Reviewerly/exHarmony huggingface dataset:
Image Retrieval Based on Learning to Rank and Multiple Loss

Image retrieval applying deep convolutional features has achieved the most advanced performance in most standard benchmark tests. In image retrieval, deep metric learning (DML) plays a key role and aims to capture semantic similarity information carried by data points. However, two factors may impede the accuracy of image retrieval. First, when learning the similarity of negative examples, current methods separate negative pairs into equal distance in the embedding space. Thus, the intraclass data distribution might be missed. Second, given a query, either a fraction of data points, or all of them, are incorporated to build up the similarity structure, which makes it rather complex to calculate similarity or to choose example pairs. In this study, in order to achieve more accurate image retrieval, we proposed a method based on learning to rank and multiple loss (LRML). To address the first problem, through learning the ranking sequence, we separate the negative pairs from the query image into different distance. To tackle the second problem, we used a positive example in the gallery and negative sets from the bottom five ranked by similarity, thereby enhancing training efficiency. Our significant experimental results demonstrate that the proposed method achieves state-of-the-art performance on three widely used benchmarks.
A High-Performance Distributed Relational Database System for Scalable OLAP Processing

The scalability of systems such as Hive and Spark SQL that are built on top of big data platforms have enabled query processing over very large data sets. However, the per-node performance of these systems is typically low compared to traditional relational databases. Conversely, Massively Parallel Processing (MPP) databases do not scale as well as these systems. We present HRDBMS, a fully implemented distributed shared-nothing relational database developed with the goal of improving the scalability of OLAP queries. HRDBMS achieves high scalability through a principled combination of techniques from relational and big data systems with novel communication and work-distribution techniques. While we also support serializable transactions, the system has not been optimized for this use case. HRDBMS runs on a custom distributed and asynchronous execution engine that was built from the ground up to support highly parallelized operator implementations. Our experimental comparison with Hive, Spark SQL, and Greenplum confirms that HRDBMS's scalability is on par with Hive and Spark SQL (up to 96 nodes) while its per-node performance can compete with MPP databases like Greenplum.
Using Blockchain to Store Teachers' Certification in Basic Education in Mexico

Blockchain has the potential to transform the financial services industry, institutional functions, business operations, and other areas such as education. The current paper focuses on one real-world illustration of Blockchain's potential - a pilot project that used a Blockchain (hosted by Ethereum) to store certifications for 1,518 teachers who participated in a teacher training in Mexico.
Analysis of the Accuracy Batch Training Method in Viewing Indonesian Fisheries Cultivation Company Development

Analysis of research is very important to do, so that research becomes more precise and directed. As well as analyzing the development of aquaculture companies in Indonesia, studies and the use of appropriate methods are needed to get optimal results. This research is expected to be widely useful, both for the Government of Indonesia and the private sector as one of the study materials in business development in the fisheries sector. The data used in this study is data on the number of aquaculture companies according to the type of cultivation obtained from the Indonesian Statistical Center from 2000 to 2016. This study uses the Batch training method with weight and bias learning rules with 5 architectural models, namely: 8-5-1, 8-10-1, 8-5-10-1, 8-10-20-1 and 8-20-40-1. Of these 5 architectural models, the best is 8-5-1 with an accuracy rate of 75%, MSE 0.0445464533, with an error rate of 0.001 - 0.07.
Adaptive Resource Management for a Virtualized Computing Platform within Edge Computing

In virtualized computing platforms, energy consumption is related to the computing-plus-communication processes. However, most of the proposed energy consumption models and energy saving solutions found in literature consider only the active Virtual Machines (VMs), thus the overall operational energy expenditure is usually related to solely the computation process. To address this shortcoming, in this paper we consider a computing-plus-communication energy model, within the Multi-access Edge Computing (MEC) paradigm, and then put forward a combination of a traffic engineering- and MEC Location Service-based online server management algorithm with Energy Harvesting (EH) capabilities, called Automated Resource Controller for Energy-aware Server (ARCES), for autoscaling and reconfiguring the computing-plus-communication resources. The main goal is to minimize the overall energy consumption, under hard per-task delay constraints (i.e., Quality of Service (QoS)). ARCES jointly performs (i) a short-term server demand and harvested solar energy forecasting, (ii) VM soft-scaling, workload and processing rate allocation and lastly, (iii) switching on/off of transmission drivers (i.e., fast tunable lasers) coupled with the location-aware traffic scheduling. Our numerical results reveal that ARCES achieves on average energy savings of 69%, and an energy consumption ranging from 31%-45%and from 21%-25% at different values of per-VM reconfiguration cost, with respect to the case where no energy management is applied.
Cyber Security and Data Governance Roles and Responsibilities at the C-Level and the Board

