Reviewerly/exHarmony huggingface dataset:
Myanmar Handwritten Character Recognition from Similar Character Groups Using K-Means and Convolutional Neural Network

The recognition of characters from similar character groups has gained popularity because it considers only corresponding similar character groups instead of all possible characters when one character is to be recognized. In this paper, we present a novel Myanmar optical character recognition (MOCR) system for handwritten text. Firstly, the system uses tensor-based subspace learning (TSL) to reduce dimension space for only relevant features. The system then gathers similar character groups using the K-means clustering algorithm. The convolutional neural network (CNN) is adopted to discriminate the input character string against the groups of characters that are the most similar to it. The system finally classifies and produces the results as the recognized characters in the phrase level. The experimental works are conducted to evaluate the results in terms of precision, recall, processing time, and error rate. The results have shown that they are superior to other contemporary MOCR works.
Reduction of Spatial Overhead in Decentralized Cloud Storage using IDA

Decentralized cloud storage reflects a significant change in large-scale storage performance and economy. Removing central control enables users to store and exchange data without depending on third-party storage [16] providers. Decentralization reduces the possibility of data failures and outages while increasing object storage protection and privacy at the same time. Decentralized cloud storage is where individuals or groups encouraged to enter, store, and keep data available are stored on a decentralized network through several locations. The servers used, rather than a single organization, are hosted by individuals. In this paper, an information dispersal algorithm is applied on decentralized cloud storage to reduce spatial overhead, which provides more efficient performance compared to the existing methodologies.
Blockchain for Education Enterprises

Education enterprises (e.g., schools, universities, online platforms) seeking digital transformation require careful evaluation of their strategies and transformative capabilities. Although transformation of teaching routines is slowly evolving into student-centric learning, individual-oriented learning routines (assess, evidence, credentials) are complex to manage compared to established teaching routines (define, engage, and enhance) using existing digital learning management systems. This has resulted in poor tracking of 21st century skills (21C) of learners, which are highly sought by teachers, learners, and employers. Based on reviews of blockchain use cases from the education literature, this study proposes a blockchain for education learning ledger (BELL) model to capture and track 21C skills demanded by Industry 4.0. The BELL model includes an innovative assess skills chain for developing strategic learning capabilities and enabling reconfiguration of learning outcomes.
Fast Training Algorithm for Genetic Fuzzy Controllers and Application to an Inverted Pendulum with Free Cart

The classical control theory cannot be applied in those systems whose complexity is too high to be analytically modeled. In these cases, mathematical methods with more degrees of freedom are used because they provide better adaptation. One method widely used in control problems is the Fuzzy Inferencing Systems. However, the process of calibration of the parameters required may involve a high computational cost. Among them, Genetic Algorithms have demonstrated great convergence towards ideal solutions. As the dimensions of the control problem (input features) increase, the optimization process requires much more time. Therefore, the present work proposes a gradual search and parameter update criteria for Genetic Fuzzy Controllers that improves several orders of magnitude the processing time. The algorithm developed has been applied to the control problem of the Inverted Pendulum with Free Cart. The results obtained demonstrate an effective parameter calibration in seconds, while the traditional method of tuning for the same problem takes more than 2 h. Currently, many of the mechanical systems of the different industries undergo sudden changes in their properties during use, therefore an instant effective recalibration of the controllers is necessary. This method allows fast adaptation and also guarantees the same performance in the control process.
Analyses of Character Networks in Dramatic Works by Using Graphs

Artificial Literature (ALit) starts seem possible with upcoming generative models. ALit consists of writing machines that generates literary works. Although there are random machines that imitates the language models, texts by the writing machine should be far beyond, they need to have the structural similarity with the reference texts. In the framework for ALit, our first task is to find structure of tragedies which are very well stated beginning with Aristotle. In this piece of work, the character networks are analyzed with graph theory in order to extract structural properties of Shakespearean texts. The character network is generated and represented as undirected weighted graphs. The weighted and betweenness centrality graphs are interpreted with and without protagonists/antagonists following the “Network Theory, Plot Analysis” by Franco Moretti. As a conclusion, we investigated symmetries or antagonism clusters. There is an antagonism behind the protago-nist/antagonists. This investigation is important to extract knowledge about the class or political struggle.
A Novel Kite Cross Hexagonal Search Algorithm for Fast Block Motion Estimation

