Reviewerly/exHarmony huggingface dataset:
Nonunitary entanglement dynamics in continuous-variable systems

We construct a random unitary Gaussian circuit for continuous-variable (CV) systems subject to Gaussian measurements. We show that when the measurement rate is nonzero, the steady state entanglement entropy saturates to an area-law scaling. This is different from a many-body qubit system, where a generic entanglement transition is widely expected. Due to the unbounded local Hilbert space, the time scale to destroy entanglement is always much shorter than the one to build it, while a balance could be achieved for a finite local Hilbert space. By the same reasoning, the absence of transition should also hold for other non-unitary Gaussian CV dynamics.
Adversarial Human Activity Recognition Using Wi-Fi CSI

Human activity recognition has been used for various applications in Internet of Things (e.g., health monitoring, security, and sport-related monitoring). Wi-Fi channel state information (CSI) is widely used for activity recognition, where CSI can capture human activities that influence wireless channel. In this paper, we study the impact of adversarial attacks on deep neural network (DNN) based human activity recognition with Wi-Fi CSI. First, we discuss the system framework, where activity recognition can be considered as a classification problem and a specific DNN model is introduced. Then, we discuss adversarial attack problem for DNN-based human activity recognition and formulate three white-box attacks. In the experiment with a public Wi-Fi CSI dataset, our results show that the performances of DNN-based human activity classification are greatly influenced by three white-box adversarial attacks.
Hybrid Metaheuristics Web Service Composition Model for QoS Aware Services

Recent advancements in cloud computing (CC) technologies signified that several distinct web services are presently developed and exist at the cloud data centre. Currently, web service composition gains maximum attention among researchers due to its significance in real-time applications. Quality of Service (QoS) aware service composition concerned regarding the election of candidate services with the maximization of the whole QoS. But these models have failed to handle the uncertainties of QoS. The resulting QoS of composite service identified by the clients become unstable and subject to risks of failing composition by end-users. On the other hand, trip planning is an essential technique in supporting digital map services. It aims to determine a set of location based services (LBS) which cover all client intended activities quantified in the query. But the available web service composition solutions do not consider the complicated spatio-temporal features. For resolving this issue, this study develops a new hybridization of the firefly optimization algorithm with fuzzy logic based web service composition model (F3L-WSCM) in a cloud environment for location awareness. The presented F3L-WSCM model involves a discovery module which enables the client to provide a query related to trip planning such as flight booking, hotels, car rentals, etc. At the next stage, the firefly algorithm is applied to generate composition plans to minimize the number of composition plans. Followed by, the fuzzy subtractive clustering (FSC) will select the best composition plan from the available composite plans. Besides, the presented F3L-WSCM model involves four input QoS parameters namely service cost, service availability, service response time, and user rating. An extensive experimental analysis takes place on CloudSim tool and exhibit the superior performance of the presented F3L-WSCM model in terms of accuracy, execution time, and efficiency.
Reinforcement Learning to Optimize Lifetime Value in Cold-Start Recommendation

Recommender system plays a crucial role in modern E-commerce platform. Due to the lack of historical interactions between users and items, cold-start recommendation is a challenging problem. In order to alleviate the cold-start issue, most existing methods introduce content and contextual information as the auxiliary information. Nevertheless, these methods assume the recommended items behave steadily over time, while in a typical E-commerce scenario, items generally have very different performances throughout their life period. In such a situation, it would be beneficial to consider the long-term return from the item perspective, which is usually ignored in conventional methods. Reinforcement learning (RL) naturally fits such a long-term optimization problem, in which the recommender could identify high potential items, proactively allocate more user impressions to boost their growth, therefore improve the multi-period cumulative gains. Inspired by this idea, we model the process as a Partially Observable and Controllable Markov Decision Process (POC-MDP), and propose an actor-critic RL framework (RL-LTV) to incorporate the item lifetime values (LTV) into the recommendation. In RL-LTV, the critic studies historical trajectories of items and predict the future LTV of fresh item, while the actor suggests a score-based policy which maximizes the future LTV expectation. Scores suggested by the actor are then combined with classical ranking scores in a dual-rank framework, therefore the recommendation is balanced with the LTV consideration. Our method outperforms the strong live baseline with a relative improvement of 8.67% and 18.03% on IPV and GMV of cold-start items, on one of the largest E-commerce platform.
Efficient Online Tracking-by-Detection With Kalman Filter

