Reviewerly/exHarmony huggingface dataset:
Implementation of Data Transportation Protocol Depending on the Lightweight Cipher Algorithms

Lightweight cryptography (LWC) takes a novel approach to produce security for low-power devices and constrained applications such as the Internet of Things (IoT) and Wireless Sensor Node (WSN). In these WSN and IoT applications, whole nodes communicate with each other over a network; therefore, there are opportunities to penetrate this node via the extrinsic attack. The most important thing here is to preserve this node from all kinds of extrinsic attacks considering the hardware implementation and memory size. Considering the above problem, finding a proposal that transfers data and ensures that it is not stolen or tampered is necessary. This proposal has been based on previous lightweight encryption algorithms. The proposal transferred the data (in xml data formats) between the start and end nodes with the least nodes. This provided a speed in the transfer process, which led to a significant improvement in data security. Essentially lightweight cipher provides the security at the node level only. We also calculated the execution time of these five lightweight algorithms, the time of data transfer, and the number of nodes for transport data.
Hypernymy Detection for Low-resource Languages: A Study for Hindi, Bengali, and Amharic

Numerous attempts for hypernymy relation (e.g., dog “is-a” animal) detection have been made for resourceful languages like English, whereas efforts made for low-resource languages are scarce primarily due to lack of gold-standard datasets and suitable distributional models. Therefore, we introduce four gold-standard datasets for hypernymy detection for each of the two languages, namely, Hindi and Bengali, and two gold-standard datasets for Amharic. Another major contribution of this work is to prepare distributional thesaurus (DT) embeddings for all three languages using three different network embedding methods (DeepWalk, role2vec, and M-NMF) for the first time on these languages and to show their utility for hypernymy detection. Posing this problem as a binary classification task, we experiment with supervised classifiers like Support Vector Machine, Random Forest, and so on, and we show that these classifiers fed with DT embeddings can obtain promising results while evaluated against proposed gold-standard datasets, specifically in an experimental setup that counteracts lexical memorization. We further incorporate DT embeddings and pre-trained fastText embeddings together using two different hybrid approaches, both of which produce an excellent performance. Additionally, we validate our methodology on gold-standard English datasets as well, where we reach a comparable performance to state-of-the-art models for hypernymy detection.
Tight Bounds for General Computation in Noisy Broadcast Networks

Let II be a protocol over the n-party broadcast channel, where in each round, a pre-specified party broadcasts a symbol to all other parties. We wish to design a scheme that takes such a protocol II as input and outputs a noise resilient protocol II’ that simulates II over the noisy broadcast channel, where each received symbol is flipped with a fixed constant probability, independently. What is the minimum overhead in the number of rounds that is incurred by any such simulation scheme? A classical result by Gallager from the 80's shows that non-interactive T-round protocols, where the bit communicated in every round is independent of the communication history, can be converted to noise resilient ones with only an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{O}(\log\log T$</tex> ) multiplicative overhead in the number of rounds. Can the same be proved for any protocol? Or, are there protocols whose simulation requires an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Omega(\log T)$</tex> overhead (which always suffices)? We answer both the above questions in the negative: We give a simulation scheme with an <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\tilde{O}(\sqrt{\log T})$</tex> overhead for every protocol and channel alphabet. We also prove an (almost) matching lower bound of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Omega(\sqrt{\log T})$</tex> on the overhead required to simulate the pointer chasing protocol with T = n and polynomial alphabet.
Perception and Motion Planning for Unknotting/untangling of Ropes of Finite Thickness

Handling physical ropes/strings is an important capability to enable robots for human interaction, surgery/suturing, and so on. This chapter will discuss the specific problem of untangling strings/ropes with finite thickness. The chapter discusses the motivation for this problem, the problem statement, the state of the art in this area, and the specific direction of this research. Subsequently, an outline of the rope simulator being developed, which is a prerequisite, is provided. A brief discussion on the employed rope model and handling of self-collisions is provided.
Surface Shape Distortion Online Measurement Method for Compact Laser Cavities Based on Phase Measuring Deflectometry

Conventional phase measuring deflectometry (PMD) takes up a large measurement space and is not suitable for compact online measurement, as the liquid crystal display (LCD) has to be placed in parallel with the mirror under test. In this paper, a compact online phase measuring deflectometry (COPMD) with the LCD screen set perpendicular to the mirror under test is presented for surface shape distortion real-time measurement. The configuration of the COPMD in an enclosed laser cavity is proposed, and the principle of the method is theoretically derived by using the vector-form reflection law. Based on the analysis model, the fringe modulation regulation of the LCD is revealed, and the measurement errors caused by misalignments of the components are illustrated. The validity and flexibility of the COPMD method are verified in the experiment by using a single-actuator deformable mirror as the mirror under test and the PMD method as the comparison. The proposed COPMD method remarkably expands the application range of the conventional PMD method, as it could make efficient use of compact space and is applicable for real-time measurement in enclosed laser facilities and assembled laser systems.
FarSick: A Persian Semantic Textual Similarity And Natural Language Inference Dataset

