Reviewerly/exHarmony huggingface dataset:
Restorable-inpainting: A novel deep learning approach for shoeprint restoration

Shoeprints are important information collected at the crime scene and are of great value for forensic analysis. Shoeprints collected in real-world scenarios are normally unclear, abrasive, and lack contextual and other kinds of missing information. In this research, we apply a novel deep learning technique called restorable inpainting to repair shoeprint contours and missing parts. Existing inpainting methods aim to fill artificially occluded areas with plausible pixels, but these methods may not restore missing information for occlusions in shoeprint images. In addition, because no ground-truth shoeprints exist for training samples, inpainting occluded regions becomes challenging. In this paper, we propose DeepShoePaint, a novel deep learning approach to perform restorable in paint ing by restoring synthetic information resembling desirable shoe print images necessary for forensics. DeepShoePaint novelly adapts a probabilistic distribution borrowed from the variational autoencoder into a U-Net-like structure forming a unified architecture trained in an unsupervised fashion to restore occluded and masked regions to produce human-verifiable shoeprints. The experimental results reveal that DeepShoePaint achieves outstanding human inspection and statistical assessment results and outperforms conventional inpainting models. We believe that this study can provide valuable insights, not limited to inpainting, into restoring desirable shoeprints to automate and facilitate the forensic investigation and examination process instead of using handcrafted methods.
Mineral exploration of Iwo-Apomu Southwestern Nigeria using aeromagnetic and remote sensing

The tectonic framework, lithostructural and mineralised zones of Iwo-Apomu region of Precambrian Basement Complex rocks were investigated using integrated aeromagnetic and satellite remote datasets. Fast Fourier Transforms (FFT) performed on aeromagnetic data were: Butterworth Filter (BF), Reduction to Magnetic Equator (RTE), Regional field, Residual field, Tilt-Angle Derivative (TDR), First and Second Vertical Derivative (FVD and SVD), Tilt angle and Total horizontal derivative (TDR and TDHR), and Total Gradient Amplitude (TGA); which range from −31.97 – 123.24, −27.12 – 131.75, 1.52 – 117.90, −58.55 – 50.42, −1.35 – 1.36, −0.0712 – 0.0643, 0.0004 – 0.0102, 0.005 – 0.261 respectively. Continuations and Radially Average Power Spectrum; revealed the variations of the deep-seated basement rocks, the structures and the mineralised anomalous bodies with depth. The upward continuation maps also show that the regional trends of the mapped lithologies and lineaments/faults in the area are generally in NW-SE, E-W, and NW-SE directions. These filters defined the lithostructures, lithological boundaries, geological structures, contacts, faults, folds and mineral deposit. The results of satellite data on Principal Component Analysis (PCA), False Colour Composite (FCC) images and Band Ratio (BR) discriminated the different rock types, lithostructural boundaries and alteration zones that hosted the mineralized ore bodies which trend in NE–SW and minor NW–SE and E–W directions. The combined techniques generated showing the probable positions and trend of suspected fault/fracture, mineral deposit as well as other basement structures.
Extrinsic topology of Floquet anomalous boundary states in quantum walks

Bulk-boundary correspondence is a fundamental principle for topological phases where bulk topology determines gapless boundary states. On the other hand, it has been known that corner or hinge modes in higher-order topological insulators may appear due to ``extrinsic'' topology of the boundaries even when the bulk topological numbers are trivial. In this paper, we find that Floquet anomalous boundary states in quantum walks have similar extrinsic topological natures. In contrast to higher-order topological insulators, the extrinsic topology in quantum walks is manifest even for first-order topological phases. We present the topological table for extrinsic topology in quantum walks and illustrate extrinsic natures of Floquet anomalous boundary states in concrete examples.
Fast Graph-based Binary Classifier Learning via Further Relaxation of Semi-Definite Relaxation

