Reviewerly/exHarmony huggingface dataset:
Greenfields gold deposit exploration techniques using conformal geometric algebra-based arsenopyrite trace element assemblage models

Predictive spatial maps of a mineralising system's geological features are highly desirable and useful from the industrial standpoint of resource exploration, mining, civil and geotechnical engineering. Additionally, such maps have academic value as theoretical models of complex natural phenomena. However, the production of these maps is a challenging endeavour using models based on geochemical data that are sparely populated with uneven statistical distributions. Using a Conformal Geometric Algebra based formulation that predicts geological features from geochemical datasets in a recent work by the authors (referred to as ‘the hyperfield formulation’), this work extends the methods and techniques to the construction of predictive spatial maps of these geological features. This contribution further introduces a novel “intersection space minimisation procedure” and the “raytracing interpolation procedure” and they are used to create comprehensive and accurate spatialised maps from a limited number of predictions in conjunction with neural networks and the hyperfield formulation. Case studies are presented where the sample's distance to mineralisation and the whole rock Au concentrations are predicted and mapped for the Mount Porter deposit, Northern Territory, Australia. • Geochemical data are featurised to form higher-dimensional multivectors • Elements of these multivectors are used to predict geological phenomena • Location of Au mineralisation is predicted using an intersection space minimisation procedure • Au concentration predictions are spatially represented using a novel interpolation procedure
Evaluation of Confidence Sets for Estimation with Piecewise Linear Constraint

Equality constrained estimation finds its application in problems like positioning of cars on roads. This paper compares two constructions of confidence sets. The first one is given by the intersection of a standard unconstrained confidence set and the constraint, the second one applies the constraint first and designs the confidence set later. Analytical results are presented for a linear constraint. A family of piecewise linear constraints is inspected numerically. It is shown that for the considered scenarios, the second construction with a properly tuned free parameter provides confidence sets that are smaller in the expectation.
A Predictive Service for Highway Hotspot Policing

Brazil's road traffic mortality rates have been increasing since the 1960s and in 2003 road traffic crashes in Brazil were responsible for over 26 percent of fatal injuries. Some actions need to be taken to make it safer, hence, we have proposed, implemented, and deployed a support system that predicts and generates, based on the prediction, a program-card for a Federal agency responsible for patrolling highways which cross most of the big Brazilian cities. We used the data from the Federal Highway Police (PRF) about road accidents to test the performance of the generated models and demonstrate the designed dashboard. We obtained a better performance than the baseline of comparison on the three groups used on the study: alcohol_drugs, lack_attention and aggressive driving, reaching a R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score of 0.440, 0.688 and 0.453 respectively.
Cloud Computing Technology for the Network Resource Allocation on the Research of Application

The information revolution has brought about the rapid development of the Internet technology services. Where users obtain required hardware platforms software and services through the network in an on-demand and easily scalable manner are becoming increasingly popular. This article introduces the three architecture models four deployment models five architectures five key technologies of cloud computing and explains how to integrate and share the network through cloud computing, flexibly configure and call software and hardware resources. Under resources it greatly reduces the difficulties encountered in the construction of power costs space costs facility maintenance costs, software and hardware construction costs data maintenance costs, etc., improves network resource utilization, reduces costs and provides users with large-scale massive data storage with handling of services.
Component Tree Computation of 2D Images

Level set methods are fruitful in computer vision, image coding, image processing, geographic information systems, and many other fields. The connected components of the level sets of an image, ordered by inclusion, are organized into a component tree. We present an algorithm to compute the component tree from image level lines. The running time is O(m), where m is the number of image level lines. Experiments show that the new algorithm runs nearly 2 times faster than the existing method.
Story and Task Issue Analysis for Agile Machine Learning Projects

The usage of Agile methodology in planning and executing machine learning (ML) and data science related software engineering projects is increasing. However, there are very few studies using real data on how effective such planning is or guidelines on how to plan such projects. In this paper, we analyze data taken from several software projects using Scrum tools. We compare the data for data science/ML and non-ML projects, in an attempt to understand if data science and ML projects are planned or executed any differently compared to normal software engineering projects. We also perform a story classification task using machine learning to analyze story logs for agile tasks for several teams. We find there are differences in what makes a good ML story as opposed to a non ML story. After analyzing this data, we propose a few ways in which software projects, whether machine learning related or not, can be better logged and executed using Scrum tools like Jira.
A Survey on Recognizing and Significance of Self-portrait Images

