Reviewerly/exHarmony huggingface dataset:
Visual Question Answering Using Deep Learning: A Survey and Performance Analysis

The Visual Question Answering (VQA) task combines challenges for processing data with both Visual and Linguistic processing, to answer basic 'common sense' questions about given images. Given an image and a question in natural language, the VQA system tries to find the correct answer to it using visual elements of the image and inference gathered from textual questions. In this survey, we cover and discuss the recent datasets released in the VQA domain dealing with various types of question-formats and robustness of the machine-learning models. Next, we discuss about new deep learning models that have shown promising results over the VQA datasets. At the end, we present and discuss some of the results computed by us over the vanilla VQA model, Stacked Attention Network and the VQA Challenge 2017 winner model. We also provide the detailed analysis along with the challenges and future research directions.
Ising formulations of some graph-theoretic problems in psychological research: Models and methods

It has been established in the fields of computer science and physics that many NP-hard and NP-complete graph-theoretic problems can be formulated and solved as Ising spin models. We discuss several problems that have a history in mathematical psychology, most notably max-cut clustering, graph coloring, a linear ordering problem related to paired comparison ranking and directed acyclic graphs, and the problem of finding a minimum subset of points necessary to contain another point within a convex hull. New Ising spin models are presented for the latter two problems. Additionally, we provide MATLAB software programs for obtaining solutions via enumeration of all spin ensembles (when computationally feasible) and simulated annealing. We are not claiming that the Ising spin model should be the preferred approach for formulating and solving graph-theoretic problems on conventional digital computers but it does provide a unifying framework for these problems, which may lead to future computational improvements. Recent progress in the development of quantum computing architecture has shown that Ising spin models can afford enormous improvements in algorithm efficiency when implemented on these platforms, potentially leading to widespread use of the methodology in the future.
Object Detection in Rural Roads Through SSD and YOLO Framework

Object detection is challenging in the computer vision area and is crucial in autonomous driving systems. The largest number of traditional techniques or the use of deep learning are evaluated in the urban area, but in rural areas, there is little research carried out. The goal of this work is object detection in rural roads, this paper presents the use of deep learning frameworks used as You Only Look Once (YOLO) and another that belongs to the same category of one-stage is the well-known Single Shot Multi-Box Detector (SSD), in the state of the literature, produces excellent results in detecting objects in real-time. These models behave differently in network architecture, for this reason, we use images of rural roads with different environments to achieve an optimal balance between precision and precision in the detection of objects. Furthermore, as there is no dataset in these environments, we created our own data set to perform the experiments due to the difficulty of this problem. The result of both detectors has produced acceptable results under certain conditions like lighting conditions, viewing perspectives, partial occlusion of the object.
Mental models of a social-ecological system facilitate social learning among a diverse management team

Managing social-ecological systems increasingly requires collaboration among diverse teams with a wide range of worldviews and perspectives. Increased attention to the social and cultural factors that shape environmental outcomes is needed for these collaborative teams to function effectively. Mental models are cognitive representations of the external world which guide an individual's thinking, decision-making, and behavior. They are critical elements of collaborative environmental management because they shape our understanding of social-ecological systems, our perceptions of environmental problems, and our preferences for certain management actions. In this paper, we describe an iterative process of constructing and revising mental models at both individual and small group levels over the course of a year in a community-based conservation area in the Ethiopian highlands. We compared mental models of the conservation area from four groups involved in management to identify commonalities and differences in the way people conceptualize the area. While we found high variability in mental models both within and across groups, most participants perceived social, economic, and political variables to be the key drivers of change in this system. Economic variables were also identified as key sensitivities, along with biotic and livelihood variables. However, groups differed considerably in how they thought about relationships between these variables, particularly political and economic variables. We used interviews with participants to assess how they learned throughout the mental modeling process, finding evidence of changes to stakeholder relationships, system understanding, and the time horizons used in planning. Women farmers differed from other groups at multiple stages in our process, both in the structure of the models they produced and in the social learning they experienced. Our study was strengthened by the iterative process that allowed individuals and small groups to reflect on their own understanding and share it with others, resulting in increased communication, mutual respect, and understanding among members of the management team. These findings point to the complementarity of both individual and group-level mental modeling for nuanced system understanding, and emphasize the need for diverse perspectives in collaborative environmental management in order for holistic understanding of both problems and solutions to emerge.
Sentiment Analysis of People During Lockdown Period of COVID-19 Using SVM and Logistic Regression Analysis

