Reviewerly/exHarmony huggingface dataset:
LSCP: Locally Selective Combination in Parallel Outlier Ensembles

In unsupervised outlier ensembles, the absence of ground truth makes the combination of base outlier detectors a challenging task. Specifically, existing parallel outlier ensembles lack a reliable way of selecting competent base detectors, affecting accuracy and stability, during model combination. In this paper, we propose a framework—called Locally Selective Combination in Parallel Outlier Ensembles (LSCP)—which addresses the issue by defining a local region around a test instance using the consensus of its nearest neighbors in randomly selected feature subspaces. The top-performing base detectors in this local region are selected and combined as the model's final output. Four variants of the LSCP framework are compared with seven widely used parallel frameworks. Experimental results demonstrate that one of these variants, LSCP_AOM, consistently outperforms baselines on the majority of twenty real-world datasets.
Deep gated attention networks for large-scale street-level scene segmentation

Street-level scene segmentation aims to label each pixel of street-view images into specific semantic categories. It has been attracting growing interest due to various real-world applications, especially in the area of autonomous driving. However, this pixel-wise labeling task is very challenging under the complex street-level scenes and large-scale object categories. Motivated by the scene layout of street-view images, in this work we propose a novel Spatial Gated Attention (SGA) module, which automatically highlights the attentive regions for pixel-wise labeling, resulting in effective street-level scene segmentation. The proposed module takes as input the multi-scale feature maps based on a Fully Convolutional Network (FCN) backbone, and produces the corresponding attention mask for each feature map. The learned attention masks can neatly highlight the regions of interest while suppress background clutter. Furthermore, we propose an efficient multi-scale feature interaction mechanism which is able to adaptively aggregate the hierarchical features. Based on the proposed mechanism, the features of different levels are adaptively re-weighted according to the local spatial structure and the surrounding contextual information. Consequently, the proposed modules are able to boost standard FCN architectures and result in an enhanced pixel-wise segmentation for street-level scene images. Extensive experiments on three public available street-level benchmarks demonstrate that the proposed Gated Attention Network (GANet) approach achieves consistently superior performance and outperforms the very recent state-of-the-art methods.
D-RaNGe: Using Commodity DRAM Devices to Generate True Random Numbers with Low Latency and High Throughput

We propose a new DRAM-based true random number generator (TRNG) that leverages DRAM cells as an entropy source. The key idea is to intentionally violate the DRAM access timing parameters and use the resulting errors as the source of randomness. Our technique specifically decreases the DRAM row activation latency (timing parameter t R a D ) below manufacturer recommended specifications, to induce read errors, or activation failures, that exhibit true random behavior. We then aggregate the resulting data from multiple cells to obtain a TRNG capable of providing a high throughput of random numbers at low latency.
Local Feature Descriptor for Image Matching: A Survey

Image registration is an important technique in many computer vision applications such as image fusion, image retrieval, object tracking, face recognition, change detection and so on. Local feature descriptors, i.e., how to detect features and how to describe them, play a fundamental and important role in image registration process, which directly influence the accuracy and robustness of image registration. This paper mainly focuses on the variety of local feature descriptors including some theoretical research, mathematical models, and methods or algorithms along with their applications in the context of image registration. The existing local feature descriptors are roughly classified into six categories to demonstrate and analyze comprehensively their own advantages. The current and future challenges of local feature descriptors are discussed. The major goal of the paper is to present a unique survey of the state-of-the-art image matching methods based on feature descriptor, from which future research may benefit.
Canceling Thermal Cross-Talk Effects in Photonic Integrated Circuits

Thermal actuators are among the most consolidated and widespread devices for the active control of photonic integrated circuits (PICs). As a main drawback, mutual thermal crosstalk among actuated devices integrated onto the same photonic chip can affect the working point of the PIC and can reduce the efficiency of automated tuning and calibration procedures. In this paper, a strategy to cancel out the effects of the phase coupling induced by thermal crosstalk is presented. In our technique, we named thermal eigenmode decomposition (TED), all the actuators of the PIC are controlled simultaneously according to the eigensolution of the thermally coupled system. The effectiveness of the TED method is validated by numerical simulations and experiments carried out on coupled microring resonator and switch fabrics of Mach-Zehnder interferometers. With respect to individual control of phase actuators, where thermal crosstalk can hinder the convergence of automated tuning algorithms, with the TED technique convergence is always reached, requires a lower number of iterations, and is less sensitive to the initial state of the PIC. The proposed TED method can be applied to generic tuning and locking algorithm, can be employed in arbitrary PIC architectures and its validity can be extended to systems where phase coupling is induced by other physical effects, such as mutual mechanical stress and electromagnetic coupling among RF lines.
Improved decisions for marketing, supply and purchasing: Mining big data through an integration of sentiment analysis and intuitionistic fuzzy multi criteria assessment

