Reviewerly/exHarmony huggingface dataset:
Blockchain-Enabled Next Generation Access Control

In the past two decades, longitudinal personal health record (LPHR) adoption rate has been low in the United States. Patients' privacy and security concerns was the primary negative factor impacting LPHR adoption. Patients desire to control the privacy of their own LPHR in multiple information systems at various facilities. However, little is known how to model and construct a scalable and interoperable LPHR with patient-controlled privacy and confidentiality that preserves patients' health information integrity and availability. Understanding this problem and proposing a practical solution are considered important to increase LPHR adoption rate and improve the efficiency as well as the quality of care. Even though having the state-of-the-art encryption methodologies being applied to patients' data, without a set of secure access control policies being implemented, LPHR patient data privacy is not guaranteed due to insider threats. We proposed a definition of "secure LPHR" and argued LPHR is secure when the security and privacy requirements are fulfilled through adopting an access control security model. In searching for an access control model, we enhanced the National Institute of Standards and Technology (NIST) next generation access control (NGAC) model by replacing the centralized access control policy database with a permissioned blockchain peer-to-peer database, which not only eases the race condition in NGAC, but also provides patient-managed access control policy update capability. We proposed a novel blockchain-enabled next generation access control (BeNGAC) model to protect security and privacy of LPHR. We sketched BeNGAC and LPHR architectures and identified limitations of the design.
Measurement of microscopic strain distributions of CFRP laminates with fiber discontinuities by sampling moiré method

Carbon fiber-reinforced plastics (CFRP) laminate structures manufactured using prepregs usually contain fiber discontinuities that result from the prepreg cutting for design and cost-efficiency. In this study, the sampling moiré method was used to measure the microscopic strain of unidirectional CFRP laminates with fiber discontinuities to investigate the deformation behavior that causes damage. In addition to the strain distributions of the laminate before and after cracking, the strain results due to thermal residual stress relaxation and residual plastic deformation during cracking were also obtained. The experimental results of this study were in good agreement with the finite element method results. Our results revealed that the sampling moiré method is an accurate method of measuring microscopic strain that can be used to evaluate the mechanical properties, damage behavior, residual strains in laminates with fiber discontinuities, and other different materials and structures.
Gender Identification in Sepedi Speech Corpus

Gender identification is the task of identifying the gender of the speaker from the audio signal. Most gender identification systems are developed using datasets belonging to well-resourced languages. There has been little focus on creating gender identification systems for under resourced African languages. This paper presents the development of a gender identification system using a Sepedi speech dataset containing a duration of 55.7 hours made of 30776 males and 28337 females. We build a gender identification system using machine learning models that are trained using multilayer Perceptron (MLP), convolutional neural network (CNN), and long short-term memory (LSTM). Mid-term features are extracted from time domain features, frequency domain features and cepstral domain features, and normalised using the Z-score normalisation technique. XGBoost is used as a feature selection method to select important features. MLP achieved the same F-score and an accuracy of 94% for data with seen speakers while LSTM and CNN achieved the same F-score and an accuracy of 97%. We further evaluated the models on data with unseen speakers. All the models achieved good performance in F-score and accuracy.
An Internet of Things Intrusion Detection Method Based on CNN-FDC

In order to overcome the problem of weak function and low accuracy when the traditional intrusion detection system is faced with complex and high-dimensional network data characteristics. This paper proposes a CNN-FDC (CNN + focus loss + Dropout-Connect) detection method based on convolutional neural networks. After converting the KDD-CUP99 data set to grayscale images, the Dropout-Connect method is used to enhance the model’s ability to eliminate overfitting, and then Focal Loss is used as a loss function to solve the problem of sample imbalance in the data set. From what have been discussed above, it can be concluded that, compared with the traditional LeNet-5 and KNN machine learning algorithms, the model adding the confusion matrix judgment performs better and relatively high accuracy rate.
Enhancing real-world adversarial patches through 3D modeling of complex target scenes

Adversarial examples have proven to be a concerning threat to deep learning models, particularly in the image domain. While many studies have examined adversarial examples in the real world, most of them relied on 2D photos of the attack scene. As a result, the attacks proposed may have limited effectiveness when implemented in realistic environments with 3D objects or varied conditions. Some studies on adversarial learning have used 3D objects, however in many cases, other researchers are unable to replicate the real-world evaluation process. In this study, we present a framework that uses 3D modeling to craft adversarial patches for an existing real-world scene. Our approach uses a 3D digital approximation of the scene to simulate the real world. With the ability to add and manipulate any element in the digital scene, our framework enables the attacker to improve the adversarial patch's impact in real-world settings. We use the framework to create a patch for an everyday scene and evaluate its performance using a novel evaluation process that ensures that our results are reproducible in both the digital space and the real world. Our evaluation results show that the framework can generate adversarial patches that are robust to different settings in the real world.
Bounds on List Decoding of Linearized Reed-Solomon Codes

