Reviewerly/exHarmony huggingface dataset:
Tamil Author Identification Using Convolutional Neural Network

Author identification is one of the significant tasks in natural language processing(NLP) which identifies the author of a given text content. Text classification applications such as news classification and sentiment classification mostly rely on features extracted from the content or the topic of the content. Author identification, on the contrary generally depends on the features representing the writing style such as lexical, character level, syntactic and semantic features. In recent years, the performance of text classification approaches has been significantly improved by applying deep learning-based text representation. For the author identification task, a good amount of research has been done for English and other European languages whereas relatively less have been done for regional languages like Tamil. This paper describes such a study for the Tamil language. This study proposes a deep-learning model for author identification, which concatenates the conventional word-level features and automatic features learned by a convolutional neural network (CNN) with FastText pre-trained word vectors. As part of the study, a dataset for Tamil author identification was created and the proposed model was analysed. The proposed model obtained better performance compared with conventional or deep learning models.
Dual Fuzzy Logic Controllers for Omnidirectional Robot Navigation and Obstacle Avoidance

This paper presents the development and implementation of a fuzzy logic control system for a three wheeled omnidirectional robot in an unknown or unstructured environment with real-time navigation planning and avoiding unknown obstacles. In this paper, we used two fuzzy logic controllers like some previous works, however, the main contributions in this work is that we aimed to optimize the navigation path as well as implement an obstacle avoidance fuzzy controller that operates using an omnidirectional robot with three degrees of freedom. To evaluate the efficiency of the dual fuzzy controller, multiple simulations were applied using MATLAB and Robotino Sim platforms.
An Efficient Kernel KNN classifier for Activity Recognition on Smartphone

The real-life mobile sensing applications use mobile sensors integrated in smartphones to predict user's physical activities, and to detect an anomaly. The precision of the human activity recognition (HAR) system depends on extracted features and robustness of the training model. This study mainly proposed a new scheme SV-KNN based on kernel K-Nearest Neighbors using a compact training data based on the support vectors (SV) to identify the ongoing activity. To reduce the sensory features as the inputs for classifier, we used the Principal Component Analysis (PCA). Comparison of our system with existing classifiers shows the efficiency of SV-KNN approach in terms of accuracy and F-score.
A Lightweight M_TRNG Design based on MUX Cell Entropy using Multiphase Sampling

True Random Number Generator (TRNG) is built on hardware-based non-deterministic noise for generating keys, initialization vectors, and random numbers in a variety of applications requiring cryptographic protection. In this paper, through the research of frequency jitter mechanism, a true random number generator based on multi-phase sampling of MUX unit is proposed. The scheme is based on the “soft macro” design of the MUX unit, which replaces the traditional look-up table entropy scheme. It completes high-precision jitter sampling on the basis of ensuring the fairness of the TRNG entropy source, and can be well transplanted to a series of FPGAs. The proposed TRNG is verified on three FPGAs of Xilinx Virtex-6, Artix-7 and Virtex-7. The experimental results show that the generated random sequences are of good quality, passing the NIST SP800-22 tests with higher p-value and passing NIST SP 800-90B tests with higher minimum entropy, while achieving 100Mbps throughput. It is worth mentioning that the resource overhead consumed by the proposed TRNG is small and single, only consuming 4 MUX units, 4 DFFs and one LUT unit, which has a good application prospect.
Text classification based on machine learning for Tibetan social network

Social network technologies have gained widespread attention in many fields. However, the research on Tibetan Social Network (TSN) is limited to the sentiment analysis of micro-blogs, and few researchers focus on text classification and data mining in TSN. It cannot meet the social needs of the majority of Tibetans and the text information they really care about. In this paper, we investigate and compare different models that we adopted for the classification of Tibetan text. Machine learning models including Naive Bayesian (NB), Random Forest (RF), Support Vector Machine (SVM), fastText and text Convolutional Neural Networks (CNN) are used as classifiers to determine the best approach in Tibetan Social Network. In addition, term frequency-inverse document frequency (TF-IDF) is used to extract hot words and generate the word cloud. The results show that the random forest is significantly better than other machine learning algorithms on Tibetan text classification.
An enhanced ECA/Chaotic-based PRNG: Hardware design and Implementation

