Reviewerly/exHarmony huggingface dataset:
Multi-robot Systems Using Broadcast Control Framework with Visual-Based Tracking

The multi-robot application is becoming more critical in achieving complex collaborative tasks. This requires an efficient multi-agent robot system that supports distributed tasks, time and energy consumption. One of the developing control methods is the broadcast control framework. We found that variants of broadcast control techniques were validated on simulation only and lacked experimental work. Therefore, this paper focus-on the experimental investigation of a variant of the broadcast control framework. To create a similar environment to the application, we developed the system using Raspberry Pi, AlphaBots, a webcam, and Bluetooth as the central controller, robots, visual tracking, and wireless communication. We successfully validated the system in an indoor environment. The experimental finding shows the travelling trajectory of the robots differs from the MATLAB simulation results. Nonetheless, the broadcast control framework system was able to achieve the final target accurately. This paper provides implementation details and challenges for future research and development of a broadcast control framework. We propose to include obstacle avoidance in robots as a new study direction.
A new algorithm of path planning in 3D environments for an implemented quadcopter robot

Path planning within three-dimensional environments is of great importance in contemporary time due to the introduction of robots in many works. Finding the shortest path reduces fuel and time. In this paper a new algorithm is proposed to plan the paths within 3D environments. The algorithm was tested in two ways: simulation and practical. A multi-propeller vehicle is built that can track the resulting path from the proposed algorithm. The spherical shape of the robot, and obstacles is the basic of the work of the proposed algorithm. This does not mean that the algorithm cannot handle other forms of obstacles. Any other shape can be represented by a set of intersecting spheres. The resulting path of this proposed algorithm consists of two pieces that can be repeated a number of times until reaching the target point. The first piece is a straight path connecting the current position of the robot with a point located on the edge of the obstacle. The second piece is a path around the obstacle connecting two points away from the obstacle with a distance equal to the radius of the robot. All points are calculated by equations derived in this paper. The proposed algorithm was compared with two other algorithms. The results proved the superiority of the proposed algorithm in terms of path length and smoothness of the path.
A small-scale UAV propeller optimization by using ant colony algorithm

UAVs are designed to create lift in the same direction as the rotation axis, hence propellers are one of the most basic components of propulsion. Although several previous propeller optimization techniques have been developed, they usually find the optimal thrust coefficient at a constant power coefficient and vice versa. This research will combine the optimal values of the thrust coefficient and the power coefficient to find the optimum thrust and the lowest power at the same time. A Simple Blade Element Theory Blade served as the foundation for all assumptions.This article examined over 80 various designs, brands, and types of propellers in a two-blade configuration with diameters ranging from 2.5 to 19 inches and varying pitch values. The data for the baseline propeller was obtained from the UIUC Propeller Database. The inputs for the optimization are the propeller type, diameter, pitch angle, rotational speed, thrust coefficient, and power coefficient. The results show that by determining the factor of interest in the thrust coefficient and the power coefficient, the algorithm can find the optimal propeller specifications. When the factor of interest in the thrust coefficient is 100%, the algorithm will ignore the effect of the power coefficient and vice versa.
Emotion Detection in Unfix-Length-Context Conversation

Emotion Detection in conversation is playing more and more important role in dialogue system. Existing approaches to Emotion Detection in Conversation (EDC) use a fixed context window to recognize speakers’ emotion, which may lead to either scantiness of key context or interference of redundant context. In response, we explore the benefits of variable-length context and propose a more effective approach to EDC. In our approach, we leverage different context windows when predicting the emotion of different utterances. New modules are included to realize variable-length context: 1) two speaker-aware units, which explicitly model inner- and inter-speaker dependencies to form distilled conversational context and 2) a top-k normalization layer, which determines the most proper context windows from the conversational context to predict emotion. Experiments and ablation study show that our approach outperforms several strong baselines on three public datasets.
Sequence Recommendation Based on Interactive Graph Attention Network

Sequence recommendation aims to model the dynamic preferences of users from their historical interactions and accurately predict the next item that the users may be interested. Sequence recommendation models based on graph neural networks (GNNs) have become popular in academic research recently with remarkable results. However, it is difficult for existing GNNs-based models to learn the rapidly changing patterns of the user interests. Therefore, this paper proposes a novel GNNs-based model with a graph attention network (GAT) for the sequence recommendation, named Interactive Graph Attention Network Sequence Recommendation, IGANSR in short. In particular, the proposed IGANSR model constructs the user attributes graph and item attributes graph respectively to acquire the dynamic characteristics of both users and items. In addition, the IGANSR model utilizes a multi-layer graph attention network to dynamically learn the higher-order features and the representations of new nodes. Afterward, the IGANSR model can aggregate various information of each user’s neighbors’ graph and capture the embedding of similar users. Lastly, the proposed IGANSR model combines the dynamic item representations with the user representations together and projected onto multiple scales for the augmented learning. Experimental results carried out on three public datasets demonstrate that the IGANSR model outperforms other existing recommendation models.
Multimodal Graph Learning for Cross-Modal Retrieval

