Reviewerly/exHarmony huggingface dataset:
Where We Stand (or Fall): An Analysis of CSRF Defenses in Web Frameworks

Cross-Site Request Forgery (CSRF) is among the oldest web vulnerabilities that, despite its popularity and severity, it is still an understudied security problem. In this paper, we undertake one of the first security evaluations of CSRF defense as implemented by popular web frameworks, with the overarching goal to identify additional explanations to the occurrences of such an old vulnerability. Starting from a review of existing literature, we identify 16 CSRF defenses and 18 potential threats agains them. Then, we evaluate the source code of the 44 most popular web frameworks across five languages (i.e., JavaScript, Python, Java, PHP, and C#) covering about 5.5 million LoCs, intending to determine the implemented defenses and their exposure to the identified threats. We also quantify the quality of web frameworks’ documentation, looking for incomplete, misleading, or insufficient information required by developers to use the implemented CSRF defenses correctly.
A pyramidal deep learning pipeline for kidney whole-slide histology images classification

Renal cell carcinoma is the most common type of kidney cancer. There are several subtypes of renal cell carcinoma with distinct clinicopathologic features. Among the subtypes, clear cell renal cell carcinoma is the most common and tends to portend poor prognosis. In contrast, clear cell papillary renal cell carcinoma has an excellent prognosis. These two subtypes are primarily classified based on the histopathologic features. However, a subset of cases can a have a significant degree of histopathologic overlap. In cases with ambiguous histologic features, the correct diagnosis is dependent on the pathologist's experience and usage of immunohistochemistry. We propose a new method to address this diagnostic task based on a deep learning pipeline for automated classification. The model can detect tumor and non-tumoral portions of kidney and classify the tumor as either clear cell renal cell carcinoma or clear cell papillary renal cell carcinoma. Our framework consists of three convolutional neural networks and the whole slide images of kidney which were divided into patches of three different sizes for input into the networks. Our approach can provide patchwise and pixelwise classification. The kidney histology images consist of 64 whole slide images. Our framework results in an image map that classifies the slide image on the pixel-level. Furthermore, we applied generalized Gauss-Markov random field smoothing to maintain consistency in the map. Our approach classified the four classes accurately and surpassed other state-of-the-art methods, such as ResNet (pixel accuracy: 0.89 Resnet18, 0.92 proposed). We conclude that deep learning has the potential to augment the pathologist's capabilities by providing automated classification for histopathological images.
NeuroCAN: Contextual Anomaly Detection in Controller Area Networks

The Controller Area Network (CAN) is an established standard for inter-connecting onboard Electronic Control Units (ECUs) in a vehicle. Through sensors and actuators, ECUs maintain critical vehicle functions such as transmission and engine control. However, security was never a part of CAN design and hence ECUs are susceptible to a wide range of attacks. Thus, in recent years, several anomaly detection systems have been proposed for the CAN bus in order to detect anomalies caused by adversarial attacks or misbehaving sensors. These systems generally try to detect deviations from individual sensor's expected behavior. As such, they are ineffective against attacks that target multiple sensors to accomplish a collective desired behavior without changing the expected behavior of each individual sensor. In this paper, we focus on detecting such attacks by identifying contextual CAN anomalies in realtime. To this end, we present NeuroCAN, a deep learning-based detection system that utilizes Linear embeddings and Long Short Term Memory (LSTM) units to learn the spatio-temporal correlations among sensor data on the CAN bus at a frame level. By exploiting such correlations, NeuroCAN is able to detect contextual anomalies that are otherwise difficult to detect by analyzing individual sensor data. We evaluate NeuroCAN using two publicly available CAN datasets and compare it against existing approaches. Our results show that NeuroCAN achieves over 95% detection accuracy and performs significantly better than the existing baselines.
Modeling Touch Point Distribution with Rotational Dual Gaussian Model

Touch point distribution models are important tools for designing touchscreen interfaces. In this paper, we investigate how the finger movement direction affects the touch point distribution, and how to account for it in modeling. We propose the Rotational Dual Gaussian model, a refinement and generalization of the Dual Gaussian model, to account for the finger movement direction in predicting touch point distribution. In this model, the major axis of the prediction ellipse of the touch point distribution is along the finger movement direction, and the minor axis is perpendicular to the finger movement direction. We also propose using projected target width and height, in lieu of nominal target width and height to model touch point distribution. Evaluation on three empirical datasets shows that the new model reflects the observation that the touch point distribution is elongated along the finger movement direction, and outperforms the original Dual Gaussian Model in all prediction tests. Compared with the original Dual Gaussian model, the Rotational Dual Gaussian model reduces the RMSE of touch error rate prediction from 8.49% to 4.95%, and more accurately predicts the touch point distribution in target acquisition. Using the Rotational Dual Gaussian model can also improve the soft keyboard decoding accuracy on smartwatches.
It’s about Time: Adopting Theoretical Constructs from Visualization for Sonification

