Reviewerly/exHarmony huggingface dataset:
The Tocantinzinho Paleoproterozoic Porphyry-Style Gold Deposit, Tapajós Mineral Province (Brazil): Geology, Petrology and Fluid Inclusion Evidence for Ore-Forming Processes

The Tocantinzinho gold deposit, located in the Tapajós Mineral Province, Amazonia, Brazil, is considered the largest gold deposit in the region. It is a stockwork-disseminated gold deposit, hosted in a 1982 ± 8 Ma hydrothermalized monzogranite of the Creporizão Intrusive Suite, with petrographic and geochemical characteristics of volcanic arc to post-collisional granites. Gold is mainly associated with phyllic alteration. Primary fluid inclusions trapped in the mineralization stages are H2O–NaCl and unsaturated and homogenize either to the vapor or to the liquid with Th(t) of 300–430 °C, salinity of 2–16 wt % NaCl eq. and density from 0.43 to 0.94 g/cm3. At these conditions, Au is expected to be transported as Au(HS)2− complexes and ore is deposited as the result of boiling in the first mineralizing stages and of mixing between magmatic fluid and meteoric water during the phyllic alteration. Compared with other deposits, Tocantinzinho has similarities with magmatic-hydrothermal oxidized calc-alkaline granite-related gold deposits classified as porphyry gold deposits but we classify as a porphyry-style gold deposit, as it lacks some characteristics of the Phanerozoic porphyry-type deposits. The results from this study can be used to elaborate and guide prospection models in Amazonia and in similar Proterozoic terrains.
Cloud-assisted privacy-conscious large-scale Markowitz portfolio

The theory of Markowitz portfolio has had enormous value and extensive applications in finance since it came into being. With the advent of the Big-Data era and the increasingly complicated financial market, the resource consumption of computing portfolio investments is significantly increasing. Cloud computing offers a good platform to efficiently compute large-scale portfolio investments, in particular, for resource-limited investors. In this paper, a Markowitz model (MM) is taken into consideration for outsourcing to a public cloud in a privacy-conscious way. As in general computation outsourcing, outsourcing MM inevitably faces four issues, namely, input/output privacy, correctness, verification, and substantial computation gain for investors; it has consistent complexity with the original methods when the cloud solves the encrypted version. However, the proposed cloud-assisted privacy-conscious MM employs location-scrambling and value-alteration encryption operations, which can protect the MM's input/output privacy well. Moreover, the correctness of solving MM over an encrypted domain in the cloud side can be demonstrated and the results returned by the cloud can be verified. Furthermore, both theoretical and experimental analyses validate that the investor can obtain a huge amount of computational gain, and the cloud complexity consistent with that of the original case when solving the encrypted version.
Artistic Object Recognition by Unsupervised Style Adaptation

Computer vision systems currently lack the ability to reliably recognize artistically rendered objects, especially when such data is limited. In this paper, we propose a method for recognizing objects in artistic modalities (such as paintings, cartoons, or sketches), without requiring any labeled data from those modalities. Our method explicitly accounts for stylistic domain shifts between and within domains. To do so, we introduce a complementary training modality constructed to be similar in artistic style to the target domain, and enforce that the network learns features that are invariant between the two training modalities. We show how such artificial labeled source domains can be generated automatically through the use of style transfer techniques, using diverse target images to represent the style in the target domain. Unlike existing methods which require a large amount of unlabeled target data, our method can work with as few as ten unlabeled images. We evaluate it on a number of cross-domain object and scene classification tasks and on a new dataset we release. Our experiments show that our approach, though conceptually simple, significantly improves the accuracy that existing domain adaptation techniques obtain for artistic object recognition.
Blockchain-Based Management of Shared Energy Assets Using a Smart Contract Ecosystem

Energy markets are facing challenges regarding a changing energy generation and consumption structure, as well as the coordination of an increasing number of assets, devices and stakeholders. We address these challenges by introducing a blockchain-based smart contract ecosystem as our contribution to extant research. Apart from blockchain-specific benefits (e.g. data integrity and smart contract execution), the ecosystem fosters energy-blockchain research through the creation of digital assets. Doing so, we address research gaps identified by previous authors. From our work, we can derive economic implications regarding the foundation of local energy markets, the incentivization of grid-stabilizing behavior and the settlement of collective action problems.
ProSecCo: progressive sequence mining with convergence guarantees

