Reviewerly/exHarmony huggingface dataset:
Weakly supervised deep metric learning on discrete metric spaces for privacy-preserved clustering

With an increase in the number of data instances, data processing operations (e.g. clustering) requires an increasing amount of computational resources, and it is often the case that for considerably large datasets such operations cannot be executed on a single workstation. This requires the use of a server computer for carrying out the operations. However, to ensure privacy of the shared data, a privacy preserving data processing workflow involves applying an encoding transformation on the set of data points prior to applying the computation. This encoding should ideally cater to two objectives—first, it should be difficult to reconstruct the data, second, the results of the operation executed on the encoded space should be as close as possible to the results of the same operation executed on the original data. While standard encoding mechanisms, such as locality sensitive hashing, caters to the first objective, the second objective may not always be adequately satisfied. In this paper, we specifically focus on ‘clustering’ as the data processing operation. We apply a deep metric learning approach to learn a parameterized encoding transformation function with an objective to maximize the alignment of the clusters in the encoded space to those in the original data. We conduct experimentation on four standard benchmark datasets, particularly MNIST, Fashion-MNIST (each dataset contains 70K grayscale images), CIFAR-10 consisting of 60K color images and 20-Newsgroups containing 18K news articles. Our experiments demonstrate that the proposed method yields better clusters in comparison to approaches where the encoding process is agnostic of the clustering objective. • Proposed an encoding-based workflow of data clustering that preserves data privacy. • Proposed solution is suitable for deployment in a distributed computing environment. • A weakly supervised approach is used to learn a parameterized similarity function. • Effective reconstruction of data is achived with additional statistical information. • Experiments on image and text datasets shows the efficacy of the proposed method.
Boosted backtracking search optimization with information exchange for photovoltaic system evaluation

Abstract The determination of photovoltaic (PV) parameters is of great importance for the reliability of solar system operation, continuity of the load power consumption, and control management of the energy source. Therefore, this study proposes an advanced backtracking search optimization algorithm (BSA) equipped with teaching and learning‐based optimization (TLBO), named TLBOBSA, to accurately simulate the PV model. During the evaluation of the proposed algorithm, the concept of teaching from TLBO is introduced into the BSA to guide optimal individuals, thus improving the convergence rate of the algorithm. The learning behavior among individuals in the student phase of TLBO facilitates interindividual learning and provides beneficial information for its evolution, which is introduced into the BSA to ensure the diversity of the population. The comprehensive test results of different PV module models in different environmental conditions show that the proposed algorithm is more advantageous for parameter extraction than other existing algorithms. This can be seen in the simulation experiments of two commercial PV models, where the simulated current is consistent with the measured current at each measured voltage. This demonstrates that the proposed TLBOBSA is an accurate and reliable tool for evaluating unknown parameters of PV models.
Estimating the optimal linear combination of predictors using spherically constrained optimization

In the context of a binary classification problem, the optimal linear combination of continuous predictors can be estimated by maximizing the area under the receiver operating characteristic curve. For ordinal responses, the optimal predictor combination can similarly be obtained by maximization of the hypervolume under the manifold (HUM). Since the empirical HUM is discontinuous, non-differentiable, and possibly multi-modal, solving this maximization problem requires a global optimization technique. Estimation of the optimal coefficient vector using existing global optimization techniques is computationally expensive, becoming prohibitive as the number of predictors and the number of outcome categories increases.We propose an efficient derivative-free black-box optimization technique based on pattern search to solve this problem, which we refer to as Spherically Constrained Optimization Routine (SCOR). Through extensive simulation studies, we demonstrate that the proposed method achieves better performance than existing methods including the step-down algorithm. Finally, we illustrate the proposed method to predict the severity of swallowing difficulty after radiation therapy for oropharyngeal cancer based on radiation dose to various structures in the head and neck.Our proposed method addresses an important challenge in combining multiple biomarkers to predict an ordinal outcome. This problem is particularly relevant to medical research, where it may be of interest to diagnose a disease with various stages of progression or a toxicity with multiple grades of severity. We provide the implementation of our proposed SCOR method as an R package, available online at https://CRAN.R-project.org/package=SCOR .
Multi-scale convolutional neural network for texture recognition

