Reviewerly/exHarmony huggingface dataset:
Secure Logging with Security Against Adaptive Crash Attack

Logging systems are an essential component of security systems and their security has been widely studied. Recently (2017) it was shown that existing secure logging protocols are vulnerable to crash attack in which the adversary modifies the log file and then crashes the system to make it indistinguishable from a normal system crash. The attacker was assumed to be non-adaptive and not be able to see the file content before modifying and crashing it (which will be immediately after modifying the file). The authors also proposed a system called SLiC that protects against this attacker. In this paper, we consider an (insider) adaptive adversary who can see the file content as new log operations are performed. This is a powerful adversary who can attempt to rewind the system to a past state. We formalize security against this adversary and introduce a scheme with provable security. We show that security against this attacker requires some (small) protected memory that can become accessible to the attacker after the system compromise. We show that existing secure logging schemes are insecure in this setting, even if the system provides some protected memory as above. We propose a novel mechanism that, in its basic form, uses a pair of keys that evolve at different rates, and employ this mechanism in an existing logging scheme that has forward integrity to obtain a system with provable security against adaptive (and hence non-adaptive) crash attack. We implemented our scheme on a desktop computer and a Raspberry Pi, and showed in addition to higher security, a significant efficiency gain over SLiC.
ARTAN: Align reviews with topics in attention network for rating prediction

Rating prediction has long been a popular research topic in the field of recommendation systems. Latent factor models, particularly matrix factorization (MF) methods, constitute the most prevalent techniques for rating prediction. However, MF-based methods suffer from the problems of data sparsity and lack of explicability. In this paper, we propose a novel model in which ratings and topic-level review information are integrated into a deep neural framework to address these problems. Specifically, we designed a topic alignment operation and a topic attention mechanism to reflect the user’s preferences and an item’s properties in terms of a topic in the reviews. We present a neural prediction layer that extends user and item representations by including both the latent factors from ratings and the textual information from reviews. The results of extensive experiments on four real-world datasets demonstrate that our proposed method consistently outperforms the state-of-the-art recommendation approaches that follow this direction in terms of rating prediction. Furthermore, our model can categorize representative reviews of users/items and group reviews into topics for the users’ reference.
Breaking CAPTCHA characters using Multi-task Learning CNN and SVM

CAPTCHAs have become the canonical methods to distinguish humans from bots or malicious programs. Most of the CAPTCHAs deployed on the websites are text-based. They are completely reliant on the assumption that bots cannot read text from an image. However, in recent times, people have been able to come up with programs that are able to recognize CAPTCHAs with significant accuracy. This paper aims to present similar methods, which aids in breaking of security. The paper presents Multi-task Learning CNN (MTL-CNN) and SVM can independently recognize text-based CAPTCHAs. With MTLCNN, no pre-processing of image is required and still, it achieves promising results. However, SVM demands some manual labor to achieve the same.
Methodology for Forecasting the Power Production of PV Plants During Solar Eclipse

On July 2, 2019, a total solar eclipse occurred in the southern part of South America. This affected mainly the electrical systems of; Chile, Argentina, Peru, Bolivia, Paraguay, and Brazil. Considering this type of events, this research proposes a procedure that allows predicting the energy production of an industrial size photovoltaic plant under conditions of high variation of solar irradiance. The methodology is broken down into two stages: in the first, a heuristic methodology is developed to forecast irradiance, while in the second, supervised learning machines are used to estimate the production of photovoltaic plants. The results show that the overall error is of the order of 21%, which were contrasted with the measurements obtained during the eclipse of 2 July.
A novel neural-network gradient optimization algorithm based on reinforcement learning

Searching appropriate step size and hyperparameter is the key to getting a robust convergence for gradient descent optimization algorithm. This study comes up with a novel gradient descent strategy based on reinforce learning, in which the gradient information of each time step is expressed as the state information of markov decision process in iterative optimization of neural network. We design a variable-view distance planner with a markov decision process as its recursive core for neural-network gradient descent. It combines the advantages of model-free learning and model-based learning, and fully utilizes the state transition information of the optimized neural-network objective function at each step. Experimental results show that the proposed method not only retains the merits of the model-free asymptotic optimal strategy but also enhances the utilization rate of samples compared with manually designed optimization algorithms.
PERCEPTION OF EDUCATED CITIZENS ON AADHAR CARD AS A TOOL FOR EFFICIENT E-GOVERNANCE IN INDIA - A CONCEPTUAL MODEL

