Reviewerly/exHarmony huggingface dataset:
SpecTerminator: Blocking Speculative Side Channels Based on Instruction Classes on RISC-V

In modern processors, speculative execution has significantly improved the performance of processors, but it has also introduced speculative execution vulnerabilities. Recent defenses are based on the delayed execution to block various speculative side channels, but we show that several of the current state-of-the-art defenses fail to block some of the available speculative side channels, and the current most secure defense introduces a performance overhead of up to 24.5%. We propose SpecTerminator, the first defense framework based on instruction classes that can comprehensively and precisely block all existing speculative side channels. In SpecTerminator, a novel speculative side channel classification scheme based on the features of secret transmission is proposed, and the sensitive instructions in the speculative window are classified and identified using optimized hardware taint tracking and instruction masking techniques to accurately determine the scope of leakage. Then, according to the execution characteristics of these instructions, dedicated delayed execution strategies, such as TLB request ignoring, selective issue, and extended delay-on-miss, are designed for each type of sensitive instruction to precisely control that these instructions are delayed only in pipeline stages that are at risk of leakage. In contrast to previous defenses based on the Gem5 simulator, we have innovatively implemented defenses against Spectre attacks based on the open-source instruction set RISC-V on an FPGA-accelerated simulation platform that is more similar to real hardware. To evaluate the security of SpecTerminator, we have replicated various existing x86-based Spectre variants on RISC-V. On SPEC 2006, SpecTerminator defends against Spectre attacks based on memory hierarchy side channels with a performance overhead of 2.6% and against all existing Spectre attacks with a performance overhead of 6.0%.
DFS-GAN: stabilizing training of generative adversarial networks through discarding fake samples

Generative adversarial networks (GANs) are generative models based on game theory. Because the relationship between generator and discriminator must be carefully adjusted during the training process, it is difficult to get stable training. Although some solutions are proposed to alleviate this issue, it is still necessary to discuss how to improve the stability of GANs. We propose a GAN we call the discarding fake samples (DFS)-GAN. During the training process, some generated samples are unable to fool the discriminator and provide a relatively invalid gradient for the discriminator. So, in the stabilized discriminator module (SDM), we discard the fake but easily discriminated samples. At the same time, we propose a new loss function, SGAN-gradient penalty 1. We explain the rationale of SDM and our loss function from a Bayesian decision perspective. We inferred the best number of discarded fake samples and verified the selected parameters’ effectiveness by experiments. The Fréchet inception distance (FID) value of DFS-GAN is 14.57 ± 0.19 on Canadian Institute for Advanced Research-10 (CIFAR-10), 20.87 ± 0.33 on CIFAR-100, and 92.42 ± 0.43 on ImageNet, which is lower than that of the current optimal method. Moreover, SDM module can be used in many GANs to decrease the FID value if their loss functions fit.
What Can You Draw?

We address the problem of which planar sets can be drawn with a pencil and eraser. The pencil draws any union of black open unit disks in the plane R2. The eraser produces any union of white open unit disks. You may switch tools as many times as desired. Our main result is that drawability cannot be characterized by local obstructions: a bounded set can be locally drawable, while not being (globally) drawable. We also show that if drawable sets are defined using closed unit disks, then the cardinality of the collection of drawable sets is strictly larger compared with the definition involving open unit disks.
Increasing inclusion through differentiated instruction in a technology-rich primary school classroom in Norway

In this case study, the aim was to investigate how primary school teachers in a leading-edge Norwegian primary school use digital technologies to differentiate instruction in order to promote a more inclusive learning environment in academically diverse classrooms. Seven teachers teaching grades 1 and 5 were observed and interviewed to collect data on their beliefs and practices regarding differentiation. Afterwards, 20 teachers in the same school answered in a survey about teaching in highly digitalised learning environments. The results suggest that teachers find a lot of potential and possibilities in using digital technologies to differentiate instruction to create an inclusive learning environment. However, pupils’ digital products indicate that they would need more guidance in taking advantage of the teachers’ intentions and flexible curricula.
Dlung: 无监督少样本微分同胚呼吸运动建模方法

Lung image registration plays an important role in lung analysis applications, such as respiratory motion modeling. Unsupervised learning-based image registration methods that can compute the deformation without the requirement of supervision attract much attention. However, it is noteworthy that they have two drawbacks: they do not handle the problem of limited data and do not guarantee diffeomorphic (topology-preserving) properties, especially when large deformation exists in lung scans. In this paper, we present an unsupervised few-shot learning-based diffeomorphic lung image registration, namely Dlung. We employ fine-tuning techniques to solve the problem of limited data and apply the scaling and squaring method to accomplish the diffeomorphic registration. Furthermore, atlas-based registration on spatio-temporal (4D) images is performed and thoroughly compared with baseline methods. Dlung achieves the highest accuracy with diffeomorphic properties. It constructs accurate and fast respiratory motion models with limited data. This research extends our knowledge of respiratory motion modeling.
A noninterference trusted dual system security guarantee method based on secure memory

