Reviewerly/exHarmony huggingface dataset:
Construct validity of digital media literacy instrument for student teachers

Abstract To measure the level of media literacy, a measuring instrument in the form of an assessment is needed. The instruments used in the study were adopted from Son which is a non-test in the form of a closed questionnaire that had been revised and validated with the conditions of students in Banda Aceh. The study aims to examine construct validity of digital media literacy instruments used. The population in the study was 242 student teachers in Biology Education Department at a public university in Banda Aceh. The sample were 168 students selected using purposive sampling. The study applied CFA method. The results of construct validity of the instrument that can be seen in the validity of all the observed variables were good with t value &gt; 2 and SLF &gt;0.3. Moreover, the construct reliability were categorized very high (0.80&lt;CR 1.00) and high (0.60&lt;CR0.80). This indicates that the instrument can be used to measure the level of digital media literacy of student teachers.
Enhanced Financial Module in Intelligent ERP Using Cryptocurrency

Rapidly changing in the technology is enforced towards to convert our business into the smart, intelligent and automated. This paper is focus on to improve the financial module by using crypto currency and bit coin in intelligent ERP. We have emphasis on the evolution of block chain from the bit coin network. It also gives brief future about the crypto currencies With ERP. In 2017 the crypto currency showcase has achieved a record of $54 billion. It shows that the following result is a persistent development. Here we also discussed about the types of bitcoin and their use to maintain in the block chain or Ledger.
Local Coordination Diagrams for Collision Avoidance in Multi-Robot Path Planning

Autonomous robots are intended to help humans performing a lot of different tasks in a safer and more efficient way. Some of those tasks must be solved by a group of autonomous robots. Also, when the task can be solved only by one robot, for cost constraints, it is cheaper (for development and maintenance) to implement solutions including a group of simple robots. Solutions including multiple robots have to solve group problems like communication and coordination; also, common problems of autonomous robots like the widely studied problem of Path Planning must be rethought. In this case, finding a collision-free path is not enough because each robot also has to avoid collisions with other robots (by coordinating their movements). In this scenario, the path planning problem turns into the multi-robot motion planning (MRMP) problem. There are two approaches for solving the MRMP problem: coupled and decoupled. This work is focused on the decoupled approach because it has the potential to solve MRMP not only in a centralized way but, also in a concurrent or distributed way. In this sense, a new parallelizable algorithm, called Local Coordination Diagrams - LCD, is presented in this paper. Experimental results show that our approach can be applied efficiently to a large number of robots.
Texturing the Digital Humanities: A Manifesto

The digital humanities is today overwhelmingly visual. Even as DH has increased access to the humanities at an astonishing rate, it has also inadvertently narrowed our sensory access to information. It is time to add new dimensions to a field structured predominately by the flat screen. Emphasizing touch, we call for a textured digital that challenges the logic governing many notions of access—that is, the unproblematic translatability of information between the senses. As an epistemological intervention, texturing DH promises new ways to approach sensorial history and to engage diverse sensory abilities and experiences—past, present, and future. As an ethical intervention, it emphasizes our responsibility to evaluate carefully the structures of digital accessibility and to increase access for all. (SA and DW)
K-Means Clustering of Ontologies Based on Graph Metrics

The growth of the semantic web in recent years have led to an increase in the development of ontologies. The increase in the number of ontologies requires users or developers to understand their design for the purposes of selection and reuse. On the other hand, the availability of various ontology metrics allows for different methods of analyzing and evaluating the ontologies. In this paper, the K-Means clustering algorithm have been implemented on a set of graph metrics for analyzing ontologies. The experiments were carried out with k values from 2 to 7, resulting to 2, 3, 4, 5, 6 and 7 clusters, respectively. The clustering results were further visualized with scatterplots. The scatterplots enabled to visualize the effect of various k values on the centroids of the clusters and to conclude that the higher the k is, the closer the centroids of the clusters are to the data points. The experiments showed that the K-Means algorithm is efficient in clustering ontologies based on their graph metrics.
A Defense against Adversarial Examples via Image Squeezing