Linearized Reed-Solomon (LRS) codes are sum-rank metric codes that fulfill the Singleton bound with equality. In the two extreme cases of the sum-rank metric, they coincide with Reed-Solomon codes (Hamming metric) and Gabidulin codes (rank metric). List decoding in these extreme cases is well-studied, and the two code classes behave very differently in terms of list size, but nothing is known for the general case. In this paper, we derive a lower bound on the list size for LRS codes, which is, for a large class of LRS codes, exponential directly above the Johnson radius. Furthermore, we show that some families of linearized Reed-Solomon codes with constant numbers of blocks cannot be list decoded beyond the unique decoding radius.
Video Action Retrieval Using Action Recognition Model

In addition to video sharing services such as YouTube, the spread of video-based social networking services such as TikTok and Instagram have led to the accumulation of vast amounts of video data. In this situation, it has become important to develop a technology to efficiently retrieve necessary information from the accumulated video data. In this paper, we propose a method to retrieve similar videos by focusing on people and their actions in the videos. We apply this method to various datasets and TV videos to demonstrate its usefulness. Demonstration video: \urlhttps://youtu.be/y86api-FXpU
Block decomposition and statistics arising from permutation tableaux

Permutation statistics wm¯ and rlm are both arising from permutation tableaux. wm¯ was introduced by Chen and Zhou, which was proved equally distributed with the number of unrestricted rows of a permutation tableau. While rlm is shown by Nadeau equally distributed with the number of 1’s in the first row of a permutation tableau. In this paper, we investigate the joint distribution of wm¯ and rlm. Statistic (rlm, wm¯, rlmin, des, (321)) is shown equally distributed with (rlm, rlmin, wm¯, des, (321)) on Sn. Then the generating function of (rlm, wm¯) follows. An involution is constructed to explain the symmetric property of the generating function. Also, we study the triple statistic (wm¯, rlm, asc), which is shown to be equally distributed with (rlmax−1, rlmin, asc) as studied by Josuat-Vergès. The main method we adopt throughout the paper is constructing bijections based on a block decomposition of permutations.
Embedded Attributes for Cuneiform Sign Spotting

In the document analysis community, intermediate representations based on binary attributes are used to perform retrieval tasks or recognize unseen categories. These visual attributes representing high-level semantics continually achieve state-of-the-art results, especially for the task of word spotting. While spotting tasks are mainly performed on Latin or Arabic scripts, the cuneiform writing system is still a less well-known domain for the document analysis community. In contrast to the Latin alphabet, the cuneiform writing system consists of many different signs written by pressing a wedge stylus into moist clay tablets. Cuneiform signs are defined by different constellations and relative positions of wedge impressions, which can be exploited to define sign representations based on visual attributes. A promising approach of representing cuneiform sign using visual attributes is based on the so-called Gottstein-System. Here, cuneiform signs are described by counting the wedge types from a holistic perspective without any spatial information for wedge positions within a sign. We extend this holistic representation by a spatial pyramid approach with a more fine-grained description of cuneiform signs. In this way, the proposed representation is capable of describing a single sign in a more detailed way and represent a more extensive set of sign categories.
Tiny CNN for feature point description for document analysis: approach and dataset

In this paper, we study the problem of feature points description in the context of document analysis and template matching. Our study shows that specific training data is required for the task especially if we are to train a lightweight neural network that will be usable on devices with limited computational resources. In this paper, we construct and provide a dataset of photo and synthetically generated images and a method of training patches generation from it. We prove the effectiveness of this data by training a lightweight neural network and show how it performs in both general and documents patches matching. The training was done on the provided dataset in comparison with HPatches training dataset and for the testing, we solve HPatches testing framework tasks and template matching task on two publicly available datasets with various documents pictured on complex backgrounds: MIDV-500 and MIDV-2019.
A More Effective Sentence-Wise Text Segmentation Approach Using BERT

Text Segmentation is a Natural Language Processing based task that is aimed to divide paragraphs and bodies of text into topical, semantic blocks. This plays an important role in creating structured, searchable text-based representations after digitizing paper-based documents for example. Traditionally, text segmentation has been approached with sub-optimal feature engineering efforts and heuristic modelling. We propose a novel supervised training procedure with a pre-labeled text corpus along with an improved neural Deep Learning model for improved predictions. Our results are evaluated with the Pk and WindowDiff metrics and show performance improvements beyond any public text segmentation system that exists currently. The proposed system utilizes Bidirectional Encoder Representations from Transformers (BERT) as an encoding mechanism, which feeds to several downstream layers with a final classification output layer, and even shows promise for improved results with future iterations of BERT.
J-Net: Asymmetric Encoder-Decoder for Medical Semantic Segmentation

