Reviewerly/exHarmony huggingface dataset:
Syntax-Driven Translation

The compiler's task is to translate the input program into a form where it can execute directly on the target machine. The scanner and parser can analyze the code presented for compilation and determine whether that code constitutes a well-formed program in the source language. If the compiler is to perform translation, optimization, and code generation, however, it must build an ir version of the code for the compiler's later use. That process requires the compiler to reason about the code at a level that is deeper than the context-free syntax. This chapter looks at some of the problems that arise in performing that first translation from source code to ir in the compiler's front end, along with the mechanisms used to solve those problems. It focuses on a particular style of syntax-driven computation that was popularized by parser generators—a style that has become common practice.
Accelerating the Machine Learning Model Deployment using MLOps

Abstract Machine Learning projects acquire a large amount of data to make predictions on the data. Deploying machine learning models is a difficult task as it is involved a lot factors such as continuous builds to improve efficiency and different libraries used for prediction. As the data grow for better predictions with proper accuracy some of the parameters are to be dynamically changed for the sake of performance tuning. To enhance the variation in the parameters it takes effort and time from the developers to make the changes in the code and deploy to the production environment. In proposed work, an end to end automation cycle to enhance the timely deployment of machine learning models with better performance is presented. This automation cycle converts the changed code with varied parameters into the container image and uploads to the container registry. Later, when the new image is uploaded it auto detects and updates the production environment with the new image data by launching a new container and shutting down the previous old one. The proposed automated model will display the updated features which are developed. The users can utilize the updated model simultaneously without any downtime to obtain the new features. The main contribution of proposed work is that it can build and deploy the machine learning models automatically, without any manual intervention. The models can also be retrained for better performance of the predictions.
Secure Pdf Text Steganography Turning Secrecy into Inconspicuous Encoding

Abstract Security has become a priority in any security system. Computer applications, Internet system applications, etc. how millions of users use the Internet. This is a research project suggests an information processing algorithm Security system using PNG (Portable Network Graphics) pictures. To our knowledge, there are very few jobs available is present in the PNG archives of steganography. A Least Significant Bit (LSB) encoded stenographic system code is slightly more advanced and is built in the Matlab programming language. The proposed system is often different from other default approaches that use the LSB input method, with the next point. This: The first point about the character set of the secret message used. It automatically detects the character set for private messages. and determine whether the text is written in Latin (only 8 bits per character) or Latin/Arabic (16 characters per character). The approach presented is implemented using special Matlab code functions and encoding algorithms. Two main algorithms for deleting encrypted messages sequentially.
Co-Resident Attack and its impact on Virtual Environment

Abstract Cloud computing (CC) is one of the emerging computing models that potentially transform the IT industry. The cloud computing is defined as a computing paradigm that enables suitable, universal, on-demand network access of various computing resources like data storage, OS (Operating System), computer applications and software as well. A Cloud model essentially is characterized with features like on-demand self-service, better network access, resource distribution, elasticity and measured services. Cloud computing is also known as Internet Computing and is indeed reshaping the way industries and organisations function. It facilitates the service taker or the client to use various IT resources like OS (Operating System), middleware and various applications without the need of downloading it. In this paper a brief view of this computing paradigm and the underlying cloud infrastructure is presented. We essentially present an analysis of various security challenges that are inherent in the Infrastructure as a Service(IaaS) cloud model and explain terms like virtualization, VM (virtual machine), SLA (Service level agreement), etc. Here, CloudSim simulator is explored to study the impact of variation of cache size of the Virtual Machine on the availability of data centres, that occurs due to co-resident attack. Further, a brief view of the co-resident attack is further presented and some of the feasible countermeasures are discussed further.
Transforming Drug-Drug Interaction Extraction from Biomedical Literature

Language Models (LM) capture the characteristics of the distribution of words sequences in natural language, learning meaningful distributed representations in the process. Recent advancements in Neural Networks and Deep Leaning have led to rapid progress in this area, greatly attributed to the emergence of the attention mechanism. Specifically, the Transformer architecture that implements attention-based encoder-decoder stacks, has advanced research in numerous Natural Language Processing tasks and produced state-of-the-art pre-trained LMs. One important task is Relationship Extraction (RE), which extracts semantic relationships from text and has significant applications in the biomedical domain, especially in literature pertaining to drug safety and Drug-Drug Interactions (DDI). In DDI extraction, the task is divided into two subtasks, Drug Named Entity Recognition and Relation Classification, consequently identifying drug mentions and classifying the potential effect of drug combinations from literature. Various methods for the extraction of DDIs have been proposed that utilize different architectures, however Transformers-based LMs continue to show the most promise. The overwhelming number of available pre-trained LMs, that each provide their own benefits and disadvantages, renders the selection of a clear baseline extremely difficult. In this paper, we investigate the most relevant LMs for biomedical RE and experiment on the DDI Extraction 2013 dataset. We propose a baseline approach for pre-trained Transformer-based LMs with shallow output architectures to effectively utilize the underlying architecture and introduce a foundation that reaches similar to state-of-the-art performance for both subtasks of DDI extraction.
Use of AI methods for handling multi-dimensionality and missing values in biomedical data