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Updatable Signatures and Message Authentication Codes

Cryptographic objects with updating capabilities have been proposed by Bellare, Goldreich and Goldwasser (CRYPTO’94) under the umbrella of incremental cryptography. They have recently seen increased interest, motivated by theoretical questions (Ananth et al., EC’17) as well as concrete practical motivations (Lehmann et al., EC’18; Groth et al. CRYPTO’18; Klooß et al., EC’19). In this work, the form of updatability we are particularly interested in is that primitives are key-updatable and allow to update “old” cryptographic objects, e.g., signatures or message authentication codes, from the “old” key to the updated key at the same time without requiring full access to the new key (i.e., only via a so-called update token).Inspired by the rigorous study of updatable encryption by Lehmann and Tackmann (EC’18) and Boyd et al. (CRYPTO’20), we introduce a definitional framework for updatable signatures (USs) and message authentication codes (UMACs). We discuss several applications demonstrating that such primitives can be useful in practical applications, especially around key rotation in various domains, as well as serve as building blocks in other cryptographic schemes. We then turn to constructions and our focus there is on ones that are secure and practically efficient. In particular, we provide generic constructions from key-homomorphic primitives (signatures and PRFs) as well as direct constructions. This allows us to instantiate these primitives from various assumptions such as DDH or CDH (latter in bilinear groups), or the (R)LWE and the SIS assumptions. As an example, we obtain highly practical US schemes from BLS signatures or UMAC schemes from the Naor-Pinkas-Reingold PRF.
A predictive framework in healthcare: Case study on cardiac arrest prediction

Data-driven healthcare uses predictive analytics to enhance decision-making and personalized healthcare. Developing prognostic models is one of the applications of predictive analytics in medical environments. Various studies have used machine learning techniques for this purpose. However, there is no specific standard for choosing prediction models for different medical purposes. In this paper, the ISAF framework was proposed for choosing appropriate prediction models with regard to the properties of the classification methods. As one of the case study applications, a prognostic model for predicting cardiac arrests in sepsis patients was developed step by step through the ISAF framework. Finally, a new modified stacking model produced the best results. We predict 85 % of heart arrest cases one hour before the incidence (sensitivity> = 0.85) and 73 % of arrest cases 25 h before the occurrence (sensitivity> = 0.73). The results indicated that the proposed prognostic model has significantly improved the prediction results compared to the two standard systems of APACHE II and MEWS. Furthermore, compared to previous research, the proposed model has extended the prediction interval and improved the performance criteria.
H-Net: Unsupervised Attention-based Stereo Depth Estimation Leveraging Epipolar Geometry

Depth estimation from a stereo image pair has become one of the most explored applications in computer vision, with most previous methods relying on fully supervised learning settings. However, due to the difficulty in acquiring accurate and scalable ground truth data, the training of fully supervised methods is challenging. As an alternative, self-supervised methods are becoming more popular to mitigate this challenge. In this paper, we introduce the H-Net, a deep-learning framework for unsupervised stereo depth estimation that leverages epipolar geometry to refine stereo matching. For the first time, a Siamese autoencoder architecture is used for depth estimation which allows mutual information between rectified stereo images to be extracted. To enforce the epipolar constraint, the mutual epipolar attention mechanism has been designed which gives more emphasis to correspondences of features that lie on the same epipolar line while learning mutual information between the input stereo pair. Stereo correspondences are further enhanced by incorporating semantic information to the proposed attention mechanism. More specifically, the optimal transport algorithm is used to suppress attention and eliminate outliers in areas not visible in both cameras. Extensive experiments on KITTI2015 and Cityscapes show that the proposed modules are able to improve the performance of the unsupervised stereo depth estimation methods while closing the gap with the fully supervised approaches.
Research on Data Association Rules Mining Method Based on Improved Apriori Algorithm

With the advent of the era of big data, how to efficiently process massive data has become a hot research direction in the field of computer science. The existing data mining technology mainly uses the association rules mining method. Aiming at the shortcomings of the Apriori algorithm in the classic association rule mining method, this paper proposes an improved Apriori algorithm. This paper describes the processing flow of classic Apriori algorithm, describes the main improvement ideas of improved Apriori algorithm, and then analyzes and evaluates the improved algorithm. The experimental results show that the efficiency of the improved Apriori algorithm is much higher than that of the traditional Apriori algorithm.
Smart Ponzi Scheme Detection using Federated Learning

