Reviewerly/exHarmony huggingface dataset:
Geochronology and fluid source constraints of the Songligou gold‐telluride deposit, western Henan Province, China: Analysis of genetic implications

Abstract The Songligou gold‐telluride deposit, located in Songxian County, western Henan Province, China, is one of many gold‐telluride deposits in the Xiaoqinling‐Xiong'ershan district. Gold orebodies occur within the Taihua Supergroup and are controlled by the WNW F101 Fault, and the fault was cut across by a granite porphyry dike. Common minerals in gold orebodies include quartz, chlorite, epidote, K‐feldspar, calcite, fluorite, sericite, phlogopite, bastnasite, pyrite, galena, chalcopyrite, sphalerite, tellurides, gold, bismuthinite, magnetite, and hematite, and pyrite is the dominant sulfide. Four mineralization stages are recognized, including pyrite‐quartz stage (I), quartz‐pyrite stage (II), gold‐telluride stage (III), and quartz‐calcite stage (IV). This work reports the Rb–Sr age of gold‐telluride‐bearing pyrite and zircon U–Pb age of granite porphyry, as well as S isotope data of pyrite and galena. The pyrite Rb–Sr isochron age is 126.6 ± 2.3 Ma (MSWD = 1.8), and the average zircon U–Pb age of granite porphyry is 166.8 ± 4.1 Ma (MSWD = 4.9). ( 87 Sr/ 86 Sr) i values of pyrite and δ 34 S values of sulfides vary from 0.7104 to 0.7105 and −11.84 to 0.28‰, respectively. The obtained Rb–Sr isochron age represents the ore formation age of the Songligou gold‐telluride deposit, which is much younger than the zircon U–Pb age of the granite porphyry. Strontium and S isotopes, together with the presence of bastnaesite, suggest that the ore‐forming fluid was derived from felsic magmas with input of a mantle component and subsequently interacted with the Taihua Supergroup. Tellurium was derived from metasomatized mantle and was related to the subduction of the Shangdan oceanic crust and Izanagi plate beneath the North China Craton (NCC). This deposit is a part of the Early Cretaceous large‐scale gold mineralization in east NCC and formed in an extensional tectonic setting.
Log-Based Control Flow Attestation for Embedded Devices

Remote attestation is a very important mechanism helping a trusted party to get the status of a remote embedded device. Most remote attestation schemes aim at checking the code integrity and leave devices vulnerable to runtime attacks. Recently a new kind of attestation called control flow attestation has been proposed to get rid of this limitation. However, previous studies on control flow attestation cannot verify the attestation result efficiently and lack secure storage. In this paper, we present a log-based attestation scheme that not only can attest the control flow path of programs on embedded devices but also can verify the attestation result very efficiently. We use a lightweight root of trust in our attestation. We implement our system on Hikey board using ARM TrustZone security extension. We evaluate the performance using a popular embedded device benchmark Mibench and demonstrate that our scheme has a high security assurance and a good performance.
A practical and communication-efficient deniable authentication with source-hiding and its application on Wi-Fi privacy

Authentication service in communication is essential. However, information flows transmitted during an authentication process might reveal some personal information. In this paper, we propose a deniable authentication protocol with source hiding which does not reveal any private information, therefore the privacy of participants is preserved. With our approach, the sender is able to deny an authentication process to any third party. The receiver can not prove that the sender has participated in this authentication, though it is sure that the sender is legitimate. We construct this protocol without using “Encryption-then-MAC” paradigm, therefore the underlying building block is not required to be CCA2 secure, and is a more realistic authentication protocol for practical privacy-preserving Internet-based applications. We also show how to apply it in Wi-Fi authentication to prevent the location leakage.
Brexit Twitter Sentiment Analysis: Changing Opinions About Brexit and UK Politicians

In this paper, we analyze weather social sentiments can be utilized for the prediction of election results. In particular, we analyze Twitter sentiments about Brexit and United Kingdom (UK) politicians. Twitter is the essential social network for sentiments analyzing and it provides useful information for mining data. Through periods, we collected Twitter data about Brexit and UK politicians using Twitter Application Program interface (API). First, we cleaned and pre-processed Tweet data for sentiment analysis. Then, we create a Twitter search and sentiment visualization interface using python. Python provides useful libraries for sentiment analysis and graphical presentations. Finally, we analyze the changing opinions about Brexit and UK politicians using sentiments. In particular, in advance, we were able to correctly predict the UK parliament voting results in January 2019. In this paper, we discuss Twitter data collection, Twitter sentiment search/visualization interface and detailed sentiment analysis results about Brexit and UK politicians.
Resource Scheduling for Tasks of a Workflow in Cloud Environment

