Reviewerly/exHarmony huggingface dataset:
SDWBF Algorithm: A Novel Pedestrian Detection Algorithm in the Aerial Scene

Due to the large amount of video data from UAV aerial photography and the small target size from the aerial perspective, pedestrian detection in drone videos remains a challenge. To detect objects in UAV images quickly and accurately, a small-sized pedestrian detection algorithm based on the weighted fusion of static and dynamic bounding boxes is proposed. First, a weighted filtration algorithm for redundant frames was applied using the inter-frame pixel difference algorithm cascading vision and structural similarity, which solved the redundancy of the UAV video data, thereby reducing the delay. Second, the pre-training and detector learning datasets were scale matched to address the feature representation loss caused by the scale mismatch between datasets. Finally, the static bounding extracted by YOLOv4 and the motion bounding boxes extracted by LiteFlowNet were subject to the weighted fusion algorithm to enhance the semantic information and solve the problem of missing and multiple detections in UAV object detection. The experimental results showed that the small object recognition method proposed in this paper enabled reaching an mAP of 70.91% and an IoU of 57.53%, which were 3.51% and 2.05% higher than the mainstream target detection algorithm.
Data intermediary

Data intermediaries serve as a mediator between those who wish to make their data available, and those who seek to leverage that data.The intermediary works to govern the data in specific ways, and provides some degree of confidence regarding how the data will be used.
Discriminate2Rec: Negation-based dynamic discriminative interest-based preference learning for semantics-aware content-based recommendation

Content-based recommender systems (CBRSs) have shown to be highly effective for enhancing user experience in various problems and application domains. Most existing CBRSs employ static preference learning techniques, neglecting that user preferences constantly change over time and are attributed to only a set of certain attributes at a certain period of time due to the existence of temporal dynamics, as only certain attributes of highly preferred items actually reflect user positive interests, while many others might have neutral or even negative influence on user preferences regardless their semantic similarity. Lacking the efficiency in handling these problems poses semantical and temporal incoherence in user profiles, which significantly affects the recommendation accuracy. This paper presents Discriminate2Rec, a three-stage preference learning model that discriminates between items’ attributes based on their influence on user temporal preferences to improve temporal and semantical attribute-level profile coherence for more accurate recommendation. Evaluation is made on three real-world data sets. The results demonstrate the effectiveness of Discriminate2Rec in outperforming state-of-the-art approaches in terms of recommendation accuracy. More in detail, the results also demonstrate that: (1) learning user profiles based on the discrimination between items’ attributes improves the attribute-level profile coherence; (2) modelling the negation in user temporal preferences allows Discriminate2Rec to recommend items whose content are semantically similar to user positive interests to which user negative interests are semantically irrelevant; (3) this novel combination significantly improves temporal and semantical attribute-level profile coherence, and maximises the utilisation of signal, thus significantly improves the recommendation accuracy consistently over time.
GTM approach towards engineering a features-oriented evaluation framework for secure authentication in IIoT environment

Industry Internet of Things (IIoT) has become the most evolving area over the last few years. The number of IoT devices connected in industry has sharply elevated but this surge has led to the vulnerability and data breach such as if a malicious entry is made to the secure network, it will forfeit all the network resources. For this purpose a full pledged secure authentication method is essential to safeguard IIoT network. There is a bulky list of number of authentications protocols available to keep network safe with a variety of features so but it becomes herculean task for network administrator to pick the strong and secure authentication method due to huge number of criteria, conflicting objectives and availability of authentication protocols in industry environment. It has become imperative to get the most rational authentication method in devices operating in IIoT. To address this issue, a feature-oriented assessment framework is put forward to provide a ground for ranking and selection of best authentication mechanism. This framework uses a mathematic approach known as Graph Theory Matrix Approach (GTMA) and selects the best authentication method based on the number of features. These features are related to authentication and covers almost every aspect of authentication method and are used as benchmark for selection purposes. This framework takes into account the most important features and helps in selecting the best and most ideal features-oriented authentication method that can be employed in IIoT to keep the integrity and security of connected devices and overall network infrastructure.
Deep Collaborative Online Learning Resource Recommendation Based on Attention Mechanism

In view of the lack of hierarchical and systematic resource recommendation caused by rich online learning resources and many learning platforms, an attention-based ADCF online learning resource recommendation model is proposed by introducing the attention mechanism into a deep collaborative DCF model. Experimental results show that the proposed ADCF model enables an accurate recommendation of online learning resources, reaching 0.626 and 0.339 on the HR and NDCG metrics, respectively, compared to the DCF models before improved, up by 1.31% and 1.25%, and the proposed ADCF models by 1.79%, 2.17%, and 2.32%, respectively, compared to the IUNeu and NeuCF models.
AMBTC-based secret image sharing by simple modular arithmetic

