Reviewerly/exHarmony huggingface dataset:
KR-Net: A Dependable Visual Kidnap Recovery Network for Indoor Spaces

In this paper, we propose a dependable visual kidnap recovery (KR) framework that pinpoints a unique pose in a given 3D map when a device is turned on. For this framework, we first develop indoor-GeM (i-GeM), which is an extension of GeM [1] but considerably more robust than other global descriptors [2]-[4], including GeM itself. Then, we propose a convolutional neural network (CNN)-based system called KR-Net, which is based on a coarse-to-fine paradigm as in [5] and [6]. To our knowledge, KR-Net is the first network that can pinpoint a wake-up pose with a confidence level near 100% within a 1.0 m translational error boundary. This dependable success rate is enabled not only by i-GeM, but also by a combinatorial pooling approach that uses multiple images around the wake-up spot, whereas previous implementations [5], [6] were constrained to a single image. Experiments were conducted in two challenging datasets: a large-scale (12,557 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) area with frequent featureless or repetitive places and a place with significant view changes due to a one-year gap between prior modeling and query acquisition. Given 59 test query sets (eight images per pose), KR-Net successfully found all wake-up poses, with average and maximum errors of 0.246 m and 0.983 m, respectively.
Research on Furniture Design System Based on Big Data and Information Technology

Abstract With the rapid development of my country’s real estate and other pillar industries and the acceleration of urbanization. The furniture industry is also showing a trend of rapid development. At present, Chinese traditional furniture has long been unable to meet the needs of consumers in some functional designs. Based on this, this paper proposes a furniture design system based on big data and information technology. Make full use of the existing network environment, hardware equipment, and software technology to build a furniture product big data cloud platform that can meet the needs of business development on the basis of ensuring that the core business structure is advanced, efficient and stable. This paper studies the huge changes that the home design system will face under the influence of big data, and conducts an in-depth analysis of the industrial environment, operation mode, information transmission security and other characteristics of the home system under the background of the big data era. Research shows that under the background of the era of big data, the establishment of a big data platform for furniture products is facing new development opportunities. The development of electronic information technology provides strong support for platform construction. Utilize electronic information technology to continuously aggregate furniture product information from various channels, deeply analyze the status of furniture products, and explore furniture product quality risks, thereby building a furniture product big data cloud platform.
Value of bifacial photovoltaics used with highly reflective ground materials on single‐axis trackers and fixed‐tilt systems: a Danish case study

The energy produced by bifacial photovoltaic (PV) arrays can be augmented via albedo enhancements. However, the value of the additional energy must outweigh the costs for such modifications to be economically viable. In this work, the electrical performance and economic value of six 13 kWp crystalline-silicon (c-Si) PV arrays with distinct configurations are evaluated. The system designs include horizontal single axis trackers (HSAT) and 25° fixed-tilt structures, monofacial and bifacial PV panels, and low and high ground albedo. The value of the system designs is assessed using onsite electrical measurements and spot prices from the Nord Pool electricity market. We find that HSAT systems increase the annual value factor (VF) by 4% and decrease levelized cost of energy (LCOE) by 3.5 EUR/MWh relative to fixed-tilt systems. The use of bifacial panels can increase the VF by 1% and decrease LCOE by 4.0 EUR/MWh. However, a negligible VF increase and modest LCOE decrease was found in systems with bifacial panels and ground albedo enhancements. Although our results show that albedo enhancements result in lower LCOE than designs without, the uncertainty in upfront and ongoing costs of altering the ground in utility-scale PV parks makes the solution presently unadvisable.
Latent Gaussian process for anomaly detection in categorical data

We propose a semi-supervised approach towards anomaly detection in multivariate categorical data. Our goal is to learn a model that can distinguish the anomalous data, given a small set of training data from the normal class. To this end, our approach learns the probability distribution of normal instances with the assumption that the categorical data are generated from a continuous latent space. Gaussian process is adopted to construct the generative model. As a non-parametric Bayesian model, Gaussian process can adapt its model complexity according to the data size. Hence, our approach can be effective when the training dataset is small. Comprehensive experiments over different benchmarks clearly demonstrate the effectiveness of our approach.
Waiting Game: Optimally Provisioning Fixed Resources for Cloud-Enabled Schedulers

