Reviewerly/exHarmony huggingface dataset:
Cross-Batch Negative Sampling for Training Two-Tower Recommenders

The two-tower architecture has been widely applied for learning item and user representations, which is important for large-scale recommender systems. Many two-tower models are trained using various in-batch negative sampling strategies, where the effects of such strategies inherently rely on the size of mini-batches. However, training two-tower models with a large batch size is inefficient, as it demands a large volume of memory for item and user contents and consumes a lot of time for feature encoding. Interestingly, we find that neural encoders can output relatively stable features for the same input after warming up in the training process. Based on such facts, we propose a simple yet effective sampling strategy called Cross-Batch Negative Sampling (CBNS), which takes advantage of the encoded item embeddings from recent mini-batches to boost the model training. Both theoretical analysis and empirical evaluations demonstrate the effectiveness and the efficiency of CBNS.
Generating Accurate Caption Units for Figure Captioning

Scientific-style figures are commonly used on the web to present numerical information. Captions that tell accurate figure information and sound natural would significantly improve figure accessibility. In this paper, we present promising results on machine figure captioning. A recent corpus analysis of real-world captions reveals that machine figure captioning systems should start by generating accurate caption units. We formulate the caption unit generation problem as a controlled captioning problem. Given a caption unit type as a control signal, a model generates an accurate caption unit of that type. As a proof-of-concept on single bar charts, we propose a model, FigJAM, that achieves this goal through utilizing metadata information and a joint static and dynamic dictionary. Quantitative evaluations with two datasets from the figure question answering task show that our model can generate more accurate caption units than competitive baseline models. A user study with ten human experts confirms the value of machine-generated caption units in their standalone accuracy and naturalness. Finally, a post-editing simulation study demonstrates the potential for models to paraphrase and stitch together single-type caption units into multi-type captions by learning from data.
Affective Dependency Graph for Sarcasm Detection

Detecting sarcastic expressions could promote the understanding of natural language in social media. In this paper, we revisit sarcasm detection from a novel perspective, so as to account for the long-range literal sentiment inconsistencies. More concretely, we explore a novel scenario of constructing an affective graph and a dependency graph for each sentence based on the affective information retrieved from external affective commonsense knowledge and the syntactical information of the sentence. Based on it, an Affective Dependency Graph Convolutional Network (ADGCN) framework is proposed to draw long-range incongruity patterns and inconsistent expressions over the context for sarcasm detection by means with interactively modeling the affective and dependency information. Experimental results on multiple benchmark datasets show that our proposed approach outperforms the current state-of-the-art methods in sarcasm detection.
Hybrid Algorithm for Multi People Counting and Tracking for Smart Surveillance

Reliable people counting and tracking is active research topic in visual surveillance. In this work, a novel approach has been proposed for estimating people and tracking their location in sequence of video frames. Initially, we used Gaussian filter and background removal techniques to preprocess the image. After preprocessing, skin verification and body point detection have been introduced for human verification. For people counting, centroid of silhouettes and jacquard similarity index are developed to track moving objects in video frames. Experimental results on Pets 2009 dataset demonstrate that proposed system give boost of 8% accuracy in terms of tracking accuracy and counting rate as compared to known state-of-the-art methods. This system should be applicable to count and track people in medium density crowd environment.
VoIP Traffic Detection in Tunneled and Anonymous Networks Using Deep Learning

Network management is facing a great challenge to analyze and identify encrypted network traffic with specific applications and protocols.A significant number of network users applying different encryption techniques to network applications and services to hide the true nature of the network communication.These challenges attract the network community to improve network security and enhance network service quality.Network managers need novel techniques to cope with the failure and shortcomings of the port-based and payload-based classification methods of encrypted network traffic due to emergent security technologies.Mainly, the famous network hopping mechanisms used to make network traffic unknown and anonymous are VPN (virtual private network) and TOR (Onion Router).This paper presents a novel scheme to unveil encrypted network traffic and easily identify the tunneled and anonymous network traffic.The proposed identification scheme uses the highly desirable deep learning techniques to easily and efficiently identify the anonymous network traffic and extract the Voice over IP (VoIP) and Non VoIP ones within encrypted traffic flows.Finally, the captured traffic has been classified into four different categories, i-e., VPN VoIP, VPN Non-VoIP, TOR VoIP, and TOR Non-VoIP.The experimental results show that our identification engine is extremely robust to VPN and TOR network traffic.
Augmented Reality Visualization for Image-Guided Surgery: A Validation Study Using a Three-Dimensional Printed Phantom

