Reviewerly/exHarmony huggingface dataset:
OA-MedSQL: Order-Aware Medical Sequence Learning for Clinical Outcome Prediction

Leveraging large amounts of longitudinal electronic health records (EHR) data has shown great potentials for predicting progression of various diseases. In this work, we proposed a deep learning algorithm based on a customized Long Short-Term Memory (LSTM) model with built-in temporal sequence learning mechanism, named as Order-Aware Medical SeQuence Learning (OA-MedSQL). The model can predict various clinical outcomes from EHR sequences with competitive accuracies, meanwhile automatically distills patient medical journeys into clinically meaningful and relevant temporal sequences. The extraction of milestone temporal sequences is mainly facilitated by enforcing an order to the neurons of the LSTM model when updating their cell states at each time step. We have evaluated the proposed approach on a real-world EHR dataset that focuses on the task of predicting first-line treatment initiation for Waldenström Macroglobulinemia (WM) patients. OA-MedSQL can achieve comparable or better performance than several competitive baseline methods. Furthermore, OA-MedSQL offers a native way to extract milestone temporal sequences that are both explanatory of patient journeys and predictive of clinical outcomes.
Reconstruction of Brain MRI Images and Detection of Tumour

The field of medical visualization of organs are needed for accurate diagnosis and treatment of any disease. Brain tumour diagnosis and surgery also requires accurate3D visualization of the brain. Detection and 3D visualization of the brain and possibly tumours from MRI area computationally time consuming and error-prone task. The proposed system presents a 3D reconstruction model of the brain which greatly helps the radiologist to effectively diagnose and analyze brain. If the subject is in motion state or there occurs a movement while taking the scan, there might be distortions in the output scan image. In order to avoid such circumstances, it is better to reconstruct the 2D image into a 3D space as it is more effective. Thus, the quality of the scan image is much better. From such reconstructed images, the diseases associated with the foetus can be identified. By the help of more features the proposed method can be used for the diagnosis of diseases in the organs. By training the proposed system with more organs and features it can be used for the detection of various diseases from the reconstructed images. More than 200 images were used for the training and around 150 images were used for testing. Here the 2D image slices are undergone image preprocessing, image registration, and then reconstruction. For the image registration, the method used is discrete wavelet transform which is more suitable for medical imaging. The proposed system is applicable for the clinical practices.
Korean Grammar Error Recognition Algorithm Based on Big Data Corpus and Semantic Analysis

Korean grammar error recognition algorithm based on big data corpus and semantic analysis is studied in this paper. Data mining directly faces massive data, and there are also some various complex relationships between these data, which leads to the surge of search space and search dimension in the mining process. Based on the traditional methods, the semantic analysis and the big data framework are combined to construct framework for the recognition algorithm. The component analysis method is mainly used in the field of word meaning research, and its use premise is to divide the word meaning into different semantic components, this model is applied into the grammar error recognition. The performance of the model is efficient, and the application scenarios are discussed.
A Modified Grasshopper Optimization Algorithm

In order to solve the problems of Grasshopper Optimization Algorithm (GOA) in the optimization process, such as easily falling into the local optimum and the slow convergence speed, a Modified Grasshopper Optimization Algorithm (MGOA) is proposed. And the proposed MGOA improves the original fixed parameters to linear decreasing parameters in order to avoid the original algorithm from falling into the local optimum problem. At the same time, weighting calculation is introduced to improve the optimization accuracy of the MGOA algorithm. Additionally, the proposed MGOA is combined with the Simulated Annealing (SA) to improve the convergence speed of the original GOA algorithm. Both the unimodal test function and the multimodal test function are respectively used for simulation experiments, and experimental results show that, compared with the other five swarm intelligent algorithms, the proposed MGOA has significant advantages in aspects of accuracy, stability and convergence speed.
Fully automatic data collection for neuro-symbolic task planning for mobile robot navigation

In this paper, we present an automatic collection method of image data for neuro-symbolic task planning for robot navigation. Collecting images for robot task planning would often be overwhelmed by laborious chores to operate the robot, change the environment, and repeatedly capture quality-assured images. We propose a method using a robotic simulator to perform a series of repetitive processes for data collection automatically. It generates (i) a random instance of the navigation problem, (ii) a simulation environment that depicts the instance, (iii) a planning problem instance described in a classical planning language, (iv) a task plan that solves the planning problem, (v) control inputs for the robot to execute the task plan, and (vi) a sequence of cropped images capturing the evolving states of the robot and the world while the robot performs the plan. We use one of the state-of-the-art neuro-symbolic planning models to validate our method. From the evaluation, the model achieves at most 92.6% of the success rate in generating task plans successfully from only a pair of images showing the initial and the desired state.
Report on Cryptographic Hardware Design using Vedic Mathematics

