Reviewerly/exHarmony huggingface dataset:
Binding BIKE Errors to a Key Pair

The KEM BIKE is a Round-3 alternative finalist in the NIST Post-Quantum Cryptography project. It uses the FO $$^{\not \bot }$$ transformation so that an instantiation with a decoder that has a DFR of $$2^{-128}$$ will make it IND-CCA secure. The current BIKE design does not bind the randomness of the ciphertexts (i.e., the error vectors) to a specific public key. We propose to change this design, although currently, there is no attack that leverages this property. This modification can be considered if BIKE is eventually standardized.
Performance Analysis in NoSQL Databases, Relational Databases and NoSQL Databases as a Service in the Cloud

Non-Relational Database Management Systems (NoSQL) arise as an alternative solution to problems not efficiently solved by traditional Database Management Systems (DBMS). NoSQL, unlike the relational model, does not respond to a Data Base type, but represents a set of Database types, with different implementations and characteristics to represent the information. This work represents the continuation of previous studies and aims to compare and analyse 4 local NoSQL Database engines, 2 NoSQL Database as a Service engines in the cloud and a Relational Database engine, using different schemas and under large data volume.
Value Iteration on Multicore Processors

Value Iteration (VI) is a powerful, though time consuming, approach to solve reinforcement learning problems modeled as Markov Decision Processes (MDPs). In this paper, we explore strategies to run the sate-of-the-art cache efficient algorithm for VI developed by us [1], [2] on a multicore processor. We demonstrate a speedup of up to 2.59 on a 10-core multiprocessor using 20 threads on popular benchmark data. The speedup for the parallelized portion of the computation is up to 5.89.
A survey of machine learning applications in digital forensics

We address the role of machine learning in digital forensics in this paper, in order to have a better understanding of where machine learning stand in today's cyber security domain when it comes to collecting digital evidence.We started by talking about Digital Forensics and its past.Then, to illustrate the fi elds of digital forensics where machine learning methods have been used to date, we recommend a brief literature review.The aim of this paper is to promote machine learning applications in digital forensics.We went through different applications of machine learning in different areas and analysed how machine learning can potentially be used in other areas by considering its current applications and we believe that the ideas presented here will provide promising directions in the pursuit of more powerful and successful digital forensics tools.
Using Bayesian Dialysis and Tetrads to Detect the Persistent Characteristics of Fraud

In this paper, we propose a methodology combining Bayesian and big data tools designed to optimize the investigation of fraud. This methodology is called Bayesian dialysis. We address three issues: a) Is it possible to capitalize on the evidence provided by data indicating fraud without a parametric model and using an interpretable approach? b) If so, would it be the best solution in any case? c) What is the effect size of all unobservable, even unknown, variables? We prove the viability of a new method using as an exemplary case the selection for VAT control in the Spanish Tax Agency (Agencia Estatal de Administración Tributaria—AEAT). The new method improves fraudster detection precision by 12,29%, which is increased from an average of 82.28% to 94.36%. We also use 2018–2019 corporate tax data to test the scope of this approach. Finally, based on the concept of tetrads, we propose a method to quantify the effect of unknown latent variables on models analysis.
Improving Domain Generalization Performance for Medical Image Segmentation by Self-Supervised Learning

In recent years, deep learning technology has been widely used for medical image analysis. However, deep neural networks tend to produce lower generalization performance for data in novel domains, which is a frequent scenario in the field of medical imaging since the domain can be easily shifted by a patient’s physical characteristics and image acquisition equipment. Meanwhile, self-supervised learning is recently known not only to further enhance the performance of a model, but also to improve the robustness of it. Based on this finding, we empirically demonstrated that a model’s domain generalization performance can be improved by using self-supervised pre-training in this study. Moreover, we additionally found that data augmentation applied to the pretext task can significantly impact on domain generalization performance of a model.
Size, Speed, and Security: An Ed25519 Case Study

Ed25519 has significant performance benefits compared to ECDSA using Weierstrass curves such as NIST P-256, therefore it is considered a state-of-the-art digital signature algorithm, specially for low performance IoT devices. However, such devices often have very limited resources and thus, implementations for these devices need to be as small and as performant as possible while being secure. In this paper we describe a scenario in which an obvious strategy to aggressively optimize an Ed25519 implementation for code size leads to a small memory footprint that is functionally correct but vulnerable to side-channel attacks. This strategy serves as an example of aggressive optimizations that might be considered by cryptography engineers, developers, and practitioners unfamiliar with the power of Side-Channel Analysis (SCA). As a solution to the flawed implementation example, we use a computer-aided cryptography tool generating formally verified finite field arithmetic to generate two secure Ed25519 implementations fulfilling different size requirements. After benchmarking and comparing these implementations to other widely used implementations our results show that computer-aided cryptography is capable of generating competitive code in terms of security, speed, and size.
Structurally enriched entity mention embedding from semi-structured textual content