Bioinformatics approaches in data-rich domains such as Medicine and Biology have presented great opportunities for the application of Deep Learning algorithms in conjunction with biomedical data. The present paper proposes the implementation of Neural Network estimators on epidemiological data collected by the Longitudinal Investigation of Aging and Diet (HELIAD), for the multiclass classification of participants with cognitive dysfunction. The proposed workflow integrates the use of TensorFlow and Keras Feedforward Neural Networks, scikit-learn Pipelines and hyper-parameter tuning via the GridsearchCV class. We exploited these techniques in an effort to avoid data leakage, to compensate for missing values using different imputation strategies and configure dimensionality reduction with autoencoders. Additionally, Kohonen Self-Organizing Maps were implemented for unsupervised learning accomplishing sufficient clustering of the existing classes. Results show that amongst the 864 candidate models that were trained, the best estimator achieved reasonable performance in classifying participants with dementia (accuracy: 0.92), although due to the high class imbalance a lower macro-f1 score of 0.80 was more representative of the best model’ s capacity.
Classifying simple human activity using synthetic accelerometer data

Identifying specific patterns of human activity is necessary when defining the interactions of people with assistive technologies. In environments where the user is an elderly person, the recognition of patterns becomes useful to analyze the possible responses of the user to certain scenarios where it is necessary to make use of the supporting technology. The participation of the users is necessary to learn from their daily habits. With the rise of ambient intelligence and assistive solutions collect information from both the user and the environment in real time to adapt and be able to learn from it. In this context, the current work proposes to learn activity patterns through training by using agent-based simulations. The objective of the training is to train avatars in 3D scenarios, which recreate those physical activities that the real user cannot perform due to their physical condition. The validation of the proposed approach was in a virtual living laboratory by using 3D simulations as assisted environment scenarios.
FarFetched: Entity-centric Reasoning and Claim Validation for the Greek Language based on Textually Represented Environments

The abundance of online information narrows our collective attention span. We address the need for automated claim validation, based on the incorporated evidence from dynamic, textually represented environments created by online news sources. We present FarFetched, an entity-centric reasoning framework based on news, where latent connections between events, actions or statements are discovered via their identified entity mentions and are represented in a structured form with the help of a graph database. We propose an evidence construction approach that combines relevant extracts of the stored information from various online sources in order to support or refute a given claim in free text, relying on entity linking and semantic similarity. We then leverage textual entailment recognition to provide a measurable way for assessing whether this claim is plausible based on the constructed evidence. Our approach tries to fill the gap in automated claim validation for less-resourced languages and is showcased on the Greek language, complemented by the training of relevant semantic textual similarity (STS) and natural language inference (NLI) models that are evaluated on translated versions of common benchmarks.
AIggregate: A Machine Learning Approach for Classifying Micelle Shape

In this work we develop AIggregate, a machine learning tool for classifying the shapes of micelles, which are clusters of surfactant molecules self assembled in aqueous solutions due to their unique amphiphilic character. For the majority of these systems, as the concentration of the surfactant increases, the micellar shape changes from spherical to elongated at a specific value defining the second critical micellar concentration (CMC). Known methods aiming to classify the micelles’ shape and to specify the second CMC with molecular modeling use heuristic approaches. These are based on shape parameters like asphericity, acylindricity, and anisotropicity. We expand upon this approach by applying machine learning and deep learning architectures to classify the shape of molecular assemblies. To achieve our goal, AIggregate uses both a point cloud representation of the micelle, where each atom or group of atoms constitutes a point in the cloud, as well as shape parameters of the assembly. We found that these methods significantly improve classification accuracy over the heuristic approach, with the deep learning, point-cloud-based method offering the maximum efficiency among the examined methods.
INTERACTION ACCORDING TO IMMERSION IN VIRTUAL ENVIRONMENTS:

