An Efficient Algorithm for the Physical Mapping of Clustered Task Graphs onto Multiprocessor Architectures

The most important issue in sequential program parallelisation is the efficient assignment of computations into different processing elements. In the past, too many approaches were devoted in efficient program parallelization considering various models for the parallel programs and the target architectures. The most widely used parallelism description model is the task graph model with precedence constraints. Nevertheless, as far as physical mapping of tasks onto parallel architectures is concerned, little research has given practical results. It is well known that the physical mapping problem is NP-hard in the strong sense, thus allowing only for heuristic approaches. Most researchers or tool programmers use exhaustive algorithms, or the classical method of simulated annealing. This paper presents an alternative approach onto the mapping problem. Given the graph of clustered tasks, and the graph of the target distributed architecture, our heuristic finds a mapping by first placi..

Evaluation of Automatic Legal Text Summarization Techniques for Greek Case Law

The increasing amount of legal information available online is overwhelming for both citizens and legal professionals, making it difficult and time-consuming to find relevant information and keep up with the latest legal developments. Automatic text summarization techniques can be highly beneficial as they save time, reduce costs, and lessen the cognitive load of legal professionals. However, applying these techniques to legal documents poses several challenges due to the complexity of legal documents and the lack of needed resources, especially in linguistically under-resourced languages, such as the Greek language. In this paper, we address automatic summarization of Greek legal documents. A major challenge in this area is the lack of suitable datasets in the Greek language. In response, we developed a new metadata-rich dataset consisting of selected judgments from the Supreme Civil and Criminal Court of Greece, alongside their reference summaries and category tags, tailored for the purpose of automated legal document summarization. We also adopted several state-of-the-art methods for abstractive and extractive summarization and conducted a comprehensive evaluation of the methods using both human and automatic metrics. Our results: (i) revealed that, while extractive methods exhibit average performance, abstractive methods generate moderately fluent and coherent text, but they tend to receive low scores in relevance and consistency metrics; (ii) indicated the need for metrics that capture better a legal document summary’s coherence, relevance, and consistency; (iii) demonstrated that fine-tuning BERT models on a specific upstream task can significantly improve the model’s performance

Optimal Scheduling for UET-UCT Generalized n-Dimensional Grid Task Graphs

The n-dimensional grid is one of the most representative patterns of data flow in parallel computation. The most frequently used scheduling models for grids is the unit execution - unit communication time (UET-UCT). In this paper we enhance the model of ndimensional grid by adding extra diagonal edges. First, we calculate the optimal makespan for the generalized UETUCT grid topology and, then, we establish the minimum number of processors required, to achieve the optimal makespan. Furthermore, we solve the scheduling problem for generalized n-dimensional grids by proposing an optimal time and space scheduling strategy. We thus prove that UET-UCT scheduling of generalized ndimensional grids is low complexity tractable. 1. Introduction Task scheduling is one of the most important and difficult problems in parallel systems. Since the general scheduling problem is known to be NP-complete (see Ullman [13]), researchers have given attention to other methods such as heuristics, approximation..

Increasing Usability of Homecare Applications for Older Adults: A Case Study

As the world’s population is ageing, the field dealing with technology adoption by seniors has made headway in the scientific community. Recent technological advances have enabled the development of intelligent homecare systems that support seniors’ independent living and allow monitoring of their health status. However, despite the amount of research to understand the requirements of systems designed for the elderly, there are still unresolved usability issues that often prevent seniors from enjoying the benefits that modern ICT technologies may offer. This work presents a usability assessment of “HeartAround”, an integrated homecare solution incorporating communication functionalities, as well as health monitoring and emergency response features. An assessment with the system usability scale (SUS) method, along with in-depth interviews and qualitative analysis, has provided valuable insights for designing homecare systems for seniors, and validated some effective practical guidelines

Evaluation of Loop Grouping Methods Based on Orthogonal Projection Spaces

This paper compares three similar loop-grouping methods. All methods are based on projecting the n-dimensional iteration space J onto a k-dimensional one, called the projected space, using (n-k) linear independent vectors. The dimension k is selected differently in each method giving various results. The projected space is divided into discrete groups of related iterations, which are assigned to different processors. Two of the methods preserve optimal time completion, by scheduling loop iterations according to the hyperplane method. The theoretical analysis of the experimental results indicates the appropriate method, for specific iteration spaces and target architectures

