Reviewerly/exHarmony huggingface dataset:
Upgrading the Digital Intranet in the Galapagos Islands

This paper describes further upgrades made to an existing Intranet system developed on Raspberry Pi servers to host offline educational resources in the Galapagos Archipelago. The project's goal was to ensure that users of the intranet are provided with reliable education services and to continue developing upon the ideal of sustainability in the Galapagos Islands. This paper reports on including additional services and infrastructure to extend its access to the broader community. The upgraded services include videoconferencing, an email server, and chat features. These were successfully implemented for more accessible communication and collaboration among students and teachers. Furthermore, the consolidation of the current software running on multiple Raspberry Pi's to one shared ProLiant server and segmentation using separate virtual machines was achieved to provide increased security and smoother and more robust server performance. The potential applications for the system and their impacts are also explored.
Real-time Person Tracking and Re-identification Based on Feature Matching

The existing video-oriented person tracking and re- recognition research are all based on face recognition, which cannot meet the requirements of high continuity and high accuracy. In order to solve this problem, this paper propose a tracking and re-identification scheme based on KM (Kuhn- Munkras) algorithm. When tracking a person, this scheme establishes the connection between the face image observed in real time, the face database, and the human body image observed in real time to determine their identity. During tracking person, the appearance characteristics can be captured all the time, and the recognition of person has high real-time performance and high continuity. In the tracking process, a real-time updated person information cache list is established to realize the re- identification function after the person tracking is interrupted for a long time.
Building Natural Language Understanding System from User Manual to Execute Office Application Functions

To improve the flexibility of using office applications, it takes a natural language interface that can execute the office application's functions. To achieve this, the office application must have an inference engine that can automatically detect the intents and the entities contained in an instruction, into an algorithm to execute the related functions in the application on the entities. Creating a natural language understanding system by manually listing various rules in a knowledge base, is very inefficient and makes the effort of using natural language to execute office application functions too expensive. The author proposes conducting research to build a natural language understanding system more efficient, by analyzing the text contained in the office application user manual, by means of natural language processing technology. We propose to build a variety of simple rules automatically, using the text in the user manual, which is specifically crafted to facilitate and support natural language processing technology. In future works, research will be conducted to automatically build various complicated rules, from analysis of the text from common and commercially available user manual. In this preliminary study, the necessary steps to execute the application functions, is executed on an office application prototype specifically built for this study.
Lateral flight control method of UAV based on small disturbance and root locus theory

The flight control is the key technology for the unmanned aerial vehicle(UAV), which can ensure the flight security of UAV. Compared with the longitudinal motion, the lateral motion mode and frequency domain characteristics of the UAV are much more complex, which makes the design of lateral flight controller more challenging. Therefore, the lateral flight control method of UAV is designed based on small disturbance and root locus theory. Based on the coupled nonlinear motion model of UAV, the longitudinal and lateral small disturbance motion equations are obtained by using decoupling grouping and small disturbance linearization strategy. The lateral motion mode and lateral frequency domain characteristics of UAV are analyzed, and the lateral flight control law based on small disturbance and root locus theory is designed by using root locus and PID control theory of classical control theory. Finally, the UAV flight control simulation experiment is carried out to verify the effectiveness and superiority of the proposed method.
Data mining technologies in a distributed medical information system

Abstract Proposed a method for solving the problem of identifying hidden relationships in hard-to-structure data that have an implicit character is considered using information mining. Proposed decision trees, the effectiveness of which is illustrated by a specific example. The use of OLAP analysis systems on data presented using in the form of a real or virtual hypercube’s information is an effective tool for the effectiveness of the management for medical monitoring
Design Requirements for Crop-Specific Online and Web-Based Portals

Legumes such as common beans (Phaseolus vulgaris L.) continue playing a critical role in making developed and developing economies food sustainable as alternative sources to animal proteins. The crops bring together different stakeholders in their value chain such as farmers, service providers, researchers and policymakers. Web-based portals are emerging as important tools that these different stakeholders can use to perform various tasks, access, and share vital information pertaining to common beans. However, designing portals that are specific to common beans has not been given adequate consideration in the literature. In this study, we administered a survey to profile challenges and design requirements for web-based portals that are specific to common beans. We present the survey findings in this paper. The findings provide useful insights to researchers and industry in developing future agricultural web-based portals, and can be applied to portals on other crops or different domains.
An Outlook on using Packet Sampling in Flow-based C2 TLS Malware Traffic Detection

