Reviewerly/exHarmony huggingface dataset:
Intrinsic complexity of partial learning

A partial learner in the limit [25], given a representation of the target language (a text), outputs a sequence of conjectures, where one correct conjecture appears infinitely many times and other conjectures each appear a finite number of times. Following [7] and [21], we define intrinsic complexity of partial learning, based on reducibilities between learning problems. Although the whole class of recursively enumerable languages is partially learnable (see [25]) and, thus, belongs to the complete learnability degree, we discovered a rich structure of incomplete degrees, reflecting different types of learning strategies (based, to some extent, on topological structures of the target language classes). We also exhibit examples of complete classes that illuminate the character of the strategies for partial learning of the hardest classes.
A Framework for Catering Software Complexity Issues Using Architectural Patterns

Software systems need to be maintained and updated regularly to meet new requirements and to adapt to improvements in technology. However, every time an update is made, more codes are added that result in the increasing complexity of the software. High complexity in software makes it challenging for developers to update and maintain. To improve the complexity issues in software systems, architectural patterns play an important role. With the in-built concept of modularity in architectural patterns, they have a general tendency to reduce the complexity of software systems. This also improves other quality factors of the software, such as maintainability and testability. This research is conducted to study the impact of commonly used architectural patterns on different types of applications by applying the patterns on both desktop and web-based applications. As per the findings of the literature review, the survey results, and the empirical evaluation, it proves that complexity issues in software can be improved using architectural patterns. The findings of this research is used to propose a framework for improving complexity issues in software using architectural patterns. This framework will help stakeholders to reduce complexity by choosing suitable architectural patterns.
Substantiation of requirements and ways of their realization at synthesis of proof-stable perspective electronic signatures

The paper identifies and substantiates the requirements and ways to implement these requirements at the synthesis of proof-stable perspective electronic signatures. Today there is a problem of building post-quantum electronic signatures. At the stages of solving this problem, a wide range of requirements for technical, technical-economical and other capabilities is formed. At the national and international levels, in the most generalized form, these requirements are implemented at the NIST USA level during the NIST PQC competition for electronic signatures, asymmetric ciphers and key encapsulation protocols. In Ukraine, work is also underway to synthesize such post-quantum cryptotransformations. Requirements in these areas are justified and approved at the state level. The peculiarity of the national requirements is that the requirements for protection against special quantum attacks and attacks through side channels have been significantly increased.
Knowledge-Based Composable Inductive Programming

Zoea is a knowledge-based inductive programming system that generates code directly from a set of test cases. It also allows developers to combine generated software in a variety of ways to form programs of arbitrary size. The Zoea compiler is built using a modern variant of the blackboard architecture. Zoea integrates a large number of knowledge sources that encode different elements of programming language and software development expertise, using test cases as a ubiquitous basis for knowledge representation. Hypotheses are managed through the creation of synthetic test cases and blackboard recursion. We briefly outline the text-based and visual specification languages, and the associated composable inductive programming development paradigm. The benefits of the approach and some plans for future development are also identified.
A Guided Differential Evolution Algorithm for Control Design of Quantum Gates

Constructing high fidelity quantum gates is a key task in quantum information technology. Gradient-based methods (e.g., GRAPE) have been widely used for numerous implementations of quantum gate control tasks but are not practical for in-situ laboratory experiments. In this paper, for the design of optimal control fields for constructing quantum gates, we propose a Guided Differential Evolution (GDE) algorithm. Numerical results verify the effectiveness of the proposed GDE algorithm for the control design of quantum gates with short evolution time and few control pulses.
A pose-based hand image classification method retrainable on embedded target devices

State of the Art solutions for image classification are nowadays mostly based on Convolutional Neural Networks (CNNs), With respect to running them for inference, training CNNs is a much more resource demanding job. For this reason, embedded devices designed to execute neural networks are normally not suitable to train the same networks. Hand image classification is becoming increasingly popular in several application areas, by empowering embedded systems with the ability to recognise hand gestures or grasp types. In this field, making it possible to retrain the hand image classifier directly on the embedded device would allow an easy customization of classification task according to application specific needs. In this paper we present a method based on transfer learning which solves this problem by first extracting hand keypoints, using with a dedicated neural network, and then applying on the resulting data a lightweight classifier, easily retrainable directly on the target embedded system. The proposal has been validated on a new freely distributed dataset, outperforming state of the art solutions in the specific task.
Lightweight Lossless Compression Algorithm for Fast Decompression Application

