Reviewerly/exHarmony huggingface dataset:
A Temporally-Aware Interpolation Network for Video Frame Inpainting

We propose the first deep learning solution to video frame inpainting, a more challenging but less ambiguous task than related problems such as general video inpainting, frame interpolation, and video prediction. We devise a pipeline composed of two modules: a bidirectional video prediction module and a temporally-aware frame interpolation module. The prediction module makes two intermediate predictions of the missing frames, each conditioned on the preceding and following frames respectively, using a shared convolutional LSTM-based encoder-decoder. The interpolation module blends the intermediate predictions, using time information and hidden activations from the video prediction module to resolve disagreements between the predictions. Our experiments demonstrate that our approach produces more accurate and qualitatively satisfying results than a state-of-the-art video prediction method and many strong frame inpainting baselines. Our code is available at https://github.com/sunxm2357/TAI_video_frame_inpainting .
Queries-Based Profile Evolution using Genetic Algorithm

User's interests are important in query reformation, filtering or recommender systems. Besides, the user profile is always dynamic due to the instability of his interests and the increase among documents. Thus, its evolution is required however identifying the changes in user's interests over time may be challenging. Recent works focused on user interests evolution, takes into account the collection by matching the document and the query or by analyzing log file of user's interactions with the system (feedbacks). However, they did not consider the queries exclusively in the evolution process. This paper proposes a new user profile evolution approach in which user's queries and interests are used to update the user profile. To do so, we use genetic algorithm technique to extract relevant interests so that to detect new interests, improve the weights of the existing ones or delete the useless among them from the profile.
Blockchain Technology: A Case Study from Greek Accountants

While most Blockchain technology seems to be new, applications of this technology, beyond electronic payments, such as supply chains, health care and asset management appear every day. All of them stem from the distinctive advantages of Blockchain technology; volatility, trust, transparency and reliability. Blockchain already affects the banking and accounting sectors, and all its economy-abided activities. Under this context, the purpose of this study is to explore the knowledge and the attitude of Greek accountants towards Blockchain technology and its advantages and disadvantages with regards to the accounting area. For the analysis, a convenient sample of 100 Greek accountants was used. Results reveal that Greek accountants use digital infrastructure and technology, but they report low knowledge on Blockchain technology and lack of trust in using it for their transactions. The questionnaire has been factor analyzed and the following factors emerged: (a) The attitude of the accountants, (b) The need for validated files and reliability of sources, (c) The reliability of Blockchain technology, and (d) The need to eliminate intermediaries. Implications for providing more training through seminars and education for the accounting sector, businesses and employees are discussed.
Hierachical classification structure based on SVM for volcano seismic events

Volcano monitoring is very important in many countries. Chile is located in the Pacific fire belt, so the country has hundreds of volcanoes along its territory that may affect the economic and social activity. The main variable to monitor is volcano seismicity, as the recognition of seismic patterns originated inside the volcano gives important information about its activity. The great number of volcanos and the huge amount of seismic records makes necessary to automate some steps of this process. This papers presents a hierarchical structure based on Support Vector Machines (SVM) to classify four classes of seismic events recorded by the seismic stations. The implementation of the structure was performed using the database of the Llaima volcano and the classifiers were tested in another volcano, the Nevados de Chillán Volcanic Complex. The results show a good performance with a mean accuracy of 89.7% for the Llaima volcano and 84.9% for the Chillán volcano, with no retraining process for this new seismic dataset.
A Roadmap to Realization Approaches in Natural Language Generation

Text realization is the most significant step involved in natural language generation. It involves the approaches used to generate syntactically and semantically valid text, given an abstract linguistic representation. Based on the data and nature of data, a typical task generation includes text-to-text generation, database-to-text generation, concept-to-text generation, and speech-to-text generation. There are many approaches of natural language generation to generate texts, usually from non-linguistic structured data, which varies from a canned text approach to methods of learning from a text corpus and generating text based on the characters, content, keywords, size of the text, context, etc. Much work has also been done in learning and generating text mimicking a writing style. For applications like tutoring systems, wherein the text has to be manipulated and validated, we have to rely more on a template-based approach. Machine learning and other probabilistic-based statistical approaches can generate text for applications like report generation, summarization. This paper presents a roadmap and a comparative analysis of various text realization approaches.
Optimizing Feature Selection Parameters using Statistically Equivalent Signature (SES) Algorithm