In recent days most of the enterprises and communities adopt cloud services to deploy their workflow-based applications due to the inherent benefits of cloud-based services. These workflow-based applications are mainly compute-intensive. The major issues of workflow deployment in a cloud environment are minimizing execution time (makespan) and monetary cost. As cloud service providers maintain adequate infrastructural resources, workflow scheduling in the cloud environment becomes a non-trivial task. Hence, in this paper, we propose a scheduling technique where monetary cost is reduced, while workflow gets completed within its minimum makespan. To analyze the performance of the proposed algorithm, the experiment is carried out in WorkflowSim and compares the results with the existing well-known algorithms, Heterogeneous Earliest Finish Time (HEFT) and Dynamic Heterogeneous Earliest Finish Time (DHEFT). In all the experiments, the proposed algorithm outperforms the existing ones.
Virtual Infrastructure Orchestration For Cloud Service Deployment

Abstract Cloud adoption has significantly increased using the infrastructure-as-a-service (IaaS) paradigm, in order to meet the growing demands of computing, storage and networking, in small as well as large enterprises. Different vendors provide their customized solutions for OpenStack deployment on bare metal or virtual infrastructure. Among these many available IaaS solutions, OpenStack stands out as being an agile and open-source platform. However, its deployment procedure is a time-taking and complex process with a learning curve. This paper addresses the lack of basic infrastructure automation in almost all of the OpenStack deployment projects. We propose a flexible framework to automate the process of infrastructure bring up for deployment of several OpenStack distributions, as well as resolving dependencies for a successful deployment. Our experimental results demonstrate the effectiveness of the proposed framework in terms of automation status and deployment time, that is, reducing the time spent in preparing a basic virtual infrastructure by four times, on average.
Pseudo-Sequence Light Field Image Scalable Encoding with Improved Random Access

A new form of digital images, light field images contain considerably more information than conventional 2D digital images. However, light field images require significantly more storage, transmission and processing resources than conventional 2D images, calling for efficient compression schemes. In this work, light field images are interpreted as multi-view sequences that are compressed using MV-HEVC. A novel scan order based on blocks of sub-aperture images and a two dimensional prediction scheme are proposed for improved random access performance, as well as to provide a scalable representation of the light field data. Our proposed compression scheme achieves PSNR gains as high as 7.8 dB over reference coding schemes based on JPEG.
Deep Facial Recognition using Tensorflow

Facial recognition is a tractable problem today because of the prevalence of Deep Learning implementations. Approaches for creating structured datasets from unstructured web data are more easily accessible as are GPUs that deep learning frameworks can use to learn from this data. In DARPA's MEMEX effort, which sought to create better search capabilities for law enforcement to scan the deep and dark web, we are interested in leveraging the Tensorflow framework to reproduce a seminal Deep Learning facial recognition model called VGG- Face. On MEMEX we desired to build the VGG-Face model and to train feature extraction for use in prioritization of leads for possible law enforcement follow-up. We describe our efforts to recreate the VGG-Face dataset, along with our efforts to create the Deep Learning network implementation for it using Tensorflow. Though other implementations of VGG-Face on Tensorflow exist, none of them fully reproduce as much of the dataset as we do today (~ 48% of the data still exists), nor have detailed documentation and steps for reproducing each step in the workflow. We contribute those instructions and leverage Texas Advanced Computing Center's Maverick2 supercomputer to perform the work. We report experimental results on building the dataset, and training the network to achieve a 77.99% validation accuracy on the 2, 622 celebrity use case from VGG-Face. This paper can be a useful recipe in building new Tensorflow facial recognition.
Multi-view SVM Classification with Feature Selection

With the rapid development of data mining technology, multi-view learning (MVL) has become a new research field, which has attracted wide attention of scholars at home and abroad. Multi-view learning is to combine multiple view data of the same entity for data classification, thereby improving learning performance. Previous multi-view research methods mainly concentrate on the relationship between different data views for classification problems. However, when the data is in high dimensions, it is necessary to perform feature selection in the multi-view data classification process. In this paper, we proposed a Multi-view Support Vector Machine Classification with Feature Selection (MSVMCFS) algorithm, which can not only classify multi-view data, but also select features for each view data in the process of classification. In the model, feature selection is performed by the l1 norm sparsity regularization, and consistency and complementarity between the two views are maintained. To achieve the optimization goal, we adopt linear programming to solve the model. The experimental results on 30 binary datasets demonstrate the validity of the model.
High-Throughput Multi-Threaded Sum-Product Network Inference in the Reconfigurable Cloud