In this research, we propose a novel and effective entity mention embedding framework that learns from semi-structured text corpus with annotated entity mentions without the aid of well-constructed knowledge graph or external semantic information other than the corpus itself. Based on the co-occurrence of words and entity mentions, we enrich the co-occurrence matrix with entity-entity, entity-word, and word-entity relationships as well as the simple structures within the documents. Experimentally, we show that our proposed entity mention embedding benefits from the structural information in link prediction task measured by mean reciprocal rank (MRR) and mean [email protected] ([email protected]) on two datasets for Named-entity recognition (NER).
An Accelerated Approach to Parallel Ensemble Techniques Targeting Healthcare and Environmental Applications

Ensemble learning techniques adopt comprehensive learning methodologies that produce optimized predictions with reduced variance and bias. The structured Random Forest ensemble learning technique equips a set of weak and diverse decision trees, resulting in an active hybrid learning ensemble. Plagued with high computational complexity, Random Forest Ensemble continues to be the preferred technique when accuracy is of primary importance for learners. Efforts to accelerate the Random Forest Ensembles are in place, however failing to efficiently utilize the data transmission bandwidth between the host and the accelerator hardware. This paper provides an architectural overview of a reconfigurable accelerator based architecture of the Random Forest Ensemble with an efficient data path model for data streaming. The paper also derives the need for an accelerated parallel ensemble method by deriving the results from equivalent sequential software implementations of the algorithm. The validation of the results have been done on healthcare application involving breast cancer classification and environmental applications involving temperature prediction and fuel consumption.
Long-Tail Items Recommendation Framework Focusing on Low Precision Rate Users

In the top-k recommendation method, the average precision rate of recommendation results is an important metrics that can be used to measure the precision of a recommendation. However, the average precision rate can only reflect the overall effect of the recommendation results, and not the reflect the recommendation effect of individual users. Therefore, the individual precision of the recommendation results is often low or 0. In addition, coverage and diversity metrics are often ignored in top-k recommendation methods, thus reducing the mining capability of long-tail items. Therefore, users with a precision rate of 0 can get neither accurate recommendation results nor novel results. For users with low levels of accuracy, recommending long-tail items that meet users’ preferences is more conducive to mining users’ interest preferences than recommending hot items for them. To solve the above-mentioned problems, this paper proposes a long-tail items recommendation framework focusing on low precision rate users and provides an auxiliary recommendation module in the recall module of the framework. The auxiliary recommendation module contains three methods (based on the minimum association value between items, based on the maximum association value between items, and the long-tail items-based diversified methods) to improve the recommendation results of low precision rate users. Experiments on Movielens 1M and Movielens ml-latest-small datasets show that the proposed long-tail items recommendation framework can improve the coverage and diversity as well as the precision of recommendations.
Collaborative Filtering Algorithm Based on SDAE and Time Mean Model

Aiming at solving the problem of data sparsity and ignorance of user interest drift in traditional collaborative filtering recommendation, this paper propose a novel collaborative filtering algorithm by combing the automatic feature extraction capability of stacked denoising autoencoder (SDAE) and the ability of real-time recommendation and rapid processing of big data of time mean model (TMM). Time factor of ratings are fully considered, and the proposed model can effectively alleviate the information distortion in feature extraction of traditional recommendation algorithms. Experimental results show that SDAE-TMM can greatly improve the performance of real-time recommendation system.
Energy efficiency evaluation based on QoS parameter specification for cloud systems

Energy efficiency evaluation (EEE) is a research difficulty in the field of cloud computing. The current research mainly considers the relevant energy efficiency indicators of cloud systems and weights the interrelationship between energy consumption, system performance and QoS requirements. However, it lacks a combination of subjective and objective, qualitative and quantitative evaluation method to accurately evaluate the energy efficiency of cloud systems. We propose a novel EEE method based on the QoS parameter specification for cloud systems (EEE-QoS). Firstly, it reduces the metric values that affect QoS requirements to the same dimension range and then establishes a belief rule base (BRB). The best-worst method is utilized to determine the initial weights of the premise attributes in the BRB model. Then, the BRB model parameters are optimized by the mean-square error, the activation weight is calculated, and the activation rules of the evidence reasoning algorithm are integrated to evaluate the belief of the conclusion. The quantitative and qualitative evaluation of the energy efficiency of cloud systems is realized. The experiments show that the proposed method can accurately and objectively evaluate the energy efficiency of cloud systems.
Expressing discrete spatial relations under granularity

