Reviewerly/exHarmony huggingface dataset:
Nonoverlapping Feature Projection Convolutional Neural Network With Differentiable Loss Function

We propose NullSpaceNet, a novel network that maps from the pixel-level image to a joint-nullspace, as opposed to the traditional feature space. The features in the proposed learned joint-nullspace have clearer interpretation and are more separable. NullSpaceNet ensures that all input images that belong to the same class are collapsed into one point in this new joint-nullspace, and the input images of different classes are collapsed into different points with high separation margins. Moreover, a novel differentiable loss function is proposed that has a closed-form solution with no free parameters. NullSpaceNet architecture consists of two components; 1) a feature extractor backbone (i.e., the convolution and pooling layers), which is used to extract features from the input, and 2) a nullspace layer, which maps from the pixel-level image to the joint-nullspace. This novel architecture and formulation results in a significant reduction in the number of learnable parameters in the network. NullSpaceNet is architecture-agnostic, which means it can use any feature extractor as a backbone in its first component. NullSpaceNet exhibits superior performance when tested over four different datasets against VGG16, MobileNet-224, and MNASNET1-0. In general, NullSpaceNet needs only 1 &#x2013; 30% of the time it takes a traditional CNN to classify a batch of images, and with better accuracy of up to +2.57%.
Learning a Head-Tracking Pointing Interface

For people with poor upper limb mobility or control, interaction with a computer may be facilitated by adaptive and alternative interfaces. Visual head tracking has proven to be a viable pointing interface, which can be used when use of the mouse or trackpad is challenging. We are interested in new mechanisms to map the user’s head motion to a pointer location on the screen. Towards this goal, we collected a data set of videos of participants as they were moving their head while following the motion of a marker on the screen. This data set could be used to training a machine learning system for pointing interface. We believe that by learning on real examples, this system may provide a more natural and satisfactory interface than current systems based on pre-defined algorithms.
An Improved Unified AES Implementation Using FPGA

Encryption is an essential process in electronic data transmission because it securely protects the data from unauthorized access. In this digital era, information and its security are of great concern with technology advancements. As we have entered into 5G technology that targets end-to-end security and speed to communicate with intelligent devices. These devices and systems need an AES module having both the operation as encryption and decryption in a single module to communicate in duplex mode to access the information in a real-time environment. This article has architecture of a unified module with modified round operation and has been implemented on Virtex-7 FPGA platform. Mix column adds vertical alteration in the algorithm and this design has managed the utilization of Mix column block to make an optimized AES algorithm. The unified AES has achieved a maximum frequency of 290.3MHz and resource utilization of 9416 slice LUTs design, including some modification in traditional AES, resulting in less resource utilization and high throughput.
A Method for Data Compression and Personal Information Suppressing in Columnar Databases

Data privacy and security is the need of the hour. The importance elevates further when we submit the data over a network. It is very easy to extract personnel’s history like illness, vulnerability etc. through the information posted. Through this research, a technique for data compression and abstraction, particularly in columnar databases is proposed. It provides domain compression at the attribute level to both row and columnar databases using Attribute Domain Compression (ADC). Large datasets may be stored more efficiently by using this approach to minimize their size. Because we want the second process to be as flexible as possible, we give it the value (n) so that it can find all $${\text {n}}-1$$ more tuples.
Calculating the Mind Change Complexity of Learning Algebraic Structures

This paper studies algorithmic learning theory applied to algebraic structures. In previous papers, we have defined our framework, where a learner, given a family of structures, receives larger and larger pieces of an arbitrary copy of a structure in the family and, at each stage, is required to output a conjecture about the isomorphism type of such a structure. The learning is successful if there is a learner that eventually stabilizes to a correct conjecture. Here, we analyze the number of mind changes that are needed to learn a given family $$\mathfrak {K}$$ . We give a descriptive set-theoretic interpretation of such mind change complexity. We also study how bounding the Turing degree of learners affects the mind change complexity of a given family of algebraic structures.
Toward a Self-adaptive Supply Chains: L-SCOR Implementation Proposal, and Case Studies Methodology Proposal

In order to improve a business process behavior, to guarantee the service continuity, to minimalize the time wasted by automatic activities execution, L-SCOR is proposed to analyze the self-adaptability of a business process and to improve this performance. This paper gives an overall overview of the reference L-SCOR, and explains a proposal of its implementation based on reinforcement learning, and a methodology to conduct case studies based on six sigma strategies.
I-Vital: Information Aided Visual Tracking with Adversarial Learning

