Existing news recommendation systems often overlook the diversity of recommended content and exhibit popularity bias, resulting in suboptimal performance. To address this issue, this paper introduces a novel news recommendation approach, Popularity- and Position-Aware Contrastive Learning for Retrieval-Driven News Recommendation (PP4RNR). It consists of two modules: Entity-Level Retrieval Augmentation (ERA) and Popularity- and Position-Aware Contrastive Learning (PPCL). The ERA module utilizes both entities and titles to retrieve relevant news. Subsequently, retrieval-augmented news is fused with candidate news using our innovative cascaded attention network, leading to richer and more diverse news semantics. The PPCL module introduces perturbations in the news representation using a Gaussian perturbation vector based on the popularity and position information and then employs contrastive learning to regularize the representation space. Hence, this approach not only deepens the understanding of content diversity but also implicitly mitigates the popularity bias prevalent in current models. Rigorous testing on benchmark datasets demonstrates that our method significantly outperforms a range of state-of-the-art techniques.

Although transformer models are main network architectures for the delineation of roads from remote sensing imagery, they have critical limitations due to their regular patch mechanism and inefficiency in local information learning. To address these limitations for enhanced road extraction, this letter presents a novel double-branch architecture and random block coding transformer (DBARCT), with the following contributions. First, to improve local spatial details’ learning, we integrate transformer with convolutional neural network (CNN) into a novel dual-branch encoder-decoder architecture, such that the resulting model is efficient at learning both the local edge information and the global context information that are highly complementary for accurate road extraction. Second, to additionally augment the learning of global contextual information, we integrate the regular patching approach in traditional transformer models with a new irregular patching approach, such that it can better capture the global spatial information correlations that might be ignored by the regular patching approach. Third, an array of tests was carried out to meticulously scrutinize the efficacy of the fundamental elements of the suggested model. The empirical findings reveal that the intersection over union (IoU) metric attained by the proposed methodology on the LRSNY dataset stands at 88.53%, thereby corroborating the efficacy and preeminence of our approach in tasks related to road extraction.

Popularity bias significantly impacts news recommendation systems, as popular news articles receive more exposure and are often delivered to irrelevant users, resulting in unsatisfactory performance. Existing methods have not adequately addressed the issue of popularity bias in news recommendations, largely due to the neglect of the time factor and the impact of news content on popularity. In this paper, we propose a novel approach called Time and Content-aware Causal Model, namely TCCM. It models the effects of three factors on user interaction behavior, i.e., the time factor, the news popularity, and the matching between news content and user interest. TCCM also estimates news popularity more accurately by incorporating the news content, i.e., the popularity of entity and words. Causal intervention techniques are applied to obtain debiased recommendations. Extensive experiments on well-known benchmark datasets demonstrate that the proposed approach outperforms a range of state-of-the-art techniques.

Most existing recommendation models learn vectorized representations for items, i.e., item embeddings to make predictions. Item embeddings inherit popularity bias from the data, which leads to biased recommendations. We use this observation to design two simple and effective strategies, which can be flexibly plugged into different backbone recommendation models, to learn popularity neutral item representations. One strategy isolates popularity bias in one embedding direction and neutralizes the popularity direction post-training. The other strategy encourages all embedding directions to be disentangled and popularity neutral. We demonstrate that the proposed strategies outperform state-of-the-art debiasing methods on various real-world datasets, and improve recommendation quality of shallow and deep backbone models.

Nowadays, sensor-cloud systems have received wide attention from both academia and industry. Sensor-cloud system not only improves performances of wireless sensor networks (WSNs), but also combines different functional WSNs together to provide comprehensive services. However, a variety of malicious attacks threaten the sensor-cloud security, such as integrity, authenticity, availability and so on. Traditional available security mechanisms (e.g., cryptography and authentication) are still vulnerable. Although there are schemes to provide security by trust evaluation, the evaluation considers whether or not a sensor is credible only by checking the communication behaviors. Furthermore, when mobile sensor sinks are employed to collect sensing data, there appears a type of attacks called replicated sink attacks that are often ignored in the previous work. These attacks may bring serious vulnerability to trustworthy data collection in sensor-cloud systems. In this paper, we propose a comprehensive trustworthy data collection (CTDC) approach for sensor-cloud systems. Three kinds of trust, i.e., direct trust, indirect trust, and functional trust are defined to evaluate the trustworthiness of both sensors and mobile sinks. Except for resisting malicious attacks, the performances of sensor-cloud, such as energy, transmission distance and network throughput are also considered. We also conduct extensive simulations to evaluate the efficiency of CTDC. The simulation results show that CTDC correctly identifies malicious nodes and offers an improved performance in the data collection.

