Reviewerly/exHarmony huggingface dataset:
The moderating effect of prior knowledge on the relationship between intelligence and complex problem solving – Testing the Elshout-Raaheim hypothesis

Although the relation between intelligence and complex problem solving (CPS) has been investigated repeatedly, the moderating effect of prior knowledge on this relation still remains an open question. The Elshout-Raaheim hypothesis (see Leutner, 2002) predicts a higher correlation between intelligence and CPS at a medium level of problem specific prior knowledge and lower correlations at higher as well as lower levels of prior knowledge, thus a curvilinear moderation. We investigated this hypothesis in a sample of N = 495 high school students by using general intelligence (g) and a minimal complex systems approach (MCS) of CPS. Strategic prior knowledge in the sense of the relative frequency of the vary-one-thing-at-a-time strategy (VOTAT; Tschirgi, 1980) increased across MCS tasks in the knowledge acquisition phase of CPS. With increasing prior knowledge, correlations followed the predicted inverted U-shaped pattern in the knowledge acquisition phase and the knowledge application phase of CPS, thus supporting the Elshout-Raaheim hypothesis. The moderating effect of strategic prior knowledge for the intelligence–CPS relation and its relevance are discussed.
Visibility Enhancer: Adaptable for Distorted Traffic Scenes by Dusty Weather

Poor weather conditions such as the presence of heavy snow, fog, rain and dust storm are considered as dangerous restrictions of the functionality of cameras via reducing clear visibility. Thus, they have bad effect on computer vision algorithms used in traffic scene understanding, such as object detection, tracking, and recognition which are vital for traffic monitoring. Current methods for image enhancement can not be utilized under the influence of weather variability from foggy to dusty situations. This paper proposes an adaptive technique for visibility enhancement based on the bright balance and Laplace filtering. The overall visibility enhancement process is composed of three main parts: color and illumination improvement, reflection and component details enhancement, and linear weighted fusion. First, the contrast of an image is enhanced by auto white balance and Gamma correction for each color channel (Red, Green, Blue) individually to achieve color enhancement and outperform the illumination. Second, the detail enhancement is achieved by the Laplace pyramid filter to process the reflection component. Third, the detail enhanced layer is added back to the corrected color layer to reconstruct the clear image. The quantitative results and visual analysis demonstrate the efficacy of the proposed technique. Comparing with the state-of-the-art image enhancement methods, the evaluation of the objective metrics have shown that the contrast of unclear images can be effectively improved by the proposed method and with well effects on both foggy and dusty situations.
Criso: An Incremental Scalable and Cost-Effective Network Architecture for Data Centers

With the explosive data growth, an enormous number of computing and networking components (e.g., servers, switches, and wires) are continuously being augmented to data centers. Data center networks (DCNs) - maintaining a high network capacity - must be cost efficient, incrementally scalable, and fault-tolerant. To address these challenges, we propose in this study a new type of DCN architecture referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Criso</i> . Different from the existing network architectures, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Criso</i> is designed hierarchically and recursively by employing two ports servers and commodity switches. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Criso</i> is constructed based on numerous isomorphic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pod</i> s, each of which leverages external interfaces supplied by switches to connect with neighboring pods. Additionally, a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">pod</i> -based and fault-tolerant routing algorithm is designed to handle multiple failures. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Criso</i> has an array of promising features, including being cost-efficient and delivering a high-network capacity that can be extended to millions of nodes. The analytic results demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Criso</i> is significantly superior to the four state-of-the-art data center structures in terms of network capacity, scalability, cost, power consumption, and other static characteristics. Furthermore, the experimental results unveil that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Criso</i> satisfies the fault-tolerant demands of modern data centers. Compared to the four existing topologies (i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DCell</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BCube</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">FiConn</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Fat-Tree</i> ) that have been widely investigated, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Criso</i> is adroit at maintaining a balanced performance in terms of throughput and latency.
FREPA: an automated and formal approach to requirement modeling and analysis in aircraft control domain

