Reviewerly/exHarmony huggingface dataset:
Visual Design as the Key Factor for Effective Environmental Analytics Dashboard

This paper proposes visual design as the key factor for an effective environmental analytics dashboard. Organisations found it challenging to organise data before emerging visualisation techniques such as using dashboards and analytics. Environmental analytics dashboards support analysis of performance and progress of a task/situation for some time, i.e., weekly, monthly or yearly. However, there is a dilemma in choosing a suitable visual design for the data, especially for the environmental domain. The objective of the papers is to conduct a mini-review on the visual design factor and subfactors in the context of environmental dashboards. These factors can be the initial focal point when producing an effective dashboard framework. The methodology chosen for this review is a systematic literature review (SLR). Based on the SLR, 23 papers were reviewed, and visual design was deemed the key factor. Two subfactors, highlighted regions and values, and different visualisation techniques mainly support the key factor. Additionally, the current research shows increased research for effective dashboards from 2006 until 2022 that focuses on visual designs. The listed subfactors can be used as guidance later to build an effective environmental analytics dashboard.
Development of Navigation System for Blind People based on Light Detection and Ranging Technology(LiDAR)

The use of blind-aided navigating systems has become very essential in the 4th industrial revolution. The essence of this is to improve navigating autonomy for the blind. Although, navigation challenges are not of serious concern to people with eye defects. However, this is a major concern for blind people due to the time-consuming navigation process. These limitations necessitate the use of walking sticks, dogs, or people to navigate their path. This project provides a blind navigation system based on Light Detection and Ranging Based Navigation System for Blind People (LiDAR). The components of this fabricated device involve a charging system, a LiDAR sensor, a microcontroller, a calling stick, two buzzers, a switch vibration motor with an RF transmitter, and a receiver to give object detection and real-time assistance to the blind. The mode of its operation is by detecting obstacles along the route of a blind person and giving a notification via the buzzer and the vibration motor. Also, during emergencies, the buzzer rings and informs the visually impaired person using the device. On the other hand, the microcontroller and other modules in the device have a continuous ongoing relationship which is beneficial to the user. Another appreciable benefit of this device is the ability to recharge the battery component, the device is of low-cost, quick, simple-to-use, and novel solution for the blind.
On Laughter and Speech-Laugh, Based on Observations of Child-Robot Interaction

In this article, we study laughter found in child-robot interaction where it had not been prompted intentionally. Different types of laughter and speech-laugh are annotated and processed. In a descriptive part, we report on the position of laughter and speech-laugh in syntax and dialogue structure, and on communicative functions. In a second part, we report on automatic classification performance and on acoustic characteristics, based on extensive feature selection procedures.
Detecting and tracking using 2D laser range finders and deep learning

Detecting and tracking people using 2D laser rangefinders (LRFs) is challenging due to the features of the human leg motion, high levels of self-occlusion and the existence of objects which are similar to the human legs. Previous approaches use datasets that are manually labelled with support of images of the scenes. We propose a system with a calibrated monocular camera and 2D LRF mounted on a mobile robot in order to generate a dataset of leg patterns through automatic labelling which is valid to achieve a robust and efficient 2D LRF-based people detector and tracker. First, both images and 2D laser data are recorded during the robot navigation in indoor environments. Second, the people detection boxes and keypoints obtained by a deep learning-based object detector are used to locate both people and their legs on the images. The coordinates frame of 2D laser is extrinsically calibrated to the camera coordinates allowing our system to automatically label the leg instances. The automatically labelled dataset is then used to achieve a leg detector by machine learning techniques. To validate the proposal, the leg detector is used to develop a Kalman filter-based people detection and tracking algorithm which is experimentally assessed. The experimentation shows that the proposed system overcomes the Angus Leigh's detector and tracker which is considered the state of the art on 2D LRF-based people detector and tracker.
Large-Scale Multiobjective Federated Neuroevolution for Privacy and Security in the Internet of Things

With the development of communication techonologies, Internet of Things (IoT) devices will be deployed in more and more places and generate large amounts of data. The corresponding analysis of these newly available data will greatly improve our daily lives. However, these IoT devices can be attacked. Traditional intrusion detection systems (lDSs) usually use a centralized approach to transmit data to the cloud or a central server for analysis. This method has a great risk of privacy leakage. Federated learning (FL) is an excellent distributed learning stategy, which will have outstanding performance in preventing privacy leakage for lDSs in IoT. However, the FL-driven IDS is still in the infancy stage and needs further exploration. Therefore, we propose a large-scale multiobjective federated neuroevolution framework based on a deep fuzzy rough convolution neural network for IoT privacy and security.
Trusting Trust: Humans in the Software Supply Chain Loop