Texture is of great significance for humans and robots to recognize the surface features of objects. In the field of texture recognition, methods based on spatial information have been widely applied. However, in the case of fine texture recognition, the methods only using spatial features for texture recognition may ignore the features of small texture and result in poor recognition accuracy. In this paper, a Multi-Scale Convolutional Neural Network (MS-CNN) is proposed to recognize millimetric fine textures. MS-CNN has three paths to extract features of different time scales from different numbers of continuous pressure images. The three paths have the same backbone network structure, but the number of convolution cores of the convolution layer in the backbone network of adjacent paths is doubled. After the convolution layer, we add SE-Net to automatically obtain the importance of each feature channel through learning, and then improve the useful features to further improve the accuracy. Finally, the output of all paths is averaged, and the classification vector is calculated through the final full connection layer. To validate MS-CNN, data sets containing 9 kinds of millimetric fine textures are obtained by flexible tactile sensors. The pressure image is transformed into one-dimensional vectors and these vectors are arranged into a sample in time order as the input of MS-CNN. In addition, the attention mechanism module is applied to MS-CNN to train the weight of each convolution channel and increase the proportion of useful features in the network. Ablation experiments prove that our modification is effective and our method achieves an accuracy of 81.83% for 9 fine textures. Compared with traditional recognition methods, our method achieves better recognition performance.
Tunnel Lining Surface Monitoring System Deployable at Maximum Vehicle Speed of 100 km/h Using View Angle Compensation Based on Self-Localization Using White Line Recognition

Vehicle-mounted inspection systems have been developed to perform daily inspections of tunnel lining. However, it is difficult to continuously scan images at the desired target angle with the camera’s limited visual field because the vehicle does not always maintain the same position in the car lane while it runs. In this study, we propose a calibration-free optical self-localization method based on white line recognition, which can stabilize the scanning angle and thus enable the scanning of the entire tunnel lining surface. Self-localization based on the present method has a processing time of 27.3 ms, making it valid for real-time view angle compensation inside tunnels, where global positioning systems cannot operate. It is robust against changes in brightness at tunnel entrances and exits and capable of dealing with car lanes with dotted lines and regular white lines. We confirmed that it is possible to perform self-localization with 97.4% accuracy using the scanned data of white lines captured while the vehicle is running. When this self-localization was combined with an inspection system capable of motion blur correction, we were able to sequentially scan the tunnel lining surface at a stable camera angle at high speed, and it was shown that the acquired images could be stitched together to produce an expansion image that can be used for inspection.
A systematic mapping study on digital game adaptation dimensions

Designing accessible digital games requires considering social, technical, and cultural aspects according to the diversity of the target audience. One way to address this diversity of audiences to promote accessibility is through adaptability, allowing a game to be customized according to the needs of its users. A Systematic Mapping Study was conducted to identify and document studies dealing with the adaptation of digital games according to the needs of their users. This paper focuses on the Mapping research questions on the following adaptation dimensions: (1) What game aspects are being adapted? (2) Why is it being adapted? (3) For whom is it being adapted? (4) When does the adaptation occur? and (5) Who is carrying out the adaptation? The main results of this study are: (i) the identification of a trend in research on adaptive systems; (ii) the use of Dynamic Difficulty Adjustment and Procedural Content Generation techniques to implement adaptive behaviour in games; (iii) the patients, non-specified users, and health professionals categories as the main results in the studies' target audience.
Flexible and simple telecentric camera calibration method

The measurement accuracy of telecentric imaging technique straightforwardly depends on the calibration of the telecentric camera. We present a flexible and simple calibration method based on a telecentric imaging model and orthogonality of rotation matrix. First, we use the orthogonality of rotation matrix to solve the magnification. Second, the external parameters are solved by the imaging model, and the ambiguity of sign is solved. Finally, we use the LM nonlinear optimization method to solve the distortion parameters. Experimental results show that the reprojection error is 0.7 pixels, which represents the actual dimension of 6.37 µm. In addition, the standard measuring block and real objects are measured, and the results verify the measurement accuracy and robustness of the proposed method.
Complete positive operator-valued measure description of multichannel quantum electro-optic sampling with monochromatic field modes

We propose a multichannel version of quantum electro-optic sampling involving monochromatic field modes. It allows for multiple simultaneous measurements of arbitrarily many $\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{X}$ and $\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{Y}$ field quadratures for a single quantum-state copy, while independently tuning the interaction strengths at each channel. In contrast to standard electro-optic sampling, the sampled midinfrared (MIR) mode undergoes a nonlinear interaction with multiple near-infrared pump beams. We present a complete positive operator-valued measure description for quantum states in the MIR mode. The probability distribution of the electro-optic signal outcomes is shown to be related to an $s$-parametrized phase-space quasiprobability distribution of the indirectly measured MIR state, with the parameter $s$ depending solely on the quantities characterizing the nonlinear interaction. Furthermore, we show that the quasiprobability distributions for the sampled and postmeasurement states are related to each other through a renormalization and a change in the parametrization. This result is then used to demonstrate that two consecutive measurements of both $\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{X}$ and $\stackrel{\ifmmode \hat{}\else \^{}\fi{}}{Y}$ quadratures can outperform eight-port homodyne detection.
MagicGAN: Multiagent Attacks Generate Interferential Category via GAN

