Reviewerly/exHarmony huggingface dataset:
Visualized Benefit Segmentation Using Supervised Self-organizing Maps: Support Tools for Persona Design and Market Analysis

This study provides a visualization technique for market segmentation by benefits using supervised self-organizing maps (SOMs). Recent use of customer personas has attracted attention in the marketing field, and is important in the development of products and services that customers want. Market segmentation tools based on clustering methods, such as SOM, k-means and neural networks, have been widely used in recent years. By associating customer benefits and demographics to classes and attributes, respectively, supervised SOM can be a useful tool to support persona design. Market segmentation is important in order to utilize personas effectively for decision-making in businesses that have accumulated large amounts of customer data. We use a real case of customer data from a hotel in Tokyo, Japan to illustrate our approach for market segmentation and customer analysis.
Part-based Fusion Feature Learning for Person Re-Identification

Person re-identification is the task to recognize the same person in gallery images from different cameras. Many previous researches aim to improve feature representation to separate different persons but features are only focus on local parts and a body part. Therefore, in this paper, we propose the Part-based Fusion Network (PFN) that extracted two global features from two layers of the ResNet50, split one global feature to form part-based features, and utilized both local and global features and concatenated to be a final feature for discriminating the same person. In addition, we combine the visual feature with the spatial temporal information to gain the better result on the testing phase. The experiment result shows that our method gained essential improvement and outperformed other state-of-the-art algorithms on two public datasets which are Market-1501 and DukeMTMC-reID.
Research on the Management and Maintenance of Computer Room in Colleges and Universities

With the continuous development of information technology, computer has been gradually used in people’s daily life. The management and system maintenance of computer room in colleges and universities are trivial and complex. It not only faces a large number of users, but also ensures the normal operation of all computer systems. This work discussed the importance of strengthening the management and maintenance of the computer room in colleges and universities and researched on the problems existing in the management and maintenance of the computer room at the present stage. Subsequently, this work proposed the effective solutions to the problems and carried on a systematic discussion from the aspects of environment maintenance, safety management, hardware management, system maintenance, and software management in order to ensure the maximum utility of computer and the normal business operation of daily work in colleges and universities.
A Compact Quantum Cost-Efficient Design of a Reversible Binary Counter

One of the important aspects of Very Large Scale Integration (VLSI) is the power dissipation as the number of devices integrated on a single chip increases progressively. In order to overcome the difficulties of high power consumption of a single chip, it is necessary to introduce reversible logic. Reversible logic allows complete reproduction of the inputs from outputs. As there is no bit or information loss, reversible logic leads to zero heat dissipation. The potential applications of reversible logic include Quantum computing, DNA computing, Nanotechnology, Optoelectronics and many more. Counter circuit stores the number of times of occurrence of a particular event based on the clock pulse. An efficient reversible binary up/down counter has been proposed in this paper implementing an efficient algorithm. MSH gate has been used as D-latch, HNG gate and TS3 gate has been used for incremental count. The proposed counter circuit has 31.25% improvement in terms of quantum cost and delay, 58.82% improvement in terms of ancillary input and 5.8% improvement in terms of garbage output compared to existing best known circuits.
ELEMENTS OF THE STEM CONCEPT IN TECHNOLOGY AND ENTREPRENEURSHIP TRAINING TO HELP PERSONAL DEVELOPMENT IN CLASS 1 - 4

STEM is the acronym of the first letters in English of science, technology, engineering, and maths. This is a training concept that is related to inspiring young people to develop in the field of science and technology. This concept implies a holistic approach in education - learning all the subjects studied to contribute to inspiring students and to educating personal qualities for successful realization in the field of science and technology. From a didactic point of view, this concept implies unity between content and organization of learning, between teaching and learning. The implementation of STEM training in elementary school corresponds to one of the applications of this concept, namely that development itself is an irreversible process characterized by favorable sensory periods. The sooner STEM training takes place, the better. In economically developed countries there are real practices of introducing STEM training even for the youngest children of 3+ years. The object of the present study is the technology and entrepreneurship education as an element of STEM training in elementary school, and the subject - the personal development of students in technology and entrepreneurship education as an element of STEM training in elementary school. The aim of the paper is to present our approaches to technology and entrepreneurship education as an element of STEM training to support the personal development of pupils of primary school age. In the course of the research we carry out the following tasks: 1. Clarifying the essence of STEM training. 2. Developing approaches to technology and entrepreneurship education to help students' personal development. 3. Matching the results of technology and entrepreneurship training to labor market requirements.
Evaluating image matching methods for book cover identification

