Reviewerly/exHarmony huggingface dataset:
Arbitrary-Shaped Text Detection With Watershed Segmentation Network

Arbitrary-shaped text detection is an important task in computer vision which has achieved great success in text reading systems. However, there still exist several challenges to be solved. For example, the regression-based text detection methods always fail to fit highly-curved text contours accurately. While the semantic segmentation-based methods could fit any geometries accurately, most of them employ single-size convolution kernels which ignore the diversity of receptive fields. To tackle the above issues, we propose a Watershed Segmentation Network (WSS-Net) which consists of two stages. Firstly, the pixel-level predictions of masks including text regions mask and text kernel regions mask are generated by the semantic segmentation network, in which two strategies i.e., deepening feature fusion and expanding receptive field are designed. Thus, the generated region masks can fit the contours of various-scale text instances better. Secondly, a rebuilding module based on the watershed algorithm is proposed to rebuild text instances according to the original image and two region masks, which is a widely used post-processing technique. Extensive experiments are carried out on Total-Text, CTW1500, and MSRA-TD500 to evaluate the effectiveness of the proposed WSS-Net. The results show that our WSS-Net achieves the highest F-measure score against several state-of-the-arts on three datasets.
Attack-Model-Agnostic Defense Against Model Poisonings in Distributed Learning

The distributed nature of distributed learning renders the learning process susceptible to model poisoning attacks. Most existing countermeasures are designed based on a presumed attack model, and can only perform under the presumed attack model. However, in reality a distributed learning system typically does not have the luxury of knowing the attack model it is going to be actually facing in its operation when the learning system is deployed, thus constituting a zero-day vulnerability of the system that has been largely overlooked so far. In this paper, we study the attack-model-agnostic defense mechanisms for distributed learning, which are capable of countering a wide-spectrum of model poisoning attacks without relying on assumptions of the specific attack model, and hence alleviating the zero-day vulnerability of the system. Extensive experiments are performed to verify the effectiveness of the proposed defense.
BMTAC: a decentralized, auditable, time-limited, multi-authority attribute access control scheme in blockchain environment

Ciphertext Policy Attribute-Based Encryption (CPABE) technology is often used in data sharing platforms. Most of the existing CP-ABE schemes do not consider time-sensitive data, which may easily lead to some unpublished information being leaked in advance. Therefore, combining time and attributes, this paper proposes a decentralized, auditable, time-limited multi-authority attribute access control scheme (BMTAC) in a blockchain environment. It can solve the single point of failure problem, the trust problem, and the cross-domain problem in a multi-authority scheme. This scheme combines the Fabric Network to manage permissions of different organizations jointly. Different organizations’ CA(Certificate Authority) issue digital certificates for entities and generate time tokens for attributes. Smart contracts collect attribute and time tokens to encrypt and decrypt ciphertext data and reduce client communication and computing costs. Furthermore, the comparative experiments show that the BMTAC scheme with the added attribute time limit has a good time cost in encryption and decryption, effectively reducing the computing overhead of users. Finally, the analysis of the security model proves the security of the scheme.
BT-CKBQA: An efficient approach for Chinese knowledge base question answering

Knowledge Base Question Answering (KBQA), as an increasingly essential application, can provide accurate responses to user queries. ensuring that users obtain relevant information and make decisions promptly. The deep learning-based approaches have achieved satisfactory QA results by leveraging the neural network models. However, these approaches require numerous parameters, which increases the workload of tuning model parameters. To address this problem, we propose BT-CKBQA, a practical and highly efficient approach incorporating BM25 and Template-based predicate mapping for CKBQA. Besides, a concept lattice based approach is proposed for summarizing the knowledge base, which can largely improve the execution efficiency of QA with little loss of performance. Concretely, BT-CKBQA leverages the BM25 algorithm and custom dictionary to detect the subject of a question sentence. A template-based predicate generation approach is then proposed to generate candidate predicates. Finally, a ranking approach is provided with the joint consideration of character similarity and semantic similarity for predicate mapping. Extensive experiments are conducted over the NLPCC-ICCPOL 2016 and 2018 KBQA datasets, and the experimental results demonstrate the superiority of the proposed approach over the compared baselines. Particularly, the averaged F1-score result of BT-CKBQA for mention detection is up to 98.25%, which outperforms the best method currently available in the literature. For question answering, the proposed approach achieves superior results than most baselines with the F1-score value of 82.68%. Compared to state-of-the-art baselines, the execution efficiency and performance of QA per unit time can be improved with up to 56.39% and 44.06% gains, respectively. The experimental results for the diversification of questions indicate that the proposed approach performs better for diversified questions than domain-specific questions. The case study over a constructed COVID-19 knowledge base illustrates the effectiveness and practicability of BT-CKBQA.
A Static Analysis Platform for Investigating Security Trends in Repositories