Deep neural networks are vulnerable to interference categories, which can deceive trained models with imperceptible adversarial perturbations. More crucially, the transferability of adversarial samples has been confirmed, specifically, an adversarial sample crafted against a source agent model can transfer to other target models, which results in the adversary posing a security threat to applications in black-box scenarios. However, the existing transfer-based attacks merely consider a single agent model to create the adversarial samples, leading to poor transferability. In this paper, we propose a novel attack method called Multiagent Attacks Generate Interferential Category via GAN (MagicGAN). Specifically, to avoid the adversarial samples overfitting a single source agent, we design a multiagent discriminator, which can fit the decision boundaries of the various target models to provide more diversified gradient information for the generation of adversarial perturbations. Therefore, the generalization of our method is effectively improved, that is, the adversarial transferability of the adversarial sample is enhanced. In addition, to avoid the pattern collapse of the GAN-based adversarial approach, we construct a novel latent data distance constraint to enhance the compatibility between the latent adversarial sample distances and the corresponding data adversarial sample distances. Therefore, MagicGAN can more effectively generate a distribution close to the adversarial data. Extensive experiments on CelebA, CIFAR-10, MNIST and ImageNet fully validate the effectiveness and superiority of our proposed method.
A Lightweight Authenticated Searchable Encryption without Bilinear Pairing for Cloud Computing

Outsourcing data to cloud services is a good solution for users with limited computing resources. Privacy and confidentiality of data is jeopardized when data is transferred and shared in the cloud. The development of searchable cryptography offers the possibility to solve these problems. Symmetric searchable encryption (SSE) is popular among researchers because it is efficient and secure. SSE often requires the data sender and data receiver to use the same key to generate key ciphertext and trapdoor, which will obviously cause the problem of key management. Searchable encryption based on public key can simplify the key management problem. A public key encryption scheme with keyword search (PEKS) allows multiple senders to encrypt keywords under the receiver’s public key. It is vulnerable to keyword guessing attacks (KGA) due to the small size of the keywords. The proposal of public key authenticated encryption with keyword search (PAEKS) is mainly to resist inside keyword guessing attacks. The previous security models do not involve the indistinguishability of the same keywords ( <math xmlns="http://www.w3.org/1998/Math/MathML" id="M1"> <msub> <mrow> <mi>w</mi> </mrow> <mrow> <mn>0</mn> </mrow> </msub> <mo>×</mo> <mo>×</mo> <mo>=</mo> <msub> <mrow> <mi>w</mi> </mrow> <mrow> <mn>1</mn> </mrow> </msub> </math> ), which brings the user’s search pattern easy to leak. The essential reason is that the trapdoor generation algorithm is deterministic. At the same time, most of the existing schemes use bilinear pair design, which greatly reduces the efficiency of the scheme. To address these problems, the paper introduces an improved PAEKS model. We design a lightweight public key authentication encryption scheme based on the Diffie-Hellman protocol. Then, we prove the ciphertext indistinguishability security and trapdoor indistinguishability security of the scheme in the improved security model. Finally, the paper demonstrates its comparable security and computational efficiency by comparing it with previous PAEKS schemes. Meanwhile, we conduct an experimental evaluation based on the cryptographic library. Experimental results show that the computational overhead of our scheme compared with the ciphertext generation algorithm, trapdoor generation algorithm and test algorithm of other schemes Our scheme reduces 274, 158 and 60 times, respectively.
A comprehensive review of the security flaws of hashing algorithms