Large-scale data clustering is an essential key for big data problem. However, no current existing approach is “optimal” for big data due to high complexity, which remains it a great challenge. In this article, a simple but fast approximate DBSCAN, namely, KNN-BLOCK DBSCAN, is proposed based on two findings: 1) the problem of identifying whether a point is a core point or not is, in fact, a kNN problem and 2) a point has a similar density distribution to its neighbors, and neighbor points are highly possible to be the same type (core point, border point, or noise). KNN-BLOCK DBSCAN uses a fast approximate kNN algorithm, namely, FLANN, to detect core-blocks (CBs), noncore-blocks, and noise-blocks within which all points have the same type, then a fast algorithm for merging CBs and assigning noncore points to proper clusters is also invented to speedup the clustering process. The experimental results show that KNN-BLOCK DBSCAN is an effective approximate DBSCAN algorithm with high accuracy, and outperforms other current variants of DBSCAN, including ρ-approximate DBSCAN and AnyDBC.

With the dramatic increase of multi-media data on the Internet, cross-modal retrieval has become an important and valuable task in searching systems. The key challenge of this task is how to build the correlation between multi-modal data. Most existing approaches only focus on dealing with paired data. They use pairwise relationship of multi-modal data for exploring the correlation between them. However, in practice, unpaired data are more common on the Internet but few methods pay attention to them. To utilize both paired and unpaired data, we propose a one-stream framework triplet fusion network hashing (TFNH), which mainly consists of two parts. The first part is a triplet network which is used to handle both kinds of data, with the help of zero padding operation. The second part consists of two data classifiers, which are used to bridge the gap between paired and unpaired data. In addition, we embed manifold learning into the framework for preserving both inter and intra modal similarity, exploring the relationship between unpaired and paired data and bridging the gap between them in learning process. Extensive experiments show that the proposed approach outperforms several state-of-the-art methods on two datasets in paired scenario. We further evaluate its ability of handling unpaired scenario and robustness in regard to pairwise constraint. The results show that even we discard 50% data under the setting in [19], the performance of TFNH is still better than that of other unpaired approaches and that only 70% pairwise relationships are preserved, TFNH can still outperform almost all paired approaches.

A fast exact nearest neighbor search algorithm over large scale data is proposed based on semi-convex hull tree, where each node represents a semi-convex hull, which is made of a set of hyper planes. When performing the task of nearest neighbor queries, unnecessary distance computations can be greatly reduced by quadratic programming. GPUs are also used to accelerate the query process. Experiments conducted on both Intel(R) HD Graphics 4400 and Nvidia Geforce GTX1050 TI, as well as theoretical analysis show that the proposed algorithm yields significant improvements and outperforms current k-d tree based nearest neighbor query algorithms and others.

Density-based clustering is one of the most popular paradigms of existing clustering approaches, most approaches of this kind, such as DBSCAN, recognize clusters of data characterized by a fixed scanning radius. However, some flaws are caused by the fixed scanning radius, e.g., the determination of a proper scanning radius is nontrivial. In order to solve these problems, we revise DBSCAN, Meanshift, DPeak, etc. based on two new features, i.e., effective radius and density heat (DHeat). Generally, we name these revised clustering algorithms as DHeat. The underlying idea is based on two assumptions: 1) the existence of clusters is raised by the nonuniformity of data distribution, and the density of one data point within its r-neighborhood is proportional to the volume of the neighborhood provided the density distribution is uniform and 2) each cluster can be divided into different density layers, such as edges, shallow inner, deep inner, etc.; the deeper inner of a point locates, the higher density of that point. The experiments conducted on various test cases show that the advantage of DHeat lies in its good performance and the self-adapting scanning radius.