Reviewerly/exHarmony huggingface dataset:
Trace element distributions in (Cu)-Pb-Sb sulfosalts from the Gutaishan Au-Sb deposit, South China: Implications for formation of high fineness native gold

Compositional data, comprising electron probe microanalysis and laser ablation-inductively coupled plasma-mass spectrometry (LA-ICP-MS) trace-element data, are presented for common (Cu)-Pb-Sb sulfosalts (bournonite, jamesonite, tetrahedrite, and boulangerite), subordinate semseyite, heteromorphite, robinsonite, and (Cu)-Pb-Bi-Sb sulfosalts, as well as for accompanying base metal sulfides (BMS) in auriferous gold veins from the Gutaishan Au-Sb deposit, southern China. The objectives of the study were to identify whether these sulfosalts represent overlooked hosts for precious metals and other trace elements of petrogenetic or economic interest, establish partitioning trends among coexisting sulfosalt species and between sulfosalts and BMS, and to seek evidence for a genetic link between the abundance of (Cu)-Pb-Sb sulfosalts and the high-fineness of native gold in the deposit. All (Cu)-Pb-Sb sulfosalts analyzed were found to be remarkably poor hosts for gold and thus do not contribute to the overall mineralogical balance for gold. Trace yet measurable concentrations of Au are, however, noted in the (Cu)-Pb-Bi-Sb sulfosalts, in agreement with published data indicating that (Cu)-Pb-Bi-Sb sulfosalts may be minor Au-hosts in some ore systems. Silver is preferentially partitioned into tetrahedrite at the expense of other sulfosalt phases, and tetrahedrite is thus the major host for Ag in the Gutaishan deposit. LA-ICP-MS trace element mapping allows partitioning relationships among different sulfosalt and BMS phases to be determined for several trace elements. Jamesonite concentrates Fe, Zn, Bi, Cd, Ag, Ni, and In over coexisting bournonite, yet boulangerite is the better host for As, Ag, Sn, Se, and Te than jamesonite. Cd and Co are typically enriched in sphalerite relative to any sulfosalt, and when present, pyrite is always enriched in Au and Co relative to all other phases. A high Au/Ag ratio in the ore-forming fluid, the presence of abundant tetrahedrite that has sequestered silver during mineral precipitation, and a lack of evidence for cooling-driven precipitation may be significant reasons for the formation of high-fineness gold throughout the deposit. Two generations of native gold are documented whereby the first is coarse-grained, Ag- and Bi-bearing, and is associated with the main (Cu)-Pb-Sb sulfosalts (bournonite, jamesonite, tetrahedrite, and boulangerite). The second generation is fine-grained and has the highest fineness. Increase in the complexity of sulfosalt assemblages, re-distribution of Ag within coarse native gold and dissolution-reprecipitation reactions among the sulfosalt-gold association increase the gold fineness. The present study shows that linking petrographic aspects at the micrometer-scale with minor/trace element distributions in complex sulfide-sulfosalt assemblages can track a complex history of Au deposition and enrichment.
Health-monitoring of pregnant women: Design requirements, and proposed reference architecture

Pregnancy is a special condition in which women go through various health complications throughout the period of gestation. Conventional health monitoring systems are either too specific or too general, therefore not flexible enough to be suited for pregnant women. In this paper, requirements and challenges in health monitoring of pregnant women are summarized. First, the specific requirements for health monitoring are derived, including adaptive monitoring, need for big data, and real time monitoring. Then a first ever reference architecture is proposed specifically for health monitoring of pregnant women. Mobile devices, body sensors, cloud and thin client of health care professionals are integrated together through the proposed architecture for an end to end self adaptive health monitoring solution design. Apart from self adaptation based on system dynamics, other features of the proposed architecture are mobile device as a gateway for body and ambient sensors, risk factor evaluation and possible action identification, and prolonged monitoring. The proposed architecture facilitates big data analytic and real time monitoring by remote thin client of health professionals through the assistance of cloud infrastructure.
What Is a Knowledge Graph?