Fast and reliable visual tracking of multiple objects in videos has a promisingly broad area of application in manufacturing, construction, traffic, logistics, etc., especially so in large-scale applications where it is not feasible to attach markers to many objects for traditional marker-enabled tracking methods. This paper presents a new approach, Kalman-intersection-over-union (KIOU) tracker, for multi-object tracking in videos that integrates a Kalman filter with IOU-based track association methods. The performance of the proposed KIOU tracker is quantitatively evaluated with UA-DETRAC, an open real-world multi-object detection and tracking benchmark. Experimental results show that the KIOU tracker outperforms the leading tracking methods. Additionally, the KIOU tracker has speed comparable to simple area overlap-based track association and quality comparable to methods with much higher computational costs, demonstrating its potential for online, real-time multi-object tracking.
Diversity-augmented intrinsic motivation for deep reinforcement learning

In many real-world problems, reward signals received by agents are delayed or sparse, which makes it challenging to train a reinforcement learning (RL) agent. An intrinsic reward signal can help an agent to explore such environments in the quest for novel states. In this work, we propose a general end-to-end diversity-augmented intrinsic motivation for deep reinforcement learning which encourages the agent to explore new states and automatically provides denser rewards. Specifically, we measure the diversity of adjacent states under a model of state sequences based on determinantal point process (DPP); this is coupled with a straight-through gradient estimator to enable end-to-end differentiability. The proposed approach is comprehensively evaluated on the MuJoCo and the Arcade Learning Environments (Atari and SuperMarioBros). The experiments show that an intrinsic reward based on the diversity measure derived from the DPP model accelerates the early stages of training in Atari games and SuperMarioBros. In MuJoCo, the approach improves on prior techniques for tasks using the standard reward setting, and achieves the state-of-the-art performance on 12 out of 15 tasks containing delayed rewards.
Learning compatibility knowledge for outfit recommendation with complementary clothing matching

With the rapid development of mobile networks and e-commerce, clothing recommendation has achieved considerable success in recent years. Fashion outfit matching has become an essential component to users while shopping, which helps users to select and present items to individuals in a personalized fashion recommendation. Apparently, it is an arduous task to guide complementary clothing matching due to the complexity and subjectivity of fashion items. Some existing solutions have been presented in recent years, which are tending to discover a series of visual cues to establish the matching relations. However, it would be mismatched easily due to these methods being hard to represent all the potential semantic information from the appearance of clothes. To thoroughly make use of the visual characteristics of clothing products and the related description information, we propose a complementary clothing matching method with some compatibility knowledge, named it CCMCK shortly. For visual compatibility, we adopt the graph neural network to model the visual relationship between items. To generate an outfit that satisfies the requirement of fashion compatibility, we propose a matching way under the compatibility constraint and seek to recommend compatible items based on multi-modal compatibility. Finally, we performed a qualitative investigation on the fill-in-the-blank and fashion outfit compatibility tasks to evaluate the proposed method.
Variational Cross-Network Embedding for Anonymized User Identity Linkage

User identity linkage (UIL) task aims to infer the identical users between different social networks/platforms. Existing models leverage the labeled inter-linkages or high-quality user attributes to make predictions. Nevertheless, it is often difficult or even impossible to obtain such information in real-world applications. To this end, we in this paper focus on studying an Anonymized User Identity Linkage (AUIL) problem wherein neither labeled anchor users nor attributes are available. To handle such a practical and challenging task, we propose a novel and concise unsupervised embedding method, VCNE, by utilizing the network structural information. Concretely, considering the inherent properties of structural diversity in the AUIL problem, we introduce a variational cross-network embedding learning framework to jointly study the Gaussian embeddings instead of the existing deterministic embedding from the angle of vector space. The multi-facet experiments on both real-world and synthetic datasets demonstrate that VCNE not only outperforms all baselines to a large extent but also be more robust to the different-level diversities and sparsities of the networks.
A Culling Procedure for Collision Avoidance Model Predictive Control with Application to Ship Autopilot Models

An optimization-based approach for collision avoidance of fully-dimensional autonomous vehicles in confined environments is considered. This is realized by means of a dual reformulation of the signed distance function which yields an exact representation of the usually nonlinear, non-convex, and non-differentiable signed distance function. However, the dual collision avoidance MPC induces additional decision variables which greatly increase problem complexity. Therefore, a culling procedure is combined with the dual approach in order to reduce the problem size by identifying and eliminating decision variables associated with faces of the polyhedral obstacles and controlled vehicle, respectively. The proposed method is illustrated using a simulative study where a path-following task is performed for a ship autopilot model and near-field obstacle information is extracted from a set of AIS data from the Kiel bay area.
BooLigero: Improved Sublinear Zero Knowledge Proofs for Boolean Circuits

