Reviewerly/exHarmony huggingface dataset:
Range Search over Encrypted Multi-Attribute Data

This work addresses expressive queries over encrypted data by presenting the first systematic study of multi-attribute range search on a symmetrically encrypted database outsourced to an honest-but-curious server. Prior work includes a thorough analysis of single-attribute range search schemes (e.g. Demertzis et al. 2016) and a proposed high-level approach for multi-attribute schemes (De Capitani di Vimercati et al. 2021). We first introduce a flexible framework for building secure range search schemes over multiple attributes (dimensions) by adapting a broad class of geometric search data structures to operate on encrypted data. Our framework encompasses widely used data structures such as multi-dimensional range trees and quadtrees, and has strong security properties that we formally prove. We then develop six concrete highly parallelizable range search schemes within our framework that offer a sliding scale of efficiency and security tradeoffs to suit the needs of the application. We evaluate our schemes with a formal complexity and security analysis, a prototype implementation, and an experimental evaluation on real-world datasets.
On Efficient Approximate Queries over Machine Learning Models

The question of answering queries over ML predictions has been gaining attention in the database community. This question is challenging because finding high quality answers by invoking an oracle such as a human expert or an expensive deep neural network model on every single item in the DB and then applying the query, can be prohibitive. We develop a novel unified framework for approximate query answering by leveraging a proxy to minimize the oracle usage of finding high quality answers for both Precision-Target (PT) and Recall-Target (RT) queries. Our framework uses a judicious combination of invoking the expensive oracle on data samples and applying the cheap proxy on the DB objects. It relies on two assumptions. Under the P roxy Q uality assumption, we develop two algorithms: PQA that efficiently finds high quality answers with high probability and no oracle calls, and PQE, a heuristic extension that achieves empirically good performance with a small number of oracle calls. Alternatively, under the C ore S et C losure assumption, we develop two algorithms: CSC that efficiently returns high quality answers with high probability and minimal oracle usage, and CSE, which extends it to more general settings. Our extensive experiments on five real-world datasets on both query types, PT and RT, demonstrate that our algorithms outperform the state-of-the-art and achieve high result quality with provable statistical guarantees.
MultiBiSage

Graph Convolutional Networks (GCN) can efficiently integrate graph structure and node features to learn high-quality node embeddings. At Pinterest, we have developed and deployed PinSage, a data-efficient GCN that learns pin embeddings from the Pin-Board graph. Pinterest relies heavily on PinSage which in turn only leverages the Pin-Board graph. However, there exist several entities at Pinterest and heterogeneous interactions among these entities. These diverse entities and interactions provide important signal for recommendations and modeling. In this work, we show that training deep learning models on graphs that captures these diverse interactions can result in learning higher-quality pin embeddings than training PinSage on only the Pin-Board graph. However, building a large-scale heterogeneous graph engine that can process the entire Pinterest size data has not yet been done. In this work, we present a clever and effective solution where we break the heterogeneous graph into multiple disjoint bipartite graphs and then develop novel data-efficient MultiBiSage model that combines the signals from them. MultiBiSage can capture the graph structure of multiple bipartite graphs to learn high-quality pin embeddings. The benefit of our approach is that individual bipartite graphs can be processed with minimal changes to Pinterest's current infrastructure, while being able to combine information from all the graphs while achieving high performance. We train MultiBiSage on six bipartite graphs including our Pin-Board graph and show that it significantly outperforms the deployed latest version of PinSage on multiple user engagement metrics. We also perform experiments on two public datasets to show that MultiBiSage is generalizable and can be applied to datasets outside of Pinterest.
Person re-identification from multiple surveillance cameras combining face and body feature matching

In this work, a new method for human re-identification (Re-ID) from multiple surveillance cameras is proposed. Unlike traditional methods in which only the body features are used for matching, our proposed method uses both body and facial features for the Re-ID process. This combination allows us to re-identify people in challenging conditions, such as people with a uniform or similar-looking outfit, a partial or occluded body, appearance changes, or illumination. The face and body feature extraction models were developed using the state-of-the-art deep neural backbones and the synthesis of existing datasets in the literature. The performance of the method was evaluated on a self-generated dataset, which contains images under challenging conditions. The evaluation results show that our method outperforms traditional methods, in which the accuracy Rank1 reaches 91.30% while the traditional ones have a Rank1 of only 86.96%. This newly introduced method can be used for many practical applications in security surveillance of buildings and offices where challenging conditions often appear.
Implementation of PBL Model on Strengthening Students' Numerical Literacy and Digital Literacy Skills

