Reviewerly/exHarmony huggingface dataset:
Data Storage Design of International Students Comprehensive Information Management System based on SqlServer Database

In the new era of continuous breakthroughs in technologies such as computers, big data, the Internet of Things and artificial intelligence, various new technologies have realized the ubiquitous and borderless sharing of information. In particular, the transition from the Internet to the mobile Internet has created a way of living, working and learning across time and space, and fundamentally changed the way of knowledge acquisition. Educational informatization promotes the development of international education, gives full play to the advantages of information technology, and develops a comprehensive information management system for international students to meet the needs of education management in the information age. Based on SqlServer database management system, this paper designs conceptual structure and logical structure, provides a complete data storage structure for the development of comprehensive information management system for international students, and solves the key problems of system development.
A New Fuzzy Approach to Dynamic Adaptation of the Marine Predator Algorithm Parameters in the Optimization of Fuzzy Controllers for Autonomous Mobile Robots

This paper presents the fuzzy variant of a recent naturally-based metaheuristic that follows the rules of the best feeding strategies with the predator and prey rate regime of marine ecosystems, called the fuzzy marine predators algorithm (FMPA). This is presented as an improved variant of the marine predators algorithm and its utility in control applications. Its novelty is presented in the application of Type-1 and Interval Type-2 Fuzzy Logic systems, which balance the degree of exploration and exploitation through its iterations and through the search for the optimum of its parameters FADS and P, the second novelty is given as the fuzzy variation Interval Type-2 of a CF parameter, which varies the advance of the predator, memorized separately by the best fitness achieved by iteration, and in this way to coordinate the actions of exploration and exploitation. the fuzzy marine predator algorithm evaluates its performance with a group of 19 benchmark functions of the CEC-2017 that is composed of 2 unimodal functions, 7 multimodal functions, and 10 hybrid functions, the results show that the fuzzy marine predators algorithm provides better results than MPA. The fuzzy marine predators algorithm is used to search for the best distribution of the values of the parameters of the membership functions of a fuzzy controller for the plant of an autonomous mobile robot, bearing in mind different degrees of noise to analyze the performance when simulating the Interval Type-2 Fuzzy Logic controller. The simulation results provide evidence that the fuzzy marine predator’s algorithm exceeds the results of the marine predator’s algorithm are compare, in the design and optimization of the Interval Type-2 Fuzzy Logic controller.
Wearable Tracking: An Effective Smartwatch Approach in Distributed Population Tracking During Pandemics

The recent trend among diverse groups and industries is towards adopting smart devices, comprising smart watches (wearable and miniature smart gadgets). These intelligent objects can serve as economical gadgets capable of regularly observing individuals and patients with various health-related ailments. This chapter highlights a generalised approach for designing and developing wearable internet of things (wIoT) with enabled health technology solutions. These solutions can act as predictive and real-time mechanisms to issue alarms and execute notifications, enhance context-aware location features, and promote contact tracing of the subject to respond with early management procedures. Big data generated, by these devices can also be used for semantic analysis, generating and examining predictive health analytic insights and developing artificial intelligence applications that utilise machine learning methodologies. We cover motivational factors for developing wIoT solutions, their relationship and the role of ambient intelligence and the internet of things to serve diverse healthcare scenarios, such as self-health and home-based care. We also discuss the methodology for wIoT solution applicability, reviewing some of their benefits during pandemics and the underlying challenges concerning security and privacy issues regarding robust design and the development and implementation of health technology systems that serve distributed populations during pandemics, including COVID-19.
Succinct graph representations as distance oracles

Distance oracles answer shortest-path queries between any pair of nodes in a graph. They are often built using succinct graph representations such as spanners, sketches, and compressors to minimize oracle size and query answering latency. Node embeddings, in particular, offer graph representations that place adjacent nodes nearby each other in a low-rank space. However, their use in the design of distance oracles has not been sufficiently studied. In this paper, we empirically compare exact distance oracles constructed based on a variety of node embeddings and other succinct representations. We evaluate twelve such oracles along three measures of efficiency: construction time, memory requirements, and query-processing time over fourteen real datasets and four synthetic graphs. We show that distances between embedding vectors are excellent estimators of graph distances when graphs are well-structured, but less so for more unstructured graphs. Overall, our findings suggest that exact oracles based on embeddings can be constructed faster than multi-dimensional scaling (MDS) but slower than compressed adjacency indexes, require less memory than landmark oracles but more than sparsifiers or indexes, can answer queries faster than indexes but slower than MDS, and are exact more often with a smaller additive error than spanners (that have multiplicative error) while not being lossless like adjacency lists. Finally, while the exactness of such oracles is infeasible to maintain for huge graphs even under large amounts of resources, we empirically demonstrate that approximate oracles based on GOSH embeddings can efficiently scale to graphs of 100M+ nodes with only small additive errors in distance estimations.
Learning relationships between audio signals based on reservoir networks