In this chapter, we introduce the basic concept of a knowledge graph (KG). While a knowledge graph seems to be a very simple way of representing information, it turns out to be quite powerful and is almost a lingua franca of sorts between humans and machines. On the one hand, knowledge graphs, if constructed properly, contain much of the useful information that can be found in 'natural language' documents like newswire or even social media. On the other hand, machines can process knowledge graphs in a variety of ways, leading to applications and breakthroughs in areas like semantic search, question answering, entity resolution, and representation learning.
Strongly Secure and Efficient Range Queries in Cloud Databases under Multiple Keys

Cloud database provides an advantageous platform for outsourcing of database service. To protect data confidentiality from an untrusted cloud, the original database is often encrypted and then uploaded to the cloud. However, in order to support functional queries, existing secure databases require users to encrypt their data under the same public/symmetric key, which restricts the usage scenarios since users do not really trust each other in practice. Imagine a scenario where a user uploaded his/her own encrypted data to the cloud database and another user wants to execute private range queries on this data. This scenario occurs in many cases of collaborative statistical analysis where the data provider and analyst are different entities. Then either the data provider must reveal its encryption key or the analyst must reveal the private queries. In this paper, we overcome this restriction for secure range queries by enabling query executions on the multi-key encryption data. We propose a secure cloud database supporting range queries under multiple keys, in which all users could preserve the confidentiality of their own different keys, and do not have to share them with each other. At a higher level, our system is constructed on a two-cloud architecture and a novel distributed two-trapdoor public key cryptosystem. We prove that the proposed scheme achieves the goal of a secure query without leaking data privacy, query privacy, and data access patterns. Finally, we use extensive experiments over a real-world dataset on a commercial cloud platform to verify the efficacy of our proposed scheme.
$L_0$ Sparse Regularization-Based Image Blind Deblurring Approach for Solid Waste Image Restoration

The new vision-based object sorting system is a fundamental module in the construction and demolition waste recycling industry, where image deblurring is vital as the system often fails due to the heavily blurred images caused by the vibration of the conveyor belt that carries solid wastes. This paper proposes a novel blind deblurring approach in which a novel penalty function is formulated as the regularization term in the total energy function. This regularization term is based on sparse prior and solved as part of a mathematical optimization problem, which is operated on the dark channel of the input image. The method not only reserves the structure information of the image but also avoids over-smoothing in the final restoration. On synthetic and natural blurred images, the method outperforms other popular methods. Even with less iterations, the convergence rate and quality of the results are superior. We apply this approach for solid waste image restoration and achieve remarkable results with high validity and reliability.
Investigation of challenges in academic institutional repositories

Purpose The purpose of this paper is to explore the breadth of the challenges and issues facing institutional repositories in academic libraries, based on a survey of academic librarians. Particularly, this study covers the challenges and barriers related to data management facing institutional repositories. Design/methodology/approach The study uses a survey method to identify the relative significance of major challenges facing institutional repositories across six dimensions, including: data, metadata, technological requirements, user needs, ethical concerns and administrative challenges. Findings The results of the survey reveal that academic librarians identify limited resources, including insufficient budget and staff, as the major factor preventing the development and/or deployment of services in institutional repositories. The study also highlights crucial challenges in different dimensions of institutional repositories, including the sheer amount of data, institutional support for metadata creation and the sensitivity of data. Originality/value This study is one of a few studies that comprehensively identified the variety of challenges that institutional repositories face in operating academic libraries with a focus on data management in institutional repositories. In this study, 37 types of challenges were identified in six dimensions of institutional repositories. More importantly, the significance of those challenges was assessed from the perspective of academic librarians involved in institutional repository services.
Fast and Efficient Filter Using Wavelet Threshold for Removal of Gaussian Noise from MRI/CT Scanned Medical Images/Color Video Sequence

We proposed an optimum de-noising filter using wavelet threshold for the removal of adaptive white Gaussian noise from degraded medical images/colour video sequence based on system noise calculation. The performance of the proposed Gaussian de-noising technique is compared with various de-noising techniques such as Bayesian least squares-Gaussian scale mixtures, bilateral filter, adaptive bilateral filter, multi-resolution bilateral filter, nonlocal-Means, wavelet threshold based filters, and kernel-based method. The proposed methods have comparable/superior performance and less computational time compared to other techniques.
Design and Development of an EOG-based System to Control Electric Wheelchair for People Suffering from Quadriplegia or Quadriparesis