Humans are capable of identifying a book only by looking at its cover, but how can computers do the same? In this paper, we explore different feature detectors and matching methods for book cover identification, and compare their performances in terms of both speed and accuracy. This will allow, for example, libraries to develop interactive services based on cover book picture. Only one single image of a cover book needs to be available through a database. Tests have been performed by taking into account different transformations of each book cover image. Encouraging results have been achieved.
Coarse-to-fine Method for Vision-based Pedestrian Traffic Light Detection

Pedestrian traffic light detection is an important technique of the navigation system for the visually impaired during road crossing. In this paper, a three-stage coarse-to-fine method ...
Pedestrian Re-identification Based on Head Detection and Region Alignment

In the pedestrian re-identification method based on region segmentation, the accuracy is low due to the presence of noise in the image and the misalignment of pedestrians. To this end, we propose a pedestrian reidentification method based on human head detection and region alignment. Firstly, a data preprocessing method for pedestrian alignment is proposed. The human head coordinates are obtained by detecting the model and the part below it is determined. This allows the model to quickly remove the noise portion of the picture pedestrian while acquiring the main feature part of the pedestrian. Secondly, using a pedestrian segmentation method, the re-obtained image is segmented so that the body semantic features corresponding to each part of the image can be reduced, and the abnormal value of each part is rearranged by using a method of redefining the region. The experimental results show that the method is tested on the Market1501 pedestrian reidentification dataset using the ResNet framework. The accuracy of Rank1 can reach 93.8%, which is better than the current segmentation based pedestrian re-identification.
Worst average encountered highway velocity

A driver’s estimate of the average velocity of vehicles on a highway is biased by the driver’s sampling of those vehicles that pass the driver or are passed by the driver. Surprisingly, the magnitude of the bias does not head towards infinity as the driver’s speed heads towards infinity. Here, we explore these interesting facts.
Residual Stress Filtering of Photoelastic Images based on Improved Anisotropic Method

Dynamic photoelastic imaging technology is an important means to observe the ultrasonic stress field inside a solid. However, the sample will generate residual stress during the manufacturing process, which will cause some interference to the observation. In this paper, the iterative formula diffusion coefficient in the anisotropic algorithm is improved according to the characteristics of residual stress in the photoelastic image, and compared with the original algorithm, the improved algorithm is more conducive to filtering out the residual stress background in the image.
An Improved Beetle Antennae Search Algorithm

The Beetle Antennae Search Algorithm has fast convergence speed and global optimization ability in low-dimensional function optimization, but for multi-dimensional functions, the convergence speed and optimization precision of the BAS is relatively low. To overcome these shortcomings, this paper proposes an Improved Beetle Antennae Search Algorithm (IBAS). There are mainly two modifications for BAS. First, in order to accelerate the convergence of the algorithm, an adaptive factor is included. Second, the simulated annealing process is used to enable the algorithm to jump out of the local optimum. Two standard test functions are used for testing and compared with the BAS, BSAS, and SA algorithms. The simulation results show that IBAS has better performance in multi-dimensional function optimization.
Efficient Word Processing Applications Using Radix Tree

Computer needs an efficient mechanism to store, retrieve, manage and process huge amount of data. When the data is organized efficiently on the storage device and on the main memory then it can be accessed quickly for processing. It will reduce the access time and will provide better working environment to the users. Data structure provides a scheme to organize the data which will lead to systematic organization of the data in such a way that data will be quickly available for the processor to process. Word processing is a powerful feature that allows the user to create, insert, modify and search the text in a file. The authors of this paper propose usage of radix trie datastructure for efficiently handling word processing applications. This datastructure can be used for storing and retrieving information for various applications such as Prefix Matching (Auto complete), Information Retrieval, Dictionary Implementation. In this paper authors discuss their experience in design, implementation of Radix Tree for word processing applications. The experimental result shows that radix tries performs better compared to other word processing datastructure such as hash tables.
Review and Analysis of Access Control Mechanism for Cloud Data Centres