Cross-modal retrieval has attracted much attention lately for its various applications in Internet data mining. Existing approaches mainly adopt the projection function learning paradigm to construct dual-stream models, which suffer from two limitations: 1) They only utilize the correlations provided by cross-modal data pairs but the multiple correlations among data are unexplored. 2) They typically face the challenge of abstractness of semantics, which means that an instance may have distinct semantic information in different scenarios. In this paper, we propose a novel graph learning based framework termed Multimodal Graph Learning for cross-modal retrieval (MGL), which aims to fully exploit multiple correlations embedded in multimodal data and leverage a graph neural network to capture complementary information to alleviate the information sparsity and abstractness of semantics. First, we propose a graph construction algorithm to explore diverse multimedia information. Second, a modal feature projector is designed to learn modality-shared information, and a co-attention mechanism module is proposed to capture complementary information and perform dynamic feature integration. Third, a fusion and gate module is proposed to fully aggregate captured information and perform denoising. Furthermore, we employ a graph sampling algorithm to make our approach flexible to large-scale scenarios. Experimental results on three benchmark datasets prove the effectiveness of MGL.KeywordsCross-modal RetrievalGraph LearningGraph Neural Network (GNN)
Single Image Dehazing Using Frequency Attention

Image degradation is a negative impact on computer vision tasks, and single image dehazing methods based on data-driven have witnessed the continuously growing and achieved great success. However, most data-driven methods are based on convolution neural network (CNN) which can be considered as be an implicit modeling of frequency domain. While discrete cosine transform (DCT) can be used in the CNN to model the features in frequency domain explicitly. Therefore, we propose an end-to-end image dehazing network with frequency attention (FA) based on DCT. Then, we select the top-K low-frequency components as the output of DCT layer, so the FA module can extract information from different frequency components. The experiments on the benchmark RESIDE demonstrate that our methods achieve better results than the previous state-of-art methods.
Virtual Try-on via Matching Relation with Landmark

Virtual try-on based on image synthesis aims to combine the customer’s photo with in-shop clothes to acquire a try-on image. The key to generating a realistic try-on image is whether the in-shop clothes is spatially aligned with the customer’s body. Prior methods usually directly adopt the spatial transformation network to complete the clothing deformation, but they cannot generate high-quality try-on images when facing the customer’s posture changes or complex clothes pattern. To address it, we propose a virtual try-on network based on landmark constraint (LCVTON) in this work. Specifically, we notice the corresponding relationship between the clothes feature points and the customer body feature points, making the clothes match more closely with the customer’s body. The matching of the feature points enables us to introduce the landmark constraint into the spatial transformer network for naturally and smoothly warping clothes. Moreover, we construct a refinement network and introduce the landmark constraint into it to preserve the texture details of clothes. We conducted experiments on the try-on dataset and compared our method with existing methods. Both qualitative and quantitative results demonstrate the superiority of our method compared to existing state-of-the-art method.
Multi-Class Anomaly Detection

We study anomaly detection for the case when the normal class consists of more than one object category. This is an obvious generalization of the standard one-class anomaly detection problem. However, we show that jointly using multiple one-class anomaly detectors to solve this problem yields poorer results as compared to training a single one-class anomaly detector on all normal object categories together. We further develop a new anomaly detector called DeepMAD that learns compact distinguishing features by exploiting the multiple normal objects categories. This algorithm achieves higher AUC values for different datasets compared to two top performing one-class algorithms that either are trained on each normal object category or jointly trained on all normal object categories combined. In addition to theoretical results we present empirical results using the CIFAR-10, fMNIST, CIFAR-100, and a new dataset we developed called RECYCLE.
Enhance Gesture Recognition via Visual-Audio Modal Embedding

