Reviewerly/exHarmony huggingface dataset:
Self-similarity Student for Partial Label Histopathology Image Segmentation

Delineation of cancerous regions in gigapixel whole slide images (WSIs) is a crucial diagnostic procedure in digital pathology. This process is time-consuming because of the large search space in the gigapixel WSIs, causing chances of omission and misinterpretation at indistinct tumor lesions. To tackle this, the development of an automated cancerous region segmentation method is imperative. We frame this issue as a modeling problem with partial label WSIs, where some cancerous regions may be misclassified as benign and vice versa, producing patches with noisy labels. To learn from these patches, we propose Self-similarity Student, combining teacher-student model paradigm with similarity learning. Specifically, for each patch, we first sample its similar and dissimilar patches according to spatial distance. A teacher-student model is then introduced, featuring the exponential moving average on both student model weights and teacher predictions ensemble. While our student model takes patches, teacher model takes all their corresponding similar and dissimilar patches for learning robust representation against noisy label patches. Following this similarity learning, our similarity ensemble merges similar patches’ ensembled predictions as the pseudo-label of a given patch to counteract its noisy label. On the CAMELYON16 dataset, our method substantially outperforms state-of-the-art noise-aware learning methods by 5% and the supervised-trained baseline by 10% in various degrees of noise. Moreover, our method is superior to the baseline on our TVGH TURP dataset with 2% improvement, demonstrating the generalizability to more clinical histopathology segmentation tasks.
Uncertainty Estimation and Sample Selection for Crowd Counting

We present a method for image-based crowd counting, one that can predict a crowd density map together with the uncertainty values pertaining to the predicted density map. To obtain prediction uncertainty, we model the crowd density values using Gaussian distributions and develop a convolutional neural network architecture to predict these distributions. A key advantage of our method over existing crowd counting methods is its ability to quantify the uncertainty of its predictions. We illustrate the benefits of knowing the prediction uncertainty by developing a method to reduce the human annotation effort needed to adapt counting networks to a new domain. We present sample selection strategies which make use of the density and uncertainty of predictions from the networks trained on one domain to select the informative images from a target domain of interest to acquire human annotation. We show that our sample selection strategy drastically reduces the amount of labeled data from the target domain needed to adapt a counting network trained on a source domain to the target domain. Empirically, the networks trained on the UCF-QNRF dataset can be adapted to surpass the performance of the previous state-of-the-art results on NWPU dataset and Shanghaitech dataset using only 17\(\%\) of the labeled training samples from the target domain.Code: https://github.com/cvlab-stonybrook/UncertaintyCrowdCounting.
Bot-Detective

Popular microblogging platforms (such as Twitter) offer a fertile ground for open communication among humans, however, they also attract many bots and automated accounts "disguised" as human users. Typically, such accounts favor malicious activities such as phishing, public opinion manipulation and hate speech spreading, to name a few. Although several AI driven bot detection methods have been implemented, the justification of bot classification and characterization remains quite opaque and AI decisions lack in ethical responsibility. Most of these approaches operate with AI black-boxed algorithms and their efficiency is often questionable. In this work we propose Bot-Detective, a web service that takes into account both the efficient detection of bot users and the interpretability of the results as well. Our main contributions are summarized as follows: i) we propose a novel explainable bot-detection approach, which, to the best of authors' knowledge, is the first one to offer interpretable, responsible, and AI driven bot identification in Twitter, ii) we deploy a publicly available bot detection Web service which integrates an explainable ML framework along with users feedback functionality under an effective crowdsourcing mechanism; iii) we build the proposed service under a newly created annotated dataset by exploiting Twitter's rules and existing tools. This dataset is publicly shared for further use. In situ experimentation has showcased that Bot-Detective produces comprehensive and accurate results, with a promising service take up at scale.
An instance selection algorithm for fuzzy K-nearest neighbor

The condensed nearest neighbor (CNN) is a pioneering instance selection algorithm for 1-nearest neighbor. Many variants of CNN for K-nearest neighbor have been proposed by different researchers. However, few studies were conducted on condensed fuzzy K-nearest neighbor. In this paper, we present a condensed fuzzy K-nearest neighbor (CFKNN) algorithm that starts from an initial instance set S and iteratively selects informative instances from training set T, moving them from T to S. Specifically, CFKNN consists of three steps. First, for each instance x ∈ T, it finds the K-nearest neighbors in S and calculates the fuzzy membership degrees of the K nearest neighbors using S rather than T. Second it computes the fuzzy membership degrees of x using the fuzzy K-nearest neighbor algorithm. Finally, it calculates the information entropy of x and selects an instance according to the calculated value. Extensive experiments on 11 datasets are conducted to compare CFKNN with four state-of-the-art algorithms (CNN, edited nearest neighbor (ENN), Tomeklinks, and OneSidedSelection) regarding the number of selected instances, the testing accuracy, and the compression ratio. The experimental results show that CFKNN provides excellent performance and outperforms the other four algorithms.
Taming the Many EdDSAs

