Reviewerly/exHarmony huggingface dataset:
An Improved ASIFT Image Feature Matching Algorithm Based on POS Information

The affine scale-invariant feature transform (ASIFT) algorithm is a feature extraction algorithm with affinity and scale invariance, which is suitable for image feature matching using unmanned aerial vehicles (UAVs). However, there are many problems in the matching process, such as the low efficiency and mismatching. In order to improve the matching efficiency, this algorithm firstly simulates image distortion based on the position and orientation system (POS) information from real-time UAV measurements to reduce the number of simulated images. Then, the scale-invariant feature transform (SIFT) algorithm is used for feature point detection, and the extracted feature points are combined with the binary robust invariant scalable keypoints (BRISK) descriptor to generate the binary feature descriptor, which is matched using the Hamming distance. Finally, in order to improve the matching accuracy of the UAV images, based on the random sample consensus (RANSAC) a false matching eliminated algorithm is proposed. Through four groups of experiments, the proposed algorithm is compared with the SIFT and ASIFT. The results show that the algorithm can optimize the matching effect and improve the matching speed.
Breast cancer detection and classification in mammogram using a three-stage deep learning framework based on PAA algorithm

In recent years, deep learning has been used to develop an automatic breast cancer detection and classification tool to assist doctors. In this paper, we proposed a three-stage deep learning framework based on an anchor-free object detection algorithm, named the Probabilistic Anchor Assignment (PAA) to improve diagnosis performance by automatically detecting breast lesions (i.e., mass and calcification) and further classifying mammograms into benign or malignant. Firstly, a single-stage PAA-based detector roundly finds suspicious breast lesions in mammogram. Secondly, we designed a two-branch ROI detector to further classify and regress these lesions that aim to reduce the number of false positives. Besides, in this stage, we introduced a threshold-adaptive post-processing algorithm with dense breast information. Finally, the benign or malignant lesions would be classified by an ROI classifier which combines local-ROI features and global-image features. In addition, considering the strong correlation between the task of detection head of PAA and the task of whole mammogram classification, we added an image classifier that utilizes the same global-image features to perform image classification. The image classifier and the ROI classifier jointly guide to enhance the feature extraction ability and further improve the performance of classification. We integrated three public datasets of mammograms (CBIS-DDSM, INbreast, MIAS) to train and test our model and compared our framework with recent state-of-the-art methods. The results show that our proposed method can improve the diagnostic efficiency of radiologists by automatically detecting and classifying breast lesions and classifying benign and malignant mammograms.
Code-based signatures from new proofs of knowledge for the syndrome decoding problem

In this paper, we study code-based signatures constructed from Proofs of Knowledge (PoK). This line of work can be traced back to Stern who introduced the first efficient PoK for the syndrome decoding problem in 1993 (Stern in A new identification scheme based on syndrome decoding. In: International cryptology conference (CRYPTO), 1993). Afterwards, different variations were proposed in order to reduce signature’s size. In practice, obtaining a smaller signature size relies on the interaction of two main considerations: (i) the underlying protocol and its soundness error and (ii) the types of optimizations which are compatible with a given protocol. In particular, optimizations related to the possibility of using random seeds instead of long vectors have a great impact on the final signature length. Over the years, different variations were proposed to improve the Stern scheme such as the Veron scheme (with public key as a noisy codeword rather than a syndrome) (Véron in Appl Algebra Eng Commun Comput 8(1):57-69, 1997), the AGS scheme which is a 5-pass protocol with soundness error asymptotically equal to 1/2 (Aguilar et al. in A new zero-knowledge code based identification scheme with reduced communication. In: IEEE information theory workshop, 2011) and more recently the FJR approach which permits to decrease the soundness probability to 1/N but induces a performance overhead (Feneuil et al. in Shared permutation for syndrome decoding: new zero-knowledge protocol and code-based signature. Cryptology ePrint archive, report 2021/1576, 2021). Overall the length of the signature depends on a trade-off between: the scheme in itself, the possible optimizations and the cost of the implementation. For instance, depending on the application one may prefer a 30% shorter signature at the cost of a ten times slower implementation rather than a longer signature but a faster implementation. The recent approaches which increase the cost of the implementation open the door to many different types of trade-offs. In this paper we propose three new schemes and different trade-offs, which are all interesting in themselves, since depending on potential future optimizations a scheme may eventually become more efficient than another. All the schemes we propose use a trusted helper: the first scheme permits to get a soundness error of 1/2, the second scheme permits to decrease the soundness error to 1/N but with a different approach than the recent FJR scheme and at last the third scheme proposes a Veron-like adaptation of the FJR scheme in which the public key is a noisy codeword rather than a syndrome. We provide extensive comparison which lists various trade-offs between our schemes and previous ones. The table highlights the benefits of our constructions for certain types of trade-offs.
A hybrid domain learning framework for unsupervised semantic segmentation