Abstract The performance quality and searching speed of Block Matching (BM) algorithm are affected by shapes and sizes of the search patterns used in the algorithm. In this paper, Kite Cross Hexagonal Search (KCHS) is proposed. This algorithm uses different search patterns (kite, cross, and hexagonal) to search for the best Motion Vector (MV). In first step, KCHS uses cross search pattern. In second step, it uses one of kite search patterns (up, down, left, or right depending on the first step). In subsequent steps, it uses large/small Hexagonal Search (HS) patterns. This new algorithm is compared with several known fast block matching algorithms. Comparisons are based on search points and Peak Signal to Noise Ratio (PSNR). According to results obtained in this paper, KCHS needs less search time than others algorithms and gives very acceptable performance quality.
α-MON: Anonymized Passive Traffic Monitoring

Packet measurements are essential for several applications, such as cyber-security, accounting and troubleshooting. They, however, threaten privacy by exposing sensitive information. Anonymization has been the answer to this challenge, i.e., replacing sensitive information by obfuscated copies. Anonymization of packet traces, however, comes with some drawbacks. First, it reduces the value of data. Second, it requires to consider diverse protocols because information may leak from many non-encrypted fields. Third, it must be performed at high speeds directly at the monitor, to prevent private data from leaking, calling for real-time solutions.We present α-MON, a flexible tool for privacy-preserving packet monitoring. It replicates input packet streams to different consumers while anonymizing values according to flexible policies that cover all protocol layers. Beside classic anonymization mechanisms such as IP address obfuscation, α-MON supports α-anonymization, a novel solution to obfuscate values that can be uniquely traced back to limited sets of users. Differently from classic anonymization approaches, α-anonymity works on a streaming fashion, with zero delay, operating at high-speed links on a packet-by-packet basis. We evaluate α-MON performance using packet traces collected from an ISP network. Results show that it enables α-anonymity in real-time. α-MON is available to the community as an open-source project.
Challenges to implement Machine Learning in Embedded Systems

Machine Learning has an important role in extraction of useful information out of sextillion bytes of data collected per day. The focus of machine learning is the development of computer programs that can access data and use it to learn for themselves. In some applications the goal of machine learning is to identify patterns by analyzing them (for e.g. in image recognition, surveillance); while in others the aim is to take actions based upon input data (for e.g. in robotics, self-driving cars). For many of such applications embedded systems are used over cloud due to latency, privacy, and security concerns. Also using machine learning with embedded system creates a problem of restricted memory and processor speed. Although the required memory and processing speed can be met with high performance hardware but still time, space, cost, security, privacy and power consumption create a challenge in implementing machine learning in embedded system. In this paper it is being discussed about these challenges and how these challenges can be addressed using various techniques.
Overview of typical application energy efficiency optimization in high-performance data centers

The rapid growth of data has brought huge challenges to the storage and computing capabilities of the cloud computing data center, which is accompanied by huge energy consumption. Hadoop, as the main framework of big data storage and calculation in the current cloud computing data center, has not been optimized for energy efficiency. This paper studies the key technologies of energy efficiency optimization of Hadoop framework components in the data center, including the latest research results of YARN energy-saving scheduling strategies and distributed file system (HDFS) energy-saving storage strategies.
Pedestrian Detection Using Quaternion Gradient Based Weber Local Descriptor

In the past decades, pedestrian detection has attracted more attention in many practical applications. In this paper, a novel pedestrian detection method using Quaternion Gradient based Weber Local Descriptor (QGWLD) is proposed. Unlike many other local pedestrian detectors that only extracted features from the gray-scale image, which ignored the color information, a new powerful and robust local pedestrian detector is developed, which integrates the advantages of both the color and texture information and can be used to address the pedestrian detection problem well. As we know, the quaternion can entirely characterize a color object well and the WLD feature consisting of the gradient orientation and differential excitation has acquired a good performance for pedestrian detection. Therefore, combining the WLD feature with the quaternion representation, the QGWLD pedestrian detector is presented. Firstly, the quaternion gradient representation is performed on the color image in the sliding window, and then the Weber Local Descriptor (WLD) is extracted over the quaternion gradient feature map. After that, the QGWLD histogram is constructed to characterize the sliding window. Experimental results on INRIA and PennFudanPed pedestrian databases validate the effectiveness of the proposed QGWLD pedestrian detector. Comparing with the similar pedestrian detectors, the proposed QGWLD pedestrian detector performs better.
Semantic Plug and Play: An Architecture Combining Linked Data and Reconfigurable Hardware

Through the mechanisms of the Semantic Web, it is possible not only to describe web content syntactically but also to relate it semantically. The properties and capabilities of hardware, instead, are hidden in documents, code documentations, repository descriptions, etc. This paper presents a methodology and architecture that can be used to describe and relate the properties and capabilities of hardware. The decentralized storage of the descriptions on a hardware adapter allows the information to be evaluated at runtime. For domain-specific applications a Model-Domain-Domainmodel Architecture (MDDM) is presented so that code can also be executed at runtime using these hardware descriptions. The architecture is presented using a home automation system with single-board computers and microcontrollers, in which sensors and actuators can be exchanged and integrated.
Eye-hand movement in 3D displays: A structural equation modeling approach

