Reviewerly/exHarmony huggingface dataset:
From research to action: the practice of decolonizing ICT4D

The production of knowledge in Information and Communication Technology for Development (ICT4D) research has been characterized by an ongoing shift from dominantly Western-based to Indigenous theory formulations. This editorial puts forward core concepts in the decolonization of ICT4D, arguing that these are fundamental to the creation, reading, and interpretation of ICT4D knowledge. Drawing on a decolonial read of the articles published in Vol. 28.3, we advance the argument that decolonizing ICT4D, rather than simply a means to read and analyze data, is an emancipatory practice to be adopted in an open challenge to Western-centric modes of doing ICT4D research.
Optimal cooling configurations for quantum information processing at milli-kelvin temperatures

So called quantum supremacy has been demonstrated by at least two groups utilising < 100 physical superconducting qubits cooled to milli-kelvin temperatures. It is acknowledged that scaling these efforts to fault-tolerant, general purpose quantum computers will require very many more logical qubits, each formed from several physical ones. Addressing and control of these qubits is an area of active research, but it is likely that increased cooling capacities, particularly at the lowest temperatures, will be a requirement for the realisation of systems beyond noisy intermediate-scale quantum processors. Here we demonstrate a method by which significantly increased cooling power at temperatures ∼ 200 mK can be generated without impacting on the performance of an adjacent stage providing cooling power at temperatures ∼ 20 mK.
Definition of a sectioning plane and place for a section containing hoped-for regions using a spare counterpart specimen

Histological examination of targets in regions of interest in histological sections is one of the most frequently used tools in biomedical research. However, it is a technical challenge to secure a multitarget section for inspection of the structure's mutual relationship of targets or a longitudinally filamentous- or tubular-formed tissue section for visitation of the overall morphological features. We present a method with a specified cutting plane and place, allowing researchers to cut directly at the multitarget centers accurately and quickly. The method is proven to be reliable with high accuracy and reproducibility and a low coefficient of variation, testing on repeat experiments of three target's position-known models. With this method, we successfully yielded single sections containing whole intraorbital optical nerves, three aortic valves, or whole thoracic tracheas in their central positions. The adjoined custom-made tools used in the study, such as various tissue-specific formulated calibrated trimming and embedding guides, an organ-shaped cavity plaster mold, and a two-time embedding technique for optimal and identical trimming or embedding, also bear great potential to become a common supplemental tool for traditional histology and may contribute to the reduction of the labor, and the number of animals needed.
A novel person re-identification network to address low-resolution problem in smart city context

We argue that accurate person re-identification is a vital problem for urban public monitoring systems in the smart city context. Since images captured from different cameras have arbitrary resolutions resulting in resolution mismatch, this work proposes a model that takes arbitrary images and converts them to a pre-defined fixed resolution. The model then passes the images to a super-resolution network, producing high-resolution images. We employ a feedback network to generate more realistic super-resolution images, which are fed to the re-identification network to acquire a unique descriptor to disclose the person’s identity. We outperformed in all measures against other state-of-the-art methods.
A deep learning classification model for Persian Hafez poetry based on the poet’s era

More than any other literary genre, poetry presents a significant challenge for Natural Language Processing (NLP) algorithms. Small poetries in the Persian language are called ghazal. Ghazal classification by document embedding technique and sequential learning on poetic era is an under-explored area of research till now. Deep learning and document embedding technique is explored in the current study. We have worked with Persian Ghazal, which Hafez writes. We have found and employed useful NLP approaches to facilitate and automate the classification of Hafez’s poetry. We developed and implemented a set of rigorous and repeatable techniques that may be extended to different types of poetries. It is a part of Persian text classification and NLP. We have implemented neural network models that automatically classify Hafez’s Persian poetry chronologically with around 85% accuracy. This proposed model is significantly better than previously reported work in the Persian Language on poetry data. In the Persian language, meter classification and machine learning-based poetry classification were done before. We have introduced a classification method based on the poet’s era using sequential architectures. We found the highest accuracy when we used the Distributed Memory model for document embedding and Long Short-Term Memory (LSTM) model for training the Persian Hafez ghazals. We have achieved approximately 87% precision, 85% F1-score and 85% recall score by using our model. To perform this classification, we have used refined Hafez ghazals’ labels and found better accuracy than Bidirectional Long Short-Term Memory (Bi-LSTM) and Gated Recurrent Units (GRU) models.
Robust Optimization for Local Differential Privacy