<h3>Background</h3> Oral and maxillofacial surgery currently relies on virtual surgery planning based on image data (CT, MRI). Three-dimensional (3D) visualizations are typically used to plan and predict the outcome of complex surgical procedures. To translate the virtual surgical plan to the operating room, it is either converted into physical 3D-printed guides or directly translated using real-time navigation systems. <h3>Purpose</h3> This study aims to improve the translation of the virtual surgery plan to a surgical procedure, such as oncologic or trauma surgery, in terms of accuracy and speed. Here we report an augmented reality visualization technique for image-guided surgery. It describes how surgeons can visualize and interact with the virtual surgery plan and navigation data while in the operating room. The user friendliness and usability is objectified by a formal user study that compared our augmented reality assisted technique to the gold standard setup of a perioperative navigation system (Brainlab). Moreover, accuracy of typical navigation tasks as reaching landmarks and following trajectories is compared. <h3>Results</h3> Overall completion time of navigation tasks was 1.71 times faster using augmented reality (<i>P</i> = .034). Accuracy improved significantly using augmented reality (<i>P</i> < .001), for reaching physical landmarks a less strong correlation was found (<i>P</i> = .087). Although the participants were relatively unfamiliar with VR/AR (rated 2.25/5) and gesture-based interaction (rated 2/5), they reported that navigation tasks become easier to perform using augmented reality (difficulty Brainlab rated 3.25/5, HoloLens 2.4/5). <h3>Conclusion</h3> The proposed workflow can be used in a wide range of image-guided surgery procedures as an addition to existing verified image guidance systems. Results of this user study imply that our technique enables typical navigation tasks to be performed faster and more accurately compared to the current gold standard. In addition, qualitative feedback on our augmented reality assisted technique was more positive compared to the standard setup.?>
Digitization of Land Record Through Blockchain-based Consensus Algorithm

At present, real estate markets hold and contribute a fair share of the Gross Domestic Product (GDP) of any country. The real estate transactions generate revenue in stamp duties, which goes directly into the government kitty. However, this suffers from land document forgery and fraudulent activities due to a lack of an efficient, distributed, and completely digital system. This paper proposes a blockchain-based framework for digitizing property transactions that mitigate the risk of document forgery and other fraudulent activities. Over time, the amount of these transactions can become overwhelming, thus increasing the number of blocks. The proposed framework is based on blockchain technology to decentralize the complete ecosystem. The framework encompasses all major activities for property transactions. The framework uses the InterPlanetary File System (IPFS) that is a Peer-to-Peer (P2P) swarm network, to integrate different region registry offices in the state/country seamlessly. In order to make it secure, a consensus algorithm is proposed that reduces overhead transmissions for multicasting nodes by about 50%. The message exchange communication overhead also reduces by 54.86%. The time taken for the consensus algorithm is around 53.7% lesser than the Proof-of-Work mechanism in which all nodes participate in consensus and 10% lesser than the existing load-based approach.
How Do You Speak about Immigrants? Taxonomy and StereoImmigrants Dataset for Identifying Stereotypes about Immigrants

Stereotype is a type of social bias massively present in texts that computational models use. There are stereotypes that present special difficulties because they do not rely on personal attributes. This is the case of stereotypes about immigrants, a social category that is a preferred target of hate speech and discrimination. We propose a new approach to detect stereotypes about immigrants in texts focusing not on the personal attributes assigned to the minority but in the frames, that is, the narrative scenarios, in which the group is placed in public speeches. We have proposed a fine-grained social psychology grounded taxonomy with six categories to capture the different dimensions of the stereotype (positive vs. negative) and annotated a novel StereoImmigrants dataset with sentences that Spanish politicians have stated in the Congress of Deputies. We aggregate these categories in two supracategories: one is Victims that expresses the positive stereotypes about immigrants and the other is Threat that expresses the negative stereotype. We carried out two preliminary experiments: first, to evaluate the automatic detection of stereotypes; and second, to distinguish between the two supracategories of immigrants’ stereotypes. In these experiments, we employed state-of-the-art transformer models (monolingual and multilingual) and four classical machine learning classifiers. We achieve above 0.83 of accuracy with the BETO model in both experiments, showing that transformers can capture stereotypes about immigrants with a high level of accuracy.
Enclosure: language-based restriction of untrusted libraries

Programming languages and systems have failed to address the security implications of the increasingly frequent use of public libraries to construct modern software. Most languages provide tools and online repositories to publish, import, and use libraries; however, this double-edged sword can incorporate a large quantity of unknown, unchecked, and unverified code into an application. The risk is real, as demonstrated by malevolent actors who have repeatedly inserted malware into popular open-source libraries.
Fairness-Aware PageRank

