Reviewerly/exHarmony huggingface dataset:
Text Encryption using Bi-Amold Cat Map and Modulus Operation

This research proposes an encryption method in text that adopts the chaotic method which is popularly used in 2D matrices such as images. Chaos methods such as Arnold's Cat Map (ACM) have properties of determinism, randomness, sensitivity to initial conditions and ergodicity. In addition, this method also uses permutation, diffusion and confusion models. A slight modification to the text by converting it to binary 8-bits every 8 characters can form a 2-dimensional matrix with a size of 8 x 8, making it easy to perform ACM operations. ACM operation, combined with the substitution method with modulus operation, improves message encryption from various kinds of differential and statistical attacks. The results are proven by several tools such as entropy analysis, avalance effect (AE), and bit error ratio (BER).
Distributed Ledger Technology: Beyond the Hype

This chapter introduces distributed ledger technology (DLT), its main features, functions, and how different types of platforms are used to solve different challenges. The chapter includes two global supply chain use cases to highlight how different DLT platforms are being commercially used to build trust, data consistency and persistence through the chain of custody for materials as they transform along the supply chain from source to end-use end (and beyond to recycling in one case). Although these use cases are not specific to international security, they are used to manage risks that are similar to those related to treaties governing the non-proliferation of chemical, biological, and nuclear materials such as provenance of trade and movement of materials, and creating greater efficiencies and security in records management and data sharing.
Comparison of Naïve Bayes Algorithm Model Combinations with Term Weighting Techniques in Sentiment Analysis

Text mining is a basic concept of sentiment analysis and a discipline that combines linguistics and computer science with machine learning techniques. Text mining is used to change the text to be more structured. While machine learning focuses on finding and developing algorithms to build a system that can simulate or imitate a pattern from a dataset. In this study, supervised learning is used which is a basic machine learning technique with comparing the Naive Bayes Classifier algorithm model, namely Multinomial Nave Bayes and Bernoulli Naive Bayes with sentiment objects from Twitter. This study also uses Term Weighting techniques, namely TFIDF and TF-RF in each model. This study was conducted to determine the best combination of each model with Term Weighting and to test the model's accuracy, the researcher uses a random and balanced dataset to find out whether the dataset is very influential in the model. The first step in this research is crawling the data using the Twitter API, then the data is labeled. After the data is labeled, the data will enter an important step in the research, namely preprocessing and term weighting. The data that has been labeled is cleaned and converted into structured data so that the data is ready for analysis. The preprocessing data are weighted using the TF-IDF and TF-RF techniques, then classified one by one using 2 NBC models, so in this study there are 4 model schemes, namely Multinomial and TF-IDF, Bernoulli and TFIDF, Multinomial and TF-RF as well as Bernoulli and TF-RF. The last stage of this research is testing using Confusion Matrix, and then validated with K-Fold Cross Validation, testing is carried out to see the best performance of the 4 schemes. The result of the 4 schemes, TF-IDF and TF-RF with Bernoulli Naive Bayes schemes from the results of the Confusion Matrix test produce the best accuracy 61%, and the average accuracy value of the 5-fold validation is 60%. And the one with the lowest accuracy value lies in the Multinomial Naive Bayes model and TF-IDF which is 58% from Confusion Matrix, with an average value of 59% from the 5-fold validation. The researcher conducted several experiments using balanced data, and Bernoulli with 2 word weights had the highest accuracy value.
CRNet: Centroid Radiation Network for Temporal Action Localization

Temporal action localization aims to localize segments in an untrimmed video that contains different actions. Since contexts at boundaries between action instances and backgrounds are similar, how to separate the action instances from their surrounding is a challenge to be solved. In fact, the similar or dissimilar contents in actions play an important role in accomplishing the task. Intuitively, the instances with the same class label are affinitive while those with different labels are divergent. In this paper, we propose a novel method to model the relations between pairs of frames and generate precise action boundaries based on the relations, namely Centroid Radiation Network (CRNet). Specifically, we propose a Relation Network (RelNet) to represent the relations between sampled pairs of frames by employing an affinity matrix. To generate action boundaries, we use an Offset Network (OffNet) to estimate centroids of each action segments and their corresponding class labels. Based on the assumption that a centroid and its propagating areas have the same action label, we obtain action boundaries by adopting random walk to propagate a centroid to its related areas. Our proposed method is an one-stage method and can be trained in an end-to-end fashion. Experimental results show that our approach outperforms the state-of-the-art methods on THUMOS14 and ActivityNet.
From Pixels to Words