Static analysis tools come in many forms and configurations, allowing them to handle various tasks in a (secure) development process: code style linting, bug/vulnerability detection, verification, etc., and adapt to the specific requirements of a software project, thus reducing the number of false positives.The wide range of configuration options poses a hurdle in their use for software developers, as the tools cannot be deployed out-of-the-box. However, static analysis tools only develop their full benefit if they are integrated into the software development workflow and used on regular. Vulnerability management should be integrated via version history to identify hotspots, for example.We present an analysis platform that integrates several static analysis tools that enable Git-based repositories to continuously monitor warnings across their version history. The framework is easily extensible with other tools and programming languages. We provide a visualization component in the form of a dashboard to display security trends and hotspots. Our tool can also be used to create a database of security alerts at a scale well-suited for machine learning applications such as bug or vulnerability detection.
An optimal scheduling algorithm considering the transactions worst-case delay for multi-channel hyperledger fabric network

As the most popular consortium blockchain platform, Hyperledger Fabric (Fabric for short) has released multiple versions that support different consensus protocols to address the risks faced in current and future network transactions. For example, Fabric v1.4 and v2.0 use Kafka and Raft mechanisms to complete consensus and ensure that the system can withstand failures such as crashes, network partitions, or network shutdowns. In a multi-channel Fabric network architecture, the system structure cannot guarantee the behavior of malicious nodes. Complex cooperation between peer groups on different channels can greatly affect the security and efficiency of the entire network architecture, which is challenging to estimate and optimize. To address this challenge, we designed a Drift Plus Penalty Algorithm (DPPA) and a Transaction Worst-case Delay Algorithm (TWDA) based on peer node random scheduling using the Lyapunov optimization framework. The DPPA ensures the stability of the system and provides the maximum transaction processing rate under the minimum safety probability. The numerical results show that this algorithm can achieve a good balance between system security probability and queue accumulation. The TWDA considers discarding transactions with excessively long delay time by setting a worst-case transaction delay threshold. When considering both the security probability and queue accumulation of the Fabric system, the optimal scheduling of peer nodes is given. Numerical simulations were conducted on two types of algorithms, and the results showed that the security of the TWDA was slightly worse than that of the DPPA, but the system queue accumulation was significantly smaller. Therefore, the simulation results not only validate the effectiveness of the two types of algorithms but also provide operators with operational strategies that consider different factors.
AutoRec++: Incorporating Debias Methods into Autoencoder-based Recommender System

The deep neural network-based (DNN-based) model has proven powerful in user data behavior representation, efficiently implementing a recommender system (RS). Most prior works focus on developing a sophisticated architecture to better-fit user data. However, user behavior data are commonly collected from multiple scenarios and generated by numerous users, resulting in various biases existing in these data. Unfortunately, prior DNN-based RSs dealing with these biases are fragmented and lack a comprehensive solution. This paper aims to comprehensively handle these biases in user behavior data in preprocessing stage and training state. By incorporating the preprocessing bias (PB) and training bias (TB) into the representative autoencoder-based AutoRec model, we proposed AutoRec++. Experimental results in five commonly used benchmark datasets demonstrate that: 1) the basic model’s preference can boost by the optimal PB and TB combinations, and 2) our proposed AutoRec++ reaches a better prediction accuracy than DNN-based and non-DNN-based state-of-the-art models.
Reinforcement Learning-based Collision Avoidance for UAV

One of the significant aspects for enabling the intelligent behavior to the Unmanned Aerial Vehicles (UAVs) is by providing an algorithm for navigation through the dynamic and unseen environment. Therefore, to be autonomous, they need sensors to perceive their surroundings and utilize gathered information to decide which action to take. Having that in mind, in this paper, the authors designed the system for obstacle avoidance and also investigate the elements of the Markov decision process and their influence on each other. The flying mobile robot used within the considered problem is quadrotor type and has an integrated Lidar sensor which is utilized to detect obstacles. The sequential decision-making model based on Q-learning is trained within the MATLAB Simulink environment. The simulation results demonstrate that the UAV can navigate through the environment in most algorithm runs without colliding with surrounding obstacles.
PickPic: A real-time and low-redundancy image selection framework for disaster perception

With the maturity of mobile crowdsensing (MCS) technology, it is possible to obtain real-time situational awareness data in disaster scenarios. However, the influx of mass data in a short time will occupy excessive bandwidth. The existing research results mainly have the problems of key information loss and poor real-time performance during data selection. In this paper, a two-stage data transmission scheme, called PickPic, is proposed to quickly acquire low-redundancy information in disaster. In the first stage of PickPic, on the server side, we proposed a metadata group redundancy detection method (GRD). GRD divides metadata provided by the client into groups based on multi-dimensional features, and detects redundant images using KNN. GRD can reduce detection time and provide comprehensive situation awareness with low redundancy. In the second stage of PickPic, we proposed an image redundancy detection method (IRD), which models similar images detection issue into a graph community problem. IRD uses Louvain algorithm to cluster the images, and designs a greedy algorithm to obtain final subset of images, which further decrease the redundancy. We implemented a prototype of PickPic and evaluated the performance with two real-world datasets. Experimental results show that our framework can effectively and accurately remove redundant images and decrease the image selection time.
GAS: GPU Allocation Strategy for Deep Learning Training Tasks