With the advent of convolutional neural networks (CNN), MDNet and Siamesefamily of trackers posed tracking as supervised learning of object’s presenceusing classification (foreground and background) and location using regression.For the first time, we have brought probability distribution into the CNN frameworkfor tracking. We have selected “Information maximization Generative AdversarialNetwork (InfoGAN)” to couple the target and background classes withtwo unique Gaussian distributions. This paper highlights the use of InfoGAN ininformation extraction & feedback to improve the tracking framework. Specifically,the novel features proposed in this tracking framework are (i) Couplingof unique probability distributions to target and background classes, (ii) Unsupervisedtracker status (success/ failure) identification and correction throughinformation feedback. We demonstrated the efficacy of the proposed I-VITALtracker in visual tracking with experimental comparisons on well-known datasets such as GOT10K, VOT2020, and OTB-2015. Compared with base works,the proposed tracker has improved performance in and precisely locating theobject of interest.
New Communication in Digital Networks and Methods (Techniques) of Its Implementation in the Process of Teaching Strategy of Students

The purpose of the article is to analyze the use of social networks as a communication tool in the process of teaching students of philological specialties, as well as to assess the relevance of social networks in terms of didactics as an informal type of education. Basic method is the survey conducted by social digital networks. Based on the survey conducted by the author among students and teachers of the philological faculty of Russian and foreign universities, every year the use of social networks as one of the teaching tools is increasing. Students and teachers of Russian higher schools communicate mainly through such social networks as: VKontakte, Odnoklassniki, Instagram and YouTube; foreign citizens studying in Russia prefer mainly: Facebook, Twitter, YouTube and Instagram; students of foreign universities are impressed by: Facebook and YouTube. Communication in social networks as a means of teaching ensures the transparency of the educational process, determined by the ability of students to drown out any learning moments both in a group and in a personal virtual conversation with a teacher, if the academic hours allocated by the university were not enough for this.
Traffic State Analysis and Prediction Technology

The complete acquisition, accurate real-time evaluation, and prediction of urban road traffic state are the basis for accurately grasping the behavior of urban road traffic system, making traffic management decisions scientifically and giving full play to the potential of traffic facilities. This chapter focuses on the analysis and prediction of traffic status to meet the actual needs of urban traffic management.
A Study of Preprocessing Techniques on Digital Microscopic Blood Smear Images to Detect Leukemia

Digital microscopic blood smear images can get distorted due to the noise as a result of excessive staining during slide preparation or external factors during the acquisition of images. Noise in the image can affect the output of further steps in image processing and can have an impact on the accuracy of results. Hence, it is always better to denoise the image before feeding it to the automatic diagnostic system. There are many noise reduction filters available; the selection of the best filter is also very important. This paper presents a comparative study of some common spatial filters like wiener filter, bilateral filter, Gaussian filter, median filter and mean filter which are efficient in noise reduction, along with their summary and experimental results. Performing comparative analysis of result based on PSNR, SNR and MSE values, it can be determined that median filter is most suitable method for denoising digital blood smear images.
GCANet: A Cross-Modal Pedestrian Detection Method Based on Gaussian Cross Attention Network

Pedestrian detection is a critical but challenging research field widely applicable in self-driving, surveillance and robotics. The performance of pedestrian detection is not ideal under the limitation of imaging conditions, especially at night or occlusion. To overcome these obstacles, we propose a cross-modal pedestrian detection network based on Gaussian Cross Attention (GCANet) improving the detection performance by a full use of multi-modal features. Through the bidirectional coupling of local features of different modals, the feature interaction and fusion between different modals are realized, and the salient features between multi-modal are effectively emphasized, thus improving the detection accuracy. Experimental results demonstrate GCANet achieves the highest accuracy with the state-of-the-art on KAIST multi-modal pedestrian dataset.
An Improved Certificateless Partial Blind Signature Scheme Based on Homomorphic Encryption

A modified Paillier homomorphic cryptography-based partial blind signature scheme is proposed to address the problems of centralized key generation in traditional blind signature schemes and the fact that the current tamper-proof protection scheme for public messages only protects them after they have been attacked or does not protect public messages. In this scheme, the certificateless idea allows the distribution of keys to be decentralized to the KGC (Public Key Generation) and the user's personal key to be protected, while the partial blind signature idea ensures that the signer's signature is not misused and protects the signed message. The experimental results show that the use of the Paillier homomorphic encryption algorithm not only satisfies the feasible operational efficiency, but also demonstrates that the scheme can be resisted before the public message is tampered with, improving the security of the scheme in terms of security and feasibility.
Shinkansen Business Opening and Afterward

