Reviewerly/exHarmony huggingface dataset:
Steganographer detection via a similarity accumulation graph convolutional network

Steganographer detection aims to identify guilty users who conceal secret information in a number of images for the purpose of covert communication in social networks. Existing steganographer detection methods focus on designing discriminative features but do not explore relationship between image features or effectively represent users based on features. In these methods, each image is recognized as an equivalent, and each user is regarded as the distribution of all images shared by the corresponding user. However, the nuances of guilty users and innocent users are difficult to recognize with this flattened method. In this paper, the steganographer detection task is formulated as a multiple-instance learning problem in which each user is considered to be a bag, and the shared images are multiple instances in the bag. Specifically, we propose a similarity accumulation graph convolutional network to represent each user as a complete weighted graph, in which each node corresponds to features extracted from an image and the weight of an edge is the similarity between each pair of images. The constructed unit in the network can take advantage of the relationships between instances so that common patterns of positive instances can be enhanced via similarity accumulations. Instead of operating on a fixed original graph, we propose a novel strategy for reconstructing and pooling graphs based on node features to iteratively operate multiple convolutions. This strategy can effectively address oversmoothing problems that render nodes indistinguishable although they share different instance-level labels. Compared with the state-of-the-art method and other representative graph-based models, the proposed framework demonstrates its effectiveness and reliability ability across image domains, even in the context of large-scale social media scenarios. Moreover, the experimental results also indicate that the proposed network can be generalized to other multiple-instance learning problems.
Research Data Management Implementation at Peking University Library: Foster and Promote Open Science and Open Data

Research Data Management (RDM) has become increasingly important for more and more academic institutions. Using the Peking University Open Research Data Repository (PKU-ORDR) project as an example, this paper will review a library-based university-wide open research data repository project and related RDM services implementation process including project kickoff, needs assessment, partnerships establishment, software investigation and selection, software customization, as well as data curation services and training. Through the review, some issues revealed during the stages of the implementation process are also discussed and addressed in the paper such as awareness of research data, demands from data providers and users, data policies and requirements from home institution, requirements from funding agencies and publishers, the collaboration between administrative units and libraries, and concerns from data providers and users. The significance of the study is that the paper shows an example of creating an Open Data repository and RDM services for other Chinese academic libraries planning to implement their RDM services for their home institutions. The authors of the paper have also observed since the PKU-ORDR and RDM services implemented in 2015, the Peking University Library (PKUL) has helped numerous researchers to support the entire research life cycle and enhanced Open Science (OS) practices on campus, as well as impacted the national OS movement in China through various national events and activities hosted by the PKUL.
Robust Anomaly Detection in Feature-Evolving Time Series

Abstract This paper addresses the anomaly detection problem in feature-evolving systems such as server machines, cyber security, financial markets and so forth where in every millisecond, $N$-dimensional feature-evolving heterogeneous time series are generated. However, due to stochasticity and uncertainty in evolving heterogeneous time series coupled with temporal dependencies, their anomaly detection are extremely challenging. Furthermore, it is practically impossible to train an anomaly detection model per single time series across millions of metrics, leave alone memory space required to maintain the model and evolving data points in memory for timely processing in feature-evolving data streams. Thus, this paper proposes $\underline{o}$ne sketch $\underline{f}$its all $\underline{a}$lgorithm (OFA), which is a real-time stochastic recurrent deep neural network anomaly detector built on assumption-free probabilistic conditional quantile regression with well-calibrated predictive uncertainty estimates. The proposed framework is capable of detecting anomalies robustly, accurately and efficiently in real time while handling randomness and variabilities in feature-evolving heterogeneous time series. Extensive experiments and rigorous evaluation on large-scale real-world data sets showcase that OFA outperforms other competitive state-of-the-art anomaly detector methods.
Cross-Modal Contrastive Learning of Representations for Navigation Using Lightweight, Low-Cost Millimeter Wave Radar for Adverse Environmental Conditions

