Reviewerly/exHarmony huggingface dataset:
Systematic Approaches for Precise and Approximate Quantum State Runtime Assertion

With the rapid growth of quantum computing technology, programmers need new tools for debugging quantum programs. Recent works show that assertions are a promising way for debugging quantum programs. However, there are two main drawbacks with the existing schemes. First, the existing schemes, including both statistical and dynamic assertions are only capable of asserting limited types of states, namely classical, superposition, and specific entanglement states. Second, the use cases of these assertions are limited, since the programmer has to know the exact/precise state to assert.In this work, we propose two systematic approaches for dynamic quantum state assertion and they can assert a much broader range of quantum states including both pure states and mixed states. We also introduce the idea of approximate quantum state assertion for the cases where the programmers only have limited knowledge of the quantum states. Approximate assertion is capable of checking membership in a set of states {|Ψ〉,|Φ〉,...}. While precise quantum state assertion can check a specific quantum state, approximate assertion enables a way to check whether the qubits of interest are in a super-set of some expected states, which is analogous to the well-known Bloom filter for membership checking in classical computing. Our experiments demonstrate that our systematic approaches can assert many more quantum states and can be used in various assertion locations for qubit state checking.
Real-time face alignment: evaluation methods, training strategies and implementation optimization

Abstract Face alignment is a crucial component in most face analysis systems. It focuses on identifying the location of several keypoints of the human faces in images or videos. Although several methods and models are available to developers in popular computer vision libraries, they still struggle with challenges such as insufficient illumination, extreme head poses, or occlusions, especially when they are constrained by the needs of real-time applications. Throughout this article, we propose a set of training strategies and implementations based on data augmentation, software optimization techniques that help in improving a large variety of models belonging to several real-time algorithms for face alignment. We propose an extended set of evaluation metrics that allow novel evaluations to mitigate the typical problems found in real-time tracking contexts. The experimental results show that the generated models using our proposed techniques are faster, smaller, more accurate, more robust in specific challenging conditions and smoother in tracking systems. In addition, the training strategy shows to be applicable across different types of devices and algorithms, making them versatile in both academic and industrial uses.
Electronic Medical Record Data Sharing Through Authentication and Integrity Management

The following topics are dealt with: learning (artificial intelligence); Internet of Things; wind power plants; diseases; feature extraction; image classification; power grids; microcontrollers; pattern classification; convolutional neural nets.
Spatial Distribution Characteristics of Heavy Metals in Surface Soil of Xilinguole Coal Mining Area Based on Semivariogram

Heavy metal pollution is a major environmental problem facing humankind. Locating the source and distribution of heavy metal pollutants around mines can provide a scientific basis for environmental control. The structure effect and random effect of a semivariogram can be used to determine the reason for spatial differences in the heavy metal content in surface soil, and the coefficient of variation and regression analysis can be used to confirm that the verification accuracy meets the geostatistical requirements. According to the maximum difference method, the content of heavy metals in the surface soil of the mining area is higher than that of the surroundings, and Cu and Zn levels are higher than the background values for Inner Mongolia. In the present case, Zn, Mn, Pb, Cr, Ni, and Cu levels exceeded the background values for the surroundings of the study area by 65.10%, 53.72%, 52.17%, 46.24%, 33.08%, and 29.49%, respectively. The results show that human activities play a decisive role in the spatial distribution of heavy metals, leading to their spatial distribution in the form of “core periphery”. This distribution pattern was significantly affected by the slope, NDVI value, and the distance from the mining area, but the spatial distribution of Pb was significantly related to high-grade roads. The research methods and conclusions have reference significance for the sources and spatial distribution characteristics of heavy metal pollution in similar mining areas and provide a target for the prevention and control of environmental pollution in the study area.
Symmetrical lattice generative adversarial network for remote sensing images compression

Image compression usually includes two important operations: compression and decompression. The compression process includes the operation of discarding information, while the decompression process is to retrieve the lost information. In order to make the decompressed image more similar to the original image, the classic compression methods generally adopt the process of approximately reversible compression and decompression. Inspired by the symmetric structure in classic compression methods, we propose a new symmetrical lattice generating adversarial network (SLGAN) for the remote sensing images (RSIs) compression in this paper. Several pairs of symmetrical encoder-decoder lattices are constructed to build a generator to first generate deep representative codes of images and then decode them. For each pair of encoded lattice and decoded lattice, one discriminator is constructed to perform adversarial learning with the generator. When multiple discriminators are used for all the lattices, a cooperative learning algorithm is proposed to train jointly pairs of symmetric lattices in the generator. Moreover, to enhance edges, contours, and textures in the decomposed RSIs, an enhanced Laplacian of gaussian (ELoG) loss is designed as a regularizer to train the SLGAN. Experimental results on the panchromatic images from GF2 satellite show that SLGAN outperforms other existing state-of-the-art methods.
Dual-path CNN with Max Gated block for text-based person re-identification

