Reviewerly/exHarmony huggingface dataset:
FINGERS: exploiting fine-grained parallelism in graph mining accelerators

Graph mining is an emerging application of high importance and also with high complexity, thus requiring efficient hardware acceleration. Current accelerator designs only utilize coarse-grained parallelism, leaving large room for further optimizations. Our key insight is to fully exploit fine-grained parallelism to overcome the existing issues of hardware underutilization, inefficient resource provision, and limited single-thread performance under imbalanced loads. Targeting pattern-aware graph mining algorithms, we first comprehensively identify and analyze the abundant fine-grained parallelism at the branch, set, and segment levels during search tree exploration and set operations. We then propose a novel graph mining accelerator, FINGERS, which effectively exploits these multiple levels of fine-grained parallelism to achieve significant performance improvements. FINGERS mainly enhances the design of each single processing element with parallel compute units for set operations, and efficient techniques for task scheduling, load balancing, and data aggregation. FINGERS outperforms the state-of-the-art design by 2.8× on average and up to 8.9× with the same chip area. We also demonstrate that different patterns and different graphs exhibit drastically different parallelism opportunities, justifying the necessity of exploiting all levels of fine-grained parallelism in FINGERS.
CoolEdge: hotspot-relievable warm water cooling for energy-efficient edge datacenters

As the computing frontier drifts to the edge, edge datacenters play a crucial role in supporting various real-time applications. Different from cloud datacenters, the requirements of proximity to end-users, high density, and heterogeneity, present new challenges to cool the edge datacenters efficiently. Although warm water cooling has become a promising cooling technique for this infrastructure, the one-size-fits-all cooling control would lower the cooling efficiency considerably because of the severe thermal imbalance across servers, hardware, and even inside one hardware component in an edge datacenter. In this work, we propose CoolEdge, a hotspot-relievable warm water cooling system for improving the cooling efficiency and saving costs of edge datacenters. Specifically, through the elaborate design of water circulations, CoolEdge can dynamically adjust the water temperature and flow rate for each heterogeneous hardware component to eliminate the hardware-level hotspots. By redesigning cold plates, CoolEdge can quickly disperse the chip-level hotspots without manual intervention. We further quantify the power saving achieved by the warm water cooling theoretically, and propose a custom-designed cooling solution to decide an appropriate water temperature and flow rate periodically. Based on a hardware prototype and real-world traces from SURFsara, the evaluation results show that CoolEdge reduces the cooling energy by 81.81% and 71.92%, respectively, compared with conventional and state-of-the-art water cooling systems.
A Software-Based Remote Attestation Scheme for Internet of Things Devices

With the rapid development of intelligent applications, many Internet of Things (IoT) devices are deployed in various application scenarios, playing an extremely important role. Remote attestation is an important method to ensure the software integrity of these devices and protect them from several attacks. Due to the lack of security hardware and no support of hardware extensions for Class-1 IoT devices, it is particularly important to design a suitable remote attestation scheme for these devices. In this paper, we first propose the delayed observation mechanism to alleviate the problem that the software-based remote attestation scheme is not suitable for wireless networks. At the same time, we propose a ”filling memory at attestation-time” mechanism, which solves the problem that attackers hide malicious code through return-oriented programming. Finally, we introduce a reputation mechanism to assist our attestation, and adopt the principle of ”making higher-performance verification nodes take on more work” to greatly reduce the time-consuming attestation. We analyze the security of the scheme and implement it on a UNO-R3 development board to prove its practicability and effectiveness. Compared with traditional software-based attestation schemes, our scheme can reduce the attestation time and resist proxy attacks.
Interpreting AI for Networking: Where We Are and Where We Are Going

In recent years, artificial intelligence (AI) techniques have been increasingly adopted to tackle networking problems. Although AI algorithms can deliver high-quality solutions, most of them are inherently intricate and erratic for human cognition. This lack of interpretability tremendously hinders the commercial success of AI-based solutions in practice. To cope with this challenge, networking researchers are starting to explore explainable AI (XAI) techniques to make AI models interpretable, manageable, and trustworthy. In this article, we overview the application of AI in networking and discuss the necessity for interpretability. Next, we review the current research on interpreting AI-based networking solutions and systems. At last, we envision future challenges and directions. The ultimate goal of this article is to present a general guideline for AI and networking practitioners and motivate the continuous advancement of AI-based solutions in modern communication networks.
The Impact of Student Characteristics for Working with AR Technologies in Higher Education—Findings from an Exploratory Study with Microsoft HoloLens

