Reviewerly/exHarmony huggingface dataset:
On the Perils of Leaking Referrers in Online Collaboration Services

Online collaboration services (OCS) are appealing since they provide ease of access to resources and the ability to collaborate on shared files. Documents on these services are frequently shared via secret links, which allows easy collaboration between different users. The security of this secret link approach relies on the fact that only those who know the location of the secret resource (i.e., its URL) can access it. In this paper, we show that the secret location of OCS files can be leaked by the improper handling of links embedded in these files. Specifically, if a user clicks on a link embedded into a file hosted on an OCS, the HTTP Referer contained in the resulting HTTP request might leak the secret URL. We present a study of 21 online collaboration services and show that seven of them are vulnerable to this kind of secret information disclosure caused by the improper handling of embedded links and HTTP Referers. We identify two root causes of these issues, both having to do with an incorrect application of the Referrer Policy, a countermeasure designed to restrict how HTTP Referers are shared with third parties. In the first case, six services leak their referrers because they do not implement a strict enough and up-to-date policy. In the second case, one service correctly implements an appropriate Referrer Policy, but some web browsers do not obey it, causing links clicked through them to leak their HTTP Referers. To fix this problem, we discuss how services can apply the Referrer Policy correctly to avoid these incidents, as well as other server and client side countermeasures.
Sa1252 THE APPLICATION OF ARTIFICIAL INTELLIGENCE TO DETECT ESOPHAGEAL SQUAMOUS CELL CARCINOMA IN MOVIES USING CONVOLUNTIONAL NEURAL NETWORKS

Esophageal cancer is the eighth most common cancer worldwide, and the sixth cause of death. When esophageal cancer is diagnosed in advanced stage, it will require highly invasive treatments and its prognosis will be poor, thus it is important to detect in early stage. We have reported the great ability of artificial intelligence (AI) to detect esophageal cancer including squamous cell carcinoma (SCC) and adenocarcinoma in still pictures. Here, we demonstrated the diagnostic ability of AI to detect superficial esophageal SCC in movies.
Adaptation Across Extreme Variations using Unlabeled Domain Bridges

We tackle an unsupervised domain adaptation problem for which the domain discrepancy between labeled source and unlabeled target domains is large, due to many factors of inter and intra-domain variation. While deep domain adaptation methods have been realized by reducing the domain discrepancy, these are difficult to apply when domains are significantly unalike. In this work, we propose to decompose domain discrepancy into multiple but smaller, and thus easier to minimize, discrepancies by introducing unlabeled bridging domains that connect the source and target domains. We realize our proposal through an extension of the domain adversarial neural network with multiple discriminators, each of which accounts for reducing discrepancies between unlabeled (bridge, target) domains and a mix of all precedent domains including source. We validate the effectiveness of our method on several adaptation tasks including object recognition and semantic segmentation.
FDCT and Perceptual Hash-Based Watermarking Algorithm for Medical Images

In order to solve the security problem of patient’s private information contained in medical images such as MRI, CT and X-Ray et al. transmitted on the Internet, a robust watermarking algorithm for medical images based on fast discrete curvelet transform (FDCT) and perceptual hash is proposed. First, perform FDCT on the medical image and select the low-frequency coefficients to do lst level discrete cosine transform. After that, a 4 × 8 size data block in the upper left corner was extracted as the visual feature vector of the medical image. Then, use logistic chaotic encryption which is sensitive to the initial value to generate the encrypted watermark. It can effectively enhance the security of the algorithm. Finally, combining with perceptual hash, cryptography and the concept of the third party, a 32 bit secret key is generated to realize zero-watermark and blind extraction. The simulation results show that the proposed algorithm performed better than the existing schemes in terms of imperceptibility and robustness, and is robust to both conventional attacks and geometric attacks.
Towards Races in Linear Logic

Process calculi based in logic, such as $$\uppi $$ DILL and CP, provide a foundation for deadlock-free concurrent programming, but exclude non-determinism and races. $$\text {HCP}$$ is a reformulation of CP which addresses a fundamental shortcoming: the fundamental operator for parallel composition from the $$\uppi $$ -calculus does not correspond to any rule of linear logic, and therefore not to any term construct in CP. We introduce $$\text {HCP}^{-} _{\text {ND}}$$ , which extends $$\text {HCP}$$ with a novel account of non-determinism. Our approach draws on bounded linear logic to provide a strongly-typed account of standard process calculus expressions of non-determinism. We show that our extension is expressive enough to capture many uses of non-determinism in untyped calculi, such as non-deterministic choice, while preserving $$\text {HCP}$$ ’s meta-theoretic properties, including deadlock freedom.
Convolution filter embedded quantum gate autoencoder