Semantic textual similarity(STS) and natural language inference(NLI) are important tasks in natural language processing(NLP) such as information retrieval, text classification, subject extraction, text summarization, machine translation and plagiarism detection. Lack of appropriate datasets in the Persian language is a major obstacle to progress in this area. Therefore, in this paper, we present FarSick, a new dataset for STS and NLI tasks in the Persian language. FarSick is the first relatively large-scale STS dataset for the Persian language. It includes 9804 pairs of Persian sentences with labels for similarity and inference for each pair of sentences. This dataset is collected by translating and editing the sentences of SICK dataset. We also measured the performance of traditional, statistical and deep learning models on it, e.g. transformers, Convolution Neural Networks, Bidirectional LSTMs, weighted average of word vectors, etc. We used different pre-trained embeddings, word2vec, glove, fastText and Bert sentence transformer. We used accuracy metric to test NLI tasks and Pearson metric to test STS tasks. The dataset is available at https://github.com/ZahraGhasemi-AI/FarSick.
Multi-Agent Off-Policy TDC with Near-Optimal Sample and Communication Complexity

The finite-time convergence of off-policy TD learning has been comprehensively studied recently. However, such a type of convergence has not been well established for off-policy TD learning in the multi-agent setting, which covers broader applications and is fundamentally more challenging. This work develops a decentralized TD with correction (TDC) algorithm for multi-agent off-policy TD learning under Markovian sampling. In particular, our algorithms preserve full privacy of the actions, policies and rewards of the agents, and adopt mini-batch sampling to reduce the sampling variance and communication frequency. Under Markovian sampling and linear function approximation, we proved that the finite-time sample complexity of the algorithm for achieving an ϵ-accurate solution is in the order of ${\mathcal{O}}\left({{ \in ^{ - 1}}\ln { \in ^{ - 1}}}\right)$, matching the near-optimal sample complexity of centralized TD(0) and TDC. Importantly, the communication complexity of our algorithm is in the order of ${\mathcal{O}}\left({\ln { \in ^{ - 1}}}\right)$, which is significantly lower than the communication complexity ${\mathcal{O}}\left({{ \in ^{ - 1}}\ln { \in ^{ - 1}}}\right)$ of the existing decentralized TD(0). Experiments corroborate our theoretical findings.
A Study on the Importance of Tactile Stimulation on Immersive Experience for Digital Communication

In the post-pandemic era, telecommuting, online education, and digital entertainment have been widely proposed when there is a need for keeping physical distance. Analyses in digital communications literature indicate that individuals heavily rely on not only visual and auditory but also tactile stimulations to generate a more vivid digital communication. However, previous studies were somewhat focused on harnessing visual and auditory stimulations while the importance of tactile stimulation of digital experience has yet to receive academic attention. Given that, this paper examines the role of tactile stimulations to discern differences in individuals’ immersive experiences between digital communications and face-to-face communications. First, literature studies analyze current immersive digital experience, people’s digital communication expectations, and the effects of tactile recognition. Thereafter the research follows a research method based on content analysis and the research results indicate that touch-enabling technologies that omit tactile stimulations deliver poor communications.
AI-Assisted Activity Detection in K-6 Classroom Environments: A Preliminary Framework to Assist in Pedagogical Performance Evaluation

Video-based classroom observation tools can provide constructive feedback to K-6 teachers and assist their training and skill development. However, accurate classroom observation ratings necessitate observer training, requiring the investment of time, money and skilled labor. We propose a new AI-assisted pipeline to automate the classroom observation rating process utilizing a deep learning framework. Specifically, we combine three streams: action recognition, object detection, and age estimation networks to detect classroom instructional activities. We conducted the experiments on a novel labeled K-6 (elementary) classroom observation video dataset to detect human activities in the classroom environment. We labeled the data with the help of experienced annotators trained in classroom observation instruments. We also conducted multiple inter-rater reliability studies to ensure reliable labels. Our preliminary experimental results show promise for detecting multiple classroom instructional activity labels.
Unifying Random-Asynchronous Algorithms for Numerical Methods, Using Switching Systems Theory

