Reviewerly/exHarmony huggingface dataset:
Style augmentation: data augmentation via style randomization

We introduce style augmentation, a new form of dataaugmentation based on random style transfer, for improvingthe robustness of Convolutional Neural Networks (CNN)over both classification and regression based tasks. Duringtraining, style augmentation randomizes texture, contrastand color, while preserving shape and semantic content.This is accomplished by adapting an arbitrary styletransfer network to perform style randomization, by samplingtarget style embeddings from a multivariate normaldistribution instead of computing them from a style image.In addition to standard classification experiments, we investigatethe effect of style augmentation (and data augmentationgenerally) on domain transfer tasks. We find that dataaugmentation significantly improves robustness to domainshift, and can be used as a simple, domain agnostic alternativeto domain adaptation. Comparing style augmentationagainst a mix of seven traditional augmentation techniques,we find that it can be readily combined with them to improvenetwork performance. We validate the efficacy of ourtechnique with domain transfer experiments in classificationand monocular depth estimation illustrating superiorperformance over benchmark tasks
Discrete Deep Hashing With Ranking Optimization for Image Retrieval

For large-scale image retrieval task, a hashing technique has attracted extensive attention due to its efficient computing and applying. By using the hashing technique in image retrieval, it is crucial to generate discrete hash codes and preserve the neighborhood ranking information simultaneously. However, both related steps are treated independently in most of the existing deep hashing methods, which lead to the loss of key category-level information in the discretization process and the decrease in discriminative ranking relationship. In order to generate discrete hash codes with notable discriminative information, we integrate the discretization process and the ranking process into one architecture. Motivated by this idea, a novel ranking optimization discrete hashing (RODH) method is proposed, which directly generates discrete hash codes (e.g., +1/-1) from raw images by balancing the effective category-level information of discretization and the discrimination of ranking information. The proposed method integrates convolutional neural network, discrete hash function learning, and ranking function optimizing into a unified framework. Meanwhile, a novel loss function based on label information and mean average precision (MAP) is proposed to preserve the label consistency and optimize the ranking information of hash codes simultaneously. Experimental results on four benchmark data sets demonstrate that RODH can achieve superior performance over the state-of-the-art hashing methods.
The gold occurrence in pyrite and Te–Bi mineralogy of the Fancha gold deposit, Xiaoqinling gold field, southern margin of the North China Craton: Implication for ore genesis

The Fancha lode gold deposit is hosted in the Neoarchean Taihua Group metamorphic rocks in the Xiaoqinling gold field, on the southern margin of the North China Craton. Pyrite, as the predominant sulphide mineral and Au‐hosting mineral, was investigated using a combination of ore microscopy and in‐situ laser ablation inductively coupled plasma mass spectrometry (LA‐ICP‐MS). Three types of pyrite were identified during the ore‐forming process, that is, the coarse euhedral grains in milky quartz veins (Py1), the fine to medium, euhedral to subhedral grains in light grey quartz (Py2), and the fine grains intergrown with abundant sulphide minerals in light grey quartz (Py3). The trace elements exhibit systematic different concentrations among three types of pyrite, implying different crystallization processes. The Py1 grains have the lowest concentrations of Au and other accompanying elements such as Ag, Te, Bi, Cu, Pb, and Zn, and the trace elements of pyrite in wall rock are similar to those of Py1. The Py2 and Py3 grains show consistently high contents of Au, Ag, Te, Bi, Cu, Pb, and Zn. The uneven distribution of gold and positive correlation between Au, Ag, Te, and Bi within Py2 and Py3 grains, indicating that gold mainly occurs as submicroscopic inclusions of Au–Ag–Te–Bi assemblages in the pyrite. This interpretation is supported by the microscopic observation that large quantities of telluride and Bi‐sulfosalt minerals contact directly with gold. Observed Te–Bi minerals include petzite, sylvanite, hessite, calaverite, rucklidgeite, altaite, volynskite, tellurobismuthite, buckhornite, tetradymite, krupkaite, and bismuthinite, and native bismuth is also found. Bi (Te) melts, as an excellent gold scavenger, have played a certain role in governing gold distribution patterns and leading to high concentrations during the mineralization process in the Fancha gold deposit. The Te–Bi mineral assemblages also reveal that the gold was precipitated under variable redox conditions with log f Te 2 ranged from &lt;−11.0 to −6.4, and log f S 2 ranged from &lt;−11.8 to −8.6 at 300°C, vertically upward from the deep fluid source. Combined with available geochronological and geochemical data, enrichment of Te and Bi indicates that the ore‐forming fluids and metals might be stemed from a mantle‐derived magmatic system.
LiteEval: A Coarse-to-Fine Framework for Resource Efficient Video Recognition

