Reviewerly/exHarmony huggingface dataset:
Value of Temporal Dynamics Information in Driving Scene Segmentation

Semantic scene segmentation has primarily been addressed by forming high-level visual representations of single images. The problem of semantic segmentation in dynamic scenes has begun to receive attention with the video object segmentation and tracking problem. While there has been some recent work attempt to use deep learning models on the video level, what is not known is how the temporal dynamics information is contributing to the full scene segmentation. Moreover, most existing datasets only provide full scene annotation on non-consecutive images to ensure the variability of scenes, making it even harder to explore novel methods on video-level modeling. To address the above issues, our work takes steps to explore the behavior of modern spatiotemporal modeling approaches by: 1) constructing the MIT DriveSeg dataset, a large-scale video driving scene segmentation dataset, densely annotated for pixel-level semantic classes with 5000 consecutive video frames, and 2) proposing a joint-learning framework that reveals the contribution of temporal dynamics information in regard to different semantic classes in the driving scene. This work is intended to help assess current methods and support further exploration of the value of temporal dynamics information in video-level scene segmentation.
Towards comprehensive profile aggregation methods for group recommendation based on the latent factor model

• We use latent factor matrices to enrich the profile aggregation. • We aim at the profile aggregation with the contribution of all group members. • The weight of each group member is determined in detail for each considered item. • The neighbors are admitted to the group to clarify the interests of group members. The aggregation of group members’ profiles is an extremely important step in group recommender systems, as it represents the whole group as a single virtual user, and is the input to traditional recommendation techniques. This paper focuses on proposing methods of aggregating profiles of group members. The highlight of the proposed methods lies in incorporating the latent factor matrices formed in the latent factor recommendation model into the profile aggregation for group recommendation. As a result, the process of the profile aggregation is ensured to more fully reflect the interests of the group members. Experimental results show that profile aggregation, enriched by latent factor matrices, can improve group recommendation performance in terms of F1-score and Normalized Discounted Cumulative Gain Measure.
Semi-Supervised Learning for Cervical Precancer Detection

Convolutional neural networks have become the paradigm of choice for medical image classification applications. Recent research results have demonstrated that deep learning can provide a promising solution for cervical precancer, which is the direct precursor to invasive cervical cancer. However, labeled large datasets are required to develop robust, reliable, and portable deep learning algorithms. This paper presents a study of semi-supervised learning with split-attention models for cervical precancer classification using data derived from two large studies conducted by the U.S. National Cancer Institute. In this work, we examine semi-supervised learning with the ResNeSt50 architecture and observe a significant boost in performance over transfer learning from pre-trained ImageNet weights. We also analyze the issue of specular reflections which is very common in cervical photographic images. Specular reflection occurs as bright spots saturated with white light from the illuminant that occurs due to the presence of moisture and can distract machine learning algorithms. We explore various augmentation techniques to solve specular reflection problems to improve the visual quality of results. As a result, our approach brings significant performance improvements (82.02% accuracy) with potential application in AI device-assisted decision-making.
Explaining in Time

Explainability has emerged as a critical AI research objective, but the breadth of proposed methods and application domains suggest that criteria for explanation vary greatly. In particular, what counts as a good explanation, and what kinds of explanation are computationally feasible, has become trickier in light of oqaque “black box” systems such as deep neural networks. Explanation in such cases has drifted from what many philosophers stipulated as having to involve deductive and causal principles to mere “interpretation,” which approximates what happened in the target system to varying degrees. However, such post hoc constructed rationalizations are highly problematic for social robots that operate interactively in spaces shared with humans. For in such social contexts, explanations of behavior, and, in particular, justifications for violations of expected behavior, should make reference to socially accepted principles and norms. In this article, we show how a social robot’s actions can face explanatory demands for how it came to act on its decision, what goals, tasks, or purposes its design had those actions pursue and what norms or social constraints the system recognizes in the course of its action. As a result, we argue that explanations for social robots will need to be accurate representations of the system’s operation along causal, purposive, and justificatory lines. These explanations will need to generate appropriate references to principles and norms—explanations based on mere “interpretability” will ultimately fail to connect the robot’s behaviors to its appropriate determinants. We then lay out the foundations for a cognitive robotic architecture for HRI, together with particular component algorithms, for generating explanations and engaging in justificatory dialogues with human interactants. Such explanations track the robot’s actual decision-making and behavior, which themselves are determined by normative principles the robot can describe and use for justifications.
A deep learning based hazardous materials (HAZMAT) sign detection robot with restricted computational resources

