Reviewerly/exHarmony huggingface dataset:
GANSlider: How Users Control Generative Models for Images using Multiple Sliders with and without Feedforward Information

We investigate how multiple sliders with and without feedforward visualizations influence users’ control of generative models. In an online study (N=138), we collected a dataset of people interacting with a generative adversarial network (StyleGAN2) in an image reconstruction task. We found that more control dimensions (sliders) significantly increase task difficulty and user actions. Visual feedforward partly mitigates this by enabling more goal-directed interaction. However, we found no evidence of faster or more accurate task performance. This indicates a tradeoff between feedforward detail and implied cognitive costs, such as attention. Moreover, we found that visualizations alone are not always sufficient for users to understand individual control dimensions. Our study quantifies fundamental UI design factors and resulting interaction behavior in this context, revealing opportunities for improvement in the UI design for interactive applications of generative models. We close by discussing design directions and further aspects.
Research on the Application of Multimedia Entropy Method in Data Mining of Retail Business

In recent years, database technology has developed rapidly and is changing with each passing day. With the support of network technology, its application scale, scope, and depth are constantly expanding. With the explosive growth of data, we are also faced with such a challenge: First, as a basic information storage and management method, database technology can only perform simple data processing, such as query, statistics, reports, etc.; lack of decision-making; analysis; prediction; and other advanced functions. Secondly, in the face of these massive data, people pay more attention to how to dig out the important information hidden in these data, rather than the data itself. Therefore, data mining technology, which integrates statistics, artificial intelligence, pattern recognition, and optimization, emerges as the times require. Data mining technology is application-oriented from the very beginning, and its great success in various industries has fully demonstrated its strong vitality, especially in the retail industry. If the data mining technology can be perfectly combined with the retail industry, it can not only bring great convenience to customers but also inject new vitality into enterprises, making them invincible in the fierce competition. This paper proposes a filtering of high-quality customer system framework based on maximum entropy, which expresses customer data as a feature vector for feature selection and feature smoothing. The filtering performance of different feature sets is compared by combining different characteristics of customer data. Experiment and conduct multimedia presentations. Experiments show that the filtration performance of this system is better than the general filtration system.
Depth Optimization of CZ, CNOT, and Clifford Circuits

We seek to develop better upper bound guarantees on the depth of quantum CZ gate, CNOT gate, and Clifford circuits than those reported previously. We focus on the number of qubits $n\,{\leq}\,$1,345,000 [1], which represents the most practical use case. Our upper bound on the depth of CZ circuits is $\lfloor n/2 + 0.4993{\cdot}\log^2(n) + 3.0191{\cdot}\log(n) - 10.9139\rfloor$, improving best known depth by a factor of roughly 2. We extend the constructions used to prove this upper bound to obtain depth upper bound of $\lfloor n + 1.9496{\cdot}\log^2(n) + 3.5075{\cdot}\log(n) - 23.4269 \rfloor$ for CNOT gate circuits, offering an improvement by a factor of roughly $4/3$ over state of the art, and depth upper bound of $\lfloor 2n + 2.9487{\cdot}\log^2(n) + 8.4909{\cdot}\log(n) - 44.4798\rfloor$ for Clifford circuits, offering an improvement by a factor of roughly $5/3$.
Quality Control for the BPG Lossy Compression of Three-Channel Remote Sensing Images

This paper deals with providing the desired quality in the Better Portable Graphics (BPG)-based lossy compression of color and three-channel remote sensing (RS) images. Quality is described by the Mean Deviation Similarity Index (MDSI), which is proven to be one of the best metrics for characterizing compressed image quality due to its high conventional and rank-order correlation with the Mean Opinion Score (MOS) values. The MDSI properties are studied and three main areas of interest are determined. It is shown that quite different quality and compression ratios (CR) can be observed for the same values of the quality parameter Q that controls compression, depending on the compressed image complexity. To provide the desired quality, a modified two-step procedure is proposed and tested. It has a preliminary stage carried out offline (in advance). At this stage, an average rate-distortion curve (MDSI on Q) is obtained and it is available until the moment when a given image has to be compressed. Then, in the first step, an image is compressed using the starting Q determined from the average rate-distortion curve for the desired MDSI. After this, the image is decompressed and the produced MDSI is calculated. In the second step, if necessary, the parameter Q is corrected using the average rate-distortion curve, and the image is compressed with the corrected Q. Such a procedure allows a decrease in the MDSI variance by around one order after two steps compared to variance after the first step. This is important for the MDSI of approximately 0.2–0.25 corresponding to the distortion invisibility threshold. The BPG performance comparison to some other coders is performed and examples of its application to real-life RS images are presented.
Large-Scale Streaming End-to-End Speech Translation with Neural Transducers