Both sonification and visualization convey information about data by effectively using our human perceptual system, but their ways to transform the data could not be more different. The sonification community has demanded a holistic perspective on data representation, including audio-visual analysis, several times during the past 30 years. A design theory of audio-visual analysis could be a first step in this direction. An indispensable foundation for this undertaking is a terminology that describes the combined design space. To build a bridge between the domains, we adopt two of the established theoretical constructs from visualization theory for the field of sonification. The two constructs are the spatial substrate and the visual mark. In our model, we choose time to be the temporal substrate of sonification. Auditory marks are then positioned in time, such as visual marks are positioned in space. The proposed definitions allow discussing visualization and sonification designs as well as multi-modal designs based on a common terminology. While the identified terminology can support audio-visual analytics research, it also provides a new perspective on sonification theory itself.
Spatial interpolation of coal properties using geographic quantile regression forest

Inaccuracies in spatial modelling of coal properties can impact coal resource and reserve estimates. Geostatistical methods which account for compositional data have previously been recommended as a preferred method for modelling of coal properties. These methods can quantify uncertainty, can use auxiliary information to improve estimates, and ensure that compositional data (such as coal proximate analysis) is modelled in a mathematically consistent way. However, geostatistical methods have drawbacks in that have rigid statistical assumptions about the distribution and stationarity of the target variable and require variogram interpretation which can be onerous when then number of domains and target variables are numerous. Due to these drawback, inverse distance weighting (a simple deterministic method) remains the most popular method for modelling coal properties in Australian resource estimates. To address the drawbacks of geostatistical methods, a machine learning approach based on the quantile regression forest algorithm is proposed as an alternative method to spatially model coal properties. This newly proposed method, accounts for spatial arrangement of data, requires minimal pre-processing steps, can quantify uncertainty and can easily incorporate auxiliary information of various types. To evaluate the performance of this method, the accuracy of predictions of coal proximate analysis properties, and coal density are compared to the predictions of inverse distance weighting and regression kriging. All methods incorporate isometric log ratio transform and back-transform of data in order to account for the compositional nature of coal proximate analysis. Data from an active coal mine in the Bowen Basin, Queensland Australia was used as basis of the results. A unique feature of the mine site in this basin is the presence of intrusion which impacts coal quality properties to various degrees. Using evaluation metrics from leave-one-out cross-validation, this paper demonstrates that quantile regression forest has higher accuracy, lower bias and higher precision than inverses distance weighting across all coal properties. The paper also shows that results of the new method are very similar or better than regression kriging. It is also demonstrated that the prediction maps produced by quantile regression forest could be used to more accurately map out coal impacted by intrusion compared to inverse distance weighting, and that inverse distance weighting overestimates the impact of intrusion and intrusion extent. A drawback of the method compared to inverse distance weighting, is that it is more computationally demanding, less intuitive and is currently not available in existing geological packages.
Sparse to Dense Depth Completion using a Generative Adversarial Network with Intelligent Sampling Strategies

Predicting dense depth accurately is essential for 3D scene understanding applications such as autonomous driving and robotics. However, the depth obtained from commercially available LiDAR and Time-of-Flight sensors is very sparse. With RGB color guidance, modern convolutional neural network (CNN) based approaches can recover the missing depth information. However, there could be scenarios such as low-light environments where it might be difficult to get an associated RGB image with the sparse depth. In this work, we propose a Generative Adversarial Network (GAN) that can accurately predict the dense depth using only sparse samples without any RGB inputs. Generally, the sparsity in the depth samples is uniformly distributed and cannot guarantee capturing all intricate details. In this study, we also explore different variants of sparse sampling strategies from uniform to feature based directed sampling. We find that feature based intelligent sampling enjoys better compression ratio without sacrificing intricate details, saving data communication bandwidth. Compared to uniform sampling, depending on how aggressively the directed sampling is done, we observe about 3% to 25% reduction in size. We can easily reduce the size by 8% with directed sampling without sacrificing the reconstruction accuracy. Although such directed sampling strategies are not readily available with commercially viable depth sensors, we believe that our study paves the way for future intelligent sensing and sampling strategies. To further investigate data reduction and reconstruction accuracy trade-offs we deploy our GAN to generate higher resolution dense depth from 4 times smaller sparse samples. With slight decrease in accuracy, our GAN is able to recover the depth successfully which shows great promise in edge Internet of Things (IoT) applications where we have very tight constraint on data transmission bandwidth. Our source code along with examples is available at: https://github.com/kocchop/depth-completion-gan
Gender and region detection from human voice using the three-layer feature extraction method with 1D CNN

