Reviewerly/exHarmony huggingface dataset:
Analysis of Lightweight Cryptographic Algorithms for Internet of Things

The exponential advancements in the electronics field resulted in the development of small sensors with the capability to collect information inreal-time, without any human intervention. This is revolutionizing human life and also leads to many security challenges that cannot be ignored. The information collected through the massive internet of things, is analyzed to make intelligent decisions for the users. But the data accumulated can also include sensitive data that needs to be protected from detrimental attacks to maintain the privacy of the user and the integrity of the data. Thus encryption/decryption becomes a critical aspect in the Internet of Things applications; to avoid compromising the data from any intentional/unintentional attacks on the data. As the devices are highly resource-constrained, lightweight cryptographic algorithms that use less power, less memory, and have lesser computation are preferred to the traditional cryptographic methods. This paper analyzes the various symmetric ciphers in hardware architecture in terms of parameters like gate area, throughput, efficiency, memory, and complexity of the algorithm.
Predicting Inductive Biases of Pre-Trained Models

Most current NLP systems are based on a pre-train-then-fine-tune paradigm, in which a large neural network is first trained in a self-supervised way designed to encourage the network to extract broadly-useful linguistic features, and then fine-tuned for a specific task of interest. Recent work attempts to understand why this recipe works and explain when it fails. Currently, such analyses have produced two sets of apparently-contradictory results. Work that analyzes the representations that result from pre-training (via classifiers) finds that rich features of linguistic structure can be decoded with high accuracy, but work that analyzes model behavior after fine-tuning (via challenge sets) indicates that decisions are often not based on such structure but rather on spurious heuristics specific to the training set. In this work, we test the hypothesis that the extent to which a feature influences a model's decisions can be predicted using a combination of two factors: The feature's extractability after pre-training (measured using information-theoretic probing techniques), and the evidence available during fine-tuning (defined as the feature's co-occurrence rate with the label). In experiments with both synthetic and natural language data, we find strong (statistically significant correlations) supporting this hypothesis.
Understanding Smart Contracts as a New Option in Transaction Cost Economics

Among different concepts associated with the term blockchain, smart contracts have been a prominent one, especially popularized by the Ethereum platform. In this study, we unpack this concept within the framework of Transaction Cost Economics (TCE). This institutional economics theory emphasizes the role of distinctive (private and public) contract law regimes in shaping firm boundaries. We propose that widespread adoption of the smart contract concept creates a new option in public contracting, which may give rise to a smart-contract-augmented contract law regime. We discuss tradeoffs involved in the attractiveness of the smart contract concept for firms and the resulting potential for change in firm boundaries. Based on our new conceptualization, we discuss potential roles the three branches of government – judicial, executive, and legislative – in enabling and using this new contract law regime. We conclude the paper by pointing out limitations of the TCE perspective and suggesting future research directions.
RETRACTED: Research on the framework of intelligent classroom based on artificial intelligence

The emergence of artificial intelligence has greatly improved the imbalance of resource allocation in different regions of China, and promoted education equity. Video is an important form of learning resources in the field of education. Educational video analysis and innovative application based on artificial intelligence technology greatly promote the innovation of education and teaching, and promote the deep integration of artificial intelligence and education. In this paper, firstly, the system architecture is introduced, secondly, the model of education knowledge map which can establish the mapping relationship among knowledge, problem and ability is proposed, and finally, the evaluation index of intelligent classroom is designed. Intelligent classroom based on artificial intelligence technology is helpful to enhance interactive learning and promote the construction of intelligent campus.
Multilayer Extreme Learning Machine as Equalizer in OFDM-based Radio-over-fiber Systems

Mobile/wireless networks aim to support diverse services with numerous and sophisticated requirements, such as energy efficiency, spectral efficiency, negligible latency, robustness against time and frequency selective channels, low hardware complexity, among others. From the central station to the base stations, radio-over-fiber orthogonal frequency division multiplexing (RoF-OFDM) schemes with direct-detection are then implemented. Unfortunately, laser phase noise, chromatic fiber dispersion, and carrier frequency offset impair the orthogonality of the subcarriers; hence, deteriorating the performance of the RoF-OFDM system. In order to take all the processing tasks to the cognitive level (the last goal in the telecommunication industry), various extreme learning machines (ELMs), composed by only a single hidden layer, have been recently adopted as equalizers. The reason behind this trend comes from the lower computational complexity, higher detection accuracy, and minimum human intervention of the ELM algorithms. In this article, we introduce a multilayer ELM-based receiver for RoF schemes transmitting phase-correlated OFDM signals affected by phase and frequency errors. Results report that by appropriately setting the hyper-parameters of the multilayer ELMs, the ELM with 3 hidden layers outperforms most of the ELMs reported in the literature (the ELM with 2 hidden layers, original ELM, regularized ELM, and 2 fully-independent ELMs defined in the real domain), as well as the benchmark pilot-assisted equalizer in terms of bit error rate. Nevertheless, this benefit comes with excessive computational cost. Finally, we show that the fully-complex ELM is still the best equalizer taking into account several key metrics.
Inclusive education in Ukraine