The logic UBiSKt is a bi-intuitionistic modal logic with universal modalities having a semantics in which formulae are interpreted as subgraphs. We use the logic to formalize the idea of looking at subgraphs at different levels of detail. We show how the logic can be used to define various spatial relations between subgraphs. These relations are considered with respect to change in level of detail. We use an existing formulation of generalized partitions on graphs and hypergraphs in terms of relations to give a novel generalization of the classical modal logic S5 to a bi-intuitionistic modal logic we call UBiSKtS5.
A Study on Bengali Stemming and Parts-of-Speech Tagging

Stemming and parts-of-speech (POS) tagging are often considered as initial steps for the natural language processing (NLP) applications. The performance of NLP applications greatly depends on stemming and POS tagging. This paper presents the research works that have been done on Bengali stemming and POS tagging. In this work, we also categorize and compare Bengali stemming and POS tagging techniques. The stemming techniques are categorized as rule-based and statistical. The existing works suggest that rule-based techniques work better for Bengali stemming. Similarly, POS tagging techniques are categorized as linguistic, statistical, and machine learning-based tagger. Machine learning-based taggers perform better than other approaches of POS tagging for Bengali.
The Role of Visual Features in Text-Based CAPTCHAs: An fNIRS Study for Usable Security

To mitigate dictionary attacks or similar undesirable automated attacks to information systems, developers mostly prefer using CAPTCHA challenges as Human Interactive Proofs (HIPs) to distinguish between human users and scripts. Appropriate use of CAPTCHA requires a setup that balances between robustness and usability during the design of a challenge. The previous research reveals that most usability studies have used accuracy and response time as measurement criteria for quantitative analysis. The present study aims at applying optical neuroimaging techniques for the analysis of CAPTCHA design. The functional Near-Infrared Spectroscopy technique was used to explore the hemodynamic responses in the prefrontal cortex elicited by CAPTCHA stimulus of varying types. )e findings suggest that regions in the left and right dorsolateral and right dorsomedial prefrontal cortex respond to the degrees of line occlusion, rotation, and wave distortions present in a CAPTCHA. The systematic addition of the visual effects introduced nonlinear effects on the behavioral and prefrontal oxygenation measures, indicative of the emergence of Gestalt effects that might have influenced the perception of the overall CAPTCHA figure.
Evaluation of Chemical Weathering Indices Using Correspondence Analysis

Chemical weathering indices are used to quantify the extent of rock weathering. Several chemical weathering indices have been proposed that account for the mobility of major element oxides in rocks, but are critically dependent on the parent rock composition and have limited application to certain rock types. Therefore, comprehensive and statistical evaluation of index applications is helpful in the choice of a proper index. In this study, we determined correlations between 11 commonly used indices and rock types using correspondence analysis (CA). The database is about 163 samples of 10 rock types and their chemical weathering indices derived from reported literatures. The CA analysis yields two orthogonal principle factor axes (F1/G1 and F2/G2), which capture 37.7% and 21.2% of total variability, respectively. These two factor axes make up a factor-loading graph, which indicate that certain indices including the chemical index of alteration (CIA), chemical index of weathering (CIW), loss on ignition (LOI), and weathering potential index (WPI) are suitable for weathering grade classification of most rocks. The factor loadings of rock samples of variable weathering grade assemble in three groups in the factor-loading graph and most of them form a linear trend, which is related with mineralogy of the parent rock and the weathering environment. The factor-loading graph has a visual presentation of weathering trends of various rock types, and correlation between weathering indices and rock types. All of the merits suggest that CA is a powerful method for evaluating the applicability of chemical weathering indices.
Comparison of Statistical Methods for Obtaining Image from Video Frames Based on Development of Quality Metric

Digital images obtained from the video frames have an important role in different areas. Many image processing techniques have been applied to these images for different purposes such as edge detection. For a better image processing application, it is very important to obtain images with less oscillation from the video. Depending on the camera and environmental conditions, there are differences between consecutive frames. This means images with oscillation. Multiple frames can be used with different statistical methods to obtain images with less oscillation. In this paper, we developed a quality metric to compare the frames or images according to the amount of oscillation. Then a comparative study of statistical methods used to obtain the images with less oscillation from the video frames was presented. Images were obtained using four statistical methods for the different numbers of frames. This study also aims to evaluate how the choice of statistical method affects the oscillation of images using the developed quality metric and to compare the processing times of the methods.
Using Grid as Teaching Strategy in Teaching Mathematics

