures in most cases. However, many of them want to learn how to write English in cursive and hope to have an English signature. Thus, this study proposes a technique for generating an English signature. We use a cubic Bezier curve for the cursive connection and an affine transformation to modify the input characters. Modifications were made in the slant, scale, space between the characters and line emphasis. In addition, we added some decorative functions, such as putting a circle around the signature. The system also provided an animation to teach users how to write cursive English and the order of strokes used in a signature. As a result, we were able to provide system-generated signatures that satisfied the users.
Color Texture Classification Combining LBP Images and Fractal Features

This paper describes a color texture classification scheme that uses fractal features like: box-counting fractal dimension and differential box-counting fractal dimension. Color textured images can be represented on multiple color spaces and recent developments for color texture analysis and classification are increasing. Following this concept, a texture image is obtained using Local Binary Pattern whereupon fractal features are extracted from it. For testing this color texture classification scheme two datasets (BarkTex and VisTex) are used, and the results are compared with three other methods. We propose a modified formula for calculating the Local Binary Pattern of a color textured image and advance the algorithm for this technique. The fractal features prove to be suitable in the classification process and the accuracy rates we had obtained are close to state-of-the-art approaches using a much smaller feature space.
Serverless Query Processing on a Budget

Relational query processing is an ideal candidate for serverless computation with its stateless, idempotent, and short-lived properties. However, current serverless offerings for query processing neither provide millisecond-based pricing nor allow users to optimize the cost of their queries. To have tradeoffs between the cost and performance of their queries, users are limited to using traditional serverful approaches, which we demonstrate to be 50% slower at approximately the same cost as serverless approaches. We propose a model that will allow service providers to dynamically provision clusters to achieve their users' desired time-cost tradeoffs.
Implementation of Student Performance Management Guidance to Improve Quality Study at Colleges

Student attendance activities can take place well and smoothly With a very active student to attend each attendance Student guidance in completing the research. With it, it takes Attentive attention to the student's attendance. Known systems that have an important role for student guidance are the Attendance assessment system and the higher education service system that is absolutely necessary for lecturers Supervisor for monitoring research at higher education. One of the media information is a very important Viewboard in the The Pen System (attendance Assessment) on the Viewboard Pen used for Assessment of attendance of students to get complete data and accuracy. However, existing Viewboard Pens do not currently have a Viewboard Performance that can be a benchmark in comparing Student attendance. There are 3 (three) issues that cause Alternative problems include the absence of information to display Reanytime Student attendance for a certain period of time. Furthermore, the absence of Performance graphs that can measure the performance of student attendance, And the absence of search filters for a certain period of time. Therefore, Designed a Viewboard using the Bootstrap template and also Using the SWOT method. Using 4 (four) methods System analysis using user requirement, design method Using Prototype, HIPO, Flowchart and UML (Unified Modelling Language). The Viewboard Pen is designed using Yii Framework and hopefully helpful in solving problems Attendance at the previous system and motivating students Guidance to be more active.
Wavelet Denoising in a Highly Secure Covert Communication

Background: Over recent years, the security of the publicly available audio, video and text data has become a big challenge, i.e., social media communication; hence the author presents an exceedingly secure communication system to hide the data from an attentive adversary. Objective: The study aims to design and analyze a novel algorithm to be implemented for message transmission using multi-image LSB steganography and crucial variable-length cryptography. Encoder, decoder and social media as a channel are the main parts of the study. Methods: Various computer techniques like cryptography and steganography with video handling are used to perform the proposed work. The main objective of this work is to implement the raw video as the cover on the QR code image. This image is to be transmitted as information by enshrouding it in the raw footage. Additionally, encryption of QR code image using VLMKG (Variable Length Mixed key Generation) cryptography enhances the reliability of work. The variablelength Key is generated separately to implement the cryptography. Multi-image LSB steganography is imposed with one-bit replacement in the spatial domain. Wavelet Transform (Daubechies family) is applied for denoising, which enhances the accuracy of the received data. Results: The proposed work provides a secure way to transmit any information in the form of QR code image successfully. It is verified with the help of five QR Code images and three raw video groupings. Conclusion: Implementation of LSB steganography, variable-length key cryptography and MATLAB usage had superbly extracted the image features; calculations and the consequences were observed to be palatable.
Application Topology Definition and Tasks Mapping for Efficient Use of Heterogeneous Resources