Technological evolution in the field of graphics has generated a new interface that allows a new form of interaction between the user and the machine through the immersive environments and Virtual Reality. This evolution is due to the improvements in the field of graphics, allowing the developers to create more complex and realistic environments. This leads us to the conclusion that the user's interaction with the machine depends on its immersion in the virtual environment, and therefore on the graphic level of the application, being the skills necessary for its development belonging to the field of Arts, and being necessary too to study these immersive environments from the Fine Arts.
Local Explanation of Dimensionality Reduction

Dimensionality reduction (DR) is a popular method for preparing and analyzing high-dimensional data. Reduced data representations are less computationally intensive and easier to manage and visualize, while retaining a significant percentage of their original information. Aside from these advantages, these reduced representations can be difficult or impossible to interpret in most circumstances, especially when the DR approach does not provide further information about which features of the original space led to their construction. This problem is addressed by Interpretable Machine Learning, a subfield of Explainable Artificial Intelligence that addresses the opacity of machine learning models. However, current research on Interpretable Machine Learning has been focused on supervised tasks, leaving unsupervised tasks like Dimensionality Reduction unexplored. In this paper, we introduce LXDR, a technique capable of providing local interpretations of the output of DR techniques. Experiment results and two LXDR use case examples are presented to evaluate its usefulness.
Weighted Reservoir Sampling On Evolving Streams: A Sampling Algorithmic Framework For Stream Event Identification

Data streams are becoming increasingly important across a wide array of fields and are generally expected to be the preferred form of big data as aggregators and smart stream analytics in general can efficiently yield stream descriptions in various levels. Among them, event detection analytics are paramount since they typically allow the identification of distinct cases of interest like the so called black swans. Reservoir sampling refers to probabilistic class of techniques for keeping representative values of a stream given limited memory capacity. In the proposed framework event detection takes place once reservoir sampling is complete by clustering its output. The rationale behind this is that repeated representative values correspond to normal stream states, whereas any outliers indicate rare yet noteworthy events. With that information a probabilistic stream state graph can be constructed in order to examine the transition dynamics between states and to evaluate the role black swans play in the overall stream stability. A major part of the descriptive power of said graph lies on its inherent geometric interpretation in addition to the algebraic one. Results from two benchmark datasets, one coming from real world and a random one, are encouraging. The proposed framework is planned to be executed in Raspberry Pi as part of an IoT stack since it is sufficiently lightweight.
Deep Learning Based Malware Traffic Classification for Power Internet of Things Network Security

The detection and classification of Malware traffic is an essential building block for the network security of power Internet of Things. Recent studies show that deep learning models can be applied for accurate network traffic classification, but most of the existing works choose to convert the traffic flows into images and turn the problem into image classification. In this work, we propose a novel deep learning-based malware traffic classification approach for power Internet of things network, which represents the traffic flows in the form of fixed-size byte sequences and builds 1D-CNN models for malware traffic detection and classification. For evaluation, we compare the proposed approach to the existing deep learning-based model by using a publicly available malware traffic data set. The results show that the 1D-CNN models trained by the proposed approach outperform the existing models throughout different scenarios in the experiments.
An Improved Bat Algorithm For Solving Nonlinear Algebraic Systems Of Equations

This paper introduces a new hybrid bat algorithm for solving nonlinear equations. Nonlinear equations are often solved by using various optimization algorithms in practice. Bat algorithm is a new intelligent optimization algorithm proposed by Yang in 2010. However, in its practical application, there are some disadvantages, for example, it is easy to fall into local optimization. Therefore, this paper introduces four methods: adaptive inertia weight, chaotic local search, differential evolution algorithm and cross entropy method to optimize the bat algorithm. In this paper, the implementation of four algorithms is first introduced, and then four examples of nonlinear system of equations are to be calculated by using the hybrid self-adaptive bat algorithm (HSABA) to prove the practical feasibility of these four methods.
Delay-tolerant Privacy-preserving Continuous Histogram Publishing Method