Summary With the continuous maturity and development of single hardware security mechanism, it has been widely used in the field of information technology, but the single hardware mechanism has insufficient security guarantee in its own supporting drivers and other key programs. In response to this problem, this article proposes a trusted isolation model based on the noninterference theory and gives a formal proof. The realization is based on the trusted platform control module (TPCM) hardware mechanism by introducing a secure memory bar to provide a way to achieve a trusted dual‐system isolation guarantee. The hardware interface of secure memory module is consistent with that of ordinary memory module, which is widely used. It can improve the security guarantee ability of trusted computing platform, and has a good reference value for the design and application of trusted computing platform equipment.
The parameter estimation of interference fringes with quadratic phase based on FRFT/DCFT

Interferometry technology has a wide range of applications because of high accuracy and sensitivity, the core of which is the processing of interference fringe pattern. The interference fringes with quadratic phase are very common in interferometry measurement, which means that any sophisticated interference fringes can be decomposed or approximatively decomposed by them. Newton’s rings are typical interference fringes with quadratic phase, the physical parameters such as curvature radius and position of ring’s center are included in the pattern. The parameter estimation algorithm of Newton’s rings based on FRFT/DCFT needs only one pattern to realize the estimation through the method of signal processing with advantages of easy operation, high accuracy, high speed and strong anti-noise performance, for reason that the Newton’s ring is actually a two-dimension Chirp signal.
Variable-ratio lateral-shearing interferometry with a vortex-splitting grating

Lateral Shearing Interferometer (LSI), as a kind of self-interference technology, can achieve high-precision wavefront sensing and phase imaging. Quadriwave Lateral Shearing Interferometry (QWLSI) divides the wavefront into four transverse dislocated beams by a checkerboard phase grating. The lateral-shearing interferogram of the four waves occurs on the image plane, and then the test wavefront is reconstructed. The reconstruction precision is determined by the shear ratio, thus the variable shear ratio can meet the requirement of the different measurement accuracy. Here we proposed variable-ratio lateral-shearing interferometry with a vortex-splitting grating. Different from the checkerboard grating, topological charge is first encoded into grating and is then optimized to obtain two shear ratios in the same interference setup. The proposed variable-ratio lateral-shearing setup including of only an axial motion device is robust, effective and variable precision for wavefront sensing.
Deep learning prediction of amplitude death

Abstract Affected by parameter drift and coupling organization, nonlinear dynamical systems exhibit suppressed oscillations. This phenomenon is called amplitude death. In various complex systems, amplitude death is a typical critical phenomenon, which may lead to the functional collapse of the system. Therefore, an important issue is how to effectively predict critical phenomena based on the data in the system oscillation state. This paper proposes an enhanced Informer model to predict amplitude death. The model employs an attention mechanism to capture the long-range associations of the system time series and tracks the effect of parameter drift on the system dynamics through an accompanying parameter input channel. The experimental results based on the coupled Rössler and Lorentz systems show that the enhanced informer has higher prediction accuracy and longer effective prediction distance than the original algorithm and can predict the amplitude death of a system.
Special Issue on Intelligent Computing for Big Data

Passion for a classic research area of computer science, artificial intelligence (AI), has experienced new momentum in recent years [...]
Redescription mining on data with background network information

Redescription mining aims at finding subsets of instances that can be re-described, characterized in multiple ways, using one or more disjoint sets of attributes that describe some set of instances. Current redescription mining algorithms either work with tabular data or with relational data — where binary relations between objects are used which allow representing descriptions as graphs. In this work, we propose novel type of redescription mining methodology that allows using tabular data in combination with background network information, where nodes of a network are instances in the tabular data. Background information is used to locate subsets of instances with some desired network property whereas tabular data are used to re-describe such interesting subsets. Methodology can be classified as constraint-based redescription mining, where we allow for a large variety of complex network-based soft constraints. The proposed framework is extensible, thus any network-related measure can be used to localize subsets of instances of interest. In addition, different types of network such as undirected, directed graphs, graph sequences or multiplex can be used as a background information. We demonstrate the applicability of the proposed framework on three use-case datasets involving country trade networks, biological (gene spatial) networks and social networks. The experimental evaluation demonstrates that the proposed approach outperforms existing, general redescription mining approaches with respect to intensity of network properties of the re-described instances without loss of accuracy, mostly even improving redescription accuracy.
Improvement of the Algorithm to Evaluate Security Strength of Share Assignment on Communication Network in SSS

