Reviewerly/exHarmony huggingface dataset:
Textual Backdoor Defense via Poisoned Sample Recognition

Deep learning models are vulnerable to backdoor attacks. The success rate of textual backdoor attacks based on data poisoning in existing research is as high as 100%. In order to enhance the natural language processing model’s defense against backdoor attacks, we propose a textual backdoor defense method via poisoned sample recognition. Our method consists of two parts: the first step is to add a controlled noise layer after the model embedding layer, and to train a preliminary model with incomplete or no backdoor embedding, which reduces the effectiveness of poisoned samples. Then, we use the model to initially identify the poisoned samples in the training set so as to narrow the search range of the poisoned samples. The second step uses all the training data to train an infection model embedded in the backdoor, which is used to reclassify the samples selected in the first step, and finally identify the poisoned samples. Through detailed experiments, we have proved that our defense method can effectively defend against a variety of backdoor attacks (character-level, word-level and sentence-level backdoor attacks), and the experimental effect is better than the baseline method. For the BERT model trained by the IMDB dataset, this method can even reduce the success rate of word-level backdoor attacks to 0%.
Forming a Indicators System of Digital Maturity an Educational Organization with Attributes of Sufficiency

This article considers the problem of assessing the digital maturity of a higher education institution in order to determine its potential and current areas of digital transformation. The digital maturity of an organization, in this context, can reasonably be thought of as the state it has reached, described by a multidimensional vector of parameters, which allows the organization to be considered compositional mature for a given set of digital attributes. In a first approximation, a indicators system of digital maturity of a higher education institution has been formed. Two existing models have been analyzed and a proprietary model for assessing the digital maturity of a higher education institution has been developed. The totality of the indicators of the three models is considered from a systematic perspective and taking into account expert assessments of the informative value of the indicators in relation to assessing the level of digital maturity. The results of the group examination made it possible to carry out the primary selection of indicators of digital maturity that have signs of sufficiency. The main message of the study of the set of indicators is that it is possible to judge competently about the digital maturity of any organization only based on attracting sufficient indicators that characterize the effectiveness of the organization due to certain digital transformation activities. At the same time, all activities should be analysed to compare the costs of resources and the results of these activities. The results are an increase in the intellectual resource of the user of the fruits of digital transformation. Ultimately, these are the additional cognitive capacity and spiritual qualities of the well-mannered and polite persons. The latter can be characterized by a dominant influence on the structure of the uncertainty of the organization’s activities. The resulting evaluation model requires further research in the context of its application in solving the problem of effective digital transformation of organizational systems.
Fully Automated, Realistic License Plate Substitution in Real-Life Images

Data augmentation techniques have been focused in recent research as they hold the promise to reduce the need for extensive data acquisition and to enable systematic sampling, e.g., in order to examine underrepresented cases. The question of how and to what extent control over the result is possible and necessary is still open. We propose a novel system for license plate substitution in the wild, which replaces a given license plate within an image crop by another one with a customized pattern. The system is based on a CycleGAN architecture, which respects the plate's pose and dominant image features, such as lighting and image sharpness. Most importantly the system is trained on a set of license plate crops without requiring any label information. We demonstrate the validity of our approach in two sets of experiments: First, a license plate recognition system is trained and evaluated with varying amounts and ratios of synthetic over real-life data, and second, the realism of image features is verified by means of a human acceptance study as well as the Fréchet Inception Distance.
Scale-Aware Multi-branch Decoder for Salient Object Detection

Recently, Salient Object Detection (SOD) has been witnessed remarkable advancements owing to the introduction of Convolution Neural Networks (CNNs). However, when faced with complex situations, such as salient objects of different sizes appearing in the same image, the detection results are far away from satisfaction. In this paper, we propose a scale-aware multi-branch decoder to detect salient objects at different scales. Specifically, different branches of the decoder are assigned to extract distinctive features and predict maps for objects of different sizes. Meanwhile, we design two flows between branches for feature aggregation. Global Distinction Flow (GDF) allows each branch to focus on the semantic information of specific sizes object, while Hierarchical Integration Flow (HIF) combines different information on each branch and forms the final detection result. In addition, we generate new masks by decomposing the ground true saliency map and use them in different branches for supervision. Comparing with existed state-of-the-art methods, experiments conducted on five datasets demonstrate the effectiveness of our method.
The Development of M.Vocset for Building Prescholers English Vocabulary Inventory

