Reviewerly/exHarmony huggingface dataset:
Decoherence and objectivity in higher spin environments

We analyze decoherence and objectivization processes in spin-spin models for arbitrary spins. We first derive the most general analytic form of the decoherence factor in the measurement limit, where the interaction Hamiltonian dominates the rest. We then analyze thermal environments and derive exact, analytic formulas for both the decoherence factor and the state fidelity of post-interaction environment states. This allows to analyze the objectivization process of the state of the central spin during the interaction. We do so using, so called, Spectrum Broadcast Structures (SBS), which are specific multipartite quantum states encoding a certain operation notion of objectivity. We analyze analytically (for short times) and numerically how higher spin influences the efficiency of decoherence and objectivization processes. As expected, the higher the spin of the environment, the more efficient decoherence and objectivization become. This work is a generalization of previous studies, limited to spin-$1/2$ systems only, and we hope will be useful in future objectivity experiments.
Practical Long‐Distance Measurement‐Device‐Independent Quantum Key Distribution By Four‐Intensity Protocol

Abstract Quantum key distribution (QKD) can provide unconditionally secure private communication between two remote parties. The measurement‐device‐independent (MDI) QKD can remove all security loopholes in measurement devices. However, the large value of the observed error rate in X basis has severely limited its key rate. The four‐intensity protocol with fully optimized post data processing can improve the key rate and distance of MDI‐QKD effectively. In this paper, the four‐intensity MDI‐QKD protocol is reviewed. In the four‐intensity protocol, each of Alice and Bob uses one intensity in Z basis and three intensities in X basis (normally including one vacuum). This improves the number of effective bits in Z basis. The joint mathematical treatment for statistical fluctuation and the parameter scan pointing directly to the final key rate are applied to reduce the effect of statistical fluctuation. The double scanning method further improves the key rate and the secure distance of the four‐intensity protocol. These have greatly improved the performance of the protocol with finite‐key effects. So far, many MDI‐QKD experiments are based on the four‐intensity protocol. The successful results of these experiments have clearly shown the superiority of the four‐intensity protocol in practical long‐distance MDI‐QKD.
Sustainability of AI: The Case of Provision of Information to Consumers

The potential of artificial intelligence (AI) and its manifold applications have fueled the discussion around how AI can be used to facilitate sustainable objectives. However, the technical, ethical, and legal literature on how AI, including its design, training, implementation, and use can be sustainable, is rather limited. At the same time, consumers incrementally pay more attention to sustainability information, whereas businesses are increasingly engaging in greenwashing practices, especially in relation to digital products and services, raising concerns about the efficiency of the existing consumer protection framework in this regard. The objective of this paper is to contribute to the discussion toward sustainable AI from a legal and consumer protection standpoint while focusing on the environmental and societal pillar of sustainability. After analyzing the multidisciplinary literature available on the topic of the environmentally sustainable AI lifecycle, as well as the latest EU policies and initiatives regarding consumer protection and sustainability, we will examine whether the current consumer protection framework is sufficient to promote sharing and substantiation of sustainability information in B2C contracts involving AI products and services. Moreover, we will assess whether AI-related AI initiatives can promote a sustainable AI development. Finally, we will propose a set of recommendations capable of encouraging a sustainable and environmentally-conscious AI lifecycle while enhancing information transparency among stakeholders, aligning the various EU policies and initiatives, and ultimately empowering consumers.
How Neural Architectures Affect Deep Learning for Communication Networks?

In recent years, there has been a surge in applying deep learning to various challenging design problems in communication networks. The early attempts adopt neural architectures inherited from applications such as computer vision, which suffer from poor generalization, scalability, and lack of interpretability. To tackle these issues, domain knowledge has been integrated into the neural architecture design, which achieves near-optimal performance in large-scale networks and generalizes well under different system settings. This paper endeavors to theoretically validate the importance and effects of neural architectures when applying deep learning to communication network design. We prove that by exploiting permutation invariance, a common property in communication networks, graph neural networks (GNNs) converge faster and generalize better than fully connected multi-layer perceptrons (MLPs), especially when the number of nodes (e.g., users, base stations, or antennas) is large. Specifically, we prove that under common assumptions, for a communication network with n nodes, GNNs converge O(n log n) times faster and their generalization error is O(n) times lower, compared with MLPs.
Detecting and Analysing Fake Opinions Using Artificial Intelligence Algorithms