Deep reinforcement learning (RL), where the agent learns from mistakes, has been successfully applied to a variety of tasks. With the aim of learning collision-free policies for unmanned vehicles, deep RL has been used for training with various types of data, such as colored images, depth images, and LiDAR point clouds, without the use of classic map--localize--plan approaches. However, existing methods are limited by their reliance on cameras and LiDAR devices, which have degraded sensing under adverse environmental conditions (e.g., smoky environments). In response, we propose the use of single-chip millimeter-wave (mmWave) radar, which is lightweight and inexpensive, for learning-based autonomous navigation. However, because mmWave radar signals are often noisy and sparse, we propose a cross-modal contrastive learning for representation (CM-CLR) method that maximizes the agreement between mmWave radar data and LiDAR data in the training stage. We evaluated our method in real-world robot compared with 1) a method with two separate networks using cross-modal generative reconstruction and an RL policy and 2) a baseline RL policy without cross-modal representation. Our proposed end-to-end deep RL policy with contrastive learning successfully navigated the robot through smoke-filled maze environments and achieved better performance compared with generative reconstruction methods, in which noisy artifact walls or obstacles were produced. All pretrained models and hardware settings are open access for reproducing this study and can be obtained at https://arg-nctu.github.io/projects/deeprl-mmWave.html
Search for Reserves in the Grip of the “Imputed”: Paradoxes of Our Education Strategy

The dominant orientation of the domestic strategy of higher education (standards, competencies) towards the current demands of the labor market slows down and constrains the development processes at universities. The ability and readiness to respond to the challenges of the future is steadily becoming a real basis for the success of higher education institutions in conditions of modern technological and socio-economic changes. Time and situation require taking into account in university strategies not only the demands of the labor market, but also the features of the future and trends in knowledge development. And this, in turn, dictates the need to liberalize the existing education strategy, as well as to “synchronize” the development of the university both with trends and prospects for the development of technologies and infrastructures, and the historical and epistemological patterns of knowledge development, its functions, ways of organization and forms of life.
A dataset and a methodology for intraoperative computer-aided diagnosis of a metastatic colon cancer in a liver

The lack of pixel-wise annotated images severely hinders the deep learning approach to computer-aided diagnosis in histopathology. This research creates a public database comprised of: (i) a dataset of 82 histopathological images of hematoxylin-eosin stained frozen sections acquired intraoperatively on 19 patients diagnosed with metastatic colon cancer in a liver; (ii) corresponding pixel-wise ground truth maps annotated by four pathologists, two residents in pathology, and one final-year student of medicine. The Fleiss' kappa equal to 0.74 indicates substantial inter-annotator agreement; (iii) two datasets with images stain-normalized relative to two target images; (iv) development of two conventional machine learning and three deep learning-based diagnostic models. The database is available at http://cocahis.irb.hr. For binary, cancer vs. non-cancer, pixel-wise diagnosis we develop: SVM, kNN, U-Net, U-Net++, and DeepLabv3 classifiers that combine results from original images and stain-normalized images, which can be interpreted as different views. On average, deep learning classifiers outperformed SVM and kNN classifiers on an independent test set 14% in terms of micro balanced accuracy, 15% in terms of the micro F1 score, and 26% in terms of micro precision. As opposed to that, the difference in performance between deep classifiers is within 2%. We found an insignificant difference in performance between deep classifiers trained from scratch and corresponding classifiers pre-trained on non-domain image datasets. The best micro balanced accuracy estimated on the independent test set by the U-Net++ classifier equals 89.34%. Corresponding amounts of F1 score and precision are, respectively, 83.67% and 81.11%.
Promoting High Diversity Ensemble Learning with EnsembleBench

Ensemble learning is gaining renewed interests in recent years. This paper presents EnsembleBench, a holistic framework for evaluating and recommending high diversity and high accuracy ensembles. The design of EnsembleBench offers three novel features: (1) EnsembleBench introduces a set of quantitative metrics for assessing the quality of ensembles and for comparing alternative ensembles constructed for the same learning tasks. (2) EnsembleBench implements a suite of baseline diversity metrics and optimized diversity metrics for identifying and selecting ensembles with high diversity and high quality, making it an effective framework for benchmarking, evaluating and recommending high diversity model ensembles. (3) Four representative ensemble consensus methods are provided in the first release of EnsembleBench, enabling empirical study on the impact of consensus methods on ensemble accuracy. A comprehensive experimental evaluation on popular benchmark datasets demonstrates the utility and effectiveness of EnsembleBench for promoting high diversity ensembles and boosting the overall performance of selected ensembles.
Quasiperiodic-Chaotic Neural Networks and Short-Term Analog Memory