In recent years, gesture recognition has achieved remarkable advances, restrained from either the mainly limited attribute of the adopted single modality or the synchronous existence of multiple involved modalities. This paper proposes a novel visual-audio modal gesture embedding framework, aiming to absorb the information from other auxiliary modalities to enhance performance. The framework includes two main learning components, i.e., multimodal joint training and visual-audio modal embedding training. Both are beneficial to exploring the fundamental semantic gesture information but with a shared recognition network or a shared gesture embedding space, respectively. The enhanced framework trained with this method can efficiently take advantage of the complementary information from other modalities. We experiment on a large-scale gesture recognition dataset. The obtained results demonstrate that the proposed framework is competitive or superior to other outstanding methods, emphasizing the importance of the proposed visual-audio learning for gesture recognition.
Relation-Guided Dual Hash Network for Unsupervised Cross-Modal Retrieval

Cross-modal hashing has attracted a great deal of attention due to its unique low storage cost and high retrieval efficiency. However, these existing cross-modal retrieval methods fail to deal with global semantic relational redundancy, leading to an unsatisfactory performance on such data. In this paper, to address this issue, we propose a novel cross-modal hashing, namely Relation-guided Dual Hash Network (RDHN) for unsupervised cross-modal retrieval. It captures both long-range dependencies within modalities and enhances the relevance of semantic relations between different modalities through a heterogeneous feature fusion module. Besides, we designs a dual hash network for image modality and text modality, two for each, which can effectively highlight the useful global semantic relations while suppressing the redundant information. In the cross-modal retrieval tasks, our proposed RDHN improves by 4.9 $$\%$$ mAP@50 for $$I \rightarrow T$$ and 9.1 $$\%$$ mAP@50 for $$T\rightarrow I$$ in 128-bit compared to the state-of-the-art AGCH on the MIRFlickr-25k dataset, respectively. Code is available at https://github.com/Z000204/RDHN .
FaceMix: Transferring Local Regions for Data Augmentation in Face Recognition

Augmentation strategies for image recognition based on local image patches have gained widespread popularity. Their main idea is to replace or remove some local regions of the image. The advantage of these methods is that they change part of the image and force the network to pay attention to the less significant parts, which leads to a greater generalization capacity of the network. While these methods work good for image recognition, they do not perform as well for face recognition tasks. The purpose of this work is to create augmentation specialized for face recognition and devoid of the shortcomings of previous works. We present FaceMix: a flexible face-specific data augmentation technique that transfers a local area of an image to another image. The method has two operating modes: it can generate new images within a class, and it can generate images for a class, using face data from other classes, and these two modes also could be combined. FaceMix is helping to solve the problems of class imbalance and insufficient number of images per identity. A feature of this method is that the number of possible artificial images grows quadratically with the growth of real images. Experiments on face recognition benchmarks, such as CFP-FP, AgeDB, CALFW, CPLFW, XQLFW, SLLFW, RFW and MegaFace, demonstrate the effectiveness of the proposed method.
Ransomware through the lens of state crime

Cybercrime and other cybersecurity harms are gaining increasing political and public attention across many countries.One of the most serious and fastest growing categories of such harms relates to ransomware attacks.Many of the groups responsible for ransomware attacks have come under political pressure in recent years as they have become more aggressive in their methods and targeting.On a geopolitical level, an area attracting increasing interest is the complex relationships between ransomware groups and states, in particular, Russia.This paper introduces the concept of state crime to ransomware groups.Starting with the concept of proxies before turning to the historical examples of privateering and piracy, we focus on the notion of "cyber privateers" to analyze two select ransomware groups -DarkSide and REvil -that are believed to be affiliated with the Russian state.We argue that approaching these ransomware groups as cyber privateers engaged in state crime has the potential to enhance our understanding of how these groups operate.We further posit that a state crime perspective also assists in identifying how ransomware may be countered, highlighting the need for policy responses that are effective even when ransomware groups may be tacitly protected by a state.
Trustworthiness and Confidence of Gait Phase Predictions in Changing Environments Using Interpretable Classifier Models

The recognition of the different phases of human gait is valuable in areas such as rehabilitation and sports. Machine Learning models have been increasingly used for such recognition tasks. However, such models are usually trained on data obtained from participants in strictly controlled environments which—needless to say—might vary quite significantly from the environment in which the models are subsequently employed. Therefore, it is advisable to analyze the confidence of the model’s predictions. To this end, we present an interpretable classifier for gait phase detection. Together with classification reliability estimation tools, classification predictions can be rejected in low confidence scenarios. Our classifier is based on a robust and distance-based Learning Vector Quantization classifier. Finally, we present our approach using a real-world application in gait phase detection, which consists of one learning scenario and two different prediction scenarios.
Brain-Inspired Attention Model for Object Counting