We propose a new algorithm for the share assignment problem, which evaluates how the security strength varies depending on where shares are stored on a communication network in the secret sharing scheme. For a long time, the secret sharing scheme was studied only in terms of how to make shares. However, a recent study indicated that the security strength depends on where shares are stored on the communications network after they have been made and proposed a method for evaluating the security strength of each allocation of shares. However, the algorithm of the proposed method is inefficient. Here, we propose an improved algorithm based on a binary tree search. Numerical experiments show that the presented method surely reduces the computer usage time compared with the original method.
Cluster Analysis of Mineral-chemical Data of Basaltic Rocks from Linga, Eastern Deccan Volcanic Province (EDVP) and its Petrogenetic Implications

Cluster analysis studies based on electron microprobe data of phenocryst mineral phases (namely olivine, clinopyroxene and plagioclase) of basaltic rocks around Linga (21° 58′ N: 78° 56′ E), eastern Deccan volcanic province have been carried out for the first time. Based on highly significant correlation coefficients obtained for relevant major oxides of phenocrystal mineral phases, it is suggested that mineralogical parameters such as Fo% in olivine, Ca/Mg# in clinopyroxene and An% of plagioclase play important role to decipher the evolution of the phase chemistry. Those highly significant major oxides were subjected to multivariate statistical analysis, involving hierarchical segmentation, cluster analysis and construction of dendrograms. For each of the minerals, the clusters decrease systematically. The ultimate cluster patterns may either correspond to liquid immiscibility-related intraflow chemical variation or to multiple staged magmatic emplacements.
Is word length inaccurate for authorship attribution?

Abstract Word length refers to a feature that is extracted from texts and used to characterize authorial style; it was quantitatively demonstrated by Mendenhall (Mendenhall, T. C., 1887, The characteristics curves of composition. Science, IX: 237–49). Many similar features for describing authorial style have been proposed; however, research indicates that compared with other features, word length identifies authors with lower accuracy. This study proposes a feature, referred to as c-wordL, to improve the accuracy of authorship attribution in texts through the classification of words into several types by following the part-of-speech (POS) tags and combining these types with the word length data. The proposed method was tested using 200 literary texts from ten different authors in Japanese, English, and Chinese. The results indicated that c-wordL was more accurate than the existing word length-based features and provided useful information that word unigrams and POS tag bigrams could not measure. In addition, the ease of interpretation of different types of features was discussed. In summary, c-wordL outperformed the existing superior features in explaining the distinct writing styles and identifying the authors.
A Survey of Learning Methods in Deep Neural Networks (DDN)

Machine learning algorithm hyperparameters for a specific dataset are mathematically expensive and it is challenging to find the best values. Machine Learning predictive modelling algorithms are governed by hyperparameters. Artificial intelligence has become a new-age solution to most of the global challenges, hitherto unravelled and resolved. Within artificial intelligence, Machine Learning and its subset Deep Learning (DL) have brought a paradigm shift with their computation power. Currently, DL is a widely used computational approach in machine learning. The uniqueness of deep learning is its capability to learn a large amount of data. This paper discusses mathematical algorithms that are associated with each of these performance-enhancing measures and demonstrates tuning results and efficiency gains for each process and analysis, and it attempts to review different types of deep learning training and learning methods, viz., supervised, unsupervised, semi-supervised, and reinforcement learning together with challenges each face. Deep Learning algorithms are evaluated by applying different machine learning techniques.
The Implementation of Object Detection Using Deep Learning for Mobility Impaired People

The Object detection technique is used to locate and identify objects in images and videos. To train a model, deep neural nets are used in conjunction with complex algorithms to detect objects in real-time using machine learning applications and deep learning. People with visual impairments have difficulties when it comes to autonomous mobility; despite walking on a well-known path, they can encounter multiple hazards along the way. In this paper, the MobileNet Single Shot Multibox Detector (SSD) algorithm is implemented using transfer learning; so that it can be integrated with a smart walking stick for mobility-impaired people using Raspberry Pi v3 B+.
Logic of alternative-I