Purpose of the study: The research study is an attempt made to understand the perception of residents on Aadhar card (Biometric identification) as a tool for E-Governance in India.&#x0D; Methodology: For this study, purposive sampling is used and 400 samples are drawn from educated residents within India. The study has adopted Structural Equation Modeling through AMOS to test the hypotheses and build the conceptual model by using five indicators and 35 items.&#x0D; Findings: The results reveal that only Reliability has a significant impact on the Efficiency of Aadhar card as an E-Governance tool. The study results indicate that Concern as a factor has a negative impact on Efficiency but statistically insignificant.&#x0D; Implications of the study: The understanding of the perception of residents on Aadhar card as a tool for E-Governance is important for policymakers to bring in better governance and proper implementation of efficient Information and Communication Technologies (ICTs).&#x0D; Novelty/Originality of the study: The study gives a unique insight into examining the perception of residents as to how efficient Aadhar card is as a tool for E-Governance.
Testing MapReduce program using Induction Method

MapReduce is "divide and conquer" applied paradigm for processing large volume of data to filter out information to solve day to day complex challenges. MapReduce is core of big data applications. The challenging part to test these applications which also represent the characteristic of these applications are variation in data due to different format and sources. In other words, poor quality of input data can deviate system towards failure if not handled properly programmatically for variety of input data. MapReduce program itself based on transformations at different level based on the program logic This paper proposes the testing technique based on the mathematical induction principle and considered as extension or conjunction other testing techniques already in used either based on transformations analysis from input to output as in MRFlow. Proposed function testing can be used in business acceptance testing and showcase the correctness of program, further can detect many defects even before shipping bigdata application in live.
Preliminary analysis learning media based on edupark science with scientific methods in the national geopark of Ranah Minang Silokek of Sijunjung

Abstract The curriculum 2013 sets learning using resources which to be orientated toward potentials and uniqueness in each region. Learning resources that discuss the potential and uniqueness of the area called the education park ( edupark ). One of edupark in Sijunjung Regency that can be integrated with learning is The National Geopark of Ranah Minang Silokek of Sijunjung area which is still used as an edupark . The use of geopark can be used as an edupark that is integrated with current technological advances. Based on the need for learning resources there, a preliminary analysis will be done to each science material at the school by using Plomp development research. The data of research uses interviews and questionnaires based on learning parameters, learning resources, students’ and environments’ characteristics then the description of potential attractions that support it among them are waves, light, and sound. Based on the results of preliminary research conducted, it was found that students were interested in learning involving the media.
Domain Randomization Using Deep Neural Networks for Estimating Positions of Bolts

Abstract Current manual practices of replacing bolts on structures are time-consuming and costly, especially because of numerous bolts. Thus, an automated method that can visually detect and localize bolt positions would be highly beneficial. We demonstrate the use of deep neural networks using domain randomization for detecting and localizing bolts on a workpiece. In contrast to previous approaches that require training on real images, the use of domain randomization enables all training in simulation. The key idea is to create a wide variety of computer-generated synthetic images by varying the texture, color, camera position and orientation, distractor objects, and noise, and train the neural network on these images such that the neural network is robust to scene variability and hence provides accurate results when deployed on real images. Using domain randomization, we train two neural networks, a faster regional convolutional neural network for detecting the bolt and placing a bounding box, and a regression convolutional neural network for estimating the x- and y-position of the bolts relative to the coordinates fixed to the workpiece. Our results indicate that in the best case, we can detect bolts with 85% accuracy and can predict 75% of bolts within 1.27 cm accuracy. The novelty of this work is in using domain randomization to detect and localize: (1) multiples of a single object and (2) small-sized objects (0.6 cm × 2.5 cm).
Automation Process of Telecommunication Networks Deployment in Microsoft Azure Cloud Technology

This paper proposes the method of cloud system automation of network deployment on Infrastructure as Code (IaC) to analyze the CPU state as traffic moves through the virtual machines of different categories. The first goal is to design a simulation model of the network infrastructure using IaC concept to show the benefits of using in comparison with manual actions needed to deploy the system in case of complex cloud systems. The second goal is to analyze the behavior of virtual machines within a deployed virtual network in terms of CPU utilization. This indicator will make a conclusion about the "endurance" of machines when loading them from an appropriate number of users, and the feasibility of using them under appropriate conditions.
Mixed Initiative Realizability Problem Resolution Using UML Composite States