Nowadays the cloud is very useful for providing many IT services. These services are delivered over the internet and accessed globally with the help of internet. The cloud service provider ensures flexibility in provisioning and scaling of resources. The cloud services are completely managed by cloud service provider (CSP), which ensures the end to end availability, reliability and security of the cloud resources. The exponential growth of cloud services has provided many opportunities but has also perplexed severe security concerns. The popularity of cloud service-based applications is rapidly increasing due to which many security and legal issues are arising. In this paper, we reviewed the existing access control method and framework for cloud data centres. The different concept of reputation and attribute-based access control system has been analyzed. This review of access control approach is helpful in designing of new access control framework and to mitigate the challenges in security concerns.
Security in Body Networks: Watermark-Based Communications on Air-Gap Acoustic Channel

Wireless body area networks (WBANs) are becoming common to collect humans’ information. For example, patients’ vital signs in hospital scenarios. Recently, the interest of cyber-criminals to this information has gone up. Physical layer security is a standalone solution for low-power sensors network and WBAN. This paper describes the application of the watermark-based blind physical layer security (WBPLSec) to audio communications. Results indicate the WBPLSec as valuable technique for WBAN scenarios in which a sensor does not have any additional radio frequency (RF) interface but only a microphone and loudspeakers. Using this technique, the legitimate receiver creates a secure region around itself up to 50 cm. Due to the low data-rate, this technique is a valuable method for key exchange protocol between nodes with a good level of confidentiality.
Empirical Analysis on the Effect of Image Compression and Denoising Using Different Wavelets on Iris Recognition

The Iris recognition is commonly used as a security system due to its robustness against imposters. Iris datasets are huge and hence those datasets occupy more space. Iris image compression has become an important part of better performance like speed and data storage. The portable Iris system is in huge demand. That portable systems need to transmit the iris images through a very small bandwidth channel. To reduce the time for transferring a huge number of data over small bandwidth channel, iris file can be compressed to some extent to minimize the size. Another problem is that when an image is captured, it captures some noise that disturbs the recognition performance, so, denoising is required for noise-free images. This paper separately analyzes the impact of wavelet compression along with denoising on iris images. The compression analysis is done using Embedded Zero Tree Wavelet, the other technique used is Set Partitioning in Hierarchical Tree and the third technique used is Spatial-Orientation Tree Wavelet. Denoising is done using different wavelets Daubechies, Haar, Biorthogonal and Fejer-Korovkin. The impact of the wavelet compression and denoising techniques on recognition performance are compared with False Rejection Rate and False Acceptance Rate. The quality of a compressed image is calculated with different quality metrics. This work establishes that compression and denoising of the images minimally affect the recognition performance.
IS2020: Updating the Information Systems Model Curriculum

The model curriculum used to develop, update, and assess IS programs (IS2010) is now nearly a decade old, and an assessment of the curriculum itself indicates that its value is decreasing due to the changing technological and skills demands in the information systems environment. Therefore, the ACM and AIS established an Exploratory Task Force that assessed IS2010 and recommended a taskforce be created to update the content and structure for a new model curriculum. One recurring theme is that current graduates' technical skills do not appear to meet industry needs. The IS discipline must express its core in terms of a standard curriculum to provide a foundation upon which to develop and offer undergraduate IS programs that meet stakeholder demands. A taskforce on the Information Systems Model Curriculum (IS2020) was created following the report and recommendation of the Exploratory Taskforce. This panel seeks to introduce the work of this taskforce as well as engage the IS education community in this effort. Panelists will introduce key components of this process and seek input and feedback. This session should be of interest to all attendees, especially faculty developing college-level curricula in Information Systems.
Real-time Target Tracking Based on PCANet-CSK Algorithm

This paper presents a real-time target tracking method by combining PCANet and CSK. To speed up PCANet feature extraction, we give a lightweight PCANet to simplify the structure of PCANet. In tracking step, in order to improve tracking performance, we integrate the scale adaptive module into traditional CSK algorithm and optimize its model updating mechanism inspired by LMCF. The experimental results on OTB50 validate the effeteness of our method. Compared with the traditional CSK algorithm using gray features, the tracking success rate of our method is about 26% higher, and the tracking accuracy increases about 29%.
A Tripartite Graph Recommendation Algorithm Based on Item Information and User Preference