Corporate governance and the roles and responsibilities of the corporate officers and the board of directors have received an increasing interest since the Enron scandal of the early 2000s. This scandal resulted in enacting policies, laws and regulations such as the Sarbanes-Oxley and others. More recently, the near daily cyber security attacks on the infrastructures and data assets of corporations have resulted in cyber security professionals taking a serious look at the roles and responsibilities of the corporate officers and the board with respect to cyber security and data governance. This paper discusses the issues and challenges for cyber security governance with an emphasis on data governance and the potential roles and responsibilities of the corporate officers and the board of directors.
Security Improvement in a Modified Merkle-tree

Two of the very well-known hash algorithms from SHA2 (SHA-2) family include SHA-256 and SHA-512. Nowadays, there exists another hash algorithms family -- SHA3(SHA-3), which includes SHA3-256 and SHA3-512 algorithms, also known as Keccak algorithms. This paper provides a comparison of the empirical performance calculations with these 4 algorithms with different input volumes of sequential data (where every next slice of information is dependent of previous one) to make it impossible for parallel computation. For the purpose of calculations, 4 totally different environments are used but with exact number of CPUs and approximately similar amount of memory (using only standard instances types) -- one personal computer from one side and 3 of the biggest public cloud providers from another -- Amazon AWS (t2.medium), Microsoft Azure (Standard D2s v3) and Google Cloud (n1-standard-2). The purpose of the research is to compare these environments in terms of additional compute calculations and additional storage space (volumes and prices) for the presented new security improvement of the Merkle-root calculation. The Merkle-root is an integral part of the blockchain and therefore, security improvement of this component is a security improvement of the whole blockchain structure.
A systems theory perspective on the translation process

Abstract The translation process has often been described as a sequence of three steps, source text (ST) analysis, source-target transfer, and target text (TT) generation. We propose a radically different view, in which the human translation process consists of a hierarchy of interacting word and phrase translations systems which organize and integrate as dissipative structures. Activation of word (or phrase) translation systems is a non-selective subliminal process in the translator’s mind not restricted to one language. Depending on the entropy (i.e., the internal order) of the word translation systems, a human translator spends more or less time and energy during the translation process, which can be measured in the form of gaze patterns and production duration.
Book Genre Classification Based on Titles with Comparative Machine Learning Algorithms

This paper presents algorithmic comparisons for producing a book's genre based on its title. While some titles are easy to interpret, some are irrelevant to the genre that they belong to. Henceforth, we seek to determine the optimal and most accurate method for accomplishing the task. Several data preprocessing steps were implemented, in which word embeddings were created to make the titles operable by the computer. Five different machine learning models were tested throughout the experiment. Each different algorithm was fine-tuned for attaining the best parameter values, while no modifications were conducted on the dataset. The results indicate that the Long Short-Term Memory (LSTM) with a dropout is the top performing architecture among the algorithms, with an accuracy of 65.58%. To the authors' knowledge, no prior study has been done about book genre classification by title, therefore the present study is the current best in the field.
Visual Result Prediction in Electromagnetic Simulations Using Machine Learning

In this letter, we present a novel approach based on using convolutional neural networks (CNNs) to visually predict solutions of electromagnetic problems. CNN models are constructed and trained such that images of surface currents obtained at the early stages of an iterative solution can be used to predict images of the final (converged) solution. Numerical experiments demonstrate that the predicted images contain significantly better visual details than the corresponding input images. The developed approach and the constructed CNN models can provide visual information on the solution of a given problem using only a few iterations without performing the whole iterative solution.
LSTM Neural Networks for Baybáyin Handwriting Recognition

The Philippine Congress has signed the House Bill 1022 which declares that the Baybáyin script will be considered as the national writing system of the country. The Department of Education, National Commission for Culture and Arts, and some other organizations have vowed to reintroduce this writing system back. This handwriting system is not taught in the schools anymore so an easier training solution is needed. To be able to make this transition easier and faster, a neural network is proposed to convert hand-drawn characters to its corresponding English alphabet syllabication. The type of neural network used in this study is the Long Short-Term Memory (LSTM) network. To gather the necessary number of training images for the neural network, 25 people were employed to draw the characters. All of them used an Android mobile app that will let them draw their own version of a character based on three characters presented to them. All of the contributed handwritten characters are manually viewed to ensure the quality of the sample data. After testing five different LSTM networks, the model with 512 and 256 units in the hidden layers and 128 units in the dense layer is the best model for classifying hand-drawn Baybáyin characters. All of the models are tested up to 50 epochs. Using 8500 images for training and 1200 images for validation, this model has achieved a 95.6% training accuracy and 92.9% validation accuracy.
Learning to Explore Intrinsic Saliency for Stereoscopic Video