Selection of important feature from the high dimensional dataset is a very important task. Irrelevant and insignificant features can hinder the important information of the dataset. For accurate classification of the dataset, selection of most predictive variable is a much-needed task. Feature selection is important for the diagnosis, prognosis and treatment of any disease in case of healthcare dataset. Traditional feature selection algorithm gives the output as a single feature subset of the predictive variable. But the selection of a single feature subset cannot give the perfect idea about the nature of the dataset. The study shows that variable which is not selected by some of the feature selection algorithms will also play a major role in defining the target value of the data sample. Statistically Equivalent Signature (SES) algorithm even consider the set of such important features by considering the Markov blanket of the target variable. The root of SES is in the causal theory and Bayesian network. SES select subset of important features based on the concept of conditional independence. Finally a subset of equally predictive features/variables selected based on the Markov Blanket of target variable T. This leads to improved accuracy when considered with other feature selection algorithms. This paper presents a detailed study for selecting multiple predictive feature subsets that are statistically equivalent and their comparison with the existing feature selection algorithms. It gives an overall idea to process high dimensional datasets especially microarray (gene expression) which play a vital role in the estimation of deadly diseases such as cancer.
Dual-objective Programming Model for Equipment Resource Allocation and Its Algorithm Implementation

Abstract This paper in view of the allocation of equipment resources in enterprise groups is studied. Firstly, it introduces the 0-1 decision variable, and takes the order group self-made processing period, equipment resource condition and self-made parts processing status as constraints, emphasizing the processing quality control of the self-made parts required for key important features. A dual-objective programming model of equipment resource allocation based on quality and cost is constructed, and the dual-objective function is transformed into a single objective function by linear weighting method, and then, the improved honey evolutionary genetic algorithm is used to solve it. Finally has carried on the simulation analysis, the simulation analysis results show that the resource allocation dual-objective programming model and the algorithm are effective.
Strong Tracking Filters: Derivation and Improved Heuristic

This paper reviews variants of &#x201C;strong tracking filters,&#x201D; and offers a new method of adjusting the innovation matrix that allows the filter to be used with measurements of varying dimensionalities over time. Strong tracking filters are more robust to model mismatches than Kalman filters. Variants of the extended strong Kalman filter are compared to the extended Kalman filter for two types of model mismatch.
A Context-Based Searching Technique by Extraction and Fusion of Metadata of Digital Photos

Memories of events hold the essence of human life. To preserve and restore the ailing memories, digital photo is the most popular approach. In this paper, we present a technique to create a semi-automatic photo memorandum for context-based photo visualization and retrieval by extracting, extending and analyzing the metadata contained in digital photos. Today people tend to capture and share any significant memory or information through photographs. This practice of capturing and storing digital photographs is leading the photo collection of an individual grow exponentially. Thus, it would be a strenuous and time consuming work to find a particular photo from this vast collection if the photos are not labeled with their descriptive properties, i.e., the context. Moreover, the value of a photo dissipates if it cannot be retrieved whenever necessary. Since the human mind stores the context of a photo rather than the creation date or its digital information, it would be convenient to fetch photos by utilizing their contextual data. This paper demonstrates an approach of context-based searching for photos, along with metadata generation and diffusion for context building which will preserve and retrieve the memory by labeling them with various data that remains unnoticed in digital photos with the help of internal and external sources. The work is underpinned by a novel search technique to extract photos using natural language.
Extended Target Tracking and Classification Using Neural Networks

Extended target/object tracking (ETT) problem involves tracking objects which potentially generate multiple measurements at a single sensor scan. State-of-the-art ETT algorithms can efficiently exploit the available information in these measurements such that they can track the dynamic behaviour of objects and learn their shapes simultaneously. Once the shape estimate of an object is formed, it can naturally be utilized by high-level tasks such as classification of the object type. In this work, we propose to use a naively deep neural network, which consists of one input, two hidden and one output layers, to classify dynamic objects regarding their shape estimates. The proposed method shows superior performance in comparison to a Bayesian classifier for simulation experiments.
A Computational Evaluation of Distributed Machine Learning Algorithms

Over the past few years, availability of data throughout the internet has scaled to petabytes. Processing this data demands a strong computing mechanism rather than usual machine learning techniques. Distributed machine learning solves this problem with distributed system environment. It refers to multi-node machine learning algorithms and systems that are designed to scale to larger input data sizes. To understand the best possible utilization for each algorithm in a parallel computing environment, a detailed study of these distributed machine learning algorithms is necessary. To summarize and evaluate their performance, parameters such as Execution time, CPU usage, Memory usage rate and Accuracy have been considered for each algorithm. The experiment conducted on LightGBM, CatBoost, AdaBoost and XGBoost demonstrated that CatBoost performed the best with an accuracy of 81.31% amongst the other algorithms. This analysis will thus help beginners and business firms chose the appropriate algorithm for their specific distributed machine learning application.
The ICT crisis in regular basic education in Peru. Teachers' perception of the incorporation and technologies used in the classroom

