Reviewerly/exHarmony huggingface dataset:
Exposedness-Based Noise-Suppressing Low-Light Image Enhancement

A noise-suppressing low-light image enhancement approach is proposed in this paper based on the extent of exposedness at each image pixel. To this end, a progressive, structure-aware exposedness estimation procedure is presented that quantifies local and global exposedness. These exposedness values are leveraged to produce a locally smooth pixel-level map that signifies the required degrees of enhancement at image pixels. This map is subsequently used in an enhancement function, which satisfies a few important properties, to generate the enhanced image. Before the enhancement, inherent noise in the low-light image is diminished employing a detail-preserving, low gradient magnitude suppression method. Subjective and quantitative analysis of results on a wide variety of natural and synthetically generated low-light images from standard databases using PSNR, iRSE, SSIM, and measures of perceptual quality, natural image statistics and brightness preservation suggests that our approach in general outperforms the state-of-the-art. Ablation studies and further experiments show the importance of a few components of our approach, and that our approach is computationally fast.
Deep Learning-Assisted TeraHertz QPSK Detection Relying on Single-Bit Quantization

TeraHertz (THz) wireless communication constitutes a promising technique of satisfying the ever-increasing appetite for high-rate services. However, the ultra-wide bandwidth of THz communications requires high-speed, high-resolution analog-to-digital converters, which are hard to implement due to their high complexity and power consumption. In this paper, a deep learning-assisted THz receiver is designed, which relies on single-bit quantization. Specifically, the imperfections of THz devices, including their in-phase/quadrature-phase imbalance, phase noise and nonlinearity are investigated. The deflection ratio of the maximum-likelihood detector used by our single-bit-quantization THz receiver is derived, which reveals the effect of phase offset on the demodulation performance, guiding the architecture design of our proposed receiver. To combat the performance loss caused by the above-mentioned distortions, a twin-phase training strategy and a neural network based demodulator are proposed, where the phase offset of the received signal is compensated before sampling. Our simulation results demonstrate that the proposed deep learning-assisted receiver is capable of achieving a satisfactory bit error rate performance, despite the grave distortions encountered.
AnchiBERT: A Pre-Trained Model for Ancient Chinese Language Understanding and Generation

Ancient Chinese is the essence of Chinese culture. There are several natural language processing tasks of ancient Chinese domain, such as ancient-modern Chinese translation, poem generation, and couplet generation. Previous studies usually use the supervised models which deeply rely on parallel data. However, it is difficult to obtain large-scale parallel data of ancient Chinese. In order to make full use of the more easily available monolingual ancient Chinese corpora, we release An-chiBERT, a pre-trained language model based on the architecture of BERT, which is trained on large-scale ancient Chinese corpora. We evaluate AnchiBERT on both language understanding and generation tasks, including poem classification, ancient-modern Chinese translation, poem generation, and couplet generation. The experimental results show that AnchiBERT outperforms BERT as well as the non-pretrained models and achieves state-of - the-art results in all cases.
VFC-SMOTE: very fast continuous synthetic minority oversampling for evolving data streams

Abstract The world is constantly changing, and so are the massive amount of data produced. However, only a few studies deal with online class imbalance learning that combines the challenges of class-imbalanced data streams and concept drift. In this paper, we propose the very fast continuous synthetic minority oversampling technique ( VFC - SMOTE ). It is a novel meta-strategy to be prepended to any streaming machine learning classification algorithm aiming at oversampling the minority class using a new version of Smote and Borderline - Smote inspired by Data Sketching. We benchmarked VFC - SMOTE pipelines on synthetic and real data streams containing different concept drifts, imbalance levels, and class distributions. We bring statistical evidence that VFC - SMOTE pipelines learn models whose minority class performances are better than state-of-the-art. Moreover, we analyze the time/memory consumption and the concept drift recovery speed.
Fully Synthesizable Unified True Random Number Generator and Cryptographic Core

This paper introduces a novel class of architectures that unify true random number generation and private-key cryptography by reusing the cryptographic core for both tasks. The unified architecture is well suited for low-cost constrained secure integrated systems, in view of the inherent area efficiency and the low design effort entailed by conventional automated design flows. Clock pulse over-stretching in pulsed latch clocking generates randomness by inducing metastability and jittered oscillations. Shannon confusion and diffusion in the cryptographic datapath enforce high entropy and robustness against variations. Conventional cryptographic operation is alternatively performed at moderate clock pulsewidths. A 40-nm CMOS testchip demonstrates the proposed unified architecture with a compact area of 0.43 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\cdot 10^{6}~F^{2}$ </tex-math></inline-formula> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$F\,\,=$ </tex-math></inline-formula> minimum feature size), based on a SIMON cryptographic core. The true random number generator (TRNG) output shows cryptographic-grade quality without any calibration across dice, process (across two manufacturing lots), voltage, and temperature variations. Energy per encryption down to 0.25 pJ/bit is demonstrated. Unification of TRNG and the cryptographic core results in inherent data locality and obfuscation of key generation within logic, improving the resilience to physical attacks.
Sense and Learn: Self-supervision for omnipresent sensors