In general, cryptography is concerned with using complex mathematical operations to encrypt and decode a communication. Most of the time, complex mathematical computations are required. This may need a lot of processing time, hardware, and electricity. The main difficulty here is to provide optimum security while using little resources. This necessitates the use of sophisticated algorithms. Ancient Rigvedic mathematics, rediscovered by Bharathi Krishna Tirtaji, offers a wealth of mathematical shortcuts that may be used to the creation of sophisticated cryptographic algorithms. This paper looks at the work that has been done in this area so far. As the need for safe financial transactions and related sectors grows, cryptographic encryption and decryption play an increasingly essential role. Nowadays, the majority of safe transactions take place on smartphones and other portable devices; thus, an algorithm that uses less space while maintaining overall speed becomes essential. Several algorithms have been developed and implemented in the past to fulfil this need, but each of these algorithms has its own limitations in terms of ASIC or FPGA implementation. This paper discusses the development of an Advanced Encryption System suited for regions needing maximum area reduction, such as mobile phones. The design is created using the Verilog hardware description language, which allows for rapid hardware implementation. When compared to traditional designs, the system’s hardware implementation is quicker. To do this, we use methods from Vedic mathematics. To demonstrate the benefits of the suggested design, comparisons are made with traditional designs.
Online track planning of logistics drones in unknown environments

In order to realize the logistics Logistics distribution problem of logistics drones, the A* algorithm is applied to real-time track planing. It can effectively realize track planing in unknown environments. However, the existing A* algorithm has some disadvantages. The A* algorithm needs to know the information of the prior map in advance, and has the problem of poor real-time performance. In view of the above shortcomings, this paper introduces the idea of jumping search, and proposes a track planing algorithm for logistics drones in unknown environments (EPA) based on the A* algorithm. Theoretical analysis and simulation show that the new algorithm is better than the existing typical A* algorithm. The real-time performance of the algorithm has been improved.
Cross-Concatenation: Tackling Uncertainty in Imbalanced Big Data Classification

In this paper, we use data projection to address uncertainty problem in imbalanced data classification. Instead of resampling the data which causes uncertainty, we project the minority and majority instances into new space using a novel technique called Cross-Concatenation. To project the minority instances into a novel space, we concatenate each minority instance with all majority instances to form M * N new double size data where, M and N are the size of minority and majority classes, respectively. The same procedure is repeated to project majority instances into new space by concatenating each majority instance with all minority instances to form N * M new double size data. Our experiments show that, Cross-Concatenation can provide the classifiers sufficient data to train more efficient models since the projected classes are no longer skewed. After training the model, each test data is concatenated with the centroid of minority and majority classes to create two different instances. Afterwards, the highest probability returned from the trained model is used as a metric to assign the label to it. Our experimental results show that, the proposed method can significantly d ecrease the uncertainty in imbalanced classification with competitive results comparing to the SMOTE and its variants which are the most popular over-sampling techniques in terms of precision, recall, F1 and Area Under Curve (AUC).
Pedagogical diagnostics of professional identity formation among cadets using expert assessment method

We consider the current issue of pedagogical diagnostics of professional identity formation among cadets with expert assessment method for its implementation. The expert assessment method is one of the system elements of pedagogical diagnostics methods, we present the features, advantages and disadvantages of its use for assessing the professional identity formation among cadets, directions are proposed to increase the effectiveness of the expert assessment method and overcome its shortcomings in the process of diagnosing the formation of professional identity of cadets. The main requirements for experts – subjects of pedagogical diagnostics are to use methods within the framework of objective (documentary, experimental) and subjective (self-assessment of competence, mutual assessment by experts of other potential experts) approaches for the selection of experts. In addition to theoretical, the study has practical significance, which consists in the fact that its results can contribute to the effective use of the method of expert assessments not only to study the dynamics of the formation of professional identity of cadets in the educational process of military universities, but also to identify signs of such a dangerous psychological phenomenon as professional marginalization – the antipode of professional identity.
Domain adaptation based on the measure of kernel-product maximum mean discrepancy