The autoencoder is one of machine learning algorithms used for feature extraction by dimension reduction of input data, denoising of images, and prior learning of neural networks. At the same time, autoencoders using quantum computers are also being developed. However, current quantum computers have a limited number of qubits, which makes it difficult to calculate big data. In this paper, as a solution to this problem, we propose a computation method that applies a convolution filter, which is one of the methods used in machine learning, to quantum computation. As a result of applying this method to a quantum autoencoder, we succeeded in denoising image data of several hundred qubits or more using only a few qubits under the autoencoding accuracy of 98%, and the effectiveness of this method was obtained. Meanwhile, we have verified the feature extraction function of the proposed autoencoder by dimensionality reduction. By projecting the MNIST data to two-dimension, we found the proposed method showed superior classification accuracy to the vanilla principle component analysis (PCA). We also verified the proposed method using IBM Q Melbourne and the actual machine failed to provide accurate results implying high error rate prevailing in the current NISQ quantum computer.
Reflecting on student mental health: Creating a meditation room in a small college library

There was a time when meditation, mindfulness, and massage were often seen as luxuries or the domain of granola-eating eccentrics. Now such activities have moved into the mainstream and are embraced by millions of Americans. Data analyzed from the 2012 National Health Interview Survey indicated that 8%, or 9.3 million, of Americans reported using meditation in the previous year. The most common reason cited, according to the report, was mental health problems. Anxiety, stress, and depression were the top mental health problems for which individuals used meditation.
Tu1931 INCORPORATION OF TEMPORAL INFORMATION IN A DEEP NEURAL NETWORK IMPROVES PERFORMANCE LEVEL FOR AUTOMATED POLYP DETECTION AND DELINEATION

As opposed to current automated polyp detection techniques, an endoscopist will use the information that he/she has seen on previous video frames to indicate the presence of a polyp. We aim to exploit this type of temporal information in an artificial intelligence (AI) system with the use of so-called recurrent neural networks. These networks contain memory cells, enabling them to remember what was seen in previous frames. Although widely used in fields such as object recognition and natural language processing, recurrent neural networks have (to the best of our knowledge) not been applied for polyp detection or endoscopic AI systems in general.
Relational Reasoning using Prior Knowledge for Visual Captioning

Exploiting relationships among objects has achieved remarkable progress in interpreting images or videos by natural language. Most existing methods resort to first detecting objects and their relationships, and then generating textual descriptions, which heavily depends on pre-trained detectors and leads to performance drop when facing problems of heavy occlusion, tiny-size objects and long-tail in object detection. In addition, the separate procedure of detecting and captioning results in semantic inconsistency between the pre-defined object/relation categories and the target lexical words. We exploit prior human commonsense knowledge for reasoning relationships between objects without any pre-trained detectors and reaching semantic coherency within one image or video in captioning. The prior knowledge (e.g., in the form of knowledge graph) provides commonsense semantic correlation and constraint between objects that are not explicit in the image and video, serving as useful guidance to build semantic graph for sentence generation. Particularly, we present a joint reasoning method that incorporates 1) commonsense reasoning for embedding image or video regions into semantic space to build semantic graph and 2) relational reasoning for encoding semantic graph to generate sentences. Extensive experiments on the MS-COCO image captioning benchmark and the MSVD video captioning benchmark validate the superiority of our method on leveraging prior commonsense knowledge to enhance relational reasoning for visual captioning.
The Generalization-Stability Tradeoff In Neural Network Pruning

Pruning neural network parameters is often viewed as a means to compress models, but pruning has also been motivated by the desire to prevent overfitting. This motivation is particularly relevant given the perhaps surprising observation that a wide variety of pruning approaches increase test accuracy despite sometimes massive reductions in parameter counts. To better understand this phenomenon, we analyze the behavior of pruning over the course of training, finding that pruning's benefit to generalization increases with pruning's instability (defined as the drop in test accuracy immediately following pruning). We demonstrate that this "generalization-stability tradeoff" is present across a wide variety of pruning settings and propose a mechanism for its cause: pruning regularizes similarly to noise injection. Supporting this, we find less pruning stability leads to more model flatness and the benefits of pruning do not depend on permanent parameter removal. These results explain the compatibility of pruning-based generalization improvements and the high generalization recently observed in overparameterized networks.
Wasserstein Weisfeiler-Lehman Graph Kernels

