Reviewerly/exHarmony huggingface dataset:
Covariance Self-Attention Dual Path UNet for Rectal Tumor Segmentation

Deep learning algorithms are recognized as the most effective method for rectal tumor segmentation. However, since the multi-scale detailed feature information of rectal tumor cannot be fully extracted and applied, the segmentation and identification results of most algorithms are not always perfect. In this work, we introduce a Covariance Self-Attention Dual Path UNet (CSA-DPUNet), that is modified on the basis of UNet network and self-attention mechanism to improve network performance in feature processing and representation. The proposed network mainly makes two improvements. First, the UNet structure with single path is extended to dual paths (DPUNet). By broadening the network connections, our network is able to learn more local features with multiple contextual scales from CT images. Second, an improved criss-cross self-attention module is incorporated into DPUNet(CSA-DPUNet), instead of correlation method, we adopt covariance operation to calculate the attention weight map of self-attention mechanism, which can adaptively enhance feature combination and characterization ability. Experiments illustrate that our network called CSA-DPUNet can obviously improves the segmentation accuracy of rectal tumors, which brings 15.31%, 7.2%, 11.8%, and 9.5% improvement in Dice coefficient, P, R, F1, respectively compared with state-of-the-art. The above characteristics make the proposed CSA-DPUNet suitable for segmenting rectal tumor in practice.
Automatic Human-Like Detection of Code Smells

Many code smell detection techniques and tools have been proposed, mainly aiming to eliminate design flaws and improve software quality. Most of them are based on heuristics which rely on a set of software metrics and corresponding threshold values. Those techniques and tools suffer from subjectivity issues, discordant results among the tools, and the reliability of the thresholds. To mitigate these problems, we used machine learning to automate developers' perception in code smells detection. Different from other existing machine learning used in code smell detection we trained our models with an extensive dataset based on more than 3000 professional reviews on 518 open source projects. We conclude by an empirical evaluation of the performance of the machine learning approach against PMD, a widely used metric-based code smell detection tool for Java. The experimental results show that the machine learning approach outperforms the PMD classifier in all evaluations.
Analysis of a High-Dimensional Extended B92 Protocol

Quantum key distribution (QKD) allows two parties to establish a shared secret key that is secure against adversaries with unlimited computational power. One such protocol named B92 is quite appealing due to its simplicity but is highly sensitive to channel noise. In this work, we investigate a high-dimensional variant of an extended version of the B92 protocol and show that it can distill a key over high noise channels. The protocol we consider requires that Alice sends only three high-dimensional states and Bob only performs partial measurements. We perform an information-theoretic security analysis of our protocol and compare its key rate to that of a high-dimensional BB84 protocol over depolarization and amplitude damping channels.
Network Compression via Cooperative Architecture Search and Distillation

Neural Architecture Search (NAS) and its variants are competitive in many computer vision tasks lately. In this paper, we develop a Cooperative Architecture Search and Distillation (CASD) method for network compression. Compared with prior art, our method achieves better performance in ResNet164 pruning on CIFAR-10 and CIFAR-100 image classifications, promising to be extended to other tasks.
Self-Supervised Pre-training on the Target Domain for Cross-Domain Person Re-identification

Most existing cluster-based cross-domain person re-identification (re-id) methods only pre-train the re-id model on the source domain. Unfortunately, the pre-trained model may not perform well on the target domain due to the large domain gap between source and target domains, which is harmful to the following optimization. In this paper, we propose a novel Self-supervised Pre-training method on the Target Domain (SPTD), which pre-trains the model on both the source and target domains in a self-supervised manner. Specifically, SPTD uses different kinds of data augmentation manners to simulate different intra-class changes and constraints the consistency between the augmented data distribution and the original data distribution. As a result, the pre-trained model involves some specific discriminative knowledge on the target domain and is beneficial to the following optimization. It is easy to combine the proposed SPTD with other cluster-based cross-domain re-id methods just by replacing the original pre-trained model with our pre-trained model. Comprehensive experiments on three widely used datasets, i.e. Market1501, DukeMTMC-ReID and MSMT17, demonstrate the effectiveness of SPTD. Especially, the final results surpass previous state-of-the-art methods by a large margin.
Why Do You Trust Siri?: The Factors Affecting Trustworthiness of Intelligent Personal Assistant

