Reviewerly/exHarmony huggingface dataset:
Learned Conjugate Gradient Descent Network for Massive MIMO Detection

In this work, we consider the use of model-driven deep learning techniques for massive multiple-input multiple-output (MIMO) detection. Compared with conventional MIMO systems, massive MIMO promises improved spectral efficiency, coverage and range. Unfortunately, these benefits are coming at the cost of significantly increased computational complexity. To reduce the complexity of signal detection and guarantee the performance, we present a learned conjugate gradient descent network (LcgNet), which is constructed by unfolding the iterative conjugate gradient descent (CG) detector. In the proposed network, instead of calculating the exact values of the scalar step-sizes, we explicitly learn their universal values. Also, we can enhance the proposed network by augmenting the dimensions of these step-sizes. Furthermore, in order to reduce the memory costs, a novel quantized LcgNet is proposed, where a low-resolution nonuniform quantizer is integrated into the LcgNet to smartly quantize the aforementioned step-sizes. The quantizer is based on a specially designed soft staircase function with learnable parameters to adjust its shape. Meanwhile, due to fact that the number of learnable parameters is limited, the proposed networks are easy and fast to train. Numerical results demonstrate that the proposed network can achieve promising performance with much lower complexity.
Bayesian multiparameter quantum metrology with limited data

A longstanding problem in quantum metrology is how to extract as much information as possible in realistic scenarios with not only multiple unknown parameters, but also limited measurement data and some degree of prior information. Here we present a practical solution to this: we derive a new Bayesian multi-parameter quantum bound, construct the optimal measurement when our bound can be saturated for a single shot and consider experiments involving a repeated sequence of these measurements. Our method properly accounts for the number of measurements and the degree of prior information, and we illustrate our ideas with a qubit sensing network and a model for phase imaging, clarifying the non-asymptotic role of local and global schemes. Crucially, our technique is a powerful way of implementing quantum protocols in a wide range of practical scenarios that tools such as the Helstrom and Holevo Cram\'{e}r-Rao bounds cannot normally access.
Unifying Inter-region Autocorrelation and Intra-region Structures for Spatial Embedding via Collective Adversarial Learning

Unsupervised spatial representation learning aims to automatically identify effective features of geographic entities (i.e., regions) from unlabeled yet structural geographical data. Existing network embedding methods can partially address the problem by: (1) regarding a region as a node in order to reformulate the problem into node embedding; (2) regarding a region as a graph in order to reformulate the problem into graph embedding. However, these studies can be improved by preserving (1) intra-region geographic structures, which are represented by multiple spatial graphs, leading to a reformulation of collective learning from relational graphs; (2) inter-region spatial autocorrelations, which are represented by pairwise graph regularization, leading to a reformulation of adversarial learning. Moreover, field data in real systems are usually lack of labels, an unsupervised fashion helps practical deployments. Along these lines, we develop an unsupervised Collective Graph-regularized dual-Adversarial Learning (CGAL) framework for multi-view graph representation learning and also a Graph-regularized dual-Adversarial Learning (GAL) framework for single-view graph representation learning. Finally, our experimental results demonstrate the enhanced effectiveness of our method.
Bespoke Booklets

We present Bespoke Booklets: a design research method utilizing booklets of situated, imaginary, and personalized conceptual sketches to co-speculatively envision alternative futures (in our case for domestic Internet of Things). The Bespoke Booklets create a space where designers and participants can co-imagine alternative futures while also engaging each other at the level of embodied experiences. After refining our method, we discovered it had many qualities previously championed by feminist HCI and STS theorists. To this end, we draw out, analyze, and critique our method using feminist concepts as a lens to emphasize four specific qualities: collaborative, post-functional, situated, and partial. We found that the booklets, as material artifacts, were a productive tool to generate a physical record of our co-speculation and a fruitful catalyst for research that reflects feminist theory, offering an example of how it can be used as a heuristic for design methodologies.
Production capacity analysis and energy saving of complex chemical processes using LSTM based on attention mechanism

The production data of complex chemical processes are multi-dimensional, uncertain and noisy, and it is difficult to directly control raw materials consumption and measure the product quality. Therefore, this paper proposes a production capacity analysis and energy saving model using long short-term memory (LSTM) based on attention mechanism (AM) (AM-LSTM). The weights of the results sequence in the hidden layer, which have great influence on final results in the output layer, are calculated by the AM. Then the production prediction model is built using the LSTM to extract features of the input data and multiple time series results of the hidden layer. Compared with the common LSTM, the multi-layer perceptron (MLP) and the extreme learning machine (ELM), the applicability and the effectiveness of the proposed model is validated based on University of California Irvine repository (UCI) datasets. Finally, the proposed model is applied to analyze the production capacity and the energy saving potential of the purified terephthalic acid (PTA) solvent system and the ethylene production system of the complex chemical process. The experimental results verify the practicability and accuracy of the proposed model. Furthermore, the results offer the operation guidance for production capacity improvement through saving energy and reducing the energy consumption.
POET