A distortionless secret image sharing scheme using finite field GF(PG) (PG is the largest prime less than a given number) is investigated for sharing the absolute moment block truncation coding (AMBTC) images. Two adjusting operations are devised to modify the AMBTC trios (i.e., quantization pixels and bit maps) suitable for GF(PG) sharing. Polynomials under GF(PG) are constructed for encrypting the quantization pixels and bit maps. When sharing the trios under GF(PG), the AMBTC image is perfectly recovered. Moreover, the bit map sharing under GF(PG) can be extended to GF(PS) (PS is the smallest prime larger than a given number). Another scheme using GF(PS) is constituted by combining quantization pixel sharing under GF(PG) and bit map sharing under GF(PS). But the scheme using GF(PS) is not always lossless. Experimental results show that the proposed schemes are effective. Since GF(PG) and GF(PS) are simple modular arithmetic, improved computational efficiency is obtained.
Efficient optimisation framework for convolutional neural networks with secure multiparty computation

In recent years, deep learning has become an increasingly popular approach to modelling data due to its ability to detect abstract underlying patterns in data. Its practical applications have been limited, however, by data privacy concerns, restricting its use in major sectors such as healthcare and banking. Secure multiparty computation (MPC) is a scheme which allows multiple parties to perform joint computations over private data, while keeping the content of their data secret. MPC can enable privacy-preserving machine learning, however current implementations are rarely applied in practice due to the prohibitively high cost of performing thousands of computations and transmitting data between parties. In this paper we propose a framework incorporating various optimisation approaches from the wider field of privacy-preserving deep learning, including privacy-preserving batch normalisation and polynomial approximation of activation functions, and evaluate their performance when applied to a privacy-preserving convolutional neural network (CNN), discussing the trade-off each offers in terms of their accuracy and efficiency. We experiment with parametric polynomial (PPoly) activations by deriving polynomial approximations to activation functions and allowing the network to tune the coefficients as learning weights. We will show that, in shallow CNNs, the application of batch normalisation in combination with a PPoly activation layer can result in faster convergence, with testing accuracy exceeding that achieved with an unencrypted network, at the cost of longer running times.
Unsupervised anomaly detection in multivariate time series with online evolving spiking neural networks

Abstract With the increasing demand for digital products, processes and services the research area of automatic detection of signal outliers in streaming data has gained a lot of attention. The range of possible applications for this kind of algorithms is versatile and ranges from the monitoring of digital machinery and predictive maintenance up to applications in analyzing big data healthcare sensor data. In this paper we present a method for detecting anomalies in streaming multivariate times series by using an adapted evolving Spiking Neural Network. As the main components of this work we contribute (1) an alternative rank-order-based learning algorithm which uses the precise times of the incoming spikes for adjusting the synaptic weights, (2) an adapted, realtime-capable and efficient encoding technique for multivariate data based on multi-dimensional Gaussian Receptive Fields and (3) a continuous outlier scoring function for an improved interpretability of the classifications. Spiking neural networks are extremely efficient when it comes to process time dependent information. We demonstrate the effectiveness of our model on a synthetic dataset based on the Numenta Anomaly Benchmark with various anomaly types. We compare our algorithm to other streaming anomaly detecting algorithms and can prove that our algorithm performs better in detecting anomalies while demanding less computational resources for processing high dimensional data.
Learning interpretable word embeddings via bidirectional alignment of dimensions with semantic concepts

We propose bidirectional imparting or BiImp, a generalized method for aligning embedding dimensions with concepts during the embedding learning phase. While preserving the semantic structure of the embedding space, BiImp makes dimensions interpretable, which has a critical role in deciphering the black-box behavior of word embeddings. BiImp separately utilizes both directions of a vector space dimension: each direction can be assigned to a different concept. This increases the number of concepts that can be represented in the embedding space. Our experimental results demonstrate the interpretability of BiImp embeddings without making compromises on the semantic task performance. We also use BiImp to reduce gender bias in word embeddings by encoding gender-opposite concepts (e.g., male–female) in a single embedding dimension. These results highlight the potential of BiImp in reducing biases and stereotypes present in word embeddings. Furthermore, task or domain-specific interpretable word embeddings can be obtained by adjusting the corresponding word groups in embedding dimensions according to task or domain. As a result, BiImp offers wide liberty in studying word embeddings without any further effort.
Low-resource Neural Machine Translation: Methods and Trends