Supervised semantic segmentation often fails to generalize well in unseen scenarios due to the domain gap between the source and the target domains. Unsupervised domain adaptation is one possible way to solve this problem. However, the existing methods suffer two limitations. First, the number limitation of samples may lead to decreasing generalization. Second, only the source dataset contains pixel-level annotations, which provide stronger supervision in the source domain and result in overfitting to the source domain. To tackle these issues, we propose a hybrid domain learning (HDL) framework where the hybrid domain acts as the intermediate domain between the source domain and the target domain. Specifically, we first generate the hybrid domain feature (HDF) by a deep feature interpolation method and discuss the characteristics of the hybrid domain feature. Then, we further design a triple domain strategy to align the distribution of the source domain, the hybrid domain, and the target domain. The experiments in the tasks of GTA5 to Cityscapes and SYNTHIA to Cityscapes demonstrate that the proposed HDL framework is robust to domain adaptation and outperforms the state-of-the-art approaches.
Prototype contrastive learning for point-supervised temporal action detection

Detecting temporal actions in a video with only single-frame annotation in each action instance or segment, a.k.a., point-level supervision, has emerged as a more challenging task, compared to fully-supervised setting where per-frame annotations are available. Generally, it faces the label sparsity problem and the serious class-imbalance problem which are not fully explored in previous works. To address them, this paper develops an efficient pseudo-label generation approach to yield more positive samples and negative samples for providing supervision, i.e., the Prototype Contrastive Learning (PCL) based point-supervised temporal action detection framework. PCL aims at explicitly discovering the class relations between labeled and unlabeled frames by adopting prototype learning, and generates pseudo labels by estimating the semantic similarity of pair-wise frames in the embedding space. Meanwhile, it imposes the class relation constraint onto the action and background prototypes by introducing contrastive representation learning, i.e., the prototypes in distinct classes are pushed away and those within the same class are pulled closer. This allows learning the discriminative representations of prototypes that well comply with the data distribution of video frames. These prototype representations are treated as the hidden pattern proxies of different classes, and their semantic relations help to generate pseudo labels for unlabeled frames. Empirical studies on three benchmarks including GTEA, BEOID, and THUMOS14, have demonstrated the favorable performance of the proposed method.
A Comparative Analysis of Bias Amplification in Graph Neural Network Approaches for Recommender Systems

Recommender Systems (RSs) are used to provide users with personalized item recommendations and help them overcome the problem of information overload. Currently, recommendation methods based on deep learning are gaining ground over traditional methods such as matrix factorization due to their ability to represent the complex relationships between users and items and to incorporate additional information. The fact that these data have a graph structure and the greater capability of Graph Neural Networks (GNNs) to learn from these structures has led to their successful incorporation into recommender systems. However, the bias amplification issue needs to be investigated while using these algorithms. Bias results in unfair decisions, which can negatively affect the company reputation and financial status due to societal disappointment and environmental harm. In this paper, we aim to comprehensively study this problem through a literature review and an analysis of the behavior against biases of different GNN-based algorithms compared to state-of-the-art methods. We also intend to explore appropriate solutions to tackle this issue with the least possible impact on the model performance.
How to Detect Online Hate towards Migrants and Refugees? Developing and Evaluating a Classifier of Racist and Xenophobic Hate Speech Using Shallow and Deep Learning

Hate speech spreading online is a matter of growing concern since social media allows for its rapid, uncontrolled, and massive dissemination. For this reason, several researchers are already working on the development of prototypes that allow for the detection of cyberhate automatically and on a large scale. However, most of them are developed to detect hate only in English, and very few focus specifically on racism and xenophobia, the category of discrimination in which the most hate crimes are recorded each year. In addition, ad hoc datasets manually generated by several trained coders are rarely used in the development of these prototypes since almost all researchers use already available datasets. The objective of this research is to overcome the limitations of those previous works by developing and evaluating classification models capable of detecting racist and/or xenophobic hate speech being spread online, first in Spanish, and later in Greek and Italian. In the development of these prototypes, three differentiated machine learning strategies are tested. First, various traditional shallow learning algorithms are used. Second, deep learning is used, specifically, an ad hoc developed RNN model. Finally, a BERT-based model is developed in which transformers and neural networks are used. The results confirm that deep learning strategies perform better in detecting anti-immigration hate speech online. It is for this reason that the deep architectures were the ones finally improved and tested for hate speech detection in Greek and Italian and in multisource. The results of this study represent an advance in the scientific literature in this field of research, since up to now, no online anti-immigration hate detectors had been tested in these languages and using this type of deep architecture.
Can information and communication technologies contribute to poverty reduction? Evidence from poor counties in China