We present ProSecCo, an algorithm for the progressive mining of frequent sequences from large transactional datasets: It processes the dataset in blocks and it outputs, after having analyzed each block, a high-quality approximation of the collection of frequent sequences. ProSecCo can be used for interactive data exploration, as the intermediate results enable the user to make informed decisions as the computation proceeds. These intermediate results have strong probabilistic approximation guarantees and the final output is the exact collection of frequent sequences. Our correctness analysis uses the Vapnik–Chervonenkis (VC) dimension, a key concept from statistical learning theory. The results of our experimental evaluation of ProSecCo on real and artificial datasets show that it produces fast-converging high-quality results almost immediately. Its practical performance is even better than what is guaranteed by the theoretical analysis, and ProSecCo can even be faster than existing state-of-the-art non-progressive algorithms. Additionally, our experimental results show that ProSecCo uses a constant amount of memory, and orders of magnitude less than other standard, non-progressive, sequential pattern mining algorithms.
Selective Timewarp Based on Embedded Motion Vectors for Interactive Cloud Virtual Reality

Interactive virtual reality (VR) services such as VR gaming require considerable computing power for rendering higher quality VR images at a high frame rate. Thus offloading such VR processing to a cloud or edge computing entity is promising, but is accompanied by long latency leads for a user to see an image of the past viewport, resulting in prohibitive motion sickness. Time warp is a technique for VR that warps a rendered image before scanning it out to the display to correct for the head movement occurring after the rendering and will play an important role in reducing the perceived latency in cloud/edge VR. However, time warp has to be applied to the image areas excluding the head-locked objects such as head-up display, menu bar, and notification that are intended to have a fixed position on a screen; otherwise, they are shown to judder. In this paper, we propose an algorithm that identifies head-locked objects in encoded VR frames at low computational load with no explicit information of the head-locked objects for selective time warp in the cloud/edge VR environments. First, we make a key observation from testbed experiments that motion vectors embedded in an encoded VR video stream are highly correlated with the user’s head motion. Based on this finding, the head-locked object detection algorithm is designed to: 1) find a raw shape of each head-locked object from motion vectors embedded in frames and 2) identify the exact pixel-level head-locked region within a limited search area (along the boundary of each raw shape) by monitoring pixel color changes over frames. In order to achieve even lower computational load, the costly part of the algorithm is activated only when a new head-locked object appears. The experimental study demonstrates that the algorithm is able to detect multiple head-locked objects with zero pixel error under limited computational load.
A Modified Gravitational Search Algorithm for Function Optimization

Gravitational search algorithm (GSA) is a population-based heuristic algorithm, which is inspired by Newton's laws of gravity and motion.Although GSA provides a good performance in solving optimization problems, it has a disadvantage of premature convergence.In this paper, the concept of repulsive force is introduced and the definition of exponential Kbest is used in a new version of GSA, which is called repulsive GSA with exponential Kbest (EKRGSA).In this algorithm, heavy particles repulse or attract all particles according to distance, and all particles search the solution space under the combined action of repulsive force and gravitational force.In this way, the exploration ability of the algorithm is improved and a proper balance between exploration and exploitation is established.Moreover, the exponential Kbest significantly decreases the computational time.The proposed algorithm is tested on a set of benchmark functions and compared with other algorithms.The experimental results confirm the high efficiency of EKRGSA.
Implementing in the VAMDC the New Paradigms for Data Citation from the Research Data Alliance

VAMDC bridged the gap between atomic and molecular (A&M) producers and users by providing an interoperable e-infrastructure connecting A&M databases, as well as tools to extract and manipulate those data. The current paper highlights how the new paradigms for data citation produced by the Research Data Alliance in order to address the citation issues in the data-driven science landscape, have successfully been implemented on the VAMDC e-infrastructure.
e-PRNU: Encrypted Domain PRNU-Based Camera Attribution for Preserving Privacy

Photo Response Non-Uniformity (PRNU) noise-based source camera attribution is a popular digital forensic method. In this method, a camera fingerprint computed from a set of known images of the camera is matched against the extracted noise of an anonymous questionable image to find out if the camera had taken the anonymous image. The possibility of privacy leak, however, is one of the main concerns of the PRNU-based method. Using the camera fingerprint (or the extracted noise), an adversary can identify the owner of the camera by matching the fingerprint with the noise of an image (or with the fingerprint computed from a set of images) crawled from a social media account. In this article, we address this privacy concern by encrypting both the fingerprint and the noise using the Boneh-Goh-Nissim (BGN) encryption scheme, and performing the matching in encrypted domain. To overcome leakage of privacy from the content of an image that is used in the fingerprint calculation, we compute the fingerprint within a trusted environment, such as ARM TrustZone. We present e-PRNU that aims at minimizing privacy loss and allows authorized forensic experts to perform camera attribution. The security analysis shows that the proposed approach is semantically secure. Experimental results show that the run-time computational overhead is 10.26 seconds when a cluster of 64 computing nodes are used.
Using Genetic Algorithm to Improve Classification of Imbalanced Datasets for Credit Card Fraud Detection