The objectives of this research are (1) to develop M-Vocset for teaching English for young learner and (2) to know the benefit of M-Vocset for teaching English in Kangaroo Preschool. In this research, researcher used Research and Development (R&amp;D) model from Borg and Gall. To simplify this research, the researcher shortened it to four stages that are: data collection and information, planning, product development and final product. Data collection technique used in this research is interview and document analysis. This research was conducted at Kangaroo Preschool with age 2-4 years old. The findings of this research are: analysis needs of students and document analysis. The result is that researcher use Holy days Â as learning material to be developed. To accomplish M-Vocset, the researcher asked for input from the expert team and also the teacher there. Regarding the benefits, it is easy to use and it is to teach students how to write a sentence in English. M-Vocset develops student character regarding tolerance.
The kangaroo problem

The kangaroo problem requires two groups of kangaroos to pass each other on a narrow bridge. The question is whether there exist solutions for this task, how they look like and how they can be constructed. This depends on the number of kangaroos on each side – the puzzle has originally been posed for two groups of 3 kangaroos each. It is shown that for every number of kangaroos on each side and the agreement among the two groups which one takes the first step the problem has a unique solution. Our analysis provides an explicit construction of the solution strategy.
Not All Data Matters: An Efficient Approach to Multi-Domain Learning in Vehicle Re-Identification

Learning a robust and discriminative feature representation has always been challenging in vehicle reidentification due to intra-class variability and inter-class similarity. While most research contributions focus on adding more attributes or modifying existing state-of-the-art architectures to enhance the learning capacity, our approach tackles the problem by multi-domain learning. Many multi-domain learning approaches blindly merge datasets and later fine-tune the model on the target dataset. These methods are computationally expensive. Our paper focuses on effectively constructing a large-scale vehicle re-id dataset by selectively choosing images sharing similar attributes to the target dataset only. Through extensive experiments, we conclude that our approach can outperform other state-of-the-art models in vehicle re-identification by large margins. We also compare our method with other multi-domain learning methods to show that ours uses less external data but still has superior performance, proving that not all data matters.
Voting control in multiple entry PBFT blockchain systems for IoT

In this work we design and model a policy which incentivizes nodes to vote in a multiple entry PBFT protocol. Each time a node votes on a block proposal, a token is generated for that node. Nodes that do not vote are penalized by denying them the right to submit proposals until they have collected a sufficient number of tokens. We present a queuing M/G/1 model of token control mechanism and integrated queuing model of the node with access regulated with token mechanism. Performance results confirm the efficiency of proposed scheme.
A multi-modal dataset for analyzing the imageability of concepts across modalities

Recently, multi-modal applications bring a need for a human-like understanding of the perception differences across modalities. For example, while something might have a clear image in a visual context, it might be perceived as too technical in a textual context. Such differences related to a semantic gap make a transfer between modalities or a combination of modalities in multi-modal processing a difficult task. Imageability as a concept from Psycholinguistics gives promising insight to the human perception of vision and language. In order to understand cross-modal differences of semantics, we create and analyze a cross-modal dataset for imageability. We estimate three imageability values grounded in 1) a visual space from a large set of images, 2) a textual space from Web-trained word embeddings, and 3) a phonetic space based on word pronunciations. A subset of the corpus is evaluated with an existing imageability dictionary to ensure a basic generalization, but otherwise targets finding cross-modal differences and outliers. We visualize the dataset and analyze it regarding outliers and differences for each modality. As additional sources of knowledge, part-of-speech and etymological origin of all words are estimated and analyzed in context of the modalities. The dataset of multi-modal imageability values and a link to an interactive browser with visualizations are made available on the Web.
Comparison of various Supervised Machine Learning Algorithms in Sentiment Analysis of Tweets on Black Fungus

People tend to share thoughts in their point of views about a specific or a random event that is happening all over the world in the form of texts, posts, images, videos and audios through social media. This in turn reflects the overall emotion or sentiment of the society regarding a particular social event. In this work, the tweets of various users across the world in a certain period regarding the spread of mucormycosis disease which spreads over in the areas of India recently is collected and the text or the tweets were processed using several natural language processing tools. The linear support vector machine model is built in order to classify the tweets of the users.
Effective Automatic Radar Bias Estimation Scheme Using Targets of Opportunity