Abstract Trust greatly contributes to human‐AI collaboration, however, human's trust to IPA is hard to establish and lacks exploration. The purpose of this paper is to recognize the factors that affect the trustworthiness of IPA. 358 questionnaires were analyzed by PLS‐SEM to construct the model, while thematic analysis was used to discover expectance of IPA. Chi‐square tests and T‐test were used to distinguish the difference between two user groups. Three factors that capability of system, personality of agent, and availability of interface have a significant impact on the trustworthiness of IPA. The capability of system is the most essential as the threshold with users’ plenty of expectations. Most users pay less attention to the availability of interface and the personality of agent has a great impact on the trustworthiness of IPA. The factors found enrich the trusted AI research and inspire insights of design of IPA.
Achieving Multitasking Robots in Multi-Robot Tasks

One simplifying assumption made in the existing and well-performing multi-robot systems is that the robots are single-tasking: each robot operates on a single task at any time. While this assumption is innocent to make in situations with sufficient resources such that robots can work independently, it becomes a restriction when they must share capabilities. In this paper, we consider multitasking robots with multi-robot tasks. Given a set of tasks, each achievable by a coalition of robots, our approach allows the coalitions to overlap by exploiting task synergies based on the physical constraints required to maintain these coalitions. The key contribution is a general and flexible framework that extends the current multi-robot systems to enable multitasking. The proposed approach is inspired by the information invariant theory, which orients around the equivalence of different information requirements. We map physical constraints to information requirements in our work, thereby allowing task synergies to be identified by reasoning about the relationships between such requirements. We show that our algorithm is sound and complete. Simulation results show its effectiveness under resource-constrained situations and in handling challenging scenarios in a realistic UAV simulator.
Learning Unified Embeddings for Recommendation via Meta-path Semantics

Heterogeneous information networks (HINs) have become a popular tool to capture complicated user-item relationships in recommendation problems in recent years. As a typical instantiation of HINs, meta-path is introduced in search of higher-level representations of user-item interactions. Though remarkable success has been achieved along this direction, existing meta-path-based recommendation methods face at least one of the following issues: 1) existing methods merely adopt simple meta-path fusion rules, which might be insufficient to exclude inconsistent information of different meta-paths that may hurt model performance; 2) the representative power is limited by shallow/stage-wise formulations. To solve these issues, we propose an end-to-end and unified embedding-based recommendation framework with graph-based learning. To address 1), we propose a flexible fusion module to integrate meta-path-based similarities into relative similarities between users and items. To address 2), we take advantage of the powerful representative ability of deep neural networks to learn more complicated and flexible latent embeddings. Finally, empirical studies on real-world datasets demonstrate the effectiveness of our proposed method.
Modeling the Spread of COVID-19 by Leveraging Machine and Deep Learning Models

Corona Virus disease 2019 (COVID-19) has caused a worldwide pandemic of cough, fever, headache, body aches, and respiratory ailments. COVID- 19 has now become a severe disease and one of the leading causes of death globally. Modeling and prediction of COVID-19 have become inevitable as it has affected people worldwide. With the availability of a large-scale universal COVID-19 dataset, machine learning (ML) techniques and algorithms occur to be the best choice for the analysis, modeling, and forecasting of this disease. In this research study, we used one deep learning algorithm called Artificial Neural Network (ANN) and several ML algorithms such as Support Vector Machine (SVM), polynomial regression, and Bayesian ridge regression (BRR) modeling for analysis, modeling, and spread prediction of COVID-19. COVID-19 dataset, maintained and updated by JOHNS HOPKINS UNIVERSITY was used for ML models training, testing, and modeling. The cost and error generated during ANN training process was reduced using technique called back propagation which dynamically adjust the synapses weights to perform better predictions. The ANN architecture included one input layer with 441 neurons, 4 hidden layers each have 90 neurons and one output layer. ANN along with other ML algorithms were trained to model the prediction of COVID-19 spread for the next 10 days. Experimental results showed that BRR technique overall performed better prediction of COVID-19 for the next 10 days. The modeling of infectious diseases can help relevant countries to take the necessary steps and make timely decisions.
Enhanced MSRCR optical frequency segmented filter algorithm for a low-light vehicle environment

To solve the problem of difficult face detection in a low illumination vehicle environment, a novel multi-scale retinex color restoration (MSRCR) approach exploiting the RGB three-channel decomposition and guided filtering (MSRCR-3CGF) is proposed. The MSRCR algorithm is employed to remove the artifacts and interference of low-light in the image based on the face detector using a multi-task cascaded convolutional neural network (MTCNN). The enhanced face image is decomposed into RGB, and GF is applied to each channel. The proposed method is tested on three widely used datasets: Dark Face, large-scale CelebFaces attributes (CelebA) and WIDER FACE, and an actual low-light scene in vehicles. The experimental results show that the proposed method suppresses the high-frequency noise of MSRCR, whilst improving the image enhancement and accuracy in the face detection in a low-light vehicle environment.
RM-IQA: A new no-reference image quality assessment framework based on range mapping method

