Reviewerly/exHarmony huggingface dataset:
Learning from Mistakes: Weakly Supervised Learning of Rocks

A standard method for teaching an object detector a new class is to fine-tune it with a fully-supervised image set. The issue with fully-supervised image sets are that they are tedious to create and rely on human annotators. In this work we demonstrate that a new class can be learned by leveraging the `mistakes' of a pre-trained object detector under a weakly supervised learning (WSL) paradigm and removing the need for a fully-supervised image set. Our method iteratively cycles over four stages: observation, filtering, generation, and fine-tuning. The observation stage uses an object detector to gather inferences on an image set containing many instances of the new class. These observed inferences are passed to the filtering stage that keeps only the most frequently observed class inferences. These filtered inferences are used as object-level annotations and are passed to the generation stage. In the generation stage a training set is created by superimposing the image content of the annotations onto a set of background images. The generated training set is then used in the fine-tuning stage to create an updated object detector. We trained an object detector to recognize the novel object class, rock using this method. We compared the average precision obtained by an object detector trained using our method to an object detector trained using the fully-supervised method. We were able to achieve ~78% of the average precision obtained by the fully-supervised version.
An Optimal Placement of ADMS in Cloud Data Center

This research is focused on finding an assignment that minimizes the number of physical machines (PMs) that are used for mapping a collection of virtual machines (VMs) for the deployment of the cloud-based Advanced Distribution Management System (ADMS). The paper compares two approaches: a) one that is based on the exhaustive search and b) the multiple constraints bounded Knapsack algorithm based approach. An example of optimal mapping for multiple ADMS deployment is used to illustrate the reduction in number of physical resources needed. Also, it compares the required duration of the exhaustive search and the Knapsack algorithm execution, and illustrates the potential resource optimization depending on the overcommitment level. It is shown that virtually enabled resource sharing enables significant reduction of allocated physical resources and that the Knapsack based approach is more efficient (as compared to the exhaustive search) and can potentially lead to a fully dynamic placement and deployment reconfiguration in real-time even for a case of a large number of VMs.
Feature Extraction from Japanese Natural Language Requirements Documents for Software Product Line Engineering

Analyzing and extracting features from requirement specifications is an indispensable activity to support Software Product Line Engineering. However, performing features extraction is a time-consuming and inefficient task, since massive textual requirements need to be analyzed and classified. Most of the current approaches exhibited limitations: hindered applicability with requirements in Japanese; the support tools proposed were not made available publicly and thus making it hard for practitioners' adoption. This paper proposes a feature extraction approach from requirement specifications in Japanese using natural language processing techniques. Also, we propose a ranking method for extracted features to reduce efforts reviewing feature candidates. A case study was conducted to evaluate the performance of the proposed approach. Initial results show that 90.7% features were extracted correctly, and the top 40% features extracted contained 79.1% true features.
A Study of New Variable Selection Method Within a Framework of Real-Coded Genetic Algorithm

Recently, variable selection and parameter optimization are becoming increasingly important. Regarding parameter optimization, real-coded genetic algorithms (RCGA) have received attention due to their strong searching ability and flexibility. The Akaike information criterion (AIC) or Bayesian information criterion (BIC) are traditionally used as variable selection criteria. These criteria estimate the relative quality of analysis models for a given set of data, but they cannot be used to evaluate the importance of the variables themselves. This paper proposes a new variable selection method that applies RCGA. This new variable selection method contains two primary components. One is a new variable selection criterion, and the other is a method for estimating the progress of RCGA optimization. The effectiveness of this new variable selection method is confirmed through application to the sum of squares function, which is a nonlinear test function.
3D Model Generation for Education Using Augmented Reality

Augmented Reality is the presentation of objects superimposed on the real world view of the environment. It is an innovative technology that allows multidimensional viewing and understanding of concepts. This paper aims to use this concept of Augmented Reality in the field of Education to create an application that generates 3D models of the images embedded in textual materials using the software Unity and Vuforia. The application uses a smartphone’s camera to scan the embedded image of a textbook, e-book or photocopy and generates a 3D working model with live animations as well as on-click functionalities to view various movements of the model by providing user interaction. This application aims to provide an active learning experience to students.
A Fast Multi-phases Demon Image Registration for Atlas Building