This paper analyzes how Grid as a graphic organizer can significantly improve the numeracy level in Mathematics of the Grade 3 pupils in addition and subtraction and identify common errors and misconceptions in adding and subtracting whole numbers. To answer this question, we compared the performance of Grade 3 pupil participants. Fifty-six percent (n= 39) from Jose Rizal Memorial School and forty-four percent (n=31) from San Juan Elementary School. Out of 70 pupils, 57 or 81.43% belonged to the non-numerate level, 13 or 18.57% were classified as instructional and non-numerate. The descriptive design was utilized. The data were analyzed using mean, frequency count, percentage, and z-test in determining the effect of the grid as a graphic organizer on the numeracy level of Grade 3 Pupils. The results of the study indicate that the numeracy level of Grade 3 pupils after using the grid as a graphic organizer revealed that the non-numerate pupils were lessened to 23 out of 70 or 32.86%, instructional pupils increased to 27 or 38.57% and numerates became 20 or 28.57%. The computed mean was increased to 5.87, having a scale description of the instructional level. The findings suggest that Grid as a graphic organizer helped a lot in proper alignment of numbers to avoid common errors and misconceptions and was found to be an effective teaching strategy in teaching Mathematics.
Stereoscopic Image-Based Rendering Technique for Low-Cost Virtual Reality

Mobile phones offer an excellent low-cost alternative for Virtual Reality. However, the hardware constraints of these devices restrict the displayable visual complexity of graphics. Image-Based Rendering techniques arise as an alternative to solve this problem, but usually, generating the stereoscopic effect to improve depth perception presents a challenging problem. In this work, we present an Image-Based Rendering technique for low-cost virtual reality that incorporates stereoscopy to improve depth perception. We also conducted a user evaluation to analyze the stereoscopic effect of the technique, especially considering the effect on depth perception, presence, and navigation. The results prove the benefits of our technique for both virtual and real-world environments.
Budgeted Batch Mode Active Learning with Generalized Cost and Utility Functions

Active learning reduces the labeling cost by actively querying labels for the most valuable data points. Typical active learning methods select the most informative examples one-at-a-time, their batch variants exist which select a set of most informative points instead of one point at a time. These points are selected in such a way that when added to the training data along with their labels, they provide maximum benefit to the underlying model. In this paper, we present a learning framework that actively selects optimal set of examples (in a batch) within a given budget, based on given utility and cost functions. The framework is generic enough to incorporate any utility and any cost function defined on a set of examples. Furthermore, we propose a novel utility function based on the Facility Location problem that considers three important characteristics of utility i.e., diversity, density and point utility. We also propose a novel cost function, by formulating the cost computation problem as an optimization problem, the solution to which turns out to be the minimum spanning tree. Thus, our framework provides the optimal batch of points within the given budget based on the cost and utility functions. We evaluate our method on several data sets and show its superior performance over baseline methods.
Integrating Secured Crypto System with Cloud for Enhancing Cloud-Based Encrypted Data Sharing Services

Cloud services are most growing technology for future business system and information technology applications, as recent advancement has proofed themselves the power and success of cloud computing network, which offers more advance services to its users on demand, such advancement for storage and computational activities motivate many users to take part into cloud computing services. The nature of cloud services model make real-time services to be processed more effectively for best streamed-based communication services. However, along with this benefits, in addition security issues are consider to be a long term concern for different types of cloud computing service models, the chances of data hijacking, loss of information putting such technology on high risk over cloud service model although many researchers and scientist has invented many algorithm and techniques to get secure and provide trustworthy cloud computing services based on demand to its users. In this paper, we proposed a data encryption technique called crypto system to overcome the weakness of existing attribute-based encryption (ABE) technique; ABE is not cooperative with cloud computing service models and other services of cloud; in this paper, we focus on the weakness of attribute-based encryption (ABE) techniques and propose a integrated crypto system for resolving data sharing and security concerns which brings efficiency, reliability, and security for cloud services model.
Label Enhancement Manifold Learning Algorithm for Multi-label Image Classification

In this paper, we propose a new label enhancement manifold learning algorithm to solve the multi-label image classification problem. Predicting multiple objects in a traffic video image is a challenging problem. Our idea is to use label distribution learning (LDL) to enrich the label space and improve label recognition in the original label space. We use the feature function representing the manifold structure to guide the geometric meaning of the label space and transform the local topology from the feature space to the label space. We first build a tag distribution learner. Next, use the LDL model for classification. The similarity between the two distributions is measured by Bayesian divergence, and the label distribution is learned through the maximum entropy model and the objective function of this paper is established. Finally, an enhanced label model of the manifold space is established to reduce the dimensionality of the feature matrix generated during the training phase, so that the supervised information in the label manifold can be used in the incremental manifold space to improve the accuracy of feature extraction. Compared to the latest multi-label learning methods, our label enhancement model has advantages in predicting performance.