This study proposes a novel decision support system for product ranking problems which integrates multi criteria decision making (MCDM) and aspect level sentiment analysis techniques. The main purpose of the developed methodology is to rank the alternative products taking into account a set of product criteria and the customer comments related to these criteria posted on websites to recommend the most appropriate alternative to potential customers. The decision support system comprises two stages, in the first stage, the online customer reviews are transformed into customer satisfaction scores through aspect level sentiment analysis to obtain performance scores corresponding to alternative products, whereas the second stage deals with ranking the alternative products via a novel MCDM methodology, namely “IF-ELECTRE integrated with VIKOR” according to the performance scores obtained in the first level. Intuitionistic fuzzy sets (IFSs) are utilized to effectively represent the customer reviews including hesitant expressions in decision matrix. The weights of criteria (the product aspects of significant importance for customers) are determined using entropy method. The applicability of the developed approach is explored by a case study, in which customer reviews about hotel experiences are evaluated using lexicon based sentiment analysis and alternative hotels are ranked according to the findings from the sentiment analysis by the Intuitionistic fuzzy (IF)-ELECTRE integrated with VIKOR methodology.
An evaluation of deep learning based object detection strategies for threat object detection in baggage security imagery

Rising crimes are likely to promote the need for effective security systems for baggage screening at airports. Therefore, technologies enabling public safety are of paramount importance. Out of all the technologies available, X-ray based baggage-screening plays a major role in threat detection. Originally the screening is done manually where a person scrutinizes the X-ray images on a screen to identify potential threat objects. Within this context, with limited dataset availability, we employ an imaging model for a generation of new X-ray images. In this article, an effort is made to perform threat object detection by using deep neural networks based framework. The framework is built upon Convolutional Neural Network (CNN) based techniques such as You Only Look Once (YOLO) and Faster Region based CNN (FRCNN) to perform threat object detection. Apart from this, to improve the model performance with limited original training data the transfer-learning paradigm is also tested out. The performance is studied on 4 classes of threat objects: 1) Gun; 2) Shuriken; 3) Razor-blade; 4) Knife. As compared to traditional Machine Learning (ML) techniques, FRCNN uses region proposal in its first stage to produce better results. On using Faster RCNN with RESNET which was pre-trained on ImageNet dataset, 98.4% accuracy is achieved for 4-class threat recognition requiring 0.16 sec per image. Comparative performance of these threat detection techniques for cluttered X-ray baggage imagery is also presented. We firmly believe that it is possible to fully automate this screening process by using these computer vision techniques
Performance Benefits of Robust Nonlinear Zeroing Neural Network for Finding Accurate Solution of Lyapunov Equation in Presence of Various Noises

In the previous work, a finite-time zeroing neural network (ZNN) has been established to find the accurate solution of Lyapunov equation in the presence of no noises. In order to further improve the convergence speed of ZNN and suppress various noises encountered in real applications, in this paper, two robust nonlinear zeroing neural networks (RNZNNs) are designed by adding two novel nonlinear activation functions (AFs) for finding the solution of the Lyapunov equation in the presence of various noises. Unlike the previous ZNN activated by known AFs (e.g., linear activation function, bipolar sigmoid activation function, and power activation function), the proposed two RNZNN models possess predefined-time convergence (instead of finite-time convergence) even in the presence of various noises. The greatest advantage of the predefined-time convergence is independent to initial states of a dynamic system, which is much superior to the finite-time convergence related to initial states, and tremendously modifies the convergence performance. In addition, the predefined-time convergence of the RNZNN models for solving the Lyapunov equation are mathematically proved in detail under various external noises. The simulation comparisons further verify the superiority of the proposed RNZNN models for finding the solution of the Lyapunov equation.
Model and Validation of Single-Axis Tracking With Bifacial PV

