Reviewerly/exHarmony huggingface dataset:
Study on 5-D light field compression using multi-focus images

We propose a novel method of 5-D dynamic light field compression using multi-focus images. A light field enables us to observe its scene from various viewpoints. However, it generally consists of 4-D enormous data, that are not suitable for storing or transmitting without effective compression. 4-D light fields are very redundant because they essentially include just 3-D scene information. Actually, a method of reconstructing a light field directly from 3-D information composed of multi-focus images without any scene estimation is successfully derived, though robust 3-D scene estimation such as depth recovery from light fields is not so easy. Previously, based on the method, we proposed novel light field compression via multi-focus images as effective representation of 3-D scenes. In this paper, we extend this method to compression of 5-D light fields composed of multiview videos including the time domain. It achieves significant improvement in compression efficiency by utilizing multiple redundancy of 5-D light fields. We show experimental results using synthetic videos. Quality of reconstructed light fields is evaluated by PSNR and SSIM for analyzing characteristics of its performance. They reveal that our method is much superior to light field compression using HEVC at practical lower bit-rates.
An Effective Hybrid Metaheuristic Algorithm for Weight Optimization of Artificial Neural Networks

Due to a number of local minimums, the gradient descent methods have difficulties in searching for the optimal weights of artificial neural networks (ANNs). To resolve this problem, recently, a number of scholars have conducted studies that utilize metaheuristics to optimize the weights of ANNs. Particularly, the swarm intelligence algorithm which is one of the metaheuristics has shown great potential in previous studies. In this study, we propose the hybrid metaheuristic algorithm that effectively optimizes the weight of ANNs by combining particle swarm optimization (PSO) and grey wolf optimizer (GWO), both of which are swarm intelligence algorithms. In the search process, the proposed algorithm resolves the convergence instability in the validation dataset using the swarm memory that was inspired by the habit of remembering the environment in which the swarm was suitable for survival. Numerical experiments demonstrate the proposed algorithm outperforms the existing swarm intelligence algorithms, stochastic gradient descent, and Adam Optimizer in terms of classification accuracy.
Analysis of Legal Basis for Computer Cyber Security Review

The development of computer and information technology enables the in-depth application for human beings in the network and information application, and accordingly, more and more fields in human society are deeply associated with computer networks. The emergence of computer network enables human beings to obtain higher efficiency in information processing and transaction. However, computer network is endowed with virtuality, complexity and openness, which pose itself in internal and external security threats. At the same time, the speed of technological development is always faster than the updating and optimization of administrative regulations, which makes it difficult to guarantee cyber security in terms of legal basis. This paper discusses the legal basis of computer cyber security review with the hope to provide support for the relevant work and theoretical research of computer cyber security review.
Robust Speaker Verification with Joint Self-Supervised and Supervised Learning

Supervised learning and self-supervised learning address different facets. Supervised learning achieves high accuracy, but it requires numerous expensive labeled data indeed. Correspondingly, self-supervised learning, makes use of abundant unlabeled data to learn, but the performance lags behind that of the supervised counterpart. To overcome the difficulty of acquiring annotated data and contain the high performance in the context of speaker verification, we propose in this work a self-supervised joint learning (SS-JL) framework which complements the supervised main task with self-supervised auxiliary tasks in joint training. These auxiliary tasks help the speaker verification pipeline to generate robust speaker representation that is closely relevant to voiceprints. Our model is trained on English dataset and tested on multilingual datasets, including English, Chinese and Korean datasets, and 13.6%, 12.7% and 13.5% improvement is achieved respectively in terms of equal error rate (EER) compared with the baselines.
Data imputation in deep neural network to enhance breast cancer detection

Abstract Breast cancer is one of the most precarious cancers that claims many women's' lives every year. The existing automated systems for mammography datasets are designed to detect the abnormalities and classify them as benign or malignant. However, these systems ignore the incompleteness of data due to missing or irrelevant values in the dataset, which severely impede the automated system's reliability. To enhance the reliability of the decision outcomes, this study proposes a composite imputed multilayer fully connected deep networks (MFCN) that can replace the incomplete values in the dataset to augment the efficient feature learning for breast cancer classification. The data imputation techniques and one‐hot encoding transformation applied to the dataset are proved to improve the feature representation. The imputed features extracted from the instances are fed into the MFCN to classify malignant and benign by utilizing mammographic mass (MM) and INbreast datasets. The performance of the proposed missing data imputation treated MFCN (MdI‐MFCN) is compared with competitive machine learning (ML) classifiers by incorporating stratified 6‐fold cross‐validation. The experimental results of the models are evaluated with and without imputed data using the five performance metrics, namely, accuracy, precision, recall, F1‐score, and specificity. The performance of the proposed MdI‐MFCN has outperformed the competitive classifiers with the highest accuracy of 93% and 98% of the area under the receiver operating characteristic (ROC) curve on MM and INbreast datasets, respectively. The simplicity and reliability of this proposed approach its potential in characterizing mammography datasets during breast cancer diagnosis.
Deriving Explainable Discriminative Attributes Using Confusion About Counterfactual Class