Formal methods are promising for modeling and analyzing system requirements. However, applying formal methods to large-scale industrial projects is a remaining challenge. The industrial engineers are suffering from the lack of automated engineering methodologies to effectively conduct precise requirement models, and rigorously validate and verify (V&V) the generated models. To tackle this challenge, in this paper, we present a systematic engineering approach, named Formal Requirement Engineering Platform in Aircraft (FREPA), for formal requirement modeling and V&V in the aerospace and aviation control domains. FREPA is an outcome of the seamless collaboration between the academy and industry over the last eight years. The main contributions of this paper include 1) an automated and systematic engineering approach FREPA to construct requirement models, validate and verify systems in the aerospace and aviation control domain, 2) a domain-specific modeling language AASRDL to describe the formal specification, and 3) a practical FREPA-based tool AeroReq which has been used by our industry partners. We have successfully adopted FREPA to seven real aerospace gesture control and two aviation engine control systems. The experimental results show that FREPA and the corresponding tool AeroReq significantly facilitate formal modeling and V&V in the industry. Moreover, we also discuss the experiences and lessons gained from using FREPA in aerospace and aviation projects.
Signal processing techniques for efficient compilation of controlled rotations in trapped ions

Quantum logic gates with many control qubits are essential in many quantum algorithms, but remain challenging to perform in current experiments. Trapped ion quantum computers natively feature a different type of entangling operation, namely the Molmer-Sorensen (MS) gate which effectively applies an Ising interaction to all qubits at the same time. We consider a sequence of equal all-to-all MS operations, interleaved with single qubit gates that act only on one special qubit. Using a connection with quantum signal processing techniques, we find that it is possible to perform an arbitray SU(2) rotation on the special qubit if and only if all other qubits are in the state |1>. Such controlled rotation gates with N-1 control qubits require 2N applications of the MS gate, and can be mapped to a conventional Toffoli gate by demoting a single qubit to ancilla.
Blockchain-based Model for Sharing Activities of Daily Living in Healthcare Applications

People aged 65 and above are considered to be the fastest-growing population in the world. It is believed that the majority of elderly people will be living alone and hence need to receive health services to ensure their well-being. One promising way is to provide caregivers access to the elderly daily activities (e.g., eating, cleaning, exercising patterns, etc.) through non-intrusive means. Therefore, recognizing and tracking their daily activities play a key role in providing timely health and emergency services that foster better day-to-day care. The solution proposed in this paper is based on analyzing and recognizing the daily activities based on the energy consumption of appliances inside homes. To ensure that elderly people's data are protected and accessible by authorized personnel within the healthcare ecosystem, blockchain technology is used as a means for maintaining and sharing daily activities patterns with healthcare providers. The main challenges of this paper are as follows: (i) How to recognize relationships between appliance usage and daily activities from concurrent streams of smart meter data; and (ii) how to construct profiles of daily activities and upload them to the blockchain system. For addressing these issues, we develop our model based on Bayesian network to recognize daily activities based on the energy consumption of appliances. To ensure that elderly people's data are protected, we use Hyperledger blockchain technology as a trustworthy mechanism for creating profiles, in order to protect data and prevent scams or frauds that might jeopardize people's privacy.
Multi-View Human Pose Estimation in Human-Robot Interaction

This paper contributes a real-time human-robot interaction system based on multi-human poses capture. First, we propose an iterative method for multi-human 3D poses estimation from multi-view. There are three mainly steps: (1) all independent 2D poses under each view are obtained by using the pose detector; (2) 2D poses from different perspectives are associated based on a greedy algorithm; (3) 3D skeletons are generated with the 2D poses that have been correctly associated. In the process of each iteration, the poses with the highest correspondence are always paired priority to ensure the accuracy of the results. Due to the occlusion in each view, previous methods that rely on the appearance of the human body and wearable devices are not suitable for multi-human 3D poses estimation, especially in crowded scenes. The proposed method does not required any markers or devices, which has good robustness even under occlusion or crowded. In addition, we also designed three human-robot interaction modes, including action following, target specification and dynamic obstacle avoidance. For each mode, target generation and correction methods are present. Combining human body pose estimation and robot motion, design experiments to verify the smoothness of the robot control method for dynamic target tracking and the effectiveness in different interaction modes.
Real-Time Semantic Segmentation Network Based on Lite Reduced Atrous Spatial Pyramid Pooling Module Group