This study aims to look at the effectiveness of project-based learning on literacy skills and digital literacy of students in elementary schools. This study used a quantitative approach with the control group research method and the pretest-posttest research design. The students used in this study were grade II elementary school students with a selected population of 5 classes consisting of 120 students, randomly selected to meet the control class consisting of 20 students and 1 experimental class in each class who realized that the ability to understand numerical concepts and arithmetic operational skills in everyday life is not in accordance with numerical literacy indicators. The overall results described in the Manova test (Multivariate Analysis of Variance) show that the project-based learning model has proven effective in increasing students' digital literacy and numerical literacy skills. This can be seen from the results of the experimental class using the Project Based Learning model which is superior to the control class using the traditional learning model. Based on the results of the analysis and discussion, it can be concluded that the Project Based Learning model is effectively used to improve the numerical literacy skills and digital literacy of second grade elementary school students. So that through this research students can achieve the expected indicators in the application of numeracy and digital literacy.
Development of a Benchmark Odia Handwritten Character Database for an Efficient Offline Handwritten Character Recognition with a Chronological Survey

A good benchmark dataset is a primary requirement in the offline handwritten character recognition (HCR) process. Only three handwritten numerals and alphabet datasets from Odia are publicly accessible for study, although many writers have used several datasets in their experiments. In this article, two tasks are done to address this issue. Those are the following: First, an extensive survey focused on various datasets is provided with the methodologies used in chronological order. The second factor is a solution to the lack of publicly available handwritten characters and numeral datasets. A new dataset of handwritten Odia characters with numerals has been developed. Anyone can access this dataset by sending an email to the authors of the article. This dataset was created with the help of 150 volunteers of various age groups, races, and qualifications. Some homogeneous experiments are conducted using deep learning models to evaluate the consistency of the dataset. One heterogeneous trial has also been performed to estimate the complexities of the characters present in the dataset by comparing them with the existing benchmark datasets.
Group-Based Personalized News Recommendation with Long- and Short-Term Fine-Grained Matching

Personalized news recommendation aims to help users find news content they prefer, which has attracted increasing attention recently. There are two core issues in news recommendation: learning news representation and matching candidate news with user interests. In this context, “candidate” indicates potential for interest. Due to the superior ability to understand natural language demonstrated by Pretrained Language Models (PLMs), recent works utilize PLMs (e.g., BERT) to strengthen news modeling, obtaining more accurate user interest matching and achieving notable improvement in news recommendation. However, the existing PLM-based methods are usually incapable of fully exploring the fine-grained (i.e., word-level) relatedness between user behaviors and candidate news due to the heavy computational cost brought by PLMs. In this article, we propose a group-based personalized news recommendation method with long- and short-term matching mechanisms between users and candidate news based on PLMs to learn fine-grained matching efficiently and effectively. In our approach, we design to group user historical clicked news into chunks with quite shorter news sequences according to their clicked timestamps, which could alleviate the computation issues of PLMs. PLMs are applied in each group jointly with the candidate news to capture their word-level interaction, and global group-level matching is learned across different groups. In addition, the group-based mechanism could be naturally adapted for long- and short-term user representation learning, in which we build users’ long preferences from the representations of all groups and treat the last group as short interests, respectively. Finally, we employ a gate network to dynamically unify the group-level, long- and short-term representations, yielding comprehensive user-news matching effectively. Extensive experiments are conducted on two real-world datasets. The results show that our proposed method achieves superior performance in news recommendations.
Parsing-Conditioned Anime Translation: A New Dataset and Method

Anime is an abstract art form that is substantially different from the human portrait, leading to a challenging misaligned image translation problem that is beyond the capability of existing methods. This can be boiled down to a highly ambiguous unconstrained translation between two domains. To this end, we design a new anime translation framework by deriving the prior knowledge of a pre-trained StyleGAN model. We introduce disentangled encoders to separately embed structure and appearance information into the same latent code, governed by four tailored losses. Moreover, we develop a FaceBank aggregation method that leverages the generated data of the StyleGAN, anchoring the prediction to produce in-domain animes. To empower our model and promote the research of anime translation, we propose the first anime portrait parsing dataset, Danbooru-Parsing , containing 4,921 densely labeled images across 17 classes. This dataset connects the face semantics with appearances, enabling our new constrained translation setting. We further show the editability of our results, and extend our method to manga images, by generating the first manga parsing pseudo data. Extensive experiments demonstrate the values of our new dataset and method, resulting in the first feasible solution on anime translation.
An unsupervised perplexity-based method for boilerplate removal