Augmented reality (AR) is being used in various fields and increasingly being used in the educational sector. Although there is still little research available on the use of this technology in education, the research literature indicates its potential and effectiveness. However, information about students’ characteristics and individual learning preconditions in relation to learning with AR technologies is still scarce. In this study, 18 student teachers for science and technology for lower secondary schools of a German-speaking university of teacher education were asked about different aspects related to motivation, interest, attitude, and self-efficacy before (questionnaire) and after (interviews) participating in an AR-supported teaching unit with Microsoft HoloLens. The results show that the students have a very positive attitude towards AR technologies and were highly motivated to work with this technology. Attitudes towards AR technologies, as well as towards new technologies in general, were found to be related to motivational factors and aspects of self-efficacy in the context of the AR teaching unit. As these aspects can have an impact on learning outcomes, it is of utmost importance to understand and recognize them. Educators and researchers should therefore pay more attention to these issues in the future.
A novel focused crawler combining Web space evolution and domain ontology

In many fields, how to catch the related-topic Web resources is crucial. As a vertical search method, focused crawler has received great attention in recent years. Currently, most focused crawlers consider multiple evaluating factors of the hyperlinks and use the weighted sum approach to compute the priorities of unvisited hyperlinks. However, the proper weighted coefficients are hard to determine, and their unsuitable values may even cause the direction of crawlers to deviate seriously from the topic. To overcome this issue, this article builds a multi-objective optimization model based on Web text and link structure and designs a crawler framework called the Web space evolution (WSE), where a hyperlink bank whose radius is gradually increased is introduced to extend the search scape of crawlers in Web space. To improve the uniformity and diversity of hyperlinks, a nearest and farthest candidate solution method is combined with the fast non-dominated sorting to choose Pareto-optimal solutions (hyperlinks). A domain ontology based on the formal concept analysis is applied to establish the topic model. By incorporating the WSE and the domain ontology into the focused crawling, a novel focused crawler called FCWSEO is proposed to collect topic-relevant webpages. The experimental results on the rainstorm disaster domain show that the FCWSEO outperforms other focused crawler strategies in terms of the quantity and quality of retrieved relevant webpages.
Automating the Creation of Machine Learning Algorithms using basic Math

The study of machine learning has progressed in several areas, including model structures and learning methodologies. The challenges of this research sparked the development of Auto ML, an area that tries to automate the building of Machine Learning algorithms by using basic mathematical operations as building blocks. This advancement on the other hand, has mostly concentrated on the architecture of deep neural networks. Introducing this novel framework leads to compensate the human intervention through a generic search space. A case in neural architecture search is a domain in which complex layers are used to automatically design neural networks. These modest accomplishments in creating ML algorithms from scratch, point to a promising new route for the research.
Robustness via Cross-Domain Ensembles

We present a method for making neural network predictions robust to shifts from the training data distribution. The proposed method is based on making predictions via a diverse set of cues (called ‘middle domains’) and ensembling them into one strong prediction. The premise of the idea is that predictions made via different cues respond differently to a distribution shift, hence one should be able to merge them into one robust final prediction. We perform the merging in a straightforward but principled manner based on the uncertainty associated with each prediction. The evaluations are performed using multiple tasks and datasets (Taskonomy, Replica, ImageNet, CIFAR) under a wide range of adversarial and non-adversarial distribution shifts which demonstrate the proposed method is considerably more robust than its standard learning counterpart, conventional deep ensembles, and several other baselines.
An Examination of Velocity and Initial Coin Offerings

Blockchain technology offers firms a novel method of raising capital via so-called initial coin offerings (ICOs). In the most common form of an ICO, a firm creates digital assets called “utility tokens” that are tracked on a blockchain-based ledger, requires that its product be purchased only with those tokens, and then, raises capital by selling these tokens to investors prior to creating any saleable product. (Some nonfungible tokens (NFTs) may function in a similar fashion.) We model a fundamental paradox with the use of ICOs involving utility tokens and similar structures. To increase capital raised by an ICO, the firm may attempt to reduce blockchain operating costs, thus expanding the quantity of goods sold. However, because of the mechanics of miner compensation, doing so increases the number of utility token transactions that take place in any time interval (i.e., increases token velocity and thus, the effective supply of tokens). By Fisher’s equation, this lowers the dollar value of tokens and the amount investors are willing to pay for them. We show that this paradox limits the value of utility token ICOs as an alternative to traditional financing options. We discuss alternatives to and variations of utility tokens that can mitigate the conundrum and promote ICOs as a more viable form of financing. This paper was accepted by Joshua Gans, business strategy.
R2Net: Residual refinement network for salient object detection