In the field of compression algorithms, lossless compression is used in various fields of data storage and transmission because it can restore data without distortion. However, with the development of network transmission speed, most of lossless compression algorithms cannot be applied due to their large algorithm code size and high resource overhead, especially in the embedded field. The Lempel Ziv Oberhumer (LZO), a lightweight compression algorithm using dictionary encoding, is currently one of the fastest algorithms and is widely used. This paper proposes a new fast decompression lossless compression algorithm based on the LZO algorithm, and adopts a new search strategy and compression formats to make the code size smaller, occupy less resources, and decompress faster. Experiments on the embedded platform show that the improved algorithm we proposed is 16% faster than the LZO algorithm in decompression speed without basically affecting the compression rate. We believe that such a lightweight lossless compression algorithm has practical value in scenarios that require fast decompression.
Introduction to PRCDS and simulation framework

In this chapter, Section 10.1 describes the definition of port-centric road collecting and distributing system (PRCDS) and challenges faced in its planning and management. Facilities composition, functions, and typical operation process of PRCDS are also introduced in detail. Thereafter, Section 10.2 provides a general simulation framework for PRCDS, which includes major entities, parameter setting, simulation logic, and modular structure.
Similarity-Aware CNN for Efficient Video Recognition at the Edge

Convolutional neural networks (CNNs) often extract similar features from successive video frames due to having identical appearances.In contrast, conventional CNNs for video recognition process individual frames with a fixed computational effort.Each video frame is independently processed, resulting in numerous redundant computations and an inefficient use of limited energy resources, particularly for edge computing applications.To alleviate the high energy requirements associated with video frame processing, this paper presented similarityaware CNNs that recognise similar feature pixels across frames and avoid computations on them.First, with a loss of less than 1% in recognition accuracy, a proposed similarity aware quantization technique increases the average number of unchanged feature pixels across frame pairs by up to 85%.Then, a proposed similarity-aware dataflow improves energy consumption by minimising redundant computations and memory accesses across frame pairs.According to simulation experiments, the proposed dataflow decreases the energy consumed by video frame processing by up to 30%.
Improved Collaborative Filtering Recommendation Model

The rise of Web 2.0 has brought about significant changes in online behavior patterns from users. Users are no longer just consumers of information, but the main producers of information. Behavioral information such as clicks and purchases will be recorded by the Internet. Therefore, user interaction data exploded at an exponential rate, leading to the problem of information overload, and the recommendation system is an effective strategy to solve this problem. In response to the above problems, this paper innovates on the basis of traditional recommendation algorithms and proposes an improved collaborative filtering recommendation model. Taking into account the different contributions of user historical interaction data to predicting the target product, the standard attention mechanism cannot play a good role due to the large difference in the length of user historical data. This paper proposes to introduce a smoothing parameter to improve this problem. At the same time, the paper introduces the residual network to learn nonlinear features to overcome the problem of network degradation in the deep network model and help improve the final recommendation result. This paper conducts experiments and evaluations on real public data sets, and compares the performance with current related models. Experiments have verified the rationality and effectiveness of the model design in this paper, and the recommendation accuracy has been significantly improved.
Controlling Agents by Constrained Policy Updates

Learning the optimal behavior is the ultimate goal in reinforcement learning. This can be achieved by many different approaches, the most successful of them are policy gradient methods. However, they can suffer from undesirably large updates of policies, leading to poor performance. In recent years there has been a clear trend toward designing more reliable algorithms. This paper addresses to examine different restriction strategies applied to the widely used Proximal Policy Optimization (PPO-Clip) technique. We also question whether the analyzed methods are able to adapt not only to low-dimensional tasks but also to complex, high-dimensional problems in control and robotic domains. The analysis of the learned behavior shows that these methods can lead to better performance compared to the original PPO-Clip algorithm, moreover, they are also able to achieve complex behavior and policies in high-dimensional environments.
Multiple Partitions Alignment with Adaptive Similarity Learning

Multi-view clustering is a hot topic which aims to utilize the consistent and complementary information among different views in unsupervised setting. Most existing methods integrate the information from different views in the data space. Thus the performance can be deteriorated considering the unavoidable noises or inconsistency in views. In this paper, we propose to construct the similarity matrix for different views by utilizing adaptive similarity learning, and fuse multi-view information in partition level instead of the original data space. We further leverage an auto-weighted strategy to avoid bad performance caused by certain views. Finally, we formulate the above principles into a unified framework successfully and present the optimization algorithm to optimize the corresponding objective function. Experimental results on several commonly used datasets demonstrate the efficacy of our approach against the state-of-the-art methods.
Cluster-TRnet: Jointed Model for Real-Time Traffic Identification with High Accuracy