An art of taking picture of oneself through selfie camera of smartphones has become ubiquitous. It has turned to be the effective way for expressing ones emotion nowadays. Researchers are analyzing selfie images for multiple aspects like personality, psychology, behavior. Authentication of such images taken under research is doubtful to be a real selfie image. Thus, researchers attempted identifying images as selfie automatically and segregating such images over the database. There are works that helps in detecting selfies through computer vision and deep learning methods. This research defines positive aspects of such investigations to elucidate on various aspects of analysis and highlights the different points of forethought for examining self portraits along with the future research direction.
Piecewise Hashing: A Deep Hashing Method for Large-Scale Fine-Grained Search

Fine-grained search task such as retrieving subordinate categories of birds, dogs or cars, has been an important but challenging problem in computer vision. Although many effective fine-grained search methods were developed, with the amount of data increasing, previous methods fail to handle the explosive fine-grained data with low storage cost and fast query speed. On the other side, since hashing sheds its light in large-scale image search for dramatically reducing the storage cost and achieving a constant or sub-linear time complexity, we leverage the power of hashing techniques to tackle this valuable yet challenging vision task, termed as fine-grained hashing in this paper. Specifically, our proposed method consists of two crucial modules, i.e., the bilinear feature learning and the binary hash code learning. While the former encodes both local and global discriminative information of a fine-grained image, the latter drives the whole network to learn the final binary hash code to present that fine-grained image. Furthermore, we also introduce a novel multi-task hash training strategy, which can learn hash codes of different lengths simultaneously. It not only accelerates training procedures, but also significantly improves the fine-grained search accuracy. By conducting comprehensive experiments on diverse fine-grained datasets, we validate that the proposed method achieves superior performance over the competing baselines.
A Deep Tracking and Segmentation Approach for Soccer Videos Visual Effects

The applications of deep learning algorithm in sports contain enormous potential. Specifically, in soccer, tracking algorithm could record the tracks of players, which could play as an assistant to assess team performance and evaluate strategies. Moreover, through segmentation model, we could extract semantic attributes of players. This auxiliary information may contribute to the special visual effects processing in broadcasting or entertainment area. Unlike general tracking tasks, soccer videos contain much more cases of deformation, blur, and occlusion. In this paper, we propose a novel model which could combine tracking and segmentation together. A novel deformable cross-similarity correlation (DF_CORR) is adopted to estimate the deformation of players. A new soccer tracking dataset is established to evaluate the performance of top-ranked trackers in soccer videos. In soccer tracking dataset, our model outperforms the state-of-the-art trackers whose accuracy is decreased significantly compared with the general tracking tasks. Moreover, our extensive experiments show comparable segmentation performance against SiamMask, while running in a real-time speed of 36.2FPS.
Model Checking Dynamic Strategy Logic with Memoryless Strategies

Recently, strategy specification and verification has been an active research direction in many areas, such as multi-agent systems, artificial intelligence, formal methods, logic, and so on. Since alternating-time temporal logic (ATL/ATL*) was proposed by Alur et al., many strategic logics, which are based on ATL/ATL*, have been introduced by many researchers. Among them strategy logic (SL) is an outstanding representative, which can express more properties like the existence of Nash Equilibrium in game theory; however, it cannot express regular temporal properties, nor epistemic properties.
Virtual Kiosk: Taman Herba

The Virtual Kiosk: Taman Herba is a virtual reality application that enables user to explore Taman Herba at UTHM in a virtual world by controlling the movement using keyboard. However, the exposure of benefits of herbs is very low due to no clear sign and there is not enough information about the herbs. Therefore, this application provide user with a virtual world of Taman Herba and the guidance to use the herbs. The virtual world of Taman Herba also includes with the information of each herbs. To ensure this application is well-developed, TRES-D methodology is used during development. This application will be implemented as a kiosk that will be located nearby the garden. This application expected to be a medium to educate committees to preserve our nature and educate them about the benefits of the herbs. After development of this application, most of the users agree that the overall function of this application is good.
Partial Reconfiguration Feature in Crypto Algorithm for Cloud Application

