Reviewerly/exHarmony huggingface dataset:
MUCH

Due to the abstraction of scenes, comprehensive scene understanding requires semantic modeling in both global and local aspects. Scene recognition is usually researched from a global point of view, while dense captioning is typically studied for local regions. Previous works separately research on the modeling of scene recognition and dense captioning. In contrast, we propose a joint learning framework that benefits from the mutual coupling of scene recognition and dense captioning models. Generally, these two tasks are coupled through two steps, 1) fusing the supervision by considering the contexts between scene labels and local captions, and 2) jointly optimizing semantically symmetric LSTM models. Particularly, in order to balance bias between dense captioning and scene recognition, a scene adaptive non-maximum suppression (NMS) method is proposed to emphasize the scene related regions in region proposal procedure, and a region-wise and category-wise weighted pooling method is proposed to avoid over attention on particular regions in local to global pooling procedure. For the model training and evaluation, scene labels are manually annotated for Visual Genome database. The experimental results on Visual Genome show the effectiveness of the proposed method. Moreover, the proposed method also can improve previous CNN based works on public scene databases, such as MIT67 and SUN397.
PAIG Rewriting: The Way to Scalable Multifunctional Digital Circuits Synthesis

Main objective of this paper is to introduce a novel methodology for scalable synthesis of multifunctional (polymorphic) digital circuits. Despite the fact that several approaches have been proposed during recent years, those are applicable for small-scale circuits only or based on various evolution-inspired techniques. Obvious, there does not exist yet scalable synthesis methodology for complex multifunctional circuits. The proposed methodology is based on And-Inverter Graphs (AIGs) with built-in extension for multifunctional circuits where the employment of rewriting techniques reduces the area by sharing common resources of two different input circuits. Experiments performed on publicly available benchmark circuits demonstrate significant area reduction.
Multi-Platform Authorship Verification

At the present time, there has been a rapid increase in the variety and popularity of messaging systems such as social network messaging, text messages, email and Twitter, with users frequently exchanging messages across various platforms. Unfortunately, in amongst the legitimate messages, there is a host of illegitimate and inappropriate content - with cyber stalking, trolling and computerassisted crime all taking place. Therefore, there is a need to identify individuals using messaging systems. Stylometry is the study of linguistic features in a text which consists of verifying an author based on his writing style that consists of checking whether a target text was written or not by a specific individual author. Whilst much research has taken place within authorship verification, studies have focused upon singular platforms, often had limited datasets and restricted methodologies that have meant it is difficult to appreciate the real-world value of the approach. This paper seeks to overcome these limitations through providing an analysis of authorship verification across four common messaging systems. This approach enables a direct comparison of recognition performance and provides a basis for analyzing the feature vectors across platforms to better understand what aspects each capitalize upon in order to achieve good classification. The experiments also include an investigation into the feature vector creation, utilizing population and user-based techniques to compare and contrast performance. The experiment involved 50 participants across four common platforms with a total 13,617; 106,359; 4,539; and 6,540 samples for Twitter, SMS, Facebook, and Email achieving an Equal Error Rate (EER) of 20.16%, 7.97%, 25% and 13.11% respectively.
External tracking devices and tracked tool calibration

Spatial measure device, i.e. tracking system, is the enabling technology for any surgical navigation system. The primary function of a tracking system is to infer the pose (orientation and position) of surgical instrument intraoperatively in real time. The secondary function is patient registration: locations of anatomical landmarks can be inferred by the use of a tracked and calibrated surgical instrument. Because the tracking system can only track its pose sensor directly, a pose sensor must be integrated into the surgical instrument; the geometry of the instrument must be related to its pose sensor via a spatial calibration process. This chapter provides a short overview on the principle tracking technology, error propagation, and specifics of surgical instrument calibration.
The Analysis of E-Learning Model with Technology of Acceptance Model (TAM) Method in Faculty of Computer Science Sriwijaya University

Abstract Sriwijaya University is one of the state universities located on the island of Sumatra, precisely in south Sumatra. It has 10 Faculties, and one of them is faculty of Computer Science (Fasilkom). As one of the best universities with A accreditation, Sriwijaya University always improves the learning system to make it easier for academic community to do teaching and learning process to be more effective and efficient. Therefore, its faculty of Computer Science also applies the learning process using e-learning. That is why we need a method of Technology Acceptance Model (TAM) to analyze the level of student acceptance of the implementation of e-learning because with the e-learning, lecturers, students and employees in Fasilkom can conduct the learning process more easily and quickly. In addition, with the implementation of e-learning, students and lecturers can do the teaching-learning process without having face-to-face sessions in the classroom. The students can also directly interact and discuss each other in terms of group work.
New features of non-linear time-dependent two-level atoms

