Reviewerly/exHarmony huggingface dataset:
A Prnu-Based Method to Expose Video Device Compositions in Open-Set Setups

As the diffusion of altered video sequences over social networks and the internet can have severe consequences (e.g., fake news spreading, false accusations, etc.), the forensic research community has started actively working toward the development of methodologies tailored to assess the authenticity and integrity of video sequences. In this work, we focus on the problem of spotting video compilations composed by temporally concatenating sequences acquired with different devices. To solve this problem, we leverage trace characteristics of each video recording device left on each video at acquisition time. Specifically, we propose a method leveraging Photo-Response Non-Uniformity (PRNU) traces along with a binary classifier in order to understand which frames of a video have been acquired with the same device used to record the first few frames. Results show that the proposed solution outperforms baselines based on standard PRNU correlation and thresholding tests. Experiments have been carried out in an open-set scenario and show promising results.
Relationship between corruption and capital flight in Kenya: 1998-2018

The study established the relationship between corruption and capital flight in Kenya over the period 1998 to 2018. Quarterly time series data for calculation of capital flight and for GDP growth rate and exchange rates were collected from the Central Bank of Kenya and Kenya National Bureau of Statistics. Corruption perception index data was collected from the Transparency International website. Two Autoregressive Distributed-lagged models were fitted. Regression coefficients for corruption were -0.114 and 0.066 in the short run and -0.501 in the long run and the p values were 0.523 and 0.691 and 0.558 respectively, indicating no significant relationship. Regression results showed a coefficient of 0.01 and 0.003 for the Gross Domestic Product growth rate in the short run, and 0.049 in the long run. The p values were 0.670, 0.855 and 0.578 respectively denoting no significant relationship. Regression results showed a coefficient of 0.002 and 0.003 for the exchange rate in the short run, 0.43 for the exchange rate in the long run. The p values were 0.891 and 0.584 and 0.095 respectively indicating that a one % increase in the exchange rate would lead to a 0.043 % increase in capital flight in the long run. Regression results of lagged capital flight on capital flight showed a coefficient of 0.904. The p-value was 0.000 meaning that a one % increase in lagged capital flight would lead to a 0.904 % increase in capital flight. The study recommended that the government devises policies that would prevent further capital flight and generate capital flight reversal.
The Implementation of Spatial Weighted Regression on Detecting the Risk Factors of Malaria Incidences In Kulonprogo District

Malaria is still a public health problem caused by Plasmodium parasites. This disease is naturally transmitted through the bite of female Anopheles mosquitoes. Kulonprogo Regency is one of the malaria endemic areas that needs to be developed to combat malaria. This research aims to describe the distribution of malaria cases to determine vulnerable areas in Kulonprogo Regency. This research was an observational analytic with cross sectional design conducted in April - October 2018. This research used the total sampling of 62 malaria cases. Data analysis was performed by using a Geographic Information System approach with overlay, buffer, cluster techniques, and spatial weighted regression. Based on the results of the partial analysis, it was found that 62 cases of malaria spread in 6 sub-districts namely: Kokap, Pengasih, Samigaluh, Kalibawang, Wates and Sentolo. The distribution of malaria cases clustered in Kokap Sub District, namely in Kalirejo Village (29 cases) and Hargorejo Village (9 cases). The overlay and buffer techniques found that the distribution of cases spread in Menoreh hills which have forest vegetation. Malaria-prone areas in Kulonprogo Regency were found in Kalirejo Village and Hargorejo Village through the results of spatial analysis using a Geographic Information System approach. Thus, malaria control efforts were focused on these vulnerable areas.
Implicitly Learning to Reason in First-Order Logic

We consider the problem of answering queries about formulas of first-order logic based on background knowledge partially represented explicitly as other formulas, and partially represented as examples independently drawn from a fixed probability distribution. PAC semantics, introduced by Valiant, is one rigorous, general proposal for learning to reason in formal languages: although weaker than classical entailment, it allows for a powerful model theoretic framework for answering queries while requiring minimal assumptions about the form of the distribution in question. To date, however, the most significant limitation of that approach, and more generally most machine learning approaches with robustness guarantees, is that the logical language is ultimately essentially propositional, with finitely many atoms. Indeed, the theoretical findings on the learning of relational theories in such generality have been resoundingly negative. This is despite the fact that first-order logic is widely argued to be most appropriate for representing human knowledge. In this work, we present a new theoretical approach to robustly learning to reason in first-order logic, and consider universally quantified clauses over a countably infinite domain. Our results exploit symmetries exhibited by constants in the language, and generalize the notion of implicit learnability to show how queries can be computed against (implicitly) learned first-order background knowledge.
Traceability for a Knowledge-Driven Software Engineering