We consider the setting of publishing data without leaking sensitive information. We do so in the framework of Robust Local Differential Privacy (RLDP). This ensures privacy for all distributions of the data in an uncertainty set. We formulate the problem of finding the optimal data release protocol as a robust optimization problem. By deriving closed-form expressions for the duals of the constraints involved we obtain a convex optimization problem. We compare the performance of four possible optimization problems depending on whether or not we require robustness in i) utility and ii) privacy.
α-GAN: Convergence and Estimation Guarantees

We prove a two-way correspondence between the min-max optimization of general CPE loss function GANs and the minimization of associated f-divergences. We then focus on α-GAN, defined via the α-loss, which interpolates several GANs (Hellinger, vanilla, Total Variation) and corresponds to the minimization of the Arimoto divergence. We show that the Arimoto divergences induced by α-GAN equivalently converge, for all α∈ℝ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;0</inf> ∪{∞}. However, under restricted learning models and finite samples, we provide estimation bounds which indicate diverse GAN behavior as a function of α. Finally, we present empirical results on a toy dataset that highlight the practical utility of tuning the α hyperparameter.
Secure codes with list decoding

Traitor tracing is a mathematical approach of protecting copyright of multimedia content. In this paper we propose a new concept of secure codes with list decoding (SCLD) for collusion-resistant multimedia fingerprinting, which could include many existing classes of fingerprinting codes as special cases. Furthermore, we build an efficient identifying algorithm for SCLD and establish bounds on its largest asymptotic code rate. In comparison with the existing fingerprinting codes, it is shown that SCLD has not only much more efficient traceability than separable codes but also a much larger code rate than frameproof codes.
Composite Anomaly Detection via Hierarchical Dynamic Search

Anomaly detection among a large number of processes arises in many applications ranging from dynamic spectrum access to cybersecurity. In such problems one can often obtain noisy observations aggregated from a chosen subset of processes that conforms to a tree structure. The distribution of these observations, based on which the presence of anomalies is detected, may be only partially known. This gives rise to the need for a search strategy designed to account for both the sample complexity and the detection accuracy, as well as cope with statistical models that are known only up to some missing parameters. In this work we propose a sequential search strategy using two variations of the Generalized Log Likelihood Ratio statistic. Our proposed Hierarchical Dynamic Search (HDS) strategy is shown to be order-optimal with respect to the size of the search space and asymptotically optimal with respect to the detection accuracy. An explicit upper bound on the error probability of HDS is established for the finite sample regime. Extensive experiments are conducted, demonstrating the performance gains of HDS over existing methods.
Unlimited Sampling via Generalized Thresholding

The Unlimited Sensing Framework (USF) provides an alternative protocol for high dynamic range (HDR) acquisition of real world signals. By incorporating a modulo non-linearity prior to sampling, an HDR signal, which is prone to clipping due to sensor saturation, is folded back into the dynamic range of the analog-to-digital converter (ADC). Thereafter, the modulo samples are algorithmically unfolded to their native dynamic range, thus allowing for recovery of HDR input signals. For bandlimited functions, a sampling density criterion akin to the Shannon-Nyquist theorem guarantees recovery from the modulo samples. Recently, a hardware implementation of the modulo ADC has motivated a generalized acquisition model called modulo-hysteresis that can handle non-idealities observed in practice. The recovery guarantees for this model are currently based on finite difference filters. Such filters can not handle practical scenarios where noise and perturbation play a role. The main goal of this work is to introduce a principled approach that explains the role of general filters for signal recovery. Based on the modulo-hysteresis acquisition model, we formulate input recovery guarantees for thresholding with general filters and give numerical simulations to show cases where the finite difference filter is not an optimal choice for reconstruction.
Regularization-wise double descent: Why it occurs and how to eliminate it

The risk of overparameterized models, in particular deep neural networks, is often double-descent shaped as a function of the model size. Recently, it was shown that the risk as a function of the early-stopping time can also be double-descent shaped, and this behavior can be explained as a super-position of bias-variance tradeoffs. In this paper, we show that the risk of explicit L2-regularized models can exhibit double descent behavior as a function of the regularization strength, both in theory and practice. We find that for linear regression, a double descent shaped risk is caused by a superposition of bias-variance tradeoffs corresponding to different parts of the model and can be mitigated by scaling the regularization strength of each part appropriately. Motivated by this result, we study a two-layer neural network and show that double descent can be eliminated by adjusting the regularization strengths for the first and second layer. Lastly, we study a 5-layer CNN and ResNet-18 trained on CIFAR-10 with label noise, and CIFAR-100 without label noise, and demonstrate that all exhibit double descent behavior as a function of the regularization strength.
High-Dimensional Quantum Conference Key Agreement