Most graph kernels are an instance of the class of $\mathcal{R}$-Convolution kernels, which measure the similarity of objects by comparing their substructures. Despite their empirical success, most graph kernels use a naive aggregation of the final set of substructures, usually a sum or average, thereby potentially discarding valuable information about the distribution of individual components. Furthermore, only a limited instance of these approaches can be extended to continuously attributed graphs. We propose a novel method that relies on the Wasserstein distance between the node feature vector distributions of two graphs, which allows to find subtler differences in data sets by considering graphs as high-dimensional objects, rather than simple means. We further propose a Weisfeiler-Lehman inspired embedding scheme for graphs with continuous node attributes and weighted edges, enhance it with the computed Wasserstein distance, and thus improve the state-of-the-art prediction performance on several graph classification tasks.
High-threshold fault-tolerant quantum computation with the Gottesman-Kitaev-Preskill qubit under noise in an optical setup

To implement fault-tolerant quantum computation (FTQC) with continuous variables, continuous variables need to be digitized using an appropriate code such as the Gottesman--Kitaev--Preskill (GKP) qubit. The scheme introduced in [K. Fukui et. al., Phys. Rev. X 8, 021054 (2018)] has reduced the threshold of a squeezing level required for continuous-variable FTQC to less than 10 dB, assuming noise derived from the GKP qubit itself. In this work, we propose a scheme to improve noise tolerance during the construction of large-scale cluster state used for FTQC with the GKP qubits. In our scheme, a small-scale cluster state is prepared by employing a maximum-likelihood method, the entanglement generation via the Bell measurement, and a probabilistic reliable measurement. Then, a large-scale cluster state is construct from the small-scale cluster states via the encoded Bell measurement. In the numerical calculations, we assume errors derived from the two-mode gate and loss in the homodyne measurement in addition to noise from the GKP qubit itself. The results show that the thresholds of a squeezing level are around 8.1, 9.6, and 12.4 dB for loss in the homodyne measurement 0, 5, and 10 %, respectively. Hence, this work provides a way toward continuous-variable FTQC with a feasible squeezing level.
Just in Time: Personal Temporal Insights for Altering Model Decisions

The interpretability of complex Machine Learning models is coming to be a critical social concern, as they are increasingly used in human-related decision-making processes such as resume filtering or loan applications. Individuals receiving an undesired classification are likely to call for an explanation - preferably one that specifies what they should do in order to alter that decision when they reapply in the future. Existing work focuses on a single ML model and a single point in time, whereas in practice, both models and data evolve over time: an explanation for an application rejection in 2018 may be irrelevant in 2019 since in the meantime both the model and the applicant's data can change. To this end, we propose a novel framework that provides users with insights and plans for changing their classification in particular future time points. The solution is based on combining state-of-the-art algorithms for (single) model explanations, ones for predicting future models, and database-style querying of the obtained explanations. We propose to demonstrate the usefulness of our solution in the context of loan applications, and interactively engage the audience in computing and viewing suggestions tailored for applicants based on their unique characteristic.
Measurement of heavy metal concentrations in groundwater

Water pollution is one of the main concerns in the world today. There are three main types of water pollution: chemical, physical, and biological. In this paper, we consider heavy metals as water pollutants. Heavy metals present a serious problem for human health and ecosystems. The purpose of this study is to identify and analyze heavy metals in groundwater (drinking water) in the region of Tapiza, Albania. Capacity of groundwater through wells installed in this area is used for supply and distribution of drinking water to the community of Tapiza. We collected a total of 16 groundwater samples. Analyses of heavy metals arsenic, cadmium, cobalt, copper, and lead in the groundwater were performed at the Institute of Applied Nuclear Physics, University of Tirana, Albania, using graphite furnace atomic absorption spectrometry (GFAAS). The results showed that concentration of heavy metals in groundwater samples are in these ranges: As (2.6–9.2) μg/L; Cd (0–0.61) μg/L; Co (4.3–17.8) μg/L; Cu (7.5–28.4) μg/L, and Pb (0.96–5.84) μg/L Also, the concentration of analyzed metals found in groundwater samples is compared with the maximum contaminant level specified by the U.S. Environmental Protection Agency for drinking water.
Question Answering as an Automatic Evaluation Metric for News Article Summarization.