Software Engineering (SE) industry views traceability as a means to obtain a comprehensive view of relevant knowledge. For a successful execution and delivery of large projects, the core Software Engineering process and artifacts need to be synchronized with processes and artifacts associated with the business domain, project management, and compliance.
Copula Gaussian graphical modelling of biological networks and Bayesian inference of model parameters

Understanding complex biological networks enable us to better understand the systems’ diseases such as cancers and heart attacks, and to produce drug targets which is one of the major research questions under the personalized medicine. But the description of these complexities is challenging since the associated data are very sparse, high dimensional and seriously correlated. The copula Gaussian graphical model (CGGM), which depends on the representation of the multivariate normal distribution via marginals and a copula term, is one of the successful modelling approaches to present such type of datasets. In this study, we apply CGGM in modelling steady-state activations of biological networks and make inference of model parameters under Bayesian settings. We suggest the reversible jump Markov chain Monte Carlo (RJMCMC) algorithm to estimate plausible interactions between the systems’ elements which are proteins or genes. We also generate the open-source R codes of RJMCMC for CGGM under diﬀerent dimensional networks. In the application, we use real datasets and evaluate the accuracy of estimates via F1-score. From the results, we observe that CGGM with RJMCMC is successful in the presentation of real and complex systems with higher accuracy and can be a promising approach to understand biological networks and diseases.
Real-time image restoration for space-object imaging

Military applications such as optical space surveillance and civilian applications such as astronomical imaging often require adaptive optics to compensate images of distant objects that are dynamically blurred by atmospheric turbulence. Many factors prevent adaptive optics (AO) from restoring a fully diffraction-limited image quality. Accordingly, restoration methods such as blind deconvolution and contrast enhancement are applied to further improve such imagery. Sometimes, the restoration must take place with low-latency and real-time frame rates because video imagery needs to be viewed promptly. This paper describes a procedure for conducting multi-frame blind deconvolution on experimental AO-compensated imagery in real time. In the procedure, registration and windowing enabled deconvolution, and subsequent enhancements improved the visibility of object features for visual assessment. This process features "multi-frame online blind deconvolution," which is a modification of the previously published "online blind deconvolution." This modified algorithm jointly processes multiple frames simultaneously, making it a true multi-frame, blind deconvolution method. The new method was tested on simulated and experimental imagery. The full procedure was implemented on a workstation with a low-end graphics processing unit, and timing tests were evaluated to estimate execution times.
Impossibility of blind quantum sampling for classical client

Blind quantum computing enables a client, who can only generate or measure single-qubit states, to delegate quantum computing to a remote quantum server in such a way that the input, output, and program are hidden from the server. It is an open problem whether a completely classical client can delegate quantum computing blindly (in the information theoretic sense). In this paper, we show that if a completely classical client can blindly delegate sampling of subuniversal models, such as the DQC1 model and the IQP model, then the polynomial-time hierarchy collapses to the third level. Our delegation protocol is the one where the client first sends a polynomial-length bit string to the server and then the server returns a single bit to the client. Generalizing the no-go result to more general setups is an open problem.
An Image-based Recommender System Based on Image Annotation

Recommender system is a software that analyzes available data to make recommendations about various products and services to their users might be interested in . These systems must perform efficient  for both users and the e-commerce sites benefiting  from such systems. Ensuring proper and reliable recommendations increases user satisfaction that results selling more products and services. Collaborative filtering, content-based, and hybrid techniques are type of methods for recommender systems. Content-based recommender systems are usually text-based systems, but image-based recommender systems have become increasingly in favour for the content-based recommender systems in recent years .    The process of an image based recommender system is to match a users’ image with the most similar image and recommend it. The recommendation images are the most likely images uploaded and widely acclaimed from the users. The most challenging problem in image-based recommneder systems is to match an image with the most similar visual words or classes based on the image’s visual content.  In this study, we are planning to solve this problem using Bag of words model, which is an effective model in computer vision, and also features are extracted with commonly used descriptors.
Application of an Enhanced Self-adapting Differential Evolution Algorithm to Workload Prediction in Cloud Computing

