Reviewerly/exHarmony huggingface dataset:
Analyzing the Provable Security Bounds of GIFT-COFB and Photon-Beetle

We study the provable security claims of two NIST Lightweight Cryptography (LwC) finalists, GIFT-COFB and Photon-Beetle, and present several attacks whose complexities contradict their claimed bounds in their final round specification documents. For GIFT-COFB, we show an attack using $$q_e$$ encryption queries and no decryption query to break privacy (IND-CPA). The success probability is $$O(q_e/2^{n/2})$$ for n-bit block while the claimed bound contains $$O(q^2_e/2^{n})$$ . This positively solves an open question posed in [Khairallah, ePrint 2021/648 (also accepted at FSE 2022)]. For Photon-Beetle, we show an attack using $$q_e$$ encryption queries (using a small number of input blocks) followed by a single decryption query and no primitive query to break authenticity (INT-CTXT). The success probability is $$O(q^2_e/2^{b})$$ for a b-bit block permutation, and it is significantly larger than what the claimed bound tells, which is independent of the number of encryption queries. We also show a simple tag guessing attack that violates the INT-CTXT bound when the rate $$r=32$$ . Then, we analyze other (improved/modified) bounds of Photon-Beetle shown in the subsequent papers [Chakraborty et al., ToSC 2020(2) and Chakraborty et al., ePrint 2019/1475]. As a side result of our security analysis of Photon-Beetle, we point out that a simple and efficient forgery attack is possible in the related-key setting. We emphasize that our results do not contradict the claimed “bit security” in the LwC specification documents for any of the schemes that we studied. That is, we do not negate the claims that GIFT-COFB is $$(n/2 - \log n)$$ -bit secure for $$n=128$$ , and Photon-Beetle is $$(b/2 - \log b/2)$$ -bit secure for $$b=256$$ and $$r=128$$ , where r is a rate. We also note that the security against related-key attacks is not included in the security requirements of NIST LwC, and is not claimed by the designers.
Interactive Reinforcement Learning With Bayesian Fusion of Multimodal Advice

Interactive Reinforcement Learning (IRL) has shown promising results in decreasing the learning times of Reinforcement Learning algorithms by incorporating human feedback and advice. In particular, the integration of multimodal feedback channels such as speech and gestures into IRL systems can enable more versatile and natural interaction of everyday users. In this letter, we propose a novel approach to integrate human advice from multiple modalities into IRL algorithms. For each advice modality we assume an individual base classifier that outputs a categorical probability distribution and fuse these distributions using the Bayesian fusion method Independent Opinion Pool. While existing approaches rely on heuristic fusion, our Bayesian approach is theoretically founded and fully exploits the uncertainty represented in the distributions. Experimental evaluations in a simulated grid world scenario and on a real-world human-robot interaction task with a 7-DoF robot arm show that our method clearly outperforms the closest related approach for multimodal IRL. In particular, our novel approach is more robust against misclassifications of the modalities’ individual base classifiers.
State of the Art on Twitter Spam Detection

Extensive use of social networking sites such as Twitter shows their popularity among users. It attracts normal users as well as illegitimate users. Twitter spam is a serious problem to be taken care of. Spammers use Twitter for illegal purposes such as advertising, phishing, spreading unwanted software or irrelevant information, hijacking trending topics. Thus spam detection and filtering become necessary to protect users' data. There are various spam detection methods proposed by the industry and researchers in previous years. Spam detection accuracy and efficiency have been significantly improved in the last few years. In this paper, our survey has been divided into three parts such as spam detection, real-time spam detection, and spammer detection. We also discussed different Twitter features used for spam detection, effectiveness, and challenges in current research work.
Comparative Analysis of the Measured and Simulated Performances of a Grid-Connected Photovoltaic Power Plant

The present study comparing actual data and simulated parameters was carried out using the PVsyst software for a 5.94 KWp grid-connected photovoltaic system, consisting of three types of mono-Si (2.04 KWp), poly-Si (2.04 KWp) and amorphous-Si (1.86 KWp) photovoltaic panels and a weather station.The research is based on data collected for one year (2020) of energy production Simulations were performed using long-term meteorological data generated by NASA and on-site measurement.A comparison of evaluated monthly and annually performance elements has also been considered: Eac, PR, FC.As shown by the results, each photovoltaic technology has certain advantages in different weather conditions.The uncertainties of the PVSyst software used to estimate the output of the photovoltaic installation have also been critically examined.
Interactive Agricultural Chatbot Based on Deep Learning