Single-axis tracking is a cost-effective deployment strategy for large-scale ground-mount photovoltaic systems in regions with high direct-normal irradiance. Bifacial modules in 1-axis-tracking systems boost energy yield by 4%-15% depending on module type and ground albedo, with a global average of 9%. This benefit is in addition to the 15%-25% energy gain already afforded by single-axis tracking relative to fixed-tilt deployments. Here, we compare model results against field performance data for two side-by-side bifacial/monofacial tracked systems - one in Albuquerque, NM, USA, and one in eastern Oregon. The Albuquerque system shows monthly rear irradiance gain of 10%-14.9%, and the Oregon bifacial system has an average performance ratio 9.4% higher than the monofacial system. Both results match bifacial irradiance model results within uncertainty. Simulations show that smart tracking algorithms can offer more than 1% improvement on annual energy yield by adjusting tilt angle under cloudy conditions. Finally, ray-tracing simulations investigated edge brightening, suggesting a 15%-25% increase in rear irradiance at the ends of tracker rows, but up to 20% loss from center-mounted torque tubes, creating multiple shadows.
Synthesizing Supervision for Learning Deep Saliency Network without Human Annotation

Recently, the research field of salient object detection is undergoing a rapid and remarkable development along with the wide usage of deep neural networks. Being trained with a large number of images annotated with strong pixel-level ground-truth masks, the deep salient object detectors have achieved the state-of-the-art performance. However, it is expensive and time-consuming to provide the pixel-level ground-truth masks for each training image. To address this problem, this paper proposes one of the earliest frameworks to learn deep salient object detectors without requiring any human annotation. The supervisory signals used in our learning framework are generated through a novel supervision synthesis scheme, in which the key insights are “knowledge source transition” and “supervision by fusion”. Specifically, in the proposed learning framework, both the external knowledge source and the internal knowledge source are explored dynamically to provide informative cues for synthesizing supervision required in our approach, while a two-stream fusion mechanism is also established to implement the supervision synthesis process. Comprehensive experiments on four benchmark datasets demonstrate that the deep salient object detector trained by our newly proposed learning framework often works well without requiring any human annotated masks, which even approaches to its upper-bound obtained under the fully supervised learning fashion (within only 3 percent performance gap). Besides, we also apply the salient object detector learnt with our annotation-free learning framework to assist the weakly supervised semantic segmentation task, which demonstrates that our approach can also alleviate the heavy supplementary supervision required in the existing weakly supervised semantic segmentation framework.
Comparison of Different Algorithms to Map Hydrothermal Alteration Zones Using ASTER Remote Sensing Data for Polymetallic Vein-Type Ore Exploration: Toroud–Chahshirin Magmatic Belt (TCMB), North Iran

Polymetallic vein-type ores are important sources of precious metal and a principal type of orebody for various base-metals. In this research, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data were used for mapping hydrothermal alteration zones associated with epithermal polymetallic vein-type mineralization in the Toroud–Chahshirin Magmatic Belt (TCMB), North of Iran. The TCMB is the largest known goldfield and base metals province in the central-north of Iran. Propylitic, phyllic, argillic, and advanced argillic alteration and silicification zones are typically associated with Au-Cu, Ag, and/or Pb-Zn mineralization in the TCMB. Specialized image processing techniques, namely Selective Principal Component Analysis (SPCA), Band Ratio Matrix Transformation (BRMT), Spectral Angle Mapper (SAM) and Mixture Tuned Matched Filtering (MTMF) were implemented and compared to map hydrothermal alteration minerals at the pixel and sub-pixel levels. Subtle differences between altered and non-altered rocks and hydrothermal alteration mineral assemblages were detected and mapped in the study area. The SPCA and BRMT spectral transformation algorithms discriminated the propylitic, phyllic, argillic and advanced argillic alteration and silicification zones as well as lithological units. The SAM and MTMF spectral mapping algorithms detected spectrally dominated mineral groups such as muscovite/montmorillonite/illite, hematite/jarosite, and chlorite/epidote/calcite mineral assemblages, systematically. Comprehensive fieldwork and laboratory analysis, including X-ray diffraction (XRD), petrographic study, and spectroscopy were conducted in the study area for verifying the remote sensing outputs. Results indicate several high potential zones of epithermal polymetallic vein-type mineralization in the northeastern and southwestern parts of the study area, which can be considered for future systematic exploration programs. The approach used in this research has great implications for the exploration of epithermal polymetallic vein-type mineralization in other base metals provinces in Iran and semi-arid regions around the world.
Short-long term anomaly detection in wireless sensor networks based on machine learning and multi-parameterized edit distance