The implementation of crypto algorithm in general-purpose computing platforms involves security protocols, secured storage, digital certificates, secure execution, digital right management, etc. The present researchers undertook systematic design explorations to implement various crypto issues in hardware in concurrence with the generation changes taking place in FPGA technology including the latest dynamic reconfigurable embedded platform. The partial reconfigurable hardware platform is an advanced and latest version in the growth path of FPGA technology and implementation of various crypto issues in such a system is an important area of today’s research interest. The performance of a software crypto implementation in processors usually takes extensive execution time, while the same implemented in hardware takes much lesser time as well as performs in much cost-effective manner in respect of resource usage and power. Hence, a new approach has to be adopted to design such systems based on metrics involving resource usage, throughput, and power along with adequate security. This new solution to security leads to an issue of optimization of crypto processors which can balance both the hardware and security issues of modern crypto systems. This paper explores three generations of crypto solutions. The upgradation issue of the generation of crypto solutions comes from the changing trend of FPGA technology as well as the crypto systems. The first-generation crypto system operated in processors has less efficiency and is more vulnerable to security, whereas the second generation operated in embedded hardware comes with more security, poor flexibility, and higher design complexity. The third generation using PR platform provides an optimum balance between first- and second-generation crypto solutions leading to high flexibility time and high efficiency. In this paper, we implement third-generation crypto hardware in a fog system which is placed between IoT device and cloud. The adoption of fog in existing architecture reduces the processing cost of the cloud. ISE14.4 suite with ZYNQ7z020-clg484 FPGA platform is used to implement third-generation crypto core.
Upper-Level Types of Occurrent Based on the Principle of Ontological Conservation

Ontologies are usually focused on describing the things that exist in time (i.e., continuants, objects), but the things that happen (i.e., occurrents, processes, events) also play an important role in representing the world. This paper explores the view of occurrents as transitions between situations to propose a framework for the ontological analysis of occurrents. We analyzed this view in light of the notion of material constitution to characterize the subtypes of occurrent according to the differences between the situation that precedes and the one that follows from the happening of the occurrent. With that, we propose the principle of ontological conservation as well as four types of occurrents.
Quantitative Assessment on the Limitations of Code Randomization for Legacy Binaries

Software development and deployment are generally fast-pacing practices, yet to date there is still a significant amount of legacy software running in various critical industries with years or even decades of lifespans. As the source code of some legacy software became unavailable, it is difficult for maintainers to actively patch the vulnerabilities, leaving the outdated binaries appealing targets of advanced security attacks. One of the most powerful attacks today is code reuse, a technique that can circumvent most existing system-level security facilities. While there have been various countermeasures against code reuse, applying them to sourceless software appears to be exceptionally challenging. Fine-grained code randomization is considered to be an effective strategy to impede modern code-reuse attacks. To apply it to legacy software, a technique called binary rewriting is employed to directly reconstruct binaries without symbol or relocation information. However, we found that current rewriting-based randomization techniques, regardless of their designs and implementations, share a common security defect such that the randomized binaries may remain vulnerable in certain cases. Indeed, our finding does not invalidate fine-grained code randomization as a meaningful defense against code reuse attacks, for it significantly raises the bar for exploits to be successful. Nevertheless, it is critical for the maintainers of legacy software systems to be aware of this problem and obtain a quantitative assessment of the risks in adopting a potentially incomprehensive defense. In this paper, we conducted a systematic investigation into the effectiveness of randomization techniques designed for hardening outdated binaries. We studied various state-of-the-art, fine-grained randomization tools, confirming that all of them can leave a certain part of the retrofitted binary code still reusable. To quantify the risks, we proposed a set of concrete criteria to classify gadgets immune to rewriting-based randomization and investigated their availability and capability.
Focused Query Expansion with Entity Cores for Patient-Centric Health Search

The Web provides a plethora of contents about diseases, symptoms and treatments. Most notably, users turn to health forums to seek advice from doctors and from peers with similar cases. However, the benefit of forums mostly lies in community QA and browsing. Expressive querying for patient-centric needs is poorly supported by search engines. This paper overcomes this issue by enriching user queries with judiciously chosen entities and classes from a large knowledge graph. Candidate entities are extracted from the full text of user posts. To counter topical drift that would arise from picking all entities, we devise ECO, a novel method that computes a focused entity core for query expansion. Experiments with contents from health forums and clinical trials demonstrate substantial gains that ECO achieves over state-of-the-art baselines.
The Method of Functional Safety Validation Test of AEBS Based on Fault Injection

In ISO 26262, the functional safety validation test of E / E system is required. Fault injection is a general method of functional safety validation test. AEBS is an important part of vehicle active safety, so it is very meaningful to study the fault injection test of AEBS. In this paper, a method of functional safety validation test of AEBS based on fault injection is studied, including the preparation, execution and passing standards of the test. This test method can be used to evaluate the effectiveness of the security mechanism and is suitable for functional security validation test.
Integration of educational and extracurricular activities of students of college as condition of formation of the general competences of students of college