Information and communication technologies (ICTs) have received increasing attention in recent years as a promising means to address poverty challenges in rural China. This paper uses a unique three-wave panel data set of household surveys, collected from seven officially recognized poor counties in rural China during 2012–2018, to examine the effect of ICT adoption on poverty. The results show that ICTs contributed positively to poverty reduction in the sampled counties, which may have been primarily achieved through increased migration and off-farm income, instead of through improved agricultural production and farm income. This suggests that ICT development facilitated households escaping poverty by helping them diversify their livelihood strategies away from agriculture in remote mountainous areas. The findings highlight the importance of creating incentives to complement the opportunities brought by ICTs for local development. It is important to strike a balance between poverty reduction and food security objectives.
OptORAMa: Optimal Oblivious RAM

Oblivious RAM (ORAM), first introduced in the ground-breaking work of Goldreich and Ostrovsky (STOC ’87 and J. ACM ’96) is a technique for provably obfuscating programs’ access patterns, such that the access patterns leak no information about the programs’ secret inputs. To compile a general program to an oblivious counterpart, it is well-known that Ω (log N ) amortized blowup in memory accesses is necessary, where N is the size of the logical memory. This was shown in Goldreich and Ostrovksy’s original ORAM work for statistical security and in a somewhat restricted model (the so-called balls-and-bins model), and recently by Larsen and Nielsen (CRYPTO ’18) for computational security. A long-standing open question is whether there exists an optimal ORAM construction that matches the aforementioned logarithmic lower bounds (without making large memory word assumptions, and assuming a constant number of CPU registers). In this article, we resolve this problem and present the first secure ORAM with O (log N ) amortized blowup, assuming one-way functions. Our result is inspired by and non-trivially improves on the recent beautiful work of Patel et al. (FOCS ’18) who gave a construction with O (log N ⋅ log log N ) amortized blowup, assuming one-way functions. One of our building blocks of independent interest is a linear-time deterministic oblivious algorithm for tight compaction: Given an array of n elements where some elements are marked, we permute the elements in the array so that all marked elements end up in the front of the array. Our O ( n ) algorithm improves the previously best-known deterministic or randomized algorithms whose running time is O ( n ⋅ log n ) or O ( n ⋅ log log n ), respectively.
The impact of climate change on photovoltaic power potential in Southwestern Colombia

In this paper, we present the first study of the long-term climate-change impact on photovoltaic power potential in Nariño, Colombia. In this region, more than half of the territory does not have a constant electricity supply, but it has great potential for solutions with renewable energy sources. Based on the Coordinated Regional Downscaling Experiment (CORDEX), we assess the change in photovoltaic power potential towards the end of this century, considering two climate change scenarios, one optimistic and the other pessimistic. Our results suggest that changes in photovoltaic power potential, by the end of the century, will have a maximum decrease of around 2.49% in the central zone of Nariño, with some non-affected areas, and a maximum increase of 2.52% on the southeastern side with respect to the pessimistic climate change scenario.
Executable Knowledge Graph for Transparent Machine Learning in Welding Monitoring at Bosch

With the development of Industry 4.0 technology, modern industries such as Bosch's welding monitoring witnessed the rapid widespread of machine learning (ML) based data analytical applications, which in the case of welding monitoring has led to more efficient and accurate welding monitoring quality. However, industrial ML is affected by the low transparency of ML towards non-ML experts needs. The lack of understanding by domain experts of ML methods hampers the application of ML methods in industry and the reuse of developed ML pipelines, as ML methods are often developed in an ad hoc manner for specific problems. To address these challenges, we propose the concept and a system of executable Knowledge Graph (KG), which formally encode ML knowledge and solutions in KGs, which serve as common language between ML experts and non-ML experts, thus facilitate their communication and increase the transparency of ML methods. We evaluated our system extensively with an industrial use case at Bosch, showing promising results.
Automating DBSCAN via Deep Reinforcement Learning