Decentralized applications (DApp) have opened up new heights for the use of blockchain and cryptocurrencies, but users with poor intentions utilize DApp websites to promote smart Ponzi schemes, causing huge losses to inexperienced investors. To alert potential investors, DApp websites tend to mark high-risk DApps uploaded by users. So far, previous works have only trained the classifiers of smart Ponzi schemes in a unilateral way. However, smart contract dataset is very skewed, there are much fewer smart Ponzi schemes than non-Ponzi schemes. Besides, due to data security and privacy, different DApp websites are generally not allowed to share their users' data and information. As a result, it is difficult for a certain DApp website to learn the patterns of smart Ponzi schemes and detect them alone.This paper proposes a novel smart Ponzi scheme detection framework named SPSD-FL (Smart Ponzi Scheme Detection in Federated Learning) for the first time to address these problems. Instead of unilateral training, our method fulfills a secure aggregation of local gradient histograms by a horizontal federated learning based on XGBoost algorithm, in which we leverage a data augmentation method and labeled smart Ponzi schemes' code features, dapp submitters' information, and investors' information to implement the collaborative training of the detection model between different DApp websites without sharing the original training data and protecting the user's sensitive information. Experiments conducted on the real dataset show that our method can be trained substantially more data-efficiently and the SPSD-FL model achieves an average F-score of 96.55%, which is better than the unilateral training model and no less than that of the centralized training model.
Distant viewing and multimodality theory: Prospects and challenges

This article discusses the prospects and challenges of combining multimodality theory with distant viewing, a recent framework proposed in the field of digital humanities. This framework advocates the use of computational methods to enable large-scale analysis of visual and multimodal materials, which must be nevertheless supported by theories that explain how these materials are structured. Multimodality theory is well-positioned to support this effort by providing descriptive schemas that impose structure on the materials under analysis. The field of multimodality research can also benefit from adopting computational methods, which help to achieve the long-term goal of building large multimodal corpora for empirical research. However, despite their immense potential for multimodality research, the use of computational methods warrants caution, because they involve a number of potentially cascading risks that arise from biases inherent to the underlying data and different approaches to the phenomenon of multimodality.
User Preferential Tour Recommendation Based on POI-Embedding Methods

Tour itinerary planning and recommendation are challenging tasks for tourists in unfamiliar countries. Many tour recommenders only consider broad POI categories and do not align well with users’ preferences and other locational constraints. We propose an algorithm to recommend personalized tours using POI-embedding methods, which provides a finer representation of POI types. Our recommendation algorithm will generate a sequence of POIs that optimizes time and locational constraints, as well as user’s preferences based on past trajectories from similar tourists. Our tour recommendation algorithm is modelled as a word embedding model in natural language processing, coupled with an iterative algorithm for generating itineraries that satisfies time constraints. Using a Flickr dataset of 4 cities, preliminary experimental results show that our algorithm is able to recommend a relevant and accurate itinerary, based on measures of recall, precision and F1-scores.
DocPal: A Voice-based EHR Assistant for Health Practitioners

Electronic health record (EHR) systems have been widely adopted across healthcare organizations. While there are many benefits of using EHR such as improved accessibility and secure sharing of patient data, a shortcoming is that its manual data input is time-consuming and error prone. Physicians spend as much as 49.2% of their office time on EHR. In this paper, we present the design, development, and evaluation of a voice-based assistant, DocPal, to assist healthcare practitioners to access and update EHR through their voice. User survey and experimental evaluation illustrate that DocPal has good usability, time efficiency, and accuracy. When applied in the healthcare industry, we expect it to reduce data entry time and provide better patient care.
Risk-aware sensor scheduling and tracking of large constellations

Commercial proposals for large constellations of satellites present a major problem in the field of Space Situational Awareness (SSA). Space traffic management systems must produce accurate and timely predictions of potential collisions for operators to perform evasive maneuvers. This requires precise orbit determination solutions, but limitations on sensor resources can impair tracker performance. This work presents an integrated tasking and tracking system that can autonomously task a network of ground sensors to track large constellations with the goal of identifying hazardous conjunctions. The sensor scheduling approach combines information gain maximization with predicted probability of collision to produce a single reward function for tasking. The labeled multi-Bernoulli filter, a multi-target filter capable of resolving data ambiguity, processes the data and the resulting catalog estimate drives another round of scheduling. A tracking simulation involving 4,545 satellites in low-Earth orbit and a network of twelve ground-based sensors demonstrates how this scheduling method can focus on high-risk targets while maintaining custody of the entire constellation. A conjunction assessment is performed using the estimated catalog at the end of the tracking phase, and the accuracy of the analysis is used to evaluate the scheduling method’s efficacy.
Analytical Proof of Origin for Raw Materials