Recently, Integrated Gradients-based (IG) methods have been commonly used to explain the decision process of deep neural networks (DNNs). However, they have only considered the information of the predicted class while neglecting the in-formation of the rest classes. In this paper, we propose a novel counterfactual explanation method, Discriminative Gradients (DiscGrad) that derives explainable discriminative attributes by considering not only the predicted class but also the counterfactual classes. Specifically, we calculate the discriminative attributes by removing the attribute of the counterfactual classes, and this process makes it possible to derive only key discriminative attributes that contrast with other decisions. Also, we determine the weights for discriminative attributes using the degree of confusion about counterfactual classes. We evaluated our method by measuring how much logit decreases by perturbing important attributes. Experimental results on the widely used image and text datasets show that our proposed method outperforms the strong baseline, IG. In addition, we examine the relationship between class correlation and the performance of discriminative attribute to demonstrate the effectiveness of our method.
Learning to Split for Automatic Bias Detection

Classifiers are biased when trained on biased datasets. As a remedy, we propose Learning to Split (ls), an algorithm for automatic bias detection. Given a dataset with input-label pairs, ls learns to split this dataset so that predictors trained on the training split cannot generalize to the testing split. This performance gap suggests that the testing split is under-represented in the dataset, which is a signal of potential bias. Identifying non-generalizable splits is challenging since we have no annotations about the bias. In this work, we show that the prediction correctness of each example in the testing split can be used as a source of weak supervision: generalization performance will drop if we move examples that are predicted correctly away from the testing split, leaving only those that are mis-predicted. ls is task-agnostic and can be applied to any supervised learning problem, ranging from natural language understanding and image classification to molecular property prediction. Empirical results show that ls is able to generate astonishingly challenging splits that correlate with human-identified biases. Moreover, we demonstrate that combining robust learning algorithms (such as group DRO) with splits identified by ls enables automatic de-biasing. Compared to previous state-of-the-art, we substantially improve the worst-group performance (23.4% on average) when the source of biases is unknown during training and validation.
Using Machine Learning for Speech Extraction and Translation: HiTEK Languages

Speech processing deals with retrieving and vocalizing conversational words/sentences i.e. articulatory phonetics, manner of articulation, place of articulation, articulatory gestures, articulatory phonology, articulatory speech recognition, and articulatory synthesis from Multilingual Source to Target language. The research focuses on multilingual speech recording in a single utterance and translation to a target language. Greedy method is used for fetching speech from the user. It consists of the grammatical structures of the speech in the dictionary using cohesion based method for term similarity. It translates speech to text then maps text to a set of phones resulting in target language speech. Multilingual Supervised Speech Dictionary is built for speech to speech translation, currently consisting of four languages Hindi, Telugu, English and Kannada with 100 phones for each language.
Predicting Human Motion Using Key Subsequences

Human motion prediction is an important task in computer vision, and has a wide range of applications, such as autonomous driving and human-robot interaction. Usually, human motion tends to repeat itself and follows patterns that are well-represented by a few short key subsequences. Based on the above observations, we propose an attention-based feed-forward network, which is explicitly guided by the key subsequences, for human motion prediction. Specifically, we obtain the key subsequences by clustering, extract motion attention by the similarity between the observed poses and the motion context of corresponding key subsequences, and aggregate the relevant key subsequences by a graph convolutional network to predict human motion. Experimental results on public human motion datasets show that our method achieves better performance over state-of-the-art methods in motion prediction.
Improving Actor-Critic Reinforcement Learning Via Hamiltonian Monte Carlo Method

