Reviewerly/exHarmony huggingface dataset:
Overview of the HASOC Subtrack at FIRE 2022: Hate Speech and Offensive Content Identification in English and Indo-Aryan Languages

In recent years, the spread of online offensive content has become of great concern, motivating researchers to develop robust systems capable of identifying such content automatically. To carry out a fair evaluation of these systems, several international shared tasks have been organized, providing the community with essential benchmark data and evaluation methods for various languages. Organized since 2019, the HASOC (Hate Speech and Offensive Content Identification) shared task is one of these initiatives. In its fourth iteration, HASOC 2022 included three tasks for English-Hindi codemix, German and Marathi. Tasks 1 and 2 were on conversational hate speech detection. The idea is to detect supporting hate speech, profanity, or other forms of offensiveness depending on the surrounding context of Twitter posts. Task 1 was offered in Hindi-English codemix and German. Task 2 was provided for Hindi-English codemix, and it was focused on further classifying the problematic tweets in conversational hate speech into standalone and contextual hate. This paper presents a brief description of tasks, data, and participation.
Atos: A Task-Parallel GPU Scheduler for Graph Analytics

We present Atos, a task-parallel GPU dynamic scheduling framework that is especially targeted at dynamic irregular applications. Compared to the dominant Bulk Synchronous Parallel (BSP) frameworks, Atos exposes additional concurrency by supporting task-parallel formulations of applications with relaxed dependencies, achieving higher GPU utilization, which is particularly significant for problems with concurrency bottlenecks. Atos also offers implicit task-parallel load balancing in addition to data-parallel load balancing, providing users the flexibility to balance between them to achieve optimal performance. Finally, Atos allows users to adapt to different use cases by controlling the kernel strategy and task-parallel granularity. We demonstrate that each of these controls is important in practice.
MFFN: Multi-view Feature Fusion Network for Camouflaged Object Detection

Recent research about camouflaged object detection (COD) aims to segment highly concealed objects hidden in complex surroundings. The tiny, fuzzy camouflaged objects result in visually indistinguishable properties. However, current single-view COD detectors are sensitive to background distractors. Therefore, blurred boundaries and variable shapes of the camouflaged objects are challenging to be fully captured with a singleview detector. To overcome these obstacles, we propose a behavior-inspired framework, called Multi-view Feature Fusion Network (MFFN), which mimics the human behaviors of finding indistinct objects in images, i.e., observing from multiple angles, distances, perspectives. Specifically, the key idea behind it is to generate multiple ways of observation (multi-view) by data augmentation and apply them as inputs. MFFN captures critical boundary and semantic information by comparing and fusing extracted multi-view features. In addition, our MFFN exploits the dependence and interaction between views and channels. Specifically, our methods leverage the complementary information between different views through a two-stage attention module called Co-attention of Multi-view (CAMV). And we design a local-overall module called Channel Fusion Unit (CFU) to explore the channel-wise contextual clues of diverse feature maps in an iterative manner. The experiment results show that our method performs favorably against existing state-of-the-art methods via training with the same data. The code will be available at https://github.com/dwardzheng/MFFN_COD.
Designing Reflective Derived Metrics for Fitness Trackers

Personal tracking devices are equipped with more and more sensors and offer an ever-increasing level of accuracy. Yet, this comes at the cost of increased complexity. To deal with that problem, fitness trackers use derived metrics---scores calculated based on sensor data, e.g. a stress score. This means that part of the agency in interpreting health data is transferred from the user to the tracker. In this paper, we investigate the consequences of that transition and study how derived metrics can be designed to offer an optimal personal informatics experience. We conducted an online survey and a series of interviews which examined a health score (a hypothetical derived metric) at three levels of abstraction. We found that the medium abstraction level led to the highest level of reflection. Further, we determined that presenting the metric without contextual information led to decreased transparency and meaning. Our work contributes guidelines for designing effective derived metrics.
Doc2Graph: A Task Agnostic Document Understanding Framework Based on Graph Neural Networks

Geometric Deep Learning has recently attracted significant interest in a wide range of machine learning fields, including document analysis. The application of Graph Neural Networks (GNNs) has become crucial in various document-related tasks since they can unravel important structural patterns, fundamental in key information extraction processes. Previous works in the literature propose task-driven models and do not take into account the full power of graphs. We propose Doc2Graph, a task-agnostic document understanding framework based on a GNN model, to solve different tasks given different types of documents. We evaluated our approach on two challenging datasets for key information extraction in form understanding, invoice layout analysis and table detection. Our code is freely accessible on https://github.com/andreagemelli/doc2graph .
CoOR: Collaborative Task Offloading and Service Caching Replacement for Vehicular Edge Computing Networks