In this research, we have designed and developed an EOG-based Electric Wheelchair system for patients suffering from Quadriplegia or Quadriparesis or similar condition where a person is not only unable to walk but also unable to move fingers to press a button. The designed EOG recorder is used to capture intentional eye blink and eye movements (i.e. EOG signals) performed by the patient sitting in the Wheelchair. The recorded EOG signals are then processed in Arduino platform and converted to command signals to control the movement of the wheelchair in four directions (i.e. left, right, front and back) from four different EOG signals (i.e. left, right, up and down eye movements) along with start and stop commands from intentional eye blink signals. The electric wheelchair is also designed and implemented by converting a normal chair to wheelchair with necessary motors (2 DC brushed motors), wheels, and control circuitry along with the battery power supply. The EOG system is portable, wireless and user friendly which can be worn by the patient to control the wheelchair. Test experiment with subjects suggests that the proposed EOG system can detect different eye movements with an average accuracy of 90% in controlling the wheelchair. The overall cost of the developed system is around 20,000 BDT which is affordable. The proposed system has a huge potential to be used by our country's rural and middle-income people suffering from similar disability who can't afford commercially available expensive automated electric wheelchairs. This work can be further modified to use in other applications for the same type of patients such as EOG controlled mouse cursor for computer use. Thus this EOG-based human-machine interface applications can significantly enhance the quality of life for such middle-income or lower-middle income patients living in the developing countries such as Bangladesh.
Deep Learning-based Frame and Timing Synchronization for End-to-End Communications

End-to-end learning-based communications systems are more likely to achieve the global optimal performance. In this work, we implement a complete communications system as an end-to-end deep neural network, including transmitter, channel model, synchronization and receiver. There are a lot of issues which can result to the out of synchronization between the transmitter and the receiver, including sampling timing offset, sampling frequency offset and so on. Therefore, we focus on frame and timing synchronization in the end-to-end learning-based communications systems. Our results show that such an end-to-end learning-based communications system is robust to the impairments that arise from sampling frequency offset. An extra synchronization model based on the convolutional neural networks is introduced to achieve frame synchronization and compensate for the impairments which arise from sampling time offset and sampling time error. The performances of the synchronization model are evaluated from the symbol error rate and the frame header detection results. It can correctly detect most of the actual position of frame header, and it is about 2dB better than that of the direct correlation detection in the end-to-end communications systems.
Shared-Private Information Bottleneck Method for Cross-Modal Clustering

Recently, the cross-modal analysis has drawn much attention due to the rapid growth and widespread emergence of multimodal data. It integrates multiple modalities to improve learning and generalization performance. However, most previous methods just focus on learning a common shared feature space for all modalities and ignore the private information hidden in each individual modality. To address this problem, we propose a novel shared-private information bottleneck (SPIB) method for cross-modal clustering. First, we devise a hybrid words model and a consensus clustering model to construct the shared information of multiple modalities, which capture the statistical correlation of low-level features and the semantic relations of the high-level clustering partitions, respectively. Second, the shared information of multiple modalities and the private information of individual modalities are maximally preserved through a unified information maximization function. Finally, the optimization of SPIB function is performed by a sequential “draw-and-merge” procedure, which guarantees the function converges to a local maximum. Besides, to solve the lack of tags in cross-modal social images, we also investigate the use of structured prior knowledge in the form of knowledge graph to enrich the information in semantic modality and design a novel semantic similarity measurement for social images. The experimental results on four types of cross-modal datasets demonstrate that our method outperforms the state-of-the-art approaches.
An improved similarity calculation method for collaborative filtering- based recommendation, considering neighbor’s liking and disliking of categorical attributes of items

Similarity measures play an important role in the accuracy of collaborative filtering based recommendation. Due to non-availability of adequate co-rated users, the accuracy of collaborative filtering decreases because it introduces the sparsity problem. In certain cases, if there is no similar user, recommendation is not possible. In order to abate the issue, we propose a novel approach that calculates the similarity between users not only based on the items rather the attributes of the items. To calculate the similarity more accurately, users’ liking and disliking of the similar attributes of a particular item are considered separately. This approach is particularly useful when there are no co-rated users in the similarity dataset. The performance of the proposed algorithm is tested on the MovieLens dataset using two accuracy metrics, MAE and RMSE.
CART, a Decision SLA Model for SaaS Providers to Keep QoS Regarding Availability and Performance

Cloud systems are becoming a powerful tool for business. The evidence of the advantages of offering infrastructure as a service (IaaS), platform as a service (PaaS), or software as a service (SaaS) is overwhelming. Therefore, for SaaS providers, it is essential to know the virtual resources required to optimize the service offered and to keep the quality of service (QoS) at the desired levels. This paper presents an analytic model cloud availability and response time (CART) for obtaining the best tradeoff between performance, cost, and availability in a cloud system aimed at providing software as a service. The model aims to guarantee the predetermined availability and response time (RT) agreed with customers in a service-level agreement (SLA) contract while minimizing the cost of the system. A client-transparent error recovery strategy has been considered along with a Poisson traffic model. A sensitivity analysis of the simulated and real workloads is presented to study how a specific SLA influences the cloud service performance regarding the guaranteed RT and availability. The results corroborate the goodness of the model proposed, adjusting the real system accurately. Furthermore, the obtained results provide useful guidelines for cloud or Web-server designers.
Analysis of Consumers Perceptions of Food Safety Risk in Social Networks