How can progress in machine learning and reinforcement learning be automated to generate its own never-ending curriculum of challenges without human intervention? The recent emergence of quality diversity (QD) algorithms offers a glimpse of the potential for such continual open-ended invention. For example, novelty search showcases the benefits of explicit novelty pressure, MAP-Elites and Innovation Engines highlight the advantage of explicit elitism within niches in an otherwise divergent process, and minimal criterion coevolution (MCC) reveals that problems and solutions can coevolve divergently. The Paired Open-Ended Trailblazer (POET) algorithm introduced in this paper combines these principles to produce a practical approach to generating an endless progression of diverse and increasingly challenging environments while at the same time explicitly optimizing their solutions. An intriguing implication is the opportunity to transfer solutions among environments, reflecting the view that innovation is a circuitous and unpredictable process. POET is tested in a 2-D obstacles course domain, where it generates diverse and sophisticated behaviors that create and solve a wide range of environmental challenges, many of which cannot be solved by direct optimization, or by a direct-path curriculum-building control algorithm. We hope that POET will inspire a new push towards open-ended discovery across many domains.
Integration of IoT with blockchain and homomorphic encryption: Challenging issues and opportunities

The advancement of new technology has taken a huge leap in the last few decades. It is bringing a drastic change in each step of human life that are capable of performing intelligent tasks. The internet of things and blockchain are disruptive technologies that have received a huge attention from industry, academic and financial technologies. There is a risk of privacy leakage of sensitive information in the centralized IoT system because the centralized servers can access the plain text data from the IoT devices. There is an extensive interest in applying the blockchain in the IoT system to provide IoT data privacy and decentralized access model. However, the previous blockchain-based IoT systems have issues related to privacy leakage of sensitive information to the servers as the servers can access the plaintext data from the IoT devices. So, we present the potential of integration of blockchain based-IoT with homomorphic encryption that can secure the IoT data with high privacy in a decentralized mode. In addition, we provide comparison of the recent technologies toward the securing and preserving the privacy of the IoT data using blockchain and homomorphic encryption technology. We have also highlighted the research challenges and possible future research directions in the integrated blockchain-based IoT with homomorphic encryption.
Improving interpolation-based oversampling for imbalanced data learning

The class imbalance problem in machine learning contains a skewed distribution of samples among different classes, resulting in a learning bias toward the majority class. Interpolation-based oversampling techniques solve this problem by yielding the synthetic minority samples to correct the imbalanced class distribution, which has become one of the most popular types of solutions. However, several non-negligible drawbacks exist in them. This study identifies that the flaws of the over constraint, low-efficiency expansion, and over generalization can occur when interpolating the synthetic samples for the inland minority samples (resided in the interior of dense regions of minority class), borderline minority samples (settled in the border of dense regions), and trapped minority samples (located in the sparse regions), respectively. To overcome these flaws, a Position characteristic-Aware Interpolation Oversampling algorithm (PAIO) is proposed. PAIO first leverages a neighborhood-based clustering algorithm to divide the minority samples into three types of samples with different position characteristics (i.e., the inland minority, borderline minority, and trapped minority samples). It then carries out ad hoc interpolation oversampling for these three different types of minority samples, so that the deficiencies can be addressed correspondingly. Moreover, another drawback in interpolation-base oversampling algorithms is the groundless populating for the categorical attributes of synthetic samples. To solve this problem, we develop a Generalized Interpolation Creation way (GIC) to fill the categorical attributes. Extensive experiments on a number of real-world datasets demonstrate that the effectiveness of the proposed PAIO and GIC.
Autonomic machine learning platform