Algorithmic fairness has attracted significant attention in the past years. In this paper, we consider fairness for link analysis and in particular for the celebrated Pagerank algorithm. Given that the nodes in a network belong to groups (for example, based on demographic or other characteristics), we provide a parity-based definition of fairness that imposes constraints on the proportion of Pagerank allocated to the members of each group. We propose two families of fair Pagerank algorithms: the first (Fairness-Sensitive Pagerank) modifies the jump vector of the Pagerank algorithm to enforce fairness; the second (Locally Fair Pagerank) imposes a fair behavior per node. We then define a stronger fairness requirement, termed universal personalized fairness, that asks that the derived personalized pageranks of all nodes are fair. We prove that the locally fair algorithms achieve also universal personalized fairness, and furthermore, we prove that this is the only family of algorithms with this property, establishing an equivalence between universal personalized fairness and local fairness. We also consider the problem of achieving fairness while minimizing the utility loss with respect to the original Pagerank algorithm. We present experiments with real and synthetic networks that examine the fairness of the original Pagerank and demonstrate qualitatively and quantitatively the properties of our algorithms.
Security Analysis and Improvement of an Image Encryption Cryptosystem Based on Bit Plane Extraction and Multi Chaos

This paper analyzes the security of image encryption systems based on bit plane extraction and multi chaos. It includes a bit-level permutation for high, 4-bit planes and bit-wise XOR diffusion, and finds that the key streams in the permutation and diffusion phases are independent of the plaintext image. Therefore, the equivalent diffusion key and the equivalent permutation key can be recovered by the chosen-plaintext attack method, in which only two special plaintext images and their corresponding cipher images are used. The effectiveness and feasibility of the proposed attack algorithm is verified by a MATLAB 2015b simulation. In the experiment, all the key streams in the original algorithm are cracked through two special plaintext images and their corresponding ciphertext images. In addition, an improved algorithm is proposed. In the improved algorithm, the generation of a random sequence is related to ciphertext, which makes the encryption algorithm have the encryption effect of a “one time pad”. The encryption effect of the improved algorithm is better than that of the original encryption algorithm in the aspects of information entropy, ciphertext correlation analysis and ciphertext sensitivity analysis.
Heterogeneous Face Interpretable Disentangled Representation for Joint Face Recognition and Synthesis

Heterogeneous faces are acquired with different sensors, which are closer to real-world scenarios and play an important role in the biometric security field. However, heterogeneous face analysis is still a challenging problem due to the large discrepancy between different modalities. Recent works either focus on designing a novel loss function or network architecture to directly extract modality-invariant features or synthesizing the same modality faces initially to decrease the modality gap. Yet, the former always lacks explicit interpretability, and the latter strategy inherently brings in synthesis bias. In this article, we explore to learn the plain interpretable representation for complex heterogeneous faces and simultaneously perform face recognition and synthesis tasks. We propose the heterogeneous face interpretable disentangled representation (HFIDR) that could explicitly interpret dimensions of face representation rather than simple mapping. Benefited from the interpretable structure, we further could extract latent identity information for cross-modality recognition and convert the modality factor to synthesize cross-modality faces. Moreover, we propose a multimodality heterogeneous face interpretable disentangled representation (M-HFIDR) to extend the basic approach suitable for the multimodality face recognition and synthesis. To evaluate the ability of generalization, we construct a novel large-scale face sketch data set. Experimental results on multiple heterogeneous face databases demonstrate the effectiveness of the proposed method.
A Survey on Bone Fracture Identification Techniques using Quantitative and Learning Based Algorithms

Machine learning algorithms help to deeply analyses and monitor the minor changes in the objects. In the medical image, a little doubtful value also creates a big transformation in the outcome, which will be sorted out using learning-based algorithms. In this discussion, a genetic bone disorder Fibrous Dysplasia (FD) is considered, which replaces the bone tissues with fibrous tissues which lead to bone fracture or bone deformity. In this paper, three different bone investigation approaches are taken for discussion as Bone Mineral Density (BMD), Quantitative Measures for bone for analyzing thickness and, Algorithm based models are used for abnormality detections. The BMD test is used to calculate the life span of the bone and estimated fractural risk. The second analysis begins with quantitative measurements on different imaging techniques, which are applied to bone scan images to monitor the abnormality. The important imaging techniques of Quantitative Computer Tomography, High Resolution based Peripheral QCT, Quantitative Ultra Sound and other techniques are discussed. In model-based techniques, which automatically extracts the essential features from the image and groups them based on similar behavior, and discover the abnormal features from the image set.
Context-Aware Recommender Systems for Social Networks: Review, Challenges and Opportunities