The demand for workload prediction approaches has recently increased to manage the cloud resources, improve the performance of the cloud services and reduce the power consumption.The prediction accuracy of these approaches affects the cloud performance.In this application paper, we apply an enhanced variant of the differential evolution (DE) algorithm named MSaDE as a learning algorithm to the artificial neural network (ANN) model of the cloud workload prediction.The ANN prediction model based on MSaDE algorithm is evaluated over two benchmark datasets for the workload traces of NASA server and Saskatchewan server at different look-ahead times.To show the improvement in accuracy of training the ANN prediction model using MSaDE algorithm, training is performed with other two algorithms: the back propagation (BP) algorithm and the self-adaptive differential evolution (SaDE) algorithm.Comparisons are made in terms of the root mean squared error (RMSE) and the average root mean squared error (ARMSE) through all prediction intervals.The results show that the ANN prediction model based on the MSaDE algorithm predicts the cloud workloads with higher prediction accuracy than the other algorithms compared with.
Learning Visual Actions Using Multiple Verb-Only Labels

This work introduces verb-only representations for both recognition and retrieval of visual actions, in video. Current methods neglect legitimate semantic ambiguities between verbs, instead choosing unambiguous subsets of verbs along with objects to disambiguate the actions. We instead propose multiple verb-only labels, which we learn through hard or soft assignment as a regression. This enables learning a much larger vocabulary of verbs, including contextual overlaps of these verbs. We collect multi-verb annotations for three action video datasets and evaluate the verb-only labelling representations for action recognition and cross-modal retrieval (video-to-text and text-to-video). We demonstrate that multi-label verb-only representations outperform conventional single verb labels. We also explore other benefits of a multi-verb representation including cross-dataset retrieval and verb type manner and result verb types) retrieval.
Higher education and economic development in Nepal

A plethora of existing literature indicates that human capital has a positive association with the economic development of a country. This chapter investigates the role of higher education in the context of the economic development in Nepal. The Nepalese education system has evolved through different phases in the past few decades that have been rife with political changes. Given the increasing focus on the importance of higher education, this chapter explores its connection to the country’s economic development. The chapter also points out the mismatches, issues, challenges as well as opportunities relating to higher education and economic development of Nepal.
Cargo Tracker: Axon Framework

We have now implemented three variations of the Cargo Tracker:
Improved Multi Model Procedure to Explore Medical Image Retrieval based on Visual Semantic Signatures

In medical sectors, medical imaging is animportant concept in real time environments.Different types of medical images are captured and stored in digital format in medical research centers.Facing this type of large volume of image data with different types of image modalities, it is very important to implement efficient content based image retrieval (CBIR) for medical research centers.Visual features based image label indexing is a limitation to explore efficient image retrieval from different medical sources.So that, in this paper, we propose Novel Multi Model Semantic Approach (NMMSA) is introduced based on recent and advanced machine learning and visual based indexing approaches.In this approach, we first investigate the semantic analysis to integrate visual features based label of image from different medical image sources to provide connection to retrieve images based on label indexing with visual features.Experimental results with high amount of medical images have been shown the performance of the proposed approach in terms of medical image indexing and image retrieval systems.
Akronim dalam Komunitas Motor di Kota Merauke

This research will identify, classify, and analyze based on the phenomena of words formation. The data has been collected from some interviews of the motorcycle community members. Theories used in this research by Sampurno and Finoza.The results of this research shows that the acronym used exclusively in the motorcycle community. According to the form of an acronym, there are four forms, namely: form initial characters of the word, syllable form, form initial character of the word and syllable, phrases form. And to the function in this research that acronym as a shorthand phrase or name, slogan or humor, distinguishing origin of a words and meaning.Keywords: acronym, form, function, community
A Survey of Blockchain Applications beyond Cryptocurrency