Nowadays, the traffic data information explodes. Variety of malicious traffic is used to frequently evade the traffic identification systems. However, the extraction of massive traffic features and identification of malicious traffic using a single machine learning approach is ineffective in real-time recognition. To this end, this paper proposes the real-time traffic recognition method of Cluster-TRnet that focuses on the temporal and spatial behavior features of the application layer packets. K-means is used to filter disruptive traffic, lessen the learning burden and gather the malicious traffic features. Furthermore, the parameters of jointed convolutional neural network (CNN) and Long Short-Term Memory (LSTM) are adjusted, while the data inputted is augmented and grouped. The malicious traffic features precisely and consequently improve the accuracy of the recognition. Based on Spirent’s traffic data-set and the public traffic data-set, the proposed model is eventually validated. The experimental results show that the proposed method strictly identifies different types of traffic with an accuracy of 99.98%. Besides, it improves the efficiency of malicious traffic identification through filtering 83.51% of invalid traffic, and this dramatically saves the detection time by 78.18%.
Evolving Systems

Evolving systems emerge from the synergy between systems with adaptive structures, and the recursive methods of machine learning. Evolving algorithms construct models and derive decision patterns from stream data produced by dynamically changing environments. Different components can be chosen to assemble the system structure, rules, trees, and neural networks being amongst the most prominent. Evolving systems concern mainly with time-varying environments, and processing of nonstationary stream data using computationally efficient recursive algorithms. They are particularly suitable for on-line, real-time applications, and dynamically changing situations, and operating conditions. This chapter gives an overview of evolving systems focusing on the model components, learning algorithms, and illustrative applications. The aim of to introduce the main ideas and a state of the art view of the area.
Emergence of Cyber Security Audit: How Cybersecurity Audit Will Be Helpful For Business in the Age of Digital Transformation

Cybersecurity has become one of the serious challenges faced by every type of organization and society. A cybersecurity audit is a complete evaluation and analysis of the IT infrastructure of a business organization. In protecting businesses in the ongoing battle against cyber threats, the cybersecurity audit plays a vital role. This article explores the new buzzword “cybersecurity audit” and we also attempt to answer how cybersecurity audit will be helpful for business firms in the age of digital transformation.
The Tele-Reference Model

At the University of Central Florida Libraries, a new alternative to traditional reference service was adopted during the COVID-19 pandemic as a remote solution for safely staffing the Research and Information Desk (RAID) at the John C. Hitt Library. Due to the challenges of a partial library re-opening at the height of COVID-19 variant infection rates in Florida, a safe alternative to physically staffing RAID was conceived and coined “Tele-Reference.” Utilizing a Zoom institutional license and readily available work equipment with audio and video capabilities, the Tele-Reference service model was developed by our research and information services librarians. The implementation, challenges, assessment, and future directions of using Tele-Reference at the University of Central Florida Libraries, as well as possible applications at other institutions, are explored herein.
Inquiry-based learning. Beliefs of trainee teachers in a physics course

Abstract The teaching of physics should be supported by practical or laboratory activities that seek to develop the scientific competencies of the students who study physics, so it is necessary to propose innovative strategies, such as research-based learning, in which students answer research questions through the analysis of data, which increases the interaction between the student and the physical concepts under investigation. Therefore, the objective of the study was to analyze the beliefs of teachers in natural science training at a public university in Colombia who take the subject of physics on research-based learning, to provide information on how to guide the use of innovative strategies. The study responds to a type of quantitative research with a descriptive approach and was based on a field design. On the other hand, the results reflect slightly positive beliefs of students in initial teacher training who study physics about research-based learning as an innovative strategy, although it is recommended to expand the sample to generalize. Finally, the analysis reveals as a conclusion that the implementation of innovative methodologies such as inquiry-based learning for science teaching, especially physics, should be promoted and supported more effectively to foster motivation, skills development, and conceptual understanding. of the scientific contents.
Cryptocurrencies: They´re really that lucrative?