Context-aware recommender systems dedicated to online social networks experienced noticeable growth in the last few years. This has led to more research being done in this area stimulated by the omnipresence of smartphones and the latest web technologies. These systems are able to detect specific user needs and adapt recommendations to actual user context. In this research, we present a comprehensive review of context-aware recommender systems developed for social networks. For this purpose, we used a systematic literature review methodology which clearly defined the scope, the objective, the timeframe, the methods, and the tools to undertake this research. Our focus is to investigate approaches and techniques used in the development of context-aware recommender systems for social networks and identify the research gaps, challenges, and opportunities in this field. In order to have a clear vision of the research potential in the field, we considered research articles published between 2015 and 2020 and used a research portal giving access to major scientific research databases. Primary research articles selected are reviewed and the recommendation process is analyzed to identify the approach, the techniques, and the context elements employed in the development of the recommendation systems. The paper presents the detail of the review study, provides a synthesis of the results, proposes an evaluation based on measurable evaluation tools developed in this study, and advocates future research and development pathways in this interesting field.
Array-Based Convolutional Neural Networks for Automatic Detection and 4D Localization of Earthquakes in Hawai‘i

Abstract The growing amount of seismic data necessitates efficient and effective methods to monitor earthquakes. Current methods are computationally expensive, ineffective under noisy environments, or labor intensive. We leverage advances in machine learning to propose an improved solution, ArrayConvNet—a convolutional neural network that uses continuous array data from a seismic network to seamlessly detect and localize events, without the intermediate steps of phase detection, association, travel-time calculation, and inversion. When testing this methodology with events at Hawai‘i, we achieve 99.4% accuracy and predict hypocenter locations within a few kilometers of the U.S. Geological Survey catalog. We demonstrate that training with relocated earthquakes reduces localization errors significantly. We outline several ways to improve the model, including enhanced data augmentation and use of relocated offshore earthquakes recorded by ocean-bottom seismometers. Application to continuous records shows that our algorithm detects 690% as many earthquakes as the published catalog, and 125% as many events than the Hawaiian Volcano Observatory internal catalog. Because of the enhanced detection sensitivity, localization granularity, and minimal computation costs, our solution is valuable, particularly for real-time earthquake monitoring.
Future-Aware Diverse Trends Framework for Recommendation

In recommender systems, modeling user-item behaviors is essential for user representation learning. Existing sequential recommenders consider the sequential correlations between historically interacted items for capturing users’ historical preferences. However, since users’ preferences are by nature time-evolving and diversified, solely modeling the historical preference (without being aware of the time-evolving trends of preferences) can be inferior for recommending complementary or fresh items and thus hurt the effectiveness of recommender systems. In this paper, we bridge the gap between the past preference and potential future preference by proposing the future-aware diverse trends (FAT) framework. By future-aware, for each inspected user, we construct the future sequences from other similar users, which comprise of behaviors that happen after the last behavior of the inspected user, based on a proposed neighbor behavior extractor. By diverse trends, supposing the future preferences can be diversified, we propose the diverse trends extractor and the time-aware mechanism to represent the possible trends of preferences for a given user with multiple vectors. We leverage both the representations of historical preference and possible future trends to obtain the final recommendation. The quantitative and qualitative results from relatively extensive experiments on real-world datasets demonstrate the proposed framework not only outperforms the state-of-the-art sequential recommendation methods across various metrics, but also makes complementary and fresh recommendations.
q-Space Novelty Detection with Variational Autoencoders

In machine learning, novelty detection is the task of identifying novel unseen data. During training, only samples from the normal class are available. Test samples are classified as normal or abnormal by assignment of a novelty score. The usage of deep neural networks for novelty detection remains an open challenge. Here we propose novelty detection methods based on training variational autoencoders (VAEs) on normal data. We apply these methods to magnetic resonance imaging, namely to the detection of diffusion-space (q-space) abnormalities in diffusion MRI scans of multiple sclerosis patients. q-Space novelty detection can reduce scan time duration and does not require any disease-specific prior knowledge, thus overcoming the disadvantages of other diffusion MRI processing methods. The methods proposed herein outperform the state of the art on q-space data in terms of quality and inference time. Our methods also outperform the state of the art on a standard novelty detection benchmark, and hence are also promising for non-MRI novelty detection.