ACM SIGAPP Applied Computing Review

We study a framework for the specification and validation of dynamic reconfigurable systems. The framework is based on configuration logic for the description of architecture styles which are families of architectures sharing common connectivity features. We express specifications in the Temporal Configuration Logic (TCL), a linear time temporal logic built from atomic formulas characterizing system configurations and temporal modalities. Two non-trivial benchmarks are introduced to show the adequacy of TCL for the specification of dynamic reconfigurable systems. We study an effective model-checking procedure based on SMT techniques for a non-trivial fragment of TCL which has been implemented in a prototype runtime verification tool. We provide preliminary experimental results illustrating the capabilities of the tool on the considered benchmark systems.

Antoine El-Hokayem

Marius Bozga

Joseph Sifakis

A framework for the specification and validation of dynamic reconfigurable systems

动态可重构系统规范与验证的框架

Semantic classification of scientific literature using machine learning approaches is challenging due to the difficulties in labeling data and the length of the texts [2, 7]. Most of the work has been done for keyword-based categorization tasks, which take care of occurrence of important terms, whereas semantic classification requires understanding of terms and the meaning of sentences in a context. In this study, we have evaluated neural network models on a semantic classification task using 1091 labeled EMF-related scientific papers listed in the Powerwatch study. The EMF-related papers are labeled into three categories: positive, null finding, and neither. We have conducted neural architecture and hyperparameter search to find the most suitable model for the task. In experiments, we compared the performance of several neural network models in terms of classification accuracy. In addition, we have tested two different types of attention mechanisms. First, a Fully Convolutional Neural Network (FCN) has been used to identify important sentences in the text for the semantic classification. Second, the Transformer, a self-attention-based model, has been tested on the dataset. The experimental result showed that the BiLSTM performed best on both unbalanced and balanced data and the FCN was able to identify important parts in input texts.

Kwanghee Won

Hyung-do Choi

Sung Shin

Deep learning-based semantic classification of EMF-related scientific literature

基于深度学习的电磁场相关科学文献语义分类

Amyotrophic Lateral Sclerosis (ALS) is a rare neurodegenerative disease that causes a rapid decline in motor functions and has a fatal trajectory. ALS is currently incurable, so the aim of the treatment is mostly to alleviate symptoms and improve quality of life (QoL) for the patients. The goal of this study is to develop a Clinical Decision Support System (CDSS) to alert clinicians when a patient is at risk of experiencing low QoL. The source of data was the Irish ALS Registry and interviews with the 90 patients and their primary informal caregiver at three time-points. In this dataset, there were two different scores to measure a person's overall QoL, based on the McGill QoL (MQoL) Questionnaire and we worked towards the prediction of both. We used Extreme Gradient Boosting (XGBoost) for the development of the predictive models, which was compared to a logistic regression baseline model. Additionally, we used Synthetic Minority Over-sampling Technique (SMOTE) to examine if that would increase model performance and SHAP (SHapley Additive explanations) as a technique to provide local and global explanations to the outputs as well as to select the most important features. The total calculated MQoL score was predicted accurately using three features - age at disease onset, ALSFRS-R score for orthopnoea and the caregiver's status pre-caregiving - with a F1-score on the test set equal to 0.81, recall of 0.78, and precision of 0.84. The addition of two extra features (caregiver's age and the ALSFRS-R score for speech) produced similar outcomes (F1-score 0.79, recall 0.70 and precision 0.90).

Anna Markella Antoniadi

Miriam Galvin

Mark Heverin

Orla Hardiman

Catherine Mooney

Prediction of quality of life in people with ALS

肌萎缩侧索硬化患者生活质量预测

Debugging failures of deployed concurrent software is important for quality assurance. However, such failures are difficult to debug because their behavior is non-deterministic and limited information can be obtained with conventional means. Reverse debuggers such as REPT [11] assists with debugging by recovering data values before the failure. This is achieved by using a hardware-tracer to log control-flow information, then using the information and a conventional coredump to recover data values via reverse-execution at machine-level. REPT's algorithm for data value recovery is reliable and fast. But the implementation cost is high because of its dependence on architecture. Applying REPT to more abstract IR (Intermediate Representation) level instructions to counter this yielded limited results with low accuracy compared to the original x86_64 implementation. The main reason for this is that the stack layout is abstracted at IR-level. In this paper, we present STRAB (State Recovery at Abstract-level), a collection of our proposed methods to solve these problems. STRAB works in two phases. First, the data values in the coredump are lifted from machine-level to IR-level using rich debug information (DWARF3) and a novel technique we call mid-recovery lifting, the latter helping to recover more heap data values at IR-level. Second, our novel hybrid memory location resolution reduces the accuracy loss due to the abstracted stack layout at IR-level. Experimental results on a variety of real-world concurrent programs show that STRAB has significantly higher accuracy compared to REPT at IR-level (+40% on average) with only minor slowdowns (x2.7 on average), while also achieving architecture-independence.