The goal of technological innovation is to assist humans in making their lives easier. This is especially true in the field of natural language processing (NLP). This is why conversational systems, often known as chatbots, have gain popularity in recent years. Chatbot has been used in different domains. These are the intelligent systems developed using machine learning algorithms and NLP. Although technology has advanced significantly in the sector of agriculture, farmers still do not have easy access to this knowledge, which necessitates extensive online searches. This is where a chatbot can help them in providing the answers to their queries quickly and easily when compared to traditional methods. In this paper, the chatbot has been intelligently built to recognize poorly grammatically defined statements, misspelled words, and unfinished phrases. Natural language processing is used by the system to read user queries and keywords, match them against the knowledge base, and offer an answer with correct results, making it easier for users to communicate with the bot. To make the responses more intelligible, classification algorithms are used to provide non-textual responses that are easily seen by the farmers.
An improved crow search algorithm with multi strategy disturbance

As a new meta heuristic intelligent algorithm, crow search algorithm simulates the behavior of crows following each other and stealing food. Due to the simplicity and robustness of crow search algorithm, it has been successfully applied in many fields. However, like other swarm intelligence optimization algorithms, crow search algorithm also has the disadvantages of slow convergence speed and easy to fall into local optimization. In order to improve the convergence accuracy and later search ability of the algorithm, a new hybrid crow search algorithm called multi strategy disturbance improved crow search algorithm (MSD-CSA) is proposed based on the traditional crow search algorithm. In MSD-CSA, the sharing mechanism is added to improve the location update mode of random tracking in the original algorithm, reduce the search blindness and improve the convergence speed. In addition, the global optimal location is perturbed with different sizes in different iterative stages, which effectively improves the probability of jumping out of the local optimal and ensures the balance between the global search ability and the local search ability of the algorithm. In order to evaluate the effectiveness of MSD-CSA algorithm, it is applied to 20 basic test functions for optimization experiments, and compared with other intelligent optimization algorithms. Experimental results show that the average convergence and robustness of the proposed algorithm are better than other algorithms, and the overall performance is good.
On Local/Global Constructibility for Mobile Robots Using Bounded Range Measurements

We propose a state-constructibility analysis for a nonholonomic vehicle moving across an environment instrumented with fixed-frame range sensors (anchors). We consider a sparse deployment in which anchors are in a small number, and have finite and non-overlapping sensing ranges. Under the most extreme conditions (i.e., the robot being in sight of two anchors at different times), we provide a sufficient condition on the manoeuvres that the robot is required to execute within the range of each of the anchors in order to achieve global constructibility. When the robot travels along straight lines, these conditions are not met, but we can still have &#x201C;local&#x201D; constructibility to a degree quantifiable through the smallest eigenvalue of the Constructibility Gramian (CG). Our second contribution is to show how this metric changes according to the geometric parameters of the linear trajectory with respect to the position of the anchors and their sensing range.
On the Analysis of Clustering Algorithms in Data Mining

The rapid development of Internet technology has led to an increasing proportion of data and information content in databases at this stage. In a large amount of data, if more valuable knowledge and information with patterns can be mined, the data can be optimally utilised in a number of fields. Data mining has been widely used in different scenarios in society, and clustering analysis is facing new content and challenges, with different clustering algorithms trying to achieve clustering results with differentiated ideas. Therefore, this research will also focus on the basic parts and types of clustering algorithms and analyse some clustering algorithms based on special requirements.
Self-Updatable Database System Based on Human Motion Assessment Framework

Recently, human motion-centric videos have been attracting attention in the field of computer vision. Observing and detecting human motion in intelligent surveillance camera systems is essential for understanding the intentions of target subjects. However, these videos have vast amounts of disparate and complex information, and hence they are difficult to process and label automatically. As a result, building and maintaining a database using motion-centric videos requires considerable labor in trimming and classifying the videos. Therefore, we propose a self-updatable motion database system based on a human motion assessment framework for evaluating complex human movements. The framework quantifies three primitive motion properties: stability, liveliness, and attention. This assessment highlights the semantics of human motion in the input video. The semantic motion sequence obtained after the motion assessment is compared with a similarity motion database to determine whether the database needs to be updated; for efficient comparison, we introduce a sequential autoencoder model with a long short-term memory neural network. The proposed system maintains the database within a surveillance camera system using a motion update algorithm; unseen motions in the database are updated using a camera-based surveillance system. In addition, this framework combines state-of-art action recognition methods to improve performance by up to 11% via the self-update of motion.
Exploration of Balanced Design in Resource-Constrained Edge Device for Efficient CNNs