In this paper, two-level atoms interacting with a single-mode radiation field alongside important properties are presented. The model presented discusses multi-photon process and it has also a nonlinear Kerr-medium and Stark-shift. In addition, the coupling parameter is discussed in a time-dependent fashion. The results show that Stark-shift, time-dependent coupling parameter, and nonlinear Kerr-medium play significant roles regarding the evolution of the von-Neumann entropy and the system's geometric phases. We've used accessible parameters to examine these observations, and some new results are obtained. Remarkably, the geometric phase shows high sensitivity to any change of the considered parameters.
Perspective Analysis of Digital Voting Systems

Detection of fake votes is a major problem in current voting system. Traditionally, Electronic voting machines have been developed to improve the reliability of the electoral process. Generally EVM's requires more manpower. Some of the recent technologies have been contributed much to improve the Election process with different voting concepts. But still there is a difficulty in detecting the fake votes. In this article we examine the various concepts to achieve transparency over the database and also explores various key challenges in digital voting.
A general construction method for Pseudo-Random Number Generators based on the Residue Number System

In this paper a general construction method for Pseudo-Random Number Generators (PRNG) is described. The main idea is to give form to a topology similar to a Hopfield neural network in which each neuron is replaced by a small PRNG. The Residue Number System (RNS) is used to perform the involved calculations in each feedback line in order to both introduce non-linearity to the system, and to use binary representations of little number of bits. Different tests are applied to measure the statistical properties of the generated sequence of numbers. These tests are basic statistical tests, the Diehard test, statistical complexity measurements, zipping complexity and the Hurst exponent.
See-Thru: Towards Minimally Obstructive Eye-Controlled Wheelchair Interfaces

Eye-tracking interfaces increase the communication bandwidth between humans and computers when using hands is not possible. For some, eyes are the only available input modality to control and interact with the various devices that enable their independence. The goal of this work is to develop and evaluate an eye-controlled wheelchair navigation interface that minimizes obstruction to the user's field of view by removing the conventional use of a computer screen as a feedback mechanism. We present See-Thru, an eye-tracking interface that provides feedback to the user without a screen while simultaneously providing a clear view of the path ahead. Our prototype is evaluated against a screen-based state of the art interface in a study with three navigation tasks completed by seven power wheelchair users. Our results show that a majority of the participants not only prefer using the See-Thru interface, but perform better at driving tasks when using it. This supports the notion that users favor minimally obstructive interfaces in navigational contexts.
Labeled Property Graphs: SQL or NoSQL?

There are two main approaches to graph databases: based on RDF model and based on labeled property graph model. RDF is well known and studied, but modern graph databases with labeled property graph model are studied much lesser. In this paper we evaluated several possible solutions for storing and querying graph data using Gremlin - general purpose graph query language from Apache TinkerPop. We used LDBC Graphalytics framework and compared NoSQL-based setups with SQL-based setups. We evaluated JanusGraph on HBase both on single machine and cluster and SQLG on top of PostgreSQL and H2. We used datasets from the different domains and of different sizes up to tens of millions vertices and edges. Evaluation results show that for the used workload SQLG with PostgreSQL is about ten times faster than JanusGraph on HBase and SQLG with H2 performance is in between.
Image Deblurring under Impulse Noise via Total Generalized Variation and Non-Convex Shrinkage

Image deblurring under the background of impulse noise is a typically ill-posed inverse problem which attracted great attention in the fields of image processing and computer vision. The fast total variation deconvolution (FTVd) algorithm proved to be an effective way to solve this problem. However, it only considers sparsity of the first-order total variation, resulting in staircase artefacts. The L1 norm is adopted in the FTVd model to depict the sparsity of the impulse noise, while the L1 norm has limited capacity of depicting it. To overcome this limitation, we present a new algorithm based on the Lp-pseudo-norm and total generalized variation (TGV) regularization. The TGV regularization puts sparse constraints on both the first-order and second-order gradients of the image, effectively preserving the image edge while relieving undesirable artefacts. The Lp-pseudo-norm constraint is employed to replace the L1 norm constraint to depict the sparsity of the impulse noise more precisely. The alternating direction method of multipliers is adopted to solve the proposed model. In the numerical experiments, the proposed algorithm is compared with some state-of-the-art algorithms in terms of peak signal-to-noise ratio (PSNR), structural similarity (SSIM), signal-to-noise ratio (SNR), operation time, and visual effects to verify its superiority.
Malignancy Determination Breast Cancer Based on Mammogram Image With K-Nearest Neighbor