As is known to all, deep neural networks have shown superior performance in many image classification tasks, but it is vulnerable to attack by adversarial examples, which are added tiny perturbations on the original examples to fool deep neural networks. Many previous defenses focused on mdifying model structure to enhance the robustness of the neural networks. In this paper, an image squeezing method proposed can help to classify images correctly, before inputing images to deep neural networks, image webp squeezng can destory the perturbation of adversarial examples. Firstly, it is necessary to generate corresponding adversarial examples of the neural networks, which can completely fool classifier, then apply webp squeezing to adversarial examples, and put them into the classifier to regain the correct image classification results. Our experiments on CIFAR-10 dataset demonstrated that webp image squeezing can help neural networsk classify adversarial examples correctly. And our method proposed in this paper can effectively resist FGSM, One-pixel attack and C&W attack, and its recognition accuracy is improved by more than 5% compared with the HGD method.
Maithili Text-to-Speech System

This paper discusses development of TTS system for Maithili language. Speech corpus spanning 5 hours borrowed from LDCIL, CIIL Mysore, and also of 3 hours is collected from native speakers in studio environment. As most Indian languages including Maithili are syllabic in nature, concatenative method is used for the purpose of speech generation taking syllable as a basic unit. To enhance naturalness of speech out, 1055 most frequently occurring words have been recorded and stored. The system supports UTF-16 for text input. C#.NET is used for development of interface. The speech database consists of 930 syllable (C*V) in total. Each position has 300 syllables and 10 independent vowels. 930 units of speech data is built from all three positions. Subjective Evaluation, MOS and MRT, are conducted by 10 native speakers. The quality of synthesized speech in terms of intelligibility and naturalness is evaluated to be approximately 84 percent. The relevance of the work lies in the fact that no TTS system exists for Maithili Language till date.
Increasing the Profit of Cloud Providers through DRAM Operation at Reduced Margins

Energy reduction is a key objective in cloud computing, and DRAM memories are responsible for an important amount of the energy consumption of data center nodes. Vendors adopt very conservative margins for DRAM operating parameters, such as the refresh rate and supply voltage, to guarantee correct operation even under the worst process variation and operating conditions. In this paper, we investigate the exploitation of DRAM margins to improve the energy efficiency of data center nodes, without triggering penalties due to service level agreement (SLA) violations. We introduce a model that captures the most important aspects of job management and system configuration. We also introduce RM-DRAM, a scheduling and node configuration policy that exploits the extended margins of DRAMs to reduce the operator's cost, considering the tradeoff between the cost of energy consumption and potential SLA violations. RM-DRAM also employs cost-aware (rather than threshold-based) VM consolidation. We extract the parameters used in the simulation (particularly power consumption and error rates) by characterizing a commercial ARM-based server. We perform simulations to evaluate the effectiveness of our approach, showing that significant gains, up to 34.84% and 29.53% in energy and cost, respectively, can be achieved compared with a state-of-the-art policy.
Deep Reconstruction Error Based Unsupervised Outlier Detection in Time-Series

With all the advanced technology nowadays, the availability of time-series data is being increased. Outlier detection is an identification of abnormal patterns that provide useful information for many kinds of applications such as fraud detection, fault diagnosis, and disease detection. However, it will require an expensive domain and professional knowledge if there is no label which indicates normal and abnormality. Therefore, an unsupervised novelty detection approach will be used. In this paper, we propose a deep learning-based approach. First, it prepares subsequences according to the optimal lag length using Autoregressive (AR) model. The selected lag length for time-series analysis defines the data context in which further analysis is performed. Then, reconstruction errors (RE) of the subsequences on deep convolutional autoencoder (CAE) models are used to estimate the outlier threshold, and density-based clustering is used to identify outliers. We have compared the proposed method with several publicly available state-of-the-art anomaly detection methods on 30 time-series benchmark datasets. These results show that our proposed deep reconstruction error based approach outperforms the compared methods in most of the cases.
A Method for Predicting Importance of Attack Players Based on Multiple Gaze Tracking Data in Soccer Videos

This paper presents a new method for predicting importance of attack players based on multiple gaze tracking data in soccer videos. In order to understand the game situation in the soccer game, experienced soccer players look at the movement, position, and space which are other players more often and faster than inexperienced players. Also, these features are different from each experienced player. For this reason, the gaze tracking data of experienced soccer player is useful for tactical analysis. Therefore, by introducing the gaze tracking data of multiple experienced soccer players into the importance prediction, more robust prediction than a method which use only one player's gaze data can be expected. Since this predicted importance is obtained from gaze data of the experienced soccer players, the obtained one is more useful data for understanding soccer game situation. Therefore, the proposed method can contribute to automatically data generation for developing novel services of video/data distribution that supports to easily understanding of video contents for many viewers. Experimental results using actual soccer videos showed the effectiveness of our method.
Application Research of two-dimensional code in mobile augmented reality