Although semi-definite relaxation (SDR) can be used to solve NP-hard, semi-supervised, graph-based binary classification problems, existing solvers for SDR generally consist of projection onto a positive semi-definite (PSD) cone that has high time complexityper iteration. A faster algorithm that solves graph-based binary classification problems with a sequence of linear programs based on Gershgorin Disc Perfect Alignment (GDPA) linearization does not require PSD cone projection, but the overall time complexity can still be as high as due to the requirement of solving multiple linear programs. In this paper, we propose a fast graph-based binary classification problem solver with only time complexity. We overcome the computation burden by further relaxation of SDR (RSDR). Specifically, we first derive an upper bound of the Shur complement of the last diagonal entry of the PSD classification matrix, and then derive a trivial solution of the dual of the semi-definite program (SDP) for the graph-based binary classification problem. The only time-consuming part of the proposed solver is the computation of the first eigenvector of the PSD classification matrix, where we adopt Locally Optimal Block Preconditioned Conjugate Gradient (LOBPCG), that is known to have linear time complexity. Experimental results show that (i) our proposed RSDR performs on average faster than GDPA linearization based SDR across all experimented datasets with comparable classification error rates, and (ii) the unrolling version of our proposed RSDR that learns the PSD Mahalanobis distance metric matrix has fewer number of trainable parameters compared to conventional black-box networks, is up to faster than the unrolling version of GDPA linearization based SDR (SDR unrolling) in inference time, and has smaller average classification error rates compared to SDR unrolling.
A feminist hacklab’s resilience towards anti-democratic forces

Makerspaces and hacklabs are believed to encourage a positive attitude towards gaining computer skills. Within these communities for peer production, citizens can apply cutting-edge technologies in DIY projects. In recent decades, mushrooming makerspaces and hacklabs were embraced by the tech industry and governments alike. Feminist makerspaces and hacklabs, however, as they are centred around a queer feminist agenda, have raised eyebrows. In order to foster diversity in tech development, they create safer spaces for self-expression. Here, feminist lay(wo)men* (To emphasise that the category ‘Woman’ is constructed and that more people than only those who identify as women are being included, one uses the sign * after the term ‘women’ ), makers, designers, artists and tinkerers experiment with open-source hardware and software. Art and design projects emerging from feminist hacklabs focus on issues of representation and democratic participation in digital media, as well as on ways of reclaiming one’s own body. This article tries to unpack how, after an exhibition on sexual health norms, a feminist hacklab was attacked by local right-wing and conservative politicians. The attack resulted in the defunding of the feminist hacklab. But it also started a transformation process within the collective, as members became aware of critical interferences of diffracting marginalisations. The crisis triggered a discussion on how each member was threatened to very different degrees; for example, there was more at stake for members depending on their legal status in the country. The right-wing and conservative campaign against the feminist hacklab damaged the initiative, but at the same time it pushed the collective to generate increased vehemence and resilience.
Minimum Adversarial Examples

Deep neural networks in the area of information security are facing a severe threat from adversarial examples (AEs). Existing methods of AE generation use two optimization models: (1) taking the successful attack as the objective function and limiting perturbations as the constraint; (2) taking the minimum of adversarial perturbations as the target and the successful attack as the constraint. These all involve two fundamental problems of AEs: the minimum boundary of constructing the AEs and whether that boundary is reachable. The reachability means whether the AEs of successful attack models exist equal to that boundary. Previous optimization models have no complete answer to the problems. Therefore, in this paper, for the first problem, we propose the definition of the minimum AEs and give the theoretical lower bound of the amplitude of the minimum AEs. For the second problem, we prove that solving the generation of the minimum AEs is an NPC problem, and then based on its computational inaccessibility, we establish a new third optimization model. This model is general and can adapt to any constraint. To verify the model, we devise two specific methods for generating controllable AEs under the widely used distance evaluation standard of adversarial perturbations, namely Lp constraint and SSIM constraint (structural similarity). This model limits the amplitude of the AEs, reduces the solution space's search cost, and is further improved in efficiency. In theory, those AEs generated by the new model which are closer to the actual minimum adversarial boundary overcome the blindness of the adversarial amplitude setting of the existing methods and further improve the attack success rate. In addition, this model can generate accurate AEs with controllable amplitude under different constraints, which is suitable for different application scenarios. In addition, through extensive experiments, they demonstrate a better attack ability under the same constraints as other baseline attacks. For all the datasets we test in the experiment, compared with other baseline methods, the attack success rate of our method is improved by approximately 10%.
Log-Rank-Type Tests for Equality of Distributions in High-Dimensional Spaces

Motivated by applications in high-dimensional settings, we propose a novel approach for testing the equality of two or more populations by constructing a class of intensity centered score processes. The resulting tests are analogous in spirit to the well-known class of weighted log-rank statistics that are widely used in survival analysis. The test statistics are nonparametric, computationally simple, and applicable to high-dimensional data. We establish the usual large sample properties by showing that the underlying log-rank score process converges weakly to a Gaussian random field with zero mean under the null hypothesis and with a drift under the contiguous alternatives. For the Kolmogorov–Smirnov-type and the Cramer-von Mises-type statistics, we also establish the consistency result for any fixed alternative. Cutoff points for the test statistics are obtained by permutations or a simulation-based resampling method. The new approach is applied to a study of brain activation measured by functional magnetic resonance imaging when performing two linguistic tasks and also to a prostate cancer DNA microarray dataset. Supplementary materials for this article are available online.
Automatic Recognition of Ragged Red Fibers in Muscle Biopsy from Patients with Mitochondrial Disorders