Convolutional Neural Networks (CNNs) have been widely used in various image recognition applications due to their high precision and high versatility. In order to improve real-time performance, many efficient CNNs with low computational complexity have been proposed. However, compared with the reduction in the amount of calculation, the demand for memory access has not decreased simultaneously. How to balance the utilization of various resources has become a challenge. This brief deeply deconstructs the CNN network and analyzes the relationship among computational resources, on-chip memory, off-chip bandwidth, and throughput. With a given bandwidth, we propose a cross-layer scheduling to minimize the on-chip memory occupancy, which can still fully utilize the computational resources. We implement a flexible FPGA-based hardware accelerator that can deploy the proposed design. According to the experiments on the MobileNetV2, our design reduces the on-chip memory usage by 91.7% and achieves state-of-the-art performance.
Image Understanding With Reinforcement Learning: Auto-Tuning Image Attributes and Model Parameters for Object Detection and Segmentation

Models for image semantics understanding, such as deep learning (DL) models and mathematical models, are often trained on specific dataset or configured with specific parameters. When deploying such models on new tasks in a different test environment, it requires considerable effort to re-train the model or extensive expertise to tune the parameters. In this paper, we propose a smart reinforcement learning (RL) agent that could learn to tune parameters automatically to enhance model performance. The learning process is formulated as a generic control task for parameter adjustment, and applied to two use scenarios: (1) image attributes tuning to improve object detection performance on fixed DL model, and (2) parameter tuning of the mathematical model (Level Set) for image segmentation. We design a novel dynamic threshold mechanism in a multi-branch RL agent to effectively tune parameters of image qualities (for object detection) and Level Set models (for object segmentation). We conduct experiments on Pascal-VOC testing set, MS COCO validation set and a proprietary dataset of industrial components, where we achieve substantial improvement on object detection accuracy. We also perform experiments on the automatic parameter tuning of Level Set models. Results show that our method facilitates considerable performance improvement on public datasets compared with baseline method.
Image Classification by Optimized Convolution Neural Networks

Considering the fact that digital images are used in almost all scientific areas and they are a big part of everyday life, it is obvious that the importance of good methods for processing and analyzing them is great. One of the most frequent tasks in various applications that use digital images is image classification. A revolutionized improvement in this area was achieved with convolutional neural networks (CNN). The convolutional neural networks managed to achieve classification accuracy significantly better compared to previously proposed and used methods. Even better results can be obtained by tuning CNN hyperparameters. Since this is a hard optimization problem, swarm intelligence algorithms can be successfully used. In this paper, we propose bare bones fireworks algorithm for tuning a selected subset of hyperparameters and it was tested on the benchmark dataset for handwritten digit recognition, MNIST. The proposed method achieved higher classification accuracy compared to the methods from the literature.
Towards Mining Public Opinion: An Attention-Based Long Short Term Memory Network Using Transfer Learning

The Internet provides a large number of tools and resources, such as social media sites, online newsgroups, blogs, electronic forums, virtual communities, and online travel sites, for consumers to express their views or opinions regarding various issues.These opinions can help organizations like tourism to improve their products and services for their consumers.Opinion mining refers to a process of identifying emotions by applying Natural Language Processing (NLP) techniques to a pool of texts.This paper mainly focuses on mining public opinion from the hotel reviews domain.To do so, we proposed a novel technique called the Attention-Based Long Short Term Memory (Attention-LSTM) Network using a transfer learning approach.We empirically analyzed several machine learning and deep learning methods and observed our proposed technique provided an adequate performance for mining public opinion in the hotel reviews domain.
Teachers experiences in relation to online teaching/learning during the Covid-19 pandemic

The current Covid-19 pandemic has completely changed the life of modern society and many of its segments. Undoubtedly, it influenced the entire education and the format of teaching so the online teaching and learning have become the most valuable tools in the emergency situation we are currently in. In this regard, the paper emphasizes, first of all, the explanation of online teaching and learning, the review of distance learning during the pandemic, as well as a brief overview of previous domestic and foreign researches on this topic. In order to research how the teachers coped in this situation, what are their experiences with online teaching, a research was conducted with foreign language teachers on the territory of Serbia. Therefore, in the second part of the paper, research methods are presented, as well as the results of a survey and interviews with foreign language teachers. The results showed that as the benefits of online teaching, teachers singled out the ease of preparation of classes, students' interest in teaching, increased quality of teaching materials, the possibility of faster and easier access to materials. On the other hand, the shortcomings of this type of teaching include insufficiently developed digital literacy of teachers, student workload, lack of time, technical equipment, poor internet connection and inability to control student work.
Sentiment Analysis of Twitter Data Using Machine Learning Approaches