Significant progress has been made in recent years in image quality assessment (IQA). In particular, the development of deep learning has provided no-reference (NR)-IQA with more impressive solutions. However, improving the generalization of NR-IQA models is still an urgent necessity. In this study, we propose a new framework that uses the range mapping method to map an existing full-reference (FR)-IQA dataset to an NR-IQA dataset, thereby further enhancing the accuracy and generalization of the NR-IQA model. First, an NR-IQA model is employed to score an FR-IQA dataset to obtain the corresponding mean opinion score (MOS) values. Then, the correlation coefficients between these MOS values and the original differential mean opinion score (DMOS) values marked by the FR-IQA dataset itself is calculated. Subsequently, the matching sequence pair is obtained according to these correlation coefficients. Then, a range mapping function is selected based on this sequence pair, and this function is used to map the entire FR-IQA dataset to the existing NR-IQA dataset, and a new NR-IQA dataset is generated. Finally, the new and the existing NR-IQA datasets are merged into a new dataset, which can train an end-to-end multi-task network to obtain the final model RM-IQA. This model exhibits better performance as it exploits more prior information. Based on the largest available NR-IQA dataset KonIQ-10k and FR-IQA dataset KADID-10K, the experimental results proved the effectiveness of the proposed framework.
A Generalized A* Algorithm for Finding Globally Optimal Paths in Weighted Colored Graphs

Both geometric and semantic information of the search space are imperative for a good plan. We encode those properties in a weighted colored graph (geometric information in terms of edge weight and semantic information in terms of edge and vertex color) and propose a generalized A<sup>&#x2217;</sup> to find the shortest path among the set of paths with minimal inclusion of low-ranked color edges. We prove the completeness and optimality of this Class-Ordered A<sup>&#x2217;</sup> (COA<sup>&#x2217;</sup> ) algorithm with respect to the hereto defined notion of optimality. The utility of COA<sup>&#x2217;</sup> is numerically validated in a ternary graph with feasible, infeasible, and unknown vertices and edges for the cases of a 2D mobile robot, a 3D robotic arm, and a 5D robotic arm with limited sensing capabilities. We compare the results of COA<sup>&#x2217;</sup> to that of the regular A<sup>&#x2217;</sup> algorithm, the latter of which finds a shortest path regardless of the semantic information, and we show that the COA<sup>&#x2217;</sup> dominates the A<sup>&#x2217;</sup> solution in terms of finding less uncertain paths.
A Multiscale Clustering Approach for Non-IID Nominal Data

Multiscale brings great benefits for people to observe objects or problems from different perspectives. Multiscale clustering has been widely studied in various disciplines. However, most of the research studies are only for the numerical dataset, which is a lack of research on the clustering of nominal dataset, especially the data are nonindependent and identically distributed (Non-IID). Aiming at the current research situation, this paper proposes a multiscale clustering framework based on Non-IID nominal data. Firstly, the benchmark-scale dataset is clustered based on coupled metric similarity measure. Secondly, it is proposed to transform the clustering results from benchmark scale to target scale that the two algorithms are named upscaling based on single chain and downscaling based on Lanczos kernel, respectively. Finally, experiments are performed using five public datasets and one real dataset of the Hebei province of China. The results showed that the method can provide us not only competitive performance but also reduce computational cost.
On Using Monte-Carlo Tree Search to Solve Puzzles

Solving puzzles has become increasingly important in artificial intelligence research since the solutions could be directly applied to real-world or general problems such as pathfinding, path planning, and exploration problems. Selecting the best approach to solve puzzles has always been an essential issue. Monte-Carlo Tree Search (MCTS) has surged into popularity as a promising approach due to its low run-time and memory complexity. Thus, it is required to know how to employ this method to solve the puzzles.
Chinese Poetry Generation with Metrical Constraints

Poetry is a kind of literary art, which conveys emotion with aesthetic expressions. Poetry automatic generation is challenging because it is required to confirm the semantic representation (content) and metrical constraints (form). Most previous work lacks the effective use of metrical information, resulting in the generated poems may break these constraints, which are essential for poetry. In this paper, we formulate the poetry generation task as a constrained text generation problem. A Transformer-based dual-encoder model is then proposed to force the poetry generation conditioned on both the writing intention and the metrical patterns. We conduct experiments on three popular genres of Chinese classical poetry: quatrains ( Open image in new window ), regulated verse ( Open image in new window ) and Song iambic ( Open image in new window ). Both automatic and human evaluation results confirm that our method (poetry generation with metrical constraints, MCPG) significantly improves metrical compliance of generated poems while maintaining coherence and fluency.
Deepfake and Security of Video Conferences

