Reviewerly/exHarmony huggingface dataset:
The Semantic Meaning of Hand Shapes and Z-Dimension Movements of Freehand Distal Pointing on Large Displays

It is difficult to select small targets in freehand distal pointing on large displays due to physical fatigue and jitter. Previous research proposed many solutions but not through the semantic meaning of hand behaviors. The purpose of this study is to investigate the hand behaviors, including hand shapes and motions in the Z-dimension between small and large target selections, by means of two controlled experiments. The generated results indicated that the following: (1) when interacting with a large display from a remote distance, users preferred to complete a low-precision task by the whole hand while using the finger in high-precision tasks. As a result, users moved small targets by the index finger gesture while moving large targets by open hand gesture; (2) selection of small targets led to hand stretching which might reduce the interaction distance; (3) the orientation had significant effects on hand motions in the Z-dimension. The relationship between orientation and hand motions in the Z-dimension is similar to a sinusoidal function; (4) mouse-like pointing had much impact on freehand interaction. Based on these results, some design guidelines for freehand interaction and design suggestions for freehand pointing were discussed.
RETRACTED: Secure BB84-type quantum key distribution with a simple phase error formula

The BB84-based quantum key distribution system is limited in high-speed and chip integration due to the requirement of preparing and measuring four states. Recently, the simplified BB84 protocol with preparation and measurement of only three states has showed the approximate secret key rate with standard BB84. However, the security proof is valid only under the collective attack and requires the basis-independent detection efficiency condition. Here, we provide a security proof against the coherent attack, which simultaneously removes the basis-independent detection efficiency condition. Importantly, the phase error rate formula is quite simple through subtle observation. We expect that the simplified BB84 protocol with the decoy-state method can be implemented widely in reality.
The Classification of Customers’ Sentiment using Data Mining Approaches

Data mining is a procedure of extracting the requisite information from unprocessed records by using certain methodologies and techniques. Data having sentiments of customers is of utmost importance for managers and decision-makers who intend to monitor the progress, to maintain the quality of their products or services and to observe the latest market trends for business support. Billions of customers are using micro-blogging websites and social media for sharing their opinions about different topics on daily basis. Therefore, it has become a source of acquiring information but to identify a particular feature of a product is still an issue as the information retrieves from varied sources. We proposed a framework for data acquisition, preprocessing, feature extraction and used three supervised machine-learning algorithms for classification of customers’ sentiments. The proposed framework also tested to evaluate the system’s performance. Our proposed methodology will be helpful for researchers, service providers, and decisionmakers.
A Multi-View Deep Learning Model for Encrypted Website Service Classification

This paper presents a multi-view deep learning model for encrypted traffic classification which uses different neural network structures to process different types of features. Compared to previous work, our multi-view deep learning model can make full use of different types of features extracted from the flow. We evaluate our deep learning model in the real- world datasets acquired from the website servers of a large internet corporation. We found that the proposed multi-view deep learning model gets better performance than the state-of-the-art. Moreover, this paper focuses on a new traffic classification scenario in which the traffic under the specific website is classified on the basis of different website services. Each website service represents a specific website function which can be composed of several webpages. Therefore, the granularity of website service classification is between website classification and webpage classification.
NEEDS ANALYSIS IN LEARNING MEDIA DEVELOPMENT BASED ON AUGMENTED REALITY (AR) FOR COMPUTER NETWORK INSTALLATION COURSES

This needs analysis research consists of field studies and literature studies. Where the purpose of this research is; 1) description of student achievement values ​​and learning processes, 2) the uses and constraints faced by students in the use of learning media Computer Network Installation, 3) formulation of instructional media needed in learning computer network installation. This research belongs to the type of qualitative descriptive research. The subjects of this study were 29 students of Informatics Engineering Education study programs and 2 lecturers of computer network installation courses. Data obtained using a questionnaire and analysis qualitatively through stages; arranging, tabulating, analyzing qualitative data and making interpretations of the results of the analysis according to the problems and objectives of the study which then makes conclusions. The results showed that; 1) there are still many students who have not reached the score above the minimum criteria and fail in carrying out the practicum process, 2) the biggest obstacle faced by students in implementing Computer Network Installation learning is the limitations of using instructional media that are less effective to support independent learning, 3) the media that need developed for learning Computer Network Installation which is learning media based on Augmented Reality (AR).
Curriculum learning for distant supervision relation extraction