DBSCAN is widely used in many scientific and engineering fields because of its simplicity and practicality. However, due to its high sensitivity parameters, the accuracy of the clustering result depends heavily on practical experience. In this paper, we first propose a novel Deep Reinforcement Learning guided automatic DBSCAN parameters search framework, namely DRL-DBSCAN. The framework models the process of adjusting the parameter search direction by perceiving the clustering environment as a Markov decision process, which aims to find the best clustering parameters without manual assistance. DRL-DBSCAN learns the optimal clustering parameter search policy for different feature distributions via interacting with the clusters, using a weakly-supervised reward training policy network. In addition, we also present a recursive search mechanism driven by the scale of the data to efficiently and controllably process large parameter spaces. Extensive experiments are conducted on five artificial and real-world datasets based on the proposed four working modes. The results of offline and online tasks show that the DRL-DBSCAN not only consistently improves DBSCAN clustering accuracy by up to 26% and 25% respectively, but also can stably find the dominant parameters with high computational efficiency. The code is available at https://github.com/RingBDStack/DRL-DBSCAN.
Temporal Contrastive Pre-Training for Sequential Recommendation

Recently, pre-training based approaches are proposed to leverage self-supervised signals for improving the performance of sequential recommendation. However, most of existing pre-training recommender systems simply model the historical behavior of a user as a sequence, while lack of sufficient consideration on temporal interaction patterns that are useful for modeling user behavior.
KEEP: An Industrial Pre-Training Framework for Online Recommendation via Knowledge Extraction and Plugging

An industrial recommender system generally presents a hybrid list that contains results from multiple subsystems. In practice, each subsystem is optimized with its own feedback data to avoid the disturbance among different subsystems. However, we argue that such data usage may lead to sub-optimal online performance because of thedata sparsity. To alleviate this issue, we propose to extract knowledge from thesuper-domain that contains web-scale and long-time impression data, and further assist the online recommendation task (downstream task). To this end, we propose a novel industrial KnowlEdge Extraction and Plugging (KEEP) framework, which is a two-stage framework that consists of 1) a supervised pre-training knowledge extraction module on super-domain, and 2) a plug-in network that incorporates the extracted knowledge into the downstream model. This makes it friendly for incremental training of online recommendation. Moreover, we design an efficient empirical approach for KEEP and introduce our hands-on experience during the implementation of KEEP in a large-scale industrial system. Experiments conducted on two real-world datasets demonstrate that KEEP can achieve promising results. It is notable that KEEP has also been deployed on the display advertising system in Alibaba, bringing a lift of +5.4% CTR and +4.7% RPM.
Frequent Itemset Mining with Local Differential Privacy

With the development of the Internet, a large amount of transaction data (e.g., shopping records, web browsing history), which represents user data, has been generated. By collecting user transaction data and learning specific patterns and association rules from it, service providers can provide better services. However, because of the increasing privacy awareness and the formulation of laws on data protection, collecting data directly from users will raise privacy concerns. The concept of local differential privacy (LDP), which provides strict data privacy protection on the user side and allows effective statistical analysis on the server side, is able to protect user privacy and perform statistics on sensitive issues at the same time. This paper adopts padding-and-sampling-based frequent oracle (PSFO), combined with an interactive query-response method satisfying local differential privacy, to identify frequent itemsets in an efficient and accurate way. Therefore, this paper proposes FIML, an improved algorithm for finding frequent itemsets in the LDP setting of transaction data. The data collector generates frequent candidate sets based on the results of the previous stage and uses them for querying, and users randomize their responses in a reduced domain to achieve local differential privacy. Extensive experiments on real-world and synthetic datasets show that the FIML algorithm can find frequent itemsets more efficiently with the same privacy protection and computational cost.
ContrastVAE

Aiming at exploiting the rich information in user behaviour sequences, sequential recommendation has been widely adopted in real-world recommender systems. However, current methods suffer from the following issues: 1) sparsity of user-item interactions, 2) uncertainty of sequential records, 3) long-tail items. In this paper, we propose to incorporate contrastive learning into the framework of Variational AutoEncoders to address these challenges simultaneously. Firstly, we introduce ContrastELBO, a novel training objective that extends the conventional single-view ELBO to two-view case and theoretically builds a connection between VAE and contrastive learning from a two-view perspective. Then we propose Contrastive Variational AutoEncoder (ContrastVAE in short), a two-branched VAE model with contrastive regularization as an embodiment of ContrastELBO for sequential recommendation. We further introduce two simple yet effective augmentation strategies named model augmentation and variational augmentation to create a second view of a sequence and thus making contrastive learning possible. Experiments on four benchmark datasets demonstrate the effectiveness of ContrastVAE and the proposed augmentation methods. Codes are available at https://github.com/YuWang-1024/ContrastVAE