Mitochondrial dysfunction is considered to be a major cause of primary mitochondrial myopathy in children and adults, as reduced mitochondrial respiration and morphological changes such as ragged red fibers (RRFs) are observed in muscle biopsies. However, it is also possible to hypothesize the role of mitochondrial dysfunction in aging muscle or in secondary mitochondrial dysfunctions. The recognition of true histological patterns of mitochondrial myopathy can avoid unnecessary genetic investigations. The aim of our study was to develop and validate machine-learning methods for RRF detection in light microscopy images of skeletal muscle tissue. We used image sets of 489 color images captured from representative areas of Gomori's trichrome-stained tissue retrieved from light microscopy images at a 20× magnification. We compared the performance of random forest, gradient boosting machine, and support vector machine classifiers. Our results suggested that the advent of scanning technologies, combined with the development of machine-learning models for image classification, make neuromuscular disorders' automated diagnostic systems a concrete possibility.
Social Network Analytic-Based Online Counterfeit Seller Detection using User Shared Images

Selling counterfeit online has become a serious problem, especially with the advancement of social media and mobile technology. Instead of investigating the products directly, one can only check the images, tags annotated by the sellers on the images, or the price to decide if a seller sells counterfeits. One of the ways to detect counterfeit sellers is to investigate their social graphs, in which counterfeit sellers show different behaviour in network measurements, such as those in centrality and EgoNet. However, social graphs are not easily accessible. They may be kept private by the operators, or there are no connections at all. This article proposes a framework to detect counterfeit sellers using their connection graphs discovered from their shared images. Based on 153 K shared images from Taobao, it is proven that counterfeit sellers have different network behaviours. It is observed that the network measurements follow Beta function well. Those distributions are formulated to detect counterfeit sellers by the proposed framework, which is 60% better than approaches using classification.
Measuring the Impact of Information Display Methods on AR HMD for Comprehending Construction Information with EEG

With the advances in Augmented Reality (AR) technology, head-mounted displays (HMDs) provide users with various information presentation options, which combine the physical world with virtual objects through a 2D or 3D display. Compared with traditional blueprint documentation, AR presents the design in a more perceptual and intuitive graphic language. However, it is necessary to understand how different information displays through HMD affect user's comprehending performance in terms of both efficiency and cognitive status. In this paper, we describe a study measuring the impact of information display types in AR HMD on the user's comprehension of complex construction information. Subjects from construction engineering background are using both 3D conformal model and 2D tag-along image displayed through an AR HMD to observe a roof deck design. The evaluation of each method includes both mental workload and comprehension performance. Significant differences were captured in the performance evaluation using electroencephalogram (EEG) analysis. The results show the advantage of 3D display in reducing mental workload and improving comprehending performance.
Pattern Recognition Characteristics and Neural Mechanism of Basketball Players’ Dribbling Tactics Based on Artificial Intelligence and Deep Learning

There are many factors that affect a player’s overall basketball ability, and different factors will have different effects. The effect is mainly manifested in the difference of offensive and defensive data of basketball players in basketball games. In the basketball field, the formulation of existing training plans mainly relies on the manual observation and personal experience of coaches. This method is inevitably subjective. The pattern recognition of dribbling tactics is one of the important factors. A proper dribbling tactic can make the team achieve better results. In order to discover different dribbling characteristics, reanalyze the connotation and manifestation of basketball speed and strive to analyze the factors that affect basketball speed reasonably and accurately. The deep learning algorithm simulates the thinking process of the human brain neurons through the computer method and then realizes the function of the computer to automatically learn the data characteristics and complete the complex data analysis task. We use artificial intelligence and deep learning to simulate various dribbling tactics of players and find out the rules to improve players’ abilities. The results of the study prove that developing a suitable dribbling tactical model for basketball players can increase their competitive ability by more than 10%, reduce the damage to players, and prolong their careers. Generally speaking, athletes’ injuries can be reduced by more than 15%. This shows that the pattern recognition characteristics and neural mechanisms of dribbling tactics are extremely important to basketball players.
Automatic breast ultrasound tumor segmentation via one-stage hierarchical target activation network