With the growing usage of credit card transactions, financial fraud crimes have also been drastically increased leading to the loss of huge amounts in the finance industry. Having an efficient fraud detection method has become a necessity for all banks in order to minimize such losses. In fact, credit card fraud detection system involves a major challenge: the credit card fraud data sets are highly imbalanced since the number of fraudulent transactions is much smaller than the legitimate ones. Thus, many of traditional classifiers often fail to detect minority class objects for these skewed data sets. This paper aims first: to enhance classified performance of the minority of credit card fraud instances in the imbalanced data set, for that we propose a sampling method based on the K-means clustering and the genetic algorithm. We used K-means algorithm to cluster and group the minority kind of sample, and in each cluster we use the genetic algorithm to gain the new samples and construct an accurate fraud detection classifier.
Leakage-resilient ring signature schemes

Ring signature schemes provide a way to sign a message without exposing the identity of authentic signer. Security of ring signature assumes that the signing keys are perfectly secret. On account of the physical imperfection of cryptosystems in practice, however, malicious attackers can easily learn partial secret information of the system by means of side-channel attacks , thus breaking the security. To overcome this problem, Wang et al. introduced the notion of leakage-resilient ring signature and presented a concrete construction. However, their scheme is only provably secure in the random oracle model and can tolerate at most ( 1 / 2 − 1 / 2 t − ϵ ) part leakage of the secret signing key, where t is the ring size. In this work, we focus on the constructions of leakage-resilient ring signature based on bounded leakage model , and combine Bender et al.'s security definitions of traditional ring signature with bounded leakage resilience, which is stronger than that considered in Wang et al.'s work. We then propose three constructions of leakage-resilient ring signature secure under the given security models. The first one is a black-box construction, and the second one is a concrete construction with leakage bound ( ( n − 2 ) log ⁡ q − ω ( log ⁡ k ) ) whose security is reduced to the intractability of computational Diffie–Hellman problem and leakage-resilient hard relation without random oracles. The third construction enjoys better efficiency and higher leakage bound, e.g. ( ( n − 1 ) log ⁡ q − ω ( log ⁡ k ) ) , but its security proof resorts to the random oracle model.
Multimodal mobile keystroke dynamics biometrics combining fixed and variable passwords

Recent works have demonstrated the possibility to craft successful statistical attacks against keystroke dynamic biometric password. Those attacks leverage the possibility to capture several keystroke dynamics samples for a given password string, and then extract and use their distributional properties to craft the attack. These approaches are by design more likely to be successful when launched against fixed passwords, as several samples of the passwords can be captured through successive login sessions. Although the dynamics obtained from specific keys or key sequences for consecutive passwords slightly vary, by definition the distributional properties remain fairly stable for the same user. One way to thwart such that attack to use a variable password, also know as one‐time password (OTP). However, the fact that the keystroke dynamic OTP is different from one session to the other, makes it extremely difficult to reconstruct a valid biometric profile for a user. Modeling accurate keystroke dynamic OTP is challenging, due to the underlying variability and the sparse amount of information involved. We tackle the aformentioned challenge by presenting, in this paper, by presenting a multimodal approach tat combines fixed and variable keystroke dynamic biometric passwords. We investigate two different fusion models and evaluate our approach using a data set involving 100 different users, yielding encouraging performance results in terms of accuracy and resistance against statistical attacks.
An N-way group association architecture and sparse data group association load balancing algorithm for sparse CNN accelerators

In recent years, ASIC CNN Accelerators have attracted great attention among researchers for the high performance and energy efficiency. Some former works utilize the sparsity of CNN networks to improve the performance and the energy efficiency. However, these methods bring tremendous overhead to the output memory, and the performance suffers from the hash collision. This paper presents: 1) an N-Way Group Association Architecture to reduce the memory overhead for Sparse CNN Accelerators; 2) a Sparse Data Group Association Load Balancing Algorithm which is implemented by the Scheduler module in the architecture to reduce the collision rate and improve the performance. Compared with the state-of-art accelerator, this work achieves either 1) 1.74x performance with 50% memory overhead reduction in the 4-way associated design or 2) 1.91x performance without memory overhead reduction the 2-way associated design, which is close to the theoretical performance limit (without collision).
Non-Blind Image Deblurring Method by the Total Variation Deep Network