Augmented Reality (AR) is an emerging technology that adds computer-generated virtual information to real-world scenarios, thereby enhancing people's understanding of the surrounding real-world environment. With the development of network, augmented reality technology is gradually applied to mobile devices based on smart phones. Augmented reality technology has been widely used in foreign cultural circles, such as ancient architecture research, Museum exhibitions, enhanced travel and campus navigation. At present, our country's mobile augmented reality technology can only aim at a small number of targets, and can not meet the needs of target recognition and tracking for mobile users. On this basis, two-dimensional code is gradually developed in the mobile augmented reality system. A large number of two-dimensional code content coding technology and fast positioning technology can meet the needs of people. Target recognition requirements. Therefore, this paper expounds the general situation of two-dimensional code and mobile augmented reality technology, studies the application of two- dimensional code in mobile augmented reality, and points out its application in library.
Image Deblurring Method Based on Fourier Transform

Image blurring caused by motion and camera jitter is a common phenomenon nowadays, and blurring directly leads to the decline of image quality. The paper provides an image deblurring method based on Fourier transform. The method includes treating the blurring problem as an inverse problem. Firstly, the blurred image is preliminarily de-noised to remove part of the noise and improve the image quality. Then, the original clear image and the blur calculator are estimated respectively by double iteration method. The experimental results show that our method has good deblurring effect.
The Applicability of Corpus Linguistics in the Field of Linguistics

Corpus Linguistics refers to an enormous collection of text through which use of language or corpus of a particular variety is represented. The importance of reviewing corpus linguistics and its principle from a linguistic perspective should be documented. Thus, this paper aims at examining the plausibility of corpus linguistics' objectives and methodologies in context of basic tenets related to linguistics. The doctrines about the data nature and usage for linguistic analysis will also be used to determine how corpus linguistics methods stand up to them. This paper also encompasses a discussion regarding the issue of representativeness in light of Douglas Biber's influential research paper “Representativeness in Corpus Design”.
Teachable object recognizers for the blind

Advances in artificial intelligence and computer vision hold great promises for increasing access to information and improving the quality of life for people with visual impairments. However, these recent technologies require an abundance of training data, which are typically obtained from sighted users. These training images tend to have different characteristics from images taken by blind users due to inherent differences in camera manipulation. My research focus is to build a teachable object recognizer, where blind users can actively train the underlying model with objects in proximity to their hand(s). Thus, the datasets used in my project are more representative of the blind population. In this article, I describe my research motivations and goals, and summarize my research plan.
Analysis and Prediction of Massive Electricity Information Based on Hadoop Ha Architecture

With the widespread use of smart grids and the Internet of Things, the amount of electricity generated by energy metering equipment is gradually increased. The traditional model has been more difficult to burden the storage, processing and analysis of massive electricity consumption information, and has gradually become the performance bottleneck of the system. Based on Hadoop with the advantages of distributed storage and parallel computing, the current performance bottleneck can be solved. In order to improve the fault tolerance of Hadoop in the production environment, the QJM (Quorum Journal Manager) mode is adopted as the high-availability shared storage mechanism, and the ZooKeeper cluster is introduced to complete the switching between the active and standby nodes to achieve smooth failover and avoidance. In order to fully analyze and explore the potential value in massive electricity consumption information, Hive data warehouse is used to realize complex query and multi-dimensional analysis. Based on statistical analysis data, a gray model is used to predict the trend of some data in future electricity usage information. Through the real-time collection of massive electricity consumption information, data cleaning and classification preprocessing, HDFS cloud storage, MapReduce parallel computing, HiveQL statistical analysis, the proposed scheme is efficient and feasible. It also verifies that the gray model can better predict the future power consumption trend under the Hadoop HA (High Available) architecture.
Combining Local Binary Pattern and Speeded-Up Robust Feature for Content-Based Image Retrieval