The purpose of this chapter is to examine inclusion and inclusive education for students with SEN as conceptualised in Ukraine’s educational policy documents. The data consist of national policy documents from 2009 (when inclusive education started to be implemented at the institutional level) to 2019; these documents were analysed using a thematic analysis. The analysis indicated three main themes: incompleteness and controversy in the concept of inclusion; the residue of a medical paradigm in the definition of concept inclusive education and tension and conflict in labelling of the concept of students with SEN. The results point to the necessity of encouraging discussions and reflections and articulating and defining what inclusion is and how Ukraine can create an effective strategy for the development of inclusive education.
RETRACTED: Research on the construction of university education information platform supported by cloud computing

The level of information construction in Colleges and universities is an important indicator to reflect the level of education in Colleges and universities. With the development of cloud computing technology, how to solve the above problems and how to use cloud computing to improve the university education information platform is a problem that the builders of university education information platform should think deeply. This paper first analyzes the current situation of university education information cloud platform, and then puts forward the education information scheme based on cloud computing, and finally realizes the education information system based on cloud computing. The system promotes the integration of cloud computing, big data and other cutting-edge technologies with the education industry, and improves the work efficiency of relevant personnel in Colleges and universities.
Universal Weakly Supervised Segmentation by Pixel-to-Segment Contrastive Learning

Weakly supervised segmentation is challenging as sparsely labeled pixels do not provide sufficient supervision: A semantic segment may contain multiple distinctive regions whereas adjacent segments may appear similar. Common approaches use the few labeled pixels in all training images to train a segmentation model, and then propagate labels within each image based on visual or feature similarity. Instead, we treat segmentation as a semi-supervised pixel-wise metric learning problem, where pixels in different segments are mapped to distinctive features. Naturally, our unlabeled pixels participate not only in data-driven grouping within each image, but also in discriminative feature learning within and across images. Our results on Pascal VOC and DensePose datasets demonstrate our substantial accuracy gain on various forms of weak supervision including image-level tags, bounding boxes, labeled points, and scribbles.
SMiRL: Surprise Minimizing Reinforcement Learning in Unstable Environments

Every living organism struggles against disruptive environmental forces to carve out and maintain an orderly niche. We propose that such a struggle to achieve and preserve order might offer a principle for the emergence of useful behaviors in artificial agents. We formalize this idea into an unsupervised reinforcement learning method called surprise minimizing reinforcement learning (SMiRL). SMiRL alternates between learning a density model to evaluate the surprise of a stimulus, and improving the policy to seek more predictable stimuli. The policy seeks out stable and repeatable situations that counteract the environment's prevailing sources of entropy. This might include avoiding other hostile agents, or finding a stable, balanced pose for a bipedal robot in the face of disturbance forces. We demonstrate that our surprise minimizing agents can successfully play Tetris, Doom, control a humanoid to avoid falls, and navigate to escape enemies in a maze without any task-specific reward supervision. We further show that SMiRL can be used together with standard task rewards to accelerate reward-driven learning.
Multi-Car Convex Feasible Set Algorithm in Trajectory Planning

Abstract Trajectory planning is an essential module for autonomous driving. To deal with multi-vehicle interactions, existing methods follow the prediction-then-plan approaches which first predict the trajectories of others then plan the trajectory for the ego vehicle given the predictions. However, since the true trajectories of others may deviate from the predictions, frequent re-planning for the ego vehicle is needed, which may cause many issues such as instability or deadlock. These issues can be overcome if all vehicles can form a consensus by solving the same multi-vehicle trajectory planning problem. Then the major challenge is how to efficiently solve the multi-vehicle trajectory planning problem in real time under the curse of dimensionality. We introduce a novel planner for multi-vehicle trajectory planning based on the convex feasible set (CFS) algorithm. The planning problem is formulated as a non-convex optimization. A novel convexification method to obtain the maximal convex feasible set is proposed, which transforms the problem into a quadratic programming. Simulations in multiple typical on-road driving situations are conducted to demonstrate the effectiveness of the proposed planning algorithm in terms of completeness and optimality.
Hateful People or Hateful Bots?

