Reviewerly/exHarmony huggingface dataset:
HUNTER: AI based holistic resource management for sustainable cloud computing

The worldwide adoption of cloud data centers (CDCs) has given rise to the ubiquitous demand for hosting application services on the cloud. Further, contemporary data-intensive industries have seen a sharp upsurge in the resource requirements of modern applications. This has led to the provisioning of an increased number of cloud servers, giving rise to higher energy consumption and, consequently, sustainability concerns. Traditional heuristics and reinforcement learning based algorithms for energy-efficient cloud resource management address the scalability and adaptability related challenges to a limited extent. Existing work often fails to capture dependencies across thermal characteristics of hosts, resource consumption of tasks and the corresponding scheduling decisions. This leads to poor scalability and an increase in the compute resource requirements, particularly in environments with non-stationary resource demands. To address these limitations, we propose an artificial intelligence (AI) based holistic resource management technique for sustainable cloud computing called HUNTER. The proposed model formulates the goal of optimizing energy efficiency in data centers as a multi-objective scheduling problem, considering three important models: energy, thermal and cooling. HUNTER utilizes a Gated Graph Convolution Network as a surrogate model for approximating the Quality of Service (QoS) for a system state and generating optimal scheduling decisions. Experiments on simulated and physical cloud environments using the CloudSim toolkit and the COSCO framework show that HUNTER outperforms state-of-the-art baselines in terms of energy consumption, SLA violation, scheduling time, cost and temperature by up to 12, 35, 43, 54 and 3 percent respectively.
Deep Discrete Cross-Modal Hashing with Multiple Supervision

Deep hashing has been widely used for large-scale cross-modal retrieval benefited from the low storage cost and fast search speed. However, most existing deep supervised methods only preserve the instance-pairwise relationship supervised by the semantic similarity matrix, which always inufficient heterogeneous correlation. Thus, we propose the Deep Discrete Cross-Modal Hashing with Multiple Supervision ( DDCH ms ) to further enhance the semantic consistency of heterogeneous modalities. It improves the performance of semantic information retrieval with the joint supervision of instance-pairwise, instance-labeled and class-wise similarities. Specifically, we firstly utilize the instance-pairwise similarity matrix to supervise the learning process of heterogeneous networks and it keeps the pairwise correlation from the perspective of instance-instance. Specially, we design a semantic network to fully exploit the semantic information implicated in labels, which is also used to supervise multi-modal networks on instance-label level. Furthermore, we propose the class-wise hash codes to cooperate with the intrinsic label matrix as the prototypes, and it guides the hash learning and further ensures the precision and compactness of the learned hash codes. In addition, we design different discrete optimization strategies to optimize the class-wise hash codes and unified hash codes, respectively. That avoids the optimization errors and ensures the high-quality of learned hash codes. Experiments on three popular datasets indicate that our method outperforms other state-of-the-art methods in terms of cross-modal retrieval.
Deep Neural Network Based Vehicle Detection and Classification of Aerial Images

The detection of the objects in the ariel image has a significant impact on the field of parking space management, traffic management activities and surveillance systems.Traditional vehicle detection algorithms have some limitations as these algorithms are not working with the complex background and with the small size of object in bigger scenes.It is observed that researchers are facing numerous problems in vehicle detection and classification, i.e., complicated background, the vehicle's modest size, other objects with similar visual appearances are not correctly addressed.A robust algorithm for vehicle detection and classification has been proposed to overcome the limitation of existing techniques in this research work.We propose an algorithm based on Convolutional Neural Network (CNN) to detect the vehicle and classify it into light and heavy vehicles.The performance of this approach was evaluated using a variety of benchmark datasets, including VEDAI, VIVID, UC Merced Land Use, and the Self database.To validate the results, various performance parameters such as accuracy, precision, recall, error, and F1-Score were calculated.The results suggest that the proposed technique has a higher detection rate, which is approximately 92.06% on the VEDAI dataset, 95.73% on the VIVID dataset, 90.17% on the UC Merced Land dataset, and 96.16% on the Self dataset.
Building Blocks of a Flip-Chip Integrated Superconducting Quantum Processor