Nowadays, with the significant increasement of the deep learning training (DLT) task workload in GPU clusters, the number and the scale of GPU clusters grow rapidly. A crucial question is how to efficiently schedule DLT tasks with limited cluster resources. Existing GPU schedulers do not fully consider the connection between users and clusters, and few methods optimize the GPU allocation of DLT tasks. In this study, we propose a scheduling framework for GPU clusters, which improves performance and reduces energy consumption of clusters. We first analyze the relationship between the characteristics of performance and energy consumption and the task configurations for DLT tasks. Then, we propose a prediction method to predict the completion time and energy consumption of DLT tasks. To make better use of cluster resources, based on the prediction model, we propose GAS, which adopts the GPU Allocation Strategy by specifying the parallelism for DLT tasks. Compared to FIFO and SJF schedulers, GAS reduces the makespan by 19.6%-19.8%, reduces the average queueing time by 84.4%-93.9% and reduces the energy consumption by 22.2%22.5%. For users, GAS also reduces the cost of users by 21.3%21.6%. The large-scale simulation experiment further illustrates the effectiveness and scalability of GAS.
Pedestrian Behavior Maps for Safety Advisories: CHAMP Framework and Real-World Data Analysis

It is critical for vehicles to prevent any collisions with pedestrians. Current methods for pedestrian collision prevention focus on integrating visual pedestrian detectors with Automatic Emergency Braking (AEB) systems which can trigger warnings and apply brakes as a pedestrian enters a vehicle’s path. Unfortunately, pedestrian-detection-based systems can be hindered in certain situations such as night-time or when pedestrians are occluded. Our system addresses such issues using an online, map-based pedestrian detection aggregation system where common pedestrian locations are learned after repeated passes of locations. Using a carefully collected and annotated dataset in La Jolla, CA, we demonstrate the system’s ability to learn pedestrian zones and generate advisory notices when a vehicle is approaching a pedestrian despite challenges like dark lighting or pedestrian occlusion. Using the number of correct advisories, false advisories, and missed advisories to define precision and recall performance metrics, we evaluate our system and discuss future positive effects with further data collection. We have made our code available at https://github.com/s7desai/ped-mapping, and a video demonstration of the CHAMP system at https://youtu.be/dxeCrS Gpkw.
A Novel Backdoor Attack Adapted to Transfer Learning

Recently, backdoor attacks pose a major security threat to deep neural networks. The attacker embeds hidden malicious behaviors into deep learning models that is activated only when the input contains specific triggers. Although backdoor attacks have a high success rate and are very stealthy, existing backdoor models often lose their attack capabilities after transfer learning. A critical question at present is how to ensure that the malicious behavior of the backdoor model is not disturbed by transfer learning. In this paper, we conducted a detailed empirical study of multiple existing backdoor defenses, and found that the existing defenses are generally based on the different recognition mechanisms of backdoor triggers and clean samples in the model. In order to resist existing defenses, we propose a method for generating backdoor triggers inversely based on the gradient information of model. In addition, for preventing the model from being disturbed by transfer learning, we use the modified Triplets-Loss to inject the backdoor only in the convolutional layer, and erase the backdoor information of the fully connected layer under the premise of ensuring the effect of the backdoor attack. Finally, we evaluate the attack and 4 potential defenses in several benchmark datasets, including MNIST, CIFAR10, GTSRB. The proposed attack method achieves 100% success rates while circumventing the interference of existing backdoor defenses.
Future Knowledge That Can Addressing Limitations In Chat GPT And Wolfram Alpha