While cloud platforms enable users to rent computing resources on demand to execute their jobs, buying fixed resources is still much cheaper than renting if their utilization is high. Thus, optimizing cloud costs requires users to determine how many fixed resources to buy versus rent based on their workload. In this paper, we introduce the concept of a waiting policy for cloud-enabled schedulers, which is the dual of a scheduling policy, and show that the optimal cost depends on it. We define multiple waiting policies and develop simple analytical models to reveal their tradeoff between fixed resource provisioning, cost, and job waiting time. We evaluate the impact of these waiting policies on a year-long production batch workload consisting of 14Mjobs run on a 14.3k-core cluster, and show that a compound waiting policy decreases the cost (by 5%) and mean job waiting time (by 7×) compared to a fixed cluster of the current size.
Online Binary Incomplete Multi-view Clustering

Multi-view clustering has attracted considerable attention in the past decades, due to its good performance on the data with multiple modalities or from diverse sources. In real-world applications, multi-view data often suffer from incompleteness of instances. Clustering on such multi-view data is called incomplete multi-view clustering (IMC). Most of the existing IMC solutions are offline and have high computational and memory costs especially for large-scale datasets. To tackle these challenges, in this paper, we propose a Online Binary Incomplete Multi-view Clustering (OBIMC) framework. OBIMC robustly learns the common compact binary codes for incomplete multi-view features. Moreover, the cluster structures are optimized with the binary codes in an online fashion. Further, we develop an iterative algorithm to solve the resultant optimization problem with linear computational complexity and theoretically prove its convergence. Experiments on four real datasets demonstrate the efficiency and effectiveness of the proposed OBIMC method. As indicated, our algorithm significantly and consistently outperforms some state-of-the-art algorithms with much less running time.
Clustering of Citizen Science Prospect to Construct Big Data-based Smart Village in Indonesia

The development of citizen science as the foundation of a smart village is one of the solutions to reduce poverty in rural areas. The main objective of this research is to map the prospect cluster of citizen science to construct big data-based smart villages in Indonesia. This research was conducted through a cluster analysis of 2018 village potential data in Indonesia, using a combination of k-means, expected maximum and density-based algorithms. The contribution of this research resulted in a map of prospect of citizen science clusters for smart villages in 33 provinces in Indonesia. The data used was limited to village administrative areas, so DKI Jakarta was not included in this study. The main factors that attribute to this cluster analysis are ICT infrastructure, management of villagers' participation in ICT activities, renewable energy, transportation, agricultural business activities and nonagricultural small and medium enterprises. The results of the clusters show that there are 3 clusters of citizen science potential to develop smart Indonesian villages, namely the very potential (11%), potential (60%) and quite potential (29%) clusters. This citizen science prospects cluster map is visualized on spatial data based on the provinces in Indonesia. Province Bangka Belitung Island, West Java, Central Java, East Java, Yogyakarta, Banten and Bali are potential provinces for developing citizen science in order to construct big data-based smart villages. The results of this cluster were validated with the dendogram structure of the systematic literature review (SLR). The dendogram structure shows the keywords that correspond to the main attributes used in the clustering process. This validation process is an innovative finding in the smart village research ecosystem.
Publicly Verifiable Proofs of Data Replication and Retrievability for Cloud Storage

Proofs of Retrievability (PORs) permit a cloud provider to prove to the client (owner) that her files are correctly stored. Extensions of PORs, called Proofs of Retrievability and Reliability (PORRs), enable to check in a single instance that replicas of those files are correctly stored as well.In this paper, we propose a publicly verifiable PORR using Verifiable Delay Functions, which are special functions being slow to compute and easy to verify. We thus ensure that the cloud provider stores both original files and their replicas at rest, rather than computing the latter on the fly when requested to prove fair storage. Moreover, the storage verification can be done by anyone, not necessarily by the client. To our knowledge, this is the first PORR that offers public verification. Future work will include implementation and evaluation of our solution in a realistic cloud setting.
Towards a Flexible Embedding Learning Framework

Representation learning is a fundamental building block for analyzing entities in a database. While the existing embedding learning methods are effective in various data mining problems, their applicability is often limited because these methods have pre-determined assumptions on the type of semantics captured by the learned embeddings, and the assumptions may not well align with specific downstream tasks. In this work, we propose an embedding learning framework that 1) uses an input format that is agnostic to input data type, 2) is flexible in terms of the relationships that can be embedded into the learned representations, and 3) provides an intuitive pathway to incorporate domain knowledge into the embedding learning process. Our proposed framework utilizes a set of entity-relation-matrices as the input, which quantifies the affinities among different entities in the database. Moreover, a sampling mechanism is carefully designed to establish a direct connection between the input and the information captured by the output embeddings. To complete the representation learning toolbox, we also outline a simple yet effective post-processing technique to properly visualize the learned embeddings. Our empirical results demonstrate that the proposed framework, in conjunction with a set of relevant entity-relation-matrices, outperforms the existing state-of-the-art approaches in various data mining tasks.
Coverless Image Steganography Based on Jigsaw Puzzle Image Generation