A model of quasiperiodic-chaotic neural networks is proposed on the basis of chaotic neural networks. A quasiperiodic-chaotic neuron exhibits quasiperiodic dynamics that an original chaotic neuron does not have. Quasiperiodic and chaotic solutions are exclusively isolated in the parameter space. The chaotic domain can be identified by the presence of a folding structure of an invariant closed curve. Using the property that the influence of perturbation is conserved in the quasiperiodic solution, we demonstrate short-term visual memory in which real numbers are acceptable for representing colors. The quasiperiodic solution is sensitive to dynamical noise when images are restored. However, the quasiperiodic synchronization among neurons can reduce the influence of noise. Short-term analog memory using quasiperiodicity is important in that it can directly store analog quantities. The quasiperiodic-chaotic neural networks are shown to work as large-scale analog storage arrays. This type of analog memory has potential applications to analog computation such as deep learning.
Chaos Based Secure Medical Image Transmission Model for IoT- Powered Healthcare Systems

Abstract Due to the extensive development of Internet of Things (IoT) in e-healthcare environment, security and integrity of the medical data especially medical images became a big issue. This paper proposes a chaotic security architecture for ensuring the security of the medical images during transmission and storage. The proposed model is built by comprising of three main stages. At first, message digest 5 algorithm is applied to the plain image for generating the seed key of Lorenz chaotic map. Subsequently Lorenz map is iterated to generate the chaotic key series utilized for further process. In second stage, dual confusion such as row-by-row and column-by-column confusion is executed on the plain image. At last, dual diffusion process is performed by applying binary reverse and compliment operation, in addition to that XOR operation is executed between diffused image and Lorenz chaotic key image. Simulation results and analysis of security level by applying different attacks indicates that the developed cryptosystem has the potential of satisfying the security requirements of IoT healthcare applications.
Re-Evaluation of the Security of a Family of Image Diffusion Mechanisms

In recent years, the use of permutation-diffusion architecture for digital image encryption has become increasingly popular. The permutation procedure scrambles the pixel locations, while the diffusion phase modifies the pixel values and gives rise to the avalanche effect. Various diffusion techniques have been developed, and their strength strongly impacts the security of the overall cryptosystem. In this paper, we re-evaluate the security of a family of image diffusion mechanisms that are based on mixing modulo addition with bitwise exclusive OR operations. The recovery of the encryption element of these diffusion mechanisms is comprehensively demonstrated, and the accuracy bounds under various conditions are proved mathematically. Compared to the state-of-the-art methods, our work improves the recovery accuracy of the encryption element while the required prior knowledge is decreased. The proposed analysis of the diffusion mechanisms is further used to cryptanalyze the whole cryptosystem theoretically and experimentally.
Using WhatsApp to support EFL reading comprehension skills with Turkish early secondary learners

The purpose of this study is to investigate the impact of reading tasks delivered through WhatsApp on students' achievements in English reading comprehension and explore students' views on the process. A mixed-method research model was used. The participants were 54 seventh grade students, aged 11-12, who were divided into experimental and control groups. Reading texts with comprehension questions were sent to the experimental group students' WhatsApp accounts. The tasks were designed to use all the facilities of WhatsApp. Finishing the reading comprehension activities over a set period of time, students sent their answers via WhatsApp to the teacher who returned the answer key to them. The control group students completed the same reading texts and activities in the classroom. The results showed that the experimental group performed better. It should be noted that the success of the WhatsApp group may be linked to the ranking and reward system which was implemented to encourage them. At the end of the study, students in the experimental group generally expressed their positive attitudes towards using WhatsApp to enhance their reading comprehension skills. We discuss the limitations and implications of this small-scale study for the further use of mobile technology to support language skills.
Decision Support System for Solving Reviewer Assignment Problem

Reviewing is the most important step in the quality accreditation of scientific works, requires the professional expertise of the reviewer as well as there is no conflict of interest in the evaluation process. However, we also acknowledge that reviewers have limited knowledge, experience and opinions about the work of others, so they may misinterpret the author’s point of view, leading to the rejection of excellent scientific work or a potentially successful project proposal. Manually selecting reviewers can lead to bias and time-consuming. To solve these problems, we have developed a support system for selecting a group of reviewers to evaluate a particular problem, such as a proposal or a research paper. Our support system consists of three main modules: data collection, reviewer identification and group prediction of reviewers. The Data Collection module collects data from a variety of sources to create the scientist profile database. The reviewer identification module recognizes reviewers on a specific topic. The reviewer prediction module provides a group of exp reviewers to evaluate a submitted paper or a proposal. Experiments on the DBLP computer science bibliography dataset showed that our system achieves better results in terms of accuracy in comparison to other methods.
One-Shot Image-to-Image Translation via Part-Global Learning With a Multi-Adversarial Framework