Extracting texts of various shapes, sizes, and orientations from images containing multiple objects is an important problem in many contexts, especially, in connection to E-commerce. In the context of the scale at which Walmart operates, the text from an image can be a richer and more accurate source of data than human inputs and can be used in several applications such as Attribute Extraction, Offensive Text Classification, Product Matching among others. The motivation of this particular work has come from different business requirements such as flagging products whose images contain words that are non-compliant with organizational policies and building an efficient automated system to identify similar products by comparing the information contained in their respective product images and many others. Existing methods fail to address domain specific challenges like high entropy, different orientations, and small texts in product images adequately. In this work, we provide a solution that not only addresses these challenges but is also proven to work at a million image scale for various retail business units within Walmart. Extensive experimentation revealed that our proposed solution has been able to save around 30% computational cost in both the training and the inference stages.
Assessing the Suitability of Semi-Supervised Learning Datasets using Item Response Theory

In practice, supervised learning algorithms require fully labeled datasets to achieve the high accuracy demanded by current modern applications. However, in industrial settings supervised learning algorithms can perform poorly because of few labeled instances. Semi-supervised learning (SSL) is an automatic labeling approach that utilizes complete labels to infer missing labels in partially complete datasets. The high number of available SSL algorithms and the lack of systematic comparison between them leaves practitioners without guidelines to select the appropriate one for their application. Moreover, each SSL algorithm is often validated and evaluated in a small number of common datasets. However, there is no research that examines what datasets are suitable for comparing different SSL algorihtms. The purpose of this paper is to empirically evaluate the suitability of the datasets commonly used to evaluate and compare different SSL algorithms. We performed a simulation study using twelve datasets of three different datatypes (numerical, text, image) on thirteen different SSL algorithms. The contributions of this paper are two-fold. First, we propose the use of Bayesian congeneric item response theory model to assess the suitability of commonly used datasets. Second, we compare the different SSL algorithms using these datasets. The results show that with except of three datasets, the others have very low discrimination factors and are easily solved by the current algorithms. Additionally, the SSL algorithms have overlapping 90% credible intervals, indicating uncertainty in the difference between the accuracy of these SSL models. The paper concludes suggesting that researchers and practitioners should better consider the choice of datasets used for comparing SSL algorithms.
Scheduling task and offloading process based on KNN and NB algorithm on cloud

Cloud Computing is quickly developing and a lot more cloud suppliers are arising. This project says how the incoming tasks are scheduled in the cloud. In this paper, presenting a classifier algorithmic technique that integrates the K-Nearest Neighbor(KNN) algorithm with the Naive Bayes algorithm. Calculation offloading innovation offers a feasible arrangement by offloading some calculation serious assignments of the K-Nearest Neighbor algorithm and the Naive Bayes algorithm to edges or distant mists that are furnished with adequate assets. However, the offloading process might lead to excessive delays and thus seriously affect the user experience. To address this significant issue, we first respect the average response time of multi-task parallel scheduling as our streamlining objective. Finally, the K-Nearest Neighbor and Naive Bayes algorithm based applications are proposed to solve the problem. Offloading a task means how the task is scheduled in Virtual Machine, first, the task is separated into types(small, medium, large and extra-large) after that it sees which Virtual Machine is available to load the task. Suppose, if the incoming task is too-large then we have to offload it by separating the task into subtask as 6 operations (split, combine, merge, demerge, promote and demote). To avoid the burden in the Virtual Machine, We analyze the above operations by using the K-Nearest Neighbour (KNN) algorithm with the Naive Bayes algorithm and then scheduled in the Virtual Machine. We want to show that the K-Nearest Neighbour (KNN) algorithm with the Naive Bayes algorithm shows better performance than Deep Neural Network(DNN).
Modelling of Intelligent Agents Using A–Prolog

Nowadays, research in artificial intelligence has widely grown in areas such as knowledge representation,goal-directed behaviour and knowledge reusability, all of them directly relevant to improving intelligent agents in computer games. In a particular way, we focus on the development of a novel algorithm that allow an agent to combine fundamental theories such as reasoning, learning and simulation. This algorithm combines a system of logical rules and a simulation mechanism based on learning that makes our agent has an infallible mechanism for decision-making into the game “connect four”. The logic system is developed in a modelling language known as Answer Set Programming or A–Prolog. This paradigm is the integration of two well-known languages, namely Prolog and ASP.
Tightly CCA-secure inner product functional encryption scheme