Nowadays, high-performance computing (HPC) not only faces challenges to reach computing performance, it also has to take in consideration the energy consumption. In this context, heterogeneous architectures are expected to tackle this challenge by proposing a mix of HPC and low-power nodes. There is a significant research effort to define methods for exploiting such computing platforms and find a trade-off between computing performance and energy consumption. To this purpose, the topology of the application and the mapping of tasks onto physical resources are of major importance. In this paper we propose an iterative approach based on the exploration of logical topologies and mappings. These solutions are executed onto the heterogeneous platform and evaluated. Based on these results a Pareto front is built, allowing users to select the most relevant configurations of the application according to the current goals and constraints. Experiments have been conducted on a heterogeneous micro-server using a video processing application running on top of a software-distributed shared memory and deployed over a mix of Intel i7 and Arm Cortex A15 processors. Results show that some counterintuitive solutions found by the exploration approach perform better than classical configurations.
Towards a Trusted Support Platform for the Job Placement Task

HR tech is a new trend in the hiring process, still facing some inefficiencies and limits. This paper sketches a high level architecture of a trusted support platform for the job placement task. Our design is based on a multifaceted analysis of current practices and requirements from technical, legal, and social perspectives. Relying on the properties of intelligent data analysis approaches, blockchain technology, and distributed identity management, this solution will enable optimisation, compliance, and improvements of the candidate selection process for job openings. The outcomes of the current work will be further applied for the identification of data formats and specific technologies to be used for the implementation. Practical use-cases are currently under development with industry partners.
Communication-Efficient Algorithms for Decentralized Optimization Over Directed Graphs.

We study the problem of decentralized optimization over a time-varying directed network whose nodes have access only to their local convex cost functions; the goal of the network is to collectively minimize the sum of the functions. To reduce the communication cost rendered high by large dimensionality of the model parameters, the nodes sparsify their updates before communicating them to their neighbours. We propose communication-efficient algorithms for both average consensus and decentralized optimization problems. Our schemes build upon the observation that compressing the messages via sparsification implicitly alters column-stochasticity of the mixing matrices of the directed network, a property that plays an important role in establishing convergence results for decentralized learning tasks. We show that by locally modifying the mixing matrices the proposed algorithms achieve $O(\frac{\mathrm{ln}T}{\sqrt{T}})$ convergence rate for decentralized optimization, and a geometric convergence rate for the average consensus problems, respectively. To our knowledge, these are the first communication-sparsifying schemes for distributed optimization over directed graphs. Experimental results on synthetic and real datasets show the efficacy of the proposed algorithms.
Neural Hybrid Recommender: Recommendation Needs Collaboration

In recent years, deep learning has gained an indisputable success in computer vision, speech recognition, and natural language processing. After its rising success on these challenging areas, it has been studied on recommender systems as well, but mostly to include content features into traditional methods. In this paper, we introduce a generalized neural network-based recommender framework that is easily extendable by additional networks. This framework named NHR, short for Neural Hybrid Recommender allows us to include more elaborate information from the same and different data sources. We have worked on item prediction problems, but the framework can be used for rating prediction problems as well with a single change on the loss function. To evaluate the effect of such a framework, we have tested our approach on benchmark and not yet experimented datasets. The results in these real-world datasets show the superior performance of our approach in comparison with the state-of-the-art methods.
Towards GPU Utilization Prediction for Cloud Deep Learning

Understanding the GPU utilization of Deep Learning (DL) workloads is important for enhancing resource-efficiency and cost-benefit decision making for DL frameworks in the cloud. Current approaches to determine DL workload GPU utilization rely on online profiling within isolated GPU devices, and must be performed for every unique DL workload submission resulting in resource under-utilization and reduced service availability. In this paper, we propose a prediction engine to proactively determine the GPU utilization of heterogeneous DL workloads without the need for in-depth or isolated online profiling. We demonstrate that it is possible to predict DL workload GPU utilization via extracting information from its model computation graph. Our experiments show that the prediction engine achieves an RMSLE of 0.154, and can be exploited by DL schedulers to achieve up to 61.5% improvement to GPU cluster utilization.
From the attic to the cloud: mobilization of endangered language resources with linked data