In multi-radar air surveillance scenario bias in individual radar measurements is an important prerequisite for effective fusion of radar data. Conventional bias computation approaches depend on GPS fitted air sorties for computation of bias in radar measurements. The method proposed in this paper computes radar bias automatically using GPS data available from Automatic Dependent Surveillance Broadcast (ADS-B) measurements from targets of opportunity. The proposed method computes the bias in radar measurements without the need of costly dedicated calibration sorties or multiple radars and thereby reducing the cost and manual effort. This paper provides details about the mathematical frame works of recursive weighted least square based bias estimation algorithm and the associated data conversion formats in different coordinate systems. The proposed method preprocess the correlated radar and AD-B measurements and selects measurement pairs from linear segments for time alignment. This paper provides a detailed scheme of automatic bias estimation process with different parameters involved in controlling the accuracy and periodicity of the bias to be computed. The scheme developed in this paper also includes a bias validation module and a unique averaging method to ensure accuracy and smooth variations across the measurement batches considered for computation. The simulation results obtained in this paper suggest the utility of the proposed approach for practical bias computation applications in a cost effective way. The method developed in this paper is based on insight obtained from analysis of recorded data from field deployed radars.
Latent Representation Prediction Networks

Modern model-based reinforcement learning methods for high-dimensional inputs often incorporate an unsupervised learning step for dimensionality reduction. The training objective of these unsupervised learning methods often leverages only static inputs such as reconstructing observations. These representations are combined with predictor functions for simulating rollouts to navigate the environment. We advance this idea by taking advantage of the fact that we navigate dynamic environments with visual stimulus and create a representation that is specifically designed with control and actions in mind. We propose to learn a feature map that is maximally predictable for a predictor function. This results in representations that are well suited for the task of planning, where the predictor is used as a forward model. To this end, we introduce a new way of learning this representation along with the prediction function, a system we dub Latent Representation Prediction Network (LARP). The prediction function is used as a forward model for a search on a graph in a viewpoint-matching task, and the representation learned to maximize predictability is found to outperform other representations. The sample efficiency and overall performance of our approach are shown to rival standard reinforcement learning methods, and our learned representation transfers successfully to unseen environments.
Domain Adaption Based on Symmetric Matrices Space Bi-Subspace Learning and Source Linear Discriminant Analysis Regularization

At present, Symmetric Positive Definite (SPD) matrix data is the most common non-Euclidean data in machine learning. Because SPD data donb’t form a linear space, most machine learning algorithms can not be carried out directly on SPD data. The first purpose of this paper is to propose a new framework of SPD data machine learning, in which SPD data are transformed into the tangent spaces of Riemannian manifold, rather than a Reproducing Kernel Hilbert Space (RKHS) as usual. Domain adaption learning is a kind of machine learning. The second purpose of this paper is to apply the proposed framework to domain adaption learning (DAL), in which the architecture of bi-subspace learning is adopted. Compared with the commonly-used one subspace learning architecture, the proposed architecture provides a broader optimization space to meet the domain adaption criterion. At last, in order to further improve the classification accuracy, Linear Discriminant Analysis (LDA) regularization of source domain data is added. The experimental results on five real-world datasets demonstrate the out-performance of the proposed algorithm over other five related state-of-the-art algorithms.
Action recognition based on dynamic mode decomposition

Abstract Based on dynamic mode decomposition (DMD), a new empirical feature for quasi-few-shot setting (QFSS) skeleton-based action recognition (SAR) is proposed in this study. DMD linearizes the system and extracts the modes in the form of flattened system matrix or stacked eigenvalues, named the DMD feature. The DMD feature has three advantages. The first advantage is its translational and rotational invariance with respect to the change in the localization and pose of the camera. The second one is its clear physical meaning, that is, if a skeleton trajectory was treated as the output of a nonlinear closed-loop system, then the modes of the system represent the intrinsic dynamic property of the motion. Finally, the last one is its compact length and its simple calculation without training. The information contained by the DMD feature is not as complete as that of the feature extracted using a deep convolutional neural network (CNN). However, the DMD feature can be concatenated with CNN features to greatly improve their performance in QFSS tasks, in which we do not have adequate samples to train a deep CNN directly or numerous support sets for standard few-shot learning methods. Four QFSS datasets of SAR named CMU, Badminton, miniNTU-xsub, and miniNTU-xview, are established based on the widely used public datasets to validate the performance of the DMD feature. A group of experiments is conducted to analyze intrinsic properties of DMD, whereas another group focuses on its auxiliary functions. Experimental results show that the DMD feature can improve the performance of most typical CNN features in QFSS SAR tasks.
Variants of Fuzzy C-Means on MRI Modality for Cancer Image Archives

