Reviewerly/exHarmony huggingface dataset:
The quantum switch is uniquely defined by its action on unitary operations

The quantum switch is a quantum process that creates a coherent control between different unitary operations, which is often described as a quantum process which transforms a pair of unitary operations <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo stretchy="false">(</mml:mo><mml:msub><mml:mi>U</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:mo>,</mml:mo><mml:msub><mml:mi>U</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo stretchy="false">)</mml:mo></mml:math> into a controlled unitary operation that coherently applies them in different orders as <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mo fence="false" stretchy="false">|</mml:mo><mml:mrow class="MJX-TeXAtom-ORD"><mml:mn>0</mml:mn></mml:mrow><mml:mo fence="false" stretchy="false">&amp;#x27E9;</mml:mo><mml:mo fence="false" stretchy="false">&amp;#x27E8;</mml:mo><mml:mrow class="MJX-TeXAtom-ORD"><mml:mn>0</mml:mn></mml:mrow><mml:mo fence="false" stretchy="false">|</mml:mo><mml:mo>&amp;#x2297;</mml:mo><mml:msub><mml:mi>U</mml:mi><mml:mn>1</mml:mn></mml:msub><mml:msub><mml:mi>U</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:mo>+</mml:mo><mml:mo fence="false" stretchy="false">|</mml:mo><mml:mrow class="MJX-TeXAtom-ORD"><mml:mn>1</mml:mn></mml:mrow><mml:mo fence="false" stretchy="false">&amp;#x27E9;</mml:mo><mml:mo fence="false" stretchy="false">&amp;#x27E8;</mml:mo><mml:mrow class="MJX-TeXAtom-ORD"><mml:mn>1</mml:mn></mml:mrow><mml:mo fence="false" stretchy="false">|</mml:mo><mml:mo>&amp;#x2297;</mml:mo><mml:msub><mml:mi>U</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mi>U</mml:mi><mml:mn>1</mml:mn></mml:msub></mml:math>. This description, however, does not directly define its action on non-unitary operations. The action of the quantum switch on non-unitary operations is then chosen to be a ``natural'' extension of its action on unitary operations. In general, the action of a process on non-unitary operations is not uniquely determined by its action on unitary operations. It may be that there could be a set of inequivalent extensions of the quantum switch for non-unitary operations. We prove, however, that the natural extension is the only possibility for the quantum switch for the 2-slot case. In other words, contrary to the general case, the action of the quantum switch on non-unitary operations (as a linear and completely CP preserving supermap) is completely determined by its action on unitary operations. We also discuss the general problem of when the complete description of a quantum process is uniquely determined by its action on unitary operations and identify a set of single-slot processes which are completely defined by their action on unitary operations.
Average coherence and entropy

Quantum coherence is one of the characteristic features of quantum mechanics and underpins many quantum mysteries. To eliminate the influence of the reference basis on the coherence of a quantum state and uncover its intrinsic properties, it is common to study coherence by averaging over different reference bases. Using the metric-adjusted skew information, we explore three natural approaches to average coherence of a state: average over all orthonormal bases, average over all elements of an operator orthonormal base, and average over a complete family of mutually unbiased bases. We establish the equivalence among these three types of average coherence and interpret the unified average coherence as the coherence of a quantum state relative to a depolarizing channel. Additionally, we employ the unified average coherence to introduce a notion of quantum $f$ entropy (where $f$ is an operator monotone function associated with the metric-adjusted skew information) and demonstrate that quantum $f$ entropy possesses properties analogous to the ubiquitous von Neumann entropy. Furthermore, we illuminate some connections between quantum $f$ entropy and quasientropy, and compare $f$ entropy with von Neumann entropy, R\'enyi entropy, and Tsallis entropy for some typical states.
A Survey on Recommendation Methods Based on Social Relationships

With the rapid development of online social networks recently, more and more online users have participated in social network activities and rich social relationships are formed accordingly. These social relationships provide a rich data source and research basis for in-depth study on recommender systems (RSs), while also promoting the development of RSs based on social networks. To solve the problems of cold start and sparsity in RSs, many recommendation algorithms are constantly being proposed. Motivated by the availability of rich social connections in today’s RSs, a large number of recommendation techniques based on social relationships have been proposed recently, achieving good recommendation results, and have become the mainstream research direction in the field of RSs, attracting more and more researchers to engage in this research. In this study, we mainly review and summarize the social relationship-based recommendation methods and techniques in RSs, and study some recent deep social relationship recommendation methods and techniques based on deep learning (DL), including the latest social matrix factorization (MF)-based recommendation methods and graph neural network (GNN)-based recommendation methods. Finally, we discuss the potential impact that may improve the RS and future direction. In this article, we aim to introduce the recent recommendation techniques integrating social relationships to solve data sparsity and cold start, and provide a new perspective for improving the performance of RSs, thereby providing useful resources in the state-of-the-art research results for future researchers.
Deep learning based image reconstruction at any speeds for faster pavement texture measurement using 3D laser technology