We have integrated single and coupled superconducting transmon qubits into flip-chip modules. Each module consists of two chips -- one quantum chip and one control chip -- that are bump-bonded together. We demonstrate time-averaged coherence times exceeding $90\,\mu s$, single-qubit gate fidelities exceeding $99.9\%$, and two-qubit gate fidelities above $98.6\%$. We also present device design methods and discuss the sensitivity of device parameters to variation in interchip spacing. Notably, the additional flip-chip fabrication steps do not degrade the qubit performance compared to our baseline state-of-the-art in single-chip, planar circuits. This integration technique can be extended to the realisation of quantum processors accommodating hundreds of qubits in one module as it offers adequate input/output wiring access to all qubits and couplers.
Human Language Understanding &amp; Reasoning

Abstract The last decade has yielded dramatic and quite surprising breakthroughs in natural language processing through the use of simple artificial neural network computations, replicated on a very large scale and trained over exceedingly large amounts of data. The resulting pretrained language models, such as BERT and GPT-3, have provided a powerful universal language understanding and generation base, which can easily be adapted to many understanding, writing, and reasoning tasks. These models show the first inklings of a more general form of artificial intelligence, which may lead to powerful foundation models in domains of sensory experience beyond just language.
Using pre-trained models to boost code review automation

Code review is a practice widely adopted in open source and industrial projects. Given the non-negligible cost of such a process, researchers started investigating the possibility of automating specific code review tasks. We recently proposed Deep Learning (DL) models targeting the automation of two tasks: the first model takes as input a code submitted for review and implements in it changes likely to be recommended by a reviewer; the second takes as input the submitted code and a reviewer comment posted in natural language and automatically implements the change required by the reviewer. While the preliminary results we achieved are encouraging, both models had been tested in rather simple code review scenarios, substantially simplifying the targeted problem. This was also due to the choices we made when designing both the technique and the experiments. In this paper, we build on top of that work by demonstrating that a pre-trained Text-To-Text Transfer Transformer (T5) model can outperform previous DL models for automating code review tasks. Also, we conducted our experiments on a larger and more realistic (and challenging) dataset of code review activities.
SparseTT: Visual Tracking with Sparse Transformers

Transformers have been successfully applied to the visual tracking task and significantly promote tracking performance. The self-attention mechanism designed to model long-range dependencies is the key to the success of Transformers. However, self-attention lacks focusing on the most relevant information in the search regions, making it easy to be distracted by background. In this paper, we relieve this issue with a sparse attention mechanism by focusing the most relevant information in the search regions, which enables a much accurate tracking. Furthermore, we introduce a double-head predictor to boost the accuracy of foreground-background classification and regression of target bounding boxes, which further improve the tracking performance. Extensive experiments show that, without bells and whistles, our method significantly outperforms the state-of-the-art approaches on LaSOT, GOT-10k, TrackingNet, and UAV123, while running at 40 FPS. Notably, the training time of our method is reduced by 75% compared to that of TransT. The source code and models are available at https://github.com/fzh0917/SparseTT.
Blockchain Scaling Using Rollups: A Comprehensive Survey

Blockchain systems have seen much growth in recent years due to the immense potential attributed to the technology behind these systems. However, this popularity has outlined a critical scalability issue that most blockchain systems are now confronted with. With their increasing popularity comes an increasing amount of load on the system. Several scaling solutions that modify either the functioning of the underlying protocol or that build on top of them have already been proposed; however, each of these solutions comes with their advantages and disadvantages. This paper aims to survey the current state-of-the-art of rollups as a scaling solution. We discuss the mode of operation of the different types of rollups, outline state-of-the-art implementations of each type together with their features and limitations. We also conduct a performance analysis comparing these implementations. Finally, we outline avenues for future research around rollups as a scaling solution.
Anomaly Detection in Autonomous Driving: A Survey

Nowadays, there are outstanding strides towards a future with autonomous vehicles on our roads. While the perception of autonomous vehicles performs well under closed-set conditions, they still struggle to handle the unexpected. This survey provides an extensive overview of anomaly detection techniques based on camera, lidar, radar, multimodal and abstract object level data. We provide a systematization including detection approach, corner case level, ability for an online application, and further attributes. We outline the state-of-the-art and point out current research gaps.
Exploration of Educational Possibilities by Four Metaverse Types in Physical Education

The metaverse has been evolving the internet-based education represented by e-learning. Metaverse technology is currently being developed as a platform centered on content-based information industries. It can be classified into four categories: augmented reality, lifelogging, mirror worlds, and virtual worlds. Although current research finds that the potential of the metaverse is not small in the education world, and metaverse technology is already being used in the sports world, concrete applications have not been investigated. The main aims of this study, which started with this purpose, can be summarized as follows. The metaverse environment is still in its rudimentary stage, and its use related to physical education subjects is only at the game level. In the future, the utilization of the metaverse by physical education subjects will be possible in universities only when more specialized technology is grafted into various sports. Ultimately, this study contributes to expanding the scope and depth of follow-up research by offering basic data showing the direction of metaverse-based physical education.
Digitalization of vocational education under crisis conditions

