Reviewerly/exHarmony huggingface dataset:
Active Learning and Case-Based Reasoning for the Deceptive Play in the Card Game of Truco

Deception is an essential behavior in many card games. Despite this fact, it is not trivial to capture the intent of a human strategist when making deceptive decisions. That is even harder when dealing with deception in card games, where components of uncertainty, hidden information, luck and randomness introduce the need of case-based decision making. Approaching this problem along with the investigation of the game of Truco, a quite popular game in Southern regions of South America, this work presents an approach that combines active learning and Case-Based Reasoning (CBR) in which agents request a human specialist to review a reused game action retrieved from a case base containing played Truco hands. That happens when the agents are confronted with game situations that are identified as opportunities for deception. The goal is to actively capture problem-solving experiences in which deception can be used, and later employ such case knowledge in the enhancement of the deceptive capabilities of the Truco agents. Experimental results show that the use of the learned cases enabled different kinds of Truco agents to play more aggressively, being more deceptive and performing a larger number of successful bluffs.
Cooperative object recognition method of multi-UAVs based on decision fusion

In this paper, a cooperative object recognition algorithm of multi-UAVs based on decision fusion is proposed. Firstly, the SURF operator is used to get the coordinate transformation matrix and stitch the images. Then, the detection results of each UAV are projected to the coordinate system of the stitched images, and the spatial coordinates of the detection information are unified. Using Jaccard overlap and Hellinger distance to synthesize position and attribute information. Then constructing the probability matrix of the detection box association. The nearest neighbor association rule based on the global optimization is used to operate multi-objective association. Finally, a dynamic switching strategy based on Jousselme distance is adopted, which can adaptively select the DST or DSmT, to fuse the information of association detection bounding boxes according to the degree of conflict. Experiments are performed on the constructed multi-UAVs cooperative object detection datasets, and the performance of several algorithms is compared. The results show that the proposed algorithm can not only increase the patrol range, but also improve the recognition accuracy of the UAV patrol system.
A Novel Image Thresholding Method Combining Entropy With Parzen Window Estimation

Abstract Image thresholding is an important and efficient image segmentation technique, which is crucial and essential for image analysis and computer vision. In this paper, we proposed a new image thresholding method based on entropy and parzen window (PW) estimation. First, the probability of each gray-level distribution is approximate by using the PW estimation. Second, by combining the obtained probability information with entropic information of the foreground and background, a new objective function is created. At last, the ideal threshold value is obtained by optimizing the objective function. By comparing with some classical thresholding methods, such as inter class variance method (OTSU), minimum error thresholding method (MET), Kapur’s entropy based method (KAPUR) and the recent methods that take spatial information into consideration (2D-D histogram method, GLLV histogram method and Gabor histogram method), the proposed method, experiment on 10 images (one synthetic image, four nondestructive testing images and five real-world images), presents a better performance on the accuracy, robustness and visual effect of segmentation.
Predicting Vulnerability for Requirements

Software security being one of the primary concerns in the software engineering community, researchers are coming up with many preemptive approaches aiming to minimize the vulnerabilities in the software. These approaches, dominated by static and dynamic analysis of the code often with machine learning (ML) techniques, are designed to detect vulnerabilities in the post-implementation stage of the software development life-cycle (SDLC). While they are found to be effective in detecting vulnerabilities, the consequences are often expensive. Accommodating changes after detecting a vulnerability in the system in later stages of the SDLC is very costly, sometimes even infeasible as it may involve changes in design or architecture. Moreover, the root of a vulnerability can often be traced back to the requirements specification. On that account, Imtiaz and Bhowmik have advocated a novel framework to provide an additional measure of predicting vulnerabilities at earlier stages of the SDLC. In this study, we build upon their proposed framework and leverage state-of-the-art ML algorithms to predict vulnerabilities for new requirements. We also present a case study on a large open-source-software (OSS) system, Firefox, evaluating the effectiveness of the extended prediction module. The results demonstrate that the framework could be a viable complement to the traditional yulnerability-fighting approaches.
A Development of Smart Coding Creative Kit to Enhance Creative Problem Solving Thinking for Children