To what extent should one trust a statement that a program is free of Trojan horses? Perhaps it is more important to trust the people who wrote the software. . . . You can’t trust code that you did not totally create yourself.
Machine Learning Security and Privacy

Our special issue explores emerging security and privacy aspects related to machine learning and artificial intelligence techniques, which are increasingly deployed for automated decisions in many critical applications today. With the advancement of machine learning and deep learning and their use in health care, finance, autonomous vehicles, personalized recommendations, and cybersecurity, understanding the security and privacy vulnerabilities of these methods and developing resilient defenses becomes extremely important. An area of research called adversarial machine learning has been developed at the intersection of cybersecurity and machine learning to understand the security of machine learning in various settings. Early work in adversarial machine learning showed the existence of adversarial examples: data samples that can create misclassifications at deployment time. Other threats against machine learning include poisoning attacks, where an adversary controls a subset of data at training time, and privacy attacks in which an adversary is interested in learning sensitive information about the training data and model parameters.
Exploring Artificial Intelligence Architecture in Data Cleaning Based on Bayesian Networks

In order to further improve the technical level of data cleaning and data mining and better avoid the defects of uncertain knowledge expression in traditional Bayesian networks, a Bayesian network algorithm based on combined data cleaning and mining technology is proposed, and a manual functional data cleaning architecture based on Hadoop is constructed. The results show that the traditional neighbor sorting algorithm with window size of 5 takes the least time to process the same amount of data. The nearest neighbor sorting algorithm with window size 7 is always the longest. The time consumption of the nonfixed window nearest neighbor sorting algorithm is similar to that of the traditional nearest neighbor sorting algorithm with a window size of 5. However, with the increase of data volume, the consumption time increases rapidly until it approaches the consumption time of the traditional sorting nearest neighbor algorithm with window size of 7. Therefore, the algorithm can improve the precision of data cleaning at the expense of cleaning speed, which proves that the artificial intelligence architecture based on combined data significantly improves the efficiency of the algorithm and can effectively analyze and process large data sets.
k-Plane Matroids and Whiteley’s Flattening Conjectures

In this short note we consider the k-plane matroid, which is a matroid on the edge set, I, of a bipartite graph, $$H=(A,B;I)$$ , defined by a counting condition. We show that 2k-connectivity of H implies that I is a spanning set for the k-plane matroid on the edge set of the complete bipartite graph on (A, B). For $$k=2$$ we explain the connections to rigidity in the plane.
Best Neural Network Approximation by using Bernstein Polynomials with GRNN Learning Application

Bernstein polynomials are one of the first and main tools for function approximation.On the other hand, neural networks have many useful applications in approximation and other fields as well.In this paper, we study how we benefit from properties of Bernstein polynomials to define a new version of neural networks, that can be fit approximating functions in terms of modulus of continuity.Numerically, we use neural networks to approximate some types of continuous functions.For that purpose, we use GRNN algorithm to approximate functions uniformly by using Matlab, giving some examples that confirm good rate approximation.
EANT: Distant Supervision for Relation Extraction with Entity Attributes via Negative Training

Distant supervision for relation extraction (DSRE) automatically acquires large-scale annotated data by aligning the corpus with the knowledge base, which dramatically reduces the cost of manual annotation. However, this technique is plagued by noisy data, which seriously affects the model’s performance. In this paper, we introduce negative training to filter them out. Specifically, we train the model with the complementary label based on the idea that “the sentence does not express the target relation”. The trained model can discriminate the noisy data from the training set. In addition, we believe that additional entity attributes (such as description, alias, and types) can provide more information for sentence representation. On this basis, we propose a DSRE model with entity attributes via negative training called EANT. While filtering noisy sentences, EANT also relabels some false negative sentences and converts them into useful training data. Our experimental results on the widely used New York Times dataset show that EANT can significantly improve the relation extraction performance over the state-of-the-art baselines.
ENHANCING TRANSACTION THROUGHPUT IN PUBLIC BLOCKCHAIN NETWORK USING NESTED CHAINS

In this paper, we propose a new transaction sharding technique that aims to improve the scalability of the 5irechain network. This sharding technique called “nested chain” can be easily implemented in a decentralized setting like the blockchain. The sharding technique reduces cross-chain dependencies by ensuring that all the transactions sent from the same address will be found in blocks along the same chain and there will be no possibility of double-spending of the same token in two different chains. Our technique does not require additional tokens and it operates only on the same token across multiple shards.
A Novel Hybrid Encryption Method Based on Honey Encryption and Advanced DNA Encoding Scheme in Key Generation