Growing public interest in getting information on the origin of raw materials used to manufacture goods for daily life has triggered the development of concepts to increase the transparency of raw material supply chains. Analytical proofs of origin (APOs) for raw materials may support those transparency concepts by giving evidence about the origin of a specific raw material shipment. For a variety of raw materials like gemstones, TTT (tantalum, tin, tungsten) minerals, and others, APOs have been developed. The identification of features that distinguish different origins, databases of those features from reliable reference samples, and a data evaluation strategy adopted to the envisaged application scenario are the key aspects of APO methods. Here, an overview is given on APO methods developed for different raw materials and application cases.
Learning digital camera pipeline for extreme low-light imaging

• In extreme low-light, cameras produce images that are dark and noisy. • Data-driven method for mapping underexposed RAW images to well-exposed sRGB image. • New loss function exploiting pixel-level and feature level image quality metrics. In low-light conditions, a conventional camera imaging pipeline produces sub-optimal images that are usually dark and noisy due to a low photon count and low signal-to-noise ratio (SNR). We present a data-driven approach that learns the desired properties of well-exposed images and reflects them in images that are captured in extremely low ambient light environments, thereby significantly improving the visual quality of these low-light images. The recent works on this problem only consider a pixel-level loss metric that ignores perceptual quality and thus generate outputs susceptible to visual artifacts. To address this problem, we propose a new loss function that exploits the characteristics of both pixel-wise and perceptual metrics, enabling our deep neural network to learn the camera processing pipeline to transform the short-exposure, low-light RAW sensor data to well-exposed sRGB images. The results show that our method outperforms the state-of-the-art according to psychophysical tests as well as pixel-wise standard metrics and recent learning-based perceptual image quality measures. In essence, the proposed model can potentially replace the conventional digital camera pipeline for the specific case of extreme low-light imaging.
Streamline Ring ORAM Accesses through Spatial and Temporal Optimization

Memory access patterns could leak temporal and spatial information in a sensitive program; therefore, obfuscated memory access patterns are desired from the security perspective. Oblivious RAM (ORAM) has been the favored candidate to eliminate the access pattern leakage through randomly remapping data blocks around the physical memory space. Meanwhile, accessing memory with ORAM protocols results in significant memory bandwidth overhead. For each memory request, after going through the ORAM obfuscation, the main memory needs to service tens of actual memory accesses, and only one real access out of them is useful for the program execution. Besides, to ensure the memory bus access patterns are indistinguishable, extra dummy blocks need to be stored and transmitted, which cause memory space waste and poor performance. In this work, we introduce a new framework, String ORAM, that accelerates the Ring ORAM accesses with Spatial and Temporal optimization schemes. First, we identify that dummy blocks could significantly waste memory space and propose a compact ORAM organization that leverages the real blocks in memory to obfuscate the memory access pattern. Then, we identify the inefficiency of current transaction-based Ring ORAM scheduling on DRAM devices and propose an effective scheduling technique that can overlap the time spent on row buffer misses while ensuring correctness and security. With a minimal modification on the hardware and software, and negligible impact on security, the framework reduces 30.05% execution time and up to 40% memory space overhead compared to the state-of-the-art bandwidth-efficient Ring ORAM.
OERFF: A Vehicle Re-Identification Method Based on Orientation Estimation and Regional Feature Fusion

Vehicle re-identification (re-id) is an important issue in the transportation and vehicle tracking area.The existing FastReID framework has made significant improvements in data preprocessing, model structure and parameter configuration of re-id.However, the FastReID did not consider the directional differences between vehicle images, which makes it difficult to balance the image pairs with different directional differences during calculating the similarity between vehicles.To make a further improvement based on the FastReID, this paper proposes an Orientation Estimation and Regional Feature Fusion method, named OERFF.In OERFF, the orientation estimation model is trained to judge the orientation and the main region of a vehicle in an image.Then the dedicated region feature model is utilized to extract the regional feature of a vehicle according to its proper orientation.Finally, a feature fusion strategy is applied with weighted distance according to the orientation difference of vehicles to further improve the identification accuracy.Extensive experiments are conducted based on three workbench datasets and results show that the proposed method can improve the mAP by 2.5% on the VeRi-766 dataset and 3.2% on the VERI-Wild dataset.The accuracy of rank-1 is improved by 2.4% on the VehicleID dataset.