Reviewerly/exHarmony huggingface dataset:
Temperature extremities-dependent global solar radiation models for country-wide applications in India

Calibration of models has been performed for estimating global solar radiation with input as daily temperature extremes. Measured data for 22 locations were obtained from the Indian Meteorological Department (India) for 15 years and ten global solar radiation model forms were selected. Data were then bifurcated, where sixteen locations were used for training and the remaining six for validation. Models were compared using the statistical tools-based error analysis. The ranking of models was introduced using a Global Performance Indicator. Period-based testing of models declared Chen et al. model as the superior one. Location-based validation of the models was further performed which indicated the best suitability of the Bristow and Campbell model for semi-arid climate, Hargreaves et al. model for Arid climate and montane climate, Samani model for Tropical wet climate, Goodin et al. model for Humid subtropical climate and wet-dry climate, with GPI values of 5.3965, 0.5325, 0.5174, 0.5182, 0.5333 and 0.5862, respectively.
A Polynomial Multiplication Accelerator for Faster Lattice Cipher Algorithm in Security Chip

Polynomial multiplication is the most computationally expensive part of the lattice-based cryptography algorithm. However, the existing acceleration schemes have problems, such as low performance and high hardware resource overhead. Based on the polynomial multiplication of number theoretic transformation (NTT), this paper proposed a simple element of Montgomery module reduction with pipeline structure to realize fast module multiplication. In order to improve the throughput of the NTT module, the block storage technology is used in the NTT hardware module to enable the computing unit to read and write data alternately. Based on the NTT hardware module, a precalculated parameter storage and real-time calculation method suitable for the hardware architecture of this paper is also proposed. Finally, the hardware of polynomial multiplier based on NTT module is implemented, and its function simulation and performance evaluation are carried out. The results show that the proposed hardware accelerator can have excellent computing performance while using fewer hardware resources, thus meeting the requirements of lattice cipher algorithms in security chips. Compared with the existing studies, the computing performance of the polynomial multiplier designed in this paper is improved by approximately 1 to 3 times, and the slice resources and storage resources used are reduced by approximately 60% and 17%, respectively.
CAE-Net: Cross-Modal Attention Enhancement Network for RGB-T Salient Object Detection

RGB salient object detection (SOD) performs poorly in low-contrast and complex background scenes. Fortunately, the thermal infrared image can capture the heat distribution of scenes as complementary information to the RGB image, so the RGB-T SOD has recently attracted more and more attention. Many researchers have committed to accelerating the development of RGB-T SOD, but some problems still remain to be solved. For example, the defective sample and interfering information contained in the RGB or thermal image hinder the model from learning proper saliency features, meanwhile the low-level features with noisy information result in incomplete salient objects or false positive detection. To solve these problems, we design a cross-modal attention enhancement network (CAE-Net). First, we concretely design a cross-modal fusion (CMF) module to fuse cross-modal features, where the cross-attention unit (CAU) is employed to enhance the two modal features, and channel attention is used to dynamically weigh and fuse the two modal features. Then, we design the joint-modality decoder (JMD) to fuse cross-level features, where the low-level features are purified by higher level features, and multi-scale features are sufficiently integrated. Besides, we add two single-modality decoder (SMD) branches to preserve more modality-specific information. Finally, we employ a multi-stream fusion (MSF) module to fuse three decoders’ features. Comprehensive experiments are conducted on three RGB-T datasets, and the results show that our CAE-Net is comparable to the other methods.
Identification of SMEs in the Critical Factors of an IS Backup System Using a Three-Stage Advanced Hybrid MDM–AHP Model