Heterogeneous wireless sensor networks are a source of large amount of different information representing environmental aspects such as light, temperature, and humidity. A very important research problem related to the analysis of the sensor data is the detection of relevant anomalies. In this work, we focus on the detection of unexpected sensor data resulting either from the sensor system itself or from the environment under scrutiny. We propose a novel approach for automatic anomaly detection in heterogeneous sensor networks based on coupling edge data analysis with cloud data analysis. The former exploits a fully unsupervised artificial neural network algorithm, whereas cloud data analysis exploits the multi-parameterized edit distance algorithm. The experimental evaluation of the proposed method is performed applying the edge and cloud analysis on real data that has been acquired in an indoor building environment and then distorted with a range of synthetic impairments. The obtained results show that the proposed method can self-adapt to the environment variations and correctly identify the anomalies. We show how the combination of edge and cloud computing can mitigate the drawbacks of purely edge-based analysis or purely cloud-based solutions.
Introduction to Human-Building Interaction (HBI)

Buildings and urban spaces increasingly incorporate artificial intelligence and new forms of interactivity, raising a wide span of research questions about the future of human experiences with, and within, built environments. We call this emerging area Human-Building Interaction (HBI) and introduce it as an interdisciplinary domain of research interfacing Human-Computer Interaction (HCI) with Architecture and Urban Design. HBI seeks to examine the involvement of HCI in studying and steering the evolution of built environments. Therefore, we need to ask foundational questions such as the following: what are the specific attributes of built environments that HCI researchers should take into account when shifting attention and scale from “artefacts” to “environments”? Are architecture and interaction design methods and processes compatible? Concretely, how can a team of interaction designers bring their tools to an architectural project, and collaborate with other stakeholders? Can and will architecture change the theory and practice of HCI? Furthermore, research in HBI should produce knowledge and practical guidelines by experimenting novel design instances that combine architecture and digital interaction. The primary aim of this article is to specify the mission, vision, and scope of research in HBI. As the introductory article to the TOCHI special issue, it also provides a summary of published manuscripts and describes their collective contribution to the development of this field.
Face image manipulation detection based on a convolutional neural network

Facial image manipulation is a particular instance of digital image tampering, which is done by compositing a region from one facial image into another facial image. Fake images generated by facial image manipulation now spread like wildfire on news websites and social networks, and are considered the greatest threat to press freedom. Previous research relied heavily on handcrafted features to analyze tampered regions which were inefficient and time-consuming. This paper introduces a framework that accurately detects manipulated face image using deep learning approach. The original contributions of this paper include (1) a customized convolutional neural network model for Manipulated Face (MANFA) identification; it contains several convolutional layers that effectively extract features of multi-levels of abstraction from a tampered region. (2) A hybrid framework (HF-MANFA) that uses Adaptive Boosting (AdaBoost) and eXtreme Gradient Boosting (XGBoost) to deal with the imbalanced dataset challenge. (3) A large manipulated face dataset that is manually collected and validated. The results from various experiments proved that proposed models outperformed existing expert and intelligent systems which were usually used for the manipulated face image detection task in terms of area under the curve (AUC), computational complexity, and robustness against imbalanced datasets. As a result, the presented framework will motivate the finding of a more powerful altered face images detection method and encourages the integration of the proposed model in applications that have to deal with manipulated images regularly.
Spatial-Temporal Attention-Aware Learning for Video-Based Person Re-Identification

In this paper, we present a spatial-temporal attention-aware learning (STAL) method for video-based person re-identification. Most existing person re-identification methods aggregate image features identically to represent persons, which are extracted from the same receptive field across video frames. However, the image quality may be varying for different spatial regions and changing over time, which shall contribute to person representation and matching adaptively. Our STAL method aims to attend to the salient parts of persons in videos jointly in both spatial and temporal domains. To achieve this, we slice the video into multiple spatial-temporal units which preserve the body structure of a person and develop a joint spatial-temporal attention model to learn the quality scores of these units. We evaluate the proposed method on three challenging datasets including iLIDS-VID, PRID-2011, and the large-scale MARS dataset, and consistently improve the rank-1 accuracy by a large margin of 5.7%, 0.9%, and 6.6% respectively, in comparison with the state-of-the-art methods.
TabletInVR

Complex virtual reality (VR) tasks, like 3D solid modelling, are challenging with standard input controllers. We propose exploiting the affordances and input capabilities when using a 3D-tracked multi-touch tablet in an immersive VR environment. Observations gained during semi-structured interviews with general users, and those experienced with 3D software, are used to define a set of design dimensions and guidelines. These are used to develop a vocabulary of interaction techniques to demonstrate how a tablet's precise touch input capability, physical shape, metaphorical associations, and natural compatibility with barehand mid-air input can be used in VR. For example, transforming objects with touch input, "cutting" objects by using the tablet as a physical "knife", navigating in 3D by using the tablet as a viewport, and triggering commands by interleaving bare-hand input around the tablet. Key aspects of the vocabulary are evaluated with users, with results validating the approach.