Reviewerly/exHarmony huggingface dataset:
Development of Reflex Technology of Action Identification in Project Planning Systems

The need for intellectualization of project management processes is shown, in particular, the creation of intelligent systems capable of effectively plan projects in the process of natural-language interaction with the project team. The analysis of existing theoretical and practical developments in this field is carried out. It is shown that the issue of intellectualization of the project planning process is still open. It is proposed to use a reflex approach to building an intelligent project planning system. The technology of reflex identification of actions in project planning systems, based on the model of non-force (information) interaction, is proposed. Scenarios of actions have been developed and their distribution has been performed in accordance with the content of natural language requests to the project planning system. Experimental studies are shown a high probability of correct recognition of actions in project planning systems from natural language requests of project team members. The results of the conducted experiments unequivocally testify to the effectiveness of the developed information technology of reflex identification of action scenarios in project planning systems.
Symmetric Private Information Retrieval at the Private Information Retrieval Rate

We consider the problem of symmetric private information retrieval (SPIR) with user-side common randomness. In SPIR, a user retrieves a message out of K messages from N non-colluding and replicated databases in such a way that no single database knows the retrieved message index (user privacy), and the user gets to know nothing further than the retrieved message (database privacy), i.e., the privacy constraint between the user and the databases is symmetric. SPIR has the following three properties: its capacity is smaller than the capacity of PIR which requires only user privacy; it is infeasible in the case of a single database; and it requires presence of shared common randomness among the databases. We introduce a new variant of SPIR where the user is provided with a random subset of the shared database common randomness, which is unknown to the databases. We determine the exact capacity region of the triple (d,ρS,ρU), where d is the download cost, ρS is the amount of shared database (server) common randomness, and ρU is the amount of available user-side common randomness. We show that with a suitable amount of ρU, this new SPIR achieves the capacity of the conventional PIR. As a corollary, single-database SPIR becomes feasible. Further, the presence of user-side ρU reduces the amount of required server-side ρS.
Few-Shot Single-View 3D Reconstruction with Memory Prior Contrastive Network

3D reconstruction of novel categories based on few-shot learning is appealing in real-world applications and attracts increasing research interests. Previous approaches mainly focus on how to design shape prior models for different categories. Their performance on unseen categories is not very competitive. In this paper, we present a Memory Prior Contrastive Network (MPCN) that can store shape prior knowledge in a few-shot learning based 3D reconstruction framework. With the shape memory, a multi-head attention module is proposed to capture different parts of a candidate shape prior and fuse these parts together to guide 3D reconstruction of novel categories. Besides, we introduce a 3D-aware contrastive learning method, which can not only complement the retrieval accuracy of memory network, but also better organize image features for downstream tasks. Compared with previous few-shot 3D reconstruction methods, MPCN can handle the inter-class variability without category annotations. Experimental results on a benchmark synthetic dataset and the Pascal3D+ real-world dataset show that our model outperforms the current state-of-the-art methods significantly.
Weakly-Supervised Temporal Action Detection for Fine-Grained Videos with Hierarchical Atomic Actions

Action understanding has evolved into the era of fine granularity, as most human behaviors in real life have only minor differences. To detect these fine-grained actions accurately in a label-efficient way, we tackle the problem of weakly-supervised fine-grained temporal action detection in videos for the first time. Without the careful design to capture subtle differences between fine-grained actions, previous weakly-supervised models for general action detection cannot perform well in the fine-grained setting. We propose to model actions as the combinations of reusable atomic actions which are automatically discovered from data through self-supervised clustering, in order to capture the commonality and individuality of fine-grained actions. The learnt atomic actions, represented by visual concepts, are further mapped to fine and coarse action labels leveraging the semantic label hierarchy. Our approach constructs a visual representation hierarchy of four levels: clip level, atomic action level, fine action class level and coarse action class level, with supervision at each level. Extensive experiments on two large-scale fine-grained video datasets, FineAction and FineGym, show the benefit of our proposed weakly-supervised model for fine-grained action detection, and it achieves state-of-the-art results.
Optimal Bidirectional Long Short Term Memory based Sentiment Analysis with Sarcasm Detection and Classification on Twitter Data