The actor-critic RL is widely used in various robotic control tasks. However, by viewing the actor-critic RL from the perspective of variational inference (VI), in practice, the actor-critic RL may yield suboptimal policy estimates due to the amortization gap and insufficient exploration. In this work, inspired by the previous use of Hamiltonian Monte Carlo (HMC) in VI, we propose to integrate the policy network of actor-critic RL with HMC, which is termed as Hamiltonian Policy. As such we propose to evolve actions from the base policy according to HMC. First, HMC can improve the policy distribution to better approximate the posterior and hence reduce the amortization gap. Second, HMC can also guide the exploration more to the regions of action spaces with higher Q values, enhancing the exploration efficiency. Further, instead of directly applying HMC into RL, we propose a new leapfrog operator to simulate the Hamiltonian dynamics. With comprehensive empirical experiments on continuous control baselines, including MuJoCo and PyBullet Roboschool, we show that the proposed approach is a data-efficient and easy-to-implement improvement over previous actor-critic methods.
An improved image dehazing method based on dark channel compensation and guided filtering

Image dehazing is an important topic in the field of computer vision. The traditional single image dehazing algorithm is susceptible to the serious halo phenomenon and color distortion. For this problem, an improved method for single image dehazing based on dark channel prior and guided filtering is proposed in this paper. Aiming at the problems of halo phenomenon by traditional dark channel prior, a compensation model was proposed firstly. This model identified halo regions by the differential operation on the two minimal filtered images and adjusted the filtering window scale to a more suitable value. Secondly, rough transmittance correction combined with sky area judgement was applied aiming at color degradation caused by serious cover influence of fog and haze. Finally, the guided filter algorithm was improved for optimal transmittance by locating edge points with Canny operator and modifying the smoothing factor in loss function with the ratio of the gradient operator to window gradient mean as edge weight. The experimental results demonstrate that the proposed algorithm can efficiently remove halo effects and keep more details whatever in near or far regions, as well as improving visual effects and color saturation of images. Objective experiment evaluations also verify the effectiveness of proposed algorithm.
AdCSE: An Adversarial Method for Contrastive Learning of Sentence Embeddings

Due to the impressive results on semantic textual similarity (STS) tasks, unsupervised sentence embedding methods based on contrastive learning have attracted much attention from researchers. Most of these approaches focus on constructing high-quality positives, while only using other in-batch sentences for negatives which are insufficient for training accurate discriminative boundaries. In this paper, we demonstrate that high-quality negative representations introduced by adversarial training help to learn powerful sentence embeddings. We design a novel method named AdCSE for unsupervised sentence embedding. It consists of an untied dual-encoder backbone network for embedding positive sentence pairs and a group of negative adversaries for training hard negatives. These two parts of AdCSE compete against each other mutually in an adversarial way for contrastive learning, obtaining the most expressive sentence representations while achieving an equilibrium. Experiments on 7 STS tasks show the effectiveness of AdCSE. The superiority of AdCSE in constructing high-quality sentence embeddings is also validated by ablation studies and quality analysis of representations.
Libra-CAM: An Activation-Based Attribution Based on the Linear Approximation of Deep Neural Nets and Threshold Calibration

Universal application of AI has increased the need to explain why an AI model makes a specific decision in a human-understandable form. Among many related works, the class activation map (CAM)-based methods have been successful recently, creating input attribution based on the weighted sum of activation maps in convolutional neural networks. However, existing methods use channel-wise importance weights with specific architectural assumptions, relying on arbitrarily chosen attribution threshold values in their quality assessment: we think these can degrade the quality of attribution. In this paper, we propose Libra-CAM, a new CAM-style attribution method based on the best linear approximation of the layer (as a function) between the penultimate activation and the target-class score output. From the approximation, we derive the base formula of Libra-CAM, which is applied with multiple reference activations from a pre-built library. We construct Libra-CAM by averaging these base attribution maps, taking a threshold calibration procedure to optimize its attribution quality. Our experiments show that Libra-CAM can be computed in a reasonable time and is superior to the existing attribution methods in quantitative and qualitative attribution quality evaluations.
Radar Pulse Signal Filtering Using Vertical Synchrosqueezing

This paper presents a method for multipath propagation effect suppression in electronic reconnaissance applications when frequency modulated radar pulses are considered. In practical systems, the aforementioned phenomenon may significantly distort the direct signal when it reaches the receiver, making it impossible to be distinguished from interference and delayed copies of the pulse. In order to retrieve the reference waveform, a vertical synchrosqueezing (VSS) transform is applied in this paper, which aims to sharpen the time-frequency (TF) distribution and allow one to obtain a strongly concentrated component and mode extraction. Therefore, this concept is utilized in this article for the purpose of reference signal extraction. The method was intensively tested and validated using simulated and real-life signals, which the selected results illustrate.
Indistinguishable photons play a crucial role in quantum technologies

Secure communications and quantum computation are critical applications of an emerging technology.
Efficacy of cloud contracts