Automatic breast ultrasound (ABUS) imaging is a well-established tool in breast cancer diagnosis. Delineating lesion on ABUS is an essential step for breast cancer computer-aided diagnosis (CAD). This work aims to develop an automated deep learning-based method for breast tumor segmentation on three-dimensional ABUS. The proposed method, one-stage hierarchical target activation network, consists of three subnetworks, i.e., fully convolutional one-state object detector (FCOS), hierarchical block and mask module. Feature extractor is used to extract informative features from ABUS. FCOS is used to locate the volume-of-interest (VOIs) of breast tumor. Hierarchical block is used to enhance the feature contrast around tumor boundary. Mask module then segment tumor from the refined feature map within the VOIs. A five-fold cross-validation on 40 patients’ cases was conducted. The ABUS breast tumors were segmented and compared with manual contours using several segmentation measurements. The Dice similarity coefficient (DSC) and mean surface distance (HD95) is 0.855&plusmn;0.090 and 1.56&plusmn;1.02 mm, respectively. These results demonstrate the feasibility and efficacy of the proposed method for breast tumor segmentation, which can further facilitate CAD for breast cancer using 3D ABUS imaging.
Broadband Variable Transmission Sphere for Fizeau Interferometry

Transmission spheres used in interferometry are specified by f-number and source wavelength. In this paper, we explore a broadband variable transmission sphere (BVTS) system based on freeform Alvarez lenses that enables variable operation across a broad range of f-numbers and wavelengths. Potential applications and performance tradeoffs are discussed in comparison to conventional spherical transmission spheres. Simulation results are presented for f/15 to f/80 configurations from visible to long-wave infrared sources in a Fizeau interferometer. Simulation results highlight that spherical, coma, and astigmatism impose limits on surface measurement quality.
Data Mining based Dimensionality Reduction Techniques

With the rapid advancement in the science domain, the explosion of available data is seen in recent years. Laboratory instruments are becoming more advanced day by day and are able to capture thousands of measurements in a single experiment therefore dealing with such a huge amount of high dimensional data creates several challenges and leads to data heterogeneity, curse of dimensionality, and inaccuracy. However, a significant portion of this high dimensional data is redundant and can be efficiently reduced to lower dimensions with the help of traditional dimensionality reduction techniques like Principal Component Analysis (PCA) and Linear discriminant Analysis (LDA). Over the last few years, many dimensionality reduction methods are introduced and used as per their application, and it has been observed that some techniques work better for a particular type of real application dataset while do not show much accuracy/efficiency with any other dataset. This paper aims to survey recent approaches which are compared as per their efficiency and accuracy along with their advantages and disadvantages.
Increasing generalizability via the principle of minimum description length

Traditional statistical model evaluation typically relies on goodness-of-fit testing and quantifying model complexity by counting parameters. Both of these practices may result in overfitting and have thereby contributed to the generalizability crisis. The information-theoretic principle of minimum description length addresses both of these concerns by filtering noise from the observed data and consequently increasing generalizability to unseen data.
Quality of governance and corporate real investment: Assessing the impact of foreign aid

Abstract Developing economies are the main recipients of foreign aid. Nonetheless, such countries must face trade‐offs regarding poor governance due to the inflow of foreign aid because aid hampers governance situations. Given that, the current study aims to examine the role of country governance in defining corporate investment decisions and to ascertain how this effect varies across those countries that receive substantial amounts of foreign aid. To test the hypotheses, we collect the financial information of non‐financial sector enterprises from seven economies and apply the generalized method of moments and two‐stage least square models. The statistical results reveal that governance has a positive influence on corporate investment choices. This positive effect is consistent across individual effects of foreign aid. However, foreign aid systematically deteriorates the governance quality of the host country, referring to donor agencies’ unnecessary interruption of the host country's institutional proceedings. This factor reduces institutional efficiency, which further deteriorates corporate investment. Our empirical analysis suggests that all the countries analysed should focus on better governance situations to promote industrial growth. Policymakers from these economies should ensure the effectiveness of foreign aid and introduce better governance practices to expediate industrial growth. HIGHLIGHTS Country level governance has a positive and significant impact on CI decisions. Foreign aid affects CI decisions positively. Foreign aid deteriorates the host country's governance quality. The interaction effect of country governance and foreign aid on CI is negative.
A robotic system for images on carpet surface

In this study, we propose a system that uses a carpet as a non-luminescent display by utilizing the phenomenon in which different shades of traces can be created by changing the direction of the fibers using a motor. The advantage of this method is that it can be used with existing cloth, does not require ink for drawing, and can be rewritten many times. The contributions of this work are (1) the ability to display images in high resolution and grayscale, and (2) the ability to automatically draw large pictures.