Sentiment analysis (SA) and sarcasm detection on social networking platforms have gained significant attention in recent times. The existence of sarcasm in social networking data like Twitter is a major cause of wrong categorization of sentiments. It remains a challenging issue from natural language processing (NLP) since it restricts the way of identifying the original sentiment of the people. Different feature engineering approaches are available in the literature to detect the presence of sarcasm in Twitter data. This study develops an Optimal Bidirectional Long Short Term Memory related Sarcasm Detection and Classification (OBiLSTM-SDC) using Twitter Data. The intention of OBiLSTM-SDC approach is for identifying and classifying the presence of sarcasm in social networks. The OBiLSTM-SDC technique encompass term frequency-inverse document frequency (TF-IDF) vectorizer. Further, the BiLSTM model can be utilized for the classification of sarcasm and the hyperparameter tuning of the BiLSTM model takes place via chicken swarm optimization (CSO) algorithm. The experimental assessment of the OBiLSTM-SDC model is taken place utilizing benchmark dataset and the outcomes were checked on various prospects. The simulation outcomes emphasized the supremacy of the OBiLSTM-SDC model over the existing approaches.
Energy-Efficient DNN Training Processors on Micro-AI Systems

Many edge/mobile devices are now able to utilize deep neural networks (DNNs) thanks to the development of mobile DNN accelerators. Mobile DNN accelerators overcame the problems of limited computing resources and battery capacity by realizing energy-efficient inference. However, its passive behavior makes it difficult for DNN to provide active customization for individual users or its service environment. The importance of on-chip training is rising more and more to provide active interaction between DNN processors and ever-changing surroundings or conditions. Despite its advantages, the DNN training has more constraints than the inference such that it was considered impractical to be realized on mobile/edge devices. Recently, there are many trials to realize mobile DNN training, and a number of prior works will be summarized. Firstly, it arranges the new challenges of the DNN accelerator induced by training functionality and discusses new hardware features related to the challenges. Secondly, it explains algorithm-hardware co-optimization methods and explains why it becomes mainstream in mobile DNN training research. Thirdly, it compares the main differences between the conventional inference accelerators and recent training processors. Finally, the conclusion is made by proposing the future directions of the DNN training processor in micro-AI systems.
Face Recognition using Open Source Computer Vision Library (OpenCV) with Python

Within a human's lifetime, faces are the visually embellished images that appear most frequently. Facial Recognition is the ability to recognize and discover someone primarily based totally on their facial features. Because the face is multidimensional, it necessitates several mathematical calculations. Thus, this work is aimed at developing an improved face recognition system that will serve in numerous application areas. Many approaches have already been developed, but they have low recognition capabilities and a high false alarm rate. As a result, there is a need for face recognition system that is more accurate and has a shorter recognition time. Due to rising security concerns and the quick development of mobile devices, face detection has recently been a hot research area. This paper focuses on the implementation of a face recognition system for human identification based on the open-source computer vision library (OpenCV) with python.At last the current application areas related to face recognition system is represented.
Cost-Aware Dynamic Multi-Workflow Scheduling in Cloud Data Center Using Evolutionary Reinforcement Learning

The Dynamic Multi-Workflow Scheduling (DMWS) problem aims to allocate highly complex tasks modeled as workflows to cloud resources while optimizing workflow brokers' interests. A workflow broker offers workflow execution services to end-users with agreed Service Level Agreements (SLA) while reducing its total VM rental fees in the meantime. Most existing DMWS-related research works focus on minimizing the workflow makespan by using either heuristics or hyper-heuristics techniques. However, these techniques were either designed for static workflow scheduling based on prior workflow information and/or the simplified cloud environment. In this paper, the DMWS problem is formulated to collectively minimize VM rental fees and SLA violation penalties. Moreover, we introduce a novel priority-based deep neural network scheduling policy that can flexibly adapt to a changing number of VMs and workflows. To train the new policy, a new Evolutionary Strategy based Reinforcement Learning (ES-RL) is developed and implemented. Different from gradient-based deep reinforcement learning algorithms, ES-RL has its advances in effectively training population-based and generally applicable policies in parallel as well as robustness to hyper-parameter settings. Our experiments with real-world datasets show that ES-RL can effectively train scheduling policies that can significantly reduce the costs by more than 90% compared to the state-of-the-art scheduling policies.
ABCP: Automatic Blockwise and Channelwise Network Pruning via Joint Search