Learning general-purpose representations from multisensor data produced by the omnipresent sensing systems (or IoT in general) has numerous applications in diverse use cases. Existing purely supervised end-to-end deep learning techniques depend on the availability of a massive amount of well-curated data, acquiring which is notoriously difficult but required to achieve a sufficient level of generalization on a task of interest. In this work, we leverage the self-supervised learning paradigm towards realizing the vision of continual learning from unlabeled inputs. We present a generalized framework named Sense and Learn for representation or feature learning from raw sensory data. It consists of several auxiliary tasks that can learn high-level and broadly useful features entirely from unannotated data without any human involvement in the tedious labeling process. We demonstrate the efficacy of our approach on several publicly available datasets from different domains and in various settings, including linear separability, semi-supervised or few shot learning, and transfer learning. Our methodology achieves results that are competitive with the supervised approaches and close the gap through fine-tuning a network while learning the downstream tasks in most cases. In particular, we show that the self-supervised network can be utilized as initialization to significantly boost the performance in a low-data regime with as few as 5 labeled instances per class, which is of high practical importance to real-world problems. Likewise, the learned representations with self-supervision are found to be highly transferable between related datasets, even when few labeled instances are available from the target domains.
Near-Infrared Spectroscopy Study of Serpentine Minerals and Assignment of the OH Group

Three different kinds of serpentine mineral samples were investigated using Fourier transform near-infrared spectroscopy (FTNIR). The results show that there are obvious differences in the characteristic infrared spectra of the three serpentine group minerals (lizardite, chrysotile, and antigorite), which can easily be used to identify these serpentine minerals. The combination of weak and strong peaks in the spectrum of lizardite appears at 3650 and 3690 cm−1, while the intensities of the peaks at 4281 and 4301 cm−1 (at 7233 and 7241 cm−1, respectively) are similar. A combination of weak and strong peaks in chrysotile appears at 3648 and 3689 cm−1 and at 4279 and 4302 cm−1, and a single strong peak appears at 7233 cm−1. In antigorite, there are strong single peaks at 3674, 4301, and 7231 cm−1, and the remaining peaks are shoulder peaks or are not obvious. The structural OH mainly appears as characteristic peaks in four regions, 500–720, 3600–3750, 4000–4600, and 7000–7600 cm−1, corresponding to the OH bending vibration, the OH stretching vibration, the OH secondary combination vibration, and the OH overtone vibration, respectively. In the combined frequency vibration region, the characteristic peak near 4300 cm−1 is formed by the combination of the internal and external stretching vibrations and bending vibrations of the structural OH group. The overtone vibrations of the OH stretching vibration appear near 7200 cm−1, and the practical factor is about 1.965. The near-infrared spectra of serpentine minerals are closely related to their structural differences and isomorphous substitutions. Therefore, near-infrared spectroscopy can be used to identify serpentine species and provides a basis for studies on the genesis and metallogenic environment of these minerals.
Robust Local Preserving and Global Aligning Network for Adversarial Domain Adaptation

Unsupervised domain adaptation (UDA) requires source domain samples with clean ground truth labels during training. Accurately labeling a large number of source domain samples is time-consuming and laborious. An alternative is to utilize samples with noisy labels for training. However, training with noisy labels can greatly reduce the performance of UDA. In this paper, we address the problem that learning UDA models only with access to noisy labels and propose a novel method called robust local preserving and global aligning network (RLPGA). RLPGA improves the robustness of the label noise from two aspects. One is learning a classifier by a robust informative-theoretic-based loss function. The other is constructing two adjacency weight matrices and two negative weight matrices by the proposed local preserving module to preserve the local topology structures of input data. We conduct theoretical analysis on the robustness of the proposed RLPGA and prove that the robust informative-theoretic-based loss and the local preserving module are beneficial to reduce the empirical risk of the target domain. A series of empirical studies show the effectiveness of our proposed RLPGA.
Hierarchical object detection for very high-resolution satellite images