This contribution is a hardware design and implementation of an enhanced Pseudo-Random Number Generator (PRNG) dedicated to security applications on a Field Programmable Gate Array (FPGA). This high-performance Lorenz chaotic-based PRNG is improved using Elementary Cellular Automata for key encoding. Then, the hardware design of the PRNG is developed with the Xilinx System Generator software tools. The implementation is targeted at an FPGA Zedboard operating at a high throughput of 49946.62 Mbps and a good operating frequency of 195.104 MHz. The NIST 800–22 SP test suite results validate the efficiency of this hardware PRNG in high-quality random numbers generation. Furthermore, an application of this enhanced hardware PRNG in an image encryption/decryption application is offered. Compared to some recent works, the experimental results indicate that this work offers better resource utilization, throughput, and power consumption results.
FUNDAE: Fault Template Attack on SUNDAE-GIFT AEAD Scheme

Fault Attacks (FA) have garnered a lot of attention from industry and academic research due to their practical and wide impact. In the framework of symmetric key cryptography, designing countermeasures against fault attacks is still an open problem with recent developments of advanced fault injection techniques. Recently proposed Fault Template Attack (FTA) has shown that without access to any ciphertext of a crypto execution, an adversary can still recover the secret key just by knowing if the computation is faulty or not. Additionally, usage of Authenticated Encryption with Associated Data (AEAD), a kind of symmetric-key operating mode, has gained momentum and become the standard for secret key communications. In this work, we first show how an adversary can very efficiently launch FTA using a combination of the right fault model and proper selection of an encryption block in AEAD operation, where we could recover full master key using 25 percent less fault requirements when compared to classical fault template attack. Then we propose a generic area redundant countermeasure scheme to thwart FTA in AEADs, where our countermeasure enabled circuit occupied only 16 percent additional area than unprotected circuit. We use SUNDAE-GIFT as the benchmark circuit for all our experiments.
Algorithmic Optimization of Vehicle Detection System based on Hybrid Metaheuristic with Machine Learning

In this paper a robust and accurate car detection system has been developed. To successfully detect vehicles in the road and taking into account the different weather conditions of the captured scenes, we use the Histogram of Oriented Gradient (HOG) method to extract features from an image and the support vector machine linear (SVM) for classification. To optimize this car detection system, a Particle Swarm Optimization (PSO) technique is used for the selection of the extracted features.
FPGA implementation of a chaos- Towfish-based cryptosystem for medical image security

In recent years, information and communication systems have experienced serious security issues due to the rising popularity of image-sharing platforms and the ubiquity of numerous smart electronic devices. The increased volume of data generated by the medical and clinical communities necessitates the use of such advanced platforms for data exchange. As a result, the implementation of improved procedures and resources in terms of storage and security is essential. This research proposes a novel medical image encryption method based on chaos sequence and the modified Twofish algorithm. A quick and more efficient algorithm than current methods is built using chaos-based image encryption methods. The modified algorithm can be applied for hardware applications.
AutoGF: Runtime Graph Filter Tuning for Community Node Ranking

A recurring graph analysis task is to rank nodes based on their relevance to overlapping communities of shared metadata attributes (e.g. the interests of social network users). To achieve this, approaches often start with a few example community members and employ graph filters that rank nodes based on their structural proximity to the examples. Choosing between well-known filters typically involves experiments on existing graphs, but their efficacy is known to depend on the structural relations between community members. Therefore, we argue that employed filters should be determined not during algorithm design but at runtime, upon receiving specific graphs and example nodes to process. To do this, we split example nodes into training and validation sets and either perform supervised selection between well-known filters, or account for granular graph dynamics by tuning parameters of the generalized graph filter form with a novel optimization algorithm. Experiments on 27 community node ranking tasks across three real-world networks of various sizes reveal that runtime algorithm selection selects near-best AUC and NDCG among a list of 8 popular alternatives, and that parameter tuning yields similar or improved results in all cases.
Unsupervised many-to-many stain translation for histological image augmentation to improve classification accuracy

Deep learning tasks, which require large numbers of images, are widely applied in digital pathology. This poses challenges especially for supervised tasks since manual image annotation is an expensive and laborious process. This situation deteriorates even more in the case of a large variability of images. Coping with this problem requires methods such as image augmentation and synthetic image generation. In this regard, unsupervised stain translation via GANs has gained much attention recently, but a separate network must be trained for each pair of source and target domains. This work enables unsupervised many-to-many translation of histopathological stains with a single network while seeking to maintain the shape and structure of the tissues.StarGAN-v2 is adapted for unsupervised many-to-many stain translation of histopathology images of breast tissues. An edge detector is incorporated to motivate the network to maintain the shape and structure of the tissues and to have an edge-preserving translation. Additionally, a subjective test is conducted on medical and technical experts in the field of digital pathology to evaluate the quality of generated images and to verify that they are indistinguishable from real images. As a proof of concept, breast cancer classifiers are trained with and without the generated images to quantify the effect of image augmentation using the synthetized images on classification accuracy.The results show that adding an edge detector helps to improve the quality of translated images and to preserve the general structure of tissues. Quality control and subjective tests on our medical and technical experts show that the real and artificial images cannot be distinguished, thereby confirming that the synthetic images are technically plausible. Moreover, this research shows that, by augmenting the training dataset with the outputs of the proposed stain translation method, the accuracy of breast cancer classifier with ResNet-50 and VGG-16 improves by 8.0% and 9.3%, respectively.This research indicates that a translation from an arbitrary source stain to other stains can be performed effectively within the proposed framework. The generated images are realistic and could be employed to train deep neural networks to improve their performance and cope with the problem of insufficient numbers of annotated images.
On the improvement of uncertain cloud service capacity

