Reviewerly/exHarmony huggingface dataset:
Geochemistry of the Athabasca Basin, Saskatchewan, Canada, and the unconformity-related uranium deposits hosted by it

ABSTRACT A large data set comprising near-total digestion analyses of whole rock samples from the Athabasca Basin, Saskatchewan, Canada (based principally on the Geological Survey of Canada open file 7495), containing more than 20,000 analyses, was used to define the average chemical composition of Athabasca Group sandstones and of unconformity-related uranium deposits hosted by the basin. The chemical composition of unaltered and un-mineralized Athabasca Group sandstones is dominated by Al (median Al2O3 of 1.14 wt.%), Fe (median Fe2O3 of 0.24 wt.%), and K (median K2O of 0.11 wt.%; Si was not measured), corresponding mostly to the presence of kaolin, illite, and hematite, in addition to the most-abundant quartz. The median concentration of U in the barren sandstones is 1 ppm, with 5 ppm Th, 3 ppm Pb, and 56 ppm ΣREE. Other trace elements present in significant amounts are Zr (median of 100 ppm), Sr (median of 69 ppm), and B (median of 43 ppm), corresponding to the presence of zircon, illite, and dravite. The elements most enriched in a typical Athabasca Basin unconformity-related uranium deposit relative to the barren sandstone are U (median enrichment of ×710), Bi (×175), V (×77), and Mg (×45), followed by five elements with enrichment factors between 20 and 30 (Co, Mo, K, As, and Ni). These correspond to the presence in the ore bodies of alteration minerals (dravite, kaolinite, illite, chlorite, aluminum-phosphate-sulfate minerals, and a suite of sulfide minerals) and are similar to what has been observed before. These elements are similar to the typical pathfinder elements described above known deposits, but their usefulness has to be assessed based on their relative mobility in the predominantly oxidizing Athabasca Basin sandstones.
An Information Visualization Tool for the Interactive Component-Based Evaluation of Search Engines

In this paper, we present an InfoVis tool based on SanKey diagrams for the exploration of large combinatorial combinations of IR components – the Grid of Points (GoP). The goal of this tool is to ease the comprehension of the behavior of single IR components within fully functioning off-the-shelf IR systems without recurring to complex statistical tools. In order to assess the quality of the proposed SanKey-based InfoVis tool we conduceted an initial user study that led to interesting conclusions, yet to be validated in a future and more comprehensive study.
A Virtual Assistant Chatbot for College Advising System

This paper proposes the merging of different Techniques for the implementation of an artificially intelligent Chatbot with which the users can interact with text or voice commands. Main objective for the Chatbot to be used as an information and advising system that helps postgraduate students in answering their different queries and respond and provide the best possible options for them. It is created using Visual C# (GUI) and SIML (Synthetic Intelligence Markup Language) as a knowledge base for the Chatbot and uses Microsoft SAPI for both speech synthesizer, and speech recognition with natural language processing abilities as well as providing a few different extra help for the students as a course list, schedule and more.
A Novel Hybrid RNN-ELM Architecture for Crime Classification

Extreme Learning Machine (ELM) is a single layer feedforward neural network (SLFN) that has shown remarkable results in regression and classification (multi-class) problems. The theories on ELM indicates that the hidden neurons can be randomly generated. In this paper, we introduce a hybrid Recurrent Neural Network(RNN) – ELM hybrid structure for crime hotspot classification. The RNN extracts the features from the data and learns using Long Short-Term Memory (LSTM) and finally ELM is applied at the end of the layers for our classification problem. The dataset used for this study is Philadelphia’s crime data. The dataset is also tested with RNN using backpropagation without ELM. With ease of implementation, fast learning speed and better accuracy, RNN-ELM clearly outperformed RNN with backpropagation.
Comparative Study on Deep Learning Frameworks for Object Detection

Object detection is one among the major sub-domains of the computer vision which deals with the identification of objects of a pre-defined class. Recognition of object is imminent to identify numerous pertinent objects from an image or video. Several deep neural learning, machine learning based techniques are used for object detection in digital images and videos. This paper discusses a comparative study of some deep learning based object detection frameworks, and analysed on the benchmark mean Average Precision (mAP) and selected models are evaluated using PASCAL VOC 2007 dataset which is the standard image data set for object class identification and recognition. Among the selected detection models, PVANet has the highest mAP (84.9) with FPS 21.7 and is considered as the best object detection method.
Chinese font translation with improved Wasserstein generative adversarial network