Relation extraction under distant supervision leverages the existing knowledge base to label data automatically, thus greatly reduced the consumption of human labors. Although distant supervision is an efficient method to obtain a large amount of labeled data, the training dataset labeled by distant supervision suffers from noise problem resulting in poor generalization ability of the relation extractor. To alleviate the noise problem, we propose a novel relation extraction method based on curriculum learning. Curriculum learning is utilized to guide the training process of relation extractor, specifically through the predefined curriculum-driven mentor network. Mentor network can dynamically adjust the weights of sentences during training, giving lower weights to noisy sentences and higher weights to truly labeled sentences. Relation extractor and mentor network are trained collaboratively to optimize joint objective. The experimental results show that the proposed method can improve the generalization ability of relation extractor in a noisy environment and obtains better performance for relation extraction.
Participant Selection Strategy With Privacy Protection for Internet of Things Search

Nowadays, with the rapid developments of the Internet of Things, the information on physical entities has shown explosive growth. The emergence of Internet of Things search technology has facilitated the storage, organization, and management of massive entity information. Aiming at the issues of security and privacy and the target to obtain the optimal perceiving results by selecting participants to ensure the quality of service of the IoT platform, this paper proposes a participant selection strategy with privacy protection for IoT search (PSSPP). Firstly, the search task requester and the task contents are anonymized and blinded respectively, and then individual attributes of participants are mapped to hide the identity information of participants. Secondly, in order to choose the participants who meet the task requirements, the participants are preliminarily selected based on the bloom filter. Finally, the perceiving trust value and relative credibility of participants in participating tasks are dynamically evaluated. Simulation results demonstrate that the proposed scheme can reduce the attack probability of malicious users toward task requesters and participants, and rationally select the appropriate task participants.
Development and validation of constraints handling in a Differential Evolution optimizer

Research work requires independent, portable optimization tools for many applications, most often for problems where derivability of objective functions is not satisfied. Differential evolution optimization represents an alternative to the more complex, encryption based genetic algorithms. Various packages are available as freeware, but they lack constraints handling, while constrained optimizations packages are commercially available. However, the literature devoted to constraints treatment is significant and the current work is devoted to the implementation of such an optimizer, to be applied in low-fidelity optimization processes. The parameter free penalty scheme is adopted for implementation, and the code is validated against the CEC2006 benchmark test problems and compared with the genetic algorithm in MATLAB. Our paper underlines the implementation of constrained differential evolution by varying two parameters, a predefined parameter for feasibility and the scaling factor, to ensure the convergence of the solution.
Research review on devices and methods for rapid measurement of paper ash

The Chinese national standard for paper ash measurement cannot meet the requirements for accurate and rapid ash measurement in actual production and scientific research because of the long measuring time, tedious procedures, and large human error. This paper reviews some worldwide devices and methods for rapid measurement of paper ash, including ceramic fiber muffle furnace, microwave muffle furnace, the addition of ash adjuvant, dry samples method, direct combustion of paper samples, oxygen-enriched combustion method, chemical analysis method, and ray method, etc. The differences and relationships are identified among these devices and methods. By comparing the different ash measurement methods, the rapid ash analyzer based on X-ray technology has the obvious advantages of short measuring time and small error. Lastly, the present situation and the development potential of these devices and methods are discussed in this review.
Digital literacy of the generation z students and their attitudes and beliefs towards ICT knowledge and skills

Technology is rapidly developing and new generations easily adopt technological innovations.The education system is beginning to realize the increasing importance of adapting its education style to suit the specific characteristics of the new generations.Generation Z represents students who are born and raised in the highly progressed digital environment.Therefore, using the survey method, an analysis within the current Generation Z was made, focused on their digital literacy as well as perception and attitudes towards ICT learning, knowledge and skills.
Improving Specimen Labelling and Data Collection in Bio-science Research using Mobile and Web Applications