The rapid development of technologies and their application in all branches of the economy calls for digitalization of education as a prerequisite of improving the quality of vocational training. Digital technologies in their turn allow to diversify the mode of training according to the needs arising under various circumstances. In some countries like Australia and Canada, online and blended learning are the only possibly form of training due to learners’ remotedness to schools. But as recent experience shows, introduction of online education was the only way out to sustain it under the conditions of the COVID-19 and now by the wartime and absence of access to educational facilities. In this was, the necessity of digitalization of education is constantly growing together with its increasing range of applicability. Now all production processes and processes of the service sector are under the influence of digital technologies, because modern machines are operated by computers. Modern military equipment is also digitally based and operated. Thus, working in modern industries and services requires a high level of digital literacy, which presents a challenge for the system of vocational education. Under modern conditions, irrespective of their positive or negative origin, vocational schools (VS) should be ready to train specialists for various spheres of industry capable of working with constantly changing digital technologies. This fact puts forwards certain requirements to digital literacy of both students and teachers, who have to cooperate through digital devices and software to attain the set educational goals. All these circumstances require the equal level of digital literacy of both teachers and students to provide educational institutions with the latest material base and digital resources.
Educational Research and AI-Generated Writing: Confronting the Coming Tsunami

The public release and surprising capacity of ChatGPT has brought AI-enabled text generation into the forefront for educators and academics. ChatGPT and similar text generation tools raise numerous questions for educational practitioners, policymakers, and researchers. We begin by first describing what large language models are and how they function, and then situate them in the history of technology’s complex interrelationship with literacy, cognition, and education. Finally, we discuss implications for the field.
Coin Classification Using a Novel Technique for Learning Characteristic Decision Trees by Controlling the Degree of Generalization

A novel method for learning characteristic decision trees is applied to the problem of learning the decision mechanism of coin-sorting machines. Decision trees constructed by ID3-like algorithms are unable to detect instances of categories not present in the set of training examples. instead of being rejected, such instances are assigned to one of the classes actually present in the training set. To solve this problem the algorithm must learn characteristic, rather than discriminative, category descriptions. In addition, the ability to control the degree of generalization is identified as an essential property of such algorithms. A novel method using the information about the statistical distribution of the feature values that can be extracted from the training examples is developed to meet these requirements. The central idea is to augment each leaf of the decision tree with a subtree that imposes further restrictions on the values of each feature in that leaf.
Cardinality Estimators do not Preserve Privacy

Abstract Cardinality estimators like HyperLogLog are sketching algorithms that estimate the number of distinct elements in a large multiset. Their use in privacy-sensitive contexts raises the question of whether they leak private information. In particular, can they provide any privacy guarantees while preserving their strong aggregation properties? We formulate an abstract notion of cardinality estimators, that captures this aggregation requirement: one can merge sketches without losing precision. We propose an attacker model and a corresponding privacy definition, strictly weaker than differential privacy: we assume that the attacker has no prior knowledge of the data. We then show that if a cardinality estimator satisfies this definition, then it cannot have a reasonable level of accuracy. We prove similar results for weaker versions of our definition, a nd a nalyze t he p rivacy o f existing algorithms, showing that their average privacy loss is significant, e ven f or m ultisets w ith l arge cardinalities. We conclude that strong aggregation requirements are incompatible with any reasonable definition o f privacy, and that cardinality estimators should be considered as sensitive as raw data. We also propose risk mitigation strategies for their real-world applications.
Multi-sense embeddings through a word sense disambiguation process

Natural Language Understanding has seen an increasing number of publications in the last few years, especially after robust word embeddings models became prominent, when they proved themselves able to capture and represent semantic relationships from massive amounts of data. Nevertheless, traditional models often fall short in intrinsic issues of linguistics, such as polysemy and homonymy. Any expert system that makes use of natural language in its core, can be affected by a weak semantic representation of text, resulting in inaccurate outcomes based on poor decisions. To mitigate such issues, we propose a novel approach called Most Suitable Sense Annotation (MSSA), that disambiguates and annotates each word by its specific sense, considering the semantic effects of its context. Our approach brings three main contributions to the semantic representation scenario: (i) an unsupervised technique that disambiguates and annotates words by their senses, (ii) a multi-sense embeddings model that can be extended to any traditional word embeddings algorithm, and (iii) a recurrent methodology that allows our models to be re-used and their representations refined. We test our approach on six different benchmarks for the word similarity task, showing that our approach can produce state-of-the-art results and outperforms several more complex state-of-the-art systems.
Video Object Detection with Locally-Weighted Deformable Neighbors