Abstract Eye-hand movements are two important parameters to evaluate the User Experience (UX) in the stereoscopic display (3D). The current study utilized structural equation modeling (SEM) to derive the causal relationship between parallax, index of difficulty (ID), eye movement, hand movement, eye index performance (IP eye), and hand index performance (IP hand) simultaneously. 10 participants voluntarily participated in the current study and they were asked to perform a multi-directional Fitts’ Law task in the projection-based stereoscopic display. The results indicated that parallax had significant effects on eye movement time and hand movement time. In addition, ID was found to have significant effects on eye movement time and hand movement time. Interestingly, parallax and ID were found to have significant effects on IP eye, however, no significant effects were found on IP hand. These findings could be very beneficial for UX researchers and projection-based stereoscopic display developers especially in terms of evaluating UX of an interface.
Research on Semi-supervised Recommendation Algorithm Based on Hybrid Model

In the algorithm model training, the scale of the labeled data and the type of algorithm model determines the accuracy of algorithm model. In reality, when training a new system, the amount of unlabeled data is often much larger than that of labeled data. There are linear models, matrix decomposition models and so on for the selection of recommended algorithm models. Given the above problems, consider using the semi-supervised co-training method to construct a dual-view hybrid model algorithm framework through co-training of recommendation algorithm models based on different mathematical foundations. The algorithm framework is verified on the movie data set MovieLens. The experimental results show that the semi-supervised co-training recommendation algorithm based on the hybrid model reduces the root-mean-square error by 1.4%, the accuracy of recommendation results is improved.
Development of an integrated augmented reality experiment module on the topic of motion kinematics on student learning motivation

Abstract This study was designed to increase student learning motivation by developing a basic physics practicum module on the topic of integrated augmented reality kinematics. This study uses the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) development model. The subjects tested in this study were students of the physics education study program, at one of the universities in Lampung in academic year 2019/2020. The data collection instrument was in the form of a student learning motivation questionnaire. The data obtained were analyzed quantitatively. The results showed that the developed module was suitable for use in learning and practical activities and was also able to increase student learning motivation.
Structured discriminative tensor dictionary learning for unsupervised domain adaptation

Unsupervised domain adaptation aims at learning a classification model robust to data distribution shift between a labeled source domain and an unlabeled target domain. Most existing approaches have overlooked the multi-dimensional nature of visual data, building classification models in vector space. Meanwhile, the issue of limited training samples is rarely considered by previous methods, yet it is ubiquitous in practical visual applications. In this paper, we develop a structured discriminative tensor dictionary learning method (SDTDL), which enables domain matching in tensor space. SDTDL produces disentangled and transferable representations by explicitly separating domain-specific factor and class-specific factor in data. Classification is achieved based on sample reconstruction fidelity and distribution alignment, which is seamlessly integrated into tensor dictionary learning. We evaluate SDTDL on cross-domain object and digit recognition tasks, paying special attention to the scenarios of limited training samples and test beyond training sample set. Experimental results show that our method outperforms existing mainstream shallow approaches and representative deep learning methods by a significant margin.
Research on On-line Monitoring Technology of Oil Wear Particles Based on Improved Otsu Algorithm

Aiming at the catastrophic consequences of large mechanical equipment due to problems with the lubrication system, a study based on an improved Otsu algorithm is proposed. This article elaborates the significance of online monitoring of abrasive particles in oil and the methods to achieve online monitoring. On the premise of the research of traditional Otsu algorithm, the existing algorithm is analyzed and improved. The experimental part is based on MATLAB simulation link to model and analyze oil abrasive grains. The experimental results show that the designed oil abrasive grain detection algorithm can meet the requirements of stable and effective detection of abrasive grain size and quantity and can distinguish quantitatively. 100um abrasive particles, and the detection rate is not less than 80%, and the performance is good, which meets the design requirements.
Towards Higher Customer Conversion Rate: An Interactive Chatbot Using the BEET Model

This paper presents an interactive Chatbot that increases the rate of customer conversion by observing the behavior emotion status. The trigger customer action accordingly using a customer behavior analysis model named "Behavioral Emotion Engagement Trigger" BEET model. BEET extends the operation of the Chatbot beyond conversational AI, sentiment analysis to evaluate the emotional status of the customer then it aligns the response using BEET to engage customer along with their own priorities. The performance of the chatbot is measured according to its ability to understand and classify both people and sentiments then it measures their conversion rate.
Manifold Adaptive Multiple Kernel K-Means for Clustering

