Reviewerly/exHarmony huggingface dataset:
Readability of English language textbooks for diploma students of the University of Cape Coast

Textbooks are important for teaching and learning at every level of education.Being one source of learning materials from which students can read on their own, with the goal of further understanding what has been discussed in class, it is important that these textbooks are written in a way that would be understandable to students.Yet, a number of researches have concluded that textbooks are usually difficult to read by the intended audience.Therefore, this paper examined the readability of English Language textbooks for diploma students of the Centre for Distance Learning, University of Cape Coast.Three textbooks (for year one -three diploma students) were used for the study.Passages were selected from each textbook for readability analyses.Flesch reading ease (FRE) and Flesch -Kincaid grade level (FKGL) indexes were used to compute readability scores of the textbooks.Measures of central tendencies, one-sample T-test, with bootstrapping, were used to analyse the data.The results revealed that the three textbooks were generally between 'fairly difficult' and 'difficult' to read ranges (M = 40.69and 52.73).The readability of the textbooks was found to be statistically different from recommended readability scores for public documents.Further, the results indicated that the average word per sentence was to be between 22 and 25 for all three textbooks.This may have contributed to the low readability of the textbooks.The researcher recommends the revision of the textbooks, so that they will serve the intended purpose and contribute to effective teaching and learning of English Language.
Accelerating parallel graph computing with speculation

Nowadays distributed graph computing is widely used to process large amount of data on the internet. Communication overhead is a critical factor in determining the overall efficiency of graph algorithms. Through speculative prediction of the content of communications, we develop an optimization technique to significantly reduce the amount of communications needed for a class of graph algorithms. We have evaluated our optimization technique using five graph algorithms, Single-source shortest path, Connected Components, PageRank, Diameter, and Random Walk, on the Amazon EC2 clusters using different graph datasets. Our optimized implementations have reduced communication overhead by 21--93% for these algorithms, while keeping the error rates under 5%.
GeoAttn: Localization of Social Media Messages via Attentional Memory Network

Recent studies have demonstrated inspiring success in leveraging geo-tagged social media data for applications such as event detection, location recommendation and mobile healthcare. However, in most real-life social media streams, only a small percentage of data have explicit geo-location metadata, which hinders the power of social media from being fully unleashed.We study the problem of inferring geo-locations from social media messages. While a number of text-based geo-locating techniques have been proposed, they either fall short of automatically identifying indicative keywords from noisy social media posts or do not integrate rich prior knowledge of geological regions. We propose an attentive memory network called GeoAttn for localization of social media messages. To capture indicative keywords for location inference, GeoAttn consists of an attentive message encoder, which selectively focuses on location-indicative terms to derive a discriminative message representation. The message embedding is then fed into a memory network, which selectively attends to relevant Points-of-Interest (POIs) for location prediction. The message encoder and key-value memory network are jointly trained in an end-to-end manner. The attention mechanisms in GeoAttn not only alleviate noisy information for higher prediction accuracy, but also provide interpretable attention scores that rationalize the predictions. Our experiments on a million-scale geo-tagged tweet dataset show that GeoAttn outperforms previous state-of-the-art location prediction methods by 15.5% in mean error distance, and is capable of locating over half of the tweets within 5km.
Effect of social media on undergraduate students achievement and interest in chemistry in the North-central geo-political zone of Nigeria

This paper gives a report of the effect of social media on undergraduate students' achievement and interest in chemistry in the north-central geo-political zone of Nigeria.The research design adopted was a cross-sectional survey.The population of this study comprised all undergraduate chemistry students from the universities in the north-central geopolitical zone of Nigeria.240 undergraduate chemistry students constituted the sample for the study and 80 students each were randomly selected from all the four levels of the three sampled Universities namely; Nasarawa State University Keffi, University of Abuja, and Federal University of Agriculture, Makurdi.Structured questionnaire tagged: Effect of social media on achievement and interest in chemistry (ESMAIC) was the only instrument used for data collection.The questionnaire used was validated by experts and pilot tested to establish a reliability coefficient of 0.88.The finding of this study reveals that 60.8% of the respondents engage on social media mainly for educational related purposes while 52.9% of them agreed that social media has helped in increasing their grade point average (GPA).The interest of 67.5% respondents was boosted when employing social media for learning, and Facebook is most frequently used among the network sites by students.Addiction, distraction, poor time management and health problems are some of the disadvantages of continuous engagement with social media.Results also show that many students did not agree that social media is not an effective tool for studying and some are still hesitant on adopting modern technology while some are not very proficient on its usage.It is therefore recommended that students should be educated on how to maximize the potential of the different social network sites for their academic benefits among others.
The Method of Predicting Changes of a Dynamic Signature Using Possibilities of Population-Based Algorithms