Quantum Conference Key Agreement (QCKA) protocols are designed to allow multiple parties to agree on a shared secret key, secure against computationally unbounded adversaries. In this paper, we consider a high-dimensional QCKA protocol and prove its information theoretic security against arbitrary, general, attacks in the finite-key scenario. Our proof technique may be useful for other high-dimensional multiparty quantum cryptographic protocols. Finally, we evaluate the protocol in a variety of settings, showing that high-dimensional states can greatly benefit QCKA protocols.
Efficient and Robust Classification for Sparse Attacks

In the past two decades we have seen the popularity of neural networks increase in conjunction with their classification accuracy. Parallel to this, we have also witnessed how fragile the very same prediction models are: tiny perturbations to the inputs can cause misclassification errors throughout entire datasets. In this paper, we consider perturbations bounded by the ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</inf> –norm, which have been shown as effective attacks in the domains of image-recognition, natural language processing, and malware-detection. To this end, we propose a novel defense method that consists of "truncation" and "adversarial training". We then theoretically study the Gaussian mixture setting and prove the asymptotic optimality of our proposed classifier. Motivated by the insights we obtain, we extend these components to neural network classifiers. We conduct numerical experiments in the domain of computer vision using the MNIST and CIFAR datasets, demonstrating significant improvement for the robust classification error of neural networks.
Singleton bounds for entanglement-assisted classical and quantum error correcting codes

We show that entirely information theoretic methods, based on von Neumann entropies and their properties, can be used to derive Singleton bounds on the performance of entanglement-assisted hybrid classical-quantum (EACQ) error correcting codes. Concretely we show that the triple-rate region of qubits, cbits and ebits of possible EACQ codes over arbitrary alphabet sizes is contained in the quantum Shannon theoretic rate region of an associated memoryless erasure channel, which turns out to be a polytope. We show that a large part of this region is attainable by certain EACQ codes, whenever the local alphabet size (i.e. Hilbert space dimension) is large enough, in keeping with known facts about classical and quantum minimum distance separable (MDS) codes: in particular all of its extreme points and several important extremal lines. Full details in [1].
Learning under Storage and Privacy Constraints

Storage-efficient privacy-guaranteed learning is crucial due to enormous amounts of sensitive user data required for increasingly many learning tasks. We propose a framework for reducing the storage cost while at the same time providing privacy guarantees, without essential loss in the utility of the data for learning. Our method comprises noise injection followed by lossy compression. We show that, when appropriately matching the lossy compression to the distribution of the added noise, the compressed examples converge, in distribution, to that of the noise-free training data. In this sense, the utility of the data for learning is essentially maintained, while reducing storage and privacy leakage by quantifiable amounts. We present experimental results on the CelebA dataset for gender classification and find that our suggested pipeline delivers in practice on the promise of the theory: the individuals in the images are unrecognizable (or less recognizable, depending on the noise level), overall storage of the data is substantially reduced, with no essential loss of the classification accuracy. As an added bonus, our experiments suggest that our method yields a substantial boost to robustness in the face of adversarial test data.
Bounding quantum capacities via partial orders and complementarity

Quantum capacities are fundamental quantities that are notoriously hard to compute and can exhibit surprising properties such as superadditivity. Thus a vast amount of literature is devoted to finding close and computable bounds on these capacities. We add a new viewpoint by giving operationally motivated bounds on several capacities, including the quantum capacity and private capacity of a channel and the one-way distillable entanglement and private key of a bipartite state. Our bounds themselves are generally given by certain capacities of the complementary channel or state. As a tool to obtain these bounds we discuss partial orders on quantum channels, such as the less noisy and the more capable order. Our bounds help to further understand the interplay between different capacities and give operational limitations on superadditivity properties and the difference between capacities. They can also be used as a new approach towards numerically bounding capacities, as discussed with some examples.
Traffic Sign Detection Algorithm Based on Improved YOLOv4

Aiming at the problems of low detection accuracy of small targets, low real-time detection and missed target detection in the current traffic sign detection, an improved algorithm based on YOLOv4 is proposed. In the YOLOv4 network, the second residual block of CSPDarknet53 is used to embed the convolution structure and the YOLO head structure, increase another one output, and perform upsampling and downsampling in the network. The Inception structure is added to the classification and regression network to further improve the detection speed and increase the network complexity. Finally, the Inception structure is added to the classification and regression network to further improve the detection speed and increase the network complexity. The experimental results show that on the TT100K (Tsinghua-Tencent 100K) traffic sign dataset, compared with the YOLOv4 network, the mAP has increased by 3.6 percent, and the recognition speed has only decreased in a small range, basically achieving a balance between recognition accuracy and speed. The experimental results demonstrate the effectiveness of the improved network in small target detection and overall detection.
Fractional-order Retinex-based low-light image enhancement fusion algorithm for energy meters