The multi-scale features and the fusion strategy of contextual features are the keys to salient object detection task. Previous multi-scale-based works often overlooked the completeness of features when acquiring multi-scale features. Moreover, the decoders were hard to accurately capture the salient object and refine the object's boundaries simultaneously when in a complex environment, which leads to unsatisfactory saliency maps. To address the above problems, we present a Residual Refinement Network (R2Net), which is composed of the Residual Pyramid Module (RPM), the Residual Fusion Module (RFM) and the Feature Optimize Module (FOM), for salient object detection. RPM integrates different feature information through different receptive fields, can not only obtain multi-scale information but also retain the local details information of features. RFM can better locate salient objects and refine the boundaries through the interweaving and fusion of multi-layer features. And FOM is designed to further refine the fused features. Furthermore, we propose a Structural Polishing (SP) loss, which better guides the network through pixel-level supervision, global supervision and boundary supervision to generate high-quality saliency maps with fine boundaries. Experimental results on 6 benchmark datasets demonstrate that the proposed method has superior performance compared with 18 state-of-the-art methods. The code and results of our method are available at https://github.com/zhangjin12138/R2Net
DHT- and blockchain-based smart identification for video conferencing

Video conferencing applications help people communicate via the Internet and provide a significant and consistent basis for virtual meetings. However, integrity, security, identification, and authentication problems are still universal. Current video conference technologies typically rely on cloud systems to provide a stable and secure basis for executing tasks and processes. At the same time, video conferencing applications are being migrated from centralized to decentralized solutions for better performance without the need for third-party interactions. This article demonstrates a decentralized smart identification scheme for video conferencing applications based on biometric technology, machine learning, and a decentralized hash table combined with blockchain technology. We store users' information on a distributed hash table and transactional events on the distributed ledger after identifying users by implementing machine learning functions. Furthermore, we leverage distributed ledger technology's immutability and traceability properties and distributed hash table unlimited storage feature to improve the system's storage capacity and immutability by evaluating three possible architectures. The experimental results show that an architecture based on blockchain and distributed hash table has better efficiency but needs a longer time to execute than the two other architectures using a centralized database.
Cascade Image Matting with Deformable Graph Refinement

Image matting refers to the estimation of the opacity of foreground objects. It requires correct contours and fine details of foreground objects for the matting results. To better accomplish human image matting tasks, we propose the Cascade Image Matting Network with Deformable Graph Refinement(CasDGR), which can automatically predict precise alpha mattes from single human images without any additional inputs. We adopt a network cascade architecture to perform matting from low-to-high resolution, which corresponds to coarse-to-fine optimization. We also introduce the Deformable Graph Refinement (DGR) module based on graph neural networks (GNNs) to overcome the limitations of convolutional neural networks (CNNs). The DGR module can effectively capture long-range relations and obtain more global and local information to help produce finer alpha mattes. We also reduce the computation complexity of the DGR module by dynamically predicting the neighbors and apply DGR module to higher–resolution features. Experimental results demonstrate the ability of our Cas-DGR to achieve state-of-the-art performance on synthetic datasets and produce good results on real human images.
A study of the influence of the sensor sampling frequency on the performance of wearable fall detectors

Last decade has witnessed a major research interest on wearable fall detection systems. Sampling rate in these detectors strongly affects the power consumption and required complexity of the employed wearables. This study investigates the effect of the sampling frequency on the efficacy of the detection process. For this purpose, we train a convolutional neural network to directly discriminate falls from conventional activities based on the raw acceleration signals captured by a transportable sensor. Then, we analyze the changes in the performance of this classifier when the sampling rate is progressively reduced. In contrast with previous studies, the detector is tested against a wide set of public repositories of benchmarking traces. The quality metrics achieved for the different frequencies and the analysis of the spectrum of the signals reveal that a sampling rate of 20 Hz can be enough to maximize the effectiveness of a fall detector.
Adversarial Attack on Deep Cross-Modal Hamming Retrieval