In vehicular edge computing networks, edge service caching has emerged as a promising technology that supports delay sensitive applications. When the vehicles pass the coverage areas of roadside units (RSUs), the vehicles can offload part/all of their tasks to the RSUs that had cached the related service data in advance. However, it is difficult to utilize the service caching resources in RSUs efficiently, for the high mobility, variable task computing requests of vehicles and the limited storage and computation capacity of RSUs. In this paper, we propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">co</u> llaborative task <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</u> ffloading and service caching <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</u> eplacement scheme from a vehicle perspective, named CoOR, the task processing cooperation between the adjacent RSUs and the service caching replacement for vehicle are mainly considered. We formulate the system computing cost and delay minimization as a mixed integer programming problem, it faces challenges of task offloading and service caching coupling, the heterogeneous computation requests and dynamic data transmission conditions. Thus, we develop an iterative algorithm combined with the Gibbs sampling and deep reinforcement learning (DRL) to find the optimal decisions. Extensive simulation results show that the proposed schemes have good convergence and better performance than the traditional baselines.
Impact of Cloud Computing on Entrepreneurship, Cost, and Security

Cloud computing is the latest computer paradigm to emerge from ICT achievements, especially Internet services and virtualization. Global cloud service companies provide cloud services to people and businesses. As companies go to the cloud, the industry grows. Cloud computing offers both pros and cons for enterprises. This chapter examines cloud computing for startups. Cloud computing is trendy. Businesses are often uninformed of the cost and security consequences of cloud computing. Cloud computing is cost-effective, but security issues remain. Cloud computing’s influence has been studied. Few studies have examined cloud computing security and cost for businesses. The report assesses the economic and security benefits and risks of cloud computing for businesses. It examines the economic risks and security benefits, recommending that businesses consider them. Data was gathered from IT pros. Outdated cloud pricing models are a cost concern. Faster data recovery and transfer, centralization, and better software procedures and maintenance boost safety. This article includes cloud computing cost-cutting and security tips. Reduced operating and administrative costs are the biggest savings. According to the study, small and medium businesses use hybrid cloud computing. Without realistic pricing models, global regulatory frameworks, and supranational security demands, costs and security can’t be avoided.
Telepresence robots from the perspective of psychology and educational sciences

Purpose Telepresence robots (TPRs) are an emerging field of application and research that have received attention from various disciplines, including computer science, telehealth and education. The purpose of this study is to conduct a bibliometric analysis of publications on TPR in the Web of Science database from 1980 to 2022 to gain a better understanding of the state of research on TPRs and explore the role of pedagogical and psychological aspects in this research. Design/methodology/approach The analysis of research publications on TPRs was made on the basis of papers published in the Web of Science database from 1980 to 2022. The following research questions were proposed: What are the main tendencies in publication years, document types, countries of origin, source titles, publication authors, affiliations of authors and the most cited articles related to TPRs? What are the main topics discussed in the publications from the perspective of psychology? What are the main topics discussed in the publications from the perspective of educational sciences? Findings The results indicate that it is in the computer science where most of the existing research has been conducted, whereas the interest in the psychology and educational science has been relatively low. The greatest regional contributor has been the USA, whereas the effort in the European Union lags behind. Research publications in psychology in the Web of Science database related to TPRs can be grouped into three broad thematic categories: features of TPRs, degree of social presence compared to physical presence or other mediated technologies and opportunities for using TPRs. The results suggest that from the perspective of psychology, TPRs are one of the approaches that could enable greater social presence in remote communication. Most of the analysed papers in educational sciences investigated the opportunities of using TPRs in various educational fields. However, while the findings of the studies indicated significant potential of TPRs for education, their acceptance for wider use is still challenged. Research limitations/implications The limitations of this research are that this study only analysed research papers in the Web of Science database and therefore only covers a limited number of scientific papers published in the field of psychology and educational sciences on TPRs. In addition, only publications with the term “telepresence robots” in the topic area of the Web of Science database were analysed. Therefore, several relevant studies are not discussed in this paper that are not reflected in the Web of Science database or were related to other keywords. Originality/value The field of TPRs has not been explored using a bibliographic analysis of publications in the Web of Science database from the perspective of psychology and educational sciences. The findings of this paper will help researchers and academic staff better understand the state of research on TPRs and the pedagogical and psychological aspects addressed in this research.
Blockchain Regulated Verifiable and Automatic Key Refreshment Mechanism for IoT