Analysing the human voice has always been a challenge to the engineering society for various purposes such as product review, emotional state detection, developing AI, and much more. Two basic grounds of voice or speech analysis are to detect human gender and the geographical region based on accent. This study presents a three-layer feature extraction method from the raw human voice to detect the gender as male or female, as well as the region from where that voice belongs. Fundamental frequency, spectral entropy, spectral flatness, and mode frequency have been calculated in the first layer of feature extraction. On the other hand, Mel Frequency Cepstral Coefficient has been used to extract the features in the second layer and linear predictive coding in the third layer. Regular voice contains some noises which have been removed with multiple audio data filtering processes to get noise-free smooth data. Multi-Output-based 1D Convolutional Neural Network has been used to recognize gender and region from a combined dataset which consists of TIMIT, RAVDESS, and BGC datasets. The model has successfully predicted the gender with 93.01% and region with 97.07% accuracy. This method works better than usual state-of-the-art methods in separate datasets along with the combined dataset on both gender and region classification.
Sentiment Analysis of Portuguese Political Parties Communication

Political communication in social media has gained increasing importance in the last years. In this study, we analyze the political parties’ communication on Twitter and understand the sentiment of their communication. First by identifying their communication performance regarding the daily number of tweets, favorite tweets, number of retweets per day and per political party. We present a sentiment analysis by the political party using tweets data. In this study, we propose an explanatory model with the main drivers of retweets. To conduct this study, our approach used data analysis and machine learning techniques methods. Results indicate the main determinants that influence future retweets of political posts globally. Here we present a comparison of the communication content between tweets posts and the political parties’ programs available on their institutional websites. We identify the similarities between tweets and formal programs per party and among all parties. This study contributes to analyze the coherence and effectiveness of the political parties’ communication.
Exact reconstruction of extended exponential sums using rational approximation of their Fourier coefficients

In this paper, we derive a new recovery procedure for the reconstruction of extended exponential sums of the form [Formula: see text], where the frequency parameters [Formula: see text] are pairwise distinct. In order to reconstruct y(t) we employ a finite set of classical Fourier coefficients of y with regard to a finite interval [Formula: see text] with [Formula: see text]. For our method, 2N + 2 Fourier coefficients [Formula: see text] are sufficient to recover all parameters of y, where [Formula: see text] denotes the order of y(t). The recovery is based on the observation that for [Formula: see text] the terms of y(t) possess Fourier coefficients with rational structure. We employ a recently proposed stable iterative rational approximation algorithm in [Y. Nakatsukasa, O. Sète and L. N. Trefethen, The AAA Algorithm for rational approximation, SIAM J. Sci. Comput. 40(3) (2018) A1494A1522]. If a sufficiently large set of L Fourier coefficients of y is available (i.e. [Formula: see text]), then our recovery method automatically detects the number M of terms of y, the multiplicities [Formula: see text] for [Formula: see text], as well as all parameters [Formula: see text], [Formula: see text], and [Formula: see text], [Formula: see text], [Formula: see text], determining y(t). Therefore, our method provides a new stable alternative to the known numerical approaches for the recovery of exponential sums that are based on Prony’s method.
Build Your Own Bundle - A Neural Combinatorial Optimization Method

In the business domain,bundling is one of the most important marketing strategies to conduct product promotions, which is commonly used in online e-commerce and offline retailers. Existing recommender systems mostly focus on recommending individual items that users may be interested in, such as the considerable research work on collaborative filtering that directly models the interaction between users and items. In this paper, we target at a practical but less explored recommendation problem named personalized bundle composition, which aims to offer an optimal bundle (i.e., a combination of items) to the target user. To tackle this specific recommendation problem, we formalize it as a combinatorial optimization problem on a set of candidate items and solve it within a neural combinatorial optimization framework. Extensive experiments on public datasets are conducted to demonstrate the superiority of the proposed method.
Commercial virtual reality displays: Issues of performance and simulator sickness from exocentric depth-perception tasks

