Reviewerly/exHarmony huggingface dataset:
Ontological Model to Represent Interactive Simulation Objects

Online education is an educational proposal which tries to facilitate access to knowledge through the web. However, although it is an objective proposal, there are multiple problems to find interactive learning objects such as simulations, which can be easily located and used without restrictions. Currently, the majority of simulations are simple executable objects stored in various repositories of the Web which do not have adequate descriptions that provide them with semantics and allow the creation of relationships or references among them, which makes it difficult to find them and access their content. For the simulations to be visible on the web, this research exposes the use of the semantic web through the creation, design, and use of an ontological model which represents the necessary description for these types of objects, based on specifications and learning object standards, to facilitate access to these resources, create relationships among them, and allow interconnection with other data.
An Improvement of Multi-exponentiation with Encrypted Bases Argument: Smaller and Faster

A cryptographic primitive, called encryption switching protocol (ESP), has been proposed recently. This two-party protocol enables interactively converting values encrypted under one scheme into another scheme without revealing the plaintexts. Given two additively and multiplicatively homomorphic encryption schemes, parties can now encrypt their data and convert underlying encryption schemes to perform different operations simultaneously. Due to its efficiency, ESP becomes an alternative to fully homomorphic encryption schemes in some privacy-preserving applications. In this paper, we propose an improvement in ESP. In particular, we consider the multi-exponentiation with encrypted bases argument (MEB) protocol. This protocol is not only the essential component and efficiency bottleneck of ESP, but also has tremendous potential in many applications. For example, it can be used to speed up many intricate cryptographic protocols, such as proof of knowledge of a double logarithm. According to our theoretical analysis and experiments, our proposed MEB protocol has lower communication and computation cost. More precisely, it reduces the communication cost by roughly $$29\%$$ compared to the original protocol. The computation cost of the verifier is reduced by 19%–42%, depending on the settings of experimental parameters. This improvement is particularly useful for verifiers with weak computing power in some applications. We also provide a formal security proof to confirm the security of the improved MEB protocol.
Handwritten Characters Segmentation using Projection Approach

In the area of optical character recognition, handwritten character segmentation is still an ongoing process. Having good segmentation result can provide the better recognition accuracy. In the proposed system, segmentation is carried out mainly on labelling and projection concepts. The input word is firstly labelled. Then, the modified word is segmented with projection approach. The experiments are performed on local dataset with 1600 words approximately and the system gets segmentation accuracy around 85.75 percentage.
GMDH modelling for mobile user throughput forecasting

This paper demonstrates the use of GMDH algorithm as an alternative approach for forecasting mobile user throughput in a cellular data network. We measure the hourly throughput per user for three weeks and use it to train a GMDH-based model for predicting the user throughput for the fourth week. We adopt a modelling strategy that employs a single next-day forecaster iteratively to estimate an entire week throughput. Our experimental results show that the GMDH-based forecaster performed very well with a mean percentage error as low as 1.87%. We also compare the performance of the GMDH-based forecaster with that developed using state-of-the-art LSTM method and show that it can achieve a comparable performance against the LSTM method. Moreover, the GMDH algorithmâs ability to select only effective input variables during model training reduces the dimensionality of the training data by 43% and allows the development of simpler and more interpretable throughput forecaster.
Data User-Based Attribute Based Encryption

Attribute Based Encryption (ABE) has emerged as an informtion-centric public key cryptographic system which allows a data owner to share data, according to an access policy, with multiple data users based on the attributes they possess, without knowing their identities. In the original ABE schemes, a central authority administrates the system and issues secret keys to data users based on their attributes and both the owner and users need to trust a specific CA. However, in certain real-world applications, the data users would not trust anyone but themselves. For such situations, we introduce a new decentralization model of ABE, termed Data User-based ABE (DU-ABE), which is managed jointly by the data users. DU-ABE is the first decentralized ABE scheme which replaces the authorities with the data users without employing any other extra entities.
Survey of Sentiment Analysis on Social Media

With the influx of massive amounts of data on various social media platforms, it has become a gold mine of unbiased user opinions. The universality of the data on these platforms allows gathering of user emotion in multiple domains such as education, entertainment and the consumer market. Hence, it has become a lucrative source for businesses to gain insights into customer mindset. Sentiment analysis has hence become a major area of research. This work aims to consolidate and analyse some of the many techniques used for the sentiment analysis of social media.
Analysis of news transmission mode based on fuzzy data classification and neural network simulation