Neural transducers have been widely used in automatic speech recognition (ASR). In this paper, we introduce it to streaming end-to-end speech translation (ST), which aims to convert audio signals to texts in other languages directly. Compared with cascaded ST that performs ASR followed by text-based machine translation (MT), the proposed Transformer transducer (TT)-based ST model drastically reduces inference latency, exploits speech information, and avoids error propagation from ASR to MT. To improve the modeling capacity, we propose attention pooling for the joint network in TT. In addition, we extend TT-based ST to multilingual ST, which generates texts of multiple languages at the same time. Experimental results on a large-scale 50 thousand (K) hours pseudo-labeled training set show that TT-based ST not only significantly reduces inference time but also outperforms non-streaming cascaded ST for English-German translation.
PIFNet: 3D Object Detection Using Joint Image and Point Cloud Features for Autonomous Driving

Owing to its wide range of applications, 3D object detection has attracted increasing attention in computer vision tasks. Most existing 3D object detection methods are based on Lidar point cloud data. However, these methods have some limitations in localization consistency and classification confidence, due to the irregularity and sparsity of Light Detection and Ranging (LiDAR) point cloud data. Inspired by the complementary characteristics of Lidar and camera sensors, we propose a new end-to-end learnable framework named Point-Image Fusion Network (PIFNet) to integrate the LiDAR point cloud and camera images. To resolve the problem of inconsistency in the localization and classification, we designed an Encoder-Decoder Fusion (EDF) module to extract the image features effectively, while maintaining the fine-grained localization information of objects. Furthermore, a new effective fusion module is proposed to integrate the color and texture features from images and the depth information from the point cloud. This module can enhance the irregularity and sparsity problem of the point cloud features by capitalizing the fine-grained information from camera images. In PIFNet, each intermediate feature map is fed into the fusion module to be integrated with its corresponding point-wise features. Furthermore, point-wise features are used instead of voxel-wise features to reduce information loss. Extensive experiments using the KITTI dataset demonstrate the superiority of PIFNet over other state-of-the-art methods. Compared with several state-of-the-art methods, our approach outperformed by 1.97% in mean Average Precision (mAP) and by 2.86% in Average Precision (AP) for the hard cases on the KITTI 3D object detection benchmark.
Re-Examining System-Level Correlations of Automatic Summarization Evaluation Metrics

How reliably an automatic summarization evaluation metric replicates human judgments of summary quality is quantified by systemlevel correlations.We identify two ways in which the definition of the system-level correlation is inconsistent with how metrics are used to evaluate systems in practice and propose changes to rectify this disconnect.First, we calculate the system score for an automatic metric using the full test set instead of the subset of summaries judged by humans, which is currently standard practice.We demonstrate how this small change leads to more precise estimates of system-level correlations.Second, we propose to calculate correlations only on pairs of systems that are separated by small differences in automatic scores which are commonly observed in practice.This allows us to demonstrate that our best estimate of the correlation of ROUGE to human judgments is near 0 in realistic scenarios.The results from the analyses point to the need to collect more high-quality human judgments and to improve automatic metrics when differences in system scores are small. 1
StepGame: A New Benchmark for Robust Multi-Hop Spatial Reasoning in Texts

Inferring spatial relations in natural language is a crucial ability an intelligent system should possess. The bAbI dataset tries to capture tasks relevant to this domain (task 17 and 19). However, these tasks have several limitations. Most importantly, they are limited to fixed expressions, they are limited in the number of reasoning steps required to solve them, and they fail to test the robustness of models to input that contains irrelevant or redundant information. In this paper, we present a new Question-Answering dataset called StepGame for robust multi-step spatial reasoning in texts. Our experiments demonstrate that state-of-the-art models on the bAbI dataset struggle on the StepGame dataset. Moreover, we propose a Tensor-Product based Memory-Augmented Neural Network (TP-MANN) specialized for spatial reasoning tasks. Experimental results on both datasets show that our model outperforms all the baselines with superior generalization and robustness performance.
Observable bound for Gaussian illumination

We propose observable bounds for Gaussian illumination to maximize the signal-to-noise ratio, which minimizes the discrimination error between the presence and absence of a low-reflectivity target using Gaussian states. The observable bounds are achieved with mode-by-mode measurements. In the quantum regime using a two-mode squeezed vacuum state, our observable receiver outperforms the other feasible receivers whereas it cannot approach the quantum Chernoff bound. The corresponding observable cannot be implemented with heterodyne detections due to the additional vacuum noise. In the classical regime using a thermal state, a receiver implemented with a photon number difference measurement approaches its bound regardless of the signal mean photon number, while it asymptotically approaches the classical bound in the limit of a huge idler mean photon number.
The Effect of Genetic Algorithm Parameters Tuning for Route Optimization in Travelling Salesman Problem through General Full Factorial Design Analysis