Breast cancer is the most common cancer in women and for both men and women is the second highest type of cancer in the world. Early diagnosis and treatment is one way to provide appropriate treatment for the presence of breast cancer so that the risk of developing cancer from benign to malignant can be minimized. Before the physical symptoms of cancer appear, mammography is an efficient tool for detecting early breast cancer. In Computer Aided Diagnosis using the detection mammogram images that are either dependent on extraction features and classification methods. In this study, the process of determining features uses first order with six characteristics which will then become the basis for the classification process with KNN. The results obtained were the highest accuracy values found when the value of K = 5 with an accuracy of 91.8% for classification with cross-validation and K = 15 with an accuracy of 91.8% with classification without cross-validation.
Towards Long-Term Coverage and Video Users Satisfaction Prediction in Cellular Networks

Network operators are interested in continuously monitoring the satisfaction of their customers to minimise the churn rate: however, collecting user feedbacks through surveys is a cumbersome task. In this work we explore the possibility of predicting the long-term user satisfaction relative to network coverage and video streaming starting from user-side network measurements only. We leverage country-wide datasets to engineer features which are then used to train several machine learning models. The obtained results suggest that, although some correlation is visible and could be exploited by the classifiers, long-term user satisfaction prediction from network measurements is a very challenging task: we therefore point out possible action points to be implemented to improve the prediction results.
Efficient Group Data Sharing In Cloud Environment Using Honey Encryption

Cloud computing is a rapid growing advanced technology which is Internet based, providing various ways for storage, resource sharing, and various features. It has brought a new way to securely store and share information and data with multiple users and groups. The cloud environment deals with many problems, and one of the most important problems in recent days is the security issues. Sharing the data in a group, in cloud conditions has turned into a blazing theme in up and coming decades. Thus the blasting interest in cloud computing, ways and measures to accomplish secure and effective information and data sharing in the cloud is a flourishing point to be engaged. In this way, the venture centers around empowering information sharing and capacity for a similar gathering inside the cloud with high security and intensity. Therefore, Honey Encryption and Advanced Encryption Standard is used for providing security for the data shared within the group by the crew members in cloud environment. In addition, an access key is provided by the Group Manager to enable access to the documents and files stored in cloud by the users for specific time period.
A polymorphic RPC calculus

The RPC calculus is a simple semantic foundation for multi-tier programming languages such as Links in which located functions can be written for the client-server model. Subsequently, the typed RPC calculus is designed to capture the location information of functions by types and to drive location type-directed slicing compilations. However, the use of locations is currently limited to monomorphic ones, which is one of the gaps to overcome to put into practice the theory of RPC calculi for client-server model. This paper proposes a polymorphic RPC calculus to allow programmers to write succinct multi-tier programs using polymorphic location constructs. Then the polymorphic multi-tier programs can be automatically translated into programs only containing location constants amenable to the existing slicing compilation methods. We formulate a type system for the polymorphic RPC calculus, and prove its type soundness. Also, we design a monomorphization translation together with proofs on its type and semantic correctness for the translation.
Learning to Disentangle Latent Physical Factors for Video Prediction

Physical scene understanding is a fundamental human ability. Empowering artificial systems with such understanding is an important step towards flexible and adaptive behavior in the real world. As a step in this direction, we propose a novel approach to physical scene understanding in video. We train a deep neural network for video prediction which embeds the video sequence in a low-dimensional recurrent latent space representation. We optimize the total correlation of the latent dimensions within a variational recurrent auto-encoder framework. This encourages the representation to disentangle the latent physical factors of variation in the training data. To train and evaluate our approach, we use synthetic video sequences in three different physical scenarios with various degrees of difficulty. Our experiments demonstrate that our model can disentangle several appearance-related properties in the unsupervised case. If we add supervision signals for the latent code, our model can further improve the disentanglement of dynamics-related properties.
How to Use Ambiguity in Problem Framing for Enabling Divergent Thinking: Integrating Problem Structuring Methods and Concept-Knowledge Theory