Abstract Researchers in bio-sciences are increasingly harnessing technology to improve processes that were traditionally pegged on pen-and-paper and highly manual. The pen-and-paper approach is used mainly to record and capture data from experiment sites. This method is typically slow and prone to errors. Also, bio-science research activities are often undertaken in remote and distributed locations. Timeliness and quality of data collected are essential. The manual method is slow to collect quality data and relay it in a timely manner. Capturing data manually and relaying it in real time is a daunting task. The data collected has to be associated to respective specimens (objects or plants). In this paper, we seek to improve specimen labelling and data collection guided by the following questions; (1) How can data collection in bio-science research be improved? (2) How can specimen labelling be improved in bio-science research activities? We present WebLog , an application that we prototyped to aid researchers generate specimen labels and collect data from experiment sites. We use the application to convert the object (specimen) identifiers into quick response (QR) codes and use them to label the specimens. Once a specimen label is successfully scanned, the application automatically invokes the data entry form. The collected data is immediately sent to the server in electronic form for analysis.
Stabilization of switched systems with all unstable modes: application to the aircraft team problem

The method of stabilizing switched systems based on the optimal control is applied, with all modes unstable, for a typical example of the multi-agent system. First, the optimal control method for stabilizing switched systems is introduced. For this purpose, a switching table rule procedure is constructed. This procedure is inspired by the optimal control that identifies the optimal trajectory for the switched systems. In the next step, the stability of a multi-agent system is studied, considering different unstable connection topologies. Finally, the optimal control method is successfully applied to an aircraft team, as an example of the multi-agent systems. Simulation results evaluate and confirm the successful application of this method in the aircraft team example.
Improving the Performance of Data Hiding by Designing Three Samples-based Smoothing in Audio

The internet is widely applied to facilitate the transferring process of data as a means of communication.Some of this transferred message has confidential information and must be protected.One of the techniques to protect is data hiding.It uses carrier media as a cover to hide information inside it, one of which is audio.However, there are some problems in the use of audio, namely the limited capacity and sound quality after data insertion.In this paper, we address these problems by interpolating the audio samples using Modification Lagrange Interpolation (MLI) and exploring Reduce Difference Expansion (RDE) for embedded the secret.Next, samples are smoothed to improve the quality based on three sampling signal points of the stego audio.The testing of proposed method is carried out on fifteen mono audio data sets from various genres the covers and ten files as confidential information data with various file sizes.The results show that using the proposed methods, the performance increases that the average quality of each audio genre raises by about 4dB and able to increase the capacity of message data that can be hidden by 20%.
Alignment tolerant dual-surface sphero-cylindrical progressive addition lens

We present the design, fabrication, and characterization of a dual-surface sphero-cylindrical progressive addition lens (DSOC-PAL), which combines the progressive cylindrical addition power of both lens surfaces to give the required spherical addition power. Both the freeform lens surfaces are numerically solved with the variational-difference numerical method based on the minimization of a merit function that employs weight functions and boundary conditions to balance between the desired spherical power distribution and the necessary cylindrical power distribution. The simulation results show that the proposed DSOC-PAL design is highly tolerant to the surface alignment errors and agree well with the experimental results obtained by the lens surfacing and polishing process.
Multi-Stage Meta-Learning for Few-Shot with Lie Group Network Constraint

Deep learning has achieved many successes in different fields but can sometimes encounter an overfitting problem when there are insufficient amounts of labeled samples. In solving the problem of learning with limited training data, meta-learning is proposed to remember some common knowledge by leveraging a large number of similar few-shot tasks and learning how to adapt a base-learner to a new task for which only a few labeled samples are available. Current meta-learning approaches typically uses Shallow Neural Networks (SNNs) to avoid overfitting, thus wasting much information in adapting to a new task. Moreover, the Euclidean space-based gradient descent in existing meta-learning approaches always lead to an inaccurate update of meta-learners, which poses a challenge to meta-learning models in extracting features from samples and updating network parameters. In this paper, we propose a novel meta-learning model called Multi-Stage Meta-Learning (MSML) to post the bottleneck during the adapting process. The proposed method constrains a network to Stiefel manifold so that a meta-learner could perform a more stable gradient descent in limited steps so that the adapting process can be accelerated. An experiment on the mini-ImageNet demonstrates that the proposed method reached a better accuracy under 5-way 1-shot and 5-way 5-shot conditions.
Evaluating University Academic Efficacy: Institutional Approach

The aim of the article is to develop methodological approach to university academic performance evaluation on the basis of instruments of institutional economic theory.The novelty of this work is that unlike other methods authors' method of quantitative evaluation of university academic performance allows for identifying the problems of institutional support of academic activities.During the research authors employed methods of system, logical and economic analysis; empirical information was processed with the help of statistical analysis and correlation analysis methods.As a result authors identified university academic efficacy institutions and suggested efficacy indicators for these institutions.Universities of the Ural Federal area were grouped according to quality parameters of academic efficacy institutions (that is presence of effective institutions, institutional traps, institutional malfunctions).
The Development of Ergonomics Risk Assessment Method using Infrared Thermal Imaging