Neural Machine Translation (NMT) brings promising improvements in translation quality, but until recently, these models rely on large-scale parallel corpora. As such corpora only exist on a handful of language pairs, the translation performance is far from the desired effect in the majority of low-resource languages. Thus, developing low-resource language translation techniques is crucial and it has become a popular research field in neural machine translation. In this article, we make an overall review of existing deep learning techniques in low-resource NMT. We first show the research status as well as some widely used low-resource datasets. Then, we categorize the existing methods and show some representative works detailedly. Finally, we summarize the common characters among them and outline the future directions in this field.
CBAM-Yolov5: improved Yolov5 based on attention model for infrared ship detection

Infrared image has the low contrast and resolution, and there are few available features for infrared small targets, so the quality of bounding box obtained by the target detection model is substandard. To solve the problems, a model named CBAM-Yolov5 is proposed. Firstly, the convolutional block attention module is added to enhance the feature extraction ability of the backbone network. Then a scale bias factor is designed to improve the regression effect on the bounding box of small targets, which will increase the weight of the small targets on the loss function. Through experimental verification, the mAP and recall rate of our model can be 93.3% and 90.4% on the infrared ship dataset, and compared with Yolov5, it has increased by 1.1% and 2.0%, respectively.
Search Interfaces for Biomedical Searching

The objective of this controlled information retrieval (IR) user experiment is to gain an understanding of domain experts' interactions with novel search interfaces within the context of biomedical information search, with a goal of better search interface design. In this paper, we examine the relationships among user perception, gaze and search behaviour and user search performance. An eye-tracking study of biomedical domain experts' interactions with novel search interfaces was conducted. A total of thirty-two users participated and searched for documents answering eight complex exploratory search tasks, using four different search interfaces. The findings suggest that gaze behaviour in terms of fixation durations based measures of areas of interest (AOI), i.e., visual attention to the elements of title, author, abstract and MeSH (Medical Subject Headings) terms in document surrogates is correlated with search performance. Users are more likely to achieve better search performance by precision-based measures when 1) search tasks are perceived as difficult; 2) users attend to the element of abstract; and 3) users can recall using the per-query suggestions during the search processes. More importantly, our findings suggest that a user search interface design that displays contextual information between the suggested keywords and the document may better support users reformulating their queries for complex search tasks in the biomedical domain. We discuss implications for the design of search user interfaces for biomedical searching.
GLPose: Global-Local Representation Learning for Human Pose Estimation

Multi-frame human pose estimation is at the core of many computer vision tasks. Although state-of-the-art approaches have demonstrated remarkable results for human pose estimation on static images, their performances inevitably come short when being applied to videos. A central issue lies in the visual degeneration of video frames induced by rapid motion and pose occlusion in dynamic environments. This problem, by nature, is insurmountable for a single frame. Therefore, incorporating complementary visual cues from other video frames becomes an intuitive paradigm. Current state-of-the-art methods usually leverage information from adjacent frames, which unfortunately place excessive focus on only the temporally nearby frames. In this paper, we argue that combining global semantically similar information and local temporal visual context will deliver more comprehensive and more robust representations for human pose estimation. Towards this end, we present an effective framework, namely global-local enhanced pose estimation ( GLPose ) network. Our framework consists of a feature processing module that conditionally incorporates global semantic information and local visual context to generate a robust human representation and a feature enhancement module that excavates complementary information from this aggregated representation to enhance keyframe features for precise estimation. We empirically find that the proposed GLpose outperforms existing methods by a large margin and achieves new state-of-the-art results on large benchmark datasets.
Clus-DR: Cluster-based pre-trained model for diverse recommendation generation

Recommender Systems are a predictive model for personalized suggestions utilizing past interactions and experiences. Collaborative filtering is the most popular and successful approach. The core idea behind this approach is that the users expressing similar preferences in the past are considered similar and will continue to like similar recommendations in future. The similarity among the items between past and future references, however, affects diversity and coverage of the recommendation system. In this work, we focus on a less usual direction for recommendation systems by increasing the probability of retrieving unusual and novel items in the recommendation list, which are, or can be, also relevant to the users. Most of prevailing techniques for incorporating diversity are based on re-ranking methodology, which shrinks the domain of user’s exposure to serendipitous items. To overcome this issue, we propose a methodology Clus-DR (Cluster-based Diversity Recommendation) that uses individual diversity of users and then pre-trained model for diverse recommendation generation. Instead of relying on re-ranking approach, we use different clustering techniques to have different groups of users with similar diversity. Experimental results using datasets of diverse domains indicate the effectiveness of the proposed Clus-DR methodology in diversity and coverage while maintaining acceptable level of accuracy.
Robust-adaptive-behavior strategy for human-following robots in unknown environments based on fuzzy inference mechanism