The human visual system excels at biasing the stereoscopic visual signals by the attention mechanisms. Traditional methods relying on the low-level features and depth relevant information for stereoscopic video saliency prediction have fundamental limitations. For example, it is cumbersome to model the interactions between multiple visual cues including spatial, temporal, and depth information as a result of the sophistication. In this paper, we argue that the high-level features are crucial and resort to the deep learning framework to learn the saliency map of stereoscopic videos. Driven by spatio-temporal coherence from consecutive frames, the model first imitates the mechanism of saliency by taking advantage of the 3D convolutional neural network. Subsequently, the saliency originated from the intrinsic depth is derived based on the correlations between left and right views in a data-driven manner. Finally, a Convolutional Long Short-Term Memory (Conv-LSTM) based fusion network is developed to model the instantaneous interactions between spatio-temporal and depth attributes, such that the ultimate stereoscopic saliency maps over time are produced. Moreover, we establish a new large-scale stereoscopic video saliency dataset (SVS) including 175 stereoscopic video sequences and their fixation density annotations, aiming to comprehensively study the intrinsic attributes for stereoscopic video saliency detection. Extensive experiments show that our proposed model can achieve superior performance compared to the state-of-the-art methods on the newly built dataset for stereoscopic videos.
Statistical strategies for the analysis of massive data sets

Abstract The advent of the big data age has changed the landscape for statisticians. Public and private organizations alike these days are interested in capturing and analyzing complex customer data in order to improve their service and drive efficiency gains. However, the large volume of data involved often means that standard statistical methods fail and new ways of thinking are needed. Although great gains can be obtained through the use of more advanced computing environments or through developing sophisticated new statistical algorithms that handle data in a more efficient way, there are also many simpler things that can be done to handle large data sets in an efficient and intuitive manner. These include the use of distributed analysis methodologies, clever subsampling, data coarsening, and clever data reductions that exploit concepts such as sufficiency. These kinds of strategies represent exciting opportunities for statisticians to remain front and center in the data science world.
FRACTIONAL SEMANTICS FOR CLASSICAL LOGIC

Abstract This article presents a new (multivalued) semantics for classical propositional logic. We begin by maximally extending the space of sequent proofs so as to admit proofs for any logical formula; then, we extract the new semantics by focusing on the axiomatic structure of proofs. In particular, the interpretation of a formula is given by the ratio between the number of identity axioms out of the total number of axioms occurring in any of its proofs. The outcome is an informational refinement of traditional Boolean semantics, obtained by breaking the symmetry between tautologies and contradictions.
Modeling Incomplete Knowledge of Semantic Web Using Bayesian Networks

Interoperable ontologies already exist in the biomedical field, enabling scientists to communicate with minimum ambiguity. Unfortunately, ontology languages, in the semantic web, such as OWL and RDF(S), are based on crisp logic and thus they cannot handle uncertain knowledge about an application field, which is unsuitable for the medical domain. In this paper, we focus on modeling incomplete knowledge in the classical OWL ontologies, using Bayesian networks, all keeping the semantic of the first ontology, and applying algorithms dedicated to learn parameters of Bayesian networks in order to generate the Bayesian networks. We use EM algorithm for learning conditional probability tables of different nodes of Bayesian network automatically, contrary to different tools of Bayesian networks where probabilities are inserted manually. To validate our work, we have applied our model on the diagnosis of liver cancer using classical ontology containing incomplete instances, in order to handle medical uncertain knowledge, for predicting a liver cancer.
Traffic Control for RDMA-Enabled Data Center Networks: A Survey

Data centers, the infrastructure of cloud computing, have been widely deployed around the world to accommodate the increasing cloud computing demands. A data center network (DCN) connects tens or hundreds of thousands of servers in the data center and uses a traffic control scheme to enable the data transmission among servers. Various new applications in cloud present new requirements on traffic control of DCNs, such as low latency and high throughput. Existing traffic control schemes in DCNs suffer from the complicated kernel processing and cannot satisfy the requirements. Remote direct memory access (RDMA), which bypasses the kernel processing to enable fast memory moving across a network, is recognized as a promising solution. In this article, we present a survey of traffic control schemes for traditional RDMA, traditional DCNs, and RDMA-enabled DCNs and explain their limitations. We also differentiate the existing schemes from congestion control, performance, and components. In order to encourage future research, we point out some potential research directions of this research.
Zonation in primary geochemical haloes for orogenic vein-type gold mineralization in the Quartz Ridge prospect, Sukari gold mine area, Eastern Desert of Egypt