Nowadays, increased information capacity and transmission processes make information security a difficult problem.As a result, most researchers employ encryption and decryption algorithms to enhance information security domains.As it progresses, new encryption methods are being used for information security.In this paper, a hybrid encryption algorithm that combines the honey encryption algorithm and an advanced DNA encoding scheme in key generation is presented.Deoxyribonucleic Acid (DNA) achieves maximal protection and powerful security with high capacity and low modification rate, it is currently being investigated as a potential carrier for information security.Honey Encryption (HE) is an important encryption method for security systems and can strongly prevent brute force attacks.However, the traditional honeyword encryption has a message space limitation problem in the message distribution process.Therefore, we use an improved honey encryption algorithm in our proposed system.By combining the benefits of the DNA-based encoding algorithm with the improved Honey encryption algorithm, a new hybrid method is created in the proposed system.In this paper, five different lookup tables are created in the DNA encoding scheme in key generation.The improved Honey encryption algorithm based on the DNA encoding scheme in key generation is discussed in detail.The passwords are generated as the keys by using the DNA methods based on five different lookup tables, and the disease names are the input messages that are encoded by using the honey encryption process.This hybrid method can reduce the storage overhead problem in the DNA method by applying the five different lookup tables and can reduce time complexity in the existing honey encryption process.
The Effect of Digital Literacy on Language Ability in Higher Education: Experience From a Developing Country

The digital literacy abilities of several developing countries are already promising. Indonesia is one of them. In fact, in the industrial revolution 4.0 era, students are agents of change to have good digital literacy skills in facing multidimensional challenges and prospects in the future. This research itself focuses on the influence of the existence of digital literacy on Indonesian language skills. Ten Respondents of Institut Technology of Sepuluh Nopember 2021 Urban and Regional Planning Students were sampled to prove this through quantitative research using survey methods and user tests. Respondents with a high duration of using social media to represent digital literacy show high Indonesian language proficiency test results. Meanwhile, respondents with a reasonable period of social media use showed more volatile results due to the influence of several other variables. Overall, a high level of digital literacy can improve one's Indonesian language skills. From this, it can be seen that digital literacy among students is critical in the future, especially in enhancing their Indonesian language skills.
Design of a Hybrid Method Exploiting Different Insolation States for Solar Radiation Forecasting

The constant expansion of solar energy has made the accurate forecasting of radiation an important issue. An accurate prediction of solar energy production is crucial for the effective integration of photovoltaic (PV) and wind generators in smart grids. The intermittent nature of solar energy poses many challenges to renewable energy system operators in terms of operational planning and scheduling. For this reason, forecasting solar radiation by means of the hybrid methods is becoming widespread. In this paper, a hybrid method for predicting solar radiation is proposed, wherein the prediction model is determined based on the clearness index. The study used two-year solar radiation data of the province of Mardin obtained from the Turkish State Meteorological Service (TSMS). As predictors, ANN, NARX networks, and Ridge regression methods were used, and the training data were modeled with all three approaches in the first stage of the study. The clearness index was determined into three ranges; slightly cloudy, cloudy, and mostly cloudy. The training data were modeled with three methods used as estimators, and the success of each method was examined in each defined clearness index range. As a result, in the hybrid prediction algorithm, the clearness index is first estimated using artificial neural networks, and then the future solar radiation value is predicted by using the most successful model within the predicted clearness index range. Experimental results show that more successful predictions are made with the proposed hybrid method than when models are used individually.
Variation in Surface Solar Radiation and the Influencing Factors in Xinjiang, Northwestern China

The variation of solar radiation has a profound effect on the surface energy balance and hydrological cycle. Although the relationship between solar radiation variation and its influencing factors has been extensively studied, they are seldom used in Xinjiang, the largest province in China. In this study, we investigated the spatial distribution and temporal variation in global radiation (Eg), water vapor content (WVC), aerosol optical depth (AOD), total cloud cover (TCC), and low-level cloud cover (LCC) in Xinjiang, northwestern China, between 1961 and 2015. The annual average Eg reported at all stations was 5126.3–6252.8 MJ·m−2 with a mean of 5672 MJ·m−2. The highest annual mean Eg of 6252.8 MJ·m−2 occurred in Hami, eastern Xinjiang, whereas the lowest annual mean Eg of 5126.3 MJ·m−2 occurred in Urumqi, northern Xinjiang. The annual Eg variation was mainly affected by WVC, AOD, TCC, and LCC. Decreases in annual, spring, summer, autumn, and winter Eg trends were recorded in Xinjiang at rates of −33.88 × 10−2, −1.92 × 10−2, −1.89 × 10−2, −3.47 × 10−2, and −3.56 × 10−2 MJ·m−2·decade−1, respectively, with decreasing ratios of 9.43%, 5.85%, 0.14%, 8%, and 20.55%, respectively. Increasing trends in annual WVC, AOD, TCC, and LCC were noted in Xinjiang at rates of 7.12 × 10−5 mm·decade−1, 2.74 × 10−6 decade−1, 8.77 × 10−5 % decade−1, and 5.73 × 10−5% decade−1, respectively. In addition, increasing trends in the annual Eg at Yining and Yanqi stations were observed. The Eg spatial distribution was complex in Xinjiang at the stations observed in this study, which were divided into six groups. Eg at group 1 showed an increasing trend associated with decreases in the WVC and TCC, whereas decreases in Eg were observed at groups 2–6, which could have been influenced by increases in AOD, TCC, and LCC.
The Effectiveness of Mnemonic Keyword Method to Improve Students' Vocabulary Mastery at The First Year of SMP Negeri 12 Parepare