Currently, an increasing number of model pruning methods are proposed to resolve the contradictions between the computer powers required by the deep learning models and the resource-constrained devices. However, for simple tasks like robotic detection, most of the traditional rule-based network pruning methods cannot reach a sufficient compression ratio with low accuracy loss and are time consuming as well as laborious. In this article, we propose automatic blockwise and channelwise network pruning (ABCP) to jointly search the blockwise and channelwise pruning action for robotic detection by deep reinforcement learning. A joint sample algorithm is proposed to simultaneously generate the pruning choice of each residual block and the channel pruning ratio of each convolutional layer from the discrete and continuous search space, respectively. The best pruning action taking both the accuracy and the complexity of the model into account is obtained finally. Compared with the traditional rule-based pruning method, this pipeline saves human labor and achieves a higher compression ratio with lower accuracy loss. Tested on the mobile robot detection data set, the pruned YOLOv3 model saves 99.5% floating-point operations, reduces 99.5% parameters, and achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\boldsymbol {37.3\times }$ </tex-math></inline-formula> speed up with only 2.8% mean of average precision (mAP) loss. On the sim2real detection data set for robotic detection task, the pruned YOLOv3 model achieves 9.6% better mAP than the baseline model, showing better robustness performance.
Non-Parametric Modeling of Spatio-Temporal Human Activity Based on Mobile Robot Observations

This work presents a non-parametric spatiotemporal model for mapping human activity by mobile autonomous robots in a long-term context. Based on Variational Gaussian Process Regression, the model incorporates prior information of spatial and temporal-periodic dependencies to create a continuous representation of human occurrences. The inhomogeneous data distribution resulting from movements of the robot is included in the model via a heteroscedastic likelihood function and can be accounted for as predictive uncertainty. Using a sparse formulation, data sets over multiple weeks and several hundred square meters can be used for model creation. The experimental evaluation, based on multi-week data sets, demonstrates that the proposed approach outperforms the state of the art both in terms of predictive quality and subsequent path planning.
GEBNet: Graph-Enhancement Branch Network for RGB-T Scene Parsing

RGB-T (red–green–blue and thermal) scene parsing has recently drawn considerable research attention. Although existing methods efficiently conduct RGB-T scene parsing, their performance remains limited by a small receptive field. Unlike methods that capture the global context by fusing multiscale features or using an attention mechanism, we propose a graph-enhancement branch network (GEBNet), which uses long-range dependencies obtained from the branch to refine a coarse semantic map generated by the decoder. Semantic and detail modules embedded in the graph-enhancement branch fuse high- and low-level features. Furthermore, inspired by the ability of graph neural networks to capture the global context, we integrate a novel graph-enhancement module into the network branch to obtain global information from both high-level semantic information and low-level details. Results from extensive experiments on the MFNet and PST900 datasets demonstrate the high performance of the proposed GEBNet and the contributions of its main components to the parsing performance.
Federated Learning-Based Cell-Free Massive MIMO System for Privacy-Preserving

Cell-free massive MIMO (CF mMIMO) is a promising next generation wireless architecture to realize federated learning (FL). However, sensitive information of user equipments (UEs) may be exposed to the involved access points or the central processing unit in practice. To guarantee data privacy, effective privacy-preserving mechanisms are defined in this paper. In particular, we demonstrate and characterize the possibility in exploiting the inherent quantization error, caused by low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs), for privacy-preserving in a FL CF mMIMO system. Furthermore, to reduce the required uplink training time in such a system, a stochastic non-convex design problem that jointly optimizing the transmit power and the data rate is formulated. To address the problem at hand, we propose a novel power control method by utilizing the successive convex approximation approach to obtain a suboptimal solution. Besides, an asynchronous protocol is established for mitigating the straggler effect to facilitate FL. Numerical results show that compared with the conventional full power transmission, adopting the proposed power control method can effectively reduce the uplink training time under various practical system settings. Also, our results unveil that our proposed asynchronous approach can reduce the waiting time at the central processing unit for receiving all user information, as there are no stragglers that requires a long time to report their local updates.
HDC-MiniROCKET: Explicit Time Encoding in Time Series Classification with Hyperdimensional Computing