Inspired by the respective human capacity, this paper investigates learning through analogies via the reservoir computing paradigm. Even though the vast majority of the literature considers modeling of patterns/objects serving classification-based applications, we move to learning of relationships characterizing input pairs. To this end, we describe the design process of a suitable echo state network capturing relationships existing between audio signals, i.e. similar, dissimilar, reverberation, and noise. Such a neural network operates on frequency-domain representations of sound belonging to the following four classes: male and female speech, applause and laughter. After extensive experiments, we demonstrate that echo state networks are able to learn through analogies and achieve superior performance when compared with Siamese neural networks.
Research on aerocraft altitude recognition based on Fuzzy SOFM network

The purposes of this study are to identify the aerocraft altitude. In consideration of the shortcoming of statistics and randomness of influencing factors in aerocraft flight, Fuzzy SOFM model is taken as an effective method to identify the aerocraft altitude. The results show that the method based on Fuzzy SOFM model is effective for recognizing the aerocraft altitude.
Usability Evaluation of a Locally Developed Software in Libya Using IsoMetrics

In the information technology era, there is a rapid noticeable increase in software application demand, especially in the last decade. Software development firms are established to meet this demand. Due to the common lack of usability feedback in locally developed systems in Libya, a usability evaluation framework is needed. In this research, a usability evaluation of locally developed software was performed and conducted on Bank Current Accounts Function users (the account handlers within the bank) on Almezan Banking Software System, in Libya. The research aims to measure the usability of Almezan software from the user point of view, using the international tool IsoMetrics (ISO) standard. The study methodology steps included preparing and describing a questionnaire, then defining the type of the sample of the population and the place of distribution of the questionnaire. The results of the data analysis show that the principles of Controllability and Suitability for learning from a user point of view, have received the best-rated feedback of the system. Unlike in Error tolerance and Suitability for individualization, which got lower-rated feedback according to users’ expectations. A comparison has been made between the software under this study’s results and another source of study. The results of the comparison have revealed that the Almezan Software got higher usability of users’ expectations in Suitability for the task, Self-descriptiveness, Controllability, and Conformity with user expectations, while their mean values were significantly different. Furthermore, Error tolerance, Suitability for individualization, and Suitability for learning results were similar, while their mean values showed that they were not particularly different.
Research on Learning Evaluation Index System in Virtual Simulation Experiment Environment

With the large-scale construction of virtual simulation experiment platform, the research on learning evaluation of virtual simulation experiment has been widely concerned. In order to solve the problem that the study of learning evaluation lacks systematic and professional evaluation system in the current virtual simulation experiment environment, the paper constructs the learning evaluation index system on the basis of summarizing the traditional experimental evaluation index system and network learning evaluation index system, according to the characteristics of virtual simulation experiment, such as virtuality, monitoring, openness, interactivity and practicality. Through literature research, Delphi, analytic hierarchy process, the paper constructed a learning evaluation index system in virtual simulation experiment environment, which included four first-level indicators, knowledge development, experimental skills, communication and interaction, and emotion and attitude, and 39 second-level indicators based on the classification theory of learning results. And the relative weight of each index item is determined through analytic hierarchy process. Finally, the sum of the product of the evaluation score Gi and weight Wi of all indicators is counted to get the final score of students' virtual simulation experiment learning, realizing the scientific and automatic evaluation of students' learning effect in virtual experiment environment.
Dynamic Relation-Aware Multiple Instance Learning for Few-Shot Learning