Deep convolutional neural networks have achieved great success on various image recognition tasks. However, it is nontrivial to transfer the existing networks to video due to the fact that most of them are developed for static image. Frame-byframe processing is suboptimal because temporal information that is vital for video understanding is totally abandoned. Furthermore, frame-by-frame processing is slow and inefficient, which can hinder the practical usage. In this paper, we propose LWDN (Locally-Weighted Deformable Neighbors) for video object detection without utilizing time-consuming optical flow extraction networks. LWDN can latently align the high-level features between keyframes and keyframes or nonkeyframes. Inspired by (Zhu et al. 2017a) and (Hetang et al. 2017) who propose to aggregate features between keyframes and keyframes, we adopt brain-inspired memory mechanism to propagate and update the memory feature from keyframes to keyframes. We call this process Memory-Guided Propagation. With such a memory mechanism, the discriminative ability of features in keyframes and non-keyframes are both enhanced, which helps to improve the detection accuracy. Extensive experiments on VID dataset demonstrate that our method achieves superior performance in a speed and accuracy trade-off, i.e., 76.3% on the challenging VID dataset while maintaining 20fps in speed on Titan X GPU.
Evaluating the performance of photon-number-resolving detectors

We analyze the performance of photon-number-resolving (PNR) detectors and introduce a figure of merit for the accuracy of such detectors. This figure of merit is the (worst-case) probability that the photon-number-resolving detector correctly predicts the input photon number. Simulations of various PNR detectors based on multiplexed single-photon ``click detectors'' is performed. We conclude that the required quantum efficiency is very high in order to achieve even moderate (up to a handful) photon resolution, we derive the required quantum efficiency as a function of the the maximal photon number one wants to resolve, and we show that the number of click detectors required grows quadratically with the maximal number of photons resolvable.
Generating Realistic Stock Market Order Streams

We propose an approach to generate realistic and high-fidelity stock market data based on generative adversarial networks (GANs). Our Stock-GAN model employs a conditional Wasserstein GAN to capture history dependence of orders. The generator design includes specially crafted aspects including components that approximate the market's auction mechanism, augmenting the order history with order-book constructions to improve the generation task. We perform an ablation study to verify the usefulness of aspects of our network structure. We provide a mathematical characterization of distribution learned by the generator. We also propose statistics to measure the quality of generated orders. We test our approach with synthetic and actual market data, compare to many baseline generative models, and find the generated data to be close to real data.
Cyber-Storms Come from Clouds: Security of Cloud Computing in the IoT Era

The Internet of Things (IoT) is rapidly changing our society to a world where every “thing” is connected to the Internet, making computing pervasive like never before. This tsunami of connectivity and data collection relies more and more on the Cloud, where data analytics and intelligence actually reside. Cloud computing has indeed revolutionized the way computational resources and services can be used and accessed, implementing the concept of utility computing whose advantages are undeniable for every business. However, despite the benefits in terms of flexibility, economic savings, and support of new services, its widespread adoption is hindered by the security issues arising with its usage. From a security perspective, the technological revolution introduced by IoT and Cloud computing can represent a disaster, as each object might become inherently remotely hackable and, as a consequence, controllable by malicious actors. While the literature mostly focuses on the security of IoT and Cloud computing as separate entities, in this article we provide an up-to-date and well-structured survey of the security issues of cloud computing in the IoT era. We give a clear picture of where security issues occur and what their potential impact is. As a result, we claim that it is not enough to secure IoT devices, as cyber-storms come from Clouds.
Adapting Double Weaving and Yarn Plying Techniques for Smart Textiles Applications

Smart textiles integrate sensing and actuation components into their structures to bring interactivity to fabrics. We describe how we adapted two existing fiber arts techniques, double weaving and yarn plying, for the purpose of creating a woven textile that changes color in response to touch. We draw from this experience to make three core contributions: descriptions of our experiments plying yarns that change between three color states; descriptions of double weaving structures that allow us to support interactivity while hiding circuitry from view; and suggestions for how these techniques could be adapted and extended by other researchers to make richly crafted and technologically sophisticated fabrics.