Classification of time series data is an important task for many application domains. One of the best existing methods for this task, in terms of accuracy and computation time, is MiniROCKET. In this work, we extend this approach to provide better global temporal encodings using hyperdimensional computing (HDC) mechanisms. HDC (also known as Vector Symbolic Architectures, VSA) is a general method to explicitly represent and process information in high-dimensional vectors. It has previously been used successfully in combination with deep neural networks and other signal processing algorithms. We argue that the internal high-dimensional representation of MiniROCKET is well suited to be complemented by the algebra of HDC. This leads to a more general formulation, HDC-MiniROCKET, where the original algorithm is only a special case. We will discuss and demonstrate that HDC-MiniROCKET can systematically overcome catastrophic failures of MiniROCKET on simple synthetic datasets. These results are confirmed by experiments on the 128 datasets from the UCR time series classification benchmark. The extension with HDC can achieve considerably better results on datasets with high temporal dependence at about the same computational effort for inference.
Autonomous Data Collection With Dynamic Goals and Communication Constraints for Marine Vehicles

In marine robotics, data-collection operations often require an autonomous underwater vehicle (AUV) to collaborate with an unmanned surface vehicle (USV). The mission for the AUV is to reach many goal locations, avoid obstacles and unsafe areas, and maintain communication with the USV. The goals, however, are not known a priori, but are dynamically discovered by the USV as it moves along a predefined path. The USV communicates the discovered goals to the AUV along with rewards for reaching each goal to incentivize the AUV to increase the sum of the rewards when obstacles, time, and communication constraints make it impossible to reach all the goals. We develop a framework comprised of an execution module and a multi-layered planner to enable the AUV to avoid collisions, maintain communication with the USV, and increase the sum of the rewards by reaching many of the discovered goals. The execution module enables the AUV to follow the planned motions, invoking the planner when new goals are discovered. To facilitate navigation, the planner constructs a 3D roadmap that captures the connectivity of the environment by sampling and connecting waypoints in the free space. The high-level planning layer is based on informed discrete search to find roadmap paths that satisfy the communication constraints and increase the sum of the goal rewards. The low-level layer uses sampling-based motion planning to expand a tree of feasible motions along these roadmap paths. The layers interact to update the planned motions as new goals are discovered. Experiments using 3D environments and an increasing number of goals demonstrate the efficiency of the approach to solve dynamic multi-goal motion-planning problems with communication constraints. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners—</i> This paper is motivated by the problem of at-sea data collection using unmanned vehicles where inter-vehicle communications constraints must be maintained. This is a challenging problem that has generally required significant human monitoring and intervention due to the communication constraints and planning challenges. In this paper, we leverage recent advances in underwater communications and focus on new problems that arise in the planning space, specifically, how we generate trajectories for an AUV that satisfies communication range constraints while still performing an underlying task. We show that it is possible to plan for an AUV in real-time while considering these constraints. This paper suggests that our approach can support these complex at-sea missions. Future research will focus on field deployments and introducing more uncertainty into the underlying sensor models during planning.
An empirical study on maintainable method size in Java

Code metrics have been widely used to estimate software maintenance effort. Metrics have generally been used to guide developer effort to reduce or avoid future maintenance burdens. Size is the simplest and most widely deployed metric. The size metric is pervasive because size correlates with many other common metrics (e.g., McCabe complexity, readability, etc.). Given the ease of computing a method's size, and the ubiquity of these metrics in industrial settings, it is surprising that no systematic study has been performed to provide developers with meaningful method size guidelines with respect to future maintenance effort. In this paper we examine the evolution of ~785K Java methods and show that developers should strive to keep their Java methods under 24 lines in length. Additionally, we show that decomposing larger methods to smaller methods also decreases overall maintenance efforts. Taken together, these findings provide empirical guidelines to help developers design their systems in a way that can reduce future maintenance.
A Multirobot Distributed Collaborative Region Coverage Search Algorithm Based on Glasius Bio-Inspired Neural Network

There are many constraints for a multirobot system to perform a region coverage search task in an unknown environment. To address this, we propose a novel multirobot distributed collaborative region coverage search algorithm based on Glasius bio-inspired neural network (GBNN). First, we develop an environmental information updating model to represent the dynamic search environment. This model converts the environmental information detected by the robot into dynamic neural activity landscape of GBNN. Second, we introduce the distributed model predictive control method in search path planning to improve search efficiency. In addition, we propose a distributed collaborative decision-making mechanism among the robots to produce several dynamic search subteams. Within each subteam, collaborative decisions are made among the robot members to optimize the solution and obtain the next movement path of each robot. Finally, we conduct experiments in three aspects to verify the effectiveness of the proposed method. Compared with three algorithms in this field, the experimental results demonstrate that the proposed algorithm exhibits good performance in a multirobot region coverage search task.