This study aims to develop the board game- Smart Coding Creative Kit to enhance creative problem-solving thinking for children based on 4 processes of Youth Scotland’s framework []. The target groups consisted of 1) 3 experts of media and innovation and 2) 30 grade 1–3 students by purposive sampling method. Statistics used to analyze quantitative data were average (\(\overline{X}\)) and standard deviation (S.D.) while interpreting and descriptive analysis were for qualitative data. Research results revealed efficiency of the evaluated Smart Coding Creative Kit that was in highest level (\(\overline{X}\) = 4.50, S.D. = 0.58) and opinion of the subjects who had board game trial was in high level as (\(\overline{X}\) = 4.40, S.D. = 0.55). This can illustrate the board game efficiency that can be used with children in according with the analysis results that found its suitability with a group of ages 5 with small members as 3.
Threat Comparison for Large-Scale Systems Using Different Browsers

The main threats in complex networks for large-scale information systems using web browsers or service browsers are analyzed. The necessary security features for these types of systems are compared. The advantages of systems developed with service-browser technology are shown.
Portuguese Neural Text Simplification Using Machine Translation

Automatic Text Simplification (ATS) has played a significant role in the Natural Language Processing (NLP) field. ATS is a sequence-to-sequence problem aiming to create a new version of the original text removing complex and domain-specific words. It can improve communication and understanding of documents from specific domains, as well as support second language learning. This paper presents an empirical study on the use of state-of-the-art ATS methods to simplify texts in Portuguese. It is important to remark that the literature reports the challenge in analyzing Portuguese texts due to the lack of resources compared to other languages (i.e., English). More specifically, this work evaluated different Neural Machine Translation (NMT) techniques for ATS in Portuguese. The experiments showed that NMT achieved promising results in Portuguese texts, obtaining 40.89 BLEU score using multiple parallel corpora and raising the overall readability score by more than 5 points.
AR - Mart: The Implementation of Augmented Reality as a Smart Self-Service Cashier in the Pandemic Era

Considering the recent booming of cashier-less checkout technology and the future trend of increasing self-service and contactless conditions due to Covid-19, it is necessary to find an alternative checkout concept suitable for local retail conditions. Using augmented reality, the aim of this study is to compare the proposed method with the conventional method of cashier checkout using barcode scanner. The method used in this study is marker-based tracking, where the marker is an image file which will be uploaded to Vuforia SDK Kit. The result of the proposed method for AR-Mart as a smart cashier is faster, more accurate and can reduces the duration for cashier checkout significantly.
Articulatory Synthesis of Vocalized /r/ Allophones in German

Articulatory synthesis relies on precise, parametric vocal tract shapes to generate natural-sounding speech. In German, a particular challenge is the accurate synthesis of the vocalic /r/ allophones following vowels or in syllable coda position. Using established phonetic conventions, no satisfying results could be achieved so far, implying a possible shortcoming of these existing conventions. This study therefore analyzed a large number of natural recordings of the sounds in question from a single speaker to find the optimal number of target vocal tract shapes. Applying clustering techniques, the manifold of vocalic /r/ allophones could be reduced to two prototypical [ɐ] variants previously undescribed in the literature. As shown by a listening experiment, which of these two sounds was preferred in which context depended not only on the respective context vowel’s tenseness, but also on its openness and acoustic distance to other context vowels ending in the same [ɐ] variant. This indicates that the two different allophones might serve as a contrastive cue to help differentiate between otherwise similar context vowels.
A Neural Architecture for Detecting Identifier Renaming from Diff

In software engineering, code review controls code quality and prevents bugs. Although many commits to a codebase add features, some commits are code refactoring, including renaming of identifiers. Reviewing code refactoring requires a bit of different efforts than that of reviewing functional changes. For instance, renaming an identifier has to make sure that the new name not only is more descriptive and follows the naming convention of the institution, but also does not collide with any other identifiers. We propose in this paper a machine learning model to automatically identify commits consisting of pure identifier renaming, from only the diff files. This technique helps code review enforce naming and coding conventions of the institution, and let quality assurance testers focus more on functional changes. In contrast to the traditional way of detecting such changes by parsing the full source code before and after the commit, which is less efficient and requires rigorous syntactical completeness and correctness, our novel approach based on neural networks is able to read only the diff and gives a confidence value of whether it is a renaming or not. Since there had been no existing labeled dataset on repository commits, we labeled a dataset with more than 1,000 repos from GitHub by Java syntax analysis. Then we trained a neural network to classify these commits as whether they are renaming, obtaining the test accuracy of 85.65% and the false positive rate of 2.03%. The methods in our experiment also have significance for general static analysis with neural network approaches.
Matching Cyber Security Ontologies through Genetic Algorithm-Based Ontology Alignment Technique