Acquiring information properly through machine learning requires familiarity with the available algorithms and understanding how they work and how to address the given problem in the best possible way. However, even for machine-learning experts in specific industrial fields, in order to predict and acquire information properly in different industrial fields, it is necessary to attempt several instances of trial and error to succeed with the application of machine learning. For non-experts, it is much more difficult to make accurate predictions through machine learning. In this paper, we propose an autonomic machine learning platform which provides the decision factors to be made during the developing of machine learning applications. In the proposed autonomic machine learning platform, machine learning processes are automated based on the specification of autonomic levels. This autonomic machine learning platform can be used to derive a high-quality learning result by minimizing experts’ interventions and reducing the number of design selections that require expert knowledge and intuition. We also demonstrate that the proposed autonomic machine learning platform is suitable for smart cities which typically require considerable amounts of security sensitive information.
The CSO Classifier: Ontology-Driven Detection of Research Topics in Scholarly Articles

Classifying research papers according to their research topics is an important task to improve their retrievability, assist the creation of smart analytics, and support a variety of approaches for analysing and making sense of the research environment. In this paper, we present the CSO Classifier, a new unsupervised approach for automatically classifying research papers according to the Computer Science Ontology (CSO), a comprehensive ontology of re-search areas in the field of Computer Science. The CSO Classifier takes as input the metadata associated with a research paper (title, abstract, keywords) and returns a selection of research concepts drawn from the ontology. The approach was evaluated on a gold standard of manually annotated articles yielding a significant improvement over alternative methods.
Vulnerabilities on Hyperledger Fabric

In this paper, we have precisely analysed Hyperledger Fabric and pointed out two security limitations with possible solutions. First, the identity of an endorser is known to all members within a channel, which opens a gateway for DoS attack on endorsers in order to either block transaction pertaining to a client, or to degrade network efficiency. Second, the technology is prone to wormhole attack i.e. within a channel, compromising a member leads to leakage of ledger information of all members, to everyone outside the channel. We have proposed a solution to remove the above mentioned weaknesses. We have proposed two different mechanisms to eliminate the first weakness. The first approach uses a random verifiable function to randomize endorsers, while the second approach uses pseudonyms to anonymize endorsers. To address the second weakness, we have anonymized sender and receiver identity inside a channel. We have used a group signature approach using bilinear pairing to hide the sender identity and a zero knowledge approach using bilinear pairing to anonymize the receiver. The approach is immune to a malleability attack. Proper security proofs have been provided for Signature Unforgeability and Unlinkability in Ciphertext (UN-C). We have provided experimental results to measure the impact of DoS attack on hyperledger network using Hyperledger Caliper. After applying DoS attack on two peers, the throughput is reduced from 125 tps to 100 tps at send rate 123 tps. The latency in increased from 1.396 s to 2.44 s at send rate 123 tps.
Adaptive Range Genetic Algorithm: A hybrid optimization approach and its application in the design and economic optimization of Shell-and-Tube Heat Exchanger

Several optimization algorithms have been developed in the past for application in a variety of complex real world problems. Herein, a new hybrid variant of Genetic Algorithm (GA), utilizing the sample space reduction technique of Cohort Intelligence (CI) Algorithm, referred to as Adaptive Range Genetic Algorithm (ARGA) is presented and discussed. The correctness of the proposed algorithm is established by solving 50 standard test functions. Also, the practical applicability of the proposed algorithm is demonstrated by applying it to the design and economic optimization problem of a Shell and Tube Heat Exchanger. Heat exchangers are critical industrial components used in a wide range of industrial applications. Owing to their high manufacturing costs and complicated design criteria, their economic efficiency of operation depends heavily on optimization of their design. A relative assessment, comparing the performance of the proposed method of solution to other existing algorithmic methods, is conducted. The quality and robustness of the proposed solution method highlights its adeptness in solving real world computational problems in core mechanical engineering applications.
Agriculture Development, Employment Generation and Poverty Reduction in West Africa

Background: The problem of poverty eradication has been limited to the Economic Community of West African States (ECOWAS) region, which accounts for more than 40% of the world's poor population. The majority of these people are rural farmers who depend solely on agriculture for livelihood. Agriculture in West Africa remains the largest means of employment in which more than 60% of the sub-region’s active labour force is involved. Objective: This study examined the potentials of agriculture to generate employment for the people, thereby reducing the level of poverty in West Africa. Methods: The Generalized Method of Moments (GMM) econometric technique was employed in this study for the panel data covering the period of 17 years (2000 to 2016). Results: Results from the study showed that agriculture provides the opportunity for the poor to increase their earnings to escape the poverty trap, whether the poor can seize these agricultural opportunities depends on their human capital development. Conclusion: The study, therefore, concluded that effective policies ( e.g . social protection) should be formulated in the agricultural development plans that will prioritize sustainable land and water management, access to markets, and the food security. To achieve this, the use of modern methods should be encouraged through farm incentives to boost agricultural production and increase farmer’s income which is earned through the sale of agricultural commodities, and thus; in the long run, increase the revenue accruing to the government and reduce the rate of poverty.
Instance selection improves geometric mean accuracy: a study on imbalanced data classification