Inner product functional encryption (IPFE) is a modern public key paradigm where the master key can derive a secret key sky for a vector y, which can then be used to decrypt a ciphertext of x to get the inner product 〈x,y〉 as output. In ASIACRYPT 2019, Tomida proposed the first tightly secure IPFE scheme in the multi-user and multi-challenge setting based on the matrix decisional Diffie-Hellman (MDDH) assumption. However, the construction achieves CPA security only. Up to now, there is no IPFE scheme with tight CCA security available. In this paper, we construct the first tightly CCA-secure IPFE scheme in the multi-user and multi-challenge setting. The security reduction to the MDDH assumption (including SXDH, k-LIN, etc.) loses only a factor O(log⁡λ) with λ the security parameter. Moreover, our scheme enjoys full compactness. To support inner product function of dimension m, our SXDH-based IPFE has (m2+8m+14) and (3m+14) group elements in the master public key and ciphertext respectively. This is comparable to the tightly CPA-secure IPFE proposed by Tomida based on the DDH assumption, whose master public key and ciphertext contain (m2+2) and 3m group elements, respectively. Furthermore, we construct the first IPFE with both tight CCA-security and function-hiding property, based on our CCA-secure IPFE. The tight function-hiding CCA security is obtained by adapting the techniques in Lin (CRYPTO 2017) and Gay (PKC 2020) to the multi-user and multi-challenge setting.
A Method to Detect Isomorphism in Planar Kinematic Chains

For synthesizing kinematic chains of a given number of links and degree of freedom, detection of isomorphism is essential. Two kinematic chains are called isomorphic if they have equivalent topological configuration. The exertion for detection of isomorphism among planar kinematic chains is being researched for a long time to avoid redundancy. In the past, researchers have used several methods like characteristic polynomial-based approaches, Hamming number method, path and distance-based techniques, and many more complex algorithms to detect isomorphism. In this work, an index for detection of isomorphism among planar kinematic chains is proposed by considering the distance matrix and degree of links matrix. Kinematic chains are isomorphic, if they have same index else they are non-isomorphic. Hence, to detect isomorphism among different kinematic chains, the proposed technique is implemented successfully for 8-link single degree of freedom and 9-link double degree of freedom kinematic chains.
BASECASS: A methodology for CAPTCHAs security assurance

Today, much of the interaction between clients and providers has moved to the Internet. Some tricksters have also learned to benefit from this new situation. New improved cons, tricks and deceptions can be found on-line. Many of these deceptions are only profitable if they are done at a large scale. In order to achieve these large numbers of interactions, these attacks require automation. CAPTCHAs/HIPs are a relatively new security mechanism against automated attacks. They try to detect when the other end of the interaction is a human or a computer program (a bot). However, CAPTCHA/HIP design is still in its initial conception as the stream of successful attacks highlight it. This paper focuses on the design of CAPTCHAs and if there is a way in which to assess a basic level of security for new CAPTCHA designs. To do so, we first review main attacks to different types of CAPTCHAs and then, we describe BASECASS, a methodology that can help in avoiding some of these design pitfalls. The application of the methodology is exemplified in three attacks to CAPTCHAs and how following the methodology designers could have avoided them.
Data-driven Intelligence System for General Recommendations of Deep Learning Architectures

Choosing optimal Deep Learning (DL) architecture and hyperparameters for a particular problem is still not a trivial task among researchers.The most common approach relies on popular architectures proven to work on specific problem domains led on the same experiment environment and setup.However, this limits the opportunity to choose or invent novel DL networks that could lead to better results.This paper proposes a novel approach for providing general recommendations of an appropriate DL architecture and its hyperparameters based on different configurations presented in thousands of published research papers that examine various problem domains.This architecture can further serve as a starting point of investigating DL architecture for a concrete data set.Natural language processing (NLP) methods are used to create structured data from unstructured scientific papers upon which intelligent models are learned to propose optimal DL architecture, layer type, and activation functions.The advantage of the proposed methodology is multifold.The first is the ability to eventually use the knowledge and experience from thousands of DL papers published through the years.The second is the contribution to the forthcoming novel researches by aiding the process of choosing optimal DL setup based on the particular problem to be analyzed.The third advantage is the scalability and flexibility of the model, meaning that it can be easily retrained as new papers are published in the future, and therefore to be constantly improved.
CATE: CAusality Tree Extractor from Natural Language Requirements

Causal relations (If A, then B) are prevalent in requirements artifacts. Automatically extracting causal relations from requirements holds great potential for various RE activities (e.g., automatic derivation of suitable test cases). However, we lack an approach capable of extracting causal relations from natural language with reasonable performance. In this paper, we present our tool CATE (CAusality Tree Extractor), which is able to parse the composition of a causal relation as a tree structure. CATE does not only provide an overview of causes and effects in a sentence, but also reveals their semantic coherence by translating the causal relation into a binary tree. We encourage fellow researchers and practitioners to use CATE at https://causalitytreeextractor.com/
People Tracking in a Smart Room Using Kinect Sensor