Shinji Hoshino

Yoshitaka Arahori

Katsuhiko Gondow

Postmortem accurate IR-level state recovery for deployed concurrent programs

部署并发程序的死后精确IR级别状态恢复

Data streaming platforms connect heterogeneous services through the publish-subscribe paradigm. Currently available platforms provide protection against crash faults, but are not resistant against Byzantine faults like arbitrary hardware faults and intrusions. State machine replication can provide this protection, but the higher resource requirements and the more elaborate communication primitives usually result in a higher overall complexity and a non-negligible performance degradation. As data streaming operates on highly-partitionable append-only state, some of these performance losses can be counteracted by applying speculative execution and sharding. We show the effectiveness of these concepts in a prototype implementation, which only results in a reasonable drop in system throughput and latency during average system utilization, when compared to state-of-the-art data streaming platforms like Apache Kafka, while providing stronger resilience guarantees.

Johannes Köstler

Hans P. Reiser

Gerhard Habiger

Franz J. Hauck

SmartStream

智能流

Concrete domains have been introduced in description logic (DL) to enable reference to concrete objects (such as numbers) and predefined predicates on these objects (such as numerical comparisons) when defining concepts. The primary research goal in this context was to find restrictions on the concrete domain such that its integration into certain DLs preserves decidability or tractability. In this article, we complement these investigations by studying the abstract expressive power of both first-order and description logics extended with concrete domains, i.e., we analyze which classes of first-order interpretations can be expressed using these logics, compared to what their counterparts without concrete domains can express. We demonstrate that, under natural conditions on the concrete domain D (which also play a role for decidability), extensions of first-order logic (FOL) or the well-known DL ALC with D share important formal characteristics with FOL, such as the compactness and the Löwenheim-Skolem properties. Nevertheless, these conditions do not ensure that the abstract expressive power of the extensions we consider is contained in that of FOL, though in some cases it is. To be more precise, we show, on the one hand, that unary concrete domains leave the abstract expressive power within FOL if we are allowed to introduce auxiliary predicates. As a by-product, we obtain (semi-)decidability results for some fragments of FOL extended with the concrete domains considered in this article. On the other hand, we show that the ability to express equality between elements of D , another condition employed in the context of showing decidability of ALC ( D ), is sufficient to push the abstract expressive power of most first-order fragments with concrete domains beyond that of FOL. While for such concrete domains D decidability is retained for ALC ( D ), we show that the availability of equality in D causes undecidability of the two-variable fragment of FOL( D ), although the two-variable fragment of FOL is decidable.

Franz Baader

Filippo De Bortoli

Logics with Concrete Domains: First-Order Properties, Abstract Expressive Power, and (Un)Decidability

具有具体领域的逻辑：一阶性质、抽象表达能力及（不）可判定性

Fog Computing is a paradigm decentralizing the Cloud by geographically distributing computation, storage, network resources and related services. It provides benefits such as reducing the number of bottlenecks, limiting unwanted data movements, etc. However, managing the size, complexity and heterogeneity of the Fog systems to be engineered is challenging and can quickly become costly. According to best practices in software engineering, verification tasks could be performed on a system design prior to its implementation and deployment. We propose a generic model-based approach for verifying Fog systems at design time, also enabling the automatic generation of corresponding deployment configuration files. Named VeriFog, this approach is notably based on a customizable Fog Modeling Language (FML). We experimented in practice by modeling three use cases, from three different application domains, and by considering three main types of non-functional properties to be verified. From this modeling and verification effort, we show that we are able to automatically generate usable deployment configuration files for different deployment tools. In direct collaboration with our industrial partner Smile, the approach and underlying language presented in this paper are necessary steps towards a more global model-based support for the complete life cycle of Fog systems.