This paper sets out the objective of analyzing the perception that regular basic education peruvian teachers have on the use of information and communication technologies (ICT) in the educational field. For this, a quantitative-descriptive research was structured and the database of the II Encuesta Nacional a Docentes (Peruvian Teachers Survey - ENDO) carried out by Ministerio de Educació n (Peruvian Ministry of Education - Minedu) in 2016 was used, the results of which were published in 2018. The sample is conformed by 9823 teachers of public and private schools of all country. The results allow us to conclude that there is a lack of knowledge about the use and benefits of ICT applied to the teaching-learning process, which is reflected in the moderately positive perception of the ICT use in the pedagogical work of teachers.
Jump Markov Nonlinear System Identification for Behavior Classification in Multi-Sensor Target Tracking

The detection of unusual behavior plays a crucial role in the prevention of illegal and harmful activities such as smuggling, piracy, arms trading, human trafficking, and illegal immigration. Furthermore, for military applications, it is useful to detect anomalous behavior to provide an alert for potential threats, especially with the more recent widespread use of drones for warfare and terrorist activities. In order to provide a solution for these emerging needs, in this work, a novel method for target dynamic behavior classification by analyzing trajectories using data gathered from multiple heterogeneous sensors is presented.
On the Design of a Privacy-Centered Data Lifecycle for Smart Living Spaces

Many living spaces, such as homes, are becoming smarter and connected by using Internet of Things (IoT) technologies. Such systems should ideally be privacy-centered by design given the sensitive and personal data they commonly deal with. Nonetheless, few systematic methodologies exist that deal with privacy threats affecting IoT-based systems. In this paper, we capture the generic function of an IoT system to model privacy so that threats affecting such contexts can be identified and categorized at system design stage. In effect, we integrate an extension to so called Data Flow Diagrams (DFD) in the model, which provides the means to handle the privacy-specific threats in IoT systems. To demonstrate the usefulness of the model, we apply it to the design of a realistic use-case involving Facebook Portal. We use that as a means to elicit the privacy threats and mitigations that can be adopted therein. Overall, we believe that the proposed extension and categorization of privacy threats provide a useful addition to IoT practitioners and researchers in support for the adoption of sound privacy-centered principles in the early stages of the smart living design process.
The Design and Development of a Web-based Smoking Complaint System

With the advancement of technology, the Internet users are increasing exponentially. In this technology enhancement era, web-based complaint management system becomes more favored and therefore, it becomes more effective and efficient to manage the complaints received, especially for the service-oriented organizations or business operators that are profit-oriented. Up to date, little literature could be found with regards to community welfare and public health complaint systems developed for the non-profit-oriented organizations or companies, especially as an intervention to combat tobacco use at public or work places. This study aims to fill in the gap by designing and developing a web-based smoking complaint management system as an innovative initiative to support the Blue Ribbon Campaign initiated by the World Health Organization, to fight the tobacco epidemics within the context of Malaysia. A framework for this web-based smoking complaint management system is proposed. This study employs the iterative waterfall model that incorporates the adapted human-centered agile work flow. The interface design and the development of the system are described. Limitation and recommendations for future works are also presented.
Assessment on various Approaches for Content Based Image Retrieval

Content Based Image retrieval(CBIR) has a important place in research field. This paper deals with the realization of different approaches used in image retrieval based on content. In CBIR, a query image is searched from larger database by selecting features from image and then exact match is retrieved using efficient algorithms. The various CBIR approaches are reviewed such as relevance feedback, SVM, graphs, wavelet transform, Gabor filter, Semantic templates, GMM, Fuzzy logic, Object ontology, Machine intelligence and Histogram. In this paper, the various graph theory algorithms used in CBIR have been discussed and compared viz. NN Graphs, Collocation Tree, Graphlets, Efficient Manifold Ranking, A* algorithm and segmentation algorithm.
Towards Improving Human Arithmetic Learning using Machine Learning