Cloud service firms often adopt a hybrid strategy in which computing capacity comes from a combination of their privately owned infrastructure and a public infrastructure provided by vendors. This combination can be considered a cloud computing supply chain. A major complexity for firms in managing such a supply chain is that computing demand and private computing capacity are both random variables. This randomness may require public cloud computing to fill the supply–demand gap and lead to additional costs. In this paper we study a cloud service firms’ problem of determining the amount of private computing capacity that minimizes their expected total capacity cost. We first demonstrate that the optimal amount of private computing capacity is unaffected by its randomness. We then establish that the reduction in expected total capacity cost, due to improvements in the mean or the variability of private computing capacity, exhibit diminishing returns, which allows characterization of parameter impacts and the optimal improvement level. We also establish that when setting their computing capacity price, public vendors do not need to know the private computing capacity of cloud service firms using a hybrid strategy. Our research results should be helpful to cloud service firms making procurement decisions regarding private computing capacity and/or instituting computing capacity improvement programs. Our research results should also be useful to public vendors for pricing.
Modelling and Simulation of Intelligent English Paper Generating Based on SSA-GA

To enhance the quality and efficiency of computer-enabled generation of papers for Test for English Majors Band 8 (TEM-8), a paper generation model supported by sparrow search algorithm-genetic algorithm was studied. First, a simplified test paper generation mathematical model was set up after analyzing and studying types and characteristics of TEM-8 tasks. In the model, quantity, type, difficulty, discrimination degree, scores, exposure, and answering time of test questions were taken into consideration. To enhance the optimizing effect of the genetic algorithm for searching test questions, the traditional genetic algorithm was improved by introducing the sparrow search algorithm into the model to achieve a better crossover rate, variance rate, optimization precision, and speed of the genetic algorithm. A new sparrow search-genetic algorithm (SSA-GA) was designed, and the optimizing effect of SSA-GA was verified to be ideal through optimizing six standard test functions. Then, SSA-GA was applied to conduct experimentation with test paper generation, and comparison with traditional genetic algorithms was also made. The values of best and average fitness of SSA-GA were better than those of the traditional genetic algorithm (GA) in the paper generation. Exposure rate and success rate in TEM-8 paper generation of SSA-GA were higher than those of traditional GA in TEM-8 paper generation. Results showed that the studied SSA-GA could implement test paper generation with higher speed and better quality.
Analysis of User Perception regarding the Mobile Healthcare Application of a Public Health Center using Big Data

PURPOSE This study aimed to investigate user perceptions regarding the mobile healthcare application of public health centers by using big data. METHODS The study data included 1,089 users’ reviews (from September 27, 2016 to December 23, 2021), which were analyzed using Python, Textom, KrKwic, UCINET 6, and the Net-draw program. RESULTS First, the evaluation of the application showed a higher number of “Good” responses (677 times) compared to “Bad” (329 times) and “Normal” responses (83 times). Second, network structures related to “Good” were “Like,” “Health care,” “Help,” “A sense of purpose,” “Grateful,” “Diet management,” “Exercise management,” “Easy,” “Recommendation,” “Satisfaction,” “Diet,” “Useful,” and so on. Third, network structures related to “Bad” were “Execution error,” “Request improvement,” “Question,” “Slow speed,” “Interlocking error,” “Lack of food type,” “Login error,” “Inconvenience,” “Delete and reinstall,” “Update error,” “Irritation,” “Connection error,” “Problem occurred,” “Direct input request,” “Not available,” “Waste of stars,” “Lack of function,” “Not enough,” “Stuffy,” “Lack of exercise,” and so on. Fourth, as a result of structural equivalence analysis, four clusters appeared: cluster 1 (negative function), cluster 2 (negative emotion), cluster 3 (positive function), and cluster 4 (positive emotion). CONCLUSIONS It is necessary to respond quickly in order to reflect on the users’ reviews, and active efforts are required to improve the program quality so that users can use it conveniently.
Label informed hierarchical transformers for sequential sentence classification in scientific abstracts