ABSTRACTRecently, the super-resolution network PT-SRGAN was developed for faster pavement texture measurement using 3D laser technology at 0.1 mm resolution from only six predetermined faster speeds. This paper further introduces an extended application of PT-SRGAN in combination with bicubic interpolation to enhance the low-resolution 3D pavement data collected at any speeds for more flexible practices. The research team collected two datasets on ten field pavement sections: (1) Dataset-1 was the true 0.1 mm 3D texture data collected at very low speeds (<1.5 mph); and (2) Dataset-2 was the low-resolution 3D texture data collected at three faster speeds (7.5, 15, and 30 mph). The efficacy of the extended PT-SRGAN was validated by reconstructing 0.1 mm data from manually downsized low-resolution Dataset-1 and real low-resolution Dataset-2. First, the superior performance of the proposed method was demonstrated by examining two evaluation metrics calculated between ground truth and reconstructed images at different speeds using Dataset-1. Further, seven 3D areal texture parameters were calculated and averaged for reconstructed Dataset-2, and compared with those of Dataset-1 to demonstrate that the proposed method shows promising performance to enhance real low-resolution 3D texture images to 0.1 mm data for any faster pavement texture evaluation.KEYWORDS: Deep learningimage enhancement3D imagepavement textureareal texture parameters Disclosure statementNo potential conflict of interest was reported by the author(s).
The MacWilliams identity for the skew rank metric

The weight distribution of an error correcting code is a crucial statistic in determining its performance.One key tool for relating the weight of a code to that of it's dual is the MacWilliams Identity, first developed for the Hamming metric.This identity has two forms: one is a functional transformation of the weight enumerators, while the other is a direct relation of the weight distributions via (generalised) Krawtchouk polynomials.The functional transformation form can in particular be used to derive important moment identities for the weight distribution of codes.In this paper, we focus on codes in the skew rank metric.In these codes, the codewords are skew-symmetric matrices, and the distance between two matrices is the skew rank metric, which is half the rank of their difference.This paper develops a q-analog MacWilliams Identity in the form of a functional transformation for codes based on skew-symmetric matrices under their associated skew rank metric.The method introduces a skew-q algebra and uses generalised Krawtchouk polynomials.Based on this new MacWilliams Identity, we then derive several moments of the skew rank distribution for these codes.
On Explanations for Hybrid Artificial Intelligence

The recent developments of machine learning (ML) approaches within artificial intelligence (AI) systems often require explainability of ML models. In order to establish trust in these systems, for example in safety critical applications, a number of different explainable artificial intelligence (XAI) methods have been proposed, either post-hoc or intrinsic models. These can help to understand why a ML model has made a particular decision. The authors of this paper point out that the abbreviation XAI is commonly used in the literature referring to explainable ML models, although the term AI encompasses many more topics than ML. To improve efficiency and effectiveness of AI, two or more AI subsystems are often combined to solve a common problem. In this case, an overall explanation has to be derived from the subsystems' explanations. In this paper we define the term hybrid AI. This is followed by reviewing the current state of XAI before proposing the use of blackboard systems (BBS) to not only share results but also to integrate and to exchange explanations of different XAI models as well, in order to derive an overall explanation for hybrid AI systems.
RAN: Region-Aware Network for Remote Sensing Image Super-Resolution

The remote sensing (RS) image super-resolution (SR) algorithm aims to reconstruct a high-resolution (HR) image with rich texture details from a given low-resolution (LR) image, improving the spatial resolution. It has been widely concerned in remote sensing image processing and application. Most current deep learning-based methods rely on paired training datasets. However, most datasets are often based on bicubic degradation. This single construction way limits the performance of the pre-trained network. Moreover, SR is an ill-posed problem in that multiple SR images are constructed from a single LR input. This paper proposes a Region-Aware Network (RAN) for remote sensing image super-resolution to alleviate the above issues. First, we introduce the contrastive learning strategy to mine the latent degraded representation of the image and serve as the prior knowledge of the network. Considering the RS images are acquired in specific scenes that have apparent self-similarity. Then, we propose a Region-Aware Module (RAM) based on attention mechanisms and the graph neural network to explore region information and cross-patch self-similarity. Extensive experiments have demonstrated that the proposed RAN adapts to RS image super-resolution tasks with various degradations and performs better in constructing texture information.
Mitigating Sybil Attacks in Federated Learning