Transfer learning is an important branch of machine learning, focusing on applying what has been learned in the old field to new problems. Maximum mean discrepancy (MMD) is used in most existing works to measure the difference between two distributions by applying a single kernel. Recent works exploit linear combination of multiple kernels and need to learn the weight of each kernel. Because of the singleness of single-kernel and the complexity of multiple-kernel, we propose a novel kernel-product maximum mean discrepancy (DA-KPMMD) approach. We choose the product of linear kernel and Gaussian kernel as the new kernel. Specifically, we reduce differences in the marginal and conditional distribution simultaneously between source and target domain by adaptively adjusting the importance of the two distributions. Further, the within-class distance is minimizing to differentiate samples of different classes. We conduct cross-domain classification experiments on three image datasets and experimental results show the superiority of DA-KPMMD compared with several domain adaptation methods.
Stack-VAE Network for Zero-Shot Learning

Zero-shot learning aims to transfer knowledge from the seen classes to unseen ones through some high-level semantics (e.g., per-class attributes), enabling the learning model to recognize novel classes without retraining. Among them, the generative methods adopt the scheme that synthesizes samples for the unseen classes, thereby converting the task into a standard classification problem. However, most existing work inevitably suffers from the domain shift problem when only the seen classes are used for supervision. Furthermore, they can not fully leverage the semantic information in data synthesis due to the limited expressiveness of the generator. In this paper, we develop a novel network, named stack-VAE, to alleviate the above problems. The proposal mainly consists of a generative module and a feature core agent. Specifically, we design the generator based on hierarchical VAE, which exploits multi-layer Gaussian distribution to improve the expressiveness, thereby better mimicking the real data distribution of the unseen classes. Besides, we propose a feature core agent based objective, which is beneficial to mitigate seen class bias by enforcing the inter-class separability and reducing the intra-class scatter. Experimental results conducted on three widely used datasets, i.e., AWA2, SUN, CUB, show that the proposed network outperforms the baselines and achieves a new state-of-the-art.
Single Sign-On Revocation Access

Single Sign-On (SSO) is a mechanism that allows users to seamlessly access multiple services using a single authentication action with an Identity Provider (IdP). OpenID Connect (OIDC) is an authentication and authorization protocol widely used in internet-based environments, and it presents the new standard for SSO. Due to its favorable usability and security, SSO has been widely used for online authentication services. Nevertheless, this technology pose a huge security risk in the case of a compromised IdP account. If an attacker gets to hijack the IdP account, he can easily access and control all the Internet-based services associated with the victim’s hijacked account. Furthermore, SSO technology does not introduce efficient mechanisms to notify services that the centralized IdP account has been compromised, and/or revoke access from all the services in the same login session with it. Thus, it is mandatory to have a mechanism to protect user’s accounts and revoke access in the case of a hijacked IdP account. In this paper, we discuss the authentication and authorization process of SSO, the protocol OIDC, and the IdP accounts compromise threat. Next, we perform a comparative analysis of four OIDC mechanisms that can be used as authentication revocation’ solutions for SSO systems: OIDC Session Management, OIDC Front Channel Logout, OIDC Back-channel Logout, and Single Sign-Off.
A Deep Neural Network-Based Target Recognition Algorithm for Robot Scenes

Intelligent robots are a key vehicle for artificial intelligence and are widely employed in all aspects of everyday life and work, not just in the industry. One of the talents required for intelligent robots to complete their jobs is the capacity to identify their environment, which is a crucial obstacle to be overcome. Deep learning-based target identification algorithms currently do not fully leverage the link between high-level semantic and low-level detail information in the prediction step and hence are less successful in recognizing tiny target objects. Target recognition via vision sensors has also improved in accuracy and efficiency because of the development of deep learning. However, due to the insufficient usage of semantic information and precise texture information of underlying characteristics, tiny target recognition remains a difficulty. To address the aforementioned issues, we propose a target detection method based on a jump-connected pyramid model to improve the target detection performance of robots in complex scenarios. In order to verify the effectiveness of the algorithm, we designed and implemented a software system for target detection of intelligent robots and performed software integration of the proposed algorithm model with excellent experimental results. These experiments reveal that, when compared to other algorithms, our suggested algorithm’s characteristics have higher flexibility and robustness and can deliver a higher scene classification accuracy rate.
LUCKe — Connecting Clustering and Correlation Clustering