One of the most challenging and non-trivial tasks in robot-based rescue operations is the Hazardous Materials (HAZMAT) sign detection in dangerous operation fields, in order to prevent further unexpected disasters.Each HAZMAT sign has a specific meaning that the rescue robot should detect and interpret to take a safe action, accordingly.Accurate HAZMAT detection and real-time processing are the two most important factors in such robotics applications.Furthermore, the rescue robot should cope with some secondary challenges such as image distortion and restricted CPU and computational resources, embedded in the robot.In this research, we propose a CNN-Based pipeline called DeepHAZMAT for HAZMAT sign detection and segmentation in four steps:(1) Input data volume optimisation before feeding into the CNN network, (2) Application of a YOLO-based structure to collect the required visual information from the hazardous areas, (3) HAZMAT sign segmentation and separation from the background using adaptive GrabCut technique, and (4) Post-processing optimisation using morphological operators and convex hull algorithms.In spite of the utilisation of a very limited CPU and memory resources, the experimental results show the proposed method has successfully maintained a better performance in terms of detection-speed and detection-accuracy, compared to classical and modern state-of-the-art methods.
Research on Cross-chain Technology of Blockchain

With the continuous development and innovation of blockchain technology, a large number of blockchain networks with different characteristics and adapting to the needs of different scenarios have emerged in the fields of payment settlement, product traceability, identity authentication and so on, forming many islands of value. Blockchain cross-chain technology is an important technical means to realize inter-chain interconnection and value transfer. This paper systematically summarizes the current blockchain mainstream cross-chain technology. Firstly, this paper summarizes the basic knowledge of cross-chain technology, and then introduces the mainstream cross-chain techniques. Finally, this paper analyzes the challenges of cross-chain technology and forecasts its future prospect.
The Impact of ICT Support and the EFQM Criteria on Sustainable Business Excellence in Higher Education Institutions

The problem of sustainable business excellence has been investigated according to different aspects in different fields. For a more detailed analysis, it is necessary to develop an integrative sustainable business excellence model, respecting the baseline model of business excellence according to the European Foundation of Quality Management, with the possibility of defining the relationships and levels of significance of different variables. The primary goal of this paper is to present a developed integrative model to simulate the effects of information and communication technologies, and quality and effects of leadership improvement, as independent variables, on sustainable business excellence in higher education institutions, as a dependent variable. The model was developed by applying dynamic system techniques and evaluated by using statistical methods. In the sample of 17 Serbian and 21 Russian universities, the authors analyzed the information and communication support, leadership and quality, and their impact on sustainable business excellence. According to the simulation analysis, it was concluded that the EFQM model of self-assessment could help produce a more customer-oriented culture in HEIs, through ICT support, leadership and quality criteria enhancements.
The Remote on the Local: Exacerbating Web Attacks Via Service Workers Caches

Service workers boost the user experience of modern web applications by taking advantage of the Cache API to improve responsiveness and support offline usage. In this paper, we present the first security analysis of the threats posed by this programming practice, identifying an attack with major security implications. In particular, we show how a traditional XSS attack can abuse the Cache API to escalate into a personin-the-middle attack against cached content, thus compromising its confidentiality and integrity. Remarkably, this attack enables new threats which are beyond the scope of traditional XSS. After defining the attack, we study its prevalence in the wild, finding that the large majority of the sites which register service workers using the Cache API are vulnerable as long as a single webpage in the same origin of the service worker is affected by an XSS. Finally, we propose a browser-side countermeasure against this attack, and we analyze its effectiveness and practicality in terms of security benefits and backward compatibility with existing web applications.
Detail-richest-channel based enhancement for retinal image and beyond

The quality of retinal photography is not always satisfactory due to the limited imaging conditions. Low contrast and insufficient brightness are common problems. By comprehensively analyzing the image formation model from an intuitive perspective, we present general principles of parameter settings for different enhancement scenarios, and this analysis provides theoretical support for designing the proposed method. In a retinal image, the green channel preserves the richest details due to the red free photography. We term the green channel as the "detail-richest-channel" in a retinal image, and it is used as the initial transmission map. Subsequently, the transmission map is corrected by the modified cubic function to reduce over enhancement. The proposed method is designed for enhancing retinal images photographed by handheld fundus camera initially, and it can be extended to other applications. A total of 1045 retinal images photographed by handheld fundus camera, 460 retinal images acquired from cataract patients using high-end fundus camera, and 890 underwater images are tested to validate the effectiveness of the proposed method. Results show that the proposed method can improve the quality of degraded images as well as preserve the natural visual perception.
Graph Jigsaw Learning for Cartoon Face Recognition