Text-based person re-identification (Re-id) is an important task in video surveillance, which consists of retrieving the corresponding person's image given a textual description from a large gallery of images. It is difficult to directly match visual contents with the textual descriptions due to the modality heterogeneity. On the one hand, the textual embedding are not discriminative enough, which originates from the high abstraction of the textual descriptions. One the other hand, Global average pooling (GAP) is commonly utilized to extract more general or smoothed features implicitly but ignores salient local features, which are more important for the cross-modal matching problem. With that in mind, a novel Dual-path CNN with Max Gated block (DCMG) is proposed to extract discriminative word embedding and make visual-textual association concern more on remarkable features of both modalities. The proposed framework is based on two deep residual CNNs jointly optimized with cross-modal projection matching (CMPM) loss and cross-modal projection classification (CMPC) loss to embed the two modalities into a joint feature space. First, the pre-trained language model, BERT, is combined with the convolutional neural network (CNN) to learn better word embedding in the text-to-image matching domain. Second, the global Max pooling (GMP) layer is applied to make the visual textual features focus more on the salient part. To further alleviate the noise of the maxed-pooled features, the gated block (GB) is proposed to produce an attention map that focuses on meaningful features of both modalities. Finally, extensive experiments are conducted on the benchmark dataset, CUHK-PEDES, in which our approach achieves the rank-1 score of 55.81% and outperforms the state-of-the-art method by 1.3%. We also evaluate our method on two generic retrieval datasets (Flickr30K, Oxford-102 Flowers) and obtain the competitive performance. Code is available at https://github.com/voriarty/Dual-path-CNN-with-Max-Gated-block-for-Text-Based-Person-Re-identification
Determining research data services maturity: The role of library leadership and stakeholder involvement

As technology and the stewardship of research data continue to improve, academic libraries have made progress in establishing themselves as hubs and leaders for research data services on campus. They have been called on to assure cross-campus collaboration and support to develop a united service to meet their community's needs. However, evidence as to how libraries play a crucial role in leadership, whether other stakeholders' involvement makes a difference, and if so, how that involvement makes a difference is not well-documented. The findings of this study add empirical evidence in supporting the value of library's leadership and stakeholder engagement in developing research data policy and services. The secondary analysis of the survey data found that libraries tend to offer more extensive services when they take primary responsibility for developing the policy and service. It also found that the more internal stakeholders involved in developing the policy and service, the higher level of service maturity the libraries offer. Partnership with external stakeholders leads to more comprehensive and deeper services.
Object recognition in performed basic daily activities with a handcrafted data glove prototype

Abstract In this paper, special attention is given to the hand. The literature provides solutions allowing the hand gestures recognition and/or object recognition for virtual reality, robotic applications, and so on. These solutions rely mainly on computer vision and data gloves. From this finding, we decided to develop our data glove prototype. The data glove is exploited to recognize common objects in the kitchen that the person can hold (e.g., hold a fork) while he/she performs basic daily activities such as drink a glass of water. The proposed approach is straightforward, cheap ( ∼ 260 $ in USD) and efficient ( ∼ 100%). Moreover, the designed data glove gives easy and direct access to the raw data provided by sensors. Besides, a comparison between classical machine learning algorithms (e.g., CART, Random Forest) and a deep neural network is given. Finally, the proposed prototype is described in a way that researchers can reproduce it for any applications involving the object recognition with the hand.
Mineral exploration targeting by combination of recursive indicator elimination with the ℓ2-regularization logistic regression based on geochemical data