Packet sampling plays an important role in keeping storage and processing requirements at a manageable level in network management. However, because it reduces the amount of available information, it can also reduce the performance of some related tasks, such as detecting security events. In this context, this work explores how packet sampling impacts machine learning-based tasks, in particular, flow-based C2 TLS malware traffic detection using a deep neural network. Based on a proposed lightweight sampling scheme, the ongoing results show a small reduction in classification accuracy compared with analysing all the traffic, while reducing in 10 fold the number of packets processed.
Visualization of Archimedian and Platonic polyhedra using a web environment in Augmented Reality and Virtual Reality

This paper shows the development of a web environment for the construction of Archimedes and Plato polyhedra in Augmented Reality (AR) and Virtual Reality (VR). In this environment we used the geometric transformations of translation and rotation with the structure of hierarchies of HTML pages, without the use of the coordinates of each polyhedra vertex. The developed environment can be used in classroom to visualize the polyhedra in Augmented Reality, with the possibility of manipulations of the graphical representations by students in the environment created in Virtual Reality. Other studies that can be developed with the polyhedra modeled are areas, volumes and the relation of Euler. Another important content that can be developed is truncation, because seven Archimedes polyhedra are obtained by using truncation of Plato's polyhedrons. With this work, it becomes possible to develop didactic materials with a simple technology, free and with great contribution to improvement of the teaching of Geometry and other areas that use representation of 3D objects.
Examining the Obligation to Preserve Electronic Evidence under the Nigerian Law

The prevalence of digital information has come with attendant obligation to ensure that relevant electronic data are not deleted, lost or destroyed where there is reasonable apprehension of litigation. This obligation has given birth to the principle of ‘Legal Holds’, which has since been recognised in some jurisdictions of the world. This article looks at the importance of the obligation to preserve electronic data and examines whether there are laws, rules, practice or procedures currently placing such obligation on parties under the Nigeria Law. It argues that although there is in fact no law specifically making provisions for or directly imposing obligation on parties to preserve electronic data upon reasonable apprehension of litigation, a careful examination of the rules of evidence, the Rules of the various Courts as well as the Rules of Professional Conduct (RPC) for Lawyers reveal that there is in existence an obligation to preserve.
Student GroupRecommendation using Extreme Learning Machinefor Collaborative Learning

Collaborative learning(CL) is defined as situations where several learners with different ways of thinking, acting, and feeling participate in solving common problems. Group work is a representative form of CL and a strong need for various online courses. In this study, we focus on the student group recommendation based on the personalized information of students, including learning styles and social closeness. Moreover, aneffective recommendation method using extreme learning machine (ELM) is proposed in this paper. The method regards studentgroup recommendation asa binary classification problem. First, learning features are extracted from group learning styles using Felder-Silverman model and group members' social relationships. These features simultaneouslyconsider all the important factors which determine the results of recommendation and guarantee the effectiveness of grouprecommendation. Then, the extracted features are input to train ELM classifier due to its fast learning speed, whichguarantees the efficiency of recommendation. Finally, comparative experiments verify the accuracy and efficiencyof our method.
The lesion detection efficacy of deep learning on automatic breast ultrasound and factors affecting its efficacy: a pilot study

The aim of this study was to investigate the detection efficacy of deep learning (DL) for automatic breast ultrasound (ABUS) and factors affecting its efficacy.Females who underwent ABUS and handheld ultrasound from May 2016 to June 2017 (N = 397) were enrolled and divided into training (n = 163 patients with breast cancer and 33 with benign lesions), test (n = 57) and control (n = 144) groups. A convolutional neural network was optimized to detect lesions in ABUS. The sensitivity and false positives (FPs) were evaluated and compared for different breast tissue compositions, lesion sizes, morphologies and echo patterns.In the training set, with 688 lesion regions (LRs), the network achieved sensitivities of 93.8%, 97.2% and 100%, based on volume, lesion and patient, respectively, with 1.9 FPs per volume. In the test group with 247 LRs, the sensitivities were 92.7%, 94.5% and 96.5%, respectively, with 2.4 FPs per volume. The control group, with 900 volumes, showed 0.24 FPs per volume. The sensitivity was 98% for lesions > 1 cm3, but 87% for those ≤1 cm3 (p < 0.05). Similar sensitivities and FPs were observed for different breast tissue compositions (homogeneous, 97.5%, 2.1; heterogeneous, 93.6%, 2.1), lesion morphologies (mass, 96.3%, 2.1; non-mass, 95.8%, 2.0) and echo patterns (homogeneous, 96.1%, 2.1; heterogeneous 96.8%, 2.1).DL had high detection sensitivity with a low FP but was affected by lesion size.DL is technically feasible for the automatic detection of lesions in ABUS.
Online Knowledge Distillation via Collaborative Learning with Enhanced Diversity and Gradual Ensemble