The blockchain is an emerging technology. It is widely used because of its efficiency and functionality. The hash function, as a supporting aspect of the data structure, is critical for assuring the blockchain's availability and security. Hash functions, which were originally designed for use in a few cryptographic schemes with specific security needs, have since become regular fare for many developers and protocol designers, who regard them as black boxes with magical characteristics. Message digesting, password verification, data structures, compiler operation and linking file name and path together are contemporary examples of hash functions applications. Since 2004, we've observed an exponential increase in the number and power of attacks against standard hash algorithms. In this paper, we investigated reported security flaws on well-known hashing algorithms and determined which of them are broken. A hash function is said to be broken when an attack is found, which, by exploiting special details of how the hash function operates, finds a preimage, a second preimage or a collision faster than the corresponding generic attack. To increase background knowledge, we also provide a summary of the types of attacks in this area. Finally, we summarized the information of the broken hash algorithms in a table which is very helpful for selecting, designing or using blockchains.
Improving breast cancer diagnosis by incorporating raw ultrasound parameters into machine learning

The improved diagnostic accuracy of ultrasound breast examinations remains an important goal. In this study, we propose a biophysical feature-based machine learning method for breast cancer detection to improve the performance beyond a benchmark deep learning algorithm and to furthermore provide a color overlay visual map of the probability of malignancy within a lesion. This overall framework is termed disease-specific imaging. Previously, 150 breast lesions were segmented and classified utilizing a modified fully convolutional network and a modified GoogLeNet, respectively. In this study multiparametric analysis was performed within the contoured lesions. Features were extracted from ultrasound radiofrequency, envelope, and log-compressed data based on biophysical and morphological models. The support vector machine with a Gaussian kernel constructed a nonlinear hyperplane, and we calculated the distance between the hyperplane and each feature's data point in multiparametric space. The distance can quantitatively assess a lesion and suggest the probability of malignancy that is color-coded and overlaid onto B-mode images. Training and evaluation were performed on in vivo patient data. The overall accuracy for the most common types and sizes of breast lesions in our study exceeded 98.0% for classification and 0.98 for an area under the receiver operating characteristic curve, which is more precise than the performance of radiologists and a deep learning system. Further, the correlation between the probability and Breast Imaging Reporting and Data System enables a quantitative guideline to predict breast cancer. Therefore, we anticipate that the proposed framework can help radiologists achieve more accurate and convenient breast cancer classification and detection.
On Multiple Paths and Change in the Language Network

Abstract Recent work in Diachronic Construction Grammar (DCxG) has foregrounded the importance of multiple sources of a particular construction, as well as promoting the idea that constructions are organised as a network of knowledge. The research presented in this article explores the ways in which multiple sources play a role at various stages in constructional change, and the effects of this on the structure of the language network. We aim to show how an account of language structure that focuses on links between constructions may be useful in tracking the various stages in the development of a new construction.
Color Alignment for Relative Color Constancy via Non-Standard References

Relative colour constancy is an essential requirement for many scientific imaging applications. However, most digital cameras differ in their image formations and native sensor output is usually inaccessible, e.g., in smartphone camera applications. This makes it hard to achieve consistent colour assessment across a range of devices, and that undermines the performance of computer vision algorithms. To resolve this issue, we propose a colour alignment model that considers the camera image formation as a black-box and formulates colour alignment as a three-step process: camera response calibration, response linearisation, and colour matching. The proposed model works with non-standard colour references, i.e., colour patches without knowing the true colour values, by utilising a novel balance-of-linear-distances feature. It is equivalent to determining the camera parameters through an unsupervised process. It also works with a minimum number of corresponding colour patches across the images to be colour aligned to deliver the applicable processing. Two challenging image datasets collected by multiple cameras under various illumination and exposure conditions were used to evaluate the model. Performance benchmarks demonstrated that our model achieved superior performance compared to other popular and state-of-the-art methods.
In Defense of Dignitary Safety: A Phenomenological Study of Student Resistance to Hate Speech on Campus

Frequent incidents of racist hate speech on college and university campuses continue to instigate an ideological battleground between legal purists, anti-racist scholars, and those otherwise situated somewhere therein. We find that arguments from legal purists are predicated upon a false-equivalency between racist and anti-racist speech where the effect, value, and embedded power dynamics of the former are often disregarded. We engage in a phenomenological analysis of a four-year, private institution – Clearview College (CVC)—where a controversial speaker was invited to campus by a conservative student organization. We specifically interrogate how the seemingly race-neutral free speech policies at CVC, which were informed by the “Chicago Principles,” were racially structured in impact. We utilize a conceptual framework that demarcates intellectual safety and dignitary safety as a foundational point of departure to analyze the responses from 20 undergraduate students. The responses from focus groups revealed two primary themes: (1) racist hate speech as a threat to dignitary safety, and (2) institutional retribution against students defending their dignitary safety. Implications for higher education policy and praxis are provided.