Verification of a signature on the basis of its dynamics is an important issue of biometrics. This kind signature is called the dynamic signature. It can be represented, among others, by the set of features determined on the basis of time characteristics: pen velocity, pen pressure on the surface of a graphics tablet, etc. Values of the features can change over time, individually for each signer. Our previous research was related to the prediction of these changes to increase the effectiveness of a signature verification process. This approach was effective. The main purpose of this work is to compare the effectiveness of the methods for a prediction of signature features changes using selected population-based algorithms. They are used for learning of the fuzzy system used for prediction. Tests of the proposed approach were performed using ATVS-SLT DB database of the dynamic signatures.
Unsupervised color texture segmentation based on multi-scale region-level Markov random field models

In the field of color texture segmentation, region-level Markov random field model (RMRF) has become a focal problem because of its efficiency in modeling the large-range spatial constraints. However, the RMRF defined on a single scale cannot describe the un-stationary essence of the image, which highly limits its robustness. Hence, by combining wavelet transformation and the RMRF model, we present a multi-scale RMRF (MsRMRF) model in wavelet domainin this paper. In the Bayesian framework, the proposed model seamlessly integrates the multi-scale information stemmed from both the original image and the region-level spatial constraints. Therefore, the new model can accurately describe the characteristics of different kinds of texture. Based on MsRMRF, an unsupervised segmentation algorithm is designed for segmenting color texture images. Both synthetic color texture images and remote sensing images are employed in the comparative experiments, and the experimental results show that the proposed method can obtain more accurate segmentation results than the competitors.
Multidimensional Crime Dataset Analysis

Data analytics (DA) is defined as the process of scrutinizing different data sets to draw out the outcomes about the information they contain with the help of specialized functional systems and software. There are different areas where data analytics applications have been operated such as transportation, detection of fraud, city planning, health department, digital advertisement, etc. One of the key area of data analytics is in the crime world. Crime Analysis and prevention is a systematic approach for identifying and analyzing patterns and trends in crime. Our proposed method can predict regions which have high probability for a particular crime occurrence from previous years records and the necessary actions that can be taken place by the police authorities to provide more and more security. The necessary steps can be initiated for security reasons so that criminals think twice before performing a crime. Instead of focusing on causes of crime occurrence like criminal background of offender, etc., we are focusing on crime patterns in different regions. Crime Analysis is concerned with exploring different crime datasets, analyzing them and finding out certain patterns from them, so data analytics is a field which helps in establishing certain patterns from the data. In this paper, we are going to represent the crime data in the form of multipolarity to find relationships between the objects and the attributes. Since, the crime data is very large in size and in unstructured manner, so there is a need to first normalize the data and then find relationships among them by representing them in the form of m-Polar Fuzzy Contexts and m-Polar Fuzzy Concepts.
Venetian Perspective Boxes: When the Images Become Environments

The contribution focuses on the comparison of two types of case studies: (1) educational devices dedicated to the historical heritage of the Interior Design and (2) artistic (on-site) installations dedicated to the experience of the landscape. Both cases are characterised by low digital technology, high level of analogic experience, translation of images between different visual and haptic media in real environments that still function as wunderkammern. Our aim is to study the semiotic device shared by these different cases.
Classifying Physical Actions of Human Models using Multi-objective Clustering based on Elephant Herding Algorithm

In artificial intelligence robot design understanding the behavior of humans play a significant role. The human behavior can be aggressive or normal which is characterized by its arm, leg and body moments. The problem to identify the human behavior is dealt here as a multi-objective clustering problem where the segregation is carried out using two objective functions: intra cluster distance and inter cluster distance. A new multi-objective clustering algorithm ‘MOEHA’ is proposed based on recently reported Elephant Herding Algorithm (EHA). The MOEHA performance on clustering is demonstrated on three synthetic and six real world datasets. Comparative results demonstrated superior performance over benchmark algorithms NSGA-II and MOPSO. Five case studies on the classification of human physical actions demonstrated better results over the comparative algorithms.
INTRODUCING A FRAMEWORK FOR CONFLATING ROAD NETWORK DATA WITH SEMANTIC WEB TECHNOLOGIES