Large number of digital image libraries containing huge amount of images have made the task of searching and retrieval tedious. Content-Based Image Retrieval (CBIR) is a field which finds solution to this problem. This paper proposes CBIR a technique which extracts interest points from texture feature at multiple resolutions of image. Local Binary Pattern (LBP) has been used to perform texture feature extraction and interest points are gathered through Speeded-Up Robust Feature (SURF) descriptors. The multiresolution decomposition of image is done using Discrete Wavelet Transform (DWT). DWT coefficients of gray scale image are computed followed by computation of LBP codes of resulting DWT coefficients. The interest points from texture image are then gathered by computing SURF descriptors of resulting LBP codes. Finally, feature vector for retrieval is constructed through Gray-Level Co-occurrence Matrix (GLCM) which is used to retrieve visually similar images. The performance of the proposed method has been tested on Corel-1 K dataset and measured in terms of precision and recall. The experimental results demonstrate that the proposed method performs better than some of the other state-of-the-art CBIR techniques in terms of precision and recall.
Using an Artificial Neural Network to Improve Email Security

Email is one of the most widely used features of internet, and it is the most convenient method of transferring messages electronically. However, email productivity has been decreased due to phishing attacks, spam emails, and viruses. Recently, filtering the email flow is a challenging task for researchers due to techniques that spammers used to avoid spam detection. This research proposes an email spam filtering system that filters the spam emails using artificial back propagation neural network (BPNN) technique. Enron1 dataset was used, and after the preprocessing, TF-IDF algorithm was used to extract features and convert them into frequency. To select best features, mutual information technique has been applied. Performance of classifiers were measured using BoW, n-gram, and chi-squared methods. BPNN model was compared with Naïve Bayes and support vector machine based on accuracy, precision, recall, and f1-score. The results show that the proposed email spam system achieved 98.6% accuracy with cross-validation.
Preliminary design of CIPP-SAW evaluation model in measuring ICT-based learning effectiveness in health colleges

Abstract Evaluation activities are essential to be conducted to measure the effectiveness level of the ICT-based learning process in colleges, including also specifically for health colleges. Evaluation models that are often used to measure ICT-based learning effectiveness in health colleges are formative-summative models. However, reality shows that the evaluation model is not effective. The problems solving to overcome that problem is to develop a new evaluation model in the form of CIPP ( Context-Input-Process-Product ) model based on SAW ( Simple Additive Weighting ) so that it can show the assessment accuracy level to ICT-based learning quality aspects. This study purpose was to know the initial design of the CIPP model based on SAW . This research uses the Borg &amp; Gall development method, which focuses on the model design phase. Subjects involved in designing this evaluation model were three peoples with academic classifications in the fields of information technology, educational evaluation, and health. The research results showed that there was a clear evaluation model design using CIPP evaluation components and SAW methods in conducting learning evaluation. The impact that arises from this study results is the effectiveness of the implementation of ICT-based learning was more clearly measured, while the implications of this study present an evaluation model innovation in the field of education which is the adoption of decision support system concept in facilitating the calculation of the effectiveness of each evaluation aspect starting from the highest level to lowest.
An Enhanced Scale Robust Network for Crowd Counting

The main challenge of crowd counting is the dramatic variations in scale and perspective. Most methods combine features of different receptive fields to deal with scale problems, but do not perform well on continuous scale variations in local areas. Moreover, they use only Euclidean loss which assumes independence of each pixel without considering local correlation in the density map. Therefore, we propose the enhanced scale robust network (ESRN) for accurate and efficient crowd counting. ESRN contains an embedded GAN module, which is followed by a well-designed enhancer. The discriminator guides the generator to strengthen the local correlation and create high-quality density maps, while the enhancer combines context information in different scales and varying among different sub-regions to further enhance the scale robustness of the network. The embedded GAN module is jointly trained with the enhancer. Extensive experimental results on three challenging datasets well demonstrate the effectiveness of our method.
Laser Strip Center Extraction Methodology for the Detection of Weld Seam

As the key technology in the weld seam visual tracking system, the laser strip center extraction is highly valued to the performance of detection system. In this paper, a laser strip center extraction methodology based on geometrical center method, direction template and gray centroid method is proposed. Firstly, the laser strip center is picked up as an approximate skeleton by geometric center method. Then a normal judgment method based on the Lagrange mean theorem is designed to determine the normal direction of the skeleton. At last, accurate laser strip center gray is obtained by gray centroid method. The experimental results indicate that suggested method is both simple and accurate, and meet the speed and precision requirement of the weld seam detection.