Deep learning is widely used to create artificial contents on the Internet. Similarly, it is also used to detect fake contents. Fake frames created and integrated with deep learning algorithms are known as deepfake. Recently, malicious users tend to use deepfake to manipulate genuine contents to carry out variety of attacks. Video conferencing applications has been a significant target of the malicious users since the beginning of Covid-19 pandemic who use deepfake models to create fake virtual identities in online video conferences. We propose a lightweight deepfake detection model that may be integrated with video conference applications to detect fake faces. Experimental analyses show that the proposed model provides acceptable accuracy to detect fake images on video conferences.
Capsule-based Object Tracking with Natural Language Specification

Tracking with Natural-Language Specification (TNL) is a joint topic of understanding the vision and natural language with a wide range of applications. In previous works, the communication between two heterogeneous features of vision and language is mainly through a simple dynamic convolution. However, the performance of prior works is capped by the difficulty of linguistic variation of natural language in modeling the dynamically changing target and its surroundings. In the meanwhile, natural language and vision are firstly fused and then utilized for tracking, which is hard to model the query-focused context. Query-focused should pay more attention to context modeling to promote the correlation between these two features. To address these issues, we propose a capsule-based network, referred to as CapsuleTNL, which performs regression tracking with natural language query. In the beginning, the visual and textual input is encoded with capsules, which can not only establish the relationship between entities but also the relationship between the parts of the entity itself. Then, we devise two interaction routing modules, which consist of visual-textual routing module to reduce the linguistic variation of input query and textual-visual routing module to precisely incorporate query-based visual cues simultaneously. To validate the potential of the proposed network for visual object tracking, we evaluate our method on two large tracking benchmarks. The experimental evaluation demonstrates the effectiveness of our capsule-based network.
Architecture Design and VLSI Implementation of 3D Hand Gesture Recognition System

With advancements in technology, more and more research is being focused on enhancing daily life quality and convenience. Along with the increase in the development of gesture control systems, many controllers, such as the keyboard, mouse, and other devices, have been replaced with remote control products, which are gradually becoming more intuitive for users. However, vision-based hand gesture recognition systems still have many problems to overcome. Most hand detection methods adopt a skin filter or motion filter for pre-processing. However, in a noisy environment, it is not easy to correctly extract interesting objects. In this paper, a VLSI design with dual-cameras has been proposed to construct a depth map with a stereo matching algorithm and recognize hand gestures. The proposed system adopts an adaptive depth filter to separate interesting foreground objects from the background. We also propose dynamic gesture recognition using depth and coordinate information. The system can perform static and dynamic gesture recognition. The ASIC design is implemented in TSMC 90 nm with about 47.3 K gate counts, and 27.8 mW of power consumption. The average accuracy of each gesture recognition is 83.98%.
Cluster and Scatter: A Multi-grained Active Semi-supervised Learning Framework for Scalable Person Re-identification

Active learning has recently attracted increasing attention in the task of person re-identification, due to its unique scalability that not only maximally reduces the annotation cost but also retains the satisfying performance. Although some preliminary active learning methods have been explored in scalable person re-identification task, they have the following two problems: 1) the inefficiency in the selection process of image pairs due to the huge search space, and 2) the ineffectiveness caused by ignoring the impact of unlabeled data in model training. Considering that, we propose a Multi-grained Active Semi-Supervised learning framework, named MASS, to address the scalable person re-identification problem existing in the practical scenarios. Specifically, we firstly design a cluster-scatter procedure to alleviate the inefficiency problem, which consists of two components: cluster step and scatter step. The cluster step shrinks the search space into individual small clusters by a coarse-grained clustering method, and the subsequent scatter step further mines the hard distinguished image pairs from unlabelled set to purify the learned clusters by a novel centrality-based adaptive purification strategy. Afterward, we introduce a customized purification loss for the purified clustering, which utilizes the complementary information in both labeled and unlabeled data to optimize the model for solving the ineffectiveness problem. The cluster-scatter procedure and the model optimization are performed in an iterative fashion to achieve the promising performance while greatly reducing the annotation cost. Extensive experimental results have demonstrated that MASS can even achieve a competitive performance with fully supervised methods in the case of extremely less annotation requirements.