Research background: Cryptocurrency is a digital currency that is intended for online trading. It uses and implements the principles of cryptography to create a distributed, decentralized and secure digital currency. Virtual money is a new and promising branch of the virtual economy that brings many advantages and disadvantages in a global sense. Many people have become involved in cryptocurrency hype because high investments in this digital money have been seized during the pandemic. The rise in revenues from this digital money has gripped the world globally. Purpose of the article: The basic purpose and chosen goal is to analyze the use of cryptomen trading during the global Covid-19 pandemic, as well as investing in these alternative sources of investment, which are gaining more attention every day precisely because of their freedom and detachment. Methods: The article will analyze data that will be compared based on the years before the Covid-19 pandemic and during the Covid-19 pandemic. Based on these data, the investment activity of people, companies, corporations is compared. Findings &amp; Value added: Based on the results in the article, it was found that during the Covid-19 pandemic, the interest in investing in cryptocurrencies increased compared to the interest in investing in cryptocurrencies before the pandemic. The overall result is that people are moving to a new way of holding money, as cryptocurrencies are a new way to the future, as banks are unable to provide such returns from client deposits as cryptocurrencies, but they are associated with much greater risk.
Open Set Recognition Using the Feature Space of Deep Neural Networks

Image classification assumes that all classes used in testing are known. Therefore, when an unknown class data is input, it cannot be recognized correctly. A method that enables unknown classes to be identified is called open set recognition. In this paper, we propose a method of open set recognition focusing on the feature space of the classifier and a Mahalanobis-based threshold. The experimental results show that the proposed method surpasses state-of-the-art methods on some datasets, demonstrating the potential of a method focusing on the feature space.
Deep Learning Frameworks for Internet of Things

Artificial intelligence is a common platform in which the concept of machine learning (ML) and deep learning (DL) appears. The DL is becoming a hot research topic in recent years as it enables various smarter applications and services, including the Internet of Things (IoTs). DL discovers characteristics and responsibilities straightforwardly from data including pixels, images, shapes, dimensions, text, and sound. DL is also considered as an end-to-end learning approach because the tasks are associated with learning from data. Several hidden layers consist of a neural network, and therefore, it is also known as a deep neural network (DNN). The convolution neural network (CNN) is commonly used in DNN which contains a significant number of hidden layers. This chapter aims to explore DL frameworks for IoT. The chapter begins with a discussion on the development and architecture of the DL framework. We then discuss various DL models associated with deep reinforcement learning approaches for IoT. The potential applications, including smart grid management, road traffic management, industrial sector, estimation of crop production, and detection of various plant diseases, are discussed. Various design issues and challenges in implementing DL are also discussed. The findings reported in this chapter provide some insights into DL frameworks for IoT that can help network researchers and engineers to contribute further toward the development of next-generation IoT.
Research on Human Fall Detection Based on Tiny-yolov3 Algorithm

Most families today have experienced a fall problem with an elderly person or child. The number one cause of accidental injury to an elderly person or child is a fall. When there is a danger, it is difficult for the elderly to save themselves in time. Children, on the other hand, have the characteristic of being active, which makes them fall frequently during running resulting in different degrees of injuries to children. In recent years, deep learning algorithms have become a research hotspot in the field of graphic image processing, and the YOLO series of algorithms [1-3], as a classical algorithm in the single-stage target detection algorithm, has the accuracy comparable to the two-stage algorithm and the speed beyond the two-stage algorithm, this paper is using the Tiny-yolov3 algorithm model to detect human falls, which has the advantages of high detection accuracy and low computational complexity. By monitoring the trajectory of the human body in real time through the camera loaded on the trolley, the abnormal posture of the human body is detected and stays in certain positions for a long time to serve as a criterion for whether the human body has fallen or not. A better result is achieved, which can provide some reference for fall monitoring related research.
Multiple Negative Samples Based on GAN for Cross-Modal Retrieval

Cross-modal retrieval has attracted wide attention for retrieving multimedia data such as images, text, audio, and video. However, due to the great differences in the underlying representation and distribution of modalities, it’s still challenging to find the semantic similarity of various modalities. Benefited from the introduction of the generative adversarial network (GAN) into cross-modal retrieval, the performance of models has been significantly improved. To generate discriminative representations, adversarial learning utilizes triplet constraint to establish connections among the anchor, the positive sample, and the negative sample. The strategy of triplet loss can separate the anchor and a certain class of the negative sample, but fails to guarantee the anchor and other categories of items could also be scattered in the subspace. This paper proposes a novel multiple negative samples model based on GAN (MNS-GAN) to increase intra-modal discrimination. Comprehensive experiments show that our proposed MNS-GAN method outperforms the state-of-the-art cross-modal retrieval methods.