Nowadays, various fonts are applied in many fields, and the generation of multiple fonts by computer plays an important role in the inheritance, development and innovation of Chinese culture. Aiming at the existing font generation methods, which have some problems such as stroke deletion, artifact and blur, this paper proposes Chinese font translation with improved wasserstein generative adversarial network. The wasserstein distance is used to measure distance and difference between the two distributions, and the gradient penalty mechanism is used instead of the weight clipping. The residual dense blocks with better flexibility are selected as the core component to extract the features fully and enhance the information transmission between network layers. It realizes the style migration between different Chinese fonts. Experiments show that the proposed method has better performance in font generation details, simplifies the font translation process, and improves the fidelity of font generation.
Closed-Set-Based Discovery of Representative Association Rules

The output of an association rule miner is often huge in practice. This is why several concise lossless representations have been proposed, such as the “essential” or “representative” rules. A previously known algorithm for mining representative rules relies on an incorrect mathematical claim, and can be seen to miss part of its intended output; in previous work, two of the authors of the present paper have offered a complete but, often, somewhat slower alternative. Here, we extend this alternative to the case of closure-based redundancy. The empirical validation shows that, in this way, we can improve on the original time efficiency, without sacrificing completeness.
STLDAS: Secure Two Level Deduplication and Auditing of Shared Data in Cloud

With the cloud repository service furnished by the cloud computing, users can comfortably arrange themselves as a cluster and distribute information effectively. In order to empower public verifier to audit the distributed information, clients in the cluster need to Figure out signatures on complete chunks of collaborative information. Every client in the cluster modifies and signs his respective chunks, and deploys in the cloud server. Hence specific chunks of shared information are normally signed by specific clients. If anyone of the customers’ is found malicious, he is immediately repudiated from the cluster. The prevailing clients in the cluster are permitted to re-sign the chunks that were earlier signed by this eliminated client. This approach is inefficient due to the massive amount of collaborative information in the cloud. By exploiting the approach of proxy re-signatures, the CSP is acknowledged to re-sign chunks in support of the prevailing clients during customer repudiation. When many clients deploy the same information to the cloud repository, repository space has identical copies, hence deduplication technology is usually utilized to lower the capacity and bandwidth prerequisites of the utilities by removing repetitious information and hoarding only an original replica of them. In order to assimilate both data honesty and deduplication in cloud, we present a novel Secure Two Level Deduplication and Auditing of Shared Data in Cloud (STLDAS) mechanism. Experimental results show that our mechanism achieves secure deduplication and appreciable improvement in tag generation.
Detail enhancement of infrared image based on BEEPS

BEEPS(bi-exponential edge-preserving filter) is used to enhance the details of infrared image in this paper. The original infrared image has a dynamic range of 12 or 14 bits, and the human observation range is only 8 bits. Usually, the original infrared image needs to be compressed and displayed by gray-scale remapped for displaying. For example, automatic gain control and histogram equalization are the most widely used image display technologies in infrared imaging systems, but they can lead to the loss of local details, and it is difficult to control the visibility of weak details in images. Therefore, an infrared image digital detail enhancement algorithm has emerged. Current digital enhancement algorithms can effectively enhance image details and avoid over-amplification of noise, but there are still some drawbacks, such as large computational load and poor application flexibility. Therefore, we use BEEPS in our algorithm to overcome these problems. This algorithm uses a two dimensional convolution to separate the detail information from an original infrared image, and turn the original image into the detail layer and the base layer. Detail layer processing is to transform two-dimensional convolution into one-dimensional convolution, and to complete one-dimensional convolution through iterative calculation. Then, the enhanced detail layer is added back to the base frequency layer of histogram equalization. This not only improves the computational efficiency, but also improves the visual quality of the original image. The BEEPS algorithm is proved to be excellent by image and data testing.
Implementation of unslotted and slotted CSMA/CA for 802.11 and 802.15.4 protocol

In a network consisting of multiple users, different users will communicate with each other, thereby overlapping with each other which results in a collision. Therefore, CSMA/CA is used to reduce the frequency of collisions and it also creates a plan as to how to proceed even if a collision occurs. This implementation has been done on a field programmable gate array (FPGA). In this paper, we do a comparison between IEEE 802.11 and IEEE 802.15.4 using simulations in Verilog. The major modules of the protocols include medium allocation and CSMA/CA for slotted protocol and random number generator, counter and CSMA/CA for unslotted CSMA/CA. We have performed power analysis and obtained useful insights. The unslotted protocol consumes more power than the slotted protocol.
Coverage Path Planning of Penaeus vannamei Feeding Based on Global and Multiple Local Areas