Backup system work represents “the last mile” of information security (IS). To avoid data loss or damage, enterprises should execute data backup periodically to ensure the integrity and availability of such data. Additionally, due to the continuous emergence of IS incidents featuring malicious attacks in recent years, major firms in countries around the world have successively reported being under attack by ransomware viruses. In particular, small and medium enterprises (SMEs) became the potential targets of malicious attacks based on their different types of IS awareness and degrees of digitalization; therefore, IS work has become one of the essential topics with special significance for numerous SMEs. To this end, this paper studied the factors influencing SMEs’ adoption of IS backup systems in the hope that the critical decision-making behaviors of SMEs regarding the issue of IS could be learned. Practical suggestions can be made for the marketing schemes adopted by IS manufacturers concerning the planning of IS backup systems. Thus, this study used three methodological stages to address the exciting issue of IS backup systems for SMEs. In the first stage, 11 factors at two hierarchies involving three constructs influencing SMEs’ adoption of IS backup systems were summarized via a literature review. The constructs included financial consideration (FC), the IS incident, and business IS decision making (BISD-M). In the second stage, an expert questionnaire was applied; an advanced hybrid modified Delphi method (MDM) and analytic hierarchy process (AHP) with expert input were constructed to identify the sorting of overall weights based on the 11 factors included in the first stage. Following the empirical conclusions, the top three critical factors were “disaster loss amount”, “enterprise’s downtime”, and “supplier’s contractual requirements”. The conclusions of this study indicated that two factors were included in the FC construct; thus, the FC construct influenced IS the most, and the BISD-M construct took second place. In the final stage, through re-checking three actual cases, the results of this study were verified with specific respect to the FC. In conclusion, to popularize IS backup systems among SMEs and fully implement IS, manufacturers may start from the FC in the hope that the severe impact caused by IS incidents featuring malicious attacks can be slowed down and the losses encountered can be lowered. The empirical results and conclusions of this study can be used for reference by SMEs, and both theoretical and empirical foundations have been provided for further studies in academic circles; the results above also show a significant application contribution of this study.
Efficient Video Steganalytic Feature Design by Exploiting Local Optimality and Lagrangian Cost Quotient

As the opponent of motion vector (MV)-based video steganography, the corresponding symmetric steganalysis has also developed a lot in recent years, among which the logic-based steganalytic schemes, e.g., AoSO, NPELO and MVC, are the most prevailing. Although currently achieving the best detection performance, these steganalytic schemes are less effective in detecting some logic-maintaining steganographic schemes. In view of the fact that the distributions of covers’ local Lagrangian cost quotients are normally more concentrated in the small value ranges than those of stegos and “spread” to the large values ranges after modifying the motion vector, the local Lagrangian cost quotient would thus be an efficient indicator to reflect the difference between cover videos and stego ones. In this regard, combining the logic-based (Lg) and local Lagrangian cost quotient (LLCQ)-based feature, we finally proposed a more effective and general steganalysis feature, i.e., Lg-LLCQ, which is composed of diverse subfeatures and performs much better than the corresponding single-type feature. Extensive experimental results show that the proposed method exhibits detection performance superior to other state-of-the-art schemes and even works well under cover sources and steganographic scheme mismatch scenes, which indicates our proposed feature is more conducive to real-world applications.
Effect of Stemming on Hindi Text Classification

Abstract. Text classification is very useful to search large amount of textual data available online by dividing it into smaller relevant units. Now a day’s large amount of digital documents are available in Indian languages. Designing text classifiers in Indian languages is one of the research areas so that people can search and read required documents in their local languages. In proposed work tried to design Text classifier for Hindi text documents and tried to show how stemmer affects the performance of Hindi text classifiers. Stemming is a process to convert words in any language to its base or root words. Stemmers are used for written documents not for spoken languages. Performance of many applications such as text summarization, Information Retrieval (IR) system,text classification systems, syntactic parsing can be improved by applying stemmers. Stemmer eliminates suffix or prefix of the word and form original root word. These root words helps in the preprocessing step required in many algorithms. We applied various stemmers on Hindi text classification models. Experiments and results show that performance of the classifiers is improved by applying stemmers.
Sentence Generator for English Language using Formal Semantics

Natural Language Processing (NLP), is more specifically the branch of ”artificial intelligence” (AI) concerned with providing computers the ability to comprehend spoken and written language in a manner similar to that of humans. It is used for practical purposes to help connects us with everyday activities such as texting, emailing, and cross-language communication. The requirement for intelligent systems that can read a text and listen to voice memos and can converse with people in a natural language like English has substantially increased in recent years. In this paper, the random clausal sentence generator which is simple, compound, and complex sentences are described. This random sentence generation is beneficial for students studying on online platforms to learn clauses as they will get a variety of exercises to practice. Initially, simple sentences get generated and subsequently moved on to compound sentence and complex sentence generation. In this method, roughly hundredverbs are used to get varied randomness along with 3-4 conjunctions and objects which nearly fit with the verbs and give a syntactically and semantically meaningful sentence as the outcome.
Denoising Of Digital Images Using Cyclespinning Algorithm With Shifted DWT