We develop a sequential Q-learning model using a recurrent neural network to count objects in images using attentional search. The proposed model, which is based on visual attention, scans images by making a sequence of attentional jumps or saccades. By integrating the information gathered by the sequence of saccades, the model counts the number of targets in the image. The model consists primarily of two modules: the Classification Network and the Saccade Network. Whereas the Classification network predicts the number of target objects in the image, the Saccade network predicts the next saccadic jump. When the probability of the best predicted class crosses a threshold, the model halts making saccades and outputs its class prediction. Correct prediction results in positive reward, which is used to train the model by Q-learning. We achieve an accuracy of 92.1% in object counting. Simulations show that there is a direct relation between the number of glimpses required and the number of objects present to achieve a high accuracy in object counting.
Efficient Double Oracle for Extensive-Form Two-Player Zero-Sum Games

Policy Space Response Oracles (PSRO) is a powerful tool for large two-player zero-sum games, which is based on the tabular Double Oracle (DO) method and has achieved state-of-the-art performance. Though having guarantee to converge to a Nash equilibrium, existing PSRO and its variants suffer from two drawbacks: (1) exponential growth of the number of iterations and (2) serious performance oscillation before convergence. To address these issues, this paper proposes Efficient Double Oracle (EDO), a tabular double oracle algorithm for extensive-form two-player zero-sum games, which is guaranteed to converge linearly in the number of infostates while decreasing exploitability every iteration. To this end, EDO first mixes best responses at every infostate so that it can make full use of current policy population and significantly reduce the number of iterations. Moreover, EDO finds the restricted policy for each player that minimizes its exploitability against an unrestricted opponent. Finally, we introduce Neural EDO (NEDO) to scale up EDO to large games, where the best response and the meta-NE are learned through deep reinforcement learning. Experiments on Leduc Poker and Kuhn Poker show that EDO achieves a lower exploitability than PSRO and XFP with the same amount of computation. We also find that NEDO outperforms PSRO and NXDO empirically on Leduc Poker and different versions of Tic Tac Toe.
Multi-relation Word Pair Tag Space for Joint Entity and Relation Extraction

Joint entity and relation extraction from unstructured texts is a crucial task in natural language processing and knowledge graph construction. Recent approaches still suffer from error propagation and exposure bias because most models decompose joint entity and relation extraction into several separate modules for cooperation. In addition, the mode of multi-module cooperation to complete the joint extraction task ignores the information interaction between entities and relations. Most modeling methods are based on the pattern of token pairs, which leads to ambiguous information about entities to a certain extent. To address these issues, in this work, we creatively propose a method to transform the extraction task of complex triples under multiple relations into a fine-grained classification problem based on word pairs. Specifically, to fully utilize entity information and facilitate decoding, the proposed model uses a tag strategy specific to the feature of the entity itself. Extensive experiments show that the performance achieved by the proposed model outperforms public benchmarks and delivers consistent gain on complex scenarios of overlapping triples.
Learning Associative Reasoning Towards Systematicity Using Modular Networks

Learning associative reasoning is necessary to implement human-level artificial intelligence even when a model faces unfamiliar associations of learned components. However, conventional memory augmented neural networks (MANNs) have shown degraded performance on systematically different data since they lack consideration of systematic generalization. In this work, we propose a novel architecture for MANNs which explicitly aims to learn recomposable representations with a modular structure of RNNs. Our method binds learned representations with a Tensor Product Representation (TPR) to manifest their associations and stores the associations into TPR-based external memory. In addition, to demonstrate the effectiveness of our approach, we introduce a new benchmark for evaluating systematic generalization performance on associative reasoning, which contains systematically different combinations of words between training and test data. From the experimental results, our method shows superior test accuracy on systematically different data compared to other models. Furthermore, we validate the models using TPR by analyzing whether the learned representations have symbolic properties.
Next POI Recommendation with Neighbor and Location Popularity

Next point-of-interest (POI) recommendation aims to predict the next destination for users. In the past, most POI recommendation models were based on the user’s historical check-in trajectory to achieve recommendations. However, when these models are trained with sparse historical trajectory data, the learned user’s sequence patterns are unstable, which is difficult to obtain good recommendations. In view of the above problem, we propose the next POI recommendation approach that combines neighbor information with location popularity to alleviate the sparsity of data. Specifically, we construct User-POI graph and POI-POI graph, and use graph neural networks (GNN) to capture neighbor information of effective users on these two graphs. In addition, considering that location popularity is influenced by different times and distances, we design a dynamic method to measure the impact of location popularity on the user’s check-in preferences. In evaluating the experimental performance of two real-world datasets, our approach outperforms several classical next POI recommendation approaches.