A natural way of handling imbalanced data is to attempt to equalise the class frequencies and train the classifier of choice on balanced data. For two-class imbalanced problems, the classification success is typically measured by the geometric mean (GM) of the true positive and true negative rates. Here we prove that GM can be improved upon by instance selection, and give the theoretical conditions for such an improvement. We demonstrate that GM is non-monotonic with respect to the number of retained instances, which discourages systematic instance selection. We also show that balancing the distribution frequencies is inferior to a direct maximisation of GM. To verify our theoretical findings, we carried out an experimental study of 12 instance selection methods for imbalanced data, using 66 standard benchmark data sets. The results reveal possible room for new instance selection methods for imbalanced data.
ColorNet: Investigating the Importance of Color Spaces for Image Classification

Image classification is a fundamental application in computer vision. Recently, deeper networks and highly connected networks have shown state of the art performance for image classification tasks. Most datasets these days consist of a finite number of color images. These color images are taken as input in the form of RGB images and classification is done without modifying them. We explore the importance of color spaces and show that color spaces (essentially transformations of original RGB images) can significantly affect classification accuracy. Further, we show that certain classes of images are better represented in particular color spaces and for a dataset with a highly varying number of classes such as CIFAR and Imagenet, using a model that considers multiple color spaces within the same model gives excellent levels of accuracy. Also, we show that such a model, where the input is preprocessed into multiple color spaces simultaneously, needs far fewer parameters to obtain high accuracy for classification. For example, our model with 1.75M parameters significantly outperforms DenseNet 100-12 that has 12M parameters and gives results comparable to Densenet-BC-190-40 that has 25.6M parameters for classification of four competitive image classification datasets namely: CIFAR-10, CIFAR-100, SVHN and Imagenet. Our model essentially takes an RGB image as input, simultaneously converts the image into 7 different color spaces and uses these as inputs to individual densenets. We use small and wide densenets to reduce computation overhead and number of hyperparameters required. We obtain significant improvement on current state of the art results on these datasets as well.
Fast and stable clustering analysis based on Grid-mapping K-means algorithm and new clustering validity index

As a classical data analysis technique, clustering plays the important role in identifying natural structures of target datasets. However, many of the existing clustering methods, including clustering algorithms and clustering validity indexes (CVIs), are still suffering from problems of low efficiency, poor clustering accuracy, poor stability and more sensitivity to noise points. In this paper, by mapping datasets to grids, the Grid-K-means algorithm is firstly proposed to overcome drawbacks of the traditional K-means algorithm. Then, by utilizing grid points as the weighted representative points to process datasets, a new clustering validity index (BCVI) is designed to better evaluate the quality of clustering results generated by the Grid-K-means algorithm. Based on the monotonous feature of BCVI and the linear combination of intra-cluster compactness and inter-cluster separation of clusters, BCVI consumes much lower time cost in finding the optimal clustering number (Kopt) than the commonly used method that utilizes the empirical rule Kmax≤n to calculate the Kopt. Experimental results on testing many types of datasets have demonstrated that the Grid-K-means algorithm is faster and more accurate than the traditional ones. Meanwhile, the experimental results on testing BCVI and seven existing CVIs have shown that the new BCVI is superior to the traditional ones in terms of clustering stability and data processing speed.
Learning Raw Image Denoising With Bayer Pattern Unification and Bayer Preserving Augmentation

In this paper, we present new data pre-processing and augmentation techniques for DNN-based raw image denoising. Compared with traditional RGB image denoising, performing this task on direct camera sensor readings presents new challenges such as how to effectively handle various Bayer patterns from different data sources, and subsequently how to perform valid data augmentation with raw images. To address the first problem, we propose a Bayer pattern unification (BayerUnify) method to unify different Bayer patterns. This allows us to fully utilize a heterogeneous dataset to train a single denoising model instead of training one model for each pattern. Furthermore, while it is essential to augment the dataset to improve model generalization and performance, we discovered that it is error-prone to modify raw images by adapting augmentation methods designed for RGB images. Towards this end, we present a Bayer preserving augmentation (BayerAug) method as an effective approach for raw image augmentation. Combining these data processing technqiues with a modified U-Net, our method achieves a PSNR of 52.11 and a SSIM of 0.9969 in NTIRE 2019 Real Image Denoising Challenge, demonstrating the state-of-the-art performance.