Object detection from satellite images is challenging and either computationally expensive or labor intense. Satellite images often cover large areas of more than 10km×10km. They include objects of different scales, which makes it hard to detect all of them at the same image resolution. Considering that airplanes are usually located in airports, ships are often distributed in ports and sea areas, and that oil depots are typically found close to airports or ports, we propose a new hierarchical object detection framework for very high-resolution satellite images. Our framework prescribes two stages: (1) detecting airports and ports in down-sampled satellite images and (2) mapping the detected object back to the original high-resolution satellite images for detecting the smaller objects near them. In order to improve the efficiency of object detection, we further propose a contextual information based deep feature extraction approach for both of the hierarchical detection steps, as well as an inclined bounding box based arbitrarily-oriented object location mechanism suitable especially for the smaller objects. Comprehensive experiments on a public dataset and two self-assembled datasets (which we made publicly available) show the superior performance of our method compared to standalone state-of-the-art object detectors.
A safe double screening strategy for elastic net support vector machine

Elastic net support vector machine (ENSVM) is an effective and popular classification technique. It has been widely used in many practical applications. However, solving large-scale problems still remains challenging. Inspired by its sparsity, a safe double screening rule (DSR) is proposed for accelerating ENSVM. Its main idea is to reduce the scale of the model by discarding the inactive features and samples simultaneously. In this way, the computational speed can be accelerated. Another superiority of DSR is safety, i.e., the discarded features and samples must be inactive. Its key strategy is to estimate the region containing optimal solution based on the feasible solution and duality gap. Thus, the DSR can be embedded into the process of solving the model until the algorithm converges. In addition, the safe keeping rule is constructed to identify the active features and samples. So, the DSR only needs to work on the remaining set after safe keeping. In this way, the screening process of DSR can be accelerated. Moreover, the Stochastic Dual Coordinate Ascent (SDCA) method is employed as an efficient solver. Numerical experiments on an artificial dataset and eighteen benchmark datasets demonstrate the feasibility and validity of our proposed method.
A Large Multi-target Dataset of Common Bengali Handwritten Graphemes

Latin has historically led the state-of-the-art in handwritten optical character recognition (OCR) research. Adapting existing systems from Latin to alpha-syllabary languages is particularly challenging due to a sharp contrast between their orthographies. Due to a cursive writing system and frequent use of diacritics, the segmentation and/or alignment of graphical constituents with corresponding characters becomes significantly convoluted. We propose a labeling scheme based on graphemes (linguistic segments of word formation) that makes segmentation inside alpha-syllabary words linear and present the first dataset of Bengali handwritten graphemes that are commonly used in everyday context. The dataset contains 411k curated samples of \( 1295 \) unique commonly used Bengali graphemes. Additionally, the test set contains \(900 \) uncommon Bengali graphemes for out of dictionary performance evaluation. The dataset is open-sourced as a part of a public Handwritten Grapheme Classification Challenge on Kaggle to benchmark vision algorithms for multi-target grapheme classification. The unique graphemes present in this dataset are selected based on commonality in the Google Bengali ASR corpus. From competition proceedings, we see that deep learning methods can generalize to a large span of out of dictionary graphemes which are absent during training (Kaggle Competition kaggle.com/c/bengaliai-cv19, Supplementary materials and Appendix https://github.com/AhmedImtiazPrio/ICDAR2021supplementary).
Exposing Fake Faces Through Deep Neural Networks Combining Content and Trace Feature Extractors

With the breakthrough of computer vision and deep learning, there has been a surge of realistic-looking fake face media manipulated by AI such as DeepFake or Face2Face that manipulate facial identities or expressions. The fake faces were mostly created for fun, but abuse has caused social unrest. For example, some celebrities have become victims of fake pornography made by DeepFake. There are also growing concerns about fake political speech videos created by Face2Face. To maintain individual privacy as well as social, political, and international security, it is imperative to develop models that detect fake faces in media. Previous research can be divided into general-purpose image forensics and face image forensics. While the former has been studied for several decades and focuses on extracting hand-crafted features of traces left in the image after manipulation, the latter is based on convolutional neural networks mainly inspired by object detection models specialized to extract images’ content features. This paper proposes a hybrid face forensics framework based on a convolutional neural network combining the two forensics approaches to enhance the manipulation detection performance. To validate the proposed framework, we used a public Face2Face dataset and a custom DeepFake dataset collected on our own. Experimental results using the two datasets showed that the proposed model is more accurate and robust at various video compression rates compared to the previous methods. Throughout class activation map visualization, the proposed framework provided information on which face parts are considered important and revealed the tempering traces invisible to naked eyes.
TDIOT: Target-Driven Inference for Deep Video Object Tracking