The article deals with the problem of formation of General competencies of students in the SPO system. The specificity of General competencies in comparison with professional competencies is determined. As one of the conditions for the formation of General competencies, the integration of forms of educational activity of students, namely, the integration of educational and extracurricular activities, is considered. This approach to achieving competencies, motivation for learning and cognitive activity has not been sufficiently studied in science and is of interest.
Characterizing the Coordinating Behaviors in a Three vs. One Ball Possession Game using Gaze Interaction

Coordinating behaviors in group activities is a fundamental skill in our social life. However, people with impairments and disabilities may have difficulty performing a whole-body movement and manipulation of devices; therefore, their opportunities for coordinating behaviors are considerably reduced, especially in virtual environments. In this study, we focus on gaze interaction as a primary interaction method and investigate coordinating behaviors in a virtual environment. In addition, we implement a three vs. one ball possession game controlled using gaze interaction. As a preliminary study, we examine the coordinating behavioral patterns of game players without impairments and disabilities. Moreover, our experimental result demonstrates that it is possible to perform three vs. one ball possession games using gaze interaction. Further, we observe the distribution of angles among the three players, with modes at 56, 72, and 50 degrees for players 1, 2, and 3, respectively. This reveals the approximate shape of the triangle constructed by the players. In the real-world player's skill, the resulted triangle allowed us to categorize the players at the intermediate level.
Improve Robustness of Deep Neural Networks by Coding

Deep neural networks (DNNs) typically have many weights. When errors appear in their weights, which are usually stored in non-volatile memories, their performance can degrade significantly. We review two recently presented approaches that improve the robustness of DNNs in complementary ways. In the first approach, we use error-correcting codes as external redundancy to protect the weights from errors. A deep reinforcement learning algorithm is used to optimize the redundancy-performance tradeoff. In the second approach, internal redundancy is added to neurons via coding. It enables neurons to perform robust inference in noisy environments.
An isomorphic Dvoretzky-Milman Theorem using general random ensembles

We construct rather general random ensembles that yield the optimal (isomorphic) estimate in the Dvoretzky-Milman Theorem. This is the first construction of non gaussian/spherical ensembles that exhibit the optimal behaviour. The ensembles constructed here need not satisfy any rotation invariance and can be heavy-tailed.
Software Effort Estimation Using Early COSMIC to Substitute Use Case Weight

In the size estimation software, there are many methods that have proven their reliability. One of them is Use Case Points (UCP). UCP has a well-known advantage based on the use case scenario which is a reformation of the user story in the software requirements specification (SRS) document. However, UCP also has several weaknesses, including the use case is a summary of the user story. User stories often do not reveal detailed data. Therefore, the potential ambiguity of the use case must be watched by a business/system analyst. On the other hand, there is an international association called COSMIC, which has developed a global standard for calculating the size of the software namely ISO/IEC 19761. The COSMIC model begins with a user story which is then carried out cascade to sequence diagrams to make an engagement between process/method flow and data. The purpose of this study is to substitute the use case weight of the pure UCP method, to become a COSMIC functional size unit (Cfsu). Then, the estimation results of the two are compared with the actual effort. The case study used as a comparison of the COSMIC and UCP methods is the Hair Salon Online Booking Application. From this study the results obtained are the deviation between the results of the original UCP estimate (keep use case weight) of the actual effort is 76.85 percent. As for software effort estimation using early COSMIC is 92.67 percent against the actual effort.
Measurement on Effect of Controlled Wave Phase in EM Fault Injection Attack

The intentional electromagnetic interference (IEMI) injection method using sinusoidal waves cannot be protected with conventional countermeasures proposed and expanded the range of target devices. Therefore, evaluating devices against side-channel attacks is more important than in the past. A control in the phase of sinusoidal waves used in this attack can affect the extraction of secret keys. Nevertheless, there was not enough discussion about the effects of the controlled phase in conventional studies. We considered the effect of control in the phase of sinusoidal waves and proposed the new evaluation method based on this consideration. This method makes it possible to evaluate the devices taking into account the effects of controlled phases that have not been considered before. This method was devised based on the idea that evaluation is performed while changing the phase of sinusoidal waves. We experimented using actual cryptographic hardware to substantiate the effect of changing the phase of the sinusoidal wave on the attack and verify the validity of the proposed method. We confirmed that the phase of sinusoidal waves affects the attack as expected and showed that the proposed method is more suitable for evaluation than the conventional method. We also pointed out the problems of the proposed method and indicated the point of possible further improvement.