This study aims to analyze the effect of genetic algorithm parameters on the distribution mileage of the largest logistics service provider in Central Jakarta using a general full factorial design and ANOVA test.This study revealed that all factors and three types of interactions were statistically significant in influencing the distribution mileage.Moreover, the combination of population size, crossover probability, mutation probability, and the number of iterations = (90, 1.00, 0.010, 800) generated the lowest mean value and was significantly different from other combinations.This study provides a different approach to analyzing those factors and interactions that influence finding the shortest route.
Reducing large adaptation spaces in self-adaptive systems using classical machine learning

Modern software systems often have to cope with uncertain operation conditions, such as changing workloads or fluctuating interference in a wireless network. To ensure that these systems meet their goals these uncertainties have to be mitigated. One approach to realize this is self-adaptation that equips a system with a feedback loop. The feedback loop implements four core functions – monitor, analyze, plan, and execute – that share knowledge in the form of runtime models. For systems with a large number of adaptation options, i.e., large adaptation spaces, deciding which option to select for adaptation may be time consuming or even infeasible within the available time window to make an adaptation decision. This is particularly the case when rigorous analysis techniques are used to select adaptation options, such as formal verification at runtime, which is widely adopted. One technique to deal with the analysis of a large number of adaptation options is reducing the adaptation space using machine learning. State of the art has showed the effectiveness of this technique, yet, a systematic solution that is able to handle different types of goals is lacking. In this paper, we present ML2ASR+, short for Machine Learning to Adaptation Space Reduction Plus. Central to ML2ASR+ is a configurable machine learning pipeline that supports effective analysis of large adaptation spaces for threshold, optimization, and setpoint goals. We evaluate ML2ASR+ for two applications with different sizes of adaptation spaces: an Internet-of-Things application and a service-based system. The results demonstrate that ML2ASR+ can be applied to deal with different types of goals and is able to reduce the adaptation space and hence the time to make adaptation decisions with over 90%, with negligible effect on the realization of the adaptation goals.
Meta-Weight Graph Neural Network: Push the Limits Beyond Global Homophily

Graph Neural Networks (GNNs) show strong expressive power on graph data mining, by aggregating information from neighbors and using the integrated representation in the downstream tasks. The same aggregation methods and parameters for each node in a graph are used to enable the GNNs to utilize the homophily relational data. However, not all graphs are homophilic, even in the same graph, the distributions may vary significantly. Using the same convolution over all nodes may lead to the ignorance of various graph patterns. Furthermore, many existing GNNs integrate node features and structure identically, which ignores the distributions of nodes and further limits the expressive power of GNNs. To solve these problems, we propose Meta Weight Graph Neural Network (MWGNN) to adaptively construct graph convolution layers for different nodes. First, we model the Node Local Distribution (NLD) from node feature, topological structure and positional identity aspects with the Meta-Weight. Then, based on the Meta-Weight, we generate the adaptive graph convolutions to perform a node-specific weighted aggregation and boost the node representations. Finally, we design extensive experiments on real-world and synthetic benchmarks to evaluate the effectiveness of MWGNN. These experiments show the excellent expressive power of MWGNN in dealing with graph data with various distributions.
Trusted Blockchain-Driven IoT Security Consensus Mechanism

Single point of failure and node attack tend to cause instability in the centralized Internet of Things (IoT). Combined with blockchain technology, the deficiency of traditional IoT architecture can be effectively alleviated. However, the existing blockchain consensus mechanism still has the problems of forks and wasting of computing power. Therefore, this paper proposes a new framework based on a two-stage credit calculation to handle these problems. Notably, the nodes are selected through the model, and these nodes will compete on the chain according to the behavior of participating in the creation of the block. Then, a comparative simulation with the existing consensus mechanism proof of work (PoW) is presented. The results show that the proposed framework can quickly eliminate malicious nodes, maintain the overall security of the blockchain and reduce consensus delay.
A novel belief rule base expert system with interval-valued references

As an essential parameter in the belief rule base (BRB), referential values refer to evaluation criteria for describing attributes using quantitative data or linguistic terms, the rationality and preciseness of which are important to the modeling accuracy. At present, the studies on referential values of BRB are mainly related to single-valued data. However, due to the inherent uncertainty, ambiguity, and vagueness of expert knowledge, the single-valued references provided by experts cannot represent qualitative information adequately. In this paper, a novel BRB with interval-valued references (BRB-IR) is proposed, in which qualitative knowledge and quantitative data can be integrated to construct models. First, the interval-valued referential values provided by experts are optimized by a nonlinear optimization algorithm to obtain the optimal referential values. Furthermore, other model parameters are optimized by the projection covariance matrix adaptation evolutionary strategy (P-CMA-ES) algorithm. Finally, a case study for pipeline leak detection is constructed to verify the model's effectiveness, and the results show that the proposed BRB-IR is more effective and characterizes expert knowledge better than the classical BRB using single-valued references.
CDANet: Common-and-Differential Attention Network for Object Detection and Instance Segmentation