Recently, Cross-Modal Hamming space Retrieval (CMHR) regains ever-increasing attention, mainly benefiting from the excellent representation capability of deep neural networks. On the other hand, the vulnerability of deep networks exposes a deep cross-modal retrieval system to various safety risks (e.g., adversarial attack). However, attacking deep cross-modal Hamming retrieval remains underexplored. In this paper, we propose an effective Adversarial Attack on Deep Cross-Modal Hamming Retrieval, dubbed AACH, which fools a target deep CMHR model in a black-box setting. Specifically, given a target model, we first construct its substitute model to exploit cross-modal correlations within hamming space, with which we create adversarial examples by limitedly querying from a target model. Furthermore, to enhance the efficiency of adversarial attacks, we design a triplet construction module to exploit cross-modal positive and negative instances. In this way, perturbations can be learned to fool the target model through pulling perturbed examples far away from the positive instances whereas pushing them close to the negative ones. Extensive experiments on three widely used cross-modal (image and text) retrieval benchmarks demonstrate the superiority of the proposed AACH. We find that AACH can successfully attack a given target deep CMHR model with fewer interactions, and that its performance is on par with previous state-of-the-art attacks.
A novel multivariable time series prediction model for acute kidney injury in general hospitalization

Early recognition and prevention are important to reduce the risk of acute kidney injury (AKI). We aimed to build a novel multivariate time series prediction model for dynamic AKI prediction in general hospitalization.Deidentified electronic data of all patients admitted in Sichuan Provincial Peoples Hospital during 1 January 2019 and 31 December 2019 was retrospectively collected. Variables including demographics, admission variables, lab investigation variables and prescription variables were extracted. The first 50 most frequently detected lab investigation variables were selected as the predictive variables. Features within three previous days were selected to predict the risk of AKI in the next 24 h. The model was built using recurrent neural network (RNN) algorithm integrated with a time series convolution module and an attention convolution module and internally validated using five-fold cross-validation. Area under the ROC curve (AUC) and recall rate were used to evaluate the performance. The model was compared with four other models built using other machine learning algorithms and published machine learning models in literature.47,960 eligible admissions were identified, among which 2694 (5.6%) admissions were complicated by AKI. Our model has an AUC of 0.908 and a recall rate of 0.869, outperforming models generated by mainstay machine learning methods and most of the published machine learning models.This study reports a novel machine learning prediction model for AKI in general hospitalization which is based on RNN algorithm. The model outperforms models generated by mainstay machine learning methods and most of the published machine learning models.
Continual Learning for Image-Based Camera Localization

For several emerging technologies such as augmented reality, autonomous driving and robotics, visual localization is a critical component.Directly regressing camera pose/3D scene coordinates from the input image using deep neural networks has shown great potential.However, such methods assume a stationary data distribution with all scenes simultaneously available during training.In this paper, we approach the problem of visual localization in a continual learning setup -whereby the model is trained on scenes in an incremental manner.Our results show that similar to the classification domain, non-stationary data induces catastrophic forgetting in deep networks for visual localization.To address this issue, a strong baseline based on storing and replaying images from a fixed buffer is proposed.Furthermore, we propose a new sampling method based on coverage score (Buff-CS) that adapts the existing sampling strategies in the buffering process to the problem of visual localization.Results demonstrate consistent improvements over standard buffering methods on two challenging datasets -7Scenes, 12Scenes, and also 19Scenes by combining the former scenes 1 .
Learning to Cut by Watching Movies

Video content creation keeps growing at an incredible pace; yet, creating engaging stories remains challenging and requires non-trivial video editing expertise. Many video editing components are astonishingly hard to automate primarily due to the lack of raw video materials. This paper focuses on a new task for computational video editing, namely the task of raking cut plausibility. Our key idea is to leverage content that has already been edited to learn fine-grained audiovisual patterns that trigger cuts. To do this, we first collected a data source of more than 10K videos, from which we extract more than 255K cuts. We devise a model that learns to discriminate between real and artificial cuts via contrastive learning. We set up a new task and a set of baselines to benchmark video cut generation. We observe that our proposed model outperforms the baselines by large margins. To demonstrate our model in real-world applications, we conduct human studies in a collection of unedited videos. The results show that our model does a better job at cutting than random and alternative baselines.
Fear of Economic Cybercrime Across Europe: A Multilevel Application of Routine Activity Theory

Abstract Despite the increasing prevalence of cybercrime and its study by criminologists, very little research has examined the extent, nature, and impact of fear of cybercrime. In this study, we conducted a multilevel analysis of the 2018 Eurobarometer Cybersecurity Survey to test the applicability of routine activities theory on fear of economic cybercrime. We contribute to the literature by demonstrating that: (1) fear of economic cybercrime varies across EU member states; (2) country-level infrastructure development and income inequality are predictive of individual-level fear; (3) individual-level routine activities and sociodemographic variables are associated with fear; (4) country-level infrastructure development moderates the effects of individual-level guardianship. This paper concludes by emphasizing the importance of including country-level and individual-level determinants in fear of cybercrime research.