LUCKe allows any purely distance-based "classic" clustering algorithm to reliably find linear correlation clusters. An elaborated distance matrix based on the points’ local PCA extracts all necessary information from high dimensional data to declare points of the same arbitrary dimensional linear correlation cluster as "similar". For that, the points’ eigensystems as well as only the relevant information about their position in space, are put together. LUCKe allows transferring known benefits from the large field of basic clustering to correlation clustering. Its applicability is shown in extensive experiments with simple representatives of diverse basic clustering approaches.
Neural Architecture Search and Multi-Objective Evolutionary Algorithms for Anomaly Detection

The processing speed and memory footprint are important factors for applications processing on resource-constrained devices such as IoT devices and embedded systems. Deep learning has been evolving continuously so that it can be used on resource-constrained devices but there are still some limitations in using it because these devices are not designed for processing complicated tasks. Further, the complexity of the Convolutional Neural Network (CNN) model is the barrier to implementation on these devices. In this paper, we have developed Neural Architecture Search (NAS) that uses a Multi-Objective Genetic Algorithm and Firefly Algorithm for creating a less complicated and robust CNN model, focusing on searching the model with faster processing time and minimum storage size. Five image datasets are applied to examine the performance of the proposed techniques, including two crack detection datasets for surface or built infrastructure inspection for industrial applications. Experimental results show that the proposed technique consistently lowers the parameter counts and processing time at comparable classification accuracies.
Cyber Security Risk Assessment Factors of Enterprise Infrastructure System

The study presents the IT infrastructure of a security operation of local and weaponized vulnerabilities. It estimates the probability of an attack of cyber security risk on the internal systems. The BSI standard 200-1 general requirement for an information security management system of cyber security risk analyzes the factors qualitative and quantitatively. The Result has discussed the breaches of the Web-facing system and worsens the attack of internal systems. The analysis specifically addressed the unidentified target attacks and popular attacks in the users and Enterprise IT structure.
Simultaneous Speech-to-Speech Translation System with Transformer-Based Incremental ASR, MT, and TTS

In this paper, we present an English-to-Japanese simultaneous speech-to-speech translation (S2ST) system. It has three Transformer-based incremental processing modules for S2ST: automatic speech recognition (ASR), machine translation (MT), and text-to-speech synthesis (TTS). We also evaluated its system-level latency in addition to the module-level latency and accuracy.
Transparency to the Extreme: An In-Depth Study of the Bitcoin Exchange Ecosystem

Bitcoin exchanges, which are digital platforms for investors to trade Bitcoins freely, operate between digital and fiat currency networks. Thus they provide an opportunity to connect real-world identities to pseudonymous Bitcoin addresses, which is critical to anti-money laundering efforts. To this end, in this paper, we conduct a systematic investigation on the whole Bitcoin exchange ecosystem and seek to characterize, understand, and identify patterns around exchanges (including exchanges’ internal wallet structure and transfer activity). Specifically, we first construct the Bitcoin Transfer Graph (BTG) using the on-chain transaction records and propose an approach to identify the Bitcoin addresses belonging to Bitcoin exchanges. Then, based on the found addresses, we conduct a preliminary analysis of mainstream Bitcoin exchanges and provide an overview of their internal wallet architecture. Lastly, we obtain many observations and findings by analyzing these graphs, which help us obtain a deep understanding of the Bitcoin exchange ecosystem.
A divide-and-conquer method for computing preferred extensions of argumentation frameworks

In this paper, we propose a divide-and-conquer method for solving the preferred extensions enumeration prob-lem, which is computationally intractable in argumentation frameworks. The rationale is to take advantage of the fact that for acyclic argumentation frameworks the computation becomes tractable with polynomial time. Concretely, we identify sufficient conditions for decomposing an argumentation framework into sub-frameworks based on certain cycles, where the soundness and completeness in computing preferred extensions are proved. Based on this conclusion, we devise the partitioning algorithm and carry out an evaluation on the International Competition on Computational Models of Argumentation (ICCMA) 2019 dataset. The results show that for the complex, time-consuming tasks our method could reduce running time when compared with the state-of-the-art solver in ICCMA. This is our first attempt in tackling the complex argumentative knowledge and many directions are yet to be explored, both theoretical and empirical.
Compressive Sensing with Optical Chaotic Encryption Based on Monolithic Silicon Chips

We propose a method that compressive sensing (CS) encryption technology based on mesoscopic optical chaos which was generated by photonic crystal microcavity. The microcavity based on integrated silicon optical mechanism is fully compatible with CMOS process. The CS encryption is realized in the scheme, the encryption is based on discrete wavelet transform (DWT) and optical chaos data generated by microcavity which also can be used to make measurement matrix. And the final result may add a new application meathod for the use of optical chaos generated by full CMOS technology.