Large cloud providers have started to make powerful FPGAs available as part of their public cloud offers. One promising application area for this kind of instances is the acceleration of machine learning tasks. This work presents an accelerator architecture that uses multiple accelerator cores for the inference in so-called Sum-Product Networks and complements it with a host software interface that overlaps data-transfer and actual computation. The evaluation shows that, the proposed architecture deployed to Amazon AWS F1 instances is able to outperform a 12-core Xeon processor by a factor of up to 1.9x and a Nvidia Tesla V100 GPU by a factor of up to 6.6x.
Cartesian Genetic Programming with Guided and Single Active Mutations for Designing Combinational Logic Circuits

The design of digital circuits using Cartesian Genetic Programming (CGP) has been widely investigated but the evolution of complex combinational logic circuits is a hard task for CGP. We introduce here a new mutation operator for CGP that aims to reduce the number of evaluations needed to find a feasible solution by modifying the subgraph of the worst output of the candidate circuits. Also, we propose a variant of the standard evolutionary strategy commonly adopted in CGP, where (i) the Single Active Mutation (SAM) and (ii) the proposed mutation operator is used in order to improve the capacity of CGP in generating feasible circuits. The proposals are applied to a benchmark of combinational logic circuits with multiple outputs and the results obtained are compared to those found by a CGP with SAM. The main advantages observed when both mutation operators are combined are the reduction of the number of objective function evaluations required to find a feasible solution and the improvement in the success rate.
Efficient depth recovering method free from projector errors by use of pixel cross-ratio invariance in fringe projection profilometry

In fringe projection profilometry, errors related to projectors are not easy to compensate for, as a projector is much more difficult to calibrate than a camera. Immune to projector errors, the depth recovering method based on pixel cross-ratio invariance enables circumventing this issue by calculating the depth of a point from the shift of its camera pixel instead of from its fringe phase. With this existing technique, however, one has to search three reference phase maps along epipolar lines for pixels having the same phases. Doing so increases the measurement time significantly. To improve measurement efficiency, this paper derives, from the pixel cross-ratio invariance, a generic function representing the relationship between the depths and the corresponding pixel shifts and suggests a calibration method for determining its coefficient matrices. Using this function allows us to recover object depths just by searching a single reference phase map, thus reducing the time duration for data processing to about one-third. Besides, different from the previous method, which depends on exactly three reference phase maps, the proposed method calculates the function coefficients from more reference phase maps in the least-squares sense and denoises the benchmark reference phases by use of averaging technique, thus improving measurement accuracy. Experimental results demonstrate this method to be effective in improving measurement accuracy and efficiency.
Hybrid Collision Avoidance with Moving Obstacles

This paper proposes a hybrid collision avoidance (COLAV) approach based on the integration of a global path planning algorithm and a reactive collision avoidance technique. This combination provides a robust path planning tool that can avoid collision with moving obstacles. Bézier curves are exploited as the basis for global path planning, while dynamic window (DW) algorithm is employed to search for optimal velocity pairs which ensure collision-free trajectory. In particular, the interface between the deliberate and reactive method is developed, enabling the vehicle to simultaneously track the generated global path towards the goal and avoid local collision. The performance and robustness of the proposed hybrid COLAV method is evaluated through numerical simulations.
Detecting Expressional Anomie in Social Media via Fine-grained Content Mining

Expression plays an important role in language inheritance, interpersonal communication, and social stability. With the rapid development of the Internet, people are becoming frequently interested in expressing themselves on social media. Meanwhile, massive anomic expressions are generated, which pollute network environments and even hinder social development. Hence, the purpose of this article is detecting anomic expressions in social media automatically, so as to reveal fine-grained status of online expressional anomie. Specifically, the authors used machine learning to detect anomic expressions and identify anomic types. Then, impacts of different factors (e.g. gender, region, time) on expressional anomie were analyzed. Finally, distributions and characteristics of expressional anomie about online contents were obtained. Empirical results indicate that the current situation of expressional anomie is severe, and scientific and effective treatments for anomic expression are necessary and urgently. Meanwhile, gender, region, and time should be taken into consideration in the formulation of treatments.
Designing for Context-Aware and Contextualized Learning