Current image steganography methods are working by assigning an image as a cover file then embed the payload within it by modifying its pixels, creating the stego image. However, the left traces that are caused by these modifications will make steganalysis algorithms easily detect the hidden payload. A coverless data hiding concept is proposed to solve this issue. Coverless does not mean that cover is not required, or the payload can be transmitted without a cover. Instead, the payload is embedded by cover generation or a secret message mapping between the cover file and the payload. In this paper, a new coverless image steganography method has been proposed based on the jigsaw puzzle image generation driven by a secret message. Firstly, the image is divided into equal rows then further divided into equal columns, creating blocks (i.e., sub-images). Then, according to secret message bits and a proposed mapping function, each block will have tabs/blanks to get the shape of a puzzle piece creating a fully shaped jigsaw puzzle stego-image. After that, the generated jigsaw puzzle image is sent to the receiver. Experimental results and analysis show a good performance in the hiding capacity, security, and robustness compared with existing coverless image steganography methods.
Automated Enforcement of the Principle of Least Privilege over Data Source Access

The state-of-the-art database-backed web applications usually assign full privileges to connections between applications and data sources. This phenomenon, which would enable a malicious attacker to easily compromise the applications through arbitrarily manipulating the data sources without the restriction of privileges, seriously breaks the principle of least privilege (PLP), a fundamental law of system security. Motivated to counter this problem, we propose a framework PDA (PLP over Data source Access) to automatically enforce this principle over data source access based on application-driven privilege separation. Our proposed PDA contributes from the following aspects: i) PDA achieves the privilege separation by intercepting database queries and enforcing privileged connections to database for each database query; ii) PDA can effectively defend against SQL-based vulnerabilities including buggy queries and SQL injection attacks. Lastly, we evaluate PDA on a widely used application platform, JForum, to demonstrate the effectiveness of PDA with a promising performance overhead of 8.13%.
Accelerated Deep Learning on HPCC Systems

Modern deep learning architectures are computationally expensive to train due to their size, complexity, and the data they typically train on. There are different approaches to accelerate the training process. Using graphics processing units (GPUs) to accelerate deep learning is an increasingly effective and accessible way for researchers to train large neural networks in a fraction of the time. In this paper, we trained neural networks on High-Performance Computing Cluster (HPCC) Systems Platform using GPU acceleration with a novel implementation that combines widely used neural network libraries and the HPCC Systems Platform. This is the first work that uses GPU acceleration on HPCC Systems and is the first to demonstrate the effectiveness of using single and multiple GPUs with HPCC Systems. We provide experiments that measure the performance increase in training time as compared to only using central processing units (CPUs). The experiments trained a convolutional neural network on image data and a multilayer perceptron trained on a Medicare fraud detection dataset, using both single and multiple GPUs to accelerate the training process. Our results show that although GPU usage does not always guarantee a significant performance increase, using one or more GPUs does generally decrease the required training time.
The Rhythm of the Robot: A Prolegomenon to Posthuman Somaesthetics

This work-in-progress paper describes an investigation into the somaesthetic qualities of exploring posthuman strategies and relations with technology. This was done through bodystorming and physical prototyping, casting soft robots directly onto the body. Through a Research-through-Design process, a soft robotic prototype, Nautica, was developed. This prototype is based on embodied design ideation, autoethnographic bodystorming and prototyping in soft robotics. Nautica explores the concept of an embodied navigation system that plays with re-embodying the disembodied virtual body found in digital navigation systems. The Nautica prototype was tested directly on the designer-researcher's body which led to discerning preliminary emergent properties. Over a short-term adoptive study it was discovered that several "material" commonalities between the wearer and robot emerged due to the materiality and behavior of soft robots. These insights may suggest new ways of experiencing hybrid somatic relations with technology. This work-in-progress ends on suggesting further work such as connecting the soft robot to a real, functioning navigation system and to expand the testing of the Nautica prototype.
Arabic Sentiment Analysis Based on 1-D Convolutional Neural Network

In recent years, Deep Learning (DL) has garnered tremendous success in a variety of application domains such as sentiment analysis. In this paper, we study the impact of different preprocessing techniques on Sentiment Analysis (SA) in Arabic. Moreover, we describe the details of selecting good Arabic word embeddings using Word2Vec and FastText models. Furthermore, a DL architecture based on Convolutional Neural Networks (CNNs) is proposed. Experiments on a total of 63K book reviews show the suitability of our proposed system on Arabic SA.
A Machine Learning based Channel Modeling for High-speed Serial Link