In this paper, we propose a simple and effective Common-and-Differential Attention Network (CDANet) for object detection and instance segmentation. For an input intermediate feature map, CDANet infers parallelly attention modules along channel and spatial dimensions respectively, then both attention modules are multiplied with the input feature map for the refined features. Specially, since redundant and confusing background may misdirect the localization at object boundary, our attention network applies Common-and-Differential operations to weaken useless background interference and focus on meaningful object features. The proposed CDANet is verified performance through comprehensive experiments on PASCAL VOC2007 and MS COCO2017 datasets for object detection and instance segmentation tasks. CDANet obtains consistently improved results on various detectors with different backbones, indicating the significant effectiveness and applicability of CDANet.
On the asymmetric effect of real exchange rate on growth: Evidence from Africa

Purpose ― This study investigates the asymmetric effect of real exchange rates on the economic growth of twenty African countries for the period 2005 to 2019. Design/Method/Approach ― A refined method of Granger and Yoon (2002) was used to decompose real exchange into appreciation and depreciation. To address the problem of endogeneity and cross-sectional dependence, a two-steps system generalized method of moments, Driscoll-Kraay estimator, and Augmented Mean group were used. Findings ― This study established the presence of asymmetries in the real exchange rate in the region. Further, the study found that real exchange rate appreciation inhibits economic growth while real exchange rate depreciation is beneficial to growth in the region. The results are robust to different estimation techniques. Practical Implications ― The outcome of this study supports the traditional view of exchange rates on macroeconomic variables. Hence, findings from this study can help investors and policymakers in the region to better understand the dynamics of the exchange rate and its effect on economic growth. Originality/Value ― This study enriches the literature on the relationship between exchange rate and growth, especially in Africa using a refined approach to decompose exchange rate into appreciation and depreciation.
Distributed quantum algorithm for Simon's problem

Limited by today's physical devices, quantum circuits with a long depth are usually noisy and difficult to realize in practice. The novel computing architecture of distributed quantum computing is expected to reduce the noise and depth of quantum circuits. In this paper, we study Simon's problem in distributed scenarios and design a distributed quantum algorithm to solve the problem. The algorithm proposed by us has the advantage of exponential acceleration compared with classical distributed computing and has the advantage of square acceleration compared with the best distributed quantum algorithm proposed before in query complexity. In particular, the previous distributed quantum algorithm for Simon's problem cannot be extended to the case of more than two computing nodes (i.e., two subproblems), but our distributed quantum algorithm can be extended to the case of multiple computing nodes (i.e., multiple subproblems).
About One Representation-Interpreter of a Monotone Measure

We consider the problem of presenting a nonclassical measure (nonadditive, but monotonous), so-called fuzzy (monotone) measure, by a classical measure, particularly by the Murofushi–Sugeno-type new representation-interpreter. The theorems on the universal interpreter of a monotone (fuzzy) measure in the Choquet integral environment and second-order dual capacities are considered.
Breast cancer diagnosis using deep belief networks on ROI images

ÖzHand-crafted features are efficient methods for image processing, recognition, and computer vision.However, the advancements in data size and image resolution lead to inconvenience in feature extraction.Moreover, they are unstable, method-dependent, and computationally intensive due to high dimensions.Especially, big data on image datasets causes unpredictable long process.It is a definite necessity to adjust the feature extraction algorithms to computer-assisted methods for image processing.Generative representational learning algorithms have been emerging approaches with the advantages of Deep Learning.In this study, I proposed employing Deep Belief Networks (DBN) for breast cancer diagnosis on ROI images.DBN models were iterated on different image sizes to evaluate the impact of dimensionality on ROI images.The proposed DBN model has achieved performance rates of 96.32%, 96.68%, 95.93%, and 96.40% for accuracy, specificity, sensitivity, and precision, respectively.Consequently, the proposed DBN with detailed representational learning is an efficient and robust algorithm for the classification of breast cancer and healthy tissues on mammograms by the advantage of generative architectures.Elle çıkarılan öznitelikler, görüntü işleme, tanıma ve bilgisayarlı görü için etkili yöntemlerdir.Ancak, veri boyutu ve görüntü çözünürlüklerindeki artış, özniteliklerin elde edilmesinde zorluklara sebep olmuştur.Kararsız, yönteme bağımlı ve hesaplama açısından yoğundurlar.Özellikle, görüntü veri kümelerindeki büyük veriler, öngörülemeyen uzun süreçler doğurur