Recent work in the field of automatic summarization and headline generation focuses on maximizing ROUGE scores for various news datasets. We present an alternative, extrinsic, evaluation metric for this task, Answering Performance for Evaluation of Summaries. APES utilizes recent progress in the field of reading-comprehension to quantify the ability of a summary to answer a set of manually created questions regarding central entities in the source article. We first analyze the strength of this metric by comparing it to known manual evaluation metrics. We then present an end-to-end neural abstractive model that maximizes APES, while increasing ROUGE scores to competitive results.
Image smoothing via adaptive fourth‐order partial differential equation model

To overcome the staircase effects and simultaneously avoid edge blurring, here the authors proposed a novel algorithm of adaptive fourth-order partial differential equation (PDE) for image smoothing. The proposed method is based on You and Kaveh (YK) model that first performs a pre-processing using coherence-enhancing diffusion filter before the smoothing. Then, the authors select an adaptive edge-stopping function founded on the Tansig function to effectively remove the noise and insignificant details without destruction of important edges and textures in the image. MATLAB simulation results validate the efficiency of the proposed model quantitatively and visually, when compared with other state-of-the-art methods.
Penerapan Sistem Informasi Raport Online

Technological advancements make it easy for everyone related to the ease of getting and managing information. One concrete example is the development of the internet which has become an icon of technological development. Especially in the era of education 4.0, internet plays a very important role in the education process. In the education era 4.0 educators and students are required to be able to integrate technology in every educational activity. One application of technology in education today is the existence of a system of information on report cards based on the internet or what we are familiar with online report cards. This research is a literature study that aims to collect information relating to the use of online report card information systems that are applied by several schools. The results obtained from this literature study are online report cards that make it easy for educators to process student value data that can be accessed by both students and parents.
Heterogeneous Graph Attention Network

Graph neural network, as a powerful graph representation technique based on deep learning, has shown superior performance and attracted considerable research interest. However, it has not been fully considered in graph neural network for heterogeneous graph which contains different types of nodes and links. The heterogeneity and rich semantic information bring great challenges for designing a graph neural network for heterogeneous graph. Recently, one of the most exciting advancements in deep learning is the attention mechanism, whose great potential has been well demonstrated in various areas. In this paper, we first propose a novel heterogeneous graph neural network based on the hierarchical attention, including node-level and semantic-level attentions. Specifically, the node-level attention aims to learn the importance between a node and its metapath based neighbors, while the semantic-level attention is able to learn the importance of different meta-paths. With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered. Then the proposed model can generate node embedding by aggregating features from meta-path based neighbors in a hierarchical manner. Extensive experimental results on three real-world heterogeneous graphs not only show the superior performance of our proposed model over the state-of-the-arts, but also demonstrate its potentially good interpretability for graph analysis.
Ekonomik Özgürlük Endeksi ve Hisse Senedi Piyasası Endeksi Arasındaki İlişkinin Analizi; Türkiye Örneği

According to the Heritage Foundation Economic Freedom Index, Turkey, with a overall score of 65.4, seems below the regional average. On the other hand, BIST 100 has a good performance with a nearly %20 rise from 2016 to 2017 except the last nine-month period performance. The objective of this study is to analyze the causality relationship between Economic Freedom Index and BIST 100 in the period of 1995-2018 by employing Fourier KPSS Test and Fourier Granger Causality Test. The results supported no causality relationship between economic freedom index and stock market indices in Turkey in the period of 1995-2018.
Amended Cross-Entropy Cost: An Approach for Encouraging Diversity in Classification Ensemble (Brief Announcement)

In the field of machine learning, the training of an ensemble of models is a very common method for reducing the variance of the prediction, and yields better results. Many researches indicate that diversity between the predictions of the models is important for the ensemble performance. However, for Deep Learning classification tasks there is no explicit way to encourage diversity. Negative Correlation Learning (NCL) is a method for doing so in regression tasks. In this work we develop a novel algorithm inspired by NCL to explicitly encourage diversity in Deep Neural Networks (DNNs) for classification. In the development of the algorithm we first assume that the same training characteristics that hold in NCL must also hold when training an ensemble for classification. We also suggest the Stacked Diversified Mixture of Classifiers (SDMC), which is based on our outcome. SDMC is a layer that aims to replace the final layer of a DNN classifier. It can be easily applied on any model, while the cost in terms of number of parameters and computational power is relatively low.
Exploring Online Music Listening Behaviors of Musically Sophisticated Users

Due to the rise of available online music, a lot of music consumption is moving from traditional offline media to online sources. Online music sources offer almost an unlimited music collection to its users. Hence, how music is consumed by users (e.g., experts) may differ from traditional offline sources. In this work we explored how musically sophisticated users (i.e. experts) consume online music in terms of diversity. To analyze this, we gathered data from two different sources: Last.fm and Spotify. As expertise is defined by the ubiquitousness of experiences, we calculated different diversity measurements to explore how ubiquitous (in terms of diversity) the listening behaviors of users are. We found that different musical sophistication levels correspond to applying diversity related to specific kind of musical characteristics (i.e., artist or genre). Our results can provide knowledge on how systems should be designed to provide better support to expert users.