On October 1, 1964, the Tokaido ShinkansenTokaido Shinkansen opened for business. The ribbon-cutting ceremony was conducted by Reisuke IshidaIshida, R., JNR president at the time. His predecessor SogoSogo, S., who had led the way in bringing the Shinkansen to fruition, had resigned 15 months earlier because the budget for the Shinkansen construction had doubled from its original amount. SogoSogo, S. accepted responsibility for failing to stay within the budget.
Semantic Segmentation for the Detection of Very Small Objects on Cervical Cell Samples Stained with the AgNOR Technique

Semantic segmentation has been used widely for cytological imaging for several body parts and different stain types. This work explores deep learning models for the segmentation of very small cellular structures in AgNOR-stained cervical cell images and compares state-of-the-art models (HRNet and SegFormer) with traditional U-nets using ResNets as their backbone. AgNOR has been used as a coadjuvant staining technique for the diagnosis of cervical and other cancer types. Our results presented the transformer model, SegFormer, as the best model to segment nuclei. HRNet is the best model to segment the very small object categories (the NORs — Nucleolar Organizer Regions). HRNet showed to be more suitable for AgNOR image segmentation as a whole, with 84.7% precision, 84.6% recall, and 84.6% F-score for nuclei, and 70.1% precision, 55.4% recall and 61.8% F-score on the detection of NORs.
Calculating the Distance between Languages with Deep Learning

Artificial intelligence (AI) has been implemented in various fields, including speech recognition. In this paper, a computational method is proposed for calculating the similarity between different languages or language varieties, with their similarity represented in terms of distance. In this process, we extracted mel spectrogram features from speech signals to provide the feature vectors and derived pairs of signal tokens based on vectors. Then, we trained a Siamese time-delay neural network to calculate the distance between two signal tokens. If the token pairs are from the same language group, the distance obtained using this Siamese network model is zero. In this preliminary experiment, three types of regional Mandarin Chinese (BJ, FJ, GD) were used as the dataset. The results gave an AUC value of 0.731 for the classification task. In addition, 10 Taiwan Mandarin (TM) native speakers participated in identification and a pair-wise discrimination experiment to allow comparison with the Siamese network model. The 10 TM natives tended to misidentify GD-accented Mandarin as FJ-accented Mandarin resulting in a much greater distance between the two in the FJ-GD discrimination task compared to the Siamese network model. Overall, the results show that the performance of our model is better than the comparative experiment in completing the identification task, with the distance between the Siamese network model and the experiment having a mean absolute error (MAE) of 0.35. One may suspect that BJ-accented Mandarin is often used to exemplify Standard Mandarin and this familiarity might be the reason why the 10 TM natives displayed a bias towards BJ-accented Mandarin Chinese. To sum up, we provide a computational method to calculate the distance between two languages or language varieties, which can help linguists pre-classify sound files.
Generation of Omnidirectional Image Without Photographer

In order to create a virtual reality (VR) space using omnidirectional images, it is desirable to use images without the photographer’s inclusion. In this study, we propose a method to generate an omnidirectional image without the photographer’s inclusion by using multiple images taken by an omnidirectional camera. In the proposed method, the photographer rotates around the omnidirectional camera and takes several images. Next, we perform feature point matching on the omnidirectional images and unify the appearance of all the images by using the amount of translation calculated from the matching. Finally, the images are combined with graph cut and Poisson image editing to produce an omnidirectional panoramic image without the photographer in it.
Densification of Sparse Optical Flow Using Edges Information

Optical flow methods, which estimate a dense motion field starting from a sparse one, are playing an important role in many visual learning and recognition applications. The proposed system is based only on sparse optical flow and line detector. It is able to densify the starting optical flow, reaching good performances in objective and subjective manner, using common applications like clustering and standard KITTI evaluation kit. In particular, an appreciable improvement has been achieved in terms of quantity of motion vectors grows (up to 540%). Since often in smart cameras both optical flow and lines are available, the proposed approach avoids overloading the Engine Control Unit to transmit the entire image flow and allows reducing the power consumption, realizing a real-time robust system.
Regulations, Business Model and Actors in the Energy Market