Cartoon face recognition is challenging as they typically have smooth color regions and emphasized edges, the key to recognizing cartoon faces is to precisely perceive their sparse and critical shape patterns. However, it is quite difficult to learn a shape-oriented representation for cartoon face recognition with convolutional neural networks (CNNs). To mitigate this issue, we propose the GraphJigsaw that constructs jigsaw puzzles at various stages in the classification network and solves the puzzles with the graph convolutional network (GCN) in a progressive manner. Solving the puzzles requires the model to spot the shape patterns of the cartoon faces as the texture information is quite limited. The key idea of GraphJigsaw is constructing a jigsaw puzzle by randomly shuffling the intermediate convolutional feature maps in the spatial dimension and exploiting the GCN to reason and recover the correct layout of the jigsaw fragments in a self-supervised manner. The proposed GraphJigsaw avoids training the classification model with the deconstructed images that would introduce noisy patterns and are harmful for the final classification. Specially, GraphJigsaw can be incorporated at various stages in a top-down manner within the classification model, which facilitates propagating the learned shape patterns gradually. GraphJigsaw does not rely on any extra manual annotation during the training process and incorporates no extra computation burden at inference time. Both quantitative and qualitative experimental results have verified the feasibility of our proposed GraphJigsaw, which consistently outperforms other face recognition or jigsaw-based methods on two popular cartoon face datasets with considerable improvements.
Teacher learning and innovative professional development through the lens of the Personal, Social and Learning to Learn European key competence

This article outlines a secondary analysis of two European studies on innovative teacher professional development, through the lens of the Personal, Social and Learning to Learn European key competence. The 2018 European Recommendation describes it as being resilient, reflecting upon oneself, working with others, and managing time, information, learning and career. Adaptive, socio-emotional and metacognitive competences can be essential to harness other competences and navigate an unpredictable world, as argued in the LifeComp European framework. To nurture this key competence, teachers have to develop it themselves throughout the career. Innovative professional development can be a catalyst, addressing the complex and situated nature of teacher learning. It entails re-thinking time, space and mode of delivery, embedding active learning, relevance to practice, collaboration, coaching, feedback, and reflection. Innovative examples are presented as reciprocally related to Personal, Social and Learning to Learn, for resilient education systems in the wake of the COVID 19 pandemic.
Miner revenue optimization algorithm based on Pareto artificial bee colony in blockchain network

Abstract In order to improve the revenue of attacking mining pools and miners under block withholding attack, we propose the miner revenue optimization algorithm (MROA) based on Pareto artificial bee colony in blockchain network. MROA establishes the revenue optimization model of each attacking mining pool and revenue optimization model of entire attacking mining pools under block withholding attack with the mathematical formulas such as attacking mining pool selection, effective computing power, mining cost and revenue. Then, MROA solves the model by using the modified artificial bee colony algorithm based on the Pareto method. Namely, the employed bee operations include evaluation value calculation, selection probability calculation, crossover operation, mutation operation and Pareto dominance method, and can update each food source. The onlooker bee operations include confirmation probability calculation, crowding degree calculation, neighborhood crossover operation, neighborhood mutation operation and Pareto dominance method, and can find the optimal food source in multidimensional space with smaller distribution density. The scout bee operations delete the local optimal food source that cannot produce new food sources to ensure the diversity of solutions. The simulation results show that no matter how the number of attacking mining pools and the number of miners change, MROA can find a reasonable miner work plan for each attacking mining pool, which increases minimum revenue, average revenue and the evaluation value of optimal solution, and reduces the spacing value and variance of revenue solution set. MROA outperforms the state of the arts such as ABC, NSGA2 and MOPSO.
Computing Resource Allocation Scheme for DAG-Based IOTA Nodes