Medical image registration is an essential branch in computer vision and image processing, and it plays a vital role in medical research, disease diagnosis, surgical navigation, and other medical treatments. However, existing methods are time-consuming; the progress of image registration is difficult to observe, and fewer works for human brain atlas building use image registration method. In this paper, we first introduce a fast multi-phase demon registration (FMDR) model for image registration and atlas building. To show the applicability of our FMDR model, we use synthetic circle data to illustrate a faster and more accurate result of our model than other benchmark methods. We also demonstrate the morphological changes of a TBI case, which shows the continuous shape changes from a diseased state to a healthy state. To illustrate the performance of our model, we use a set of T2 MRIs to estimate the template image for atlas building.
Formalizing Graph Database and Graph Warehouse for On-Line Analytical Processing in Social Networks

Graph databases have been widely employed for representing connected pieces of information for different kind of domains. The data model embraces relationships as a core aspect to connect objects, and organizes everything into a network for efficient query processing of versatile applications, e.g., on-line social networking, metropolitan traffic modeling, marketing channels simulations or even counterterrorism analysis. As an emerging technology for encoding network structures, graph databases are also widely used as an infrastructure for social network analytics, which help us understand some phenomenon or hidden knowledge in buzz marketing, technology trends or public issues regarding social behaviors. Although many graph database management systems have been developed, there are still no formal definitions for theoretical graph database modeling. In this paper, we will present a formal definition for graph database model, extend the concept of data warehouse into graph warehouse, and define the basic elements of a graph warehouse for the development and derivation of graph-based multi-dimensional business intelligence through on-line analytical processing (OLAP) on graph databases.
Ambulance Detection System

In this paper, we aim to develop a computer vision system to detect robustly the ambulances observed from a static camera. Robust and reliable ambulance detection plays an important role for priority systems. These aim to find the shortest possible paths of ambulances till their destination by managing signaling networks with traffic lights. If the emergency vehicle gets stuck in a traffic jam and its arrival at the incident location is delayed, it can cause the loss of lives and property. The approach is based on two main stages: Feature extraction step and classification step. For the first step, we used many descriptors to extract features, such as HOG (Histogram of Oriented Gradient), LBP (Local Binary Patterns), and Gabor filter. Then, we classified these features using machine learning algorithms like SVM (Support Vector Machines) and kNN (K-nearest neighbor). To evaluate the performance of our constructed models, we calculated many metrics: True Positive, False Positive, False Negative, True Negative, accuracy and runtime. In fact, our achieved results show that the ambulance detection system can be successfully exploited for the overall system.
Locations and session types in a language with higher-order reflection

We propose the localised ρ-calculus, a process calculus that uses an identification of processes and names in higher-order communication along with a notion of locations. These are named, bounded areas for computation, organised in a hierarchy, and processes can move from one to another by crossing their boundaries. Process migration becomes higher-order communication. To control the usage of channels we develop a type system with binary session types for the localised ρ-calculus, based on work by Giunti and Vasconcelos, and prove that it is sound.
Active Scene Recognition

Detailed technical presentation of our contributions that are related to Active Scene Recognition. This includes our approaches to Object Pose Prediction and Next-Best-View estimation.
Model of Shared Secret Applied to a Voting System for the National Electoral Council of Ecuador

The vote is one of the pillars of Democracy to make the decision to choose different dignities, ICTs have an important role in governance to optimize resources. The problem is to improve the information security in the electoral process. The objective is to define a trust model that applies Secret Sharing to a voting system, to protect electoral information in the stages of electronic voting. The deductive method and the exploration were applied to examine the information of the articles cited. It resulted a Conceptual model of voting system, General design of voting system, SS Algorithm applied to voting system expressed in flow chart, Application prototype and Prognosis of electronic voters. It was concluded that to improve the information integrity in a voting system, the application of a cryptographic algorithm is necessary; Ecuadorian population has 66.36% of internet access that should be exploited to maximize benefits and minimize errors.
Self-adaptive Speckle Pattern Based 3D Measurement System