Online news media websites and mobile news applications can report hot events in real time. With the change of news duration, we urgently need a tool that can automatically extract hot events from massive data and show how events change dynamically with time. In this paper, the authors analyze the news transmission mode based on fuzzy data classification and neural network simulation. Due to the limitation of BP neural network algorithm, there are some problems in the prediction of information transmission. Therefore, we use fuzzy algorithm to optimize BP neural network, which is easy to fall into local minimum and slow convergence speed, so that BP neural network has higher prediction accuracy. The simulation results show that after the introduction of the neural network model, the features of neural network with richer semantic information can be used, and the new event line can be processed at the same time. The training speed of news content processing is much faster than that of probability graph model. It can be seen that under the influence of new media, news communication shows new characteristics, which further affects people’s news reading habits.
Dynamic Configuration Tuning of Working Database Management Systems

A database management system (DBMS) is a critical component in various systems. DBMSs record data related to our daily activities and analyze them to run services on the Internet. DBMSs have many configuration knobs that need to be set to appropriate values to achieve optimum performance. However, optimizing the tuning of knobs is difficult to achieve, even for experts. This paper describes a system that tunes the configuration of active DBMSs. The proposed system calculates the optimal values of the configuration knobs, tests them using the same SQL queries that the DBMS receives, and applies the optimal knob values to the DBMS. Thus the proposed system enables DBMSs to adapt to workload variation and perform efficiently.
Text mining for incoming tasks based on the urgency/importance factors and task classification using machine learning tools

In workplaces, there is a massive amount of unstructured data from different sources. In this paper, we present a case study that explains how can through communications between employees, we can help to prioritize tasks requests to increase the efficiency of their works for both technical and non-technical workers. This involves managing daily incoming tasks based on their level of urgency and importance.To allow all workers to utilize the urgency-importance matrix as a time-management tool, we need to automate this tool. The textual content of incoming tasks are analyzed, and metrics related to urgency and importance are extracted. A third factor (i.e., the response variable) is defined based on the two input variables (urgency and importance). Then, machine learning applied to the data to predict the class of incoming tasks based on data outcome desired. We used ordinal regression, neural networks, and decision tree algorithms to predict the four levels of task priority. We measure the performance of all using recalls, precisions, and F-scores. All classifiers perform higher than 89% in terms of all measures.
Linearly Constrained Weights: Reducing Activation Shift for Faster Training of Neural Networks

In this paper, we first identify activation shift, a simple but remarkable phenomenon in a neural network in which the preactivation value of a neuron has non-zero mean that depends on the angle between the weight vector of the neuron and the mean of the activation vector in the previous layer. We then propose linearly constrained weights (LCW) to reduce the activation shift in both fully connected and convolutional layers. The impact of reducing the activation shift in a neural network is studied from the perspective of how the variance of variables in the network changes through layer operations in both forward and backward chains. We also discuss its relationship to the vanishing gradient problem. Experimental results show that LCW enables a deep feedforward network with sigmoid activation functions to be trained efficiently by resolving the vanishing gradient problem. Moreover, combined with batch normalization, LCW improves generalization performance of both feedforward and convolutional networks.
Anonymizing Personal Images Using Generative Adversarial Networks

This paper introduces a first approach on using Generative Adversarial Networks (GANs) for the generation of fake images, with the objective of anonymizing patients information in the health sector. This is intended to create valuable images that can be used both, in educational and research areas, while avoiding the risk of a sensitive data leakage. For this purpose, firstly a thorough research on GAN’s state of the art and available databases has been developed. The outcome of the research is a GAN system prototype adapted to generate personal images that imitates provided samples. The performance of this prototype has been checked and satisfactory results have been obtained. Moreover, a novel research pathway has been opened so further research can be developed.
An Energy-Efficient Allocation Technique for Distributing Resources in a Heterogeneous Data Center

