Reviewerly/exHarmony huggingface dataset:
A comparative study of classifier algorithms for Twitter’s sentiment based spam detection

Abstract In today’s time Social media is a vital part of everybody’s life. People are expressing their feeling and emotions on social media platforms. Emotions are associated with the daily life experiences of everyone. Twitter is one of the mostly used social media platform. From analysis on social media, we can predict the mental status of users. In this paper a comparative study of user’s posts on Social media has been done. The approach is based on relevant keywords. Sentiment analysis and classification of emotions is done using Bayes Network Classifier, Naive Bayes Classifier, Logistic Regression, Simple Logistic, SMO, J48 pruned tree, and Random Forest. Finally, accuracy of classification is evaluated by different classifiers.
Adversarial Laws of Large Numbers and Optimal Regret in Online Classification

Laws of large numbers guarantee that given a large enough sample from some population, the measure of any fixed subpopulation is well-estimated by its frequency in the sample. We study laws of large numbers in sampling processes that can affect the environment they are acting upon and interact with it. Specifically, we consider the sequential sampling model proposed by [O. Ben-Eliezer and E. Yogev, The adversarial robustness of sampling, in Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems (PODS), 2020, pp. 49–62] and characterize the classes which admit a uniform law of large numbers in this model: these are exactly the classes that are online learnable. Our characterization may be interpreted as an online analogue to the equivalence between learnability and uniform convergence in statistical (PAC) learning. The sample-complexity bounds we obtain are tight for many parameter regimes, and as an application, we determine the optimal regret bounds in online learning, stated in terms of Littlestone’s dimension, thus resolving the main open question from [S. Ben-David, D. Pál, and S. Shalev-Shwartz, Agnostic online learning, in Proceedings of the 22nd Conference on Learning Theory (COLT), 2009], which was also posed by [A. Rakhlin, K. Sridharan, and A. Tewari, J. Mach. Learn. Res., 16 (2015), pp. 155–186].
Application Research of Blockchain Technology in Energy Internet

Abstract At present, the application of blockchain in the energy field has begun to take shape. The decentralization, cooperative autonomy, marketization and intelligence of blockchain technology coincide with the concept of energy Internet. Some high-tech companies in Europe and the United States have begun to apply blockchain technology to the energy sector. Based on the basic research of blockchain technology and energy Internet coupling, this paper deeply analyzes the application of blockchain technology in energy internet, and analyzes the business model of energy blockchain. The aim is to lay the foundation for the transformation and upgrading of energy companies and the early deployment of relevant fields.
A Sentiment Similarity-Oriented Attention Model with Multi-task Learning for Text-Based Emotion Recognition

Emotion recognition based on text modality has been one of the major topics in the field of emotion recognition in conversation. How to extract efficient emotional features is still a challenge. Previous studies utilize contextual semantics and emotion lexicon for affect modeling. However, they ignore information that may be conveyed by the emotion labels themselves. To address this problem, we propose the sentiment similarity-oriented attention (SSOA) mechanism, which uses the semantics of emotion labels to guide the model’s attention when encoding the input conversations. Thus to extract emotion-related information from sentences. Then we use the convolutional neural network (CNN) to extract complex informative features. In addition, as discrete emotions are highly related with the Valence, Arousal, and Dominance (VAD) in psychophysiology, we train the VAD regression and emotion classification tasks together by using multi-task learning to extract more robust features. The proposed method outperforms the benchmarks by an absolute increase of over 3.65% in terms of the average F1 for the emotion classification task, and also outperforms previous strategies for the VAD regression task on the IEMOCAP database.
Real-time Contact Tracing During a Pandemic using Multi-camera Video Object Tracking

Due to the COVID19 pandemic, contact tracing and moving object tracking are gaining more popularity in automated video surveillance systems in computer vision and video processing. The application of contact tracing and moving object tracking is critical in applying pandemic control measures and is getting more important day by day. This work proposes a computer vision-based algorithm for contact tracing using stationary surveillance cameras. The input videos are converted into a bird's eye view where all moving objects are detected, and the distances between them are calculated. The algorithm performs background subtraction to isolate foreground objects, morphological operations to remove the noise, and blob analysis to identify the connected regions in the resulting foreground video. Kalman filters to estimate objects' motion in the video calculates Euclidean distance between the objects to trace object contacts. This algorithm can be utilized in almost all public places such as shopping malls, airport terminals, and educational institutions. It allows identifying, assessing, and managing people who might have been exposed to the disease. The testing data was collected in a home environment, and the stationary camera was replaced with a mobile phone camera fixed on a tripod. The work was implemented and tested, and the results verified the feasibility and effectiveness of the proposed method. The system was able to detect the objects in the input video frame and estimate the distance between them across multiple cameras.
The Forgotten Hyperparameter:

In the evaluation of side-channel resilience, convolutional neural network-based techniques have been proved to be very effective, even in the presence of countermeasures. This work is introducing the use of dilated convolution in the context of profiling side-channel attacks. We show that the convolutional neural network that uses dilated convolution increases its performance by taking advantage of the leakage distributed through scattered points in leakage traces. We have validated the feasibility of the proposal by comparing it with the state-of-the-art approach. We have conducted experiments using ASCAD (with random key), and as a result the guessing entropy of the attack converges to zero for around 550 synchronized traces and for 3 000 desynchronised traces. In both groups of experiments, we have used the same architecture to train the model, changing just dilatation rate and kernel length, which indicates a reduction of the complexity in the deep learning model.
Behavioral Basis of Cryptocurrencies Markets: Examining Effects of Public Sentiment, Fear and Uncertainty on Price Formation

In recent years, cryptocurrencies have emerged as an exciting, innovative, and highly unorthodox asset class, primarily used for investment and trading purposes by globally-distributed investors. Although cryptocurrencies have attracted significant academic attention, there are currently no credible universally-accepted methodologies for determining their prices and returns. This study explores the use of sentiment analysis to model the effects of four different categories of sentiments towards the cryptocurrency markets to predict the direction of price: positivity/negativity (towards the underlying technology, development, and price of each cryptocurrency) and fear, uncertainty, and bullishness/bearishness in the financial markets. Investor sentiment is shown to successfully predict the price direction of cryptocurrencies, indicating that there is a potential for herding and anchoring biases among investors in crypto assets. Moreover, our analysis shows that cryptocurrencies can be used as a hedge against the stock market during times of market uncertainty, though not necessarily during times of investor fear.
Generative Multi-Label Zero-Shot Learning

Multi-label zero-shot learning strives to classify images into multiple unseen categories for which no data is available during training. The test samples can additionally contain seen categories in the generalized variant. Existing approaches rely on learning either shared or label-specific attention from the seen classes. Nevertheless, computing reliable attention maps for unseen classes during inference in a multi-label setting is still a challenge. In contrast, state-of-the-art single-label generative adversarial network (GAN) based approaches learn to directly synthesize the class-specific visual features from the corresponding class attribute embeddings. However, synthesizing multi-label features from GANs is still unexplored in the context of zero-shot setting. When multiple objects occur jointly in a single image, a critical question is how to effectively fuse multi-class information. In this work, we introduce different fusion approaches at the attribute-level, feature-level and cross-level (across attribute and feature-levels) for synthesizing multi-label features from their corresponding multi-label class embeddings. To the best of our knowledge, our work is the first to tackle the problem of multi-label feature synthesis in the (generalized) zero-shot setting. Our cross-level fusion-based generative approach outperforms the state-of-the-art on three zero-shot benchmarks: NUS-WIDE, Open Images and MS COCO. Furthermore, we show the generalization capabilities of our fusion approach in the zero-shot detection task on MS COCO, achieving favorable performance against existing methods.
Modeling Solar Radiation in Peninsular Malaysia Using ARIMA Model

The objective of this chapter is to build the ARIMA model and forecast the in-sample and out-sample daily solar radiation data in Peninsular Malaysia. Moreover, the study also investigates the stationarity, reliability, accuracy, and performance of the model. This study involves 12 states, but Perlis’s data are removed because Perlis’s data have the same value as Kedah. The study utilizes the Box and Jenkins methodology to develop the best model for each state. Based on the three stages of the Box and Jenkins methodology, each state can be represented by the best ARIMA model. All the ARIMA models also produced smaller error values which indicate the fitted or forecasted values follow the same trend as the actual data.
Joint Structure and Parameter Optimization of Multiobjective Sparse Neural Network