In e-commerce and on social media, identifying fake opinions has become a tremendous challenge. Such opinions are widely generated on the internet by fake viewers, also called fraudsters. They write deceptive reviews that purport to reflect actual user experience either to promote some products or to defame others. They also target the reputations of e-businesses. Their aim is to mislead customers to make a wrong purchase decision by selecting undesired products. Such reviewers are often paid by rival e-business companies to compose positive reviews of their products and/or negative reviews of other companies' products. The main objective of this paper is to detect, analyze and calculate the difference between fake and truthful product reviews. To do this, the methodology has planned to have seven phases: reviewing online products, analyzing features through linguistic enquiry and word count (LIWC), preprocessing the data to clean and normalize them, embedding words (Word2Vec) and analyzing performance using artificial deep-learning algorithms for classifying fake and truthful reviews. Two deep-learning neural network models have been evaluated based on standard Yelp product reviews. These models are bidirectional long-short term memory (BiLSTM) and convolutional neural network (CNN). The results from comparing the performance of the two models showed that the BiLSTM model provided higher accuracy for detecting fake reviews than the CNN model.
Framework of data privacy preservation and location obfuscation in vehicular cloud networks

Summary The leakage of sensitive data of users and global positioning system location trajectories from different cloud computation sources have increased in daily lives. This critical problem can influence users' lives because attackers try to misuse the leaked information. In this study, we addressed the critical problem pertaining to the privacy of personal identification information (PII) and location information over vehicular cloud networks (VCNs). The problem was addressed by proposing a data and location privacy (DLP) framework that preserves data privacy in terms of the anonymity of PII and provides location privacy for users' trajectories through the obfuscation technique. The proposed DLP framework contains five stages: registration with VCN server, processing of PII/data privacy block, segregation of obfuscation data, authentication, and provision of cryptographic security. The result analysis shows that the proposed mechanism is able to generate obfuscated trajectories and successfully recomputes the original trajectories at the server side.
Quantization Step Estimation for JPEG Image Forensics

In image forensics, quantization step estimation plays a crucial role in revealing the JPEG compression history. This study focuses on the estimation for images that have been JPEG compressed and re-saved in lossless formats. Several effective methods have been developed, whereas the performance on small sized images still needs to be improved. In this paper, a novel JPEG quantization step estimation method with low complexity and high efficiency is proposed. First, for a given decompressed image, as the special shape of its DCT coefficients distribution is highly related to the quantization step, a function of the candidate step taking a similar shape is designed. Then, the quantization step is determined as the candidate leading to the maximum response of the designed function on the probability density function of DCT coefficients. The relation between the maximum response and the quantization step is verified mathematically, which provides a theoretical basis for the proposed method. In addition, to further improve the estimation performance, two fine adjustment procedures are adopted. Experimental results demonstrate that the proposed method outperforms some state-of-the-art works, especially on small sized images.
Appearance and Motion Based Persistent Multiple Object Tracking in Wide Area Motion Imagery

Wide Area Motion Imagery (WAMI) data acquired by an airborne sensor for ground observation offers great potential for various applications ranging from the protection of borders and critical infrastructure to city monitoring and surveillance. Persistent multiple object tracking, which is a prerequisite for these applications, is generally based on moving object detection, as the characteristics of existing WAMI datasets, e.g. weak appearance of objects, impede the usage of appearance based features. Complex and computationally expensive strategies such as exploiting multiple trackers in parallel or classifier-based local search are typically utilized to detect slow and stopping vehicles that are missed by moving object detection. In this paper, we propose a novel and much simpler tracking-by-detection approach for persistent tracking in WAMI data, which avoids such strategies. To overcome limitations caused by image quality of existing WAMI datasets, our proposed tracker was developed on self-acquired WAMI data recorded with a state-of-the-art industrial camera. The improved image quality enables appearance based object detection by Convolutional Neural Networks (CNNs) in WAMI, which we fuse with motion detection to compensate for missed detections in image regions with partial occlusion or shadows. Our proposed tracker is an extension of Deep SORT with modified track management and data association, which is able to yield high recall even in such difficult image regions as well as for slow or stopping vehicles, outperforming state-of-the-art on our self-acquired dataset.
Updating incomplete framework of target recognition database based on fuzzy gap statistic

