Reviewerly/exHarmony huggingface dataset:
Systemic And Hole Semantics In Human-Machine Language Interfaces

This paper concerns the progress of earlier related work based in an experiment to enhance the intelligence of robotic systems, with the aim of achieving more language communication capabilities between humans and robots. For that purpose, a simple artificial language was used and a systemic model of language communication. The robot, which was implemented through a computer simulation, is initially taught both the artificial language and the systemic model of human communication. The main goal of improvement was to allow a robot using the implied information of incoming sentences and ask questions whenever the given instructions are not complete. This process was based on the voids of an output data structure that corresponds to a systemic model of language communication, augmented with hole semantics, as grammar formalism, in order to handle partial/incomplete information. As expected, the improvement of learning abilities was observed. Some problems were also noticed in the process that had to be handled. This research comes to redefine the project in relation to these problems, as well as the final form of the language that was planned to be used. The usage of a dictionary of approximately 200 words in Greek natural language, the interchange of the programming language from Java to Python, as well as a new word learning procedures, are the new points in this paper. New words help the robot to expand the initial dictionary. The learning procedures start at the moment that a unknown word, included in a sentence addressed to the robot, arrives in its hearing input system. In addition, Python is an evolving programming language in various artificial intelligence's applications. Finally, the natural language helps in a more direct and friendly communication with most users, in free expressions with fewer restrictions.
Sovereign Critique Network (SCN) Based Super-Resolution for chest X-rays images

A prevalent and serious illness that affects people all over the globe is tuberculosis. A successful diagnosis of tuberculosis is essential for better survival rates and a successful course of treatment. Different techniques for detecting tuberculosis have been developed recently as a result of advancements in medical technology. These techniques have greatly increased the reliability and precision of tuberculosis detection. Finding tuberculosis at an early state, in which it is most treatable, is still a challenge. In order to improve the resolution of X-ray chest images for the early detection of tuberculosis, study is currently being done in this area. The Sovereign Critique Network (SCN) model is suggested in this article as a means of generating super resolution images from low-resolution X-ray images. The suggested SCN model is evaluated on the Tuberculosis (TB) Chest X-ray database for super resolution factors of 2, 4, and 6 separately with PSNR values of 31.85, 33.79, and 35.93 and SSIM values of 0.84, 0.91, and 0.96 for super resolution factors 2, 4, and 6, respectively. The proposed model shows promising results than any of the existing models.
Conversational Chatbot Builder – Smarter Virtual Assistance with Domain Specific AI

Artificial intelligence (AI) has the potential to revolutionize the healthcare industry by providing efficient and accurate medical assistance to patients and healthcare professionals. In this literature survey paper, the focus is on the use of Generative Pretrained Transformer (GPT) technology in developing medical consultant chatbots. The objective is to demonstrate the potential for AI-powered chatbots to improve the efficiency and accuracy of medical assistance. The paper provides an overview of the current state of the art medical chatbots, the challenges and limitations of using GPT for medical consultation and insights into future directions for the development of these chatbots are also discussed. Finally, the paper concludes with a discussion of the potential benefits of using GPT-based medical consultant chatbots in providing accessible and efficient health information to patients.
Improving Assessment of Programming Pattern Knowledge through Code Editing and Revision

How well do code-writing tasks measure students’ knowledge of programming patterns and anti-patterns? How can we assess this knowledge more accurately? To explore these questions, we surveyed 328 intermediate CS students and measured their performance on different types of tasks, including writing code, editing someone else’s code, and, if applicable, revising their own alternatively-structured code. Our tasks targeted returning a Boolean expression and using unique code within an if and else.We found that code writing sometimes under-estimated student knowledge. For tasks targeting returning a Boolean expression, over 55% of students who initially wrote with non-expert structure successfully revised to expert structure when prompted - even though the prompt did not include guidance on how to improve their code. Further, over 25% of students who initially wrote non-expert code could properly edit someone else’s non-expert code to expert structure. These results show that non-expert code is not a reliable indicator of deep misconceptions about the structure of expert code. Finally, although code writing is correlated with code editing, the relationship is weak: a model with code writing as the sole predictor of code editing explains less than 15% of the variance. Model accuracy improves when we include additional predictors that reflect other facets of knowledge, namely the identification of expert code and selection of expert code as more readable than non-expert code. Together, these results indicate that a combination of code writing, revising, editing, and identification tasks can provide a more accurate assessment of student knowledge of programming patterns than code writing alone.
Text Indexing for Long Patterns: Anchors are All you Need