Contracts are a life line of any commercial activity and the technology also had an impact on this basic document but the essence and relevance is the same even in the cloud computing world. Cloud contracts have a very significant role in addressing the legal issues emerging from cloud computing services by defining the roles and responsibilit ies of the stakeholders. In the present article, the efficacy of cloud contracts in resolving the legal issues emerging from the adoption of cloud com puting has been determined. The research paper is divided in three major sections. The first part contains the introduction to contracts and difference between the cloud contracts from other kinds of Information Technology Agreements. The second part includes comparative analysis of the contracts of three leading cloud companies i.e. Amazon Web Service, Google and Facebook. The third part deals with the findings of the comparative analysis and few suggest ions to tackle the legal dichot omy regarding application of laws to cloud services
Implementation Report of the Kohel–Lauter–Petit–Tignol Algorithm for the Constructive Deuring Correspondence

For an odd prime p, let $$E_0$$ be a supersingular elliptic curve over $$\mathbb {F}_{p^2}$$ with . The Deuring correspondence gives a one-to-one correspondence between isogenies $$E_0 \longrightarrow E$$ and left -ideals. In 2014, Kohel–Lauter–Petit–Tignol provided a probabilistic algorithm, called the KLPT algorithm, that finds an equivalent ideal of a given left -ideal with some powersmooth norm. It is useful for both cryptanalyses and constructions in supersingular isogeny-based cryptography. In this paper, we modify the original KLPT algorithm to improve its output quality so that an output ideal has smaller norm. This would give an efficiency for the constructive Deuring correspondence, in which we compute the supersingular elliptic curve corresponding to a given left -ideal via the Deuring correspondence. We also report implementation results of our modified KLPT algorithm for primes p up to around 45 bits. This is the largest scale of implementation reports for the original KLPT algorithm in the literature.
Equalization of Directional Multidimensional Histograms of Matrix and Tensor Images

New approaches are proposed for the equalization of directional multidimensional histograms of 2D-matrix and 3D-tensor images, obtained from CTI or MRI sequences, video, etc. Such equalization opens new possibilities for quality improvement of the tensor images in a selected direction of the 3D space. The directional contrast enhancement is of high importance for example, for the detection of objects oriented in the same direction. In the paper the algorithms for the calculation of the multidimensional directional histograms of 2D and 3D images are defined: in the first case, in the 4 directions of the 2D plane, and in the second—in 13 directions of the 3D space. The conditions for the directional equalization 2D and 3D histograms are defined, on the basis of which is enhanced the contrast of the processed images. In the paper are also defined the criteria for the evaluation of contrast enhancement in 3D images. The new method is illustrated through a digital example. The future application areas of the proposed approach are in the processing of underground images, 3D medical and dental images, etc. In the future the method will be aimed at the double transform of the grey levels of neighboring triples of voxels in correspondence with the selected approximation 3D model of the directional histogram, the local directional equalization of monochrome images, etc.
Take Note

Hi, I’m Danny Oppenheimer. If laptops are banned in your child’s classroom, you may have heard of me. That’s because it was my lab that showed that students perform better when they take notes by hand than when they use laptops. Before I knew it, those studies were used as justification for laptop bans in classrooms around the country. I receive a lot of angry emails berating me about these policies—which is unfortunate, since I don’t actually advocate for blanket laptop bans, and never have. In fact, I don’t even ban laptops in my own classes.
An Improved Unscented Kalman Filter for Maneuvering Target Tracking*

Abstract The photoelectric pod provides angular information and distance information for the UAV (Unmanned Aerial Vehicle), and the UAV uses it to estimate the status information of the moving target. Since the measurement information of the photoelectric pod is the angle of sight and relative distance, the measurement equation contains some nonlinear functions in the Cartesian coordinate system, and the output frequency of the photoelectric pod is low. The improved unscented Kalman filter combines the function of prediction and correction, introduces the prior information of the target acceleration constraint, and uses the offline data to obtain steady gain, and then estimates the target state online. The simulation result show that the algorithm can track the target and need less time compared with the traditional unscented Kalman filter.
Report on the 2nd international conference on design of experimental search &amp; information retrieval systems (DESIRES 2021)

This is a report on the second edition of the International Conference on Design of Experimental Search &amp; Information REtrieval Systems ( DESIRES 2021) held at the Department of Information Engineering of the University od Padua (Padua, Italy) from September 15 to September 18, 2021. Date : 15--18 September, 2021. Website : http://desires.dei.unipd.it/.