Internet of Things (IoT) has proved its applicability in numerous domains such as healthcare, agriculture, automobile, industrial production, logistics and supply chain management.Looking at the current trend, we expect massive proliferation of such IoT devices all around us.However, one of the issues with the widespread use of IoT is the increasing complexity of the underlying architecture, which lead to difficulty in ensuring security compliance.One of the critical security processes is key refreshment, which needs to be regularly executed in order to guarantee strong security management.However, in most of the cases, keys are not updated and if they are updated, the update-logs are not available for all the entities to verify and build trust.Furthermore, the rules for key refreshment are also not defined in a transparent way.In this paper, we propose secure, decentralized, and automatic key refreshment mechanism for an IoT system.The proposed mechanism enables users to verify the freshness of the security keys (being used) thereby relying on the data from IoT devices and establishing trust in an IoT system.The proposed mechanism is driven by blockchain technology and smart contract.To demonstrate the viability of the proposed solution we have implemented it using both Ethereum and Hyperledger Fabric blockchains.
Experimental Activation of Strong Local Passive States with Quantum Information

Strong local passivity is a property of multipartite quantum systems from which it is impossible to extract energy locally. Surprisingly, if the strong local passive state displays entanglement, it could be possible to locally activate energy density by adding classical communication between different partitions of the system, through so-called "quantum energy teleportation" protocols. Here, we report both the first experimental observation of local activation of energy density on an entangled state and the first realization of a quantum energy teleportation protocol using nuclear magnetic resonance on a bipartite quantum system.
Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation

Multi-behavior recommendation exploits multiple types of user-item interactions, such as view and cart , to learn user preferences and has demonstrated to be an effective solution to alleviate the data sparsity problem faced by the traditional models that often utilize only one type of interaction for recommendation. In real scenarios, users often take a sequence of actions to interact with an item, in order to get more information about the item and thus accurately evaluate whether an item fits their personal preferences. Those interaction behaviors often obey a certain order, and more importantly, different behaviors reveal different information or aspects of user preferences towards the target item. Most existing multi-behavior recommendation methods take the strategy to first extract information from different behaviors separately and then fuse them for final prediction. However, they have not exploited the connections between different behaviors to learn user preferences. Besides, they often introduce complex model structures and more parameters to model multiple behaviors, largely increasing the space and time complexity. In this work, we propose a lightweight multi-behavior recommendation model named Cascading Residual Graph Convolutional Network ( CRGCN for short) for multi-behavior recommendation, which can explicitly exploit the connections between different behaviors into the embedding learning process without introducing any additional parameters (with comparison to the single-behavior based recommendation model). In particular, we design a cascading residual graph convolutional network (GCN) structure, which enables our model to learn user preferences by continuously refining the embeddings across different types of behaviors. The multi-task learning method is adopted to jointly optimize our model based on different behaviors. Extensive experimental results on three real-world benchmark datasets show that CRGCN can substantially outperform the state-of-the-art methods, achieving 24.76%, 27.28%, and 25.10% relative gains on average in terms of HR@K (K = {10,20,50,80}) over the best baseline across the three datasets. Further studies also analyze the effects of leveraging multi-behaviors in different numbers and orders on the final performance.
A survey of automated data augmentation algorithms for deep learning-based image classification tasks

Abstract In recent years, one of the most popular techniques in the computer vision community has been the deep learning technique. As a data-driven technique, deep model requires enormous amounts of accurately labelled training data, which is often inaccessible in many real-world applications. A data-space solution is Data Augmentation (DA), that can artificially generate new images out of original samples. Image augmentation strategies can vary by dataset, as different data types might require different augmentations to facilitate model training. However, the design of DA policies has been largely decided by the human experts with domain knowledge, which is considered to be highly subjective and error-prone. To mitigate such problem, a novel direction is to automatically learn the image augmentation policies from the given dataset using Automated Data Augmentation (AutoDA) techniques. The goal of AutoDA models is to find the optimal DA policies that can maximize the model performance gains. This survey discusses the underlying reasons of the emergence of AutoDA technology from the perspective of image classification. We identify three key components of a standard AutoDA model: a search space, a search algorithm and an evaluation function. Based on their architecture, we provide a systematic taxonomy of existing image AutoDA approaches. This paper presents the major works in AutoDA field, discussing their pros and cons, and proposing several potential directions for future improvements.
Cascaded deep graphical convolutional neural network for 2D hand pose estimation