Realizability problems may emerge when specifications of complex distributed reactive systems are mapped to local distributed designs that may contain undesired behaviours, sometimes termed as implied scenarios. In this paper, we focus on resolving one of these realizability problems named as mixed initiative that occurs when independent and concurrent entities take uncoordinated initiatives autonomously. The mixed initiative problem is unavoidable in the execution of some distributed reactive systems and can normally not be prevented. Mixed initiative resolution guidelines have been proposed in the past that use UML activity diagrams and/or additional transitions in UML state machines. The novelty in our approach lies in the use of UML composite states for resolving the mixed initiative problem. However, UML does not have well-defined guidelines on serialization/unfolding of composite states. This paper, therefore, first proposes guidelines on how to serialize/unfold a UML composite state and then how to use it for addressing mixed initiative realizability problem using a case study named Call Centre.
Compiling NCV Quantum Circuits for Nearest Neighbour Realization

Quantum computers that are based on superconducting and quantum dots have a fundamental requirement called Nearest Neighbour (NN) constraint satisfaction. They require interacting qubits to be directly connected (neighbours). If two non-adjacent qubits are to interact, they should be brought together first, so that interaction can occur. In this paper, we summarized state-of-the-art techniques and proposed a local ordering algorithm for transforming a given quantum circuit to a 2D NN-compliant circuit. Using the algorithm, a given circuit is decomposed into non-overlapping sub-circuits where each sub-circuit contains unique qubits. The insertion of swap gates occurs between every two consecutive sub-circuits (C <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</inf> and $C_{i+1}$) if $C_{i+1}$ do not satisfy the NN-constraint. Up to 50% reduction in swap count was obtained when compared to the most recent existing work on NCV benchmark circuits.
Improved Single Target Accuracy Using Feature Selection for Multitarget Regression

Multi- Target Regression (MTR) has recently gained a great deal of attention due to a large number of realworld applications being identified. Several MTR algorithms have been proposed and more continue to be developed. The main objective of all MTR prediction strategies is to use inter-target dependencies to improve prediction efficiency. In this research, we explore the affect of feature selection on MTR problems. We observe that using ordinary feature selection techniques with Single Target (ST) approach can give at par results compared to MTR methods, and in some cases, even surpass them.
Modified Hybrid Edge Detection in MR image via Image Processing Techniques

Specialists are realizes a day’s dynamic to create apparatus for distinguishing tumor and procedure therapeutic pictures. Picture division is a solid apparatus for the most part used to identify tumor. This paper speaks to a mixture strategy to acquire top notch MR pictures in which edges of articles present in the picture are saved. Half and half edge indicator is the blend of watchful and Sobel administrators. For powerful and proficient edge identification in MR pictures filtration is the basic part due to the significant level of clamor. This proposed methodology is the blend of two phases one of them is filtration and the other one is edge identification. In MR pictures ˙˙salt and pepper˙˙ clamor is one of the most significant issue looked by doctors and specialists during the recognizable proof and medical procedures of various body parts simply like mind tumor. In our procedure middle channel is applied so as to expel clamor from MR picture. This channel worked effectively with the various degrees of commotion in examination with Gaussian channel. After the end of commotion MR picture is exposed to proposed mixture edge identifier. Half and half edge indicator demonstrated appropriate and thorough recognition of edges, particularly for the outskirts. After the subjective and quantitative investigation of results, it has discovered that proposed method reflects increasingly proper outcomes as far as picture quality estimated based on some huge parameters like PSNR and MSE.
Malaria Parasite Detection using Deep Learning

Malaria may be a life-threatening disease caused by parasites that are transmitted to people through the bites of infected mosquitoes. An Early detection of malaria is very crucial for ensuring the proper diagnosis and increasing chances of cure. By considering the seriousness and also the number of fatalities claimed by this malaria disease, it's rational to simply accept potential small implementation errors introduced by an automatic system. Automation could be a process within which the diagnosis process will enable accurate diagnosis of the disease and hence holds the promise of delivering reliable health and care to resource-scarce areas.I.
Ascent of Pre-trained State-of-the-Art Language Models

The applications involving natural language processing (NLP) can be seen everywhere these days. From sentence completion in emails to machine translation, NLP is everywhere around us even if we do not realize it. The swift increase in the development and adoption of NLP can be largely credited to the development of a number of pre-trained models which rely on transfer learning. Language modeling is said to be one of the core problems of a number of NLP tasks such as speech synthesis, sentence paraphrasing and question answering, and this problem is effectively addressed by such pre-trained models. In this paper, we review some state-of-the-art pre-trained models, namely ULMFiT, ELMo, BERT, Transformer-XL, XLNet and OpenAI’s GPT-2. The models mentioned have attained state-of-the-art scores in standard benchmark tests on a number of tasks. We go through each individual model in detail while evaluating them and performing certain subtle comparisons.
Deep Learning for Graphs