With the development of deep learning, breakthroughs have been made in the field of semantic segmentation. However, it is difficult to generate a fine mask on the same medical images because medical images have low contrast, high resolution, and insufficient semantic information. In most scenarios, existing approaches mostly use a pooling layer to reduce the resolution of feature maps. Therefore, it is difficult for them to consider the whole image features, resulting in information loss and performance degradation. In this paper, a multiscale asymmetric encoder-decoder semantic segmentation network is proposed. The network consists of two parts, which perform feature extraction and image restoration on the input, respectively. The encoder network obtains multiscale feature information by connecting multiple ASPP modules to form a feature pyramid. Meanwhile, the upsampling layer of each decoder can be connected to the feature map generated by the corresponding ASPP module. Finally, the classification information of each pixel is obtained through the sigmoid function. The performance of the proposed method can be verified on publicly available datasets. The experimental evidence shows that the proposed method can take full advantage of multiscale feature information and achieve superior performance with less inference computational cost.
Blockchain-based health care monitoring for privacy preservation of COVID-19 medical records

The explosion and unpredictable global spread of COVID-19 has revealed the shortcomings of conventional health care systems in dealing with public health emergencies in a timely manner. The health care system enabled by the Internet of Things (IoT) is competent for tracking COVID-19 patients efficiently through an integrated network. The popular technologies in digital health care monitoring pose serious risks in terms of privacy and security issues such as data transfer and data transaction logging. Such medical data confidentiality and privacy issues could result in a delay in the advancement of treatment, thus endangering the patient's life. This chapter aims to assist infected people online by smart devices using IoT and Blockchain technologies. IoT-based health care smart devices gather valuable information and provide additional perceptions into symptoms and behaviors. Blockchain facilitates the secure exchange of information concerning patient health and it controls the medical delivery network. Blockchains are computationally expensive and require a high bandwidth with extra computing power, and therefore they are not completely suitable for most resource-constrained IoT devices intended for smart cities. Here, a customized blockchain model framework is proposed that is appropriate for the dispersed IoT devices, which are added with enhanced network confidentiality characteristics. Here, proxy reencryption and advanced cryptographic primitives are used to satisfy the additional privacy and security properties. This study provides a structure for COVID-19 infectious disease patients, and discusses electronic medical record issues and diagnostics. The work also highlights the data and transactions over a blockchain-based network that is more secure and anonymous. A widespread security analysis demonstrates that the model being introduced is certifiably successful for ensuring the privacy and resistance to intrusion. Finally, the performance assessment signifies that the projected model realizes superior overall efficiency as compared to other techniques.
A Practical Framework for Secure Document Retrieval in Encrypted Cloud File Systems

With the development of cloud computing, more and more data owners are motivated to outsource their documents to the cloud and share them with the authorized data users securely and flexibly. To protect data privacy, the documents are generally encrypted before being outsourced to the cloud and hence their searchability decreases. Though many privacy-preserving document search schemes have been proposed, they cannot reach a proper balance among functionality, flexibility, security and efficiency. In this paper, a new encrypted document retrieval system is designed and a proxy server is integrated into the system to alleviate data owner's workload and improve the whole system's security level. In this process, we consider a more practical and stronger threat model in which the cloud server can collude with a small number of data users. To support multiple document search patterns, we construct two AVL trees for the filenames and authors, and a Hierarchical Retrieval Features tree (HRF tree) for the document vectors. A depth-first search algorithm is designed for the HRF tree and the Enhanced Asymmetric Scalar-Product-Preserving Encryption (Enhanced ASPE) algorithm is utilized to encrypt the HRF tree. All the three index trees are linked with each other to efficiently support the search requests with multiple parameters. Theoretical analysis and simulation results illustrate the security and efficiency of the proposed framework.
Efficient Detection Using Soft Computing Approach of Modified Fuzzy C-Means Based Outlier Detection in Electronics Patient Records Systems

Outliers are the data objects or data elements which are deviated from the observation or population in the dataset. Normally outliers are considered as noise data but in recent days outliers are taken vital role in the applications like healthcare systems to energetically check for any malignant, irregular, or abnormal behaviour. Accuracy of the outlier detection is purely based upon the efficiency of outlier detection methods and application where the outlier detection is involved. In this paper we design hybrid three phase modified fuzzy c-means and diverse distance based outlier detection method for distributed dataset to detect the unusual usage of data and illegitimate approach in large-scale integral networks, especially in healthcare sectors (EPR systems). The proposed algorithm combining the features of modified C-Means Fuzzification, Z-score and Manhattan distance in outlier identification. The proposed algorithm provides efficiency in outlier detection on univariate EPRdata. This paper also mainly focuses the application intrusion detection in healthcare data. The algorithm is tested on real world dataset of machine learning database repository (UCIML).
Optimum Attack on 3-Round Feistel-2 Structure