Human hands, an essential component of the human body, play a vital role in interacting with and sensing real-world objects and are a reliable medium in modern technology for developing human-computer-interaction (HCI). Human Hand Pose Estimation (HPE) is challenging for numerous Artificial Intelligence (AI) applications due to the strong self-occlusion of the hands, depth ambiguity, and agile movement. Implementation of vision-based hand pose estimation algorithms can give a breath of innovation of these AI applications to overcome the challenges. We proposed a framework called Cascaded Deep Graphical Convolutional Neural Network (DCGCN, where Deep Convolutional Neural Network (DCnet) is used for computing unary and pairwise potential functions. A graphical model inference module is used for cascading unary and pairwise potentials. Evaluating the generated results via subjective and objective analysis, our DCDCN outperforms the state-of-the-art models in terms of accuracy and computational cost.
Deep transfer learning for IDC breast cancer detection using fast AI technique and Sqeezenet architecture

One of the most effective approaches for identifying breast cancer is histology, which is the meticulous inspection of tissues under a microscope. The kind of cancer cells, or whether they are cancerous (malignant) or non-cancerous, is typically determined by the type of tissue that is analyzed by the test performed by the technician (benign). The goal of this study was to automate IDC classification within breast cancer histology samples using a transfer learning technique. To improve our outcomes, we combined a Gradient Color Activation Mapping (Grad CAM) and image coloring mechanism with a discriminative fine-tuning methodology employing a one-cycle strategy using FastAI techniques. There have been lots of research studies related to deep transfer learning which use the same mechanism, but this report uses a transfer learning mechanism based on lightweight Squeeze Net architecture, a variant of CNN (Convolution neural network). This strategy demonstrates that fine-tuning on Squeeze Net makes it possible to achieve satisfactory results when transitioning generic features from natural images to medical images.
SiamTHN: Siamese Target Highlight Network for Visual Tracking

Siamese network based trackers develop rapidly in the field of visual object tracking in recent years. The majority of Siamese network based trackers now in use treat each channel in the feature maps generated by the backbone network equally, making the similarity response map sensitive to background influence and hence challenging to focus on the target region. Additionally, there are no structural links between the classification and regression branches in these trackers, and the two branches are optimized separately during training. Therefore, there is a misalignment between the classification and regression branches, which results in less accurate tracking results. In this paper, a Target Highlight Module is proposed to help the generated similarity response maps to be more focused on the target region. To reduce the misalignment and produce more precise tracking results, we propose a corrective loss to train the model. The two branches of the model are jointly tuned with the use of corrective loss to produce more reliable prediction results. Experiments on 5 challenging benchmark datasets reveal that the method outperforms current models in terms of performance, and runs at 38 fps, proving its effectiveness and efficiency.
Multi-granularity knowledge distillation and prototype consistency regularization for class-incremental learning

Deep neural networks (DNNs) are prone to the notorious catastrophic forgetting problem when learning new tasks incrementally. Class-incremental learning (CIL) is a promising solution to tackle the challenge and learn new classes while not forgetting old ones. Existing CIL approaches adopted stored representative exemplars or complex generative models to achieve good performance. However, storing data from previous tasks causes memory or privacy issues, and the training of generative models is unstable and inefficient. This paper proposes a method based on multi-granularity knowledge distillation and prototype consistency regularization (MDPCR) that performs well even when the previous training data is unavailable. First, we propose to design knowledge distillation losses in the deep feature space to constrain the incremental model trained on the new data. Thereby, multi-granularity is captured from three aspects: by distilling multi-scale self-attentive features, the feature similarity probability, and global features to maximize the retention of previous knowledge, effectively alleviating catastrophic forgetting. Conversely, we preserve the prototype of each old class and employ prototype consistency regularization (PCR) to ensure that the old prototypes and semantically enhanced prototypes produce consistent prediction, which excels in enhancing the robustness of old prototypes and reduces the classification bias. Extensive experiments on three CIL benchmark datasets confirm that MDPCR performs significantly better over exemplar-free methods and outperforms typical exemplar-based approaches.
Swift chiral quantum walks

A continuous-time quantum walk (CTQW) is sedentary if the return probability in the starting vertex is close to one at all times. Recent results imply that, when starting from a maximal degree vertex, the CTQW dynamics generated by the Laplacian and adjacency matrices are typically sedentary. In this paper, we show that the addition of appropriate complex phases to the edges of the graph, defining a chiral CTQW, can cure sedentarity and lead to swift chiral quantum walks of the adjacency type, which bring the return probability to zero in the shortest time possible. We also provide a no-go theorem for swift chiral CTQWs of the Laplacian type. Our results provide one of the first, general characterization of tasks that can and cannot be achieved with chiral CTQWs.