We provide a modified version of the Ligero sublinear zero knowledge proof system for arithmetic circuits provided by Ames et al. (CCS '17). Our modification "BooLigero" tailors Ligero for use in Boolean circuits to achieve a significant improvement in proof size. Although the original Ligero system could be used for Boolean circuits, Ligero generally requires allocating an entire field element to represent a single bit on a wire in a Boolean circuit. In contrast, our system performs operations over words of bits, allowing a proof size savings of between $$O((\log \vert \mathbb {F} \vert )^{1/4})$$ and $$O((\log \vert \mathbb {F} \vert )^{1/2})$$ compared to Ligero, where $$\mathbb {F} $$ is the field that leads to the optimal proof size in original Ligero. We achieve improvements in proof size of approximately 1.1–1.6x for SHA-2 and 1.7–2.8x for SHA-3. In addition to checking constraints of standard Boolean operations such as AND, XOR, and NOT over words, BooLigero also supports several other constraints such as multiplication in $$\text {GF} (2^w) $$ , bit masking, testing for zero bits, bit rearrangement within and across words, and bitwise outer product. Most of these techniques batch very efficiently, with only a constant overhead regardless of how many constraints of the same type are tested. Like Ligero, our construction requires no trusted setup and no computational assumptions, which is ideal for blockchain applications. It is plausibly post-quantum secure in the standard model. Furthermore, it is public-coin, perfect honest-verifier zero knowledge, and can be made non-interactive in the random oracle model using the Fiat-Shamir transform.
Where to Encode: A Performance Analysis of x86 and Arm-based Amazon EC2 Instances

Video streaming became an undivided part of the Internet. To efficiently utilise the limited network bandwidth it is essential to encode the video content. However, encoding is a computationally intensive task, involving high-performance resources provided by private infrastructures or public clouds. Public clouds, such as Amazon EC2, provide a large portfolio of services and instances optimized for specific purposes and budgets. The majority of Amazon’s instances use x86 processors, such as Intel Xeon or AMD EPYC. However, following the recent trends in computer architecture, Amazon introduced Arm-based instances that promise up to 40% better cost performance ratio than comparable x86 instances for specific workloads. We evaluate in this paper the video encoding performance of x86 and Arm instances of four instance families using the latest FFmpeg version and two video codecs. We examine the impact of the encoding parameters, such as different presets and bitrates, on the time and cost for encoding. Our experiments reveal that Arm instances show high time and cost saving potential of up to 33.63% for specific bitrates and presets, especially for the x264 codec. However, the x86 instances are more general and achieve low encoding times, regardless of the codec.
Unsupervised Large-Scale Social Network Alignment via Cross Network Embedding

Nowadays, it is common for a person to possess different identities on multiple social platforms. Social network alignment aims to match the identities that from different networks. Recently, unsupervised network alignment methods have received significant attention since no identity anchor is required. However, to capture the relevance between identities, the existing unsupervised methods generally rely heavily on user profiles, which is unobtainable and unreliable in real-world scenarios. In this paper, we propose an unsupervised alignment framework named Large-Scale Network Alignment (LSNA) to integrate the network information and reduce the requirement on user profile. The embedding module of LSNA, named Cross Network Embedding Model (CNEM), aims to integrate the topology information and the network correlation to simultaneously guide the embedding process. Moreover, in order to adapt LSNA to large-scale networks, we propose a network disassembling strategy to divide the costly large-scale network alignment problem into multiple executable sub-problems. The proposed method is evaluated over multiple real-world social network datasets, and the results demonstrate that the proposed method outperforms the state-of-the-art methods.
Schools in an evolving digital environment: Digital renewal and its maturity

Evolving digital technologies are infiltrating schools wave after wave. The changes taking place are viewed as the schools’ digital renewal process (SDRP). The SDRP is complex (multidimensional). It includes changes in the educational environment (physical and virtual), the educational process, and the way the school operates. The SDRP goes uneven, with individual schools at different stages. One-time observation of the SDRP allows you to fix its current state (statics). The longitudinal observations allows you to see changes in the schools’ digital renewal (kinematics). The connection of the observed changes with the impact on the general education system makes it possible to discuss the development of digital renewal under the influence of individual control actions (dynamics). The stages of penetration of digital technologies into the work of the school: computerization, early and mature informatization, digital transformation (transition to the “Smart School”) can be considered as the stages of maturity of the SDRP. The article discusses a framework for describing the processes of digital renewal of schools in an evolving digital environment and an assessment of the SDRP’s maturity.
TutorialLens: Authoring Interactive Augmented Reality Tutorials Through Narration and Demonstration