Security ontology can be used to build a shared knowledge model for an application domain to overcome the data heterogeneity issue, but it suffers from its own heterogeneity issue. Finding identical entities in two ontologies, i.e., ontology alignment, is a solution. It is important to select an effective similarity measure (SM) to distinguish heterogeneous entities. However, due to the complex semantic relationships among concepts, no SM is ensured to be effective in all alignment tasks. The aggregation of SMs so that their advantages and disadvantages complement each other directly affects the quality of alignments. In this work, we formally define this problem, discuss its challenges, and present a problem-specific genetic algorithm (GA) to effectively address it. We experimentally test our approach on bibliographic tracks provided by OAEI and five pairs of security ontologies. The results show that GA can effectively address different heterogeneous ontology-alignment tasks and determine high-quality security ontology alignments.
Research on key technology of 3D digitization for secondary system of power grid based on model application analysis

Abstract In order to solve the problems of long development time, poor expansibility and small application scope in the digital design of secondary system of intelligent substation at present, this paper puts forward the 3D assembly techniques based on the expanded model by analyzing the integration of secondary model files of intelligent substation. In the design process of this method, the relationship between primary equipment and secondary equipment, the mapping between logical circuit and real circuit are fully considered. By using standard family library techniques, file analysis techniques and automatic mapping techniques, the coupling between physical model and logical relationship is realized. Taking the actual 220kV intelligent substation as the pilot, the three-dimensional model in secondary system simulation analysis, online monitoring and the application of fault diagnosis shows that the techniques can effectively improve the efficiency of three-dimensional design and maintenance of secondary system.
Survey and a New Taxonomy of Proofs of Retrievability on the Cloud Storage

Proof of Retrievability (PoR) is a technique used to ensure the authenticity of data on outsourced storage services. It improves the soundness and the robustness of the data integrity scheme and allows clients to recover the remote data. Under the circumstance of considering untrusted parties including the Cloud Storage Provider (CSP) and Third Party Authenticator (TPA), incorporating PoR and zero-knowledge Proofs, which is another technique used to allow a prover to convince a verifier that a secret exists without revealing the secret itself, will ensure client integrity verification, strengthen privacy, and improve fairness to both sides. In this paper, we present, on one hand, the state-of-the-art of PoR under zero-knowledge constructs following an existing data integrity scheme taxonomy of cloud storage. We analyze the PoR scheme formalism and its similarities with zero-knowledge concepts, in addition to the techniques used to settle robustness and zero-knowledge proofs methods. On the other hand, we propose our improved taxonomy of proofs of retrievability enriched by the zero-knowledge, the cryptography model, and the cryptographic setup. The proposed taxonomy equips researchers with a tool to think about the PoR scheme from those perspectives. In the end, we state some fruitful lines of works that PoR can take advantage of; i.e Bulletproofs, Interactive Oracle Proofs, and Interactive Oracle Proofs of Proximity For Reed-Solomon.
Accelerometer-Based Alcohol Consumption Detection from Physical Activity

Smartphones have become a common tool for researchers to collect, process, and analyze large quantities of data. This will lead to the creation of solutions that will mostly come in the form of smartphone apps, which will help solve real-life problems. One such real-life problem is the over-consumption of alcohol, since it can lead to many problems including fatality. Currently, there are very expensive or tedious alternative procedures for testing blood alcohol consumption in the market. This paper offers a cheaper alternative to address this problem by detecting if the user has consumed alcohol or not by using a smartphone. We describe an experiment and propose two features derived from accelerometer data that can help us distinguish between sober and intoxicated individuals.
An Efficient Encryption-Authentication Scheme for Electrocardiogram Data using the 3DES and Water Cycle Optimization Algorithm

To share the recorded ECG data with the cardiologist in Golden Hours in an efficient and secured manner via tele-cardiology may save the lives of the population residing in rural areas of a country. This paper proposes an encryption-authentication scheme for secure the ECG data. The main contribution of this work is to generate a one-time padding key and deploying an encryption algorithm in authentication mode to achieve encryption and authentication. This is achieved by a water cycle optimization algorithm that generates a completely random one-time padding key and Triple Data Encryption Standard (3DES) algorithm for encrypting the ECG data. To validate the accuracy of the proposed encryption authentication scheme, experimental results were performed on standard ECG data and various performance parameters were calculated for it. The results show that the proposed algorithm improves security and passes the statistical key generation test.
Comprehensive Foreign Language Teaching Mode in the Era of Big Data