With the emergence of privacy protection in data analysis, continuous publishing of data stream privacy protection statistical histograms has received widespread attention. Existing researches mainly concentrates on high-real-time publishing scenarios. If the proposed methods are applied to low-real-time publishing scenarios, they cannot effectively identify the stable part of the data stream, and cannot balance grouping errors and noise errors, resulting in pool availability. To solve the problem of continuous publishing of statistical histograms of low real-time data streams, under the premise of satisfying differential privacy constraints, a low-delay tolerant histogram continuous publication method 1-delay HCP (Histogram Continuous Publication Delayed by 1 Time Unit) and a high-delay tolerant histogram continuous publication method w-delay HCP (Histogram Continuous Publication Delayed by w Time Unit) were proposed. By estimating the histogram of the newly added data stream at each time, for the low-delay tolerant scene and the high-delay tolerant scene, based on the cached data from the time to be released to the latest time, two adaptive grouping methods for bucket count streams are proposed to group the bucket counts to be released; the original bucket count were replaced by the disturbed group mean, effectively balance the grouping error and the Laplace error, and reduce the release histogram error. Based on the real data set, the baseline method that directly adds noise to the histogram at each time, the RG method that uses retrospective grouping, the 1-delay HCP and w-delay HCP methods are compared and analyzed. The results show that under the same privacy budget constraint, the error of histogram released by 1-delay HCP is lower than Baseline and RG; w-delay HCP is better than 1-delay HCP in scenarios where higher delays can be tolerated.
Light-BCube:A Scalable and High Performance Network Structure for Modular Data Center

With the rapid development of cloud computing services, thousands of data centers are built to meet the demands. A fundamental challenge in data center network is how to interconnect an exponentially increasing number of servers efficiently and provide efficient and fault-tolerant routing services for many cloud applications. This paper presents Light-BCube, a novel architecture derived from BCube and designed explicitly for a shipping-container-based modular data center. Like BCube structure, our Light-BCube is also recursively defined, in which many low-level Light-BCubes construct a high-level structure. The light-BCube architecture combines the advantages of both BCube and DCell structures. For DCell, its design scales too fast and has performance bottlenecks. As for BCube, it uses too many redundant switches, so it costs a lot. To make a trade-off between high performance and high hardware cost, we, as a result of this, propose a novel data center network.
Supervised Information Extraction of Chinese Equipment Maintenance Documents

The service data of equipment maintenance is scattered, complex and uncorrelated, and depends on the experience of experts. This paper focuses on the knowledge extraction technology of equipment maintenance documents. However, equipment maintenance documents are characterized by no obvious boundary symbols, many professional terms, and rich context information. Therefore, Convolution Neural Network - Bi-directional Long Short-Term Memory - Conditional Random Field(CNN-BiLSTM-CRF) entity extraction model is designed in this paper on the basis of Bi-directional Long Short-Term Memory – Conditional Random Field(BiLSTM-CRF) model. Aiming at the normative and implicit characteristics of equipment documents, this paper designed a relationship extraction method based on the fusion of pattern and CNN. Experimental results show that both models have good results.
An Approach for an Application of Cloud Computing in Testing Connectivity of Monitoring Systems

The rapid development of Cloud Computing makes them one of the most widely used technologies in the process of digital transformation. Consideration of their features and capabilities would be a prerequisite for success in their implementation and use. Thus, their implementation would lead to competitive advantages in business. This paper presents an approach for the application of Cloud Computing for connectivity tests of monitoring systems for monitoring of material objects.
Implementation and analysis of sparse random search adversarial attack

Adversarial attacks have shown that deep neural networks can drastically change their output based on a relatively small input perturbation. One of the most promising adversarial attacks is based on random search (RS). RS algorithm iteratively moves the current solution to the better one in the search space, which is sampled from a hypersphere surrounding the current solution. In the case of adversarial attacks, RS randomly modifies a given number of pixels in an input image to change the network's original prediction. This paper presents the implementation and analysis of the Sparse-RS algorithm for adversarial attacks generation. Furthermore, we study and compare several extensions of the original algorithm to improve its effectiveness. In the performed experiments, (1) we analyze the impact of limiting attack search space to the edges of the image and to the most significant pixels indicated by saliency maps, (2) we evaluate the process of greedily minimizing the number of perturbed pixels in a successful attack, (3) we propose a novel schedule to dynamically adjust how many pixels should be replaced in the next iteration.
A Local-Global Metric Learning Method for Facial Expression Animation

Facial expression animation plays an important role in character animation. The Expression Blendshape Model (EBM) provides a simple representation of various expressions through a linear combination of base blendshapes with expression coefficients. However, it is challenging to distinguish subtle expression changes. In this paper, we propose a method that combines local features and global features to regress the expression coefficients. Furthermore, local metric leaning (LML) and global metric learning (GML) are proposed to enhance the recognizability of cross-individual expression features. Specifically, the LML increases the feature distance of each blendshape that appears or disappears from the perspective of local representation, resulting in better capture of local appearance changes, while the GML raises feature distance between neutral and emotional expression in the high dimensional feature space from the global perspective. Experimental results and feature visualizations on the FEAFA dataset show the effectiveness of local and global metric learning.