This paper presents LiteEval, a simple yet effective coarse-to-fine framework for resource efficient video recognition, suitable for both online and offline scenarios. Exploiting decent yet computationally efficient features derived at a coarse scale with a lightweight CNN model, LiteEval dynamically decides on-the-fly whether to compute more powerful features for incoming video frames at a finer scale to obtain more details. This is achieved by a coarse LSTM and a fine LSTM operating cooperatively, as well as a conditional gating module to learn when to allocate more computation. Extensive experiments are conducted on two large-scale video benchmarks, FCVID and ActivityNet, and the results demonstrate LiteEval requires substantially less computation while offering excellent classification accuracy for both online and offline predictions.
Categorizing Comparative Sentences

We tackle the tasks of automatically identifying comparative sentences and categorizing the intended preference (e.g., "Python has better NLP libraries than MATLAB" → Python, better, MATLAB).To this end, we manually annotate 7,199 sentences for 217 distinct target item pairs from several domains (27% of the sentences contain an oriented comparison in the sense of "better" or "worse").A gradient boosting model based on pre-trained sentence embeddings reaches an F1 score of 85% in our experimental evaluation.The model can be used to extract comparative sentences for pro/con argumentation in comparative / argument search engines or debating technologies.
Where do the improvements come from in sequence-to-sequence neural TTS?

Sequence-to-sequence neural networks with attention mechanisms have recently been widely adopted for text-to-speech. Compared with older, more modular statistical parametric synthesis systems, sequ ...
Training to Detect Phishing Emails: Effects of the Frequency of Experienced Phishing Emails

We studied people’s success on the detection of phishing emails after they were trained under one of three phishing frequency conditions, where the proportion of the phishing emails during training varied as: low frequency (25% phishing emails), medium frequency (50% phishing emails) and high frequency (75% phishing emails). Individual base susceptibility to phishing emails was measured in a pre-training phase in which 20% of the emails were phishing; this performance was then compared to a post-training phase in which participants aimed at detecting new rare phishing emails (20% were phishing emails). The Hit rates, False Alarm rates, sensitivities and response criterion were analyzed. Results revealed that participants receiving higher frequency of phishing emails had a higher hit rate but also higher false alarm rate at detecting phishing emails at post-training compared to participants encountering lower frequency levels during training. These results have implications for designing new training protocols for improving detection of phishing emails.
Virtual and Augmented Reality in Liver Surgery

Department of General, Visceral and Transplant Surgery, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany. [email protected]. Funding: Tobias Huber receives funding from the German federal Ministry of Education and Research (BMBF-FKZ: 16SV8057 “AVATAR”). The authors declare no conflict of interest.
A survey on automatic speech recognition systems for Portuguese language and its variations

Communication has been an essential part of being human and living in society. There are several different languages and variations of them, so you can speak English in one place and not be able to communicate effectively with someone who speaks English with a different accent. There are several application areas where voice/speech data can be of importance, such as health, security, biometric analysis or education. However, most studies focus on English, Arabic or Asian languages, neglecting other relevant languages, such as Portuguese, which leaves their investigations wide open. Thus, it is crucial to understand the area, where the main focus is: what are the most used techniques for feature extraction and classification, and so on. This paper presents a survey on automatic speech recognition components for Portuguese-based language and its variations, as an understudied language. With a total of 101 papers from 2012 to 2018, the Portuguese-based automatic speech recognition field tendency will be explained, and several possible unexplored methods will be presented and discussed in a collaborative and overall way as our main contribution.
Learning misclassification costs for imbalanced classification on gene expression data

Cost-sensitive algorithm is an effective strategy to solve imbalanced classification problem. However, the misclassification costs are usually determined empirically based on user expertise, which leads to unstable performance of cost-sensitive classification. Therefore, an efficient and accurate method is needed to calculate the optimal cost weights.In this paper, two approaches are proposed to search for the optimal cost weights, targeting at the highest weighted classification accuracy (WCA). One is the optimal cost weights grid searching and the other is the function fitting. Comparisons are made between these between the two algorithms above. In experiments, we classify imbalanced gene expression data using extreme learning machine to test the cost weights obtained by the two approaches.Comprehensive experimental results show that the function fitting method is generally more efficient, which can well find the optimal cost weights with acceptable WCA.
Improved Ant Lion Optimizer Based on Spiral Complex Path Searching Patterns