Leveraging patch-level embedding in few-shot learning is widely studied by recent works. However, a fundamental challenge is that labels are actually assigned at image level, whereas patch-level annotations are missing. To deal with this problem, we observe that it exactly matches the applications of multiple instance learning (MIL) and novelly incorporate multiple instance learning with few-shot learning. Specifically, we propose a dynamic relation-aware multiple instance learning framework that explicitly models the spatial and semantic relation on instances and performs iterative aggregation. Extensive experiments demonstrate that the proposed method achieves competitive results compared with state-of-the-arts methods.
Virtual Fully-Connected Layer for a Large-Scale Speaker Verification Dataset

Recently, convolutional neural networks (CNNs) have been widely used in speaker verification tasks and achieved the state-of-the-art performance in most dominant datasets, such as NIST SREs, VoxCeleb, CNCeleb and etc. However, suppose the speaker classification is performed by one-hot coding, the weight shape of the last fully-connected layer is $$ B \times N $$ , B is the min-batch size, and N is the number of speakers, which will require large GPU memory as the number of speakers increases. To address this problem, we introduce a virtual fully-connected (Virtual FC) layer in the field of face recognition to the large-scale speaker verification by re-grouping strategy, mapping N to M(M is a hyperparameter less than N), so that the number of weight parameters in this layer becomes M/N times to the original. We also explored the effect of the number of utterances per speaker in each min-batch on the performance.
Echo state network ensembles for surrogate models with an application to urban mobility

In this paper we investigate the use of nonlinear embeddings to represent dynamic inputs in surrogate models, used to estimate the outcome of time-consuming simulation tools in feasible time. By encoding the temporal sequences, together with non-dynamic inputs, into a space of static features, we are able to formalize the surrogate modeling problem as a standard supervised learning one. To this purpose, we propose to use the echo state network (ESN) paradigm to generate the embeddings of the dynamic inputs. The main advantage of this approach is that the embedding does not require training, since it is provided by a reservoir of neurons with randomly generated parameters. In order to enhance the robustness of this method based on randomization, we propose to use an ensemble of different ESN embeddings. Within this scheme, we consider a procedure aimed at controlling diversity among the ensemble elements before the simulations are run. Furthermore, in order to improve accuracy, we investigate also nonlinear mappings for the generation of the ESN outputs. The proposed approach is applied to an urban traffic network example, a typical case in which simulation models are generally complex and very time-consuming, and the scenarios to be simulated involve both static inputs and time-varying quantities.
A Reward Function Using Image Processing for a Deep Reinforcement Learning Approach Applied to the Sonic the Hedgehog Game

Research in the Deep Reinforcement Learning (DRL) field has made great use of video game environments for benchmarking performance over the last few years. Most researches advocate for learning through high-dimensional sensory inputs (i.e. images) as observations in order to simulate scenarios that more closely approach reality. Though, when using these video game environments, the common practice is to provide the agent a reward signal calculated through accessing the environment’s internal state. However, this type of resource is hardly available when applying DRL to real-world problems. Thus, we propose a reward function that uses only the images received as observations. The proposal is evaluated in the Sonic the Hedgehog game. We analyzed the agent’s learning capabilities of the proposed reward function and, in most cases, its performance is similar to that obtained when accessing the environment’s internal state.
DISCRIMINATIVE HASH CODE BOOK CO-CONSTRUCTION FOR EFFICIENTLY MUTUALLY LOCALIZING PANCHROMATIC AND MULTISPECTRAL IMAGES

We explore the problem of efficiently mutually localizing panchromatic and multispectral images. We pose the problem as that of cross modal remote sensing image retrieval between panchromatic and multispectral images, and explore the employment of hash code co-construction strategy to achieve efficient retrieval. We design two special discriminative feature extractors for panchromatic and multispectral images according to the characteristics of them, and co-construct two discriminative hash code books for them. The two discriminative hash code books generate hash codes for panchromatic and multispectral images separately. Sorting the Hamming distance between the panchromatic and the multispectral image hash codes achieves efficient cross modal remote sensing image retrieval between panchromatic and multispectral images. Extensive experiments on the public data set validate the effectiveness of our method.
The TaleMaker database of mixed-initiative co-created stories