Federated learning (FL) is a distributed learning paradigm that facilities a basic data-privacy level, as the clients do not have to share their raw data. Since the clients send local model updates, it increases the attack surface of FL—with possible attackers sharing poisoning updates with the aggregation server. In this work, we focus on the Sybil attacks, a type of poisoning attack where attackers can have multiple identities to overpower the honest clients in the system. In particular, we define a cosine-similarity-based measurement to track the clients' behavior. To mitigate the Sybil attacks, we propose FedSybil, a behavior-based defense with a reputation mechanism for FL under independent and identically distributed (IID) and non-IID data settings. In extensive experiments, we demonstrate the effectiveness of our approach with an improved model accuracy over the state-of-the-art approaches reaching over 50% improvement under attacks.
Lever: Making Intensive Validation Practical on Blockchain

Blockchain heralds the dawn of decentralized applications that coordinate proper computations without the need for prior trust. Existing blockchain solutions, however, are incapable of dealing with intensive validation. Duplicated execution leads to limited throughput and unacceptable expenses. Furthermore, the absence of secure incentive mechanisms derives undesired dilemmas among rational verifiers. This work presents Lever, the first off-chain solution that makes intensive validation cost-efficient and scalable among rational verifiers. To achieve the best scalability, Lever curtails the scale of each validation to a single node and introduces novel challenge-response games between potential adversaries and rational stakeholders, optimizing validation redundancy according to the practical adversarial capability confronted. Meanwhile, compelling incentive design efficiently transfers adversary collateral to specialized rewards for honest participants, therefore allowing the user to lever sufficient endorsement with minimum cost. A backstop protocol is designed to resolve intractable disputes and circumvent the well-known Verifier’s Dilemma. Experiments show that Lever significantly improves the throughput and reduces expenses of intensive validation with a slight tradeoff in latency. It is also robust to conceivable attacks on validation and performs distinguishable ability to purify Byzantine participants.
Text sentiment analysis based on BERT-CBLBGA

Current word vector techniques can generate effective word representations from massive text data, and different deep learning models can extract various textual semantic and grammatical features from different perspectives and levels. To extract richer text sentiment features, we propose a fused model of text sentiment classification called BERT-CBLBGA, which combines BERT with multiple feature extraction deep learning models. First, BERT is utilized to generate word embedding of the text, and the obtained feature representations are then fed into TextCNN, BiLSTM, and BiGRU to extract local and global sentiment features of the text. Then we use self-attention to fuse the output of TextCNN, BiLSTM, and BiGRU to enhance the ability to extract text sentiment classification features. Finally, a classifier is employed to classify the extracted features. Experimental results on four different domain text datasets demonstrate that the BERT-CBLBGA outperforms the baseline models in terms of extracting text sentiment features and achieves the best classification performance.
Locality Matters! Traffic Demand Modeling in Datacenter Networks

Understanding and modeling traffic demand characteristics in datacenter networks is of great importance for datacenter network optimization. However, prior traffic models are over-simplified and insufficient in capturing the complex locality properties of traffic demand. We analyze real-world traffic traces and discover strong dependency between the spatial attributes (source, destination) and non-spatial attributes (interarrival time, flow size) of traffic demand. We propose Lomas to model the joint distribution of multi-dimensional traffic demand attributes and generate synthetic traces. Lomas is a novel extension of hierarchical Bayes model that can represent the relationships among these attributes as a dependency graph. We validate Lomas by showing its ability to recreate the flow-level traffic demand patterns of real-world traffic traces. Our approach can be easily adapted to different datacenters with heterogeneous traffic demand patterns, making it a convenient tool for practitioners to utilize.
Integer Programming Based Optimization of Power Consumption for Data Center Networks

With the quickly developing data centers in smart cities, reducing energy consumption and improving network performance, as well as economic benefits, are essential research topics. In particular, Data Center Networks do not always run at full capacity, which leads to significant energy consumption. This paper experiments with a range of optimization tools to find the optimal solutions for the Integer Linear Programming (ILP) model of network power consumption. The study reports on experiments under three communication patterns (near, long, and random), measuring runtime and memory consumption in order to evaluate the performance of different ILP solvers.While the results show that, for near traffic pattern, most of the tools rapidly converge to the optimal solution, CP-SAT provides the most stable performance and outperforms the other solvers for the long traffic pattern. On the other hand, for random traffic pattern, Gurobi can be considered to be the best choice, since it is able to solve all the benchmark instances under the time limit and finds solutions faster by 1 or 2 orders of magnitude than the other solvers do.
Weighted distance classification method based on data intelligence