Ant Lion Optimizer (ALO) is a new meta-heuristic algorithm that simulates the ant lion predator mechanism in nature.Five main steps of hunting include: random walks of ants, building traps, trapping in antlion's pits, sliding ants towards antlion, catching prey and re-building pits.As the predator radius of antlion decreases with the number of iterations, there is an unbalanced between the ant lion optimizer between exploration and exploitation, and it is easy to fall into the local optimal solution.An improved ant lion optimizer based on spiral complex path searching pattern is proposed, where eight spiral paths (Hypotrochoid, Rose spiral curve, Logarithmic spiral curve, Archimedes spiral curve, Epitrochoid, Inverse spiral curve, Cycloid, Overshoot parameter setting of the spiral) searching strategies were adopted to improve the diversity of the population and the ability of the algorithm to balance exploration and exploitation.The proposed algorithm can accelerate the convergence speed of ALO and improve its performance.The algorithm is verified by simulation experiments in three parts.Firstly, 28 function optimization problems were adopted to test the optimization performance of the improved ALO.Secondly, it is applied to the lightest design engineering problem of pressure vessels.Finally, the spiral complex path searching patterns are introduced into the muti-objective ALO and 4 typical muti-objective functions are optimized.Simulation results show that the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration.The improved algorithm can better solve function optimization, classical engineering problems with constraints and multi-objective function optimization problems.The improved ALO based on the spiral complex path searching mode has the characteristics of balanced exploration and exploitation, fast convergence speed and high precision.
Machine learning methods in psychiatry: a brief introduction

Machine learning (ML) techniques have been widely used to address mental health questions. We discuss two main aspects of ML in psychiatry in this paper, that is, supervised learning and unsupervised learning. Examples are used to illustrate how ML has been implemented in recent mental health research.
Chemical variations in <i>Quercus</i> pollen as a tool for taxonomic identification: Implications for long‐term ecological and biogeographical research

Abstract Aim Fossil pollen is an important tool for understanding biogeographical patterns in the past, but the taxonomic resolution of the fossil‐pollen record may be limited to genus or even family level. Chemical analysis of pollen grains has the potential to increase the taxonomic resolution of pollen analysis, but present‐day chemical variability is poorly understood. This study aims to investigate whether a phylogenetic signal is present in the chemical variations of Quercus L. pollen and to assess the prospects of chemical techniques for identification in biogeographical research. Location Portugal. Taxon Six taxa (five species, one subspecies) of Quercus L., Q. faginea , Q. robur , Q. robur ssp. estremadurensis , Q. coccifera , Q. rotundifolia and Q. suber belonging to three sections: Cerris , Ilex and Quercus (Denk, Grimm, Manos, Deng, &amp; Hipp, 2017). Methods We collected pollen samples from 297 individual Quercus trees across a 4° (~450 km) latitudinal gradient and determined chemical differences using Fourier‐transform infrared spectroscopy (FTIR). We used canonical powered partial least squares regression (CPPLS) and discriminant analysis to describe within‐ and between‐species chemical variability. Results We find clear differences in the FTIR spectra from Quercus pollen at the section level ( Cerris : ~98%; Ilex : ~100%; Quercus : ~97%). Successful discrimination is based on spectral signals related to lipids and sporopollenins. However, discrimination of species within individual Quercus sections is more challenging: overall, species recall is ~76% and species misidentifications within sections lie between 18% and 31% of the test set. Main Conclusions Our results demonstrate that subgenus level differentiation of Quercus pollen is possible using FTIR methods, with successful classification at the section level. This indicates that operator‐independent FTIR approaches can surpass traditional morphological techniques using light microscopy. Our results have implications both for providing new insights into past colonization pathways of Quercus , and likewise for forecasting future responses to climate change. However, before FTIR techniques can be applied more broadly across palaeoecology and biogeography, our results also highlight a number of research challenges that still need to be addressed, including developing sporopollenin‐specific taxonomic discriminators and determining a more complete understanding of the effects of environmental variation on pollen‐chemical signatures in Quercus .
Spatio-Temporal Ranked-Attention Networks for Video Captioning

Generating video descriptions automatically is a challenging task that involves a complex interplay between spatio-temporal visual features and language models. Given that videos consist of spatial (frame-level) features and their temporal evolutions, an effective captioning model should be able to attend to these different cues selectively. To this end, we propose a Spatio-Temporal and Temporo-Spatial (STaTS) attention model which, conditioned on the language state, hierarchically combines spatial and temporal attention to videos in two different orders: (i) a spatiotemporal (ST) sub-model, which first attends to regions that have temporal evolution, then temporally pools the features from these regions; and (ii) a temporo-spatial (TS) sub-model, which first decides a single frame to attend to, then applies spatial attention within that frame. We propose a novel LSTM-based temporal ranking function, which we call ranked attention, for the ST model to capture action dynamics. Our entire framework is trained end-to-end. We provide experiments on two benchmark datasets: MSVD and MSR-VTT. Our results demonstrate the synergy between the ST and TS modules, outperforming recent state-of-the-art methods.
Source Camera Verification for Strongly Stabilized Videos