Speckle size plays an important role in speckle-based 3D measurement systems. In real-time systems, frequent speckle size variation may be required as the object goes out of the specified range or the camera aperture has to be adjusted to observe the optimal speckle size on the image plane. In this paper, we present a system that adapts itself to achieve the desired speckle size when the measuring distance is changed. The system uses a motorized stage to vary the distance of the diffuser and the observation plane to change the speckle size which is calculated in real time. The performance of the proposed system is compared with the conventional speckle pattern and the result indicates that the proposed speckle method substantially improves the optimal range of the measurement system.
Secure signal processing using fully homomorphic encryption

In a world becoming dependent on cloud computing, multiple and many techniques are needed to meet security and privacy requirements. This study investigates using Fully Homomorphic Encryption (FHE) in the cloud for a set of signal processing algorithms. Using FHE provides a level of security and privacy for cloud computing. This research implements a framework with a set of real number processing formats that allows FHE to be used in the cloud. Developing formats for real numbers is required because FHE operates over integers. Both a fixed point binary format and a rational number format are implemented to allow for real number processing. Experimentally, the framework is implemented and verified with three algorithms: fast Fourier transform, speeded up robust features, and histogram of oriented gradients. Each implementation is bounded with error caused by the formats and examined against images (two-dimensional signals).
Exploring Gaze Interaction Design in Games

Gaze interaction in games is rapidly increasing with examples in mainstream franchises. This research focuses on investigating new opportunities for gaze-enable game design moving away from sensing-based dynamics grounded in gaze pointing and consider a user-centred approach which has its starting point in visual capabilities and metaphors of looking. This approach will lead to the development of examples that illustrate novel and playful experiences that can be designed thinking about how the players see rather than where and what they look at. This research aims to contribute to the understanding of how to create playful gaze-based experiences by providing a design framework and inspire designers to engage in the conversation to shape the future of gaze interaction in play.
Dynamics Analysis of Artistic Gymnastics with Video Motion Capture: Pommel Horse and Vault

Gymnastics motions are difficult to measure with any sensors. Any sensors on the body cannot endure to peel off from the body or to disturb the range of motion. Without any burden like sensors, we measured 2 types of gymnastics: pommel horse, vault. It was realized by Video Motion Capture system, which is markerless 3D motion capture system with RGB cameras. This paper reports the overview of this measurement and shows the followings of analysis result: joint torques, estimated contact forces and torques, the principal inertia moments of center of gravity and energy of wholebody. Also, we visualize rotation easiness of body with the principal inertia moments.
Management of Legal Processes: Statement of the problem

Introduction. The article considers the category of management from the standpoint of history, theory and philosophy. The law and the processes of its implementation in practice have been chosen as the social context within which the importance and potential of management has been studied. Materials and methods. The article actively uses both contributions of natural science and papers written for the purpose of understanding the humani-tarian knowledge. The methodological basis of the article includes the universal method of cognizability of the world, such logical methods as induction and deduction, the achievements of comparative jurisprudence, the method of text interpretation (mainly judicial decisions and regulations). The results of the study. The paper tries to answer the questions what management is in principle, what are the possible and acceptable forms of its manifestation in jurisprudence. The meanings in which management is used in the natural and human sciences are compared. A distinction is made between the concepts of “management of legal processes” and “public administration”. Law enforcement processes are much rationalized, and law has maintenance of order as its highest goal. The absence of reference to management in the procedural legislation serves as an indirect but sure sign that governance in law does not exist in the form in which it is represented in the natural sciences. Discussions and conclusions. Subject to the above it was concluded that management in law is manifested and realized in a peculiar way. It is implemented in legal processes through decentralized principles, i.e. by the participants themselves on a parity and consensual basis. The role of guides is played by symbolic means - the principles of law and the values protected by it. The role of doers is played by instrumental means - law enforcement agencies and institutions. The role of organizes is exercised by the subjects of law (individual, society and the state), and the function of consumers is realized by the subjects of specific legal relations.
INSIGHTS! - a modern deep learning approach to data analysis using Feature Name Substitution Network

The core of technological advancements in the current trend is based on the manipulation of the inestimable amount of data that is generated every second around us. Gaining interesting insights from the data is of utmost importance and the need of the hour. The proposed system makes use of advancements in the domain of deep learning by implementing various algorithms and methodologies to automate the process of data analytics. The intended insights platform is developed using various deep learning frameworks such as Tensorflow, Keras and delivered to the end user as a web platform using Django Framework. The underlying algorithm of insights which makes the automation of analytics possible relies on the efficacy of feature name substitution network implemented using LSTM and the enhanced correlation analysis. These are then used to determine a measure called Insight Relevance Index (IRI) which then updates the global rule set records in the centralized data store accordingly. Employing the proposed system will definitely aid the profit and future growth of an institution or an organization.
Bitcoin�s Crypto Currency: An Analysis from Operational Research and Information Management