Objective: With the development of mobile devices and robots, semantic segmentation network begins to pay more and more attention to the efficiency of the model. To speed up the model inference, most of the existing real-time semantic segmentation methods always reduce the size of the image before segmentation, which sacrifices the details and spatial information of the image. In order to achieve a good balance between speed and accuracy, we propose a efficient real-time semantic segmentation network based on lite reduced atrous pyramid pooling. Method: The proposed network structure uses the encoder decoder structure to construct an end-to-end semantic segmentation network. In the encoder part, the image is quickly sampled underground to avoid too much operation on the large-resolution feature map and increase the calculation amount. In the decoder part, the pyramid pooling module of light-weight empty space is used to further obtain the context information to expand the feeling field of the model. The large-size pooling core of lite reduced atrous spatial pyramid pooling module group can quickly and effectively extract the context information from the large-size input. Then, skip connection and feature fusion module can fuse multi-scale feature information. Finally, we get the semantic output through up sampling. Result: The network proposed in this paper has been tested on Cityscape data set. For 2048*1024 resolution input image, using a NVIDIA Tesla V100 video card, the network achieves 88.4FPS speed and 74.5% Miou accuracy. Conclusion: The experimental results show that the network structure proposed in this paper is faster than other networks in the control group when dealing with 2048*1024 size pictures, which shows that the research in this paper is valuable.
Multi-Level Area Balancing of Clustered Graphs

We present a multi-level area balancing technique for laying out clustered graphs to facilitate a comprehensive understanding of the complex relationships that exist in various fields, such as life sciences and sociology. Clustered graphs are often used to model relationships that are accompanied by attribute-based grouping information. Such information is essential for robust data analysis, such as for the study of biological taxonomies or educational backgrounds. Hence, the ability to smartly arrange textual labels and packing graphs within a certain screen space is therefore desired to successfully convey the attribute data . Here we propose to hierarchically partition the input screen space using Voronoi tessellations in multiple levels of detail. In our method, the position of textual labels is guided by the blending of constrained forces and the forces derived from centroidal Voronoi cells. The proposed algorithm considers three main factors: (1) area balancing, (2) schematized space partitioning, and (3) hairball management. We primarily focus on area balancing, which aims to allocate a uniform area for each textual label in the diagram. We achieve this by first untangling a general graph to a clustered graph through textual label duplication, and then coupling with spanning-tree-like visual integration. We illustrate the feasibility of our approach with examples and then evaluate our method by comparing it with well-known conventional approaches and collecting feedback from domain experts.
Determining the Relevance of Features for Deep Neural Networks

Deep neural networks are tremendously successful in many applications, but end-to-end trained networks often result in hard to understand black-box classifiers or predictors. In this work, we present a novel method to identify whether a specific feature is relevant to a classifier's decision or not. This relevance is determined at the level of the learned mapping, instead of for a single example. The approach does neither need retraining of the network nor information on intermediate results or gradients. The key idea of our approach builds upon concepts from causal inference. We interpret machine learning in a structural causal model and use Reichenbach's common cause principle to infer whether a feature is relevant. We demonstrate empirically that the method is able to successfully evaluate the relevance of given features on three real-life data sets, namely MS COCO, CUB200 and HAM10000.
Tunable transverse spin–motion coupling for quantum information processing

Laser-controlled entanglement between atomic qubits (`spins') and collective motion in trapped ion Coulomb crystals requires conditional momentum transfer from the laser. Since the spin-dependent force is derived from a spatial gradient in the spin-light interaction, this force is typically longitudinal -- parallel and proportional to the average laser $k$-vector (or two beams' $k$-vector difference), which constrains both the direction and relative magnitude of the accessible spin-motion coupling. Here, we show how momentum can also be transferred perpendicular to a single laser beam due to the gradient in its transverse profile. By controlling the transverse gradient at the position of the ion through beam shaping, the relative strength of the sidebands and carrier can be tuned to optimize the desired interaction and suppress undesired, off-resonant effects that can degrade gate fidelity. We also discuss how this effect may already be playing an unappreciated role in recent experiments.
Some Classes of Mixed Alphabet Codes With Few Weights

Mixed alphabet codes are the generalizations of the classical linear codes over finite fields and rings. In this letter we introduce a valid method of constructing \mathbb F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> R-additive codes, where p is a prime number, and R = \mathbb F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> +u \mathbb F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> with u <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =0. These codes can be viewed as a combination of irreducible cyclic codes over \mathbb F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> and trace codes over R. In particular, by using the Gray map, part of the obtained image codes meets the Greismer bound with equality, and can be applied to construct new secret sharing scheme.
VR-DANN: Real-Time Video Recognition via Decoder-Assisted Neural Network Acceleration