Arabic Twitter space is crawling with bots that fuel political feuds, spread misinformation, and proliferate sectarian rhetoric. While efforts have long existed to analyze and detect English bots, Arabic bot detection and characterization remains largely understudied. In this work, we contribute new insights into the role of bots in spreading religious hatred on Arabic Twitter and introduce a novel regression model that can accurately identify Arabic language bots. Our assessment shows that existing tools that are highly accurate in detecting English bots don't perform as well on Arabic bots. We identify the possible reasons for this poor performance, perform a thorough analysis of linguistic, content, behavioral and network features, and report on the most informative features that distinguish Arabic bots from humans as well as the differences between Arabic and English bots. Our results mark an important step toward understanding the behavior of malicious bots on Arabic Twitter and pave the way for a more effective Arabic bot detection tools.
Text sentiment analysis based on Glove model and United Network

Abstract Traditional convolutional neural network has been proved to have good classification effect in text sentiment analysis experiments. However, CNN ignores the context dependent information of sentences, which affects the classification effect. Therefore, this paper proposes a text sentiment analysis model based on CapsuleNets and Attention mechanism. Firstly, the word vector is trained by using Glove model; secondly, the local features are extracted by using the features of double-layer capsule network; then, attention mechanism is added to assign the corresponding weight to the extracted text information; finally, we complete classification. The experimental results show that the proposed model can effectively improve the accuracy of emotion classification.
Deep Residual Transform for Multi-scale Image Decomposition

Multi-scale image decomposition (MID) is a fundamental task in computer vision and image processing that involves the transformation of an image into a hierarchical representation comprising of different levels of visual granularity from coarse structures to fine details. A well-engineered MID disentangles the image signal into meaningful components which can be used in a variety of applications such as image denoising, image compression, and object classification. Traditional MID approaches such as wavelet transforms tackle the problem through carefully designed basis functions under rigid decomposition structure assumptions. However, as the information distribution varies from one type of image content to another, rigid decomposition assumptions lead to inefficiently representation, i.e., some scales can contain little to no information. To address this issue, we present Deep Residual Transform (DRT), a data-driven MID strategy where the input signal is transformed into a hierarchy of non-linear representations at different scales, with each representation being independently learned as the representational residual of previous scales at a user-controlled detail level. As such, the proposed DRT progressively disentangles scale information from the original signal by sequentially learning residual representations. The decomposition flexibility of this approach allows for highly tailored representations cater to specific types of image content, and results in greater representational efficiency and compactness. In this study, we realize the proposed transform by leveraging a hierarchy of sequentially trained autoencoders. To explore the efficacy of the proposed DRT, we leverage two datasets comprising of very different types of image content: 1) CelebFaces and 2) Cityscapes. Experimental results show that the proposed DRT achieved highly efficient information decomposition on both datasets amid their very different visual granularity characteristics.
Collaborative filtering recommendation algorithm based on KNN and Xgboost hybrid

Abstract In the traditional collaborative filtering recommendation algorithm, it is easy to fall into the dilemma of local optimization due to single classification, which affects the recommendation effect of the algorithm. A hybrid collaborative filtering recommendation algorithm based on KNN-Xgboost is proposed. The algorithm uses KNN to fill in the user’s predictive evaluation with less project evaluation, reducing the sparseness of the matrix. Then the Xgboost algorithm is used to implement multi-classifiers to predict the data, and the multi-class calculation results are calculated to form the recommended results. The experimental results show that the sparseness of the matrix is solved to a certain extent, and the recommendation accuracy is improved.
Mobilization Effects of Multilateral Development Banks

We use loan-level data on syndicated lending to a large sample of developing countries between 1993and 2017 to estimate the mobilization effects of multilateral development banks (MDBs), controllingfor a large set of fixed effects. We find evidence of positive and significant direct and indirectmobilization effects of multilateral lending on the number of deals and on the total size of bankinflows. The number of lending banks and the average maturity of syndicated loans also increase afterMDB lending. These effects are present not only on impact, but they last up to three years and are notoffset by a decline in bond financing. There is no evidence of anticipation effects and the results arenot driven by confounding factors, such as the presence of large global banks, Chinese lending andaid flows. Finally, the economic effects are sizable, suggesting that MBDs can play a vital role tomobilize private sector financing to achieve the goals of the 2030 Development Agenda.
RETRACTED: Intelligent grading system based on deep learning