This paper analyses security of concrete instantiations of EdDSA by identifying exploitable inconsistencies between standardization recommendations and Ed25519 implementations. We mainly focus on current ambiguity regarding signature verification equations, binding and malleability guarantees, and incompatibilities between randomized batch and single verification. We give a formulation of Ed25519 signature scheme that achieves the highest level of security, explaining how each step of the algorithm links with the formal security properties. We develop optimizations to allow for more efficient secure implementations. Finally, we designed a set of edge-case test-vectors and run them by some of the most popular Ed25519 libraries. The results allowed to understand the security level of those implementations and showed that most libraries do not comply with the latest standardization recommendations. The methodology allows to test compatibility of different Ed25519 implementations which is of practical importance for consensus-driven applications.
COMET: Context-Aware IoU-Guided Network for Small Object Tracking

We consider the problem of tracking an unknown small target from aerial videos of medium to high altitudes. This is a challenging problem, which is even more pronounced in unavoidable scenarios of drastic camera motion and high density. To address this problem, we introduce a context-aware IoU-guided tracker (COMET) that exploits a multitask two-stream network and an offline reference proposal generation strategy. The proposed network fully exploits target-related information by multi-scale feature learning and attention modules. The proposed strategy introduces an efficient sampling strategy to generalize the network on the target and its parts without imposing extra computational complexity during online tracking. These strategies contribute considerably in handling significant occlusions and viewpoint changes. Empirically, COMET outperforms the state-of-the-arts in a range of aerial view datasets that focusing on tracking small objects. Specifically, COMET outperforms the celebrated ATOM tracker by an average margin of \(6.2\%\) (and \(7\%\)) in precision (and success) score on challenging benchmarks of UAVDT, VisDrone-2019, and Small-90.
A Study of Pre-trained Language Models in Natural Language Processing

Pre-trained Language Model (PLM) is a very popular topic in natural language processing (NLP). It is the rapid development of pre-trained language models (PLMs) that has led to the achievements of natural language today. In this article, we give a review of important PLMs. First, we generally introduce the development history and achievements of PLMs. Second, we present several extraordinary PLMs, including BERT, the variants of BERT, Multimodal PLMs, PLMs combined with Knowledge Graph and PLMs applied to natural language generation. In the end, we summarize and look into the future of PLMs. We expect this article will provide a practical guide for learners to understanding, using and developing PLMs with the abundant literature existing for various NLP tasks.
Eurus: Towards an Efficient Searchable Symmetric Encryption With Size Pattern Protection

To achieve efficiently search and update on outsourced encrypted data, dynamic searchable symmetric encryption (DSSE) was proposed by just leaking some well-defined leakages. Though small, many recent works show that an attacker can exploit these leakages to undermine the security of existing DSSE schemes. In particular, an attacker can exploit even seemingly harmless size pattern to perform severe attacks. Many exiting schemes resort to oblivious RAM (ORAM) to hide search/access pattern; however, even such powerful cryptographic primitive cannot protect size pattern leakage. In this article, we first show that size pattern can lead to more information leakages, which is not well studied or protected by existing schemes. We then extend the existing privacy notion for DSSE to capture the size pattern leakage, achieving a strong forward and backward privacy definition. Following the definition, we propose a new DSSE scheme Eurus. Eurus can eliminate search/access pattern by relying on a multi-server ORAM scheme, meanwhile reducing size pattern with reasonable efficiency. We show that Eurus can reduce leakage significantly with better efficiency, compared with state-of-the-art leakage reduction schemes.
Sample sizes of prediction model studies in prostate cancer were rarely justified and often insufficient

ObjectiveDeveloping clinical prediction models (CPMs) on data of sufficient sample size is critical to help minimize overfitting. Using prostate cancer as a clinical exemplar, we aimed to investigate to what extent existing CPMs adhere to recent formal sample size criteria, or historic rules of thumb of events per predictor parameter (EPP)≥10.Study Design and SettingA systematic review to identify CPMs related to prostate cancer, which provided enough information to calculate minimum sample size. We compared the reported sample size of each CPM against the traditional 10 EPP rule of thumb and formal sample size criteria.ResultsAbout 211 CPMs were included. Three of the studies justified the sample size used, mostly using EPP rules of thumb. Overall, 69% of the CPMs were derived on sample sizes that surpassed the traditional EPP≥10 rule of thumb, but only 48% surpassed recent formal sample size criteria. For most CPMs, the required sample size based on formal criteria was higher than the sample sizes to surpass 10 EPP.ConclusionFew of the CPMs included in this study justified their sample size, with most justifications being based on EPP. This study shows that, in real-world data sets, adhering to the classic EPP rules of thumb is insufficient to adhere to recent formal sample size criteria.
Treatment effect prediction with adversarial deep learning using electronic health records