Data analysis is increasingly becoming a fundamental competence for the success of companies, but also in many social phenomena. Particular attention deserves the health sector because compared to others, and especially compared to the economic one, it has fewer case studies in which the social network analysis has been applied and, therefore, its potential has not yet been fully understood and adequately exploited. Taking into account this consideration, in this paper we investigate a methodology for semantic analysis of textual information obtained from social media streams, in order to perform an early identification of food contaminations. As a case study, we consider a set of reviews gathered from the social network Yelp [1], on which we perform the textual analysis foreseen in the proposed methodology.
Improved Parallax Image Stitching Algorithm Based on Feature Block

Image stitching aims at generating high-quality panoramas with the lowest computational cost. In this paper, we present an improved parallax image-stitching algorithm using feature blocks (PIFB), which achieves a more accurate alignment and faster calculation speed. First, each image is divided into feature blocks using an improved fuzzy C-Means (FCM) algorithm, and the characteristic descriptor of each feature block is extracted using scale invariant feature transform (SIFT). The feature matching block of the reference image and the target image are matched and then determined, and the image is pre-registered using the homography calculated by the feature points in the feature block. Finally, the overlapping area is optimized to avoid ghosting and shape distortion. The improved algorithm considering pre-blocking and block stitching effectively reduced the iterative process of feature point matching and homography calculation. More importantly, the problem that the calculated homography matrix was not global has been solved. Ghosting and shape warping are significantly eliminated by re-optimizing the overlap of the image. The performance of the proposed approach is demonstrated using several challenging cases.
An Approach to Analyze User’s Emotion in HCI Experiments Using Psychophysiological Measures

Emotional experiences have aroused HCI researchers' interest once these types of experience involve many parts of our body, such as muscles, body postures, and psychophysiological measures.Hedonic experiences give us pleasure and good emotions, and this paper focuses on it.Some psychophysiological measures have already been correlated with both valence and arousal, the main dimensions of emotion.However, a consensus has not been reached on which psychophysiological measure better represents the emotion's dimensions.In this paper, three combined non-invasive psychophysiological measures were used to verify which of them represents the emotion's dimensions.Besides that, an approach to studying the tendency of user's emotion is presented, assisting HCI researchers in HCI experiments.An experiment was conducted using quantitative and qualitative data analysis, and the results show important correlations that were used in the proposed approach.
Assessing Feasibility of Secure Quantum Communications Involving Underwater Assets

In this paper, we apply a mathematical model of the quantum bit error rate to assess the viability of quantum key distribution (QKD) protocols for secure and covert underwater communications. We consider scenarios that may include passive sensor arrays, coordinated underwater vehicles, and surface and aerial assets. This analysis suggests that single-photon QKD can potentially be used effectively at depths between 60 and 110 m in clear waters, with the precise depth depending in part on ambient illumination levels. We discuss how diurnal variations in background illumination can be exploited in some applications to achieve improved communications rate and/or reliability when using QKD or other secure optical protocol.
Can dependency distance and direction be used to differentiate translational language from native language?

Translational language is called "the third code" or "the third language" by some researchers. However, whether "the third code" truly exists or not has been an issue of controversy for decades ever since it was proposed. In this study, in the framework of dependency grammar, we use mean dependency distances (MDD) and dependency direction for analyzing translated texts and native texts in an attempt to differentiate translational language from native language in a quantitative way, while displaying holistic features. We built a dependency-annotated treebank consisting of translated English texts, native English texts and native Chinese texts. To eliminate the influence of sentence length and text size on the MDD and dependency direction, we calculated the MDD of each sentence length, and made sure each text was similar in size. We found that: (1) the MDD of the translated English texts was significantly different from that of native English texts, with the MDD of the former much longer than that of the latter; (2) texts of the two types were also significantly different in dependency direction, with the translated English texts being more head-initial compared with the native English texts. Differences in the MDD is probably influenced by the bigger cognitive effort in dealing with Chinese source language and difference in dependency direction is probably due to translators' tendency of putting the informational focus ahead while making decisions. These findings suggest that (1) dependency distance and direction can be used to differentiate translational language from native language; (2) typologically speaking, translational language, a third language which is distinctive from native language may exist; (3) translational language is produced under the combined effect of cognitive effort and decision-making process by translators; and 4) syntactic quantitative methods can be applied to translation studies.