Penaeus vannamei (whiteleg shrimp) has high aquaculture economic benefits, and the high-frequency feeding method is crucial for the rapid growth of shrimp during the breeding process. In order to cope with the high-frequency feeding method, the unmanned surface vehicle can greatly improve feeding efficiency and precision. Furthermore, the labor intensity of the personnel also reduced. The path planning of the unmanned surface vehicle (USV) is a prerequisite for improving feeding efficiency. Based on the actual growth process of shrimp, a two-stage feeding path planning strategy is proposed. In the seedling stage of shrimp, the range of activities is small, and it is necessary to uniformly cover the whole aquaculture area. In this paper, a higher coverage internal and external spiral traversal coverage path planning method is proposed. In the mature stage of shrimp, local area aggregation will be formed because of the larger activity space of shrimp, then it needs to cover and feed the aggregation areas. So we proposed a path planning strategy combining global and multiple local areas coverages, and an improved simulated annealing genetic algorithm is adopted to solve the global path planning. Finally, the application of two different path planning strategies achieves the path planning of the whole growth cycle of the South America shrimp, which improves the feeding efficiency of the bait and reduces the cost of breeding.
A New Type Wireless Data Center of Comb Topology

In recent years, group communication has become more and more popular. In the current data center network, multicast plays an extremely important role in group communication. Traditional data center network is designed based on wires. Researchers have proposed Dcell, Bcube and other data center topologies successively. However, in the traditional data center network, the wired topology structure has the disadvantages of high overhead, complex deployment, high maintenance cost, slow heat dissipation and so on. Moreover, the existing wireless data center Flyways structure and Graphite structure have the problems of low connectivity and poor scalability. So this paper proposes and designs a new data center network topology Comb structure (Comb model is a multi-layer cellular structure model, which is a multi-layer structure composed of single-layer honeycomb-like structure interlaced with each other). The structure can effectively improve the connectivity of the data center, shorten the routing path, reduce the probability of node failure in the network, and ensure the smooth network.
Research on the frequency modulation continuous wave ranging method based on spectral feature matching

The near-sensing detection system based on the software-defined radio (SDR) platform is widely used due to its versatility and algorithm reconfigurability. Frequency modulation continuous wave (FMCW) detection has high precision and strong anti-interference ability. To solve the problem where the near-sensing detection system has a large fixed error when the modulation frequency deviation is small, a ranging method based on spectral feature matching is proposed in this paper. Compared with traditional algorithms, such as harmonic detection, zero-padding Fast Fourier Transform (FFT) and beat frequency correction, the proposed algorithm makes full use of the spectral characteristics of the beat signal. By establishing a spectral feature library of different distances, a matching evaluation function is given to transform the problem of distance recognition into a feature matching problem. The accuracy of the algorithm is verified by comparing the ranging error of the feature matching algorithm and other ranging algorithms at different signal-noise ratio (SNR) levels.
Annotation-Based Development of Explorable Immersive VR/AR Environments

Virtual and augmented reality environments consist of objects that typically interact with other objects and users, leading to evolution of 3D objects and scenes over time. In multiple VR/AR applications in different domains, interactions and temporal properties of 3D content may be represented using general or domain knowledge, which makes them comprehensible to average users or domain experts without an expertise in IT. Logging interactions and their results can be especially useful in VR/AR environments that are intended to monitor and gain knowledge about the system behavior as well as users’ behavior and preferences. However, the available approaches to development of VR/AR environments do not enable logging interactions in an explorable way. The main contribution of this paper is a method of developing explorable VR/AR environments on the basis of existing environments developed using well established tools, such as game engines and imperative programming languages. In the approach, interactions can be represented with general or domain knowledge. The method is discussed in the context of an immersive car showroom, which enables acquisition of knowledge about customers’ interests and preferences for marketing and merchandising purposes.
A Multi-Objective Genetic Algorithm Approach for Path Planning of an Underwater Vehicle Manipulator

In this work the kinematic redundancy resolution based on Multi-Objective Genetic Algorithm (MOGA) has been exploited to path planning of a underwater vehicle-manipulator system (UVMS). Some objective functions are analyzed with the purpose to achieve the desired evolution of the configuration of the mobile manipulator when the position and orientation of the effector is imposed. In the proposed method, the generalized coordinates, additional kinematic constraints and relevant objectives functions are selected, in such a way that the base motion is implicitly limited while maximizing the whole system manipulability. Simulations for a ten dof vehicle-arm are developed, with the consideration of three objective functions for optimization. The results reveal that it is possible to choose among several solutions from the Pareto Front, according to the importance of each individual objective or in other way, there are an objective function that dominated the optimization process.
Sparse representation of images using substitution of wavelet by patches

