Reviewerly/exHarmony huggingface dataset:
The purpose reflex

Abstract In our research on the so-called conditional reflexes we often came across the phenomena of sleep. Since these phenomena seriously complicated the experiments we were conducting, disturbed them, disrupted their usual progression, we naturally ended up being forced to take on these sleep phenomena directly. Besides collecting isolated facts, two of our colleagues—N. A. Rozhanski? and M. K. Petrova—delved into this question especially systematically. N. A. Rozhanski? researched that type of sleep, that somnolent state of the animal which occurs, evidently, under the influence of monotonous indifferent stimuli, such as the isolation of the environment in which the animal finds itself during the experiments.
The Governance of Big Data and Artificial Intelligence in Network Industries

An important precondition for the development of artificial intelligence (AI) in network industries is the access to big data and the attendant necessities of data sharing and data portability. The goal of this paper is to analyze the changing needs of entrepreneurial decision-making to exhaust the innovation potential of AI-driven big data value chains taking into account AI-specific ethical, security and privacy regulations. The analytical concept of AI-powered big data virtual networks is investigated with a focus on the governance of 5G-based big data value chains required for Internet of Things (IoT) applications in particular smart networks. Although several actors may be involved—such as broadband providers, cloud service providers, geopositioning service providers, or sensor network service providers—the final responsibility for bundling these different service components lies in the hands of the AI-powered big data virtual network providers. In addition to the required data privacy and security regulations, the exploration of new liability rules for AI interacting with traditional technologies is becoming relevant, taking into account AI-specific ethical and transparency obligations. Firstly, the complementary roles of the EU data regulatory framework and European AI regulatory framework are examined. Secondly, the network economic concept of AI-powered big data virtual networks is elaborated taking into account the required regulations. Thirdly, the heterogeneity of AI systems—required for a variety of IoT applications—is considered, with a particular focus on the application of AI within the transportation sector.
Fast Resampling and Monte Carlo Methods in Python

In the 1935 book The Design of Experiments (Fisher, 1951), Ronald Fisher analyzed the results of an experiment performed by Charles Darwin to determine the effect of cross-fertilization on plant growth (Darwin, 1877).In these experiments, Darwin collected seeds from both crossand self-fertilized flowers of a parent plant and germinated the seeds under identical conditions.When a pair of cross-and self-fertilized seeds happened to germinate at the same time, the two were planted in opposite sides of the same pot and nurtured in the same environment.At the end of the study, the heights of the plants were measured.To infer whether the greater height of the cross-fertilized plants should be attributed to random chance or the change in fertilization method, Fisher computed the differences in heights between paired plants and used a paired-sample t-test (Student, 1908) to estimate the statistical significance of the results.He noted that the t-test relies on assumptions about the population of height differences, and he proposed the following statistical test, which makes no such assumptions.
An edge re-ordering based acceleration architecture for improving data locality in graph analytics applications

Data structure is the key in Edge Computing where various types of data are continuously generated by ubiquitous devices. Within all common data structures, graphs are used to express relationships and dependencies among human identities, objects, and locations. They are also expected to become one of the most important data infrastructures in the near future. Furthermore, as graph processing often requires random accesses to vast memory spaces, conventional memory hierarchies with caches cannot work efficiently. To alleviate such memory access bottlenecks in graph processing, we present a solution through vertex accesses scheduling and edge array re-ordering, in parallel with the execution of graph processing applications to improve both temporal and spatial locality of memory accesses, especially for edge-centric graph analytics which are popular means in handling dynamic graphs. Our proposed architecture is evaluated and tested through both trace-based cache simulations and cycle-accurate FPGA-based prototyping. Evaluation results show that our proposal has a potential of significantly reducing the Last Level Cache (LLC) misses by 62.60% in general among PageRank and BFS algorithms. Meanwhile, evaluations with the FPGA prototype successfully reduce the quantity of Miss-Per-Kilo-Instructions (MPKI) for LLC by 56.27% on average.
Catalysing assistive solutions by deploying light-weight deep learning model on edge devices