According to the survey, off-line examination is still the main examination method in universities, primary and secondary schools. The grading processing of off-line examination is time-consuming. Besides, since the off-line grading is subjective, it is error-prone. In order to address the challenges in off-line examinations of universities, primary and secondary schools, it is very urgent to improve the efficiency of off-line grading. In order to realize intelligent grading for off-line examinations, we exploit deep learning techniques to off-line grading. First, we propose an image processing method for English letters. Second, we propose a image recognition method based on deep learning for English letters. Third, we propose a lightweight framework for grading. Based on the above designs, we design an intelligent grading system based on deep learning. We implement the system and the result shows that the intelligent grading system can batch grading efficiently. Besides, compared with related designs, the proposed system is more flexible and intelligent.
Towards Topic-Aware Slide Generation For Academic Papers With Unsupervised Mutual Learning

Slides are commonly used to present information and tell stories. In academic and research communities, slides are typically used to summarize findings in accepted papers for presentation in meetings and conferences. These slides for academic papers usually contain common and essential topics such as major contributions, model design, experiment details and future work. In this paper, we aim to automatically generate slides for academic papers. We first conducted an in-depth analysis of how humans create slides. We then mined frequently used slide topics. Given a topic, our approach extracts relevant sentences in the paper to provide the draft slides. Due to the lack of labeling data, we integrate prior knowledge of ground truth sentences into a log-linear model to create an initial pseudo-target distribution. Two sentence extractors are learned collaboratively and bootstrap the performance of each other. Evaluation results on a labeled test set show that our model can extract more relevant sentences than baseline methods. Human evaluation also shows slides generated by our model can serve as a good basis for preparing the final presentations.
Construction of SIBI Datasets for Sign Language Recognition Using a Webcam

SIBI (Sistem Isyarat Bahasa Indonesia) is a sign language established by the Government of Indonesia as a standard of communication that used in Special School.Hence, not everyone can understand the sign language, even though people have ability to hear.They also must be able to understand the sign language in order to conduct a communication.Currently, technology of recognition has been applied to hand gestures that can translate sign language.Various studies regarding sign language recognition have been developed and there are many applications that can recognize sign language through hand gesture as datasets using webcam, but there still restricted on the datasets in SIBI.Therefore, the study aims to create SIBI datasets via hand gesture using experimental method.So that, the application can be used by SIBI users.SIBI datasets were successfully created with signs that consisting of 8 static word.Based on the confusion matrix, all the signs that has been created have a precision of 100%, meanwhile based on the test results with sufficient lighting conditions and plain background, SIBI datasets has a prediction success rate of 97,5%.
Security Professionals Must Reinforce Detect Attacks to Avoid Unauthorized Data Exposure

Organizations face the probability of being hacked because of weak and inadequate cybersecurity implementations. Hackers are still able to breach a system when security tools such as firewalls, SIEM, anti-virus software, encryption, and IDPS are readily in place within an organization. Digital criminals are responsible for increased network breaches using elusive security tools to penetrate secure environments with sophistication. Cyberattacks are continually increasing due to the sophistication and innovation of cyber attackers. Many vulnerable areas must be reinforced against cybercriminals, Insider threats, inadequate employee training, and negligence. Monetary investment in cybersecurity and management support plays a significant role in assuring the implementation of information security throughout any organizational processes. The implication for practice can provide organizations with approaches on how to mitigate cyber exploits and safeguard the confidentiality, integrity, and availability of information by bridging the gap between incident detection and response.
Software model of a highly undetectable stegosystem – ⊕HUGO model

Abstract The article is devoted to the research of a software model of the hidden message transmission process using one of the new methods of steganography proposed by the authors. The presented model implements a hard-to-detect method for embedding hidden messages in digital still images used as covering objects (containers). The main principle of using the model is to minimize adequately specified distortion using a new efficient encoding algorithm. Distortion is defined as an operation ⊕ (excluding OR - XOR) between the next half-byte of the hidden message and the right half-byte of the specially selected byte of the covering object, which is selected from the condition for minimizing the penalty function calculated on some selected neighbourhood of this byte. This allows authors to keep the original image almost unchanged and make it indistinguishable from the original, even after large payloads are embedded in it. Thus, the embedded hidden message may not be visible when the steganalysis even for the experienced steganalytic. The ⊕ operation has the property of bijectivity (it is reversible), which makes quite easy to use it to extract a hidden message on the recipient’s side, if the recipient has key information. The stegosystem model is called the ⊕Highly Undetectable SteGOsystem model. In short, it can be referred to as the ⊕HUGO model.