Blockchain since its inception in 2008 is being gaining a wide publicity. It is in early phase and its adoption is a general concern. There is a need of more interdisciplinary research to understand the barriers, enablers and diffusion of this technology. This paper reviews on the key applications of blockchain technology along with its benefits. Smart Vehicle Management, Agriculture, Healthcare, Cryptocurrency and Waste Management are the potential Areas for research in this technology. The research would be further carried out to build a prototype model for waste management using this technology.
Comprehensive Evaluation System of Teaching Quality Based on Big Data Architecture

Aiming at the problems of long response time and high error rate in traditional teaching quality comprehensive evaluation system, a teaching quality comprehensive evaluation system based on big data structure is proposed. Firstly, different evaluation indicators are designed, the principle of teaching quality comprehensive evaluation is designed by using data dictionary, the requirement analysis of teaching quality comprehensive evaluation system is realised by using UML use case model, then the overall framework of the system is designed, and the comprehensive evaluation system of teaching quality is developed and studied by using Asp.net related technology. The experimental results show that the response time of the designed system is shorter than that of the traditional teaching quality comprehensive evaluation system, and the fitting degree between the evaluation results and the actual situation is higher, which fully proves the superiority of the designed system.
Destructive Method with High Quality and Speed to Counter Information Hiding

Hidden information behavior is becoming more difficult to be detected, causing illegal information spreading. To counter information hiding, more complex and high-dimensional steganalysis methods are applied, and have good detection for some specific information hiding algorithms. However, huge time consumption is needed and only suitable for offline usage with this method. In this paper, a destructive method with high quality and speed is proposed to counter information hiding. The experimental result presents that the extracting error rate of hiding information is around 50%, which means the method can destroy the possible covert communication completely. At the same time, the quality of covers after the destructive operation is good and the average time of operating an image is millisecond.
ACM RecSys'19 late-breaking results (posters)

As part of the main program of the 2019 ACM Recommender System Conference, the Late-Breaking Results offers a unique opportunity to share with the community the latest ideas related to recommender systems. This year, we received 42 submissions for the track, out of which 13 were accepted, resulting in a acceptance rate of 31%.
Comments on “The definition of half-Gabor filter” by Lunke Fei [Pattern Recognit. Lett. 69 (2016) 35–41]

The aim of this comment is to point out wrong definition given by Fei (2016) on continuous half-Gabor filters, the conflict between the definition and the figure about the appearance of the half-Gabor has been clarified as well, and then the correct definition of the continuous half-Gabor filters is provided. Theoretical analysis and illustrative simulation show that our inference is correct.
GSODR: Global Surface Summary of the Day ('GSOD') Weather Data Client

Provides automated downloading, parsing, cleaning, unit conversion and formatting of Global Surface Summary of the Day ('GSOD') weather data from the from the USA National Centers for Environmental Information ('NCEI'). Units are converted from from United States Customary System ('USCS') units to International System of Units ('SI'). Stations may be individually checked for number of missing days defined by the user, where stations with too many missing observations are omitted. Only stations with valid reported latitude and longitude values are permitted in the final data. Additional useful elements, saturation vapour pressure ('es'), actual vapour pressure ('ea') and relative humidity are calculated from the original data and included in the final data set. The resulting metadata include station identification information, country, state, latitude, longitude, elevation, weather observations and associated flags.
A SIFT with RANSAC Based Spatial Tampering Detection in Digital Video

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Security model to protect patient data in mHealth systems through a Blockchain network

On this research paper we propose a security model to protect patient data on mobile health systems (mHealth) through a Blockchain network. This model is implemented under a Blockchain platform that allows collecting, sharing and integrating data in a safe way through a mobile app for mHealth devices, for medical care in Peruvian clinics. This security model consists of three stages: 1. Data collection, 2. Data processing, 3. System monitoring. It should be noted that the patient is autonomous in the management of his information, and that each user requires a single identifier to get access to the data. A test scenario was defined to validate the proposed model. Also, the study was conducted with a group of users through a health mobile app and the used medical data was provided by a hospital in Peru as anonymized research data. During the study, we validated the following topics: access control to the network, access to medical information of authorized users, data integrity on each transaction and performance evaluation of the system under a high user transaction load. Preliminary results showthe system average response time is 4.72 seconds for 10,000 users carrying out requests simultaneously.