Abstract In today's world, data is essential for enhancing an organization's development and decision‐making processes. Implementing artificial intelligence is necessary to analyse data and make meaningful recommendations. Machine learning distance classification methods are used to classify observations in various algorithms, such as K ‐nearest neighbours (KNN), learning vector quantization and support vector machines, and are commonly used in academia and industry. However, this procedure faces a significant challenge in finding optimal parameters (i.e., distance metrics and the desired number of neighbours) in multidimensional datasets. This study presents a novel variation of a general method for classifying new observations. The method defines a new measure called closeness , which represents the proximity between an observation and the distribution. The advantages of this method are the use of both parametric and non‐parametric distance metrics and the ability to classify observations in cases where the simple method does not provide a clear answer. This method was demonstrated using KNN over three datasets and was observed to succeed in providing correct classifications, while the simple KNN method did not. The results showed that the proposed method increased the accuracy score to 40.7% in two of the three cases and that the closeness values were well defined by the proximity between the new observation and the given distribution. In addition, the F1 score increased up to 47.97%. The innovative method introduced here may be examined and used in various distance classification algorithms.
Evaluating Container Debloaters

DOCKER containers have been widely used by organizations because they are lightweight and single hardware can run multiple instances of a container. However, this ease of virtualization comes with weaker isolation as compared to virtual machines. A compromised container can allow the attacker to escape to the host and gain privileged access. Several approaches have been developed to reduce the attack surface of containers either through the reduction of system calls or through slimming container images. Unfortunately, measuring the performance of container debloaters is challenging as there exists no platform for this purpose. This paper aims to address this gap, by building a unified platform to benchmark them.Currently, our benchmark includes 7 workload applications, and 3 container debloaters, i.e., SPEAKER, CONFINE (syscalls reduction tools), and SLIMTOOLKIT (image size reduction tool). We added several evaluation metrics in the framework, which include category-based system call reduction, CVEs mitigated, size reduction, and execution correctness.Our evaluation revealed interesting insights into the existing techniques. Both the system call reduction tools were able to produce correct debloated containers as compared to SLIMTOOLKIT (tool to reduce image size) which worked well too by reducing almost 79 percent of the size of the image but it failed to produce correct results on 2 out of 7 applications.
Federated Ensemble YOLOv5 – A Better Generalized Object Detection Algorithm

Federated learning (FL) has gained significant traction as a privacy-preserving algorithm, but the underlying resemblances of federated learning algorithms like Federated averaging (FedAvg) or Federated SGD (Fed SGD) to ensemble learning algorithms has not been fully explored. The purpose of this paper is to examine the application of FL to object detection as a method to enhance generalizability, and to compare its performance against a centralized training approach for an object detection algorithm. Specifically, we investigate the performance of a YOLOv5 model trained using FL across multiple clients and employ a random sampling strategy without replacement, so each client holds a portion of the same dataset used for centralized training. Our experimental results showcase the superior efficiency of the FL object detector's global model in generating accurate bounding boxes for unseen objects, with the test set being a mixture of objects from two distinct clients not represented in the training dataset. These findings suggest that FL can be viewed from an ensemble algorithm perspective, akin to a synergistic blend of Bagging and Boosting techniques. As a result, FL can be seen not only as a method to enhance privacy, but also as a method to enhance the performance of a machine learning model.
Prism: Revealing Hidden Functional Clusters from Massive Instances in Cloud Systems

Ensuring the reliability of cloud systems is critical for both cloud vendors and customers. Cloud systems often rely on virtualization techniques to create instances of hardware resources, such as virtual machines. However, virtualization hinders the observability of cloud systems, making it challenging to diagnose platform-level issues. To improve system observability, we propose to infer functional clusters of instances, i.e., groups of instances having similar functionalities. We first conduct a pilot study on a large-scale cloud system, i.e., Huawei Cloud, demonstrating that instances having similar functionalities share similar communication and resource usage patterns. Motivated by these findings, we formulate the identification of functional clusters as a clustering problem and propose a non-intrusive solution called Prism. Prism adopts a coarse-to-fine clustering strategy. It first partitions instances into coarse-grained chunks based on communication patterns. Within each chunk, Prism further groups instances with similar resource usage patterns to produce fine-grained functional clusters. Such a design reduces noises in the data and allows Prism to process massive instances efficiently. We evaluate Prism on two datasets collected from the real-world production environment of Huawei Cloud. Our experiments show that Prism achieves a v-measure of ∼0.95, surpassing existing state-of-the-art solutions. Additionally, we illustrate the integration of Prism within monitoring systems for enhanced cloud reliability through two real-world use cases.