Since the advent of computer and artificial intelligence technology, the whole world has set off an information technology revolution, especially now the information technology is more and more developed and mature, which also indicates that the era of BDT has come. Under the influence of big data, great changes have taken place in the working methods and modes of all walks of life. In the new era of education industry, the traditional teaching concept can not adapt to the development of the times, especially the comprehensive foreign language education vigorously promoted by the Ministry of Education. Now, due to the emergence of BDT (big data technology), many educators try to apply BDT to the comprehensive foreign language teaching mode. This paper selects two foreign language universities as experimental objects to explore the effects and repercussions of the combination of BDT and comprehensive foreign language teaching mode. BDT is used in the comprehensive foreign language teaching mode of University A, while the traditional teaching method is still used in University B. The results show that the teachers and students of University A have a high evaluation of the new teaching mode, and the teachers' score is basically above 85, even the highest score is 93. The percentage of students who are satisfied with the new teaching mode is 75%, which is far more than half the total number of people surveyed.
Security Assessment of Heterogeneous SoC-FPGA: On the Practicality of Cache Timing Attacks

Cache attacks are widespread on microprocessors and multi-processor system-on-chips but have not yet spread to heterogeneous systems-on-chip such as SoC-FPGA that are found in increasing numbers of applications on servers or in the cloud. This type of SoC has two parts, a processing system that includes hard components and ARM processor cores and a programmable logic part that includes logic gates to be used to implement custom designs. The two parts communicate via memory mapped interfaces. One of these interfaces is the accelerator coherency port that provides optional cache coherency between the two parts. In this paper, we discuss the practicability and potential threat of inside-SoC cache attacks using the cache coherency mechanism of a complex heterogeneous SoC-FPGA. For the first time, we provide proof of two cache timing attacks Flush+Reload and Evict+Time when SoC-FPGA is targeted, and proof of hidden communication using a cache-based covert channel. The heterogeneous SoC-FPGA Xilinx Zynq-7010 is used as an experimental target.
Sensor Fusion for State Estimation of AUVs Using a Novel Combination of Image Processing Technique

The performance of ROV control systems is highly dependent on the quality of their sensor measurements and state estimation methods. In this work, underwater images are used to estimate position an orientation of a ROV with respect to a surface or to other objects in the scene. Since, underwater images are liable to lack of contrast, color distortion, blurring, and are hazy in nature they have to be preprocessed for restoration and enhancement. In this work, the combination of an image formation model (IFM) based method, for reduction of Image Blurriness and Light Absorption, and a Contrast Limited Adaptive Histogram Equalization method is proposed to treat the images before applying registering techniques to extract the changes in displacement and orientation. Afterward, the Bayesian Estimation Algorithm and the Extended Kalman Filter are used to finally estimate the surge and roll motions of a BlueROV2.
The power of the snake: number theory with Python

The Python programming language is a powerful tool in teaching Number Theory and other pure mathematics classes. Python can be used to make the content more interactive and applicable. I am illustrating this with a few code snippets that I have used in my Number Theory and Cryptography classes.
Developing Computer Applications without any OS or Kernel in a Multi-core Architecture

Over the years, operating systems (OSs) have grown significantly in complexity and size providing attackers with more avenues to compromise their security. By eliminating the OS, it becomes possible to develop general-purpose non-embedded applications that are free of typical OS-related vulnerabilities. Such applications are simpler and smaller in size, making it easier secure the application code. Bare machine computing (BMC) applications run on ordinary desktops and laptops without the support of any operating system or centralized kernel. Many BMC applications have been developed previously for single-core systems. We show how to build BMC applications for multicore systems by presenting the design and implementation of a novel UDP-based bare machine prototype Web server for a multicore architecture. We also include preliminary experimental results from running the server on the Internet. This work provides a foundation for building secure computer applications that run on multicore systems without the need for intermediary software.
An Encryption-as-a-service Architecture on Cloud Native Platform

The Kubernetes is currently one of the most popular and powerful open-source cloud-native container orchestration platforms. Security at the container level of a Kubernetes cluster is known to be lacking. In this paper, we study an encryption-as-a-service architecture for Kubernetes pods, which provides a unified, flexible and universal method for encrypting the data flow internally and externally between containers. We inject an HAProxy container on the fly to the application pod to perform the encryption. We use Elasticsearch-based microservices as a case study to implement our architecture. In the experiments, we tested the overhead and performance degradation with this framework using various experimental settings. The results show that our proposed solution achieves communication encryption at the cost of only 7% of the write performance degradation. The query/read performance hardly drops.