There are a lot of non-blind image deblurring methods, especially with the total variation (TV) model-based method. However, how to choose the parameters adaptively for regularization is a major open problem. We proposed a very novel method that is based on the TV deep network to learn the best parameters adaptively for regularization. We used deep learning and prior knowledge to set up a TV-based deep network and calculate the parameters of regularization, such as biases and weights. Therefore, we used the idea of a deep network to update these parameters automatically to avoid sophisticated calculations. Our experimental results by our proposed network are significantly better than several other methods, in respect of detail retention and anti-noise performance. At the same time, we can achieve the same effect with a minimum number of training sets, thus speeding up the calculation.
LiSHT: Non-parametric Linearly Scaled Hyperbolic Tangent Activation Function for Neural Networks

The activation function in neural network introduces the non-linearity required to deal with the complex tasks. Several activation/non-linearity functions are developed for deep learning models. However, most of the existing activation functions suffer due to the dying gradient problem and non-utilization of the large negative input values. In this paper, we propose a Linearly Scaled Hyperbolic Tangent (LiSHT) for Neural Networks (NNs) by scaling the Tanh linearly. The proposed LiSHT is non-parametric and tackles the dying gradient problem. We perform the experiments on benchmark datasets of different type, such as vector data, image data and natural language data. We observe the superior performance using Multi-layer Perceptron (MLP), Residual Network (ResNet) and Long-short term memory (LSTM) for data classification, image classification and tweets classification tasks, respectively. The accuracy on CIFAR100 dataset using ResNet model with LiSHT is improved by 9.48, 3.40, 3.16, 4.26, and 1.17% as compared to Tanh, ReLU, PReLU, LReLU, and Swish, respectively. We also show the qualitative results using loss landscape, weight distribution and activations maps in support of the proposed activation function.
Privacy risk assessment in context: A meta-model based on contextual integrity

Publishing data in open format is a growing trend, particularly for public bodies who have a legal obligation to make data available as open data. We look at the privacy implications of publishing open data and, in particular, how organisations can make informed decisions around privacy risks in relation to open data publishing before publication occurs. Using a well established theoretical privacy assessment framework, Contextual Integrity, we illustrate how this can be translated into a practical meta-model that can assist public bodies in assessing what privacy implications or risks might be associated with making a particular dataset available as open data. We validate the meta-model by providing a worked example and illustrate the effectiveness of this by reference to a case study application where the meta-model was successfully applied in practice.
Story Ending Selection by Finding Hints From Pairwise Candidate Endings

The ability of story comprehension is a strong indicator of natural language understanding. Recently, Story Cloze Test has been introduced as a new task of machine reading comprehension, i.e., selecting a correct ending from two candidate endings given a four-sentence story context. Most existing methods for Story Cloze Test are essentially matching-based that operate by comparing an individual ending with a given context, therefore suffering from the evidence bias issue: both candidate endings can obtain supporting evidence from the story context, which misleads the classifier to choose an incorrect ending. To address this issue, we present a novel idea to improve story comprehension by utilizing the hints that are obtained through comparing two candidate endings. The proposed model firstly anticipates a feature vector for a possible ending solely based on the context, and then refines the feature prediction using the hints which encode the difference between two candidates. The candidate ending whose feature vector is more similar to the predicted ending vector is regarded as correct. Experimental results demonstrate that our approach can alleviate the evidence bias issue and improve story comprehension.
Assistive Grasping Based on Laser-point Detection with Application to Wheelchair-mounted Robotic Arms

As the aging of the population becomes more severe, wheelchair-mounted robotic arms (WMRAs) are gaining an increased amount of attention. Laser pointer interactions are an attractive method enabling humans to unambiguously point out objects and pick them up. In addition, they bring about a greater sense of participation in the interaction process as an intuitive interaction mode. However, the issue of human⁻robot interactions remains to be properly tackled, and traditional laser point interactions still suffer from poor real-time performance and low accuracy amid dynamic backgrounds. In this study, combined with an advanced laser point detection method and an improved pose estimation algorithm, a laser pointer is used to facilitate the interactions between humans and a WMRA in an indoor environment. Assistive grasping using a laser selection consists of two key steps. In the first step, the images captured using an RGB-D camera are pre-processed, and then fed to a convolutional neural network (CNN) to determine the 2D coordinates of the laser point and objects within the image. Meanwhile, the centroid coordinates of the selected object are also obtained using the depth information. In this way, the object to be picked up and its location are determined. The experimental results show that the laser point can be detected with almost 100% accuracy in a complex environment. In the second step, a compound pose-estimation algorithm aiming at a sparse use of multi-view templates is applied, which consists of both coarse- and precise-matching of the target to the template objects, greatly improving the grasping performance. The proposed algorithms were implemented on a Kinova Jaco robotic arm, and the experimental results demonstrate their effectiveness. Compared with commonly accepted methods, the time consumption of the pose generation can be reduced from 5.36 to 4.43 s, and synchronously, the pose estimation error is significantly improved from 21.31% to 3.91%.