Now a days currently there is a lot of power consumption in data centers due to high demand in cloud services like online data storage services, software services on cloud like Google Apps, Sales force and platform as a service on cloud. Due to heavy usage of all these services over the Cloud, now a day's Data Centers are consuming a heavy amount of Energy. This heavy Energy Consumption by data centers is not only including the higher running cost of data centers but it is also effecting the environment inversely. There are basically two ways in which we can reduce energy consumption in Data Centers, first way is by minimising the parameter of data centers while the second method is by exercising an efficiently constructed asset allocation technique to get the optimal balance between energy consumption and performance of the data centers. In this paper, we are dealing with the second approach which is a software based approach i.e. designing an efficient resource allocation technique while the first approach is a hardware based approach. Furthermore, here we will not only deal with homogeneous data centers but we are also considering the heterogeneous data centers. So, our prime focus of this research work is to allocate the resources in homogeneous and heterogeneous data centers in such a manner that energy consumed by data centers usage becomes optimal and energy consumption is reduced without effecting in the performance of data centers. Therefore, for the above we have come up with a novel algorithm toward energy efficiency in such a way that it takes care of scheduling the algorithm fairly while allocating the resources of data centers.
Cross-Platform Mobile CALL Environment for Pronunciation Teaching and Learning

Mobile technologies promote computer-assisted language learning (CALL) while mobile applications, being learner-oriented by design, provide a powerful founding to build individual self-paced environments for language study. Mobile CALL (MALL) tools are able to offer new educational contexts and fix, at least, partially, the problems of previous generations of CALL software. Nonetheless, mobile technologies alone are not able to respond to CALL challenges without cooperation and interaction with language theory and pedagogy. To facilitate and formalize this interaction several criteria sets for CALL software has been worked out in recent years. That is why an approach based on using mobile devices is a natural way to transfer the learning process from teaching-centered classroom to a process, which is oriented to individual learners and groups of learners with better emphasis on supporting individual learning styles, user collaboration and different teaching strategies. Pronunciation teaching technology in one of areas, where the automated speech processing algorithms and corresponding software meet the problems of practical phonology. Computer-assisted prosody teaching (CAPT), a sub-domain of CALL, is a relatively new topic of interest for computer scientists and software developers. Present-day advancement of mobile CAPT tools is supported by evolutionary processes in the theory of language learning and teaching. This paper explores language–technology relations using a case of StudyIntonation – a cross-platform multi-functional mobile CAPT tool based on a digital processing core for speech processing, visualization and estimation developed by the authors. We particularly address the problems of developing CAPT evaluation frameworks. To define the problematic points of the project and understand the directions for future work, we discuss an approach to formalized evaluation using a set of CAPT-specific criteria drawing attention to such evaluation factors as general descriptive information, instructional purposes, functionality, usability, and presentation.
Micro-ecologies of sound: Elemental dialog at work

This article aims to investigate interactions between biologic elements of human bodies and soundwaves, paying attention to both the appropriate material of theoretic research and the artistic practices of recent years.Starting from the specificity of sound perception and its difference from visual one, thus highlighting the immediacy of soundwaves and their immersive cosmic character, the author goes on with consideration of approaches which try to operate with these features of sound equalizing humans with other «objects» on the planet as subjects of sound affection.Giving soundscape practices their due, the author demonstrates their still «all too human» intention to reorder «natural» sound assemblages to the please of human ear and suggests to explore artworks which immerse «listening bodies» into technically supported interaction with sound structures on the elemental level causing their modulations and mutations.Elemental.Human.Agent.
Spatio-temporal Editing Method and Application in Virtual Reality Video

VR, as a new media video game expression form, differs in its temporal and spatial order from the real world. Through cognitive psychology experiments, data on eye movements and head movements were collected, factors affecting presence and continuity were analyzed, and a spatio-temporal editing design method based on regions of interest (ROI) and transition effects was studied. Then, develop a ROI visualization analysis system, and use successive over relaxation optical flow algorithms to efficiently calculate texture, motion, and depth of field. The experimental results show that continuous editing will inhibit spatial presence and weaken the psychological experience of spatial fusion; the significant position deviation before and after editing and the double significant position design will enhance behavioral exploration and at the same time weaken temporal continuity. Finally, the application practice was carried out through spatio-temporal editing design, which verified the effectiveness of the design method.
Handwritten Gujarati Word Image Matching using Autoencode