The Quartz Ridge prospect is one of >100 gold occurrences distributed in the Neoproterozoic basement rocks of the Eastern Desert of Egypt. The mineralization is hosted in quartz veins along NE-trending shear zones, and their associated altered wallrocks. Opaque minerals include pyrite and arsenopyrite with less abundant chalcopyrite, galena, and sphalerite. Gold occurs as tiny inclusions and as microfracture-filling grains mainly in arsenopyrite and in deformed quartz. Sericitization, carbonatization, and silicification are the main alteration signs accompanying ore formation. The chemical analysis of 640 core samples from four selected boreholes from the study area was used for characterizing the primary geochemical haloes and their zonation developed around detected subsurface mineralized zones. Statistical analysis of the geochemical data applying techniques based on median absolute deviation (MAD) values and log-ratio transformation (CLR) was carried out to address the problems of outliers and closed compositional data, whereas statistical factor analysis (FA) combined with enrichment factor (EF) associations were used to investigate the multi-element associations describing the gold mineralization and its primary dispersion haloes. Mineralization (Au-Ag-As-Sb ± S), alteration (Ba ± Cu ± Mo) and wall rock (Ni-Cr ±Co) associations can be inferred directly by factor analysis study. However, the dispersion of trace elements around mineralized zones can better be described by their enrichment factors calculated for data subsets. More than one stage of hydrothermal activity has been suggested and believed to be responsible for precipitating ore metals and their associated elements. Molybdenum and copper were found to be good indicators for upper zones, while tungsten and zinc are good indicators for the lower or down-dip zones. The current study manifests the success of using statistical procedures based on data transformation for compositional data analysis (i.e. centered log-ratios) coupled with enrichment factors in characterizing the primary geochemical haloes and their zoning which can be effectively used to follow similar occurrences of orogenic hydrothermal gold deposits.
Turn Your Head Half Round

Rotational tracking enables Virtual Reality (VR) users to turn their head around freely 360° while looking around the environment. However, there are situations when physical head rotation is only possible for not more than a certain range, e. g., when the user sits in a bus or plane while she is wearing a VR headset. For these situations, rotation gains were introduced to decouple virtual and real rotations. We present two more techniques that allow 360° virtual turning in a physically limited space: Dynamic Rotation Gains and Scrolling. We conducted an experiment to compare those three rotation techniques and a baseline condition regarding VR sickness, spatial orientation, and usability. We found a significant underestimation of rotation angles for the dynamic rotation gains which might mean that this technique is more subtle than others. Furthermore, usability was higher and VR sickness lower for the dynamic rotation gains while scrolling caused the highest VR sickness. Finally, we conducted a confirmatory study to prove the applicability of dynamic rotation gains in an actual VR experience and got promising feedback.
Towards Attention Based Vulnerability Discovery Using Source Code Representation

Vulnerability discovery in software is an important task in the field of computer security. As vulnerabilities can be abused to enable cyber criminals and other malicious actors to exploit systems, it is crucial to keep software as free from vulnerabilities as is possible. Traditional approaches often comprise code scanning tasks to find specific and already-known classes of cyber vulnerabilities. However these approaches do not in general discover new classes of vulnerabilities. In this paper, we leverage a machine learning approach to model source code representation using syntax, semantics and control flow of source code and to infer vulnerable code patterns to tackle large code bases and identify potential vulnerabilities that missed by any existing static software analysis tools. In addition, our attention-based bidirectional long short-term memory framework adaptively localise regions of code illustrating where the possible vulnerable code fragment exists. The highlighted region may provide informative guidance to human developers or security experts. The experimental results demonstrate the feasibility of the proposed approach in the problem of software vulnerability discovery.
Research on Robot Path Planning Based on Improved Adaptive Ant Colony Algorithm

Aiming at the problem that ant colony algorithm is easy to fall into local optimal and slow convergence in robot path planning, a path planning method based on improved ant colony algorithm is proposed for static obstacle environment.This method improves the search efficiency of the algorithm by using the adaptive adjustment heuristic function; The attenuation coefficient is adjusted dynamically to accelerate the convergence speed of the algorithm based on ant colony rule, pheromone is updated and the maximum and minimum of pheromone concentration is limited.Simulation results show that compared with other algorithms in the same environment, the improved algorithm has a faster convergence rate when the path planning results are the same.The improved algorithm has obtained the optimal path in different complexity environments, which also shows the effectiveness and reliability of the algorithm.
Digital Quantum Simulation of Linear and Nonlinear Optical Elements

We provide a recipe for the digitalization of linear and nonlinear quantum optics in networks of superconducting qubits. By combining digital techniques with boson-qubit mappings we address relevant problems which are typically considered in analog simulators, such as the dynamical Casimir effect or molecular force fields, including nonlinearities. In this way, the benefits of digitalization are extended in principle to a new realm of physical problems. We present preliminary examples launched in IBM Q 5 Tenerife.