This work addresses the problem of network pruning and proposes a novel joint training method based on a multiobjective optimization model. Most of the state-of-the-art pruning methods rely on user experience for selecting the sparsity ratio of the weight matrices or tensors, and thus suffer from severe performance reduction with inappropriate user-defined parameters. Moreover, networks might be inferior due to the inefficient connecting architecture search, especially when it is highly sparse. It is revealed in this work that the network model might maintain sparse characteristic in the early stage of the backpropagation (BP) training process, and evolutionary computation-based algorithms can accurately discover the connecting architecture with satisfying network performance. In particular, we establish a multiobjective sparse model for network pruning and propose an efficient approach that combines BP training and two modified multiobjective evolutionary algorithms (MOEAs). The BP algorithm converges quickly, and the two MOEAs can search for the optimal sparse structure and refine the weights, respectively. Experiments are also included to prove the benefits of the proposed algorithm. We show that the proposed method can obtain a desired Pareto front (PF), leading to a better pruning result comparing to the state-of-the-art methods, especially when the network structure is highly sparse.
Differential Evolution-Based Wingsuit Flying Search for Optimization

In the past few years, meta-heuristic algorithms have rapid development. More and more scholars started research in this area. Wingsuit flying search (WFS) is a recently proposed nature-inspired meta-heuristic algorithm which is able to solve complex optimization problems rapidly and effectively. However, due to the number of initial points (individuals) in WFS is small, the algorithm lacks of population diversity. Therefore, we use differential evolution (DE) as a search strategy to improve it. DE is a classical evolutionary algorithm which has fast convergence ability and good convergence results. Thus, we incorporate its learning operator into WFS. This hybrid algorithm enhances the exploration ability via DE on the basis of WFS. Finally, we proved that the proposed algorithm has superior performance in comparison with other state-of-the-art algorithms in terms of effectiveness and robustness based on thirty classical benchmark functions.
A Dynamic Gesture Recognition System for Mute Person

For a person with inability to speak, communication is the primary challenge. It is really difficult to express to a normal person understand what they are trying to communicate. The sign language is a common method used by a person with inability to speak and a normal person face difficulty in understanding signs. The gesture recognition system acts as a translator between a normal person and a mute person. The field of pattern recognition is a developing field of research and provides many techniques for image processing and learning. This paper is centered on the different approaches to gesture recognition and the impact of each technique used. In this paper kNN classification, hue saturation value model and rule-based classifier methods are discussed, and comparison is presented.
New S-Box Transformation Based on Chaotic System for Image Encryption

Chaos theory one of the complex approaches with encryption methods is known as a perfect candidate. This is due to using cryptography applications to its properties like unpredictability and sensitive initial states. S-box based on Logistics Map has been suggested. in this paper establish a new method of composition, saving time in encryption, allowing a high-efficiency non-linearity of random sequences of the created values. showed a new diffusion system and suggested utilizing the dispersion principle, based on the Lorenz Map, disengage the color points. (R, G, B). Initiate this proposal by shuffling all pixels in tow steps for each color channel to swap the pixels horizontally, vertically, or diagonally, last step marked with an XOR. The image encryption method reasonably requires a few milliseconds, and the considerable period for the familiar image to be decrypted fairly close as the images. block encoder is reversible to the developed algorithm. The unique experiments merely demonstrate that the information security capabilities will be both safe more efficient, the current S-Box analysis with statistical criteria, including balanced criteria (BC), completeness criteria (CC), avalanche criteria (AC), strict avalanche criteria (SAC), and invertibility, the analysis of performance tests demonstrate the strong cryptographic properties of the S-box discussed in this new S-Box. The consistent result of our critical analysis of picture quality evaluation, differential attacks (NPSR), (UACI), entropy information, and pixels correlation show the peculiar quality and political strength of encryption processing.
A stochastic approach for automated brain MRI segmentation

This paper presents an approach to segment lesions from brain magnetic resonance images in a fully automatic manner. The proposed idea leverages the strength of classical random walker algorithm and graph cut optimization technique in a single framework. We demonstrate that formulating a “prior” from a stochastic model can ameliorate the need of manually selected seed selection process in the random walker framework, thus making the algorithm fully automatic in a generic manner. By analytically solving a linear system of equations in the random walk process, initial labelling ∈ [0, 1] of each pixel in the image are computed to obtain the likelihood probability. These probabilities are then used to compute the likelihood for the data fidelity term in the energy function to compute the final segmentation, which is minimized by min cut max flow algorithm. Experimental results show the superiority of the proposed method over the state-of-the-art techniques on publicly available datasets.
Maximum Entropy Methods for Texture Synthesis: Theory and Practice