Nowadays, real-time object detection, which is a crucial task, is being performed through image processing and deep learning techniques. As there are several high-performance computing edge devices available, selecting the best-fit device for a particular problem is a tough task and keeping in mind the cost, performance, and weight of the device in mind. One faces several challenges while performing this task in real-time such as a lack of resources in terms of power and mobility. We have provided an insight into the computation power of devices in terms of Frames per Second (FPS) by deploying object detection models on them. This paper will provide insight into selecting the appropriate combination of device and object detection models for real-time applications. Raspberry Pi 3 (RPi3), Raspberry Pi 4 (RPi4), Intel Neural Compute Stick 2 (NCS2), and Nvidia Jetson NANO are popular devices with high computation power used for real-time applications. The memory constraints of devices along with the deployment of different You Only Look Once (YOLO) and Single-Shot Detector (SSD) are the two object detection models that have been explained in this paper. A deep learning inference optimiser, TensorRT, has been used in NANO to achieve high throughput in the performance of object detection. The precision, recall, and F1 score achieved on deploying each tested model have been presented. After observing the devices during experimentation, RPi4+NCS2 showed the best execution with the blend of factors i.e. speed, portability, and user-friendliness.
Autonomous Following for Unmanned Ground Vehicles on Unstructured Scenario: Risk and Performance Assessment

Abstract Military unmanned ground vehicles are often used in wild natural environment, autonomous following and accompanying security are typical functions. In order to make a quantitative assessment about the autonomous following performance of unmanned ground vehicles on unstructured scenarios, a set of evaluation index system construction method is proposed based on the combination of risk analysis and key performance indexes. First, the kinematics model of autonomous vehicle following in straight and curve based on meta-trajectory is established. Second, the key indexes affecting the following performance are analyzed and obtained from collision risk and lost risk. Third, the evaluation index system of autonomous following performance is constructed from five dimensions: average following speed, collision risk, collision hazard degree, following stability and lost risk. Finally, two unmanned vehicles are tested, the results show that the conclusion based on the constructed evaluation index system can significantly reflect the difference of autonomous following performance, which is basically consistent with the subjective evaluation of the test personnel.
Low-light image enhancement based on simulated multi-exposure fusion

Abstract We propose an efficient and novel framework for low-light image enhancement, which aims to reveal information hidden in the darkness and improve overall brightness and local contrast. Inspired by exposure fusion technique, we employ simulated multi-exposure images fusion to derive bright, natural and satisfactory results, while images are taken under poor conditions such as insufficient or uneven illumination, back-lit and limited exposure time. Specifically, we first design a novel method to generate synthesized images with varying exposure time from a single image. Thus, each image of these artificial sequences contains necessary information for the final desired enhanced result. We then introduce a flexible multi-exposure fusion framework to achieve fused images, which comprises a weight map prediction module and a multi-scale fusion module. Extensive experiments show that our approach can achieve similar or better performance compared to serval state-of-the-art methods.
Over comparative study of text summarization techniques based on graph neural networks

Due to the enormous content of text available online through emails, social media, and news articles, it has become complicated to summarize the textual information from multiple documents. Text summarization automatically creates a comprehensive description of the document that retains its informative contents through the keywords, where Multi-Document Summarization (MDS) is a productive tool for data accumulation that creates a concise and informative summary from the documents. In order to extract the relevant information from the documents, Graph neural networks (GNNs) is the neural structure that detains the interrelation of the graph by progressing the messages between the graphical nodes. In the current years, the advanced version of GNNs, such as graph attention network (GAN), graph recurrent network, and graph convolutional network (GCN) provides a remarkable performance in text summarization with the advantage of deep learning techniques. Hence, in this survey, graph approaches for text summarization has been analyzed and discussed, where the recent text summarization model based on Deep learning techniques are highlighted. Further, the article provides the taxonomy to abstract the design pattern of Neural Networks and conducts a comprehensive of the existing text summarization model. Finally, the review article enlists the future direction of the researcher, which would motivate the enthusiastic and novel contributions in text summarizations.
Intelligent voice system design for smart classroom construction based on speech enhancement algorithm

Online education brings both opportunities and challenges to educational institutions. The current application of traditional intelligent mode is relatively backward, in the reform of English learning, but also pays attention to students' learning participation, which will directly affect their learning effect. Therefore, this article explores and offers appropriate recommendations. For example, the development of cloud collection technology, through the installation of classroom intelligent equipment, through intelligent voice system, identify each student's parameters and classroom environment, record students' learning state, then the machine learning results are applied to intelligent classroom management to improve the classroom time utilization rate and students' learning efficiency. The whole system also includes data acquisition equipment, management node, cloud server and cloud data management equipment platform. After designing the relevant system, this paper conducts a practical research on college students' English learning under the intelligent classroom environment. After practice, questionnaire survey was conducted on the construction and use of wisdom classroom. The data show that the use of wireless sensor network and intelligent voice system, and applied in intelligent classroom, greatly improve the students' learning participation.
A Case Study on Formative Elements and Visual Perception Theory Expressed in Virtual Art Contents