Abstract The availability of large web-based corpora has led to significant advances in a wide range of technologies, including massive retrieval systems or deep neural networks. However, leveraging this data is challenging, since web content is plagued by the so-called boilerplate: ads, incomplete or noisy text and rests of the navigation structure, such as menus or navigation bars. In this work, we present a novel and efficient approach to extract useful and well-formed content from web-scraped data. Our approach takes advantage of Language Models and their implicit knowledge about correctly formed text, and we demonstrate here that perplexity is a valuable artefact that can contribute in terms of effectiveness and efficiency. As a matter of fact, the removal of noisy parts leads to lighter AI or search solutions that are effective and entail important reductions in resources spent. We exemplify here the usefulness of our method with two downstream tasks, search and classification, and a cleaning task. We also provide a Python package with pre-trained models and a web demo demonstrating the capabilities of our approach.
Research on the Path Planning of Unmanned Sweepers Based on a Fusion Algorithm

Path planning is one of the key technologies for unmanned driving. However, global paths are unable to avoid unknown obstacles, while local paths tend to fall into local optimality. To solve the problem of unsmooth and inefficient paths on multi-angle roads in a park which cannot avoid unknown obstacles, we designed a new fusion algorithm based on the improved A* and Open_Planner algorithms (A-OP). In order to make the global route smoother and more efficient, we first extracted the key points of the A* algorithm and improved the node search structure using heap sorting, and then improved the smoothness of the path using the minimum snap method; secondly, we extracted the key points of the A* algorithm as intermediate nodes in the planning of the Open_Planner algorithm, and used the A-OP algorithm to implement the path planning of the unmanned sweeper. The simulation results show that the improved A* algorithm significantly improved the planning efficiency, the nodes are less computed and the path is smoother. The fused A-OP algorithm not only accomplished global planning effectively, but also avoided unknown obstacles in the path.
What Are the Binding Constraints for a Knowledge-Based Economy in Qatar?

This study aimed to investigate the binding constraints on building a knowledge-based economy (KBE) in Qatar. The research used descriptive and qualitative approaches within the new institutional economics paradigm using data from the Global Entrepreneurship Monitor. Taking cognizance that natural-resource-driven economic development may not be sustainable, the Qatar National Vision 2030 was launched with the expectation that educational expansion and reform would turn Qatar’s carbon economy into a “knowledge economy”. The Qatari government’s National Development Strategy 2018–2022 has anchored the economic diversification agenda on building a knowledge-based economy. The findings demonstrated that per the Global Entrepreneurship Monitor analysis, compared with selected countries, Qatar scored relatively high across various dimensions of new institutional economics, including institution, governance, market, and culture. This shows that the knowledge-based economy in Qatar is developing. Several studies examined a variety of issues in building a knowledge-based economy in Qatar, but this is the first study to explore the binding constraints of building a knowledge-based economy in Qatar using the new institutional economics theory as a tool of analysis.
Shot Boundary Detection Based on Global Features and the Target Features

Video processing plays an important role in the intelligent monitoring and management system of agricultural information. Video shot boundary detection is the basic symmetry step underlying video processing techniques. According to the current shot boundary detection algorithm, the feature changes between gradual transition frames are difficult to detect, and the misdetection situation is caused by ignoring the attention of the target feature during the feature extraction. A novel symmetry multi-step comparative scheme of shot boundary detection algorithm based on global features and target features is proposed. First, the RGB color histogram features of the video frame are extracted. Second, foreground object detection for the video frames is performed using the Gaussian Mixture Model (GMM), and the scale-invariant features transformation (SIFT) of the foreground targets is extracted. Finally, global features and target features fusion through weights, calculating the difference between adjacent frames across multiple steps, generate a pattern distance map. The pattern distance map of the gradual transition and the cut detection is different; we can judge the gradual transition and the cut detection according to the pattern distance map. Experiments show that the proposed symmetry method improves by about 2% in recall and accuracy compared to other algorithms.
Combining bidirectional long short‐term memory and self‐attention mechanism for code search