Recent years have seen the rise of convolutional neural network techniques in exemplar-based image synthesis.These methods often rely on the minimization of some variational formulation on the image space for which the minimizers are assumed to be the solutions of the synthesis problem.In this paper we investigate, both theoretically and experimentally, another framework to deal with this problem using an alternate sampling/minimization scheme.First, we use results from information geometry to assess that our method yields a probability measure which has maximum entropy under some constraints in expectation.Then, we turn to the analysis of our method and we show, using recent results from the Markov chain literature, that its error can be explicitly bounded with constants which depend polynomially in the dimension even in the non-convex setting.This includes the case where the constraints are defined via a differentiable neural network.Finally, we present an extensive experimental study of the model, including a comparison with state-of-the-art methods and an extension to style transfer.
Input Optimisation Network for Semantic Segmentation of Underexposed Images

Autonomous Vehicles are required to operate robustly across widely varied scenarios and conditions, however environmental factors such as weather and lighting can impede the capabilities of the perception systems required for safe operation. In this work we investigate the effects lighting changes can have on semantic segmentation of urban road scenes, specifically how segmentation performance is affected by underexposed imagery. Using two publicly available datasets, we simulate incorrectly set camera exposure and compare the performance of a standard pre-trained deep semantic segmentation network on correctly and incorrectly exposed images. We then introduce a novel input optimization network, which aims to modify a given image such that it generates an optimal response from a pre-trained semantic segmentation network. We compare our approach to an adversarially-trained model and demonstrate significantly improved semantic segmentation performance over that of unoptimised images.
A General Framework for Making Context-Recognition Systems More Energy Efficient

Context recognition using wearable devices is a mature research area, but one of the biggest issues it faces is the high energy consumption of the device that is sensing and processing the data. In this work we propose three different methods for optimizing its energy use. We also show how to combine all three methods to further increase the energy savings. The methods work by adapting system settings (sensors used, sampling frequency, duty cycling, etc.) to both the detected context and directly to the sensor data. This is done by mathematically modeling the influence of different system settings and using multiobjective optimization to find the best ones. The proposed methodology is tested on four different context-recognition tasks where we show that it can generate accurate energy-efficient solutions—in one case reducing energy consumption by 95% in exchange for only four percentage points of accuracy. We also show that the method is general, requires next to no expert knowledge about the domain being optimized, and that it outperforms two approaches from the related work.
Time-Layered Gamic Interaction with a Virtual Museum Template

This paper discusses a simplified workflow and interactive learning opportunities for exporting map and location data using a free tool, Recogito into a Unity game environment with a simple virtual museum room template. The aim was to create simple interactive virtual museums for humanities scholars and students with a minimum of programming or gaming experience, while still allowing for interesting time-related tasks. The virtual environment template was created for the Oculus Quest and controllers but can be easily adapted to other head-mounted displays or run on a normal desktop computer. Although this is an experimental design, it is part of a project to increase the use of time-layered cultural data and related mapping technology by humanities researchers.
An urban flood inundation and damage assessment for disaster risk reduction in Johan Pahlawan Sub-district, West Aceh Regency

Residential areas with the high rainfall intensity, flow pattern, and channel condition caused recurrent inundation flood in West Aceh Regency, Aceh Province, Indonesia. The inundation results in the physical damages to the housing components and the number of losses. This study aimed to devise the flood inundation map. The method was field survey to identifying the location, the inundation depth, and the conditions of the exposed residential in 20-24 September 2018. The results showed that the pattern of land flow and drainage conformed with the topography of the area. However, the channel condition, such as bushes, narrowing of the channel, and certain stations with low contoured, caused the vulnerability of inundation. The results showed that the area of the inundation was 26.66 Ha when the flood reached a maximum water level. The inundation varied in the study area, with a duration of 4 to 5 days. Forty housing units in different areas were exposed to the inundation flood, with varying damage conditions depending on the flood depth. It depended on the water depth of the flood. This approach can be a guideline for designing flood disaster mitigation strategies, especially for the elements of residential areas.
Modeling SMART Technology Mobility of Students for Open Education System

The article represents the process of the Smart-Technology model development to automate decision-making in the field of educational mobility organization for open education system. The novelty is that for the first time SMART-technologies are considered as an integrated IT-environment with the common interface for making optimal decisions when students make their own choice of route, content, travel mode, accommodation, meals, etc. The practical importance of the study is that the system of open education is enriched with the new opportunity for mobile learning.