Feistel-2 structure is a variant of Feistel structure such that the \(i^{th}\) round function is given by \(\mathrm {F}_i(k_i \oplus x)\), where \(\mathrm {F}_i\) is a public random function and \(k_i\) is a key of n/2 bits. Lampe and Seurin showed that 3-round Feistel-2 structure is secure if \(D+T \ll 2^{n/4}\) (which is equivalent to \(D \ll 2^{n/4}\) and \(T \ll 2^{n/4}\)), where D is the number of queries to the encryption oracle and T is the number of queries to each \(\mathrm {F}_i\) oracle. On the other hand, only the meet-in-the-middle attack is known for 3-round Feistel-2 structure which works only for \((D,T)=(O(1), O(2^{n/2}))\) with \(O(2^{n/2})\) amount of memory.In this paper, we first show that 3-round Feistel-2 structure is broken by a key recovery attack if \(DT \ge 2^{n/2}\) (which requires \(O(D+T)\) amount of memory). Since it works for \(D=T=O(2^{n/4})\), this attack proves that the security bound of Lampe and Seurin is tight at \(D=T=O(2^{n/4})\). We next present a memoryless key recovery attack for \((D,T)=(O(1), O(2^{n/2}))\). We finally show a memoryless key recovery attack for \(D=O(2^{n/4})\) and \(T=O(2^{n/4})\).
Effective Query Formulation in Conversation Contextualization: A Query Specificity-based Approach

Proactively retrieving relevant information to contextualize conversations has potential applications in better understanding the conversational content between communicating parties. Since in contrast to traditional IR, there is no explicitly formulated user-query, an important research challenge is to first identify the candidate segments of text that may require contextualization for a better comprehension of their content, and then make use of these identified segments to formulate a query and eventually retrieve the potentially relevant information to augment a conversation. In this paper, we propose a generic unsupervised framework that involves shifting overlapping windows of terms through a conversation and estimating scores indicating the likelihood of the existence of an information need within these segments. Within our proposed framework, we investigate a query performance prediction (QPP) based approach for scoring these candidate term windows with the hypothesis that a term window that indicates a higher specificity is likely to be indicative of a potential information need requiring contextualization. Our experiments revealed that the QPP approaches of scoring the term windows provide better contextualization than other term extraction approaches. A post-retrieval QPP approach was observed to yield better results than a pre-retrieval one.
An Digital Economy Mode based on Blockchain

The digital economy is a new form of social economy. In order to enrich the diversity of society and economy, this paper puts forward a new digital economy mode based on blockchain. The new ecological system framework of blockchain enabling digital economy was constructed. The characteristics and prospects of the new digital economy mode based on blockchain are analyzed and verified.
EvoStencils: a grammar-based genetic programming approach for constructing efficient geometric multigrid methods

Abstract For many systems of linear equations that arise from the discretization of partial differential equations, the construction of an efficient multigrid solver is challenging. Here we present EvoStencils, a novel approach for optimizing geometric multigrid methods with grammar-guided genetic programming, a stochastic program optimization technique inspired by the principle of natural evolution. A multigrid solver is represented as a tree of mathematical expressions that we generate based on a formal grammar. The quality of each solver is evaluated in terms of convergence and compute performance by automatically generating an optimized implementation using code generation that is then executed on the target platform to measure all relevant performance metrics. Based on this, a multi-objective optimization is performed using a non-dominated sorting-based selection. To evaluate a large number of solvers in parallel, they are distributed to multiple compute nodes. We demonstrate the effectiveness of our implementation by constructing geometric multigrid solvers that are able to outperform hand-crafted methods for Poisson’s equation and a linear elastic boundary value problem with up to 16 million unknowns on multi-core processors with Ivy Bridge and Broadwell microarchitecture.
Development of Solar Energy in Chile and the World

Chile is a country with a huge potential for solar energy.This paper presents an analyses of the global situation of solar energy, identifying the geographical regions with the maximum potential source of solar energy.These areas tend to be in desert locations, since this is where the greatest irradiance is concentrated.A prediction of the potential situation in 2030 is considered.The Chilean desert is considered, identifying the photovoltaic electricity potential and mentioning the current and future projects that will significantly increase the photovoltaic generation of the country.