Generalized evidence theory (GET) is a generalization of Dempster–Shafer evidence theory. It copes with information in an open world, which makes up for the shortcoming that Dempster–Shafer evidence theory cannot handle information conflict effectively. However, GET also faces an unavoidable problem: how to determine the number of unknown targets in the incomplete frame of discernment (FOD). Fuzzy C-means (FCM) is a clustering algorithm that divides the original data set into different clusters and summarizes similar data into the same cluster. Therefore, determining the number of unknown targets in the open world can be transformed into finding the number of clusters. However, FCM has the disadvantage of subjectively controlling the number of clusters. In order to overcome this shortcoming, we use fuzzy gap statistic algorithm (FGS) to optimize it. FGS can effectively determine the optimal number of clusters in FCM. Therefore, this paper proposes a new method based on FGS to determine the number of unknown targets in the open world. In addition, to verify the method’s accuracy, we conducted seven experiments based on the University of California Irvine (UCI) data sets, including Iris, glass, Haberman, Knowledge, Robot, seeds, and WDBC. Finally, the experimental results illustrate that the proposed method to determine the number of unknown targets in the incomplete FOD has high effectiveness.
Evaluating Human-AI Hybrid Conversational Systems with Chatbot Message Suggestions

AI chatbots can offer suggestions to help humans answer questions by reducing text entry effort and providing relevant knowledge for unfamiliar questions. We study whether chatbot suggestions can help people answer knowledge-demanding questions in a conversation and influence response quality and efficiency. We conducted a large-scale crowdsourcing user study and evaluated 20 hybrid system variants and a human-only baseline. The hybrid systems used four chatbots of varied response quality and differed in the number of suggestions and whether to preset the message box with top suggestions.
MFNet: Multiclass Few-Shot Segmentation Network With Pixel-Wise Metric Learning

In visual recognition tasks, few-shot learning requires the ability to learn object categories with few support examples. Its re-popularity in light of the deep learning development is mainly in image classification. This work focuses on few-shot semantic segmentation, which is still a largely unexplored field. A few recent advances are often restricted to single-class few-shot segmentation. In this paper, we first present a novel multi-way (class) encoding and decoding architecture which effectively fuses multi-scale query information and multi-class support information into one query-support embedding. Multi-class segmentation is directly decoded upon this embedding. For better feature fusion, a multi-level attention mechanism is proposed within the architecture, which includes the attention for support feature modulation and attention for multi-scale combination. Last, to enhance the embedding space learning, an additional pixel-wise metric learning module is introduced with triplet loss formulated on the pixel-level of the query-support embedding. Extensive experiments on standard benchmarks PASCAL- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5^{i}$ </tex-math></inline-formula> and COCO- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$20^{i}$ </tex-math></inline-formula> show clear benefits of our method over the state of the art in multi-class few-shot segmentation.
Machine Learning Approaches to Identifying Changes in Eruptive State Using Multi‐Parameter Datasets From the 2006 Eruption of Augustine Volcano, Alaska

Abstract Understanding the timing of critical changes in volcanic systems, such as the beginning and end of eruptive behavior, is a key goal of volcanic monitoring. Traditional approaches to forecasting these changes have used models motivated by the underlying physics of eruption onset, which assume that geophysical precursors will consistently display similar patterns prior to transition in volcanic state. We present a machine learning classification approach for detecting significant changes in patterns of volcanic activity, potentially signaling transitions during the onset or end of volcanic activity, which does not require a model of the physical processes underlying critical changes. We apply novelty detection, where models are trained only on data prior to eruption, to the precursory unrest at Augustine Volcano, Alaska in 2005. This approach looks promising for geophysically monitored volcanic systems which have been in repose for some time, as no eruptive data is required for model training. We compare novelty detection results with multi‐class classification, where models are trained on examples of both non‐eruptive and eruptive data. We contextualize the results of these classification models using constraints from petrological, satellite and visual observations from the 2006 eruption of Augustine Volcano. The transition from non‐eruptive to eruptive behavior we identify in mid‐November 2005 is in agreement with previous estimates of the initiation of dike intrusion prior to the 2006 eruption. We find that models which include multiple types of data (seismic, deformation, and gas emissions) can better distinguish between non‐eruptive and eruptive data than models formulated on single data types.
Neural Additive Vector Autoregression Models for Causal Discovery in Time Series

Causal structure discovery in complex dynamical systems is an important challenge for many scientific domains. Although data from (interventional) experiments is usually limited, large amounts of observational time series data sets are usually available. Current methods that learn causal structure from time series often assume linear relationships. Hence, they may fail in realistic settings that contain nonlinear relations between the variables. We propose Neural Additive Vector Autoregression (NAVAR) models, a neural approach to causal structure learning that can discover nonlinear relationships. We train deep neural networks that extract the (additive) Granger causal influences from the time evolution in multi-variate time series. The method achieves state-of-the-art results on various benchmark data sets for causal discovery, while providing clear interpretations of the mapped causal relations.
MagX