In geochemical exploration, how to choose indicator elements and how to synthesize the concentrations of indicator elements into metallogenic favorability are two key problems that need to be solved properly in mineral exploration targeting. Developing new geochemical data processing techniques is one of feasible ways to solve these two key problems. Therefore, a recursive indicator elimination (RIE) method was proposed in this paper to simultaneously determine the optimal subset of geochemical elements and to complete mineral exploration targeting. The method is a combination of a recursive elimination process and machine learning techniques. The recursive elimination process is an iterative procedure to determine the optimal subset of geochemical elements; and the machine learning techniques are used to model the relationship between the subset of geochemical elements and the mineral deposits in each iteration. The optimal subset of geochemical elements determined by the recursive indicator elimination has the closest relationship with the mineral deposits. Thus, it is expected that the machine learning model established on the optimal subset of geochemical elements will have the best performance in mineral exploration targeting. A case study was conducted in the Helong area, Jilin Province, China. Either the recursive indicator elimination or the area under the curve (AUC) was used to determine the optimal subset of geochemical elements based on the 1:50, 000 stream sediment survey data. In addition, principal component analysis (PCA) was used to synthesize all geochemical elements into a few principal components (i.e., comprehensive indicators). Finally, the optimal subset of geochemical elements and the principal components were used to establish the logistic regression model for mineral exploration targeting. The results show that the RIE combined with logistic regression has the best performance in mineral exploration targeting. The performance of AUC combined with logistic regression is similar to that of PCA combined with logistic regression. From the geological point of view, there is a strong consistency between the mineral exploration targeting results and the metallogenic characteristics of the study area. Therefore, the combination of RIE and logistic regression is an effective method to synthesize geochemical element concentrations into metallogenic favorability for mineral exploration targeting.
Loop-Closure Detection Using Local Relative Orientation Matching

Loop-closure detection (LCD), which aims to recognize a previously visited location, is a crucial component of the simultaneous localization and mapping system. In this paper, a novel appearance-based LCD method is presented. In particular, we propose a simple yet surprisingly useful feature matching algorithm for real-time geometrical verification of candidate loop-closures, termed as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">local relative orientation</i> matching (LRO). It aims to efficiently establish reliable feature correspondences based on preserving local topological structures between the query image and candidate frame. To effectively retrieve candidate loop closures, we introduce the aggregated selective match kernel framework into the LCD task, which can effectively represent images and reduce the quantization noise of the traditional bag-of-words framework. In addition, the SuperPoint neural network is employed to extract reliable interest points and feature descriptors. Extensive experimental results demonstrate that our LRO can significantly improve the LCD performance, and the proposed overall LCD method can achieve much better performance over the current state-of-the-art on six publicly available datasets.
Towards a Blockchain-SDN Architecture for Secure and Trustworthy 5G Massive IoT Networks

The emerging 5G mobile network is a prominent technology for addressing networking related challenges of Internet of Things (IoT). The forthcoming 5G is expected to allow low-power massive IoT devices to produce high volumes of data that can be transmitted over ultra-reliable, low-latency wireless communication services. However, IoT systems encounter several security and privacy issues to prevent unauthorized access to IoT nodes. To address these challenges, this paper introduces a novel blockchain-based architecture that leverages Software Defined Network (SDN) and Network Function Virtualization (NFV) for securing IoT transactions. A novel security appliance is introduced in a form of Virtualized Network Functions (VNFs) for improving the scalability and performance of IoT networks. Then, we introduce a novel consensus algorithm to detect and report suspected IoT nodes and mitigate malicious traffic. We evaluate and compare our proposed solution against three well-known consensus algorithms, i.e., Proof of Work (PoW), Proof of Elapsed Time (PoET), and Proof of Stake (PoS). We demonstrate that the proposed solution provides substantially lower latency and higher throughput as well as trustworthy IoT communication.
Remote attestation and integrity measurements with Intel SGX for virtual machines

With response to the emerging virtualization trend, we focus on a Virtual Machine (VM) remote attestation process assuming that it is running in an uncontrolled and untrusted cloud infrastructure. We present a solution that is able to establish a chain of trust in a cloud environment. Our solution is based on a set of CPU instructions and it does not need any additional components to track host modifications. Our solution enables integrity verification of a filesystem. Additionally, it ensures trust level assessment for remote VMs during their startup or while triggered manually at any point in time afterwards. We identify security properties for our solution and show how it meets them. The security analysis shows that with necessary countermeasures, the proposed model can ensure the required level of security. Additionally, We evaluate the performance impact of the prototype and virtualization overhead for a real-life scenario. Here, we assume that small configuration files, binaries, and executables are most critical. The results show that important filesystem components can be verified with a minimum impact on a startup time. This way, the whole proposal allows for making VM a part of a trusted compute resource pool.
A filter based genetic algorithm and neural network technique for Image Classification

We present a method for classification of images using GA-NN approach. We will take standard image data set from UCI repository or we can create image data set by clicking images using camera and further extracting features using mazda software. In either case we will be getting feature-set of images, which will be reduced (optimized) by feature subset selection using Mutual Information based filter approach of Genetic algorithm. This optimal feature set will be further classified using neural network (nprtool). Finally we are comparing our classification results with the results of existing Multi-SVM method.
Erase then grow: Generating correct class activation maps for weakly-supervised semantic segmentation