The segmentation, identification and mining of contaminated tumor region from MRI images is a primary concern in medical image analysis. However, it is a monotonous and time-consuming process done by radiologists or clinical experts, and its precision is subjected to their expertise. To overcome these constraints, the use of supporting technology turns out to be very important. In this study, performance improvement and reduction of the complexity involved in the segmentation of medical images is been focused. We have investigated cancer cells using various fuzzy c-means methods and its different variants for Breast, Brain, Liver and Prostate dataset. The empirical outcomes of the various methods have been tested and corroborated on MRI for efficiency and quality analysis based on four well-known cluster validity indices. The results are very encouraging and robust in nature.
Verification Model for Meteorological Early Warning Information Based on Natural Language Processing

Increasing attention is being paid to early warning information; hence, designing an approach to rapidly and automatically identify the accuracy of meteorological early warning description text has become increasingly important. In this work, we propose a verification model for meteorological early warning description text based on meteorological early warning vocabulary and an N-gram language model. In the vocabulary, some correct words are made up of place names, typhoon names and early warning category names. Additionally, we propose a method to unify parallel place names that regards multiple continuous place names as a single symbol. Then, count the probability of the symbol occurring with the previous word. The perplexity examination data showed that the model works effectively, particularly when the data contain parallel but disordered place names.
An Improved Algorithm for The <i>k</i> -Dyck Edit Distance Problem

A Dyck sequence is a sequence of opening and closing parentheses (of various types) that is balanced. The Dyck edit distance of a given sequence of parentheses S is the smallest number of edit operations (insertions, deletions, and substitutions) needed to transform S into a Dyck sequence. We consider the threshold Dyck edit distance problem, where the input is a sequence of parentheses S and a positive integer k , and the goal is to compute the Dyck edit distance of S only if the distance is at most k , and otherwise report that the distance is larger than k . Backurs and Onak [PODS’16] showed that the threshold Dyck edit distance problem can be solved in O ( n + k 16 ) time. In this work, we design new algorithms for the threshold Dyck edit distance problem which costs O ( n + k 4.544184 ) time with high probability or O ( n + k 4.853059 ) deterministically. Our algorithms combine several new structural properties of the Dyck edit distance problem, a refined algorithm for fast (min , +) matrix product, and a careful modification of ideas used in Valiant’s parsing algorithm.
Big Book Based on SAS Method Using Kvisoft Application to Strengthen Reading Literacy for Student

Covid-19 has brought changes to online learning activities. Therefore, learning media that can be accessed online are very necessary for students to be able to learn from home. The purpose of this research is to develop a big book based on the SAS method using the Kvisof application and apply it to strengthen reading literacy for students in elementary school. The research method used is ADDIE. Data collection used questionnaires, observation, interviews, and documentation. The product has been successfully developed and based on the validation results from material and language experts, it shows that the product is valid for use in elementary schools. When implementing the product, it was observed that the SAS-based big book could be used by teachers and students in lower grades in learning literacy.
Facial Monitoring Using Gradient Based Approach

Security is a major concern for every organization and various security methodologies are applied to keep the work area safe and secure. The proposed algorithm computes the local gradients from an input facial image and detects the 68 landmark points to identify any unknown person. The images of all recognized and authorized persons are stored in the database after performing some encryption of the images to make database data secure. During the monitoring process, the faces encountered in the camera frame in real time need to be matched with the database images. For matching, the retrieved database images need to be decrypted and encrypted again to make the format same as used by matching algorithm. After the matching process, a notification is sent to the concerned authority for some unknown face which is also highlighted by a rectangular box. The performance and robustness of the algorithm are evaluated by testing images with various postures, background, lighting condition that is shown in experimental results.
Verifiable Obtained Random Subsets for Improving SPHINCS+

SPHINCS+ is a stateless hash-based digital signature scheme and an alternate candidate in round 3 of the NIST Post-Quantum Cryptography standardization competition. Although not considered as a finalist because of its performance, SPHINCS+may be considered for standardization by NIST after another round of evaluations. In this paper, we propose a Verifiable Obtained Random Subsets (v-ORS) generation mechanism which with one extra hash computation binds the message with the signing FORS instance (the underlying few-time signature algorithm). This enables SPHINCS+ to offer more security against generic attacks because the proposed modification restricts the ORS generation to use a hash key from the utilized signing FORS instance. Consequently, such a modification enables the exploration of different parameter sets for FORS to achieve better performance at the same security level. For instance, when using v-ORS, one parameter set for SPHINCS+-256s provides 82.9% reduction in the computation cost of FORS which leads to around 27% reduction in the number of hash calls of the signing procedure. Given that NIST has identified the performance of SPHINCS+ as its main drawback, these results are a step forward in the path to standardization.