Basic arithmetic is an essential skill that is used in almost all career paths in some way. Ensuring that young children have a solid foundation in simple mathematical concepts is a worldwide goal and new methods to improve arithmetic learning are constantly being developed. Our aim is to utilise machine learning to assist learners with developing their basic mathematics skills by identifying the types of problems a user struggles with and presenting them with targeted questions to improve in these areas. In this paper we focus only on the prediction component: given a set of arithmetic questions and corresponding answers, can we predict which future questions a user will answer incorrectly? The accuracy and suitability of four machine learning models are evaluated using data from computer-generated agents as well as human users. On simulated agents, our models achieve accuracies of around 79% to 96% with decision trees performing the best. On human data, our models achieve accuracies in the range of 63% to 69%, with the decision tree once again outperforming other approaches. We hope that these error predictions models could be incorporated into future E-learning systems targeted at human arithmetic learning.
Preserving the Privacy of Audio File Using Ideal Secret Sharing Scheme with Cloud Storage

Mohan, Meghna Ashoka Jayakumar, T. V. Praveen, K.Cloud storages like Google Drive, Firebase and Dropbox can be used for storing huge data owned by the users. Privacy has always been an issue with such technologies. The properties of security—confidentiality, integrity and availability are vital when privacy is concerned. Secret sharing schemes have been widely used for preserving the privacy of the user's data in the following way. The data can be divided into multiple shares using secret sharing scheme, and each shares can be stored in different cloud storages. This paper puts forward a novel approach of splitting audio files into shares using ideal visual secret sharing scheme which is a lightweight approach instead of number theory-based secret sharing method. In an ideal visual secret sharing scheme, after reconstruction of the secret using secret shares, we will get back the original data without any changes. This method also makes sure that with the help of malwares an attacker cannot retrieve the information of the secret from any of the shares alone which is stored in the cloud.
n-m-Variant Systems

Web servers are a popular target for adversaries as they are publicly accessible and often vulnerable to compromise. Compromises can go unnoticed for months, if not years, and recovery often involves a complete system rebuild. In this paper, we propose n-m-Variant Systems, an adversarial-resistant software rejuvenation framework for cloud-based web applications. We improve the state-of-the-art by introducing a variable m that provides a knob for administrators to tune an environment to balance resource usage, performance overhead, and security guarantees. Using m, security guarantees can be tuned for seconds, minutes, days, or complete resistance. We design and implement an n-m-Variant System prototype to protect a Mediawiki PHP application serving dynamic content from an external SQL persistent storage. Our performance evaluation shows a throughput reduction of 65% for 108 seconds of resistance and 83% for 12 days of resistance to sophisticated adversaries, given appropriate resource allocation. Furthermore, we use theoretical analysis and simulation to characterize the impact of system parameters on resilience to adversaries. Through these efforts, our work demonstrates how properties of cloud-based servers can enhance the integrity of Web servers.
Maximize the Cloud Profit to Improved QoS in Cloud Computing: Design and Analysis

the cloud computing offers the efficient computation and the storage for providing the high performance computing. In this context the different job management schemes and scheduling techniques are implemented for improving the performance of cloud servers. Because the cloud servers are much costly it is required to utilize the maximum resources for gaining the profit from the cloud infrastructure. In order to perform this task two major cases are considered for providing the solution. First the server waiting for job and all the resources are free, secondly the job achieving their waiting time and rent the third party server temporarily. In this context for demonstrating the issues and challenges first the job allocation system is explained using the suitable algorithm steps. In further for improving the performance of the server three additional algorithms are implemented which computes the maximum possible profit on the basis of available server speed and size. The implementation of the proposed system is provided using the cloudsim simulation tool and with the help of JAVA technology. In addition of that the performance of the system is also identified in terms of net profit, optimal profit and the maximum profit. The proposed technique is found acceptable for the real world scenarios also.
Extractive Text Summarization Technique Using Fuzzy C-Means Clustering Algorithm

Text summarization process has become one of the significant research areas for years owing to cope up with the astounding increase of virtual textual materials. Text summarization is the process to keep the relevant important information of the original text in a shorter version with the main ideas of the original text. There are two main classifications of text summarization process Extractive and Abstractive text summarization, Extractive summarization processes by using most important fragments of existing words, phrases or sentences from the original document, A sentence based mode) using Fuzzy C-Means clustering has been proposed in this research. Six best key features including a new feature “Sentence Highlighter Feature” have been introduced for the sentence scoring. Performance of the proposed FCM mode) is evaluated by ROUGE, which has been gauged with the precision, recall and f-measure. The result shows that this FCM model extractive techniques with a less outline repetition and profundity of data.