Objective: This article focuses on an analysis that interrelates elements of information management with aspects of operations research, with the intention of demonstrating how the behavior of the Bitcoin crypto currency can be predicted.Methods/Statistical Analysis: Initially, Bitcoin crypto currency is characterized (price history, behavior in the market, variation over a period of time, etc.).Then, the data and information collected are applied to two techniques associated with operations research: Markov Chains and Queue Theory.Finally, the information that has emerged is transformed into valuable knowledge based on an information management process.Findings: This research demonstrates how techniques associated with operations research and information management (quantitative analysis and qualitative analysis) can be combined to characterize an object or process, capture data and information associated with it, predict its behavior and transform that information into a knowledge that generates added value, allowing the right decision making, in a specific context.In this way, it is possible to demonstrate how Bitcoin crypto currency is a really stable resource and an asset capable of supporting and transferring value in current and future markets, with a constant upward trend and a behavior very similar to that of conventional currency.Application/Improvements: This analysis establishes that no matter how complex or novel an object or process may be, it can be studied with tools such as these.The key is to implement them in other contexts.
Research on Diagnosis and Treatment Methods of Common Malfunction in Electronic Circuits

Power electronic technology plays an important role in all walks of life. Especially the combination of weak current electronic technology and strong current electronic technology can change dynamic phase structure and promote stable operation of the whole circuit system. It can be seen that stable operation of electronic circuit is directly related to working quality and working efficiency of the whole system. However, in the process of designing, installing and debugging of the electronic circuit, various system failures may occur, which affect normal working operation. Therefore, it is necessary to test the electronic circuit repeatedly to gradually determine the cause of the fault and solve it.
USINN modeler

Interaction and navigation affect how a user will use the system, get content of interest to their, and responses to their requests. It is necessary to plan how these perspectives will be designed, in order to avoid that problems in the use affect the wide adoption of the systems. In order to support the design of these perspectives, it was proposed in recent research in the HCI area, a notation that allows the elaboration of models representing design solutions that can express aspects of usability during interaction and navigation, called USINN (USability-oriented INteraction and Navigation model). However, the notation did not yet have a specific tool to support the development of these models. The absence of a tool made the use of notation laborious and susceptible to syntax errors in the models. This paper presents the preliminary results of the design and evaluation of a tool for modeling interaction and navigation with the USINN notation: the USINN Modeler tool, an open source tool developed for the web. The initial results indicated that the tool facilitates the construction of the models and contributes strongly to the use of the notation.
Enhancing a Keyword Search Using Segmentation and Similarity Measure Algorithms: A Case Study of Phuket Attractions

A system to support an incorrectly typed input keyword search in Thai language is proposed in this work. Segmentation and similarity measure algorithms are employed to enhance the traditional keyword search engine. The average of six similarity measure algorithms including Levenshtein, Overlap (bi-gram), Overlap (tri-gram), Jaccard, Dice (bi-gram), and Dice (tri-gram). The prototype is tested by 93 subjects including both native and non-native Phuket subjects. Top twenty-five Phuket attraction names are used as the data set. The experimental results show that the proposed system can improve the efficiency of the original search from 54.4% to 91.6% while the execution time of the extra steps can be negligible. Moreover, Bi-gram algorithms seem to outperform their Tri-gram counterpaths in this experiment and Jaccard seems to be outperformed by other similarity measure algorithms.
Polymorphic extractors for semantic and portable pattern matching (short paper)

This paper introduces polymorphic extractors, a technique for tackling two main issues with the existing pattern matching techniques in functional languages. First, this technique defines semantic pattern matching rather than a syntactic one. Second, this technique solves the portability issue when defining a set of patterns based on different underlying data-structure design choices. Furthermore, polymorphic extractors can be further improved by performing optimizations and multi-stage programming. The key technique behind polymorphic extractors is using the tagless-final technique (a.k.a. polymorphic embedding/object algebras) for defining different extraction semantics over expression terms.