With the development of media, the form of expression has rapidly grown into the field of virtual art. The trend of development and generalization of virtual space art through virtual space and virtual reality is forming interest in virtual art using computer graphics, such as information and material, space and time, reality and virtuality, and active participation. The purpose of this paper is to analyze the image contents expressed in virtual art based on the Gestalt theory of form, and to study the aesthetic characteristics of the formative elements and principles of visual perception expressed in virtual art contents. As a result, the aesthetic expression of formative elements and principles of visual perception can feel illusion or virtual expression. In addition, it was confirmed that virtual art was applied with free spatiality that is not limited by space. It was found that the Gestalt theory could play a role in identifying and understanding the meaning expressed in virtual art.
A new time–frequency representation based on the tight framelet packet for telephone-band speech coding

To improve the quality and intelligibility of telephone-band speech coding, a new time–frequency representation based on a tight framelet packet transform is proposed in this paper. In the context of speech coding, the effectiveness of this representation stems from its resilience to quantization noise, and reconstruction stability. Moreover, it offers a sub-band decomposition and good time–frequency localization according to the critical bands of the human ear. The coded signal is obtained using dynamic bit allocation and optimal quantization of normalized framelet coefficients. The performances of the corresponding method are compared to the critically sampled wavelet packet transform. Extensive simulation revealed that the proposed speech coding scheme, which incorporates the tight framelet packet transform performs better than that based on the critically sampled wavelet packet transform. Furthermore, it ensures a high bit-rate reduction with negligible degradation in speech quality. The proposed coder is found to outperform the standard telephone-band speech coders in term of objective measures and subjective evaluations including a formal listening test. The subjective quality of our codec at 4 kbps is almost identical to the reference G.711 codec operating at 64 kbps.
An adaptive growing grid model for a non-stationary environment

The self-organizing map (SOM) represents high-dimensional input samples by a 2-dimensional output topological structure, whereby similar input samples are mapped onto the same output unit or neighboring units on a map for visualization. Although many extended SOM models have been proposed, the need to determine a grid structure of SOM before learning, and the lack of adaptability to rapid changes in input data have not yet been fully overcome. This research proposes an adaptive growing grid (AGG) model, which is a novel neural self-organizing map (SOM), for projecting high-dimensional input samples onto an output grid. Due to the need for a grid structure for visualization, the AGG uses both growing and pruning functions and an adaptive learning process in order to adapt its output grid structure and learning function to constantly and rapidly changing input data in a non-stationary environment. The proposed AGG is tested on four basic data sets and one cross-domain data set. In addition, the t-test is used to test whether the proposed AGG outperforms the benchmark model, the growing grid (GG). Based on three evaluation measures, i.e. average quantization error (AQE), topographic error (TE) and dead unit ratio (DUR), the AGG significantly outperforms the GG in a non-stationary environment.
AI Based Real-Time Privacy-Aware Camera Data Processing in Autonomous Vehicles

The three V's, namely volume, velocity and variety of sensor data are ubiquitous for decision-making in autonomous self-driving vehicles. The sensor data contain information about living and non-living entities in the neighbourhood of the moving vehicle. While identifying these objects are essential, details such as human faces, vehicle number plates, building names, etc., are not necessary for decision-making. Thus, we consider the following issues related to data collection, 1) the problem of data privacy, and 2) the problem of misuse of data by an adversary having unauthorized access. This paper proposes a method that first locates private objects (non-essential for decision-making) from frames captured by cameras installed on self-driving cars and then augments it with noise and blurring effects to make them unrecognizable. The performance results show that a combination of blurring and noise can hide private data while retaining information essential for the car to drive. Also, as the proposed approach processes within the limits of the interframe capture time, it is feasible for use in real-time. Moreover, results show that the proposed method can defend the adversarial attacks for the reconstruction of image frames from a given augmented frame.
A Multi-Level Association Rule Mining Algorithm Based on NSGA-II for Market Basket Analysis