Contextualized and contextContext-aware learning refer to active and passive approaches of utilizing contexts in educational designsEducational design. Both are at the core of many mobile learningMobile learning solutions. For scaling mobile learningMobile learning in educational institutions, it is important to understand that mobile learningMobile learning is neither an independent nor a stand-alone educational approach, but part of a rich repertoire of tools and practices that shape complex learning processes and are embedded in increasingly smart environments. Moreover, mobile learningMobile learning combines solutions for a range of different educational interventions. Educators have to choose and integrate each solution into their educational concepts in order to utilize the ubiquitously available technologies for leveraging on the learners’ contexts. This requires a better conceptual understanding on the role and function of contextContext in educational designEducational design. Seamless learningSeamless learning addresses this understanding by generalizing contextual influences on learning processes beyond mobile learningMobile learning, which is lacking in conventional educational designEducational design models. However, seamless learningSeamless learning is not an educational designEducational design model that educators can use directly for deducing design principles. Seamless learningSeamless learning is rather a concept that best understood in relation to integrated approaches of context-awarenessContext-awareness and contextualizationContextualization that contrast of existing educational designEducational design models. Because much research on mobile learningMobile learning focuses on the active role of contexts, the question comes into mind, whether contextContext is always an explicit design element? This chapter addresses this question in two parts. First, by operationalizing the concept of seamless learningSeamless learning for planning and orchestrating contextual and contextContext-aware mobile learningMobile learning. Secondly by analyzing potential contextual affordancesAffordance contextual affordance of a mobile app with minimized contextual dependenciesContextual dependencies.
On the Number of Limit Cycles in Diluted Neural Networks

We consider the storage properties of temporal patterns, i.e. cycles of finite lengths, in neural networks represented by (generally asymmetric) spin glasses defined on random graphs. Inspired by the observation that dynamics on sparse systems has more basins of attractions than the dynamics of densely connected ones, we consider the attractors of a greedy dynamics in sparse topologies, considered as proxy for the stored memories. We enumerate them using numerical simulations and extend the analysis to large systems sizes using belief propagation. We find that the logarithm of the number of such cycles is a non monotonic function of the mean connectivity and we discuss the similarities with biological neural networks describing the memory capacity of the hippocampus.
Novel navigation assistive device for deaf drivers

There are over 466 million people in the world with disabling hearing loss. People with severe-to-profound hearing impairment need to lipread or use sign language, even with hearing aids. Assistive Technologies play a vital role in helping these people interact efficiently with their environment. Deaf drivers are not currently able to take full advantage of voice-based navigation applications. In this paper, we describe research that is aimed at developing an assistive device that (1) recognizes voice-stream navigation instructions from GPS-based navigation applications, and (2) maps each voiced navigation instruction to a vibrotactile stimulus that can be perceived and understood by deaf drivers. A 13-element feature vector is extracted from each voice stream, and classified into one of six categories, where each category represents a unique navigation instruction. The classification of the feature vectors is done using a K-Nearest-Neighbor classifier (with an accuracy of 99.05%) which was found to outperform five other classifiers. Each category is then mapped to a unique vibration pattern, which drives vibration motors in real time. A usability study was conducted with ten participants. Three different alternatives were tested, to find the best body locations for mounting the vibration motors. The solution ultimately chosen was two sets of five vibrator motors, where each set was mounted on a bracelet. Ten drivers were asked to rate the proposed device (based on eight different factors) after they used the assistive device on 8 driving routes. The overall mean rating across all eight factors was 4.67 (out of 5) This indicates that the proposed assistive device was seen as useful and effective.
Deep Supervised Hashing through Ensemble CNN Feature Extraction and Low-Rank Matrix Factorization for Retinal Image Retrieval of Diabetic Retinopathy

During the past few decades, content-based image retrieval (CBIR) has been a prominent research area in medical image analysis. It enables retrieving images from an image database that are similar to a given query image. Numerous types of medical image retrieval approaches have been proposed by different research groups. In particular, supervised, deep neural network-based methods have achieved higher accuracy than others. However, they are computationally very expensive and an effective and comprehensive deep neural network-based retinal image retrieval model for diabetic retinopathy (DR) is not available in the literature. The principal objective of CBIR for DR is to efficiently retrieve retinal images that are semantically similar to a given query for effective treatment based on the severity stage of the disease. We propose to use a deep, supervised hashing approach in order to perform efficient retinal image retrieval, where we implicitly learn a good image representation along with a similarity-preserving compact binary hash code for each image by extracting features using an ensemble of deep convolutional neural networks through transfer learning and then feed these extracted features to an ANN classifier. This approach maps the image pixels to a lower-dimensional space and then generates compact binary codes to speedup the retrieval process. Moreover, our approach requires less memory and computational time, which can constructively accelerate the training process. Our experimental results show a considerable improvement compare to the other several state-of-the-art hashing techniques on the retinal dataset. We further analyze the effectiveness and efficiency of our approach using another medical dataset, KVASIR, which includes Gastrointestinal tract endoscopic imagery.