It is well known that humans can learn and recognize objects effectively from several limited image samples. However, learning from just a few images is still a tremendous challenge for existing main-stream deep neural networks. Inspired by analogical reasoning in the human mind, a feasible strategy is to &#x201C;translate&#x201D; the abundant images of a rich source domain to enrich the relevant yet different target domain with insufficient image data. To achieve this goal, we propose a novel, effective multi-adversarial framework (<b>MA</b>) based on part-global learning, which accomplishes the one-shot cross-domain image-to-image translation. In specific, we first devise a part-global adversarial training scheme to provide an efficient way for feature extraction and prevent discriminators from being overfitted. Then, a multi-adversarial mechanism is employed to enhance the image-to-image translation ability to unearth the high-level semantic representation. Moreover, a balanced adversarial loss function is presented, which aims to balance the training data and stabilize the training process. Extensive experiments demonstrate that the proposed approach can obtain impressive results on various datasets between two extremely imbalanced image domains and outperform state-of-the-art methods on one-shot image-to-image translation. Our code will be released with this paper at <uri>https://github.com/zhengziqiang/OST</uri>.
Deep-FDA: Using Functional Data Analysis and Neural Networks to Characterize Network Services Time Series

In network management, it is important to model baselines, trends, and regular behaviors to adequately deliver network services. However, their characterization is complex, so network operation and system alarming become a challenge. Several problems exist: Gaussian assumptions cannot be made, time series have different trends, and it is difficult to reduce their dimensionality. To overcome this situation, we propose Deep-FDA, a novel approach for network service modeling that combines functional data analysis (FDA) and neural networks. Specifically, we explore the use of functional clustering and functional depth measurements to characterize network services with time series generated from enriched flow records, showing how this method can detect different separated trends. Moreover, we augment this statistical approach with the use of autoencoder neural networks, improving the classification results. To evaluate and check the applicability of our proposal, we performed experiments with synthetic and real-world data, where we show graphically and numerically the performance of our method compared to other state-of-the-art alternatives. We also exemplify its application in different network management use cases. The results show that FDA and neural networks are complementary, as they can help each other to improve the drawbacks that both analysis methods have when are applied separately.
LSVP: A visual based deep neural direction learning model for point-of-interest recommendation on sparse check-in data

Recently accumulated massive amounts of geo-tagged photos provide an excellent opportunity to understand human behaviors and can be used for personalized POI recommendation. However, no existing work has considered both the visual contents in these photos and the sequential patterns of users' check-ins for POI recommendation. To this end, in this paper, we propose an attentional network named LSVP for POI recommendation, which adaptively considers the joint effects of users' long-term, short-term and visual preferences. Specifically, we first extract visual preferences from photos, then extract long-term and short-term preferences from check-in sequences. At last, an adaptive attention mechanism is used to balance all the extracted users' preferences. Experimental results on two real-world datasets collected show that LSPV provides significantly superior performances compared to other state-of-the-art POI recommendation models in terms of accuracy.
Applicability of Mobile Contact Tracing in Fighting Pandemic (COVID-19): Issues, Challenges and Solutions

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Fine-Grained Named Entity Recognition with Distant Supervision in COVID-19 Literature

Biomedical named entity recognition (BioNER) is a fundamental step for mining COVID-19 literature.Existing BioNER datasets cover a few common coarse-grained entity types (e.g., genes, chemicals, and diseases), which cannot be used to recognize highly domain-specific entity types (e.g., animal models of diseases) or emerging ones (e.g., coronaviruses) for COVID-19 studies.We present CORD-NER, a fine-grained named entity recognized dataset of COVID-19 literature (up until May 19, 2020).CORD-NER contains over 12 million sentences annotated via distant supervision.Also included in CORD-NER are 2,000 manually-curated sentences as a test set for performance evaluation.CORD-NER covers 75 fine-grained entity types.In addition to the common biomedical entity types, it covers new entity types specifically related to COVID-19 studies, such as coronaviruses, viral proteins, evolution, and immune responses.The dictionaries of these fine-grained entity types are collected from existing knowledge bases and human-input seed sets.We further present DISTNER, a distantly supervised NER model that relies on a massive unlabeled corpus and a collection of dictionaries to annotate the COVID-19 corpus.DISTNER provides a benchmark performance on the CORD-NER test set for future research.