Classical signal representation techniques generally use a description of the components on a basis on which the representation of the signal is unique such as wavelets network. Conversely, sparse representations consist in the decomposition of the signal on a dictionary comprising a number of elements much larger than the dimension of the signal. This technique can be widely used for representation, compression, denoising and separation of all types of signals. Consequently, some researches have confirmed that the use of a predefined dictionary is less efficient than a dictionary from training data. So, the idea of this paper is to propose a new technique for the creation of a dictionary using the wavelet decomposition to enhance the sparse representation of images. This technique is based on the combination of sparse coding and the fast wavelet transform algorithms for image representation. Our results obtained using different universal image databases showed greater performances in the representation of images when compared to some methods from the state of the art.
Research on Privacy Security Risk Evaluation of Intelligent Recommendation Mobile Applications Based on a Hierarchical Risk Factor Set

Intelligent recommendation applications based on data mining have appeared as prospective solution for consumer's demand recognition in large-scale data, and it has contained a great deal of consumer data, which become the most valuable wealth of application providers. However, the increasing threat to consumer privacy security in intelligent recommendation mobile application (IR App) makes it necessary to have a risk evaluation to narrow the gap between consumers' need for convenience with efficiency and need for privacy security. For the previous risk evaluation researches mainly focus on the network security or information security for a single work, few of which consider the whole data lifecycle oriented privacy security risk evaluation, especially for IR App. In this paper, we analyze the IR App's features based on the survey on both algorithm research and market prospect, then provide a hierarchical factor set based privacy security risk evaluation method, which includes whole data lifecycle factors in different layers.
Bayesian Tensor Network and Optimization Algorithm for Probabilistic Machine Learning

It is known that describing or calculating the conditional probabilities of multiple events is exponentially expensive. In this work, Bayesian tensor network (BTN) is proposed to efficiently capture the conditional probabilities of multiple sets of events with polynomial complexity. BTN is a directed acyclic graphical model that forms a subset of TN. To testify its validity for exponentially many events, BTN is implemented to the image recognition, where the classification is mapped to capturing the conditional probabilities in an exponentially large sample space. Competitive performance is achieved by the BTN with simple tree network structures. Analogous to the tensor network simulations of quantum systems, the validity of the simple-tree BTN implies an ``area law'' of fluctuations in image recognition problems.
Developing a framework for the explanation of interlingual features for native and other language influence detection

This article demonstrates the benefit of taking an explanation-based approach in the development of features for computationally supported systems used for linguistic analysis in forensic contexts. As a focal point it considers Other Language Infuence Detection (OLID) as well as its related field of Native Language Identifcation (NLI). An explanation-based approach allows the forensic linguist to understand the implications of the presence or absence of features as they vary across the contex ts and situations s/he might encounter. The authors present a qualitative framework for types of explanation and show how different types of explanations are needed to develop a full and rich language-influence feature set. The authors are not advocating a strict or inflexible typology of feature explanation but are seeking a richness of explanation at a variety of levels of analysis instead. This in turn can be developed into computational approaches, which the authors contend will therefore be stronger and more applicable to forensic casework contexts.
A method of detecting end-to-end curves of limited curvature

In this paper we consider a method for detecting end-to-end curves of limited curvature like the k-link polylines with bending angle between adjacent segments in a given range. The approximation accuracy is achieved by maximization of the quality function in the image matrix. The method is based on a dynamic programming scheme constructed over Fast Hough Transform calculation results for image bands. The proposed method asymptotic complexity is O(h⋅(w+h/k)⋅log(h/k)), where h and w are the image size, and k is the approximating polyline links number, which is an analogue of the complexity of the fast Fourier transform or the fast Hough transform. We also show the results of the proposed method on synthetic and real data.
Interpretable diagnosis of breast cancer from histological images using Siamese neural networks

Breast cancer is one of the most widespread causes of women’s death worldwide. Successful treatment can be achieved only by the early and accurate tumor diagnosis. The main method of tissue diagnosis taken by biopsy is based on the observation of its significant structures. We propose a novel approach of classifying microscopy tissue images into 4 main cancer classes (normal, benign, In Situ and invasive). Our method is based on comparing and determining the similarity of the new tissue sample with previously by specialists annotated examples that are compiled in the collection with other labeled samples. The most probable class is statistically determined by comparing a new sample with several annotated samples. The usual problem of medical datasets is the small number of training images. We have applied suitable dataset augmentation techniques, using the fact that flipping or mirroring of the sample does not change the information about the diagnosis. Our other contribution is that we show the histopathologist the reason why the algorithm has classified tissue into the particular cancer class by ordering the collection of correctly annotated samples by their similarity to the input sample. Histopathologists can focus on searching for the key structures corresponding to the predicted classes.