Exploring unfamiliar devices and interfaces through trial and error can be challenging and frustrating. Existing video tutorials require frequent context switching between the device showing the tutorial and the device being used. While augmented reality (AR) has been adopted to create user manuals, many are inflexible for diverse tasks, and usually require programming and AR development experience. We present TutorialLens, a system for authoring interactive AR tutorials through narration and demonstration. To use TutorialLens, authors demonstrate tasks step-by-step while verbally explaining what they are doing. TutorialLens automatically detects and records 3D finger positions and guides authors to capture important changes of the device. Using the created tutorials, TutorialLens then provides AR visual guidance and feedback for novice device users to complete the demonstrated tasks. TutorialLens is automated, friendly to users without AR development experience, and applicable to a variety of devices and tasks.
Exploring Residual Networks for Breast Cancer Detection from Ultrasound Images

Breast cancer is one of the most leading causes of cancer globally, affecting mostly women at any age after their puberty. Although the disease is fatal and leads to thousands of deaths yearly, it is mostly curable if detected in its early stages. As a result, fast and accurate detection techniques play a crucial role in patient survival. Earlier, doctors resorted to manual detection techniques for this purpose. However, these techniques were slow and often subjective to the expertise of the doctor. Thus, with the advancement of technology, these primitive methods were replaced by computer-aided detection algorithms. One such algorithm is the convolutional neural network (CNN). It is a deep learning algorithm that has demonstrated promising results in medical image analysis in the past few years. It possesses special neural receptors called filters, which can automatically extract features from an image without human intervention. This fascinating property has urged several researchers to propose different CNN architectures in the past few years, each having its unique methodology. In this work, the residual network (ResNet) architecture is studied for detecting breast cancer from ultrasound images. ResNets have special skip connections which make them superior to other architectures. Depending on the depth, a ResNet can be categorized into different types - ResNet-18, ResNet-34, ResNet-50, ResNet-101, and ResNet-152 being some widely used ones. In this research, each of these five ResNets is trained on the Breast Ultrasound Images (BUSI) dataset twice - first from scratch and second using transfer learning. Thereafter, their performances are evaluated and compared using four metrics - F1-score, precision, recall, and accuracy. The confusion matrices are also plotted for a better understanding of the results. Lastly, the class activation heatmaps are generated using GradCAM++ to visualize the crucial regions of the ultrasound images as interpreted by the ResNet architectures. The code used to conduct this research can be accessed at https://github.com/iamarijit/US-cancer-resnet.
Universal steganalysis of spatial content-independent and content-adaptive steganographic algorithms using normalized feature derived from empirical mode decomposed components

Steganography is a practice of covert communication by hiding secret or payload information inside a cover source medium. Fraudulent entities may make use of it to communicate secretly and pose a great threat to law enforcement agencies. Universal steganalysis is a counter attack designed to identify steganography regardless of the steganographic process involved. As minute payloads that ride over covers, evade detection, steganalysts need to resort to high dimensional feature sets that extract residues in every possible way and a powerful classifier with higher computational power. In this paper, a novel universal steganalyzer driven by Empirical Mode Decomposition technique which is an adaptive and highly efficient method to analyze non-linear and non-stationary signals is proposed. Using this technique, the suspicious digital images are decomposed into intrinsic mode functions and residues to gradually remove the image contents to reveal the feeble artifacts caused by steganographic algorithms. Frobenius norm computed from these components further highlight the distortions. A low dimensional feature with a simple thresholding function assisting the steganalytic community in a blind and generic way can definitely be viewed as a boon to the domain. The proposed method is tested upon spatial content-adaptive steganographic algorithms (HUGO, WOW, S-UNIWARD) and content-independent least significant bit steganographic algorithms (LSBR, LSBM, LSBMR, LSBR2). The experimental results show that the proposed method is able to achieve improved accuracies when compared with Spatial Rich Model, Wu Net, Ye Net and Hybrid CNN. The execution time for the proposed method is also far less compared to state-of-the-art steganalyzers.
A continuum of hate: delimiting the field of hate studies

Abstract A relatively nascent discipline, the field of hate studies has been explored and theorised from a multiplicity of disciplinary contexts. However, the field is ill-defined, and the relationship between hate crime and other related concepts unexplored. Here, we consider the range of phenomena which might fall within or without the field of hate studies, initiating a discussion of the boundaries of the field. We signal both the continuities and discontinuities among and between an array of strategies intended to sort and maintain hierarchical relationships, with the purpose of provoking scholars in the field of hate studies to reflect on its scope.