Purpose To follow and maintain an appropriate distance to the selected target person (STP), the mobile robot is required to have capabilities: the human detection and tracking and an efficient following strategy with a smooth manner that does not appear threatening to the STP and surroundings. The efficient following strategy must integrate the STP position and the obstacle information to achieve smooth and safe human-following behaviors, especially in unknown environments where robot does not have understandings in advance. The purpose of this study is to propose a robust-adaptive-behavior strategy for mobile robots. Design/methodology/approach This paper presents a robust-adaptive-behavior strategy (RABS) based on the fuzzy inference mechanism to help the robot follow the STP effectively in various unknown environments with the real-time obstacle avoidance, both indoor and outdoor and on different robot platforms. In which, the traversability of robots’ unknown surrounding environments is analyzed by using the STP position and the obstacle information obtained from the two dimensional laser scan, whose purpose is to choose the highest-traversability-score direction (HTSD) and an adaptive-safe-following distance (ASFD). Then, the HTSD, the ASFD and the current velocity of the robot are considered as inputs of the fuzzy system to adjust its velocity smoothly. Findings The proposed RABS is verified by a set of experiments using a real big-heavy autonomous mobile robot (BH-AMR), with the dimension 0.8 × 1.2 (m), weight 150 (kg), full-load 500 (kg), aiding smart factories. The obtained results have shown that the proposed RABS equips the BH-AMR with the ability to follow the STP smoothly and safely even when the robot is moving at the maximum speed 1.5 (m/s). Research limitations/implications In this paper, the autonomous mobile robot considers all environments as unknown even when it is working in mapped environments. This limitation is presented clearly in the future works section. Practical implications This proposed method can be used to help the autonomous mobile robot support persons in factories, hospitals, restaurants, supermarkets or at the airports. Originality/value This paper presents a RABS, including three new features: a fuzzy-based solution to help human-following robots maintain an appropriate distance to the STP safely and smoothly with the maximum velocity 1.5 (m/s); the proposed fuzzy-based solution, an adaptive vector field histogram and a new approach for the STP tracking is combined to follow the STP and avoid the collision simultaneously in unknown indoor and outdoor environments; the proposed RABS is considered for BH-AMRs (with the dimension 0.8 × 1.2 (m), weight 150 (kg), full-load 500 (kg)) to serve real tasks in smart factories.
Weakly supervised fine-grained image classification via two-level attention activation model

Fine-grained image classification is challenging due to the fact that categories can only be distinguished by subtle and local differences. Existing weakly supervised fine-grained image classification methods usually directly extract discriminative regions from the high-level feature maps. We observe that the operation of overlaying local receptive fields in the convolutional neural network makes the discriminative regions spread in the high-level feature maps, which can cause inaccurate region localization. In this paper, we propose an end-to-end Two-Level Attention Activation Model (TL-AAM), which can solve the problem of discriminative region spreading and obtain more effective fine-grained features. Specifically, the TL-AAM consists of: (1) an object attention activation module (OAAM), which links the correct classification score with the object region localization through gradient reflow to accurately localize the object region in a mutually reinforcing way, (2) a multi-scale pyramid attention localization module (MPALM), which locates local feature region by selecting the region with the largest response value in the feature channel, and this module can accurately obtain the detailed features in the local region, (3) a local cross-channel attention module (LCAM), which can filter irrelevant information in the high-level semantic feature maps by giving higher weights to the feature channels with high response values in the feature maps. Extensive experiments verify that TL-AAM yields the state-of-the-art performance under the same settings with the most competitive approaches, in CUB-200-2011, FGVC-Aircrafts, and Stanford-Cars datasets.
Single underwater image restoration based on descattering and color correction

Underwater images typically exhibit color distortion and low contrast due to the absorption and scattering of the non-uniform medium during the propagation of light underwater. To address these problems, an effective underwater image restoration method is presented in this paper. Firstly, we adopted an adaptive dark channel prior that can be applied to underwater images with different color tones. Then, the local water light with strong robustness is obtained by utilizing maximum filtering and Gaussian low-pass filtering. Additionally, a linear relationship between blurred and clear images in the dark channel was introduced combining with the attenuation coefficient compensation to estimate the transmission of r, g and b channels. Ultimately, we designed a new adaptive color compensation method by taking into account the attenuation difference of the three color channels. Simulation and experimental results show that the proposed method can effectively improve the clarity and contrast of the image and eliminate color distortion.