Twitter is one of the most widely used social media microblogging platforms. It generates a large amount of opinioned data every day in the form of tweets. This makes it a suitable choice for sentiment analyses and many other studies. This work investigates different machine learning approaches for analyzing sentiments of tweets aiming at enhanced accuracy. In particular, we have suggested a framework that is based on a hybrid neural network model by combining Convolutional Neural Network (CNN) and Bi-Directional Long Short Term Memory (BiLSTM). Twitter API is used to collect tweets with specific keywords related to sports, politics, news, healthcare, and technology. While fetching data, hashtags and emojis are used, which are later converted into text form for assessing their effect on overall performance. The textual data is encoded using modified TF-IDF and Glove Vector embedding in Neural Network (NN) models with vectors pre-trained on Twitter data. A comparative analysis is also presented for the various machine learning algorithms (SVM, Naïve-based, Decision tree, KNN) with different datasets (IMDB, CMRD, 1.6M Kaggle, and custom-built datasets) based on accuracy and other metrics such as f-score, precision and recall. A considerable improvement is observed when hashtags and emojis are utilised with the hybrid CNN-BiLSTM model.
Minimap: An Interactive Dynamic Decision Making Game for Search and Rescue Missions

Many aspects of human dynamic decision making (DDM) behaviors can be studied with computer-simulated games, called microworlds. However, most microworlds only emphasize some aspects of DDM and are not flexible in terms of generating different decision situations. In this paper, we introduce Minimap, a dynamic interactive game for studying DDM in search and rescue missions, which incorporates all the essential characteristics of DDM, and offers flexibility regarding experimental setups and manipulating experiment scenarios. We present an experiment constructed with Minimap to demonstrate the development of scenarios that vary in structural complexity (simple vs. complex) and opaqueness (field of view-FoV vs. map view) under time constraints. We found that: humans performed more effectively and efficiently with the Map view than with FoV; humans made fewer redundant movements in the Map view compared to FoV; human learning in terms of effectiveness and efficiency was not affected by structural complexity, but it impacted their redundancy over time; and humanâ€™s coverage was not affected even when opaqueness and structural complexity increase. These findings demonstrate how Minimap can be used to investigate many aspects of DDM behavior and inform the design of scenarios that have implications for building advisory support systems in search and rescue missions.
Intelligent Tutor for Designing Function Interface in a Programming Language

Intelligent tutoring systems play an essential role in learning. In programming learning, the specificity of the learning process is related to creating code in a programming language and developing appropriate skills. One of the basic skills in code development is designing functions and their interfaces in a programming language. For these skills mastering using ITS, it is important to detect the student’s mistakes early and provide formative explanatory feedback for the student to help them find and fix the errors. In this paper, we propose the intelligent approach to explanatory feedback generation for the task of function prototype creation training. We developed an approach to automatic teaching function design, a formal model of the subject domain based on OWL ontology and Jena rules to detect errors in the students’ answers using software reasoners, and intelligent tutor based on the developed formal model.
Location-Free Camouflage Generation Network

Camouflage is a common visual phenomenon, which refers to hiding the foreground objects into the background images, making them briefly invisible to the human eye. Previous work has typically been implemented by an iterative optimization process. However, these methods struggle in 1) efficiently generating camouflage images using foreground and background with flexible structure; 2) camouflaging foreground objects to regions with multiple appearances (e.g. the junction of the vegetation and the mountains), which limit their practical application. To address these problems, this paper proposes a novel <italic/><b>L</b><i>ocation-free</i> <b>C</b><i>amouflage</i> <b>G</b><i>eneration</i> <b>Net</b><i>work</i> (<b>LCG-Net</b>) that fuse high-level features of foreground and background image, and generate result by one inference. Specifically, a <i>Position-aligned Structure Fusion</i> (PSF) module is devised to guide structure feature fusion based on the point-to-point structure similarity of foreground and background, and introduce local appearance features point-by-point. To retain the necessary identifiable features, a new immerse loss is adopted under our pipeline, while a background patch appearance loss is utilized to ensure that the hidden objects look continuous and natural at regions with multiple appearances. Experiments show that our method has results as satisfactory as state-of-the-art in the single-appearance regions and are less likely to be completely invisible, but far exceed the quality of the state-of-the-art in the multi-appearance regions. Moreover, our method is hundreds of times faster than previous methods. Benefitting from the unique advantages of our method, we provide some downstream applications for camouflage generation, which show its potential. The related code and dataset will be released at <uri>https://github.com/Tale17/LCG-Net</uri>.
Implementing Linear Regression with Homomorphic Encryption

Benefiting from the computing power and storage power of cloud computing, machine learning tasks usually choose to upload data and models to a third-party server. However, third-party servers may face data privacy leaks in the process of data acquisition, storage, and use, especially in the fields of finance, medical treatment, and biometrics. Homomorphic encryption technology supports ciphertext calculation without decryption, and can be used for machine learning model training and prediction in the ciphertext domain. We use the homomorphic encryption library SEAL to reconstruct the linear regression protocol, use six data sets to conduct experiments, and analyze its performance and security.