In this paper, a new method for people tracking in a smart room using a Kinect sensor is proposed. The approach is based on the skeleton data with the (X, Y, Z) coordinate values of each joint in the human body which is provided by the Kinect sensor. For data classification, the Support Vector Machine (SVM) technique is used. To achieve this goal 14 movement classes are defined. Experiments were conducted on 12 subjects each one performs 14 movements in each experiment, the training dataset is created manually by capturing the subject movements during all experiments. The result of these we get after training the SVM model shows that the average accuracy is 90.2%.
A Case Study on Handwritten <i>Indic</i> Script Classification: Benchmarking of the Results at Page, Block, Text-line, and Word Levels

Handwritten script classification is still considered as a challenging research problem in the domain of document image analysis. Although some research attempts have been made by the researchers for solving the challenging issues, a comprehensive solution is yet to be achieved. The case study, undertaken here, analyzes the performances of various state-of-the art handwritten script classification methods for Indian scripts where features, needed for the script classification task, are extracted from the script images at four different granularity levels, i.e., page, block, text line, or word. The results of handwritten script classification at each level have been obtained and compared using eight different feature sets and six different state-of-the-art classifiers. Based on the classification results, an ideal level for performing the handwritten script classification task is suggested among these four classification levels. The results have also been improved by using two feature dimensionality reduction methods. All these experiments are done on two different handwritten Indic script databases, of which one is an in-house developed dataset and the other one is a freely available dataset. Finally, some future research directions that may be undertaken by the researchers as an application of the handwritten Indic script classification problem are also highlighted. The work presented here provides a basic foundation for the construction of a comprehensive handwritten script classification method for official Indian scripts.
Local Mutual Metric Network for Few-Shot Image Classification

Few-shot image classification aims to recognize unseen categories with only a few labeled training samples. Recent metric-based approaches tend to represent each sample with a high-level semantic representation and make decisions according to the similarities between the query sample and support categories. However, high-level concepts are identified to be poor at generalizing to novel concepts that differ from previous seen concepts due to domain shifts. Moreover, most existing methods conduct one-way instance-level metric without involving more discriminative local relations. In this paper, we propose a Local Mutual Metric Network (LM2N), which combines low-level structural representations with high-level semantic representations by unifying all abstraction levels of the embedding network to achieve a balance between discrimination and generalization ability. We also propose a novel local mutual metric strategy to collect and reweight local relations in a bidirectional manner. Extensive experiments on five benchmark datasets (i.e. miniImageNet, tieredImageNet and three fine-grained datasets) show the superiority of our proposed method.
Comparative Analysis and Modern Applications of PoW, PoS, PPoS Blockchain Consensus Mechanisms and New Distributed Ledger Technologies

Central authorities have registered economic transactions based on trust since the dawn of time.With the advent of paper, methods for documenting transactions and records became more difficult and detailed.With the widespread use of computers in recent years, digital recording has made record keeping easier and since then every technical advancement has made data recording more convenient and faster.Transactions have accelerated as technology has advanced, but the maintenance of records has remained under the jurisdiction of the authorities.The use of distributed ledger systems allows data to be released from the control of central authorities.A distributed ledger is a peer-to-peer network in which non-centralized data is shared by participants.The Bitcoin payment system and cryptocurrency, which arose after the 2008 financial crisis, was the first distributed ledger application.Central authorities' poor economic decisions aided in the rise of Bitcoin.The aim of Bitcoin is to make the current financial systems liberal from the influence of central authorities.With the rise in popularity of Bitcoin, the blockchain technology that underpins it has begun to garner interest.While blockchain technology was initially associated with the financial sector due to Bitcoin, research into its use in other sectors such as supply chain, records management, electoral systems, notary services, file systems, energy and artificial intelligence has begun.Several Blockchain infrastructures have been built to allow the use of blockchain technology in a variety of industries.However, it has been revealed in practical approaches that blockchain technology has limitations in terms of speed and scalability.As a result, new distributed ledger technologies with increased speed and scalability have been established.Hashgraph, Tangle, Tempo, Holochain are examples of newly developed distributed ledger technologies.Different influential features distinguish new generation distributed ledger technologies from the conventional Blockchain methods, which can yield into several and practical modern applications.In this study, Blockchain and new generation Distributed Ledger Technologies are compared and possible future applications are outlined.