Abstract This paper aims to construct a logic of alternative-I that provides a proper conceptual framework for talk of possible-I in decision-making context, and thereby solves what we call the paradox of possible-I . The model of our logic, Alt-I model, is an adaptation of N. Belnap’s branching-time model, and the STIT ( see to it that ) operator defined on the model serves to represent choices and decisions made by actual and counterfactual agents. We conclude this paper by discussing the application of Alt-I model to the case of digital twins , digital copies of a person.
Learning to segment subcortical structures from noisy annotations with a novel uncertainty-reliability aware learning framework

Accurate segmentation of subcortical structures is an important task in quantitative brain image analysis. Convolutional neural networks (CNNs) have achieved remarkable results in medical image segmentation. However, due to the difficulty of acquiring high-quality annotations of brain subcortical structures, learning segmentation networks using noisy annotations is an inevitable topic. A common practice is to select images or pixels with reliable annotations for training, which usually may not make full use of the information from the training samples, thus affecting the performance of the learned segmentation model. To address the above problem, in this work, we propose a novel robust learning method and denote it as uncertainty-reliability awareness learning (URAL), which can make sufficient use of all training pixels. At each training iteration, the proposed method first selects training pixels with reliable annotations from the set of pixels with uncertain network prediction, by utilizing a small clean validation set following a meta-learning paradigm. Meanwhile, we propose the online prototypical soft label correction (PSLC) method to estimate the pseudo-labels of label-unreliable pixels. Then, the segmentation loss of label-reliable pixels and the semi-supervised segmentation loss of label-unreliable pixels are used to calibrate the total segmentation loss. Finally, we propose a category-wise contrastive regularization to learn compact feature representations of all uncertain training pixels. Comprehensive experiments are performed on two publicly available brain MRI datasets. The proposed method achieves the best Dice scores and MHD values on both datasets compared to several recent state-of-the-art methods under all label noise settings. Our code is available at https://github.com/neulxlx/URAL.
Deep Hashing and Sparse Representation of Abnormal Events Detection

Abstract Due to its widespread application in the field of public security, anomaly detection in crowd scenes has recently become a hot topic. Some deep learning-based methods led to significant accomplishments in this field. Nevertheless, due to the scarcity of data and the misclassification of queries which most of them suffer to some extent from a sudden and infrequent overfitting. Though, we tried to solve the above problems, understand the long video streams and establish an accurate and reliable security system in order to improve its performance in detecting anomalies. We also referred to the hash technique, which has proven to be the most efficient method used when researching about large-scale image recovery. Thus, this article offers a smart video anomaly detection solution. In this paper, we combine the advantages of both deep hashing and deep auto-encoders to show that tracking changes in deep hash components across time and can be used to detect local anomalies. More precisely, we start with a new technique to minimize the mass of input data and information in order to decrease the time of calculation using a new dynamic frame skipping technique. Then, we propose to measure local anomalies by combining semantic with low-level optical flows to balance the performance and perceptibility. The experimental results illustrate that the proposed methods surpass these baselines for the detection and localization of anomalies.
Research on resilience and disaster resilience evaluation system for older communities under hierarchical analysis and delphi method

In order to solve the problem of resilience and disaster resistance evaluation of old communities, this paper establishes a resilience and disaster resistance evaluation index system with 5 primary indicators, 10 secondary indicators, and 28 tertiary indicators, and then constructs a resilience and disaster resistance evaluation index model for old communities based on hierarchical analysis and Delphi method. The weights of each index are clarified and scored to effectively evaluate the resilience and disaster resistance of communities to cope with major disaster risks. This paper takes the Wuhan Huayuanshan community as a case study, and systematically evaluates the resilience and disaster-resilience evaluation index system of the old community by means of a field survey and questionnaire.
Pengembangan Media Game Edukasi untuk Meningkatkan Pemahaman Siswa pada Mata Pelajaran IPA Kelas IV

This study aims to develop learning media for android-based educational games in science subjects for energy sources, so as to increase the activity of students and increase their understanding of various energy sources and their uses. This type of research is research and development (R&amp;D), which refers to the ADDIE development model (Analysis, Design, Development, Implement, and Evaluation). The data obtained will be analyzed with descriptive quantitative and qualitative data. The feasibility of learning media for Android-based educational games from the research results is "very feasible" to be used as a learning medium for fourth grade students. This can be seen from the results of the percentage of media expert validation of 82%, material expert validation of 88%, and linguistic expert validation of 88%. The three criteria are declared to have valid categories with additional input and general suggestions that are used as consideration in improving educational game media. Furthermore, based on the questionnaire responses, students obtained results of 92.9% which indicated the "very good" criteria. Meanwhile, based on the results of the class tests (pre-test and post-test) the average value was initially only 55.6, which increased to 85.6 after using educational game media.