In spite of extremely challenging, the weakly-supervised semantic segmentation using image-level labels has made encouraging progress in the recent phase. The existing methods mainly adopt two-stage training procedures: a) optimizing class activation map (CAM) produced by the multi-label classification network to generate pseudo ground truth; b) training a conventional fully supervised semantic segmentation network through pseudo ground truth. When optimizing CAM, most advanced methods just consider the problem that CAM can only activate the sparse and discriminative regions for each class. However, since the loss function of the classification task is image-level supervision, classification network is weak in capturing intricate contextual information, which results in another problem that many misclassified regions are activated in CAM. Compared with classification networks, the loss function of semantic segmentation tasks is pixel-level supervision, which makes it better at capturing intricate contextual information. Thus, based on this ability of the segmentation network, we propose an erasing module to erase the misclassified regions in the CAM. Furthermore, to transform the sparse CAM into high-quality dense pseudo ground truth, we apply the proposed hierarchical deep seeded region growing (H-DSRG) on the erased CAM. Finally, we conduct extensive analysis to validate the proposed method. The proposed method achieves 66.8 of mIoU for Pascal voc 2012 val dataset and 67.6 of mIoU for Pascal voc 2012 test dataset, harvesting new state-of-the-art results.
Capturing the grouping and compactness of high-level semantic feature for saliency detection

Saliency detection is an important and challenging research topic due to the variety and complexity of the background and saliency regions. In this paper, we present a novel unsupervised saliency detection approach by exploiting the grouping and compactness characteristics of the high-level semantic features. First, for the high-level semantic feature, the elastic net based hypergraph model is adopted to discover the group structure relationships of salient regional points, and the calculation of the spatial distribution is constructed to detect the compactness of the saliency regions. Next, the grouping-based and compactness-based saliency maps are improved by a propagation algorithm. The propagation process uses an enhanced similarity matrix, which fuses the low-level deep feature and the high-level semantic feature through cross diffusion. Results on four benchmark datasets with pixel-wise accurate labeling demonstrate the effectiveness of the proposed method. Particularly, the proposed unsupervised method achieves competitive performance with deep learning-based methods.
Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python

This section will review those books whose content and level reflect the general editorial policy of Technometrics. Publishers should send books for review to Ejaz Ahmed, Department of Mathematics ...
MAT-AGCA: Multi Augmentation Technique on small dataset for Balinese character recognition using Convolutional Neural Network

The lontar manuscript is an ancient Balinese cultural heritage written using Balinese characters on palm leaves. The recognition of Balinese characters in lontar is challenging because it has noise and limited data availability. To solve these problems, data augmentation is needed to increase the variety and amount of data to improve recognition performance. In this study, we collected Balinese character images from 50 lontar manuscript writers. We proposed MAT-AGCA that combines Adaptive Gaussian Thresholding and Convolutional Autoencoder for data augmentation. Based on experiments using InceptionResnetV2, DenseNet169, ResNet152V2, VGG19, and MobileNetV2, our proposed method achieved the best performance with 96.29% accuracy.
Anomaly Detection in Seismic Data–Metadata Using Simple Machine-Learning Models

Abstract In modern seismological analysis, it is not unusual to process huge amounts of data, as illustrated by two case studies exemplified in this work, both assessing the quality of several millions of segments selected for computing local and energy magnitudes. In this scenario, quality control tools to filter, discard, or rank data are of extreme importance and should ideally be simple, fast, and generalizable. Using machine-learning tools, we present here a simple and efficient model based on the isolation forest algorithm for detecting amplitude anomalies on any seismic waveform segment, with no restriction on the segment record content (earthquake vs. noise) and no additional requirements than the segment metadata. By considering a simple feature space composed of amplitudes of each segment’s power spectral density (PSD) evaluated at selected periods suitable for both local and teleseismic applications, feature selection revealed that one single feature, the PSD at 5 s, is sufficient to achieve the best predicting performances. The evaluation results report average precision scores around 0.97, and maximum F1 scores above 0.9, both remarkable results with respect to the simplicity of the approach used and the generality of the problem tackled. The trained model producing the best evaluation results is the backbone of a publicly available software, which computes an amplitude anomaly score in [0, 1] for any given seismic waveform, and can be beneficial in several applications such as discarding anomalies from data sets, ideally in a preprocessing stage, and detecting potential metadata problems on data center side. When applied to our two case studies, the software was revealed to be fast and effective, and the computed anomaly scores allow additional flexibility in addition to the proven wide-range applicability.