Recent tracking-by-detection approaches use deep object detectors as target detection baseline, because of their high performance on still images. For effective video object tracking, object detection is integrated with a data association step performed by either a custom design inference architecture or an end-to-end joint training for tracking purpose. In this work, we adopt the former approach and use the pre-trained Mask R-CNN deep object detector as the baseline. We introduce a novel inference architecture placed on top of FPN-ResNet101 backbone of Mask R-CNN to jointly perform detection and tracking, without requiring additional training for tracking purpose. The proposed single object tracker, TDIOT, applies an appearance similarity-based temporal matching for data association. In order to tackle tracking discontinuities, we incorporate a local search and matching module into the inference head layer that exploits SiamFC for short term tracking. Moreover, in order to improve robustness to scale changes, we introduce a scale adaptive region proposal network that enables to search the target at an adaptively enlarged spatial neighborhood specified by the trace of the target. In order to meet long term tracking requirements, a low cost verification layer is incorporated into the inference architecture to monitor presence of the target based on its LBP histogram model. Performance evaluation on videos from VOT2016, VOT2018 and VOT-LT2018 datasets demonstrate that TDIOT achieves higher accuracy compared to the state-of-the-art short-term trackers while it provides comparable performance in long term tracking.
Using Boolean Functions of Context Factors for Adaptive Mental Model Aggregation in Organisational Learning

Aggregation of individual mental models to obtain shared mental models for an organization is a crucial process for organizational learning. This aggregation process usually depends on several context factors that may vary over circumstances. It is explored how Boolean functions of these context factors can be used to model this form of adaptation. For adaptation of aggregation of mental model connections (represented by first-order self-model states), a second-order adaptive self-modeling network model for organizational learning was designed. It is shown how in such a network model, Boolean functions can be used to express logical combinations of context factors and based on this can exert context-sensitive control over the mental model aggregation process.
Epidemic management and control through risk-dependent individual contact interventions

Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts. Exposure notification apps have been developed to digitally scale up TTI by harnessing contact data obtained from mobile devices; however, exposure notification apps provide users only with limited binary information when they have been directly exposed to a known infection source. Here we demonstrate a scalable improvement to TTI and exposure notification apps that uses data assimilation (DA) on a contact network. Network DA exploits diverse sources of health data together with the proximity data from mobile devices that exposure notification apps rely upon. It provides users with continuously assessed individual risks of exposure and infection, which can form the basis for targeting individual contact interventions. Simulations of the early COVID-19 epidemic in New York City prove the concepts. In the simulations, network DA identifies up to a factor 2 more infections than contact tracing when both harness the same contact data and diagnostic test data. This remains true even when only a relatively small fraction of the population uses network DA. When a sufficiently large fraction of the population ($\gtrsim 75\%$) uses network DA and complies with individual contact interventions, targeting contact interventions with network DA reduces deaths by up to a factor 4 relative to TTI. Network DA can be implemented by expanding the computational backend of existing exposure notification apps, thus greatly enhancing their capabilities. Implemented at scale, it has the potential to precisely and effectively control future epidemics while minimizing economic disruption.
Construction of Optimized Dynamic S-Boxes Based on a Cubic Modular Transform and the Sine Function

The protection of sensitive data from illegitimate users is one of the main challenges in today’s technological era. To handle prevailing security-related problems and challenges amicably, cryptographic techniques are applied for the fortification of data. State-of-the-art cryptographic ciphers generally use substitution-boxes (S-boxes) that help in accomplishing robust sanctuary of data. Provision of data security by a cipher is proportionate directly to the cryptographic strength of an S-box employed in the respective cipher. This research paper proposes to project a simple and innovative scheme for the generation of dynamic S-boxes by employing a novel cubic modular transformation along with the trigonometric sine function. A pioneering optimization phase, dynamic in nature, is also suggested that improvises the nonlinearity of the initial configuration of S-box. The overall proposed scheme possesses the potential to spawn a large count of strong S-boxes by smearing a minute variation in input parameters used in initial and optimization phases. Cipher key is used to employ values to the input parameters for the creation of dynamic S-boxes. A specimen S-box is presented, and its performance has been achieved through standard criteria of S-box evaluation along with the comparative analysis with some existing Sboxes. Recital and comparative investigations validate that the anticipated S-box possesses the real capability for its usage in cryptosystems for much needed data security.