In general, knowledge distillation transfers knowledge from a large pre-trained teacher network to a small student network. This is usually referred to as offline distillation and requires two stages of learning: learning of the teacher network and that of the student network. In contrast, in online distillation, the outputs of multiple student networks are integrated and regarded as the output of the teacher network. Compared to offline distillation, this method has the advantage that it does not require a large pre-trained teacher network and can be trained online. However, since online distillation uses ensemble learning, it is extremely important to consider the following two aspects. First, it is important to maintain the diversity of individual student networks during learning. Second, since the performance of each student network is poor at the beginning of the learning and improves as the learning progresses, the distillation needs to account for this change in performance. In this paper, we propose a novel online knowledge distillation method that takes these two points into account: the diversity of individual student networks is enhanced by providing different training data to each student network, and the degree of the ensemble learning is gradually increased by introducing a rump-up function into the loss function. Experiments using CIFAR-100 and Tiny ImageNet show that the proposed method can improve the generalization ability.
The Cultivation System of the Thought-assisted Classroom Learning Community Based on Big Data

In the context of the new curriculum reform led by big data, education staff are facing more and more challenges. In order to meet new challenges and promote the development of the education industry, the country and the government, as well as schools and teachers themselves, are exploring and practicing effective ways to improve the overall quality of teaching. As a result, an ideological-assisted classroom learning community has emerged. This article first elaborates the related concepts of the thought-assisted classroom learning community in detail, then establishes a big data-based thought-assisted classroom detailed community cultivation system, and finally uses the system to use the actual teaching, through the comparison of student performance analyze with satisfaction degree, and the analysis results show that the thought-assisted classroom learning community can help students improve their performance.
Innovative CAPTCHA to Both Exclude Robots and Detect Humans with Color Blindness

This paper presents a design concept of an innovative CAPTCHA that can filter the color-vision–recognition states of different users. It can simultaneously verify the real-human-user identity, differentiate between the color-vision needs, and decide the content to be presented automatically.
Laplacian Eigenmaps based Semi-supervised Metric Fuzzy Clustering algorithm

Semi-supervised Metric Fuzzy Clustering (SMUC) is known for taking advantage of prior information of membership to guide clustering. However, SMUC has the following problem: it is easy for SMUC to reduce the effectiveness of priori information of membership guidance because of the sensitivity of algorithm to random noise, which has a negative impact on the performance of SMUC algorithm. In order to solve the problem, we propose a Laplacian Eigenmaps based Semi-supervised Metric Fuzzy Clustering algorithm (LESMUC). Firstly, K nearest neighbors are selected in the data to construct the connected graph; secondly, the weight of the graph is calculated; finally, the objective function is minimized to get the mapping matrix, and the mapping matrix is used to map the data to a new space. This process can reduce the influence of random noise in the data set on the prior information and achieve better clustering effect. Experiments on UCI data and COVID-19 CT images show the effectiveness of the proposed clustering algorithm.
Distilling Blockchain

The purpose of this chapter is to demystify blockchain technology and present potential use cases to ignite the potential blockchain brings. This chapter brings together thought leaders from both academia and industry to ensure a more holistic perspective on blockchain, both pros and cons. Primarily riding on the potential of the smart contract, this chapter expounds on the potential innovation that can be derived from blockchain. Simultaneously, this chapter expands on potential ideas that would bring value to mankind. It discusses how blockchain can be used to do social good and to rebuild trust between citizens, government, and more importantly, politicians. This could be achieved by rebuilding thrust through transparency and efficiency. While it is not without any drawback, suggested areas of future research were also proposed at the end of this chapter. This chapter also proposed a possible digital framework to be considered.
A study on non-linear discrete-time state-space models and adaptive extended Kalman filter application on oscillatıon of an object tied to the end of spring

In this work, Adaptive Extended Kalman Filter (AEKF) is introduced and its use for oscillation of an object connected to the end of a spring is shown. As a new approach, an AEKF is used as a nonlinear estimation tool for online estimation of the states and parameters of an oscillating object attached to the end of a spring model. Parameter states that do not change with time were examined. The simulation results revealed that with proper selection of initial values of AEKF, AEKF is a very useful tool for this particular application.
Explore Design Evaluation Method Based on Universal Design in Human-Products Interactive of the Product Development