Noise determination and estimating a signal along with all its details proves a challenging task in signal processing. This issue has been addressed in the past using various discrete wavelet transform (DWT) based techniques. The signal is estimated as linear average of individual estimates derived from translated and wavelet-thresholded versions of a noisy signal by cycle spinning technique. In this paper, we propose a modified cycle zpinning algorithm with a new scaled down threshold of wavelet shrinkage for denoising images containing zero mean Gaussian noise using linear average of reconstructions obtained from shifted sequences’ DWT. This considerably improves the denoising performance of the conventional recursive cycle spinning algorithm and requires drasticallyless computations. Denoising performance of the proposed algorithm is benchmarked with published Recursive Cycle spinning, Buades NL means and Dual tree Complex Wavelet algorithms visually and quantitatively.
Realtime Hand Gesture Recognition System for Human Computer Interaction

Humans are using various devices for interacting with the system like mouse, keyboard, joystick etc. We have developed a real time human computer interaction system for virtual mouse based on the hand gestures. The system is designed in 3 modules as detection of hand, recognition of gestures and human computer interaction with control of mouse events to achieve the higher degree of gesture recognition. We first capture the video using the built-in webcam or USB webcam. Each frame of hand is recognized using a media Pipe palm detection model and using opencv fingertips. The user can move the mouse cursor by moving their fingertip and can perform a click by bringing two fingertips to close. So, this system captures frames using a webcam and detects the hand and fingertips and clicks or moves of the cursor. The system does not require a physical device for cursor movement. The developed system can be extended in other scenarios where human-machine interaction is required with more complex command formats rather than just mouse events.
Inspirations and Traces in the Works of Pál Frenák

I have known Frenák’s choreographies for almost twenty years, and since 2017, I have been regularly presenting discussions at domestic and international conferences regarding studies of the aesthetic and philosophical aspects of Frenák’s work. We started to work together some years ago. I assisted in the creation of the productions Cage (2019), Spid_er (2020), Fig_Ht (2021) and Secret Off_Man (2022) as a philosophical consultant. When researching and collecting the inspirational material, I experienced impressions that affected my other works as well. My work as a philosopher with dance and the conversations with Frenák opened new pathways to self-knowledge. When it seems that everything points in the same direction and everything is about the same topic, it is time to create a trace. This was my feeling when I wrote and published my book on Frenák in 2022, entitled L’abécédaire of Pál Frenák—Transverses Between Philosophy and the Organic Movement Language of FrenÁk (Published in Hungarian and in English in one book: Frenák Pál Abécédaire-je—Átjárások a filozófia és FrenÁk organikus mozgásnyelve között, Kortárs Táncért és Jelelő Színházért Alapítvány, Budapest, 2022). In this paper, I am going to display some essential inspirations of the fantastically creative art world of Frenák.
Creating an Animal Identifier Model by Deep Learning

This paper attempts to make a YOLOv3 model which can detect and identify animals. YOLOv3 is a deep-learning technique for object identification. Suppose, there’re many animals in a place. When the picture of the animals are converted as picture data and then, if the researcher input that picture data in the research model and searches for a cat from that picture data and if there’s a cat in that picture data then the research model, the YOLOv3 model can identify and identify the cat from that picture data easily. The research model, the YOLOv3 model will do the same for other animals too. So, the researcher makes a model which can identify animals and show which animal it is by using a deep-learning algorithm called YOLOv3.
Kernel Learning by Spectral Representation and Gaussian Mixtures

One of the main tasks in kernel methods is the selection of adequate mappings into higher dimension in order to improve class classification. However, this tends to be time consuming, and it may not finish with the best separation between classes. Therefore, there is a need for better methods that are able to extract distance and class separation from data. This work presents a novel approach for learning such mappings by using locally stationary kernels, spectral representations and Gaussian mixtures.
Estimation Method Based on Extended Kalman Filter for Uncertain Phase Shifts in Phase-Measuring Profilometry

Phase-measuring profilometry (PMP) is increasingly applied in high-accuracy three-dimensional shape measurement. However, various factors may result in the uncertainty of phase shift values in the PMP system, and phase errors induced by actual phase shift often bring about the reconstruction failure of a measured object. A quadratic phase estimation method using the extended Kalman filter is proposed to remove the phase error introduced by uncertain phase shift. After eliminating the background and fringe modulation, the state estimation is employed to evaluate the quadratic phase coefficients in a selected mask window, and the phase shifts of adjacent fringe patterns can be estimated to compute the unwrapping phase. This paper presents a novel method for improving the accuracy of the PMP system influenced by phase shift errors, and the proposed region-wise method significantly enhances the reconstruction quality and efficiency. Experimental results show that the proposed algorithm effectively evaluates the actual phase shift and directly compensates the phase error, and has the advantages of high speed, high accuracy, and robustness.
A novel approach for automatic annotation of human actions in 3D point clouds for flexible collaborative tasks with industrial robots