In many real-world database systems, a large fraction of the data is represented by strings: sequences of letters over some alphabet. This is because strings can easily encode data arising from different sources. It is often crucial to represent such string datasets in a compact form but also to simultaneously enable fast pattern matching queries. This is the classic text indexing problem. The four absolute measures anyone should pay attention to when designing or implementing a text index are: (i) index space; (ii) query time; (iii) construction space; and (iv) construction time. Unfortunately, however, most (if not all) widely-used indexes (e.g., suffix tree, suffix array, or their compressed counterparts) are not optimized for all four measures simultaneously, as it is difficult to have the best of all four worlds. Here, we take an important step in this direction by showing that text indexing with locally consistent anchors (lc-anchors) offers remarkably good performance in all four measures, when we have at hand a lower bound l on the length of the queried patterns --- which is arguably a quite reasonable assumption in practical applications. Specifically, we improve on the construction of the index proposed by Loukides and Pissis, which is based on bidirectional string anchors (bd-anchors), a new type of lc-anchors, by: (i) designing an average-case linear-time algorithm to compute bd-anchors; and (ii) developing a semi-external-memory implementation to construct the index in small space using near-optimal work. We then present an extensive experimental evaluation, based on the four measures, using real benchmark datasets. The results show that, for long patterns, the index constructed using our improved algorithms compares favorably to all classic indexes: (compressed) suffix tree; (compressed) suffix array; and the FM-index.
On the use of static analysis to engage students with software quality improvement: An experience with PMD

Static analysis tools are frequently used to scan the source code and detect deviations from the project coding guidelines. Given their importance, linters are often introduced to classrooms to educate students on how to detect and potentially avoid these code anti-patterns. However, little is known about their effectiveness in raising students’ awareness, given that these linters tend to generate a large number of false positives. To increase the awareness of potential coding issues that violate coding standards, in this paper, we aim to reflect on our experience with teaching the use of static analysis for the purpose of evaluating its effectiveness in helping students with respect to improving software quality. This paper discusses the results of an experiment in the classroom, over a period of 3 academic semesters, involving 65 submissions that carried out code review activity of 690 rules using PMD. The results of the quantitative and qualitative analysis show that the presence of a set of PMD quality issues influences the acceptance or rejection of the issues, design, and best practices-related categories that take longer time to be resolved, and students acknowledge the potential of using static analysis tools during code review. Through this experiment, code review can turn into a vital part of the educational computing plan. We envision our findings enabling educators to support students with code review strategies in order to raise students’ awareness about static analysis tools and scaffold their coding skills.
The FastLanes Compression Layout: Decoding &gt; 100 Billion Integers per Second with Scalar Code

The open-source FastLanes project aims to improve big data formats, such as Parquet, ORC and columnar database formats, in multiple ways. In this paper, we significantly accelerate decoding of all common Light-Weight Compression (LWC) schemes: DICT, FOR, DELTA and RLE through better data-parallelism. We do so by re-designing the compression layout using two main ideas: (i) generalizing the value interleaving technique in the basic operation of bit-(un)packing by targeting a virtual 1024-bits SIMD register, (ii) reordering the tuples in all columns of a table in the same Unified Transposed Layout that puts tuple chunks in a common "04261537" order (explained in the paper); allowing for maximum independent work for all possible basic SIMD lane widths: 8, 16, 32, and 64 bits. We address the software development, maintenance and future-proofness challenges of increasing hardware diversity, by defining a virtual 1024-bits instruction set that consists of simple operators supported by all SIMD dialects; and also, importantly, by scalar code. The interleaved and tuple-reordered layout actually makes scalar decoding faster, extracting more data-parallelism from today's wide-issue CPUs. Importantly, the scalar version can be fully auto-vectorized by modern compilers, eliminating technical debt in software caused by platform-specific SIMD intrinsics. Micro-benchmarks on Intel, AMD, Apple and AWS CPUs show that FastLanes accelerates decoding by factors (decoding &gt;40 values per CPU cycle). FastLanes can make queries faster, as compressing the data reduces bandwidth needs, while decoding is almost free.
GRADESTYLE: GitHub-Integrated and Automated Assessment of Java Code Style

Every programming language has its own style conventions and best practices, which help developers to write readable and maintainable code. Learning code style is an essential skill that every professional software engineer should master. As such, students should develop good habits for code style early on, when they start learning how to program. Unfortunately, manually assessing students’ code with timely and detailed feedback is often infeasible, and professional static analysis tools are unsuitable for educational contexts. This paper presents GRADESTYLE, a tool for automatically assessing the code style of Java assignments. GRADESTYLE automatically checks for violations of some of the most important Google Java Style conventions, and Java best practices. Students receive a report with a code style mark, a list of violations, and their source code locations. GRADESTYLE nicely integrates with GitHub and GitHub Classroom, and can be configured to provide continuous feedback every time a student pushes new code. We adopted our tool in a second-year software engineering programming course with 327 students and observed consistent improvements in the code style of their assignments.
Optimizing Hybrid Binary-Unary Hardware Accelerators Using Self-Similarity Measures