As we all know, product development involves many factors, so the evaluation results of key frames in product development are very important. The Universal Design Principles have a significant guiding for product design, especially in terms of human-robot interaction. This article mainly to focused on how to use of Universal Design Principles in the product development process, to help design teams evaluate and screen design options, especially for evaluating the influence on human-robot interaction cause by the forms. In this article, there will be mainly using the Universal Design to re-evaluate the development process of a product based on the universal evaluation method was used for three times to promote the generation of product solutions.
Boosting Hidden Graph Node Classification for Large Social Networks

Identifying hidden nodes in social networks is a critical issue in security-related applications. In contrast to the conventional node classification on graphs with all nodes being observable, it is more challenging to classify the hidden nodes that are unobservable during the training process, also known as the “inductive learning” in previous research. Existing approaches for inductive node classification mainly adopt graph neural network models to learn node representations. Although these methods are advantageous to modeling the topology of graph-structured data, they rely heavily on node features which may vary significantly in different specific application scenarios. In addition, the inherently changeable graph structure induced by hidden nodes may cause the over-fitting problem. To address the above issues and boost the performances of hidden node classification, we propose a deep generative model based on variational auto-encoders. Specifically, we design a novel graph neural network to aggregate the multi-hop neighbor information of each node. Meanwhile, to better utilize the graph structure information as a supplement to node features, we consider the heterogeneous node influences and introduce a gated attention mechanism using node degrees. Moreover, our proposed model can be trained by minibatches and thus is applicable to large social networks. We conduct experiments on four real-world datasets, and verify the effectiveness of our method for hidden graph node classification.
A Novel Approach on Conducting Reviewer Recommendations Based on Conflict of Interest

the reviewer&#x0027;s recommendation in accordance with the field of research is crucial where this is directly proportional to the results of the review on the research. In finding suitable reviewers, conflicts of interest (COI) are often found. In this paper, we propose an approach to reviewer recommendations with topic extraction and author extraction to prevent COI. First, we separate the process into 2, namely to do topic extraction using Latent Dirichlet Allocation (LDA), and also to do author extraction using Cosine Similarity. The next step is to combine the two results to rank the 10 recommended authors. The experimental results show that our approach has succeeded in getting 10 recommended reviewers to avoid COI.
Research on Image Clustering Algorithm Based on Multi-features Extraction

Image clustering is one of the classical problems in the field of machine learning and image processing. The extraction of image features is the most important aspect of image clustering. In view of the poor performance of traditional image feature extraction, the characteristics of various image feature extraction algorithms including SIFT, ORB, and color histogram are discussed. A proposed method is of preprocessing the image first, then performing multi-features extraction and fusion, finally proceeding clustering. At the same time, multiple groups of comparative experiments are carried out. It can be seen from the experimental results that both clustering accuracy and clustering speed are taken into account by the image clustering method. Among them, the clustering accuracy can reach 99%, which shows that this method has more advantages in image clustering tasks.
English to Indian Language and Back Transliteration with Phonetic Transcription for Computational Linguistics Tools based on Conventional Transliteration Schemes

Transliteration is the method of converting a word or text from one writing style to another. Transliteration attempts to translate the same word from one language into another. This allows the reader to reconstruct the transliterated words’ original pronunciation. For faster encoding and storage, transliterated texts or words are often used in Computational Linguistics Tools like Morphological Generators and Dictionaries. Transliteration’s main goal is to provide a new way of representing documents by using a foreign language or writing system. Its aim is to preserve sound or phonetic syllables. Transliteration is determined to keep the sounds of the original language. As a side benefit, readers who don’t even know the source language can transcribe this to the prescribed language. Forward transliteration is the process of transliterating the source language into the recommended target language, while back transliteration is the process of converting the transliterated word back to its original state. The employment of symbols to represent speech sounds is known as phonetic transcription, linguistic writing, or pronunciation notation. In linguistic terms, transcription is the formal reproduction of the spoken language in printed language. The root may be pronouncements, voice, gestures, or previously written text in another writing form. A phonetic transcription is distinct from a ‘orthographic’ transcription. Using a special alphabet of phonetic symbols, the transcriber takes care of how the spoken words are sounded. The IPA is the most general. In the most generic version of generative phonology, a phonetic alphabet, such as the "International Phonetic Alphabet," is employed. Where there are several syllables in a word, one of them would be often sounded louder. This is referred to as word stress, as well as the word or phrase is stressed.