Knotify+: Toward the Prediction of RNA H-Type Pseudoknots, Including Bulges and Internal Loops

The accurate “base pairing” in RNA molecules, which leads to the prediction of RNA secondary structures, is crucial in order to explain unknown biological operations. Recently, COVID-19, a widespread disease, has caused many deaths, affecting humanity in an unprecedented way. SARS-CoV-2, a single-stranded RNA virus, has shown the significance of analyzing these molecules and their structures. This paper aims to create a pioneering framework in the direction of predicting specific RNA structures, leveraging syntactic pattern recognition. The proposed framework, Knotify+, addresses the problem of predicting H-type pseudoknots, including bulges and internal loops, by featuring the power of context-free grammar (CFG). We combine the grammar’s advantages with maximum base pairing and minimum free energy to tackle this ambiguous task in a performant way. Specifically, our proposed methodology, Knotify+, outperforms state-of-the-art frameworks with regards to its accuracy in core stems prediction. Additionally, it performs more accurately in small sequences and presents a comparable accuracy rate in larger ones, while it requires a smaller execution time compared to well-known platforms. The Knotify+ source code and implementation details are available as a public repository on GitHub

Knotify+: Toward the Prediction of RNA H-Type Pseudoknots, Including Bulges and Internal Loops

The accurate “base pairing” in RNA molecules, which leads to the prediction of RNA secondary structures, is crucial in order to explain unknown biological operations. Recently, COVID-19, a widespread disease, has caused many deaths, affecting humanity in an unprecedented way. SARS-CoV-2, a single-stranded RNA virus, has shown the significance of analyzing these molecules and their structures. This paper aims to create a pioneering framework in the direction of predicting specific RNA structures, leveraging syntactic pattern recognition. The proposed framework, Knotify+, addresses the problem of predicting H-type pseudoknots, including bulges and internal loops, by featuring the power of context-free grammar (CFG). We combine the grammar’s advantages with maximum base pairing and minimum free energy to tackle this ambiguous task in a performant way. Specifically, our proposed methodology, Knotify+, outperforms state-of-the-art frameworks with regards to its accuracy in core stems prediction. Additionally, it performs more accurately in small sequences and presents a comparable accuracy rate in larger ones, while it requires a smaller execution time compared to well-known platforms. The Knotify+ source code and implementation details are available as a public repository on GitHub

Knotify: An Efficient Parallel Platform for RNA Pseudoknot Prediction Using Syntactic Pattern Recognition

Obtaining valuable clues for noncoding RNA (ribonucleic acid) subsequences remains a significant challenge, acknowledging that most of the human genome transcribes into noncoding RNA parts related to unknown biological operations. Capturing these clues relies on accurate “base pairing” prediction, also known as “RNA secondary structure prediction”. As COVID-19 is considered a severe global threat, the single-stranded SARS-CoV-2 virus reveals the importance of establishing an efficient RNA analysis toolkit. This work aimed to contribute to that by introducing a novel system committed to predicting RNA secondary structure patterns (i.e., RNA’s pseudoknots) that leverage syntactic pattern-recognition strategies. Having focused on the pseudoknot predictions, we formalized the secondary structure prediction of the RNA to be primarily a parsing and, secondly, an optimization problem. The proposed methodology addresses the problem of predicting pseudoknots of the first order (H-type). We introduce a context-free grammar (CFG) that affords enough expression power to recognize potential pseudoknot pattern. In addition, an alternative methodology of detecting possible pseudoknots is also implemented as well, using a brute-force algorithm. Any input sequence may highlight multiple potential folding patterns requiring a strict methodology to determine the single biologically realistic one. We conscripted a novel heuristic over the widely accepted notion of free-energy minimization to tackle such ambiguity in a performant way by utilizing each pattern’s context to unveil the most prominent pseudoknot pattern. The overall process features polynomial-time complexity, while its parallel implementation enhances the end performance, as proportional to the deployed hardware. The proposed methodology does succeed in predicting the core stems of any RNA pseudoknot of the test dataset by performing a 76.4% recall ratio. The methodology achieved a F1-score equal to 0.774 and MCC equal 0.543 in discovering all the stems of an RNA sequence, outperforming the particular task. Measurements were taken using a dataset of 262 RNA sequences establishing a performance speed of 1.31, 3.45, and 7.76 compared to three well-known platforms. The implementation source code is publicly available under knotify github repo