Multiple kernel methods based on k-means aims to integrate a group of kernels to improve the performance of kernel k-means clustering. However, we observe that most existing multiple kernel k-means methods exploit the nonlinear relationship within kernels, whereas the local manifold structure among multiple kernel space is not sufficiently considered. In this paper, we adopt the manifold adaptive kernel, instead of the original kernel, to integrate the local manifold structure of kernels. Thus, the induced multiple manifold adaptive kernels not only reflect the nonlinear relationship but also the local manifold structure. We then perform multiple kernel clustering within the multiple kernel k-means clustering framework. It has been verified that the proposed method outperforms several state-of-the-art baseline methods on a variety of data sets.
Vein-rock in the «Dark kingdom» Marble Deposit (South Ural) and their Possible Connection with Gold Ore Mineralization

Abstract During the geological training of RUDN students in the Miass district of the Chelyabinsk region, one of the abandoned open stone quarry for the extraction of crushed marble was studied. Examination of the walls of the stone quarry revealed a wide development of vein and dike bodies spatially associated with the apical part of the Syrostan granitoid massif. The noted vein and dyke complexes are accompanied by halos of skarn, greisen, and pegmatite mineralization, which allows us to consider the Dark Kingdom deposit as a promising object for identifying valuable minerals, including gold.
Cascade protocol with parameters and backtracking optimization

In the quantum key distribution (QKD) process, Post-processing is one of the most important stages, Post-processing consists of four processes: Kibby right, Error rate estimation, Error code negotiation, and Private amplification. Error negotiation is the main performance bottleneck, this paper improves the Cascade protocol, optimizes parameters and backtracking process, and improves negotiation efficiency and negotiation time. The simulation results show that the negotiation efficiency and the negotiation speed are improved.
Rotor surface defect and symmetry detection based on point cloud registration

During the production process of the blower rotor, detecting its surface defects and symmetry can avoid the safety hazards of the rotor during operation. Therefore, this paper has improved the traditional ICP algorithm. For the rotor point cloud collected by the 3D measuring instrument, the standard point cloud and the test point cloud are unified in the same coordinate system, and the incomplete rotor surface is collected. The point cloud is locally registered, and stitching is completed, and then it is registered with the standard point cloud. The defect detection of the point cloud is completed by comparing the unevenness of the two-point clouds, and the symmetry detection is completed by comparing the shape features of the two-point clouds.
A Design and Experimental Method of Perception Fusion

This paper proposes one feature-level target fusion method solving the problem of perception processing based on multi-source heterogeneity of target information. By mining the correlation between target data, basic data, meteorological data and hydrological data, the multi-source heterogeneous data could be effectively used. According to the results of testing in complex scenes, the proposed method could solve the difficult problem of occlusion interleaving and recognition redundancy which is involved by traditional single-source homogeneity perception. The proposed method could identify targets more accurately and have a good fusion effect.
Issues and Research Challenges in Sequential Pattern Mining

The Mining of data is the way to recognize the substantial data from huge databases. Sequential Pattern Mining (SPM), is one of the important aspect of data mining. The SPM is utilized in discovering the consecutive patterns which takes place in huge databases. It likewise recognizes the incessant subsequences as examples from a database. In today's era, an enormous data is required to be accumulated and get saved in the databases. Various industries are focusing towards mining sequential patterns from these databases. SPM is a very latest approach, in which the researchers uncover the sequential patterns. This following paper gives a methodical study on techniques of SPM. Also, this paper provides an organized study on SPM along with the analysis of their techniques. It also deals with the sequential pattern mining problems, research challenges and future trends of SPM.
Multi‐scale supervised network for crowd counting

Crowd counting is getting more and more attention in our daily life, because it can effectively prevent some safety problems. However, due to scale variations and background noise in the image, such as buildings and trees, getting the accurate number from image is a hard work. In order to address these problems, this work introduces a new multi-scale supervised network. The proposed model uses part of vgg16 model as the backbone to extract feature. In the training process, a multi-scale dilated convolution module is added at the end of each stage of the backbone network to generate attention map with different resolutions to help the model focus on the head area in feature map. In addition, the dilated convolution adopts three dilation ratios to fit different sizes of head in the image. Finally, in order to get the high-quality density map with high-resolution, the authors employ the upsampling operation to restore the density map size to the quarter size of original image. A large number of experiments on these four datasets show that the proposed network has greatly improved the counting accuracy of many existing methods.