We introduce an overview of methods for learning in structured domains covering foundational works developed within the last twenty years to deal with a whole range of complex data representations, including hierarchical structures, graphs and networks, and giving special attention to recent deep learning models for graphs. While we provide a general introduction to the field, we explicitly focus on the neural network paradigm showing how, across the years, these models have been extended to the adaptive processing of incrementally more complex classes of structured data. The ultimate aim is to show how to cope with the fundamental issue of learning adaptive representations for samples with varying size and topology.
Hybrid handwriting authentication model for online testing using a Siamese neural network and MLP

We propose a hybrid authentication model for online testing that combines a handwriting model using a Siamese neural network and a dynamic information model using a multilayer perceptron. The resulting hybrid authentication model obtained better accuracy than either the handwriting model alone or the dynamic information model alone.
Quantitatively Measuring Privacy in Interactive Query Settings Within RDBMS Framework

Little attention has been paid to the measurement of risk to privacy in Database Management Systems, despite their prevalence as a modality of data access. This paper proposes, PriDe, a quantitative privacy metric that provides a measure (privacy score) of privacy risk when executing queries in relational database management systems. PriDe measures the degree to which attribute values, retrieved by a principal (user) engag- ing in an interactive query session, represent a reduction of privacy with respect to the attribute values previously retrieved by the principal. It can be deployed in interactive query settings where the user sends SQL queries to the database and gets results at run-time and provide privacy conscious organisations with a way to monitor the usage the application data, made available to third-parties, in privacy sense. The proposed ap- proach, without the loss of generality, is applicable to BigSQL style tech- nologies. Additionally, the paper proposes a privacy equivalence relation that facilitates the computation of privacy score.
The Model of Photovoltaic Power Short-Term Prediction Based on Dynamic Time Warping Algorithm of Partial Least Squares

Dynamic Time Warping(DTW) algorithm has strong resistance to synchronous error. Aiming at the similarity and asynchrony of meteorological factor, a method of applying DTW to photovoltaic power prediction is proposed. The meteorological factors which have great influence on photovoltaic power generation are selected by Pearson correlation coefficient method. Based on the DTW algorithm, the similarity of meteorological factors between the predicting days and the historical days can be measured. Through weighted calculation of similarity, the similar days are used to obtain training data of Partial Least Squares(PLS) prediction model. The simulation study on a photovoltaic power station verifies the correctness and effective of the proposed prediction method. The results show that the prediction model has great forecasting accuracy and certain feasibility and practicability.
Ocean energy data stream management system

In recent years a new class of data intensive application has emerged. In these applications data is modeled best not as persistent databases, but rather as transient data streams. In this chapter we assess tidal and wave energy systems through data stream management systems. This chapter includes blocking parameters of tidal and wave energy system and also assesses such types of ocean energy systems by different types of sampling methods. At the end the financial assessment of ocean energy systems is undertaken.
Short Text Classification Method Combining Word Vector and WTTM

Aimed at the problem that the characteristics of news headlines are sparse and the semantic relationship between words and words is weak, which led to the difficulty of obtaining good results in traditional text classification methods, a short text based on word vector and WTTM model is proposed. A classification method that models the topic of short text from the co-occurrence of words. Firstly, the Word2Vec tool is used to train the word vector in the short text corpus and the average word vector is synthesized by the additive averaging method. Then the theme of the short text corpus is modeled by the WTTM model to obtain the topic extended feature vector. Finally, the average word vector and the topic extended feature vector are merged. And used a random forest model to construct a classifier for classification. Compared with other short text classification methods, the short text classification method based on word vector and WTTM model has improved accuracy, recall rate and F1 value, which verified the feasibility of the method.
An efficient garbage collection in java virtual machine via swap I/O optimization

Various applications, frameworks, and services are built on Java Virtual Machine (JVM) (e.g., Big data analytics) due to its cross-platform portability. However, many of them suffer from long latency of Garbage Collection (GC) which also drops throughput, efficiency, and availability of the system. For example, when clients demand larger memory than available system memory, Operating System (OS) usually uses its swap space to release the space by evicting some inactive contents stored in memory. In this case, GC latency can be longer due to the I/O time (i.e., swap in/out). In this paper, we present a performance analysis of the existing GC policy in JVM. Based on the result of analysis, we propose an efficient GC scheme to improve the GC performance via swap I/O optimization to complement the existing GC policy. In this scheme, we selectively compact JVM heap by interacting with OS swap system during GC. The experimental results demonstrate that our scheme reduces overall GC overhead by 77.5% and improves the throughput by 82.5% respectively.