Image stabilization performed during imaging and/or post-processing poses one of the most significant challenges to photo-response non-uniformity based source camera attribution from videos. When performed digitally, stabilization involves cropping, warping, and inpainting of video frames to eliminate unwanted camera motion. Hence, successful attribution requires the inversion of these transformations in a blind manner. To address this challenge, we introduce a source camera verification method for videos that takes into account the spatially variant nature of stabilization transformations and assumes a larger degree of freedom in their search. Our method identifies transformations at a sub-frame level, incorporates a number of constraints to validate their correctness, and offers computational flexibility in the search for the correct transformation. The method also adopts a holistic approach in countering disruptive effects of other video generation steps, such as video coding and downsizing, for more reliable attribution. Tests performed on one public and two custom datasets show that the proposed method is able to verify the source of 23-30% of all videos that underwent stronger stabilization, depending on computation load, without a significant impact on false attribution.
Optimized Implementation of SIKE Round 2 on 64-bit ARM Cortex-A Processors

In this work, we present the first highly-optimized implementation of Supersingular Isogeny Key Encapsulation (SIKE) submitted to NIST's second round of post quantum standardization process, on 64-bit ARMv8 processors. To the best of our knowledge, this work is the first optimized implementation of SIKE round 2 on 64-bit ARM over SIKEp434 and SIKEp610. The proposed library is explicitly optimized for these two security levels and provides constant-time implementation of the SIKE mechanism on ARMv8-powered embedded devices. We adapt different optimization techniques to reduce the total number of underlying arithmetic operations on the field level. In particular, benchmark results on embedded processors equipped with ARM Cortex-A55@1.766GHz and ARM Cortex-A75@2.803GHz show that the entire SIKE round 2 Key Encapsulation Mechanism (KEM) takes only 98.6 ms and 85.3ms at NIST's security level 1, respectively. We also evaluated the compressed version of NIST's security level 1, which requires 134.7 ms and 113.7 ms for Cortex-A55 and Cortex-A75, respectively. Considering SIKE's extremely small key size in comparison to other post-quantum cryptography candidates, our result implies that SIKE is one of the promising candidates for key encapsulation mechanism on embedded devices in the quantum era.
Generalizing to generalize: Humans flexibly switch between compositional and conjunctive structures during reinforcement learning

Humans routinely face novel environments in which they have to generalize in order to act adaptively. However, doing so involves the non-trivial challenge of deciding which aspects of a task domain to generalize. While it is sometimes appropriate to simply re-use a learned behavior, often adaptive generalization entails recombining distinct components of knowledge acquired across multiple contexts. Theoretical work has suggested a computational trade-off in which it can be more or less useful to learn and generalize aspects of task structure jointly or compositionally, depending on previous task statistics, but it is unknown whether humans modulate their generalization strategy accordingly. Here we develop a series of navigation tasks that separately manipulate the statistics of goal values (“what to do”) and state transitions (“how to do it”) across contexts and assess whether human subjects generalize these task components separately or conjunctively. We find that human generalization is sensitive to the statistics of the previously experienced task domain, favoring compositional or conjunctive generalization when the task statistics are indicative of such structures, and a mixture of the two when they are more ambiguous. These results support a normative “meta-generalization” account and suggests that people not only generalize previous task components but also generalize the statistical structure most likely to support generalization.
Controllable Time-Delay Transformer for Real-Time Punctuation Prediction and Disfluency Detection

With the increased applications of automatic speech recognition (ASR) in recent years, it is essential to automatically insert punctuation marks and remove disfluencies in transcripts, to improve the readability of the transcripts as well as the performance of subsequent applications, such as machine translation, dialogue systems, and so forth. In this paper, we propose a Controllable Time-delay Transformer (CT-Transformer) model that jointly completes the punctuation prediction and disfluency detection tasks in real time. The CT-Transformer model facilitates freezing partial outputs with controllable time delay to fulfill the real-time constraints in partial decoding required by subsequent applications. We further propose a fast decoding strategy to minimize latency while maintaining competitive performance. Experimental results on the IWSLT2011 benchmark dataset and an in-house Chinese annotated dataset demonstrate that the proposed approach outperforms the previous state-of-the-art models on F-scores and achieves a competitive inference speed.
Implementing a Mobile Voting System Utilizing Blockchain Technology and Two-Factor Authentication in Nigeria

The voting system in Nigeria has always been filled with different forms of manipulation, and m-voting was suggested as the solution but has a major problem which is securely storing the casted votes. Blockchain was proposed to mitigate this problem and with the utilization of two-factor authentication to prevent illegible voters from casting their votes. The objective of this paper is to develop a mobile voting system that utilizes two-factor authentication to authenticate the voters and blockchain technology to securely store the votes. The system was then evaluated using the ISO 9241-11 usability model, and the results showed that the proposed system had a good usability rating which implies that it can be utilized in a voting procedure.