In high-speed signal transmission, a large amount of channel data is needed to optimize the serial link and improve the signal integrity. To overcome some drawbacks of traditional channel modeling, such as inconvenient and long simulation time, a machine learning based channel modeling is presented, which can improve the efficiency but also ensure the accuracy. By using random forest regression (RFR) algorithm and selecting several feature variables that affect channel characteristics as its inputs, an output in terms of S-parameter at each frequency point of the channel can be obtained. Furthermore, an improved modeling method, in which accurate model instead of rough one is employed for some interested frequency range, is proposed to make tradeoff between the accuracy and efficiency. Experiments results illustrate that our RFR based method can speed up the modeling process significantly. Meanwhile, the accuracy is ensured by combing the rough and accurate model effectively.
Real-Time Semantic Segmentation of Remote Sensing Images Based on Bilateral Attention Refined Network

The trade-off between feature representation capability and spatial positioning accuracy is crucial to dense classification or semantic segmentation of remote sensing images.In order to better balance the low-level spatial details in the shallow network and the high-level abstract semantics in the deep network, the bilateral attention refinement lightweight network BARNet is introduced.In this way, we can use the fine-grained features in the shallow layer to further supplement and capture the deeper information of the high-level semantic features.The network employs an asymmetric encoder decoder architecture for the task of real-time semantic segmentation.Encoder part proposes a lightweight network residual unit with the split, concatenate and split bottleneck structure to achieve more light weighted, effificient and powerful feature extraction.In the decoding section, we propose an adaptive method to enhance feature representation in local attention enhancement module.In addition, the global context embedding module is introduced to divide the high-level features into two branches.One branch gets the weight vector to guide the low-level learning, and the other branch will get a semantic vector, which is used to calculate the multi-label category loss and further introduce into the overall loss function to regulate the training process better.The effectiveness and efficiency of the network are verified on ISPRS Potsdam data set and CCF data set, respectively.The results show that the models using these strategies outperform the baseline network on MIoU, PA and F1, which increase by 18.86%, 16.21% and 15.64% on the Potsdam dataset; 10.51%, 6.53% and 8.19% on the CCF dataset.
Squeezing Correlated Neurons for Resource-Efficient Deep Neural Networks

DNNs are abundantly represented in real-life applications because of their accuracy in challenging problems, yet their demanding memory and computational costs challenge their applicability to resource-constrained environments. Taming computational costs has hitherto focused on first-order techniques, such as eliminating numerically insignificant neurons/filters through numerical contribution metric prioritizations, yielding passable improvements. Yet redundancy in DNNs extends well beyond the limits of numerical insignificance. Modern DNN layers exhibit a significant correlation among output activations; hence, the number of extracted orthogonal features at each layer rarely exceeds a small fraction of the layer size. The exploitation of this observation necessitates the quantification of information content at layer outputs. To this end, we employ practical data analysis techniques coupled with a novel feature elimination algorithm to identify a minimal set of computation units that capture the information content of the layer and squash the rest. Linear transformations on the subsequent layer ensure accuracy retention despite the removal of a significant portion of the computation units. The one-shot application of the outlined technique can shrink the VGG-16 model size \(4.9{\times }\) and speed up its execution by \(3.4{\times }\) with negligible accuracy loss while requiring no additional fine-tuning. The proposed approach, in addition to delivering results overwhelmingly superior to hitherto promulgated heuristics, furthermore promises to spearhead the design of more compact deep learning models through an improved understanding of DNN redundancy.
Exploiting the Solar Energy Surplus for Edge Computing

In the context of the global energy ecosystem transformation, we introduce a new approach to reduce the carbon emissions of the cloud-computing sector and, at the same time, foster the deployment of small-scale private photovoltaic plants. We consider the opportunity cost of moving some cloud services to private, distributed, solar-powered computing facilities. To this end, we compare the potential revenue of leasing computing resources to a cloud pool with the revenue obtained by selling the surplus energy to the grid. We first estimate the consumption of virtualized cloud computing instances, establishing a metric of computational efficiency per nominal photovoltaic power installed. Based on this metric and characterizing the site's annual solar production, we estimate the total return and payback. The results show that the model is economically viable and technically feasible. We finally depict the still many questions open, such as security, and the fundamental barriers to address, mainly related with a cloud model ruled by a few big players.