Treatment effect prediction (TEP) plays an important role in disease management by ensuring that the expected clinical outcomes are obtained after performing specialized and sophisticated treatments on patients given their personalized clinical status. In recent years, the wide adoption of electronic health records (EHRs) has provided a comprehensive data source for intelligent clinical applications including the TEP investigated in this study.We examined the problem of using a large volume of heterogeneous EHR data to predict treatment effects and developed an adversarial deep treatment effect prediction model to address the problem. Our model employed two auto-encoders for learning the representative and discriminative features of both patient characteristics and treatments from EHR data. The discriminative power of the learned features was further enhanced by decoding the correlational information between the patient characteristics and subsequent treatments by means of a generated adversarial learning strategy. Thereafter, a logistic regression layer was appended on the top of the resulting feature representation layer for TEP.The proposed model was evaluated on two real clinical datasets collected from the cardiology department of a Chinese hospital. In particular, on acute coronary syndrome (ACS) dataset, the proposed adversarial deep treatment effect prediction (ADTEP) (0.662) exhibited 1.4, 2.2, and 6.3% performance gains in terms of the area under the ROC curve (AUC) over deep treatment effect prediction (DTEP) (0.653), logistic regression (LR) (0.648), and support vector machine (SVM) (0.621), respectively. As for heart failure (HF) case study, the proposed ADTEP also outperformed all benchmarks. The experimental results demonstrated that our proposed model achieved competitive performance compared to state-of-the-art models in tackling the TEP problem.In this work, we propose a novel model to address the TEP problem by utilizing a large volume of observational data from EHR. With adversarial learning strategy, our proposed model can further explore the correlational information between patient statuses and treatments to extract more robust and discriminative representation of patient samples from their EHR data. Such representation finally benefits the model on TEP. The experimental results of two case studies demonstrate the superiority of our proposed method compared to state-of-the-art methods.
RETRACTED: Research on 3D simulation of swimming technique training based on FPGA and virtual reality technology

Swimming is one of the most popular attractions for college students but difficult to master. Virtual reality technology can solve mutual inductance and low efficiency of a software framework for physical and virtual reality model of the proposed system problems. Ecosystem swimming can improve. One solution to the effectiveness of cognitive-behavioral issues separate system; cognitive-behavioral research has broad prospects. Swimming through the students' correct behavior showed the incorrect comparison analysis, virtual reality system to solve the real problems of education swimming monotonous. It uses an efficient 3D processing algorithm to estimate the real-time Meta attributes displayed through the key input and multi-touch input and the synthesized sound's fast physical modeling. The mobile computing device having relatively limited resources, we can provide a large number of multi-mode feedback, according to user input in real time. Support the development of the 3D simulation of swimming skills. 3D virtual reality image improved, and further supports the use of virtual reality FPGA design.
Bacterial colony algorithm with adaptive attribute learning strategy for feature selection in classification of customers for personalized recommendation

This paper investigates a new bacterial colony-based feature selection algorithm to improve the classification accuracy of customers for personalized products recommendation. An attribute learning strategy is developed in this study to update the feature related population. Specifically, the features can be weighted according to their historic contributions to both the individual- and group-based subsets. Additionally, the frequency of appearance is also recorded for the feature candidates to improve the diversity of feature distribution and avoid the over-fitting. Based on the weight-based feature indexes and frequency of appearance records, the performance of feature subsets are enhanced by replacing the features being repeatedly appeared in a same vector. To explore the feasibility of the proposed method for the missing feature problems, the objective of the optimization is to minimize the classification error using the acceptable number of features. K-Nearest Neighbor is employed as the learning technique to cooperate with the proposed feature selection method. The effectiveness of the proposed feature selection method is demonstrated by performing test on the datasets from UCI machine learning repository and real-world data from Amazon customer reviews of products. Compared with other seven feature selections methods, the proposed feature selection algorithm outperforms the other algorithms by achieving higher classification accuracy rate using smaller features.
Improving the Methods for Visualization of Middle Ear Pathologies Based on Telemedicine Services in Remote Treatment