This article discusses the limitations of Chat GPT and Wolfram Alpha, specifically in terms of their restricted knowledge and their inability to answer some Quadratic Surd questions. Chat GPT's knowledge is confined to data up until 2021. However, these limitations can be addressed by incorporating future knowledge, Peter Chew Theorem for Quadratic Surds and Peter Chew Quadratic Surd Diagram into the Peter Chew Quadratic Surd Diagram (PCQSD) Calculator.By utilizing the PCQSD calculator, significant advancements can be achieved. This innovative tool allows for the conversion of any Quadratic Surd values, including decimal representations, into a sum or difference of two real numbers or a sum or difference of two complex numbers. The integration of Peter Chew's theorem and the PCQSD diagram provides a promising solution to the existing limitations in Chat GPT and Wolfram Alpha.By programming the PCQSD calculator with future knowledge, these systems can enhance their capabilities and offer more comprehensive and up-to-date answers to all range of Quadratic Surds questions. This approach demonstrates the potential of leveraging advanced mathematical concepts and diagrams to overcome the limitations posed by restricted knowledge in AI systems like Chat GPT and Wolfram Alpha.By incorporating future knowledge, Peter Chew Theorem for Quadratic Surds and Peter Chew Quadratic Surd Diagram and leveraging advanced mathematical tools, AI systems can expand their capabilities and offer more accurate and comprehensive responses in the domain of Quadratic Surds.
Privacy-Enhanced Living: A Local Differential Privacy Approach to Secure Smart Home Data

The rapid expansion of Internet of Things (IoT) devices in smart homes has significantly improved the quality of life, offering enhanced convenience, automation, and energy efficiency. However, this proliferation of connected devices raises critical concerns regarding security and privacy of the user data. In this paper, we propose a differential privacy-based system to ensure comprehensive security for data generated by smart homes. We employ the randomized response technique for the data and utilize Local Differential Privacy (LDP) to achieve data privacy. The data is then transmitted to an aggregator, where an obfuscation method is applied to ensure individual anonymity. Furthermore, we implement the Hidden Markov Model (HMM) technique at the aggregator level and apply differential privacy to the private data received from smart homes. Consequently, our approach achieves a dual layer of privacy protection, addressing the security concerns associated with IoT devices in smart cities.
From RSSE to BotSE: Potentials and Challenges Revisited after 15 Years

Both recommender systems and bots should proactively and smartly answer the questions of software developers or other project stakeholders to assist them in performing their tasks more efficiently. This paper reflects on the achievements from the more mature area of Recommendation Systems in Software Engineering (RSSE) as well as the rising area of Bots in Software Engineering (BotSE). We discuss the similarities and differences, briefly review current state of the art, and highlight three particular areas, in which the full potential is yet to be tapped: a more socio-technical context awareness, assisting knowledge sharing in addition to knowledge access, as well as covering repetitive or stimulative scenarios related to requirements and user-developer interaction.
Research On Dialogue Management Model Based On A3C Algorithm

With the development of Internet and AI related technologies, task-based dialogue system has become a research hot-spot. This paper uses the common characteristics of dialogue management and reinforcement learning in task-based dialogue system to introduce deep reinforcement learning into the process of building dialogue management model, and proposes a dialogue management model based on A3C algorithm to complete the learning task of dialogue strategy. The model is based on Actor-Critical architecture, which can dynamically improve the learning result of dialogue strategies through internal evaluation mechanism, and improve the prediction accuracy of dialogue management model. At the same time, the model is trained in a multi-threaded way, which can execute multiple sub-nets at the same time, and can effectively improve the training efficiency of the model.
Self-Supervised Occupancy Grid Map Completion for Automated Driving

This paper investigates methods for enhancing the quality of occupancy grid maps (OGMs) using a combination of a self-supervised data generation procedure using only unlabeled data and a deep learning approach. OGMs are grid-structured environment representations, commonly used in automated driving systems to encode occupancy of the surrounding area. However, due to limited sensor range and resolution, their quality degrades significantly in distant and occluded areas, posing a challenge for a subsequent decision making. We introduce OGM completion, whose goal is to provide a more complete representation of the environment by extrapolating potential occupancy to distant and occluded areas. In particular, we propose and implement a complete framework for OGM completion. We develop a method for self-supervised data generation, identify an existing class of adoptable deep learning architectures, adapt loss functions and a quantitative performance metric, and derive a generic baseline method. Finally, we validate the functionality of the implemented framework by thorough experimentation and inspection of real-world examples of OGM completion in automated driving, significantly outperforming a baseline method.
Expert-driven Rule-based Refinement of Semantic Segmentation Maps for Autonomous Vehicles

Semantic segmentation aims at assigning labels to every pixel of a given image. In the context of autonomous vehicles, semantic segmentation models should be trained with data collected from the traffic network through which vehicles are expected to circulate. Road regulation, weather conditions, and other context features may differ between regions, making local semantic segmentation datasets extremely valuable. However, the high ground truth annotation costs represent a hindrance to the development of such models. The upsurge of powerful feature learning architectures leaves room for semantic segmentation models trained on an unsupervised fashion. This observation vertebrates the purpose of this work: to produce coarse segmentation maps for scene understanding without the need of annotated data. We depart from an unsupervised model that yields low-quality results. The proposed methodology establishes a set of guidelines for the enhancement of segmentation maps. Obtained results expose an improvement of the segmentation quality thanks to the application of our devised guidelines, paving the way for the automatic generation of semantic segmentation datasets.