This script was conducted to examine in teaching vocabulary by using the mnemonic keyword method in improving students’ vocabulary mastery at first-year SMP Negeri 12 Parepare. This script used a quasi-experimental design. The population of this script was class VII at SMP Negeri 12 Parepare that comprises 103 students. The writer had chosen two classes as the sample by using a purposive sampling technique, one class as an experimental class and one class as the control class where quantity is 50 samples, 25 students in the experimental class and 25 students in the control class. The research findings show that there are significant differences in vocabulary mastery between students who are taught using the mnemonic keyword method and those who are not taught by using these methods in the first year of SMP Negeri 12 Parepare. This can be seen from the result show that the t-test value of the experimental class (13,05) was greater than the t-test value of the control class (9,03) it means that the alternative hypothesis (Ha) concluded that the mnemonic keyword can improve vocabulary mastery students’.It was concluded that the null hypothesis (H0) was rejected. And also, it can be seen from the average pre-test and post-test scores in the experimental class that is 57,44 and 76,88 (19,44) while the pre-test and post-test average score in the control class are 58,08 and 72,8 (14,72) with a large value of the treatment are 4,72. It can be concluded that the mnemonic keyword method is an effective method that can be used in teaching vocabulary mastery learning.
The Use of Code-Mixing and Code-Switching on Instagram by Students of English Program at IAIN Parepare

The objective of the this research are to find the type of code mixing, code switching user which using two languages of english eduaction department at IAIN Parepare. This reserach will answer these main question : what kinds of code mixing and code switching used in instagram, what are the functions of code mixing and code switching used instagram. This reserach used qualitative approach, it used content analysis the data it was from students status posted mei until october 2020 the researcher collected the data by using documentation and observation cheklist the data was anaylzed the types of code mixing based poplack theory, code switching based suwito theory, analyzed what function of code mixing and code swithing on students instagram based on marasigan theory, and analyzed what reason of code mixing and code switching on students instagram based on hoffman theory.The result showed that the analysis the total data 14 unit of analysis of code switching, all three types found that 9 data (42,9%) used inter-sentential switching. It showed that inter-sentential switching is commonly use on students instagram. The type had found 3 insertion of words (14,3%). Based on the data, students status on instagram commonly used insertion of word and insertion of phrase to mix the code. The researcher found functions that used by students on instagram in order to perform both code mixing and code switching, facility expression (F7) 8 data or (38,1%).&#x0D; Keywords: Analysis, code-switching. Code-mixing, Instagram
Diagnostic Value of Specialist Systems in Sports Knee Injuries

In order to explore the diagnostic effect of the expert system in knee sports injury, a method of diagnostic value of expert system in sports knee sports injury is proposed. This paper mainly takes 200 professional football players in sports higher vocational colleges as the research object. There are 146 male athletes and 44 female athletes; we establish a football injury ontology knowledge base and use a reasoning engine to build an intelligent expert system diagnosis system, allowing users to quickly discover diseases, accurately diagnose injuries, and obtain the best means of rehabilitation. Through the investigation, it can be seen that the body parts caused by football injuries are more complex, and the types of injuries in each part are also different. Therefore, it is particularly important to establish an intelligent retrieval system with convenient query and clear diagnosis by the expert system. With the birth and development of computer and artificial intelligence technology, the development of artificial intelligence expert systems in the medical field has become a reality. The construction of this system will have theoretical and practical significance and application value.
Subscription to e-resource database jstor digital library: A usage statistics of university of liberal arts bangladesh library

Universities and academic institutions invest capital for subscriptions to e-resources and online resources for their users. E-resources and online resources are becoming an essential part of their academic life along with print resources. Subscriptions to electronic resources are being increased day by day as well as their usage. Usage of e-resources also will be increased more if the users have both options to use electronic resources through i) on-campus access and, ii) remote access/off-campus access around the clock. JSTOR Digital Library is one of the leading electronic and online resource databases that meet the educational requirement of the users.