Gujarati is the native language of the state of Gujarat. In worldwide more than 65.5 million people speak Gujarati. Large numbers of Gujarati printed and penmanship documents are accessible in digital format. However, fetching information in these digital document images is a crucial yet important task. Proposed work focuses on Gujarati handwritten word-matching. As it is very difficult to extract features manually from handwritten word images, a convolutional Autoencoder model is deployed to automatically learn optimum features from data. The trained model is expected to convert the two-dimensional word-image into a compact feature vector without losing the discriminative strength of features. A moderate-sized Gujarati handwritten word-image database was also generated for experiment purpose since there is no such database available in the literature. The initial results are encouraging and warrant further investigation in this direction.
Enhancing Breast Cancer Classification via Information and Multi-model Integration

The integration of different sources of information for proper classification is of utter importance, specially in the biomedical field. Many different sources of information can be collected from a patient and they all may contribute to an accurate diagnosis. For example in cancer disease these can include gene expression (RNA-Seq) or Tissue Slide Imaging, however, their integration in order to correctly train a classification model is not straightforward. Making use of Whole-Slide-Images, this work presents a novel information integration model when different sources of data from a patient are available, named as Multi-source integration model (MSIM). Using two different Convolutional Neural Networks architectures and a Feed Forward Neural Network, the potential of a multi-model integration process which combines the information of different sources is introduced and its results are presented for Breast Cancer classification.
Social Networks Spam Detection Using Graph-Based Features Analysis and Sequence of Interactions Between Users

The growing interest in the different social network platforms leads to huge number of interactions between different users all around the globe. These unlimited interactions provide a suitable environment for spammers to spread as the complexity of the social networks increases. Automatic detection of such malicious users inside this crowd of complex interactions is one of the most difficult research problems. Different approaches have been adopted to contend against malicious activities. Among the different promising approaches is the one relying on using graph analysis techniques. In this paper, we suggest two representation models for social interaction's graph-based datasets. The representation models are mainly developed based on analyzing interactions and relations between users. The first model is developed based on graph-based analysis, while the other one is developed based on sequential processing of user interactions. Based on the conducted experiments, we conclude that the two representation models show high spam detection accuracy. However, graph-based analysis models produce higher accuracy levels compared to those produced by interaction sequences processing models.
SPA: Verbal Interactions between Agents and Avatars in Shared Virtual Environments using Propositional Planning

We present a novel approach for generating plausible verbal interactions between virtual human-like agents and user avatars in shared virtual environments. Sense-Plan-Ask, or SPA, extends prior work in propositional planning and natural language processing to enable agents to plan with uncertain information, and leverage question and answer dialogue with other agents and avatars to obtain the needed information and complete their goals. The agents are additionally able to respond to questions from the avatars and other agents using natural-language enabling real-time multi-agent multi-avatar communication environments.Our algorithm can simulate tens of virtual agents at interactive rates interacting, moving, communicating, planning, and replanning. We find that our algorithm creates a small runtime cost and enables agents to complete their goals more effectively than agents without the ability to leverage natural-language communication. We demonstrate quantitative results on a set of simulated benchmarks and detail the results of a preliminary user-study conducted to evaluate the plausibility of the virtual interactions generated by SPA. Overall, we find that participants prefer SPA to prior techniques in 84% of responses including significant benefits in terms of the plausibility of natural-language interactions and the positive impact of those interactions.
A Hybrid Approach for Effective ImageDeduplication Using PCA, SPIHT and Compressive Sensing

Data deduplication technology is known as lossless data compression strategy primarily focuses on the strategy of deletion of repeated data. So, with the use of this technology the cost of data transmission and storage can be considerably reduced. In the facebook or whatsapp applications there is a huge volume of image and video data. If a celebrity tweets on twitter then it will be broadcasted. Famous images are also duplicated number of times. If similar operations of this type occur every time, it will cause a lot of waste of available storage space on the server. So just to increase available storage volume is not going to eradicate the problem. So there should be an image deduplication process that should be applied to the all available social networks worldwide. To increase the security in the deduplication system, it is proposed that the image will be encrypted as well as decrypted with a convergent encryption key which is acquired by mathematical computing the hash cost of the available image data. So there is need of encryption, compression and data deduplication strategy to deal with this type of situation. So the main objective of this paper is to propose a new hybrid compression strategy that can compress the data at suitable dimensions with the help of a suitable encryption technique at client side before compression mechanism. In the end of process the final image of fed to server for the purpose of image deduplication strategy that can remove or delete the duplicated data. Comparison of proposed scheme with the existing searchable encryption schemes with respect to memory size will also be done. The comparison results show the efficiency of proposed scheme.