The electricity market is a particular market with an inherent complexity. The mechanisms that regulate the movement of energy volumes are managed in a precise and systematic order to ensure transparency and competition on the one hand, and reliability and continuity of service on the other. The electricity market is made up of several well-defined phases, and in each phase one or more players can operate according to the purpose of the market phase itself. There are many players involved in the energy business, each with its own role and identity. In general they can be distinguished: Prosumers, Aggregators, Energy Market Manager, Balance Responsible Party, Balance Service Provider, TSO, DSO and Energy Retailer. As the electricity system evolves, the electricity market and the role of the different actors is also changing, enabling new business models that need to be properly regulated. This chapter aims to describe the various phases of the Italian electricity market and the actors involved in each phase, defining their functions and tasks, the new business models and the main regulations. Special attention is paid to the players involved in regulatory services and, consequently, to transactions connected to the balancing of the electricity grid.
Affluence (A)

Modern society will find no solution to the ecological problem unless it takes a serious look at its lifestyle. Pope John Paul II Equation (1.1) (repeated below for convenience) is our conceptual framework for considering sustainability. Chapter 2 discussed planetary boundaries and the impact of human activities on those boundaries, that is, the left-hand side of Equation (1.1). Chapter 3 is about the first term on the right-hand side of Equation (1.1), world population and its dynamics. Discussion questions in Chapter 3 reveal a variety of ways in which it is practically and morally difficult to reduce impacts (I) by reducing population (P). This chapter examines the second term on the right-hand side of Equation (1.1), affluence ( A), and the role of human economic activity in sustainability challenges. Chapters 5–6 finish the development of our framework for sustainability.
The Mobile App Application Base on Artificial Intelligence in English Smart Classroom

Under the current situation, the education ministry has put forward the guiding ideology of “stopping classes without stopping school, and teaching without stopping classes”, and schools all over the country have launched online teaching. As a mobile Internet terminal teaching software, the mobile smart classroom APP has effectively promoted the online teaching development. This article reflects and analyzes how to use AI mobile APP in college English smart classrooms, and the effects of online teaching, aiming to explore effective ways of online teaching.
Learning Rbfns by Tlbo for Classification:A Special Attention Towards Handling Outliers

An outlier is a datum that is far from other data points in which it occurs. It can have a great impact on the output, therefore, removing or resolving it before the analysis is important to prevent any skewing. Outliers in a survey sampling can have a major outcome on statistical results. The goal of discovering outliers in data mining is to find a pattern in data that does not conform to expected behaviour. In this paper, we have proposed a framework in which a popular statistical approach termed Inter-Quartile Range (IQR) is used to detect outliers in data and deal with them by Winsorizing method. A radial basis function network trained by teaching learning-based optimization model is developed using the preprocessed dataset under this framework. To measure the effectiveness of the framework, a few standard UCI datasets are employed. The outcome of the experiments show that our proposed framework can be a viable alternative tool for the classification task of data mining where prior outliers preprocessing is necessary.
DIDACTICAL GAME AS ONE OF THE METHODS OF FORMING THE ECONOMIC COMPETENCE OF HIGH SCHOOL STUDENTS

The article deals with the problems associated with the experience of developing and applying didactic games in the process of forming the economic competence of high school students, conclusions are drawn about the possibility of using didactic games in teaching economics to schoolchildren.
Biologically Motivated Hexagonal Framework for Machine Vision

In view to the current and traditional state of art, all image analyzing device for capturing, recording and display are exclusively meant to utilize square grids. But biological structure models, however, pinpoint an alternative, evolutionary motivated base structure. Motivated by the optical perception system of human, hexagonal image processing (HIP) offers many robust advantages and benefits for the researchers. Hexagonal structure offers decisive advantages like radical symmetry, equidistant and unique neighbours, homogeneity in the lattice and 13.4% increased transformation efficiency. Even though hexagonal image processing is known for good image quality but the implementation approach made it difficult to work as there is no working hardware that applies hexagonal imaging concept. Although with these much of processing power of computers and graphic devices, hexagonal representation has not yet developed to a standardized framework or library for HIP, so the time is ripe to review the various hexagonal grid structure rendering algorithms and hexagonal sampling.
A Predictive, Context-Dependent Stochastic Model for Engineering Applications

This work explores the architecture of a context-dependent probabilistic model. We identify opportunities for providing reminders to operators in their environment as a means to address information overload. Hence, there is a need to represent a state of knowledge and help them stay vigilant during their jobs. Along with the architectural improvements, which further specialize information flows and develop a data-driven approach, continual learning techniques covered events in a probabilistic graphical model called Context-Dependent Recommendation Systems (CD-RS). We demonstrated, as a result, the use of statistical thinking and Design of Experiments (DoE), which are most clear in conducting a suitable experiment. Moreover, the validation of the model and experiments of the novel architecture based on the collected data from a real case study demonstrates the value of the proposed methods.