Unary computing is a relatively new method for implementing non-linear functions using few hardware resources compared to binary computing. In its original form, unary computing provides no trade-off between accuracy and hardware cost. In this work, we propose a novel self-similarity-based method to optimize the previous hybrid binary-unary method and provide it with the trade-off between accuracy and hardware cost by introducing controlled levels of approximation. Given a target maximum error, our method breaks a function into sub-functions and tries to find the minimum set of unique sub-functions that can derive all the other ones through trivial bit-wise transformations. We compare our method to previous works such as HBU (hybrid binary-unary) and FloPoCo-PPA (piece-wise polynomial approximation) on a number of non-linear functions including Log, Exp, Sigmoid, GELU, Sin, and Sqr, which are used in neural networks and image processing applications. Without any loss of accuracy, our method can improve the area-delay-product hardware cost of HBU on average by 7% at 8-bit, 20% at 10-bit, and 35% at 12-bit resolutions. Given the approximation of the least significant bit, our method reduces the hardware cost of HBU on average by 21% at 8-bit, 49% at 10-bit, and 60% at 12-bit resolutions, and using the same error budget as given to FloPoCo-PPA, it reduces the hardware cost of FloPoCo-PPA on average by 79% at 8-bit, 58% at 10-bit, and 9% at 12-bit resolutions. We finally show the benefits of our method by implementing a 10-bit homomorphic filter, which is used in image processing applications. Our method can implement the filter with no quality loss at lower hardware cost than what the previous approximate and exact methods can achieve.
HARFLOW3D: A Latency-Oriented 3D-CNN Accelerator Toolflow for HAR on FPGA Devices

For Human Action Recognition tasks (HAR), 3D Convolutional Neural Networks have proven to be highly effective, achieving state-of-the-art results. This study introduces a novel streaming architecture-based toolflow for mapping such models onto FPGAs considering the model's inherent characteristics and the features of the targeted FPGA device. The HARFLOW3D toolflow takes as input a 3D CNN in ONNX format and a description of the FPGA characteristics, generating a design that minimises the latency of the computation. The toolflow is comprised of a number of parts, including (i) a 3D CNN parser, (ii) a performance and resource model, (iii) a scheduling algorithm for executing 3D models on the generated hardware, (iv) a resource-aware optimisation engine tailored for 3D models, (v) an automated mapping to synthesizable code for FPGAs. The ability of the toolflow to support a broad range of models and devices is shown through a number of experiments on various 3D CNN and FPGA system pairs. Furthermore, the toolflow has produced high-performing results for 3D CNN models that have not been mapped to FPGAs before, demonstrating the potential of FPGA-based systems in this space. Overall, HARFLOW3D has demonstrated its ability to deliver competitive latency compared to a range of state-of-the-art hand-tuned approaches, being able to achieve up to 5× better performance compared to some of the existing works. The tool is available at https://github.com/ptoupas/harflow3d.
An Empirical Study of Container Image Configurations and Their Impact on Start Times

A core selling point of application containers is their fast start times compared to other virtualization approaches like virtual machines. Predictable and fast container start times are crucial for improving and guaranteeing the performance of containerized cloud, serverless, and edge applications. While previous work has investigated container starts, there remains a lack of understanding of how start times may vary across container configurations. We address this shortcoming by presenting and analyzing a dataset of approximately 200,000 open-source Docker Hub images featuring different image configurations (e.g., image size and exposed ports). Leveraging this dataset, we investigate the start times of containers in two environments and identify the most influential features. Our experiments show that container start times can vary between hundreds of milliseconds and tens of seconds in the same environment. Moreover, we conclude that no single dominant configuration feature determines a container's start time, and hardware and software parameters must be considered together for an accurate assessment.
A Pseudolinear Maximum Correntropy Kalman Filter Framework for Bearings-Only Target Tracking

This article presents a framework for a pseudolinear Kalman filter (PLKF) based on the maximum correntropy criterion for the bearings-only target tracking problem in non-Gaussian environments. We first derive a pseudolinear maximum correntropy Kalman filter (PMCKF). To solve the offset problem, bias compensation is merged into PMCKF to realize bias-compensated PMCKF (BC-PMCKF). In the real scenario, the speed variation of the target is continuous during motion. Based on this premise, we implement the speed-constrained PMCKF (SC-PMCKF) algorithm in this framework, which suppresses the effect of impulsive noise on velocity estimation well. Finally, a posterior Cramér–Rao lower bound (PCRLB) under non-Gaussian noises is derived for the framework. Simulations and physical experiments show that the proposed estimation method is better than the traditional Kalman filter in non-Gaussian noise environments.
Model-Driven Approaches for Conversational Agents Development: A Systematic Mapping Study