Summary With the wide application of deep learning in code search, especially the proposed code search model based on attention mechanism, the accuracy of code search has been greatly improved. However, the attention mechanism only captures the attention weight relationship between two words in the code fragment, without considering the contextual semantic relationship that exists between words in the code fragment, which can help improve the accuracy of code search. To address this problem, this paper proposes a model that combining bidirectional long short‐term memory and self‐attention mechanisms for code search (CBLSAM‐CS). The model first captures the contextual semantic relationship of each word in the code fragment by long‐short term memory network, and then uses the self‐attention mechanism to extract deep‐level features of the sequence. In order to verify the effectiveness of the proposed model, the paper has been conducted an experimental comparison with three other baseline models, CODEnn, CARLCS‐CNN, and SAN‐CS, on the basis of a public dataset containing 18 million code fragments. The experimental results show that the proposed model in this paper achieves 92.24% and 93.55% in mean reciprocal rank value and normalized discounted cumulative gain metrics, respectively, which are better than the baseline model. Therefore, it shows that the CBLSAM‐CS model proposed in this paper can effectively improve the accuracy and efficiency of code search.
The Improvement and Application of the Electrogeochemical Exploration Method

The anionic and cationic species of elements from deeply buried deposits migrate to the near surface driven by various geological forces. The applied electric field is used to collect these active ions to the designed electrode, and ions with a negative charge go to the anode and positive to the cathode, which is called electrogeochemical exploration method, also called CHIM. Prospecting effect has been investigated by researchers since the coming up of CHIM. However, the cumbersome technical equipment, complex techniques and low production efficiency have restricted its potential application in field geological survey. This paper presents the newly developed CHIM alternatives that are electrified by a low voltage dipole. The improved technique allows both anionic and cationic species to be extracted simultaneously in an anode and in a cathode. Compared with the conventional CHIM method, the innovative techniques called dipole electrogeochemical method are characterized by simple instrumentation, low cost and easy operation in field, and in particular enables simultaneous extraction of anionic and cationic species of elements, from which more information can be derived with higher extraction efficiency. The “dipole” electrogeochemical extraction method was proposed and applied in the experiments of the Yingezhuang gold ore in Zhaoyuan, Shandong Province, the 210 gold ore in Jinwozi, Xinjiang Province, and the Daiyinzhang gold polymetallic deposit in Wutaishan, Shanxi Province. There are clearly anomalies above the gold ore body, indicating the effectiveness and feasibility of the improved dipole electrogeochemical method in both scientific research and mineral exploration. The results of anode extraction in several mining areas have shown good results, indicating that gold may be mainly negatively charged. In fact, many metal nanoparticles, clay minerals, or complexes of metal ions are negatively charged, so they migrate to the anode electrode and enrich.
Ontology-based video retrieval using modified classification technique by learning in smart surveillance applications

With the rapid advancements in communication and multimedia computing technology, multimedia information, particularly video data, has recently become disproportionately accessible. Video is widely used in a variety of applications, making efficient management and retrieval of the expanding volume of video data critical. To manage such tasks, include automated recognition of the image queries in video retrieval with a reduced degree of system memory usage, spot detection, maintenance time, identification of exact duplicate videos, and so on. This research determination demonstrates the Modified R-Ratio with Viola-Jones Classification Method (MRVJCM) with the relevance of developing techniques and algorithms for automatic recognition of image queries. The R-Ratio Viola–Jones framework has two distinguishable feature maintenance processes, such as Feature Selection and Integration and Feature Cascading. These two distinct features are applied to the motion features (texture, emotions, elements, and shape) within the three features. The proposed techniques and their relevant algorithms are used to retrieve the most accurate videos and to assure the mathematical operation of video retrieval in the operation of the comparable protected system. As a result, the proposed MRVJCM achieves 98% of accuracy, 93% of precision, 92% of recall, and 42% of RMSE.
Visual object tracking: Progress, challenge, and future

Visual object tracking aims to continuously localize the target object of interest in a video sequence. As one of the most fundamental problems in computer vision, visual object tracking has a long list of critical applications including video surveillance, autonomous driving, human-machine interaction, augmented reality, robotics, etc., in which the tracking system provides the capacity to report target positions in real time for subsequent visual analysis. In the past decades, visual object tracking has been extensively explored and has witnessed considerable progress, especially in deep-learning-based tracking.
Image amodal completion: A survey

Existing computer vision systems can compete with humans in understanding the visible parts of objects, but still fall far short of humans when it comes to depicting the invisible parts of partially occluded objects. Image amodal completion aims to equip computers with human-like amodal completion functions to understand an intact object despite it being partially occluded. The main purpose of this survey is to provide an intuitive understanding of the research hotspots, key technologies and future trends in the field of image amodal completion. Firstly, we present a comprehensive review of the latest literature in this emerging field, exploring three key tasks in image amodal completion, including amodal shape completion, amodal appearance completion, and order perception. Then we examine popular datasets related to image amodal completion along with their common data collection methods and evaluation metrics. Finally, we discuss real-world applications and future research directions for image amodal completion, facilitating the reader’s understanding of the challenges of existing technologies and upcoming research trends.