Nowadays, high-definition video object recognition (segmentation and detection) is not within the easy reach of a real-time task in a consumer SoC due to the limited on-chip computing power for neural network (NN) processing. Although many accelerators have been optimized heavily, they are still isolated from the intrinsic video compression expertise in a decoder. Given the fact that a great portion of frames can be dynamically reconstructed by a few key frames with high fidelity in a video, we envision that the recognition can also be reconstructed in a similar way so as to save a large amount of NN computing power. In this paper, we study the feasibility and efficiency of a novel decoder-assisted NN accelerator architecture for video recognition (VR-DANN) in a conventional SoC-styled design, which for the first time tightly couples the working principle of a video decoder with the NN accelerator to provide smooth high-definition video recognition experience. We leverage motion vectors, the simple tempo-spatial information already available in the decoding process to facilitate the recognition process, and propose a lightweight NN-based refinement scheme to suppress the non-pixel recognition noise. We also propose the corresponding microarchitecture design, which can be built upon any existing commercial IPs with minimal hardware overhead but significant speedup. Our experimental results show that the VR-DANN-parallel architecture achieves 2.9× performance improvement with less than 1% accuracy loss compared with the state-of-the-art "FAVOS" scheme widely used for video recognition. Compared with optical flow assisted "DFF" scheme, it can achieve 2.2× performance gain and 3% accuracy improvement. As to another "Euphrates" scheme, VR-DANN can achieve 40% performance gain and comparable accuracy.
A Hybrid Recommendation System for Marine Science Observation Data Based on Content and Literature Filtering

With the development of ocean exploration technology and the rapid growth in the amount of marine science observation data, people are faced with a great challenge to identify valuable data from the massive ocean observation data. A recommendation system is an effective method to improve retrieval capabilities to help users obtain valuable data. The two most popular recommendation algorithms are collaborative filtering algorithms and content-based filtering algorithms, which may not work well for marine science observation data given the complexity of data attributes and lack of user information. In this study, an approach was proposed based on data similarity and data correlation. Data similarity was calculated by analyzing the subject, source, spatial, and temporal attributes to obtain the recommendation list. Then, data correlation was calculated based on the literature on marine science data and ranking of the recommendation list to obtain the re-rank recommendation list. The approach was tested by simulated datasets collected from multiple marine data sharing websites, and the result suggested that the proposed method exhibits better effectiveness.
A Systematic Literature Review on Machine Learning in Object Detection Security

This paper is a Systematic literature review on the topic of machine learning in object detection security, where the work done covers a number of 73 research papers related to this topic, and answers three different research questions, while providing charts, tables, and statistics to summarize the data and give the reader and easy to read summarized overview of the papers related to this topic.
Hierarchical Temporal Memory-Based One-Pass Learning for Real-Time Anomaly Detection and Simultaneous Data Prediction in Smart Grids

A neuro-cognitive inspired architecture based on the Hierarchical Temporal Memory (HTM) is proposed for anomaly detection and simultaneous data prediction in real-time for smart grid <inline-formula><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> PMU data. The key technical idea is that the HTM learns a <i>sparse distributed temporal representation</i> of sequential data that turns out to be very useful for anomaly detection and simultaneous data prediction in real-time. Our results show that the proposed HTM can predict anomalies within 83–90 percent accuracy for three different application profiles, namely <i>Standard, Reward Few False Positive, Reward Few False Negative</i> for two different datasets. We show that the HTM is competitive to five state-of-the-art algorithms for anomaly detection. Moreover, for the multi-step prediction in the online setting, the same HTM achieves a low 0.0001 normalized mean square error, a low negative log-likelihood score of 1.5 and is also competitive to six state-of-the-art prediction algorithms. We demonstrate that the same HTM model can be used for <i>both the tasks</i> and can learn online in one-pass, in an unsupervised fashion and adapt to changing statistics. <i>The other state-of-the-art algorithms are either less accurate or are limited to one of the tasks or cannot learn online in one-pass, and adapt to changing statistics.</i>