Collective behaviors and participatory models could be hampered by the presence of ambiguity that reflects the multiplicity of interpretations that different actors bring to a modeling exercise. Despite commonly overlooked in modeling, how ambiguity in subjective problem frames is embraced determines the quality of the participatory modeling process. This work describes an innovative approach based on the integration of Problem Structuring Methods, specifically Fuzzy Cognitive Mapping (FCM), and Concept-Knowledge (C-K) design theory, as a means to transform ambiguity from a barrier to an enabling factor of divergent thinking in participatory modeling. The integration of methods allows to identify and analyze ambiguity in problem framing, avoiding viewpoints’ polarization that hampers the development of collective behaviors. However, individualistic problem frames can still yield organized collective actions when these frames are sufficiently aligned. Often environmental policies fail because decision-makers are not aware of the misalignment and their decisions are based on wrong assumptions about the others’ problem frames. This work discusses the results of two case studies aimed to design environmental policies for groundwater protection in Kokkinochoria area (Republic of Cyprus) and Apulia Region (South-East Italy), demonstrating the potential of FCM and C-K theory integration in supporting divergent thinking in participatory modeling.
Identifying and Allocating Resources during Out of Hospital Cardiac Arrest

In out of hospital cardiac arrest (OHCA) situations, public access to an automated external defibrillator (AED) increases patient survival when it is applied to the patient before the arrival of an emergency team. Time is of essence during an OHCA event and it is crucial to identify the nearest functional AED, and a user to transport the device to the patient in the shortest time. Determining the best combination of the user and AED for an emergency situation when there are multiple users and AEDs available becomes an issue. There are multiple factors that can change the selection of the pair; for example, the walking pace of the user, AED status, battery level of the user's phone, the AED itself, and other factors. It is important to optimize these factors so that the best user and AED combination can be identified and directed so they will take the least amount of time to reach the emergency. The new IoT AEDs being developed will have a communication ability to transmit information. A multi-objective optimization approach has been proposed in this paper to prioritize a multitude of factors dependent upon the level of importance of each factor to the OHCA situation. Each factor will be individually prioritized and then optimized so the best combination of user and AED device can reach the emergency location in minimal time.
Designing VR and AR Systems with Large Scale Adoption in Mind

Large scale adoption of novel solutions is the ultimate goal in many domains, and numerous factors need to be addressed to reach that success. This process is even more challenging when those systems are intended for human operators. Not only the technical performance of the system needs to be of the desired quality, but a range of other characteristics also gets scrutinized as well. The design and development of learning and training solutions will be encumbered by additional factors characteristic of learning and training processes. Current adoption of learning and training solutions is far from the desired state: the extent to which learning and training solutions became an every-day practice of their intended users is still much lower than the investment made in this domain. Our research suggests that a good part of that blame can be laid on elements of system design that did not match users’ needs, skills, and expectations. In this paper, we report the results and lessons learned in multiple efforts focused on design and prototyping of a diverse set of training systems that used both immersive and non-immersive virtual reality technologies and a variety of 3D user interface solutions. Approaches discussed and suggested in this paper are equally applicable to the design of systems intended for other human activities in both civilian and military domains.
A Two-Level Method for Image Deblurring Problem

Image deblurring model with computationally expensive regularization is used to improve the quality of the deblurred images. These models are very efficient in preserving edges and removing staircase effect and other nice properties. However, the associated Euler-Lagrange equations involve high order derivatives or dense matrices which complicate developing an efficient numerical algorithm. In this research work, we present an efficient and robust Two-Level method to overcome these difficulties. The Two-Level method started by reducing the problem to one small, nonlinear system on image with small number of pixels (Level-I) and one less expensive linear system with large number of pixels (Level-II). So by Two-Level method our results will be effective and less expensive.
Evaluation of SQL Injection Prevention Methods

In the last few years, the usage and dependency on web applications and websites has significantly increased across a number of different areas such as online banking, shopping, financial transactions etc. amongst the several other areas. This has even directly multiplied the threat of SQL injection issue. A number of past studies have suggested that SQL injection should be handled as effectively as possible in order to avoid long term threats and dangers. This paper in specific attempts to discuss and evaluate some of the main SQL injection prevention methods.
Analytical Data Mart for the Monitoring of University Accreditation Indicators

The CAACES (Consejo de Evaluación, Acreditación y Aseguramiento de la Calidad de la Educación Superior), develops evaluation processes to accredit Higher Education Institutions of Ecuador. In order to support the accreditation process we propose an analytical data mart to a) obtain suitable information for its fast understanding and management, b) avoid the dispersion of the data required for the university accreditation. The implementation was initially developed with the indicators of students' criterion according to Institutional Evaluation Model of Universities and Polytechnic Schools incorporated by CEAACES and in the future, other indicators can be added by other accreditation criteria. The project was developed using the Kimball methodology and an open source tool of BI (Business Intelligence), the Pentaho suite. The BI solution allows the efficient monitoring of indicators prior to university accreditation, reducing the response time and resources in the report generation process.