Issues in both depth-perception performance and simulator-sickness effects may hinder future applications of virtual reality. Applications for depth perception are varied, including military, sports, and architecture domains. The purpose of this study is to compare different commercial-off-the-shelf head-mounted displays and their content, through depth-perception tasks. We also investigate simulator sickness profiles to promote further risk mitigation in such displays. Specifically, the HTC Vive and Oculus Rift Consumer Version 1 were examined in between-groups and within-groups designs, with both head-mounted displays supplying the same exocentric depth-perception tasks across three virtual environments of increasing ecological complexity. Depth-perception performance was measured objectively and simulator sickness was measured subjectively via questionnaire. Results, analyzed using non-parametric statistics, indicated significant differences for performance and simulator sickness, for both between- and within-groups studies. Although distance changes were detected equally between displays, a tradeoff occurred where the Vive had better distance classification, but the Rift had shorter time duration. Hardware issues are considered for performance differences. For post-test simulator sickness, Vive and Rift only differed in terms of fatigue, an oculomotor-based issue with probable user-avoidance strategies; the difference may relate to latency. Performance within Rift and Vive only partially showed a benefit for high-realism scenarios, yet both displays provide distinct scenario-change baselines for personnel selection. Simulator sickness for both devices showed Oculomotor subscale increases in pre-to-post test questionnaire scores, with the Vive having an additional increase in Total scores. The latter Oculomotor increase may be driven by blur. In respect to the observations, multiple factors exist for selection, including performance and simulator sickness tradeoffs.
Learning to Compose Stylistic Calligraphy Artwork with Emotions

Emotion plays a critical role in calligraphy composition, which makes the calligraphy artwork impressive and have a soul. However, previous research on calligraphy generation all neglected the emotion as a major contributor to the artistry of calligraphy. Such defects prevent them from generating aesthetic, stylistic, and diverse calligraphy artworks, but only static handwriting font library instead. To address this problem, we propose a novel cross-modal approach to generate stylistic and diverse Chinese calligraphy artwork driven by different emotions automatically. We firstly detect the emotions in the text by a classifier, then generate the emotional Chinese character images via a novel modified Generative Adversarial Network (GAN) structure, finally we predict the layout for all character images with a recurrent neural network. We also collect a large-scale stylistic Chinese calligraphy image dataset with rich emotions. Experimental results demonstrate that our model outperforms all baseline image translation models significantly for different emotional styles in terms of content accuracy and style discrepancy. Besides, our layout algorithm can also learn the patterns and habits of calligrapher, and makes the generated calligraphy more artistic. To the best of our knowledge, we are the first to work on emotion-driven discourse-level Chinese calligraphy artwork composition.
Improving the Performance of Deduplication-Based Backup Systems via Container Utilization Based Hot Fingerprint Entry Distilling

Data deduplication techniques construct an index consisting of fingerprint entries to identify and eliminate duplicated copies of repeating data. The bottleneck of disk-based index lookup and data fragmentation caused by eliminating duplicated chunks are two challenging issues in data deduplication. Deduplication-based backup systems generally employ containers storing contiguous chunks together with their fingerprints to preserve data locality for alleviating the two issues, which is still inadequate. To address these two issues, we propose a container utilization based hot fingerprint entry distilling strategy to improve the performance of deduplication-based backup systems. We divide the index into three parts: hot fingerprint entries, fragmented fingerprint entries, and useless fingerprint entries. A container with utilization smaller than a given threshold is called a sparse container . Fingerprint entries that point to non-sparse containers are hot fingerprint entries. For the remaining fingerprint entries, if a fingerprint entry matches any fingerprint of forthcoming backup chunks, it is classified as a fragmented fingerprint entry. Otherwise, it is classified as a useless fingerprint entry. We observe that hot fingerprint entries account for a small part of the index, whereas the remaining fingerprint entries account for the majority of the index. This intriguing observation inspires us to develop a hot fingerprint entry distilling approach named HID . HID segregates useless fingerprint entries from the index to improve memory utilization and bypass disk accesses. In addition, HID separates fragmented fingerprint entries to make a deduplication-based backup system directly rewrite fragmented chunks, thereby alleviating adverse fragmentation. Moreover, HID introduces a feature to treat fragmented chunks as unique chunks. This feature compensates for the shortcoming that a Bloom filter cannot directly identify certain duplicated chunks (i.e., the fragmented chunks). To take full advantage of the preceding feature, we propose an evolved HID strategy called EHID . EHID incorporates a Bloom filter, to which only hot fingerprints are mapped. In doing so, EHID exhibits two salient features: (i) EHID avoids disk accesses to identify unique chunks and the fragmented chunks; (ii) EHID slashes the false positive rate of the integrated Bloom filter. These salient features push EHID into the high-efficiency mode. Our experimental results show our approach reduces the average memory overhead of the index by 34.11% and 25.13% when using the Linux dataset and the FSL dataset, respectively. Furthermore, compared with the state-of-the-art method HAR, EHID boosts the average backup throughput by up to a factor of 2.25 with the Linux dataset, and EHID reduces the average disk I/O traffic by up to 66.21% when it comes to the FSL dataset. EHID also marginally improves the system's restore performance.