Randomized variants of iterative techniques such as Kaczmarz method, Gauss-Seidel method and asynchronous fixed-point iterations have been of interest in recent years. Due to their randomized nature, these techniques are better suited for processing of large scale and distributed data. Despite their effectiveness, their theoretical analysis has been of interest only in recent years. In the mean-time, control theory literature has studied switching systems rigorously in order to understand the behavior of systems whose dynamics change over time. This paper shows that randomized iterative algorithms can be represented as switching systems. Thus, convergence properties of such randomized algorithms follow directly from the stability theory already developed for switching systems. As an example, alternative proofs are provided for the mean-squared and almost sure convergence of randomized Kaczmarz and Gauss-Seidel methods. The necessary and sufficient condition for the mean-squared convergence of random asynchronous fixed-point iterations is also provided.
My Diary: A Web Application to Accumulate Necessary Files

Document preservation system is a helpful thing in daily life. People need to deal with various necessary tasks, such as utility bills, managing bank accounts, regular necessary official documents, and so on. They always maintain the individual hardcopy of these things and also gather them in different places from where they can be lost, torn or shattered. Again, they maintain a one-to-one system with all these essential files, which is really troublesome. For these reasons, it is necessary to gather all the necessary work of a person in one place, from where they can access those things through a one-to-many system. It can be mentioned as-all the necessary documents in one place. Thus, the one-to-one system has motivated us to make a combined system where a person will be able to assemble all their information and papers together. This paper presents a web application to accumulate necessary files for users for their personal uses. A user can sign in, login and store their important documents-utility bills, medical prescriptions, passport copies, license papers, biography pages, account details, and so on. They will also be notified about the deadlines for any event. It will be a great help for every individual to find their important things altogether in one place, which will be time-effective and easily accessible.
Research and Simulation of Image Specific Region Recognition Technology

The development of computer technology has promoted the wide application of computer vision technology, especially in the complex background environment, accurate and fast recognition of specific areas of objects has become a hot spot of image recognition technology based on computer technology. The traditional recognition methods usually use comparison method, that is, to extract image contour by edge detection and compare with given model to complete the recognition. The efficiency of this method is low, and it is difficult to achieve efficient and fast recognition. Gabor filter of neural network is chosen as the recognition tool. For the main shortcomings of its slow recognition speed and low efficiency of extracting feature points, this paper improves the recognition method by simplifying the system model and improving the efficiency of feature point extraction, it can complete the task of real-time recognition of specific recognition targets.
A Weighted TF-IDF-based Approach for Authorship Attribution

Authorship Attribution (AA) is a task in which a disputed text is automatically assigned to an author chosen from a list of candidate authors. To this end, a model is trained on a dataset of textual documents with known authors, which can be considered as a multi-class single-label classification task. In this paper, we approach this task differently by extending information retrieval techniques to train an AA model. It is based on weighting the AARR technique, presented in our previous study, to relax the value of term frequency. The efficiency of the proposed solution has been evaluated by conducting several experiments on six datasets. The results show the superiority of the proposed solution by improving the accuracy of IMDB, Gutenberg books, Poetry, Blogs, PAN2011, and Twitter datasets by 33%, 31%, 31%, 19%, 6%, and 1%, respectively, where the average improvement is 19.94% over all datasets. The best accuracy over these datasets is 88%, 82%, 67%, 90%, 65%, and 81% in the same respect. In addition, compared to the baseline system, the computation time of the proposed solution has been improved significantly (21.44X) by employing a dictionary-based indexing technique.
States of Attack Under Incomplete Information

Autonomous systems are special kinds of systems that are complex, consist of many modules, different producers, different technologies like programming, machine learning, and sometimes different manufacturers. As such, checking for their properties, or even determining which state they are at a given moment, is a difficult task. To study the latter problem we use two models, originally developed in security theory, namely we combine attack trees and the information flow security. Hence a question of whether we can decide that a system has reached a given state (i.e., whether the root node of the attack tree is reached) is translated to checking current state opacity,
On Relaxation of Simon's Algorithm

Simon's algorithm is one of the most widely used quantum algorithms in cryptanalysis of symmetric ciphers, which has a significant advantage over its classical counterparts. However, if the number of unwanted collisions of the target function is large or if the number of adversary's quantum superposition queries is limited, the Simon's algorithm is not able to unambiguously compute the actual period. This problem can lead to the failure of period-finding-based quantum attacks. In this paper we first show that using Simon's algorithm, one can find proper partial periods of Boolean vector functions, such that the corresponding probabilities, independent of the target function, are directly related to the number of quantum queries. Next, we examine how to use the partial period instead of actual one in quantum period-finding-based attacks. As a result, the advantage of our proposed relaxing method is twofold: It improves success probabilities of quantum period-finding-based distinguishers, provided that the adversary is limited to a specified small number of queries, in contrast to the previous ones. On the other hand, our proposed method generalizes the previous forgery attacks on modes of operation for message authentication codes.
Towards Owner-Controlled Data Sharing