Accurate tracking of the hands and fingers allows users to employ natural gestures in various interactive applications. Hand tracking also supports health applications, such as monitoring face-touching, a common vector for infectious disease. However, for both types of applications, the utility of hand tracking is often limited by the impracticality of bulky tethered systems (e.g., instrumented gloves) or inherent limitations (e.g., Line of Sight or privacy concerns with vision-based systems). These limitations have severely restricted the adoption of hand tracking in real-world applications. We present MagX, a fully untethered on-body hand tracking system utilizing passive magnets and a novel magnetic sensing platform. Since passive magnets require no maintenance, they can be worn on the hands indefinitely, and only the sensor board needs recharging, akin to a smartwatch.
Redesigning Data Centers for Renewable Energy

Renewable energy is becoming an important power source for data centers, especially with the zero-carbon waste pledges made by big cloud providers. However, one of the main challenges of renewable energy sources is the high variability of power produced. Traditional approaches such as batteries or transmitting to the grid fall short on scale, overhead, or "green-ness". We propose Virtual Battery: instead of adapting the availability of power to match the computation demand we shift computational demand to meet the availability of power. Virtual batteries shift demand by requiring applications to either be flexible and delay-tolerant or proactively migrating to where power is (going to be) available. We show that using multiple virtual battery sites in combination can meet the needs of modern applications. Moreover, we show how an intelligent network and power aware co-scheduler can not only provide availability despite variability but also help mitigate migration related network overhead by over 30% in total and 4.2x at peak.
Set-Membership Estimation in Shared Situational Awareness for Automated Vehicles in Occluded Scenarios

One of the main challenges in developing autonomous transport systems based on connected and automated vehicles is the comprehension and understanding of the environment around each vehicle. In many situations, the understanding is limited to the information gathered by the sensors mounted on the ego-vehicle, and it might be severely affected by occlusion caused by other vehicles or fixed obstacles along the road. Situational awareness is the ability to perceive and comprehend a traffic situation and to predict the intent of vehicles and road users in the surrounding of the ego-vehicle. The main objective of this paper is to propose a framework for how to automatically increase the situational awareness for an automatic bus in a realistic scenario when a pedestrian behind a parked truck might decide to walk across the road. Depending on the ego-vehicle's ability to fuse information from sensors in other vehicles or in the infrastructure, shared situational awareness is developed using a set-based estimation technique that provides robust guarantees for the location of the pedestrian. A two-level information fusion architecture is adopted, where sensor measurements are fused locally, and then the corresponding estimates are shared between vehicles and units in the infrastructure. Thanks to the provided safety guarantees, it is possible to adjust the ego-vehicle speed appropriately to maintain a proper safety margin. Three scenarios of growing information complexity are considered throughout the study. Simulations show how the increased situational awareness allows the ego-vehicle to maintain a reasonable speed without sacrificing safety.
Robust Adaptive-weighting Multi-view Classification

As data sources become ever more numerous, classification for multi-view data represented by heterogeneous features has been involved in many data mining applications. Most existing methods either directly concatenate all views or separately tackle each view, neglecting the correlation and diversity among views. Moreover, they often encounter an extra hyper-parameter that needs to be manually tuned, degenerating the applicability of models. In this paper, we present a robust supervised learning framework for multi-view classification, seeking a better representation and fusion of multiple views. Specifically, our framework discriminates different views with adaptively optimized view-wise weight factors and coalesces them to learn a joint projection subspace compatible across multiple views in an adaptive-weighting manner, thereby avoiding the intractable hyper-parameter. Meanwhile, the consensus and complementary information of original views can be naturally integrated into the learned subspace, in turn enhancing the discrimination of the subspace for subsequent classification. An efficient convergent algorithm is developed to iteratively optimize the formulated framework. Experiments on real datasets demonstrate the effectiveness and superiority of the proposed method.
Meta-learning for fake news detection surrounding the Syrian war

In this article, we pursue the automatic detection of fake news reporting on the Syrian war using machine learning and meta-learning. The proposed approach is based on a suite of features that include a given article's linguistic style; its level of subjectivity, sensationalism, and sectarianism; the strength of its attribution; and its consistency with other news articles from the same "media camp". To train our models, we use FA-KES, a fake news dataset about the Syrian war. A suite of basic machine learning models is explored, as well as the model-agnostic meta-learning algorithm (MAML) suitable for few-shot learning, using datasets of a modest size. Feature-importance analysis confirms that the collected features specific to the Syrian war are indeed very important predictors for the output label. The meta-learning model achieves the best performance, improving upon the baseline approaches that are trained exclusively on text features in FA-KES.