Manual annotation for human action recognition with content semantics using 3D Point Cloud (3D-PC) in industrial environments consumes a lot of time and resources. This work aims to recognize, analyze, and model human actions to develop a framework for automatically extracting content semantics. Main Contributions of this work: 1. design a multi-layer structure of various DNN classifiers to detect and extract humans and dynamic objects using 3D-PC preciously, 2. empirical experiments with over 10 subjects for collecting datasets of human actions and activities in one industrial setting, 3. development of an intuitive GUI to verify human actions and its interaction activities with the environment, 4. design and implement a methodology for automatic sequence matching of human actions in 3D-PC. All these procedures are merged in the proposed framework and evaluated in one industrial Use-Case with flexible patch sizes. Comparing the new approach with standard methods has shown that the annotation process can be accelerated by 5.2 times through automation.
A Novel Method for Obstacle Detection in Front of Vehicles Based on the Local Spatial Features of Point Cloud

Obstacle detection is the primary task of the Advanced Driving Assistance System (ADAS). However, it is very difficult to achieve accurate obstacle detection in complex traffic scenes. To this end, this paper proposes an obstacle detection method based on the local spatial features of point clouds. Firstly, the local spatial point cloud of a superpixel is obtained through stereo matching and the SLIC image segmentation algorithm. Then, the probability of the obstacle in the corresponding area is estimated from the spatial feature information of the local plane normal vector and the superpixel point-cloud height, respectively. Finally, the detection results of the two methods are input into the Bayesian framework in the form of probabilities for the final decision. In order to describe the traffic scene efficiently and accurately, the detection results are further transformed into a multi-layer stixel representation. We carried out experiments on the KITTI dataset and compared several obstacle detection methods. The experimental results indicate that the proposed method has advantages in terms of its Pixel-wise True Positive Rate (PTPR) and Pixel-wise False Positive Rate (PFPR), particularly in complex traffic scenes, such as uneven roads.
A sophisticated semantic analysis framework using an intelligent tweet data clustering and classification methodologies

Developing an efficient twitter sentiment analysis framework is one of the most challenging and demanding chores in current days. In the conventional works, various machine/deep learning based detection mechanisms are developed for tweet analysis. It encompasses the methodologies of data clustering, optimization, and classification, but it has the major problems of difficult to understand, complexity in mathematical computations, high time consumption, and error rate. Therefore, the proposed research work intends to implement an intelligent and advanced data mining techniques to design and develop a sentiment analysis framework. At first, the preprocessing is performed to normalize the dataset for generating the noise free balanced dataset. After that, the Hierarchical Tweet Expression Clustering (HTEC) algorithm is used to cluster the attributes according to the distance for simplifying the process of classification. Moreover, the multi-faceted features such as vector space Bag-of-Words (BOW), and Term Frequency – Inverse Document Frequency (IDM) are extracted for classifier training and testing operations. Finally, the Spatial Dense Bi-directional Long Short Memory (SDBi-LSTM) classification methodology is used for predicting the sentiment as positive, negative, or neutral. For evaluation, the Stanford Twitter Sentiment Test Set (STS-Test/Sentiment140), GOP debate and IMDB datasets are used to validate the results of the proposed system by using the parameters of accuracy, f1-score, training time, testing time, true positive rate, false positive rate, and etc. Also, the obtained results are compared with the recent prediction models for proving the superiority of the proposed framework.
Beta network for boundary detection under nondeterministic labels

Supervised data are not always uncontroversial, especially for boundary detection tasks. Considering that we have a portrait, the face contour is naturally the most salient boundary. Interestingly, some people, but not everyone, regard their eyes or eyebrows as boundaries. This means that humans might have different understandings of the same thing, which leads to nondeterministic labels. In this paper, we propose a novel head function based on the Beta distribution for boundary detection. Different from learning the probability in the Bernoulli distribution, it introduces more abundant information. It can be viewed as the distribution of the former's parameters and captures the uncertainty for binary classification problems. To this end, we employ the maximum likelihood and knowledge distillation as loss functions to train a deep network with the Beta head function. Moreover, we propose an efficient data processing method based on a recurrent voting strategy for merging these nondeterministic labels. In implementation, our Beta head function is lightweight and not limited to specific models. It can be seamlessly employed in many existing boundary detection networks without modifying their backbones. After introducing our Beta head function, the performances of three well-known boundary detection networks have been obviously improved. Multiple experiments indicate the effectiveness and the advantage of introducing uncertainty by the proposed Beta head function.