We discuss some of the main problems related to allowing data owners to share their data with interested consumers in a controlled way in the context of digital data markets. Since resorting to the cloud for data storage reduces the burden at the owner’s side, we first address the problem of supporting owners in selecting suitable cloud plans for storing their data collections. To this end, we illustrate some recent proposals for the specification and enforcement, in a friendly and flexible way, of the owners’ requirements and preferences that should guide the selection process. We also address the problem of ensuring that data owners remain in control of their data and that they receive rewards for making their data available to others, and illustrate recent proposals addressing it.
BER Minimization by User Pairing in Downlink NOMA Using Laser Chaos-Based MAB Algorithm

Non-Orthogonal Multiple Access (NOMA) is the technology that allows multiple users’ downlink communications to be transmitted in the same resource block by proper user pairing. In realizing real-time operations, an ultrafast pairing decision scheme is required. Previous studies have shown that decision making using laser chaos is ultra-fast and effective as a Multi-Armed Bandit (MAB) algorithm. In this paper, we demonstrate user pairing using laser chaos-based MAB algorithm on the basis of the bit error rate of the physical layer. That is, we define the conditions for successful or failed communication by bit error, leading to benefits from the efficient decision-making ability of the laser chaos-based MAB strategy. The numerical results show that the proposed method provides better performances than conventional ones, C-NOMA and UCGD-NOMA.
Hyperspectral Image Classification Based on Stacked Extreme Learning Machine

Hyperspectral image classification has become a research focus in recent literature. However, extraction of deep features in a short time is still an open issue to increase classification performance. In this paper, combining the deep network structure model of deep learning and deep feature extraction with the fast network learning algorithm of extreme learning machine, a deep neural network model based on a stacked extreme learning machine is proposed. The network structure of the model contains multiple limits. First, the learning machine is constructed in an integrated cascading manner. The original features are initially learned using a multi-scale scanning strategy and used as model input. Second different attribution features learned by multiple extreme learning machines in each layer of the network are used as the input of the next layer of the network. And then separable attribution features are got for hyperspectral image classification. The experimental results demonstrate that the proposed method can obtain better classification performance.
RBC Classification using Deep Learning

The therapeutic investigation of microscopic blood smear starts with recognizing blood cells of various groups as well as estimating cell toll in blood sample. The idiosyncratic blood cell grading and counting generates priceless knowledge to the pathologist about assorted infections. This exercise can be easily concluded if the shapes of blood cells are identified first and then using the shapes, we classify the blood cells. In this research work we build and test an automatic microscopic blood smear red blood cell (RBC) classification by using Convolutional Neural Network (CNN) based deep learning approach. We train and test the statistical CNN data models based on probabilistic pattern recognition to classify the RBC of a blood smear into Normal Cells, Echinocytes, Elliptocytes and Sickle cells. The H - minimum Transform (HmT) and Watershed Transform (WT) are used in pre-processing of images to increase the accuracy if segmentation shape extraction of the blood cells. Then the Image Data Store created considering every strongest feature of each type of blood cell convolutional layers of various sizes. Training takes place through 11 layered CNN whose performance measured by using Mean Average Precision (mAP) justifies that the deep learning-based classifiers provide satisfactory results.
Randomized Linear Programming for Tabular Average-Cost Multi-agent Reinforcement Learning

We focus on multi-agent reinforcement learning in tabular average-cost settings: a team of agents sequentially interacts with the environment and observes localized incentives. The setting we focus on is one in which the global reward is a sum of all local rewards, the joint policy factorizes into agents' marginals, and full observability. To date, exceptionally few global optimality guarantees exist for this simple setting, as most results, asymptotic or non-asymptotic, yield convergence to stationarity under parameterized settings for possibly large/continuous spaces. To strengthen performance guarantees in MARL, we focus on linear programming (LP) reformulations of RL for which stochastic primal-dual method has recently been shown to achieve optimal sample complexity in the centralized tabular case. We develop multi-agent LP extensions, whereby agents solve their local saddle point problems and then compose their variable estimates with weighted averaging steps to diffuse information between agents across time. We establish that the number of samples required to attain near-globally optimal solutions matches tight dependencies on the cardinality of the state and action spaces, and exhibits classical scalings with the size of the team in accordance with multi-agent optimization. Experiments then demonstrate the merits of this approach for cooperative navigation problems.