Data sparsity and cold start are common problems in item-based collaborative ranking. To address these problems, some bipartite-graph-based algorithms are proposed, but two flaws are still involved in the proposed bipartite-graph-based algorithms. First, they cannot introduce the information of tags into recommendation model, and second, they can't effectively utilize the priorities information from users' rating data. In order to overcome the shortcomings of current bipartite-graph-based collaborative ranking methods, a tripartite-graph-based method called TGRIP is proposed in this research. In the TGRIP algorithm, a new tripartite graph is applied to introduce the information from users' rating data and make links between user layer and item layer. For the reason that users' past preference information and tag information of items have a great effect on improving the data sparsity issue and cold start problem of items, users' recommendation lists are created based on these auxiliary information. The proposed algorithm keeps the items most likely to be accessed by a target user at the top of recommendation result. This means these items come under top-n recommendation list, which are most likely chosen by target users. Experiments show that the proposed approach achieves a remarkable improvement compared to bipartite-graph-based recommendation algorithm in terms of recall, precision, F1-score and normalized discounted cumulative gain metrics.
Research and Development of Wireless Intercom APP Based on PSO-BP Neural Network Algorithm

The development of wireless intercom APP based on PSO-BP neural network algorithm mainly adopts Android studio 3.0 software, in which PSO-BP neural network algorithm is used to realize the speech recognition of railway standard term of trainers in the railway train operation training system. The APP mainly realizes the information management of trainers, the speech recognition of the railway standard term during the training process, and the collection and transmission of the state of the switch in different stations. By introducing the speech recognition technology realized by PSO-BP neural network algorithm into the railway train operation training system, the new train service personnel can master the various operations on the site more effectively and intelligently, and it also provides an effective platform for the assessment and evaluation of railway personnel.
Improving Spam Email Filtering Systems Using Data Mining Techniques

Email is the most popular choice of communication due to its low-cost and easy accessibility, which makes email spam a major issue. Emails can be incorrectly marked by a spam filter and legitimate emails can get lost in the spam folder or the spam emails can deluge the users' inboxes. Therefore, various methods based on statistics and machine learning have been developed to classify emails accurately. In this chapter, the existing spam filtering methods were studied comprehensively, and a spam email classifier based on the genetic algorithm was proposed. The proposed algorithm was successful in achieving high accuracy by reducing the rate of false positives, but at the same time, it also maintained an acceptable rate of false negatives. The proposed algorithm was tested on 2000 emails from the two popular spam datasets, Enron and LingSpam, and the accuracy was found to be nearly 90%. The results showed that the genetic algorithm is an effective method for spam classification and with further enhancements that will provide a more robust spam filter.
Health Assessment of Artillery Command Information System Based on Combination Model

Aiming at the characteristics of artillery command information system, such as high cost of physical modeling of complex structure, difficult acquisition of deep coupling signals of state characteristic parameters, and weak description ability of technological evolution process, this paper combines AHP with HMM to establish a health assessment model of the system. Example results show that the model can effectively evaluate the technical status of the system, provide some reference for the management and use of equipment in the army, and also provide some decision support for the maintenance work of the army.
A Monarch Butterfly Optimization Approach to Dynamic Task Scheduling

Parallel processing has been employed many years in high-performance computing. Parallel processing is mostly used because it provides concurrency, maximizes load balancing, and minimizes system idle time and execution time which depend on various computer architecture. Task scheduling is a parallel processing technique which allocates the tasks to various processors. Mapping of heterogeneous tasks to a heterogeneous processor or core dynamically in a distributed environment is one of the active area of research in the field of parallel computing system. Here in this paper, monarch butterfly optimization (MBO) [1], a nature-inspired metaheuristic algorithm is implemented for a task scheduling problem. A monarch, a North American butterfly is best known for its migratory behavior in the summer season. There are mainly two processes to get the best solution. First, using a migration operator, a new generation is created. Second, they update their position by using a butterfly adjusting operator. The fitness value is evaluated and updated the population with a higher fitness value to satisfy the objective of the problem. Minimizing the cost and time of the scheduling strategy is the main objective of the proposed work.
Sentimental Analysis and Deep Learning : A Survey

Sentiment Analysis is an ongoing field of research in text mining. Sentiment Analysis is the computational treatment of opinions, Sentiments, and subjectivity of text. Many recently proposed algorithms enhancements and various Sentiment Analysis applications are investigated and presented briefly in this survey. The related fields to Sentiment Analysis that attracted researchers recently are discussed. The main target of this survey is to give nearly full image of Sentiment Analysis techniques and the related fields with brief details. In recent years machine learning has received greater attention with the success of deep learning. Deep learning can create deep models of complex multivariate structures in structured data. Though deep learning can be characterized in several different ways, the most important is that deep learning can learn higher-order interactions among features using a cascade of many layers. Deep learning has been applied to neural networks and across many fields, with significant successes in many applications. Convolution neural networks, deep belief networks, and many other approaches have been proposed to enhance the abilities of deep structure networks