Abstract. Road network asset management is a challenging task as many data sources with different road asset location accuracies are available. In Australia and New Zealand transport agencies are investigating into harmonisation of road asset data, whereby two or more data sets are merged to create a new data set. Currently, identifying relations between road assets of the same meaning is not always possible, as road authorities of these countries use their own data structures and standards. This paper employs SemanticWeb Technologies, such as RDF/Turtle ontologies and semantic rules to enable road network conflation (merge multiple data sets without creating a new data set) as a first step towards data harmonisation by means of information exchange, and shifts road network data from intersections and road nodes to data sets considering the accuracy of the data sets in the selected area. The data integration from GeoJSON into RDF/Turtle files is processed with Python. A geographic coordinates shifting algorithm reads unique data entries that have been extracted from RDF/Turtle into JSON-LD and saves the processed data in their origin file format, so that a closed data flow can be approached.
Dynamic Clustering of Time Series with Echo State Networks

In this paper we introduce a novel methodology for unsupervised analysis of time series, based upon the iterative implementation of a clustering algorithm embedded into the evolution of a recurrent Echo State Network. The main features of the temporal data are captured by the dynamical evolution of the network states, which are then subject to a clustering procedure. We apply the proposed algorithm to time series coming from records of eye movements, called saccades, which are recorded for diagnosis of a neurodegenerative form of ataxia. This is a hard classification problem, since saccades from patients at an early stage of the disease are practically indistinguishable from those coming from healthy subjects. The unsupervised clustering algorithm implanted within the recurrent network produces more compact clusters, compared to conventional clustering of static data, and provides a source of information that could aid diagnosis and assessment of the disease.
Cultural Issues in Indonesian Language Learning for Foreign Speakers

This study shows that the existence of culture in Indonesian language learning for foreign speakers&#x0D; (BIPA) has an important role. In addition to being used to enrich teaching materials in BIPA&#x0D; learning, cultural content is able to introduce Indonesian cultural identity to the international realm,&#x0D; both locally and nationally. Cultural content in BIPA learning is also one of the strategic steps that&#x0D; can be applied as a strategy to face MEA. Through cultural-based BIPA learning, it is expected that&#x0D; foreign students who learn Indonesian will be closer and know about multiculturalism in Indonesia.&#x0D; This research was conducted at State Universities in Surakarta City, namely UNS and IAIN. Data&#x0D; collection techniques are carried out in three ways, namely (1) Observation, (2) document analysis,&#x0D; and (3) interview. The data validity technique in this study is triangulation technique. Meanwhile,&#x0D; the studies discussed in the study were about the implementation of culture-based Indonesian&#x0D; language learning carried out at state universities holding the BIPA program in Surakarta and&#x0D; several relevant literature studies
Enhancing reconstruction precision of zonal methods under low sampling density in non-uniform meshes

The fringe reflection method is widely used to determine the three-dimensional shape of mirror objects. The three-dimensional shape is produced by integrating local gradient data. Notably, the loss of middle- or high-frequency information often leads to the appearance of error in surface reconstruction. It is necessary to make full use of gradient data if we want to reconstruct the surface accurately. After reconstruction under low sampling density by existing algorithms, some high-frequency information will inevitably be lost. Considering the unequally spaced or non-uniform sampling, this paper proposes a new algorithm based on zonal methods that are applicable to non-uniform meshes. The key to obtaining improvement in precision is that our algorithm introduces surface compensation. On the basis of simulation results, we conclude that the proposed algorithm has stronger applicability and higher reconstruction precision under low sampling density.
Social Strategy of Particles in Optimization Problems

The article presents a particle swarm optimization algorithm (SoPSO) in which a novel effective acceleration coefficient has been proposed. In the presented approach, the proposed acceleration coefficient is a nonlinear function that depends on the performance of the algorithm and is affected by a number of iterations. This strategy allows to more precisely specify the search direction and better control velocity of the algorithm according to which it travels in the search space to discover the best, optimal solution of the considered problem. The presented strategy was examined on the collection of benchmark functions described in the literature. The test results were compared with those achieved by the improved IPSO algorithm and the standard PSO (SPSO).
Evaluation for Task Achievement of Robotics Programming Based on Image Information

It is difficult to make an evaluation based solely on whether programming has been achieved. However, appropriate evaluation according to the achievement level leads to learners’ subjective programming learning, and to learning support that corresponds to individuals. Therefore, the quantitative evaluation of programming learning is as important as teaching and educational methods. Since programming education is being introduced to elementary and junior high schools, a programming evaluation method for this age is necessary. Therefore, this paper proposes a new concept of quantitatively evaluating the degree of achievement of tasks by programming. It assumes a unit in junior high school technology classes for learning measurement control, and uses teaching materials that have an autonomous cleaning robot as a theme. It is aimed at quantitatively evaluating the degree of accomplishment of a task by evaluating the extent to which the robot ran in a field, as seen in the room from a still picture taken multiple times.
Exploring Tracking Needs and Practices of Recreational Athletes