This paper describes a collection of 20k ELAN annotation files harvested from five different endangered language archives. The ELAN files form a very heterogeneous set, but the hierarchical configuration of their tiers allow, in conjunction with the tier content, to identify transcriptions, translations, and glosses. These transcriptions, translations, and glosses are queryable across archives. Small analyses of graphemes (transcription tier), grammatical and lexical glosses (gloss tier), and semantic concepts (translation tier) show the viability of the approach. The use of identifiers from OLAC, Wikidata and Glottolog allows for a better integration of the data from these archives into the Linguistic Linked Open Data Cloud.
Research & Innovation Activities' Impact Assessment: The Data4Impact System.

Cat. 2 Show-case: We present the Data4Impact (D4I) platform, a novel end-to-end system for evidence-based, timely and accurate monitoring and evaluation of research and innovation (R&I) activities. Using the latest technological advances in Human Language Technology (HLT) and our data-driven methodology, we build a novel set of indicators in order to track funded projects and their impact on science, the economy and the society as a whole, during and after the project life-cycle. We develop our methodology by targeting Health-related EC projects from 2007 to 2019 to produce solutions that meet the needs of stakeholders (mainly policy-makers and research funders). Various D4I text analytics workflows process datasets and their metadata, extract valuable insights and estimate intermediate results and metrics, culminating in a set of robust indicators that the users can interact with through our dashboard, the D4I Monitor (available at monitor.data4impact.eu). Therefore, our approach, which can be generalized to different contexts, is multidimensional (technology, tools, indicators, dashboard) and the resulting system can provide an innovative solution for public administrators in their policy-making needs related to RDI funding allocation.
Proceedings of the 19th Annual Software Engineering Workshop

The Software Engineering Laboratory (SEL) is an organization sponsored by NASA/GSFC and created to investigate the effectiveness of software engineering technologies when applied to the development of applications software. The goals of the SEL are: (1) to understand the software development process in the GSFC environment; (2) to measure the effects of various methodologies, tools, and models on this process; and (3) to identify and then to apply successful development practices. The activities, findings, and recommendations of the SEL are recorded in the Software Engineering Laboratory Series, a continuing series of reports that include this document.
Effective computations of module inverses with the Approximating k-ary GDD Algorithm by Ishmukhametov

Abstract Finite field calculations are used in modern cryptographic protocols for generating keys, encrypting and decrypting data, and building an electronic digital signature. The module inversing is necessary part of these calculations based on the extended Euclidean algorithm. Ishmukhametov developed a new algorithm for calculating the greatest common divisor of natural numbers called the approximat-ing algorithm which is a variant of the k-ary GCD Algorithm by J. Sorenson. In this paper we develop an extension version of this algorithm.
Hardware Implementation of Roadway Classification System in FPGA Platform

In this paper we present the implementation of a roadway classification system in the FPGA embedded platform. This system is based on the combination of an acoustic signal description algorithm (discrete wavelet transform) and an artificial intelligence algorithm (Artificial Neural Network ANN), in order to classify the different types of roadway (asphalt, gravel, snowy road, stone road). A hardware implementation of this system is done in order to equip the system with the functional autonomy of real-time systems. For this we have exploited the tools of this platform such as blocks Megafunctions. The results obtained in terms of execution time and hardware resources have shown that our implementation is a fast and optimal implementation.
Grammar Compression with Probabilistic Context-Free Grammar

We propose a new approach for universal lossless text compression, based on grammar compression. In the literature, a target string T has been compressed as a context-free grammar G in Chomsky normal form satisfying L(G) = T. Such a grammar is often called a straight-line program (SLP). In this paper, we consider a probabilistic grammar G that generates T, but not necessarily as a unique element of L(G). In order to recover the original text T unambiguously, we keep both the grammar G and the derivation tree of T from the start symbol in G, in compressed form. We show some simple evidence that our proposal is indeed more efficient than SLPs for certain texts, both from theoretical and practical points of view.
Sparse Cholesky Covariance Parametrization for Recovering Latent Structure in Ordered Data