Several datasets of stories and text have been proven useful for a variety of research fields. Yet, many of these datasets have suffered from the burden of being manually authored and/or annotated, affecting their size and potential to grow. To overcome this problem, we propose a novel database of stories collected from TaleMaker, an online multiplayer game that facilitates the playful co-creation of a story in order to eliminate the tedious task of authoring and annotating a dataset of stories. TaleMaker’s database relational schema provides a simple story representation, in which stories are named and clearly annotated. A story is composed of a sequence of plot points, each with several slots (e.g. action, character, location) filled with sense-annotated tokens (words) associated with a WordNet synset. In this paper, we describe in detail the database schema of TaleMaker’s stories repository. In addition, we suggest some of the potential applications of this repository of stories, including fostering research in fields such as story generation, narrative world generation, and word sense disambiguation.
Spatial–Temporal Graph Transformer With Sign Mesh Regression for Skinned-Based Sign Language Production

Sign language production aims to automatically generate coordinated sign language videos from spoken language. As a typical sequence to sequence task, the existing methods are mostly to regard the skeletons as a whole sequence, however, those do not take the rich graph information among both joints and edges into consideration. In this paper, we propose a novel method named Spatial-Temporal Graph Transformer (STGT) to deal with this problem. Specifically, according to kinesiology, we first design a novel graph representation to achieve graph features from skeletons. Then the spatial-temporal graph self-attention utilizes graph topology to capture the intra-frame and inter-frame correlations, respectively. Our key innovation is that the attention maps are calculated on both spatial and temporal dimensions in turn, meanwhile, graph convolution is used to strengthen the short-term features of skeletal structure. Finally, due to the generated skeletons are based on the form of skeleton points and lines so far. In order to visualize the generated sign language videos, we design a sign mesh regression module to render the skeletons into skinned animations including body and hands posture. Comparing with states of art baseline on RWTH-PHONEIX Weather-2014T in Experiment Section, STGT can obtain the highest values on BLEU and ROUGE, which indicates our method produces most accurate and intuitive sign language videos.
Java: Interfaces and Lambda Expressions

AbstractIn Chaps. 9 and 10 you learnt how to create and use classes and objects in Python. In Chaps. 17 and 18 you saw how to do the same thing in Java, and you will have noticed quite a contrast in the way the two languages approach object-oriented programming, with Java being what could be described as more of a “classical” object-oriented language. As such, Java has many high-level features which we will explore some of these in this chapter.
A Study on Predicting Software Defects with Machine Learning Algorithms

Software Defect Prediction (SDP), even in its early stages, is a crucial and significant activity. SDP has recently received a lot of attention as a quality assurance method. Massive amounts of reports and defect data may be generated by the component services. Although much emphasis has been placed on developing defect prediction models using machine learning (ML), some work has been done to determine how effective source code is. ML is a supervised algorithm that is used to produce better results. To appreciate defect prediction in SOS better, this paper suggests a fault diagnosis framework based on the web access to care.ML tools such as Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM) are used to evaluate the model’s utility, and certain metrics are also constructed using feature extraction techniques. Finally, the performances of the ML algorithms are compared and the better one is analyzed.
A Comparison of Quality Assessment Metrics on Image Resolution Enhancement Artifacts

Enhancing videos or images on resolution, frame rate, dynamic range, color gamut, noise, or scratches removing have attracted the interest of experts. Efficient image quality assessment methods for these image enhancement algorithms should be available. Many image quality assessment metrics work well on the general image distortion assessment databases. However, they may not be able to distinguish artifacts caused by the image enhancement algorithms. In this paper, we study the reliability of state-of-the-art prominent full-reference and no-reference quality assessment metrics on image resolution enhancement artifacts. Firstly, we construct a 4K resolution database containing 1152 enhanced images with different superresolution distortion types and different distortion levels. Then, a subjective study with over 20000 human judgments is organized to reach reliable references from human visual perception. The performance comparison experiments on the proposed database show that IFC, FSIM, and DISTS are the top 3 perceptualconsistent metrics for image super-resolution artifacts assessment.
Interval Type-2 Fuzzy Logic System Based on Semi-Tensor Product

Compared with type-1 fuzzy sets, interval type-2 fuzzy sets have stronger ability to deal with uncertainty. In this paper, the semi-tensor product approach is used to study interval type-2 fuzzy logic systems (IT2 FLSs). Firstly, the semi-tensor product of matrix is extended to interval matrix. Secondly, using the semi-tensor product algorithm, IT2 FLS is improved, and a new IT2 FLS is designed. Finally, its effectiveness is tested by simulation. The simulation results demonstrate that the designed IT2 FLS has a similar control effect to the conventional IT2 FLS, and can effectively simplify the fuzzy reasoning process of the multi-input multi-output systems.