There are at least 8 ergonomics risk assessment methods were study for comparison purpose in this paper.Based on the comparison result, there are a few limitations that can be found.The evaluation result may be dispute due to human biased.It is because, the evaluation result come from the perception of workers that taken into account.Other than that, there is limitation to observing a specific body part.Current 8 ergonomics risk assessment method had a limited to observing a body part and also the body posture of the subjects.Based on these limitations, this study explores the using of infrared thermal imaging to improve current ergonomic risk assessment method.This study also will discuss a new approach of ergonomics risk assessment method that enable to increased worker's safety through the assessment with specific feedback, due to availability of non-bias data and prevention of work-related musculoskeletal disorders (WMSD's)
A Mobile Malaria Fever Clinical Diagnosis System Based on Non-Nested Generalized Exemplar (NNGE)

The world today is a world of internet technology which has been deployed in all fields of endeavours to change the way humans do their daily activities and turn things around positively.The whole world is full of smart phones that house various mobile applications.A mobile application for malaria diagnosis was developed in this work to provide a wider coverage and accessibility.A data mining technique -Non-Nested Generalized Exemplar (NNGE) was used on malaria labelled dataset collected from a reputable hospital in Ado-Ekiti, Ekiti State, Nigeria to generate a classification model for malaria diagnosis.The model was tested on both the training and testing sets, attaining 100% and 98.04% detection rates respectively.The rules generated by NNGE were used as the inference engine of the user friendly mobile diagnosis application.The mobile app was developed using Java, HTML and PhP as front end, MySQL as the backend and Apache as the webserver.
Concept Lattice Method for Spatial Association Discovery in the Urban Service Industry

A relative lag in research methods, technical means and research paradigms has restricted the rapid development of geography and urban computing. Hence, there is a certain gap between urban data and industry applications. In this paper, a spatial association discovery framework for the urban service industry based on a concept lattice is proposed. First, location data are used to form the formal context expressed by 0 and 1. Frequent closed itemsets and a concept lattice are computed on the basis of the formal context of the urban service industry. Frequent closed itemsets can filter out redundant information in frequent itemsets, uniquely determine the complete set of all frequent itemsets, and be orders of magnitude smaller than the latter. Second, spatial frequent closed itemsets and association rules discovery algorithms are designed and built based on the formal context. The inputs of the frequent closed itemsets discovery algorithms include the given formal context and frequent threshold value, while the outputs are all frequent closed itemsets and the partial order relationship between them. Newly added attributes create new concepts to guarantee the uniqueness of the new spatial association concept. The inputs of spatial association rules discovery algorithms include frequent closed itemsets and confidence threshold values, and a rule is confident when and only if its confidence degree is not less than the confidence threshold value. Third, the spatial association of the urban service industry in Nanning, China is taken as a case to verify the method. The results are basically consistent with the spatial distribution of the urban service industry in Nanning City. This study enriches the theories and methods of geography as well as urban computing, and these findings can provide guidance for location-based service planning and management of urban services.
Track-to-Track Association for Intelligent Vehicles by Preserving Local Track Geometry

Track-to-track association (T2TA) is a challenging task in situational awareness in intelligent vehicles and surveillance systems. In this paper, the problem of track-to-track association with sensor bias (T2TASB) is considered. Traditional T2TASB algorithms only consider a statistical distance cost between local tracks from different sensors, without exploiting the geometric relationship between one track and its neighboring ones from each sensor. However, the relative geometry among neighboring local tracks is usually stable, at least for a while, and thus helpful in improving the T2TASB. In this paper, we propose a probabilistic method, called the local track geometry preservation (LTGP) algorithm, which takes advantage of the geometry of tracks. Assuming that the local tracks of one sensor are represented by Gaussian mixture model (GMM) centroids, the corresponding local tracks of the other sensor are fitted to those of the first sensor. In this regard, a geometrical descriptor connectivity matrix is constructed to exploit the relative geometry of these tracks. The track association problem is formulated as a maximum likelihood estimation problem with a local track geometry constraint, and an expectation–maximization (EM) algorithm is developed to find the solution. Simulation results demonstrate that the proposed methods offer better performance than the state-of-the-art methods.