IOTA is a distributed ledger technology (DLT) platform proposed for the internet of things (IoT) systems in order to tackle the limitations of Blockchain in terms of latency, scalability, and transaction cost. The main concepts used in IOTA to reach this objective are a directed acyclic graph (DAG) based ledger, called Tangle, used instead of the chain of blocks, and a new validation mechanism that, instead of relying on the miners as it is the case in Blockchain, relies on participating nodes that cooperate to validate the new transactions. Due to the different IoT capabilities, IOTA classifies these devices into full and light nodes. The light nodes are nodes with low computing resources which seek full nodes’ help to validate and attach its transaction to the Tangle. The light nodes are manually connected to the full nodes by using the full node IP address or the IOTA client load balancer. This task distribution method overcharges the active full nodes and, thus, reduces the platform’s performance. In this paper, we introduce an efficient mechanism to distribute the tasks fairly among full nodes and hence achieve load balancing. To do so, we consider the task allocation between the nodes by introducing an enhanced resource allocation scheme based on the weight least connection algorithm (WLC). To assess its performance, we investigate and test different implementation scenarios. The results show an improved balancing of data traffic among full nodes based on their weights and number of active connections.
Performance Analysis on Artificial Bee Colony Algorithm for Path Planning and Collision Avoidance in Swarm Unmanned Aerial Vehicle

With the rapid advancement of wireless communication, sensors, and battery technologies, Swarm Unmanned Aerial Vehicles (UAVs) have been widely used for traffic surveillance, and military application. Swarm UAVs, however, need to plan paths through the atmosphere, effectively preventing any collision that can occur when flying a multiple UAV simultaneously. This study proposes to design an anti-collision and a path planning system of swarm UAVs by using Artificial Bee Colony (ABC) algorithm. The ABC algorithm is an optimization method inspired by the foraging behavior of honeybees. The self-organization trait of honeybees enables them to coordinate themselves to create a global and local optimum. The proposed system, however, uses the ABC algorithm to optimize UAV's velocity, i.e., to reach its destination efficiently in the shortest path while avoiding collision among drones. The establishment of a constraint of a minimum acceptable distance among UAVs enables the algorithm to search for an alternative path in avoiding a collision. The simulation, however, reveals a successful convergence of a swarm UAVs towards a destination with no collision. During the trial with 12 and 20 drones, for instance, all UAVs successfully arrive at their goals with 0 potential collisions. However, during the test with 50 drones, there are 12 possible collisions. Once swarm drones reach their goal position at rest, the cluster will not overlap with other agents, as demonstrated in the visualization. Therefore, the ABC algorithm has satisfied the success criteria for this project and is suitable for swarm drone applications.
Covariance Pooling Layer for Text Classification

In this paper, we address the text categorization problem using a variant of traditional Convolutional Neural Networks (CNN). Our novel approach computes covariance of CNN features extracted for a given document. That is we add a covariance pooling layer on top of the convolutional features. The covariance pooling layer has more discrimination ability for classification tasks over regular max pooling layers that are used widely with CNN. This combination of CNN features and covariance pooling layers shows better classification accuracy against the traditional max pooling when tested on standard evaluation benchmarks.
Private Weighted Sum Aggregation

As large amounts of data are circulated both from users to a cloud server and between users, privately aggregating the shared data is critical. This article considers the problem of <i>private weighted sum aggregation with secret weights</i>, where an aggregator wants to compute the weighted sum of the local data of some agents. Based on the privacy requirements posed on the weights, there are different secure multiparty computation schemes exploiting the knowledge structure. First, we review schemes for when each agent has a local private value and local private weight, and when agents have a local private value, but the aggregator holds the corresponding weights. Our main focus is the more general case, where the weights are known neither by the agents nor by the aggregator&#x2014;they are generated and kept private by a system operator, and the aggregator has to compute the weighted sum without learning the agents&#x2019; data or the weights. We give solutions that achieve aggregator obliviousness and design more efficient communication and computation strategies for multidimensional data by batching the data into fewer ciphertexts. Finally, we implement our schemes and discuss the numerical results.
Occlusion Resistant Face Detection and Recognition System

The application of face recognition has become more and more popular with the development of deep learning algorithms and the calculation of hardware chips for accelerating neural networks. In face recognition, the recognition accuracy is easily affected by light, distance and occlusion. However, the occlusion is the most difficult issue to deal with. This work presents a convolutional neural network which was trained to improve the accuracy of face detection with the ability to capture facial features. The proposed method overcomes the situation when the face accompany with occlusion. All of the face regions and facial landmark are calculated via the face detection network for the inputted image. The face is then aligned by facial landmark and input into the face recognition network for identification. The experimental results of accuracy could reach 96.15% and 88.46% with the occlusion ration 25% and 50%, respectively. The proposed system successfully improves the face recognition accuracy while the face is occluded.