Sports are the primary physical activity for over 52 million people in the United States, a vast majority of which are recreational athletes.The general tracking practices and needs of this population have not been studied.In this paper, we explore how recreational athletes use tracking technologies to track their sports performance.We conducted interviews with 25 recreational athletes that are runners, soccer, tennis, and basketball players.We found our participants engaged in supplementary physical activities like exercise and strength training to improve their sports performance.They used wearables and mobile applications to track general physical activity data.However, they were unable to track sport-specific techniques due to limitations of tracking technologies, and desired better tracking support for the same.We present design opportunities for future personal informatics tools to better support the needs of recreational athletes. CCS CONCEPTS• Human-centered computing → Ubiquitous and mobile computing systems and tools.
User-Adaptive Preparation of Mathematical Puzzles Using Item Response Theory and Deep Learning

The growing use of computer-like tablets and PCs in educational settings is enabling more students to study online courses featuring computer-aided tests. Preparing these tests imposes a large burden on teachers who have to prepare a large number of questions because they cannot reuse the same questions many times as students can easily memorize their solutions and share them with other students, which degrades test reliability. Another burden is appropriately setting the level of question difficulty to ensure test discriminability. Using magic square puzzles as examples of mathematical questions, we developed a method for automatically preparing puzzles with appropriate levels of difficulty. We used crowdsourcing to collect answers to sample questions to evaluate their difficulty. Item response theory was used to evaluate the difficulty of the questions from crowdworkers’ answers. Deep learning was then used to build a model for predicting the difficulty of new questions.
Visual Content Analysis and Linked Data for Automatic Enrichment of Architecture-Related Images

Built form dominates the urban space where most people live and work and provides a visual reflection of the local, regional and global esthetical, social, cultural, technological and economic factors and values. Street-view images and historical photo archives are therefore an invaluable source for sociological or historical study; however, they often lack metadata to start any comparative analysis. Date and location are two basic annotations often missing from historical images. Depending on the research question other annotations might be useful, that either could be visually derived (e.g. the number or age of cars, the fashion people wear, the amount of street decay) or extracted from other data sources (e.g. crime statistics for the neighborhood where the picture was taken). Recent advances in automatic visual analysis and the increasing amount of linked open data triggered the research described in this paper. We provide an overview of the current status of automated image analysis and linked data technology and present a case study and methodology to automatically enrich a large database of historical images of buildings in the city of Amsterdam.
Text-driven Visual Prosody Generation for Embodied Conversational Agents

In face-to-face conversations, head motions play a crucial role in encoding information, and humans are very skilled at decoding multiple messages from interlocutors' head motions. It is of great importance to endow embodied conversational agents (ECAs) with the capability of conveying communicative intention through head movements. Our work is aimed at automatically synthesizing head motions for an ECA speaking Chinese. We propose to take only transcripts as input to compute head movements, based on a statistical framework. Subjective experiments are conducted to validate the proposed statistical framework. The results show that the generated head animation is able to improve human perception in terms of naturalness and demonstrate that the head animation is synchronized with the input of synthetic speech.
Cryptanalysis and improvement of the robust quantum dialogue protocols based on the entanglement swapping between any two logical Bell states and the shared auxiliary logical Bell state

As we know, it does not allow that a secure quantum communication protocol has the information leakage problem. Unfortunately, we find that there is the information leakage problem in the two quantum dialogue (QD) protocols which are respectively based on entanglement swapping between two logical Bell states under the collective-dephasing noise and the collective-rotation noise. To mend this loophole, they are masterly improved. It is proven that the improved QD protocols are without information leakage problem. Incidentally, they have some other obvious advantages compared to the previous ones.
Printed Odia Numeral Recognition Using Stacked Autoencoder

Automatic recognition of both printed and handwritten characters is the most progressive research area since last few periods. The printed character recognition rate still desires concentration of researchers because of variations in shape, size and design of characters. Odia is the native language of Odisha. Less works have been accounted in Odia script classification. Complex printed character recognition constantly demands a better feature extraction method for such diversified characters. Purpose of this manuscript is to apply autoencoder technique considering a suitable classifier for Odia printed numeral recognition. Autoencoder proved to be a better method in performance for dimensionality reduction as well as classification of Odia numerals.