The sparse Cholesky parametrization of the inverse covariance matrix is directly related to Gaussian Bayesian networks.Its counterpart, the covariance Cholesky factorization model, has a natural interpretation as a hidden variable model for ordered signal data.Despite this, it has received little attention so far, with few notable exceptions.To fill this gap, in this paper we focus on arbitrary zero patterns in the Cholesky factor of a covariance matrix.We discuss how these models can also be extended, in analogy with Gaussian Bayesian networks, to data where no apparent order is available.For the ordered scenario, we propose a novel estimation method that is based on matrix loss penalization, as opposed to the existing regression-based approaches.The performance of this sparse model for the Cholesky factor, together with our novel estimator, is assessed in a simulation setting , as well as over spatial and temporal real data where a natural ordering arises among the variables.We give guidelines, based on the empirical results, about which of the methods analysed is more appropriate for each setting.
Avoid Deadlock Resource Allocation (ADRA) Model V VM-out-of-N PM: Avoid Deadlock Resource Allocation (ADRA) Model V VM-out-of-N PM

This paper presents an avoid deadlock resource allocation (ADRA) for model V VM-out-of-N PM since cloud computing is a new computing paradigm composed of grid computing, distributed computing and utility concepts. Cloud computing presents a different resource allocation paradigm than either grids or distributed systems. Cloud service providers dynamically scale virtualized computing resources as a service over the internet. Due to variable number of users and limited resources, cloud is prone to deadlock at very large scale. Resource allocation and the associated deadlock avoidance is problem originated in the design and the implementation of the distributed computing, grid computing. In this paper, a new concept of free space cloud is proposed to avoid deadlock by collecting available free resource from all allocated users. New algorithms are developed for allocating multiple resources to competing services running in virtual machines on a heterogeneous distributed platform.  An experiment is tested in CloudSim. The performance of resource pool manager is evaluated by using CloudSim and resource utilization and indicating good results.
A Novel Texture Classification Scheme based on Completed Multiple Adaptive Threshold Patterns

Local binary patterns and their extensions are heavily used in image texture classification. However traditional LBP codes are sensitive to noise and have limited capability of discriminating various texture patterns. This study presents a novel Completed multiple adaptive threshold patterns (CMATP) descriptor for texture classification to ensure higher classification rates. For each patch of pixels statistical parameters like mean, standard deviation and value of centre pixel are computed to determine structural properties. Based on these observations pixel patches are categorized in to uniform, moderate and non-uniform structures. A suitable adaptive threshold is then obtained for each class of pixel groups, which can generate binary pattern that are more uniform and can produce distinct binary code for different texture patterns. Further to extract centre pixel and contrast information from each patch, multiple threshold based complementary sign and magnitude patterns are generated in a similar way. The sign and magnitude pattern are then combined form CMATP descriptor. We have compared the proposed CMATP pattern with some state of the art LBP based descriptors on some benchmark datasets like Outex, Brodatz and UMD. The experimental results shows superior performance of the CMATP as compared to others.
Research on Design and Implementation of Knowledge Map Storage Access System Based on Ontology Reasoning

Abstract In order to support the intelligent combat capability of future wars, the network information system needs in-depth research and exploration in the field of artificial intelligence. This paper designs a distributed aggregation storage mode with the characteristics of balanced storage load distribution and local node aggregation storage under Big Table model. A distributed parallel query engine that uses the Group-By mode to distribute parallel computation query trees. A knowledge map completion method based on Bayesian inference is proposed. Bayesian probabilistic inference theory and RDF implication inference rules are used to jointly infer the potential relationships between entity nodes to predict the relationship between new nodes and original nodes, thus improving the mining efficiency of potential factors in the model and the accuracy of prediction of unknown relationships. Based on Big Table model, the entity sets stored row by row are evenly divided and stored through random prefix and pre-partition operations to realize load balancing. At the same time, random prefixes can be uniformly distributed to nodes for storing entities of the same type, and aggregated by entity category on a single node. With the continuous development of knowledge map technology, future knowledge map learning and reasoning technology can be integrated with new fields such as machine depth learning, cloud computing, block chain, big data, biological genetic engineering, etc. to play an important social value.