Conversational agents are a piece of software widely used nowadays in several domains, such as e-commerce, customer support, education, among others. To build one, proficiency in distinct areas of knowledge is necessary. Moreover, its development demands the availability of frameworks, tools, and proposals to facilitate its application in several domain areas. Recently, the adoption of models in a software development process has increased through the application of Model-Driven Development in several areas to improve and increase productivity in software development. This work aims to provide state of the art through a Systematic Mapping Study regarding to Model-Driven Development approaches to automate or semi-automate the development of Chatbots. For this study, 429 scientific articles were analyzed, of which, after the inclusion and exclusion criteria, only 20 were considered primary studies.
DSANet: Dual-Branch Shape-Aware Network for Echocardiography Segmentation in Apical Views

Echocardiography is an essential examination for cardiac disease diagnosis, from which anatomical structures segmentation is the key to assessing various cardiac functions. However, the obscure boundaries and large shape deformations due to cardiac motion make it challenging to accurately identify the anatomical structures in echocardiography, especially for automatic segmentation. In this study, we propose a dual-branch shape-aware network (DSANet) to segment the left ventricle, left atrium, and myocardium from the echocardiography. Specifically, the elaborate dual-branch architecture integrating shape-aware modules boosts the corresponding feature representation and segmentation performance, which guides the model to explore shape priors and anatomical dependence using an anisotropic strip attention mechanism and cross-branch skip connections. Moreover, we develop a boundary-aware rectification module together with a boundary loss to regulate boundary consistency, adaptively rectifying the estimation errors nearby the ambiguous pixels. We evaluate our proposed method on the publicly available and in-house echocardiography dataset. Comparative experiments with other state-of-the-art methods demonstrate the superiority of DSANet, which suggests its potential in advancing echocardiography segmentation.
A Semi-Soft Label-Guided Network With Self-Distillation for SAR Inshore Ship Detection

With the soaring development of deep learning (DL) mechanisms in recent years, convolution neural network (CNN)-based methods have been extensively investigated to achieve high accuracy of ship detection in Synthetic Aperture Radar (SAR) images. However, existing CNN-based SAR ship detection methods still suffer from challenges in complex inshore scenarios due to the strong interference therein. To tackle this issue, a novel Semi-Soft Label-guided network based on Self-Distillation (SD) for SAR ship detection (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> LSDNet) is proposed in this article. First, different from the existing CNN-based detectors to extract features from the image domain only under the guidance of one-hot label, an efficient SD training strategy is devised to extract semi-soft label information to boost the inshore ship detection accuracy. Second, an angle-related and Balanced Intersection-over-Union (ArBIoU) loss is developed to enhance the inshore ship positioning performance by using the adaptive weights of center point bias and the aspect ratio difference. Experiments on the open SAR ship detection datasets demonstrate the effectiveness and superiority of the proposed method compared with the existing state-of-the-art approaches, especially in inshore scenes.
ReplaceDGA: BiLSTM-Based Adversarial DGA With High Anti-Detection Ability

Botnets extensively leverage Domain Generation Algorithms (DGAs) to establish reliable communication channels between bots and Command and Control (C&C) servers. Numerous character-level DGA classifiers have been extensively studied to detect and classify domain names generated by DGAs. Meanwhile, a series of adversarial domain generation algorithms have been proposed to evade DGA classifiers. Although the existing domain name generation algorithms have progressed against DGA classifier, their anti-detection abilities are still weak. This paper proposes a Bidirectional Long Short-Term Memory (BiLSTM) network-based adversarial DGA with high anti-detection ability, referred to as ReplaceDGA. ReplaceDGA requires no knowledge of the targeted DGA classifiers. It first builds a prediction model for benign domain names using the BiLSTM network to model the semantic relationship hidden within benign domain names and then replaces two characters of each input benign domain name based on the prediction model to maximize the similarity between the benign and generated domain names. Our experimental results validate that ReplaceDGA successfully evades various character-level DGA classifiers even after they are retrained by domain names generated by ReplaceDGA and outperforms the state-of-the-art adversarial DGAs in anti-detection ability, repetition rate, and collision rate. Our study of ReplaceDGA promotes the urgent need for developing more comprehensive and robust DGA classifiers that consider other factors besides character-level information of domain names.