As online ordering gradually replaces the traditional offline dining model, more and more businesses are using online systems to sell their products, leaving behind a large amount of order information. By mining the correlations between products from a large amount of order information and discov-ering customers’ purchasing behavior patterns, businesses can develop effective marketing strategy, enhance customer loyalty and improve economic benefits. Traditional precise algorithms such as Apriori and FP-growth algorithms are widely used for multi-level association rule mining, but they have problems with high computational complexity and space occupation. To solve these problems, intelligent evolution algorithms have been introduced. Considering the limitations of single-objective evo-lutionary algorithms that they can only optimize one objective, this paper simultaneously considers multiple optimization indi-cators, transforms the Market Basket Analysis problem into a multi-objective optimization problem, and proposes a multi-level association rule mining algorithm based on NSGA-II. At each level, we first use the Apriori algorithm to obtain frequent itemset which are then utilized for population initialization in NSGA-II and then use the NSGA-II algorithm for shopping basket data association rule mining. In addition, we have also designed an acceleration module to improve the computational efficiency of the algorithm. We applied this algorithm to actual production data from a coffee shop and the experimental results show that the algorithm is efficient in processing large volumes of orders and can effectively mine high-quality rules.
Interpretable Fault Detection Approach With Deep Neural Networks to Industrial Applications

Different explainable techniques have been introduced to overcome the challenges in complex machine learning models, such as uncertainty and lack of interpretability in sensitive processes. This paper presents an interpretable deep-leaning-based fault detection approach for two separate but relatively sensitive use cases. The first use case includes a vessel engine that aims to replicate a real-life ferry crossing. Furthermore, the second use case is an industrial, medical device assembly line that mounts and engages different product components. In this approach, first, we investigate two deep-learning models that can classify the samples as normal and abnormal. Then different explainable algorithms are studied to explain the prediction outcome for both models. Furthermore, the quantitative and qualitative evaluations of these methods are also carried on. Ultimately the deep learning model with the best-performing explainable algorithm is chosen as the final interpretable fault detector. However, depending on the use case, diverse classifiers and explainable techniques should be selected. For example, for the fault detection of the medical device assembly, the DeepLiftShap algorithm is most aligned with the expert knowledge and therefore has higher qualitative results. On the other hand, the Occlusion algorithm has lower sensitivity, and therefore, higher quantitative results. Consequently, choosing the final explainable algorithm compromises the qualitative and quantitative performance of the method.
Optimized Density Matrix Representations : Improving the Basis for Noise-Aware Quantum Circuit Design Tools

By exploiting quantum mechanical effects, quantum computers can tackle problems that are infeasible for classical computers. At the same time, these quantum mechanical properties make handling quantum states exponentially hard—imposing major challenges on design tools. In the past, methods such as tensor networks or decision diagrams have shown that they can often keep those resource requirements in check by exploiting redundancies within the description of quantum states. But developments thus far focused on pure quantum states which do not provide a physically complete picture and, e.g., ignore frequently occurring noise effects. Density matrix representations provide such a complete picture, but are substantially larger. At the same time, they come with characteristics that allow for a more compact representation. In this work, we unveil this untapped potential and use it to provide a decision diagram representation that is optimized for density matrix representations. By this, we are providing a basis for more efficient design tools such as quantum circuit simulation which explicitly takes noise/error effects into account.
Developing Accessible Email Encryption Using a Certificateless One-Way Key Agreement Scheme

Secure email encryption is increasingly fundamental to successful personal and corporate communication. This is especially true as perpetual technological innovation makes assuring the integrity and authenticity of data more challenging. However, current information technology standards place little emphasis on email encryption. CLOW-GKA (Certificateless One Way Group Key Agreement Scheme) is an email encryption system that eliminates the need for third party verification. The cryptosystem draws from such other schemes as Phil Zimmerman’s PGP (Pretty Good Privacy), Shamir’s ID-PKC (Identity Based Public Key Cryptosystem), and Al-Riyami’s CL-PKC (Certificateless PKC). This work is a continuous and a follow up effort to build a CLOW-GKA prototype by developing an accessible graphical user-interface (GUI) that implements CLOW-GKA. In particular, we focus on devising a GUI that is compatible with Gmail services, performs with comparable functionality, and offers better protection for sensitive information.
An Inductive Construction for Many-Valued Coalgebraic Modal Logic

In this paper, we present an abstract framework of many-valued modal logic with the interpretation of atomic propositions and modal operators as predicate lifting over coalgebras for an endofunctor on the category of sets. It generalizes Pattinson’s stratification method for colagebraic modal logic to the many-valued setting. In contrast to standard techniques of canonical model construction and filtration, this method employs an induction principle to prove the soundness, completeness, and finite model property of the logic. As a consequence, we can lift a restriction on the previous approach [1] that requires the underlying language must have the expressive power to internalize the meta-level truth valuation operations.