In this article the authors conduct research to improve computer-based image analysis systems in diagnostic procedures, which allows us to abandon the rather subjective characteristics of the diagnosis of a number of diseases, such as acute inflammatory diseases of the upper respiratory tract and middle ear. The possibility of using digital information processing of the image of the state of the upper respiratory tract and middle ear, recorded by a digital camera, and processing the results using specialized software, which allows achieving the objectivity of instrumental research, is considered. This approach can be used for telemedicine consultations, which is especially important in conditions of self-isolation caused by the COVID-19 pandemic.
Dark web in the dark: Investigating when transactions take place on cryptomarkets

Online illicit marketplaces known as cryptomarkets have gained considerable attention from the media, government authorities, law enforcement agencies, and researchers. An increasing number of studies have investigated various aspects of these cryptomarkets' characteristics, such as product categories, sale volumes, and the number of listings and vendors. However, there is a gap in the literature regarding whether illegal transactions (of illicit drugs) take place during the day or week. This study fills this gap by tracing Bitcoin addresses associated with the six previously leading and most active cryptomarkets—Silk Road, Silk Road 2.0, Agora, Evolution, Nucleus, and Abraxas—to identify the specific timings of these transactions. This study reveals clear patterns of activity on the marketplaces. First, transactions more often take place at night in European countries (Germany, Netherlands, the UK), the US, and Canada, where the cryptomarket drug trade is most active. Second, there are more transactions on Mondays, Tuesdays, and Wednesdays, and fewer on Saturdays and Sundays. This indicates that the retail drug trade accounts for a large part of the cryptomarkets. Further, this study examines the impact of a cryptomarket policing effort known as Operation Onymous, and indicates that this policing effort only displaced users among these marketplaces and did not deter their activity, even in the short-term. It also suggests that Operation Onymous did not alter users' transaction patterns.
Deep Learning for Radar Signal Detection in Electronic Warfare Systems

Detection of radar signals is the initial step for passive systems. Since these systems do not have prior information about received signal, application of matched filter and general likelihood ratio tests are infeasible. In this paper, we propose a new method for detecting received pulses automatically with no restriction of having intentional modulation or pulse on pulse situation. Our method utilizes a cognitive detector incorporating bidirectional long-short term memory based deep denoising autoencoders. Moreover, a novel loss function for detection is developed. Performance of the proposed method is compared to two well known detectors, namely: energy detector and time-frequency domain detector. Qualitative experiments show that the proposed method is able to detect presence of a signal with low probability of false alarm and it outperforms the other methods in all signal-to-noise ratio cases.
Massive Open Online Courses (MOOC) within the Framework of International Developmental Cooperation as a Strategy to Achieve Sustainable Development Goals

Massive Online Open Courses (MOOCs) offer the opportunity to implement a quality education timetable for those who lack the means due to economic, travel, or temporary availability limitations. Because of this, some non-governmental development organizations (NGOs), working in Latin American countries, are trying to implement this type of educational model within their educational projects. This article presents a case study on the development of a MOOC within the framework of international development cooperation carried out by an NGO and the National Autonomous University of Nicaragua. The research aims to analyze the opportunities and challenges of free, open, online teaching as a tool for achieving Sustainable Development Goal 4, and explore new educational possibilities to train people and contribute to the development of the communities in which they live. From a qualitative approach, grounded theory has been used as a holistic methodology for collecting, analyzing, and interpreting data, allowing the generation of theory in a systematic way. The most effective pedagogical models are evidenced to achieve the learning objectives and observe the challenges to be faced in order to achieve the effectiveness of MOOCs in this context. In conclusion, more applied research is needed to address the challenges that today’s societies, in times of pandemic, are facing at an educational and sustainability level.
How to Reduce the Bit-Width of an Ising Model by Adding Auxiliary Spins

Annealing machines have been developed as non-von Neumann computers aimed at solving combinatorial optimization problems efficiently. To use annealing machines for solving combinatorial optimization problems, we have to represent the objective function and constraints by an Ising model, which is a theoretical model in statistical physics. Further, it is necessary to transform the Ising model according to the hardware limitations. In the transformation, the process of effectively reducing the bit-widths of coefficients in the Ising model has hardly been studied so far. Thus, when we consider the Ising model with a large bit-width, a naive method, which means right bit-shift, has to be applied. Since it is expected that obtaining highly accurate solutions is difficult by the naive method, it is necessary to construct a method for efficiently reducing the bit-width. This article proposes methods for reducing the bit-widths of interaction and external magnetic field coefficients in the Ising model and proves that the reduction gives theoretically the same ground state of the original Ising model. The experimental evaluations also demonstrate the effectiveness of our proposed methods.