For the haze, rainy days and night and other detection environment, the smart meter collection image parameters difficult to identify the problem, A fractional-order improved Retinex-based low-light image enhancement fusion algorithm is proposed; The image is enhanced by the fractional-order improved Retniex-based low-light enhancement algorithm, and the environmental noise is filtered out by the Fast ABF algorithm, and the second enhancement process is performed; the original image, the first low-light enhanced image, the Fast ABF enhanced image and the second low-light enhanced image are decomposed by the Laplace pyramid and multiplied by the corresponding Gaussian weight matrix, and the final enhancement is obtained by fusion image; Among them, the self-built smart meter dataset is used, and the YOLOv5 model is adopted to extract the target meter dial area; the results show that the algorithm outperforms other comparison algorithms in terms of image naturalness retention and image sharpness, so that the detection accuracy is improved.
Construction and Application of Knowledge Graph for Bridge Inspection

This paper constructs a knowledge graph and complex question answering corpus for bridge inspection, which lays the foundation for constructing an automatic question answering system oriented to the characteristics of this field. Many related works are currently presented for the open domain due to the characteristics of massive scale, high data quality, and easy availability of general domain knowledge graph data, but there is only a little research on domain knowledge graph construction and complex question answering. This paper proposes a naming method of “component name_(center pile number)” to distinguish the same components of different bridges. Then given the unique tree structure of the knowledge graph for bridge inspection, we creatively propose a three-level relational knowledge graph construction method and annotate datasets containing five types of complex question answering. Finally, we use our corpus to conduct experiments on mainstream complex question answering models and further demonstrate the effectiveness of the domain-specific knowledge graph construction method.
Research on Solid Rate Filtering Technique based on Inverse Distance Weighted Interpolation of Navigation Radar

An inverse distance weighted interpolation solid rate filtering method is proposed to plot radar images by echo data of surface X-band navigation radar. The method proposes an algorithm based on inverse distance weighted interpolation filtering according to the characteristics of the echo data, converting the original inhomogeneous 2D echo data into a radially equidistant data format, and thus plots the navigation radar images in polar coordinate system. and then realizes effective suppression of wave clutter via multi-stage DBSCAN clustering-based solid rate filtering. Finally, the single-channel grayscale map is converted into a three-channel pseudo-color map for subsequent detection of the target. The experimental results show that this algorithm has a good denoising function while maintaining the grayscale information of radar images.
The Logistics Barcode ID Character Recognition Method Based on AKAZE Feature Localization

With the continuous development of science and technology, the rapid rise of e-commerce, and the continuous influx of the logistics industry into our lives. In the transportation of logistics, the sorting is the mainly work, it needs machines or staff using barcode scanners to identify barcodes to achieve sorting. Meantime, it is necessary to align the barcode of the item manually or mechanically with the barcode scanner to accurately identify it. Undoubtedly, a lot of manpower and material resources are required in the sorting process, so the method of computer vision processing is used to achieve automatic detection and identification of barcode ID will have great significance. The authors propose a logistics barcode ID character recognition method based on local positioning of AKAZE features to realize the detection and identification of logistics barcode ID in a long distance and a large range. First, extracting the AKAZE feature of the barcode in the image, using the matching algorithm to localize the barcode area, and then use the OCR (Optical Character Recognition) method based on deep learning to perform the character recognition of the barcode ID. A large number of experiments have been carried out with the real existing parcel barcodes, in which the accuracy rate reaches 99.6%, the speed reaches 0.35s/frame. Besides, the detection and identification of barcode IDs of different types and different length codes can be realized. The adoption in the future will further promote the feasibility of the logistics and transportation industry.
State Assessment of Key Equipment of Microgrid Based on Multi-source Data Fusion Method

Aiming at the difficulty of evaluating the status of power equipment and high maintenance costs in the actual operation of the power grid, a power equipment status evaluation method based on multi-dimensional feature data fusion is proposed. First, multi-source data is processed, and corresponding recursive graphs are generated based on structured time series data, and unstructured data and structured data are unified into the same description space. Then, feature extraction is performed on the recursive graph and existing image data based on the convolutional neural network, and the corresponding feature vector is obtained. The feature vector is spliced and fused according to the adaptive weight, and the state of the fused multi-modal data feature is evaluated. Finally, the effectiveness of the evaluation method is verified by an example of a switch cabinet.