Abstract Segmenting scientific abstracts into discourse categories like background, objective, method, result, and conclusion is useful in many downstream tasks like search, recommendation and summarization. This task of classifying each sentence in the abstract into one of a given set of discourse categories is called sequential sentence classification. Existing machine learning‐based approaches to this problem consider the content of only the abstract to obtain the neural representation of each sentence, which is then labelled with a discourse category. But this ignores the semantic information offered by the discourse labels themselves. In this paper, we propose LIHT, Label Informed Hierarchical Transformers – a method for sequential sentence classification that explicitly and hierarchically exploits the semantic information in the labels to learn label‐aware neural sentence representations. The hierarchical model helps to capture not only the fine‐grained interactions between the discourse labels and the words in the abstract at the sentence level but also the potential dependencies that may exist in the label sequence. Thus, LIHT generates label‐aware contextual sentence representations that are then labelled with a conditional random field. We evaluate LIHT on three publicly available datasets, namely, PUBMED‐RCT, NICTA‐PIBOSO and CSAbstract. The incremental gain in F1‐score in all the three cases over the respective state‐of‐the‐art approaches is around . Though the gains are modest, LIHT establishes a new performance benchmark for this task and is a novel technique of independent interest. We also perform an ablation study to identify the contribution of each component of LIHT in the observed performance, and a case study to visualize the roles of the different components of our model.
Conceptual understanding of sequence stratigraphy

Sequence stratigraphic and Wheeler diagram interpretations require a strong combination of conceptual understanding and diagram reasoning skills. Students are generally exposed to the foundational concepts within sequence stratigraphy (relative sea level, eustasy, base level, and accommodation) in a variety of courses along their degree path, however, one prior study found that few students have a clear scientific conceptual understanding. This study serves two purposes: First, we explore the impact of Wheeler diagram instruction on student learning. Second, we assess students’ common errors on an interpretation task prior to and after Wheeler diagram interpretation. Student conceptual errors are grouped into four categories (fundamental concepts, subaerial erosional unconformities, correlative conformities and sequence identification). In general, student errors were consistent before and after Wheeler diagram instruction. The most common errors related to students identifying the contact between two facies as a stratigraphic or sedimentological feature rather than sedimentary units. Additionally, we provide a set of suggestions for teaching sequence stratigraphy that may help target the common errors and increase student interpretation skill.
TS-Net: Two-stage deformable medical image registration network based on new smooth constraints

Medical image registration can establish the spatial consistency of the corresponding anatomical structures between different medical images, which is important in medical image analysis. In recent years, with the rapid development of deep learning, the image registration methods based on deep learning greatly improve the speed, accuracy, and robustness of registration. Regrettably, these methods typically do not work well for large deformations and complex deformations in the image, and neglect to preserve the topological properties of the image during deformation. Aiming at these problems, we propose a new network TS-Net that learns deformation from coarse to fine and transmits information of different scales in the two stages. Two-stage network learning deformation from coarse to fine can gradually learn the large and complex deformations in images. In the second stage, the feature maps downsampled in the first stage for skip connection can expand the local receptive field and obtain more local information. The smooth constraints function used in the past is to impose the same restriction on the global, which is not targeted. In this paper, we propose a new smooth constraints function for each voxel deformation, which can better ensure the smoothness of the transformation and maintain the topological properties of the image. The experiments on brain datasets with complex deformations and heart datasets with large deformations show that our proposed method achieves better results while maintaining the topological properties of deformations compared to existing deep learning-based registration methods.
Incremental Linear Regression Attack

Linear Regression Attack (LRA) is an effective Side-Channel Analysis (SCA) distinguisher designed to overcome the inaccuracies in leakage models (e.g, Hamming Weight). However, the implementation of the original LRA (termed as baseline LRA) involves many matrix arithmetic operations. The sizes of these matrices are determined by the scale of the Physical Leakage Information (PLI) traces. When processing large-scale PLI traces, extremely high memory capacity is required to execute the baseline LRA. In this paper, we propose a new implementation of LRA coined as incremental LRA. Theoretically, we reformulate the process of baseline LRA to break down the large dataset and process smaller batches of the dataset iteratively. Experimentally, we first validate that our proposed incremental LRA provides flexible choice of batch size and enables a progressive increase on the PLI traces to present attack results incrementally. Second, our proposed incremental LRA reduces execution memory and time significantly as compared to the baseline LRA. We demonstrate that the best execution performance of our incremental LRA requires only 0.65% of memory requirement (154x smaller) and takes only 3.37% of the processing time (30x speed-up) of the baseline LRA while attacking the same amount of traces.