Hiba Awad

Abdelghani Alidra

Hugo Bruneliere

Thomas Ledoux

Jonathan Rivalan

VeriFog: A Generic Model-based Approach for Verifying Fog Systems at Design Time and Generating Deployment Configurations

VeriFog：一种基于模型的验证雾计算系统设计时方法及生成部署配置的通用模型

The retention error has become one of the most challenging reliability issues of flash memory due to the shrinking of the technology nodes. To enhance data integrity by resolving the retention error issues for 3D MLC flash memory devices (e.g., SSDs and SD cards), many excellent works that exploited in-place reprogramming and data refreshing concepts have been proposed in recent years. However, these approaches could result in additional issues, such as programming disturbance and performance overhead (e.g., unavoidable data refreshing and a larger amount of program and verify shots). This work is motivated by the need to explore a low-cost solution to resolve retention error issues without incurring negative impacts caused by conventional refresh-based and in-place reprogramming approaches. As a result, this work exploits the characteristics of the cell's V t distribution and proposes the novel concept of "retention-refilling" to enhance data integrity. With such an idea, a retention-refillable programming scheme is proposed to improve flash reliability and mitigate performance overheads by trading data refreshing with retention-refilling. The capability of the proposed scheme is evaluated by a series of experiments, for which we have very encouraging results.

Kun-Chi Chiang

Yung-Chun Li

Wei-Chen Wang

Wei-Kuan Shih

Chien-Chung Ho

Enhancing Data Integrity with Efficient Retention-Refilling Programming Schemes

提高数据完整性的高效保留-重填编程方案

This work focuses on the Gaussian Mixture Model (GMM), a machine learning model used for density estimation and cluster analysis in healthcare, networking, etc. The Expectation-Maximization (EM) algorithm is commonly used to train GMMs. One of the main challenges facing this algorithm when running on embedded systems is the crippling memory constraints. In fact, EM requires several scans of the dataset and we observed that when the dataset cannot fully reside in the main memory, its execution is dramatically slowed down by I/O movements. In this paper, we present an optimization of the EM algorithm for GMMs that reduces the number of I/O operations thanks to two main contributions: (1) A divide-and-conquer strategy that divides the dataset into chunks, learns the GMM separately on each chunk and combines the results incrementally. By doing so, we prevent data from being swapped several times during the learning process. (2) Restricting the training on a subset of data whose volume is inferred online using data properties while producing good accuracy. On average, our results show a 63% improvement in overall execution time with comparable accuracy. We also adapted GMM learning to run in a limited time budget while hitting a good trade-off between execution time and energy consumption. This solution succeeded in meeting the fixed deadline in 100% of the cases and in reducing the energy consumption by up to 68.77%.

Meriem Bouzouad

Yasmine Benhamadi

Camélia Slimani

Jalil Boukhobza

Adapting Gaussian Mixture Model Training to Embedded/Edge Devices: A Low I/O, Deadline-Aware and Energy Efficient Design

适应嵌入式/边缘设备的混合高斯模型训练：一种低I/O、截止时间感知和能效设计

Artificial intelligence (AI) has significantly optimized processes across various sectors, enhancing efficiency and transforming digital interactions. However, as AI becomes more integrated into daily life, concerns about its social impacts and inherent biases have emerged. This study explores how AI technologies, such as facial recognition and Automatic Gender Recognition (AGR), can perpetuate and amplify societal prejudices, especially against transgender and non-binary individuals. The 2018 case of Amazon's Rekognition technology, which exhibited high false positive rates for individuals with dark skin, highlights the risks of algorithmic bias and mass surveillance. Given these challenges, this research performed performed a systematic mapping study of the literature on AI to present an analysis of problems and respective causes brought by facial recognition and AGR applications to trans and non-binary people. In a second phase, we developed and empirically assessed a catalog of 19 best practices for an ethical AI development grounded in Justice, Equity, Diversity, and Inclusion principles. We aim to establish ethical standards that promote inclusivity to trans and non-binary people, mitigating algorithmic discrimination.

Michel Perilo

George Valença

Aldenir Telles

Non-Binary and Trans-Inclusive AI: A Catalogue of Best Practices for Developing Automatic Gender Recognition Solutions

非二元和跨性别包容性AI：开发自动性别识别解决方案的最佳实践目录

短名	SIGAPP Appl. Comput. Rev.
Journal Impact	0.27
国际分区	COMPUTER SCIENCE, INFORMATION SYSTEMS(Q4)

涉及主题	计算机科学操作系统程序设计语言工程类数学人工智能生物哲学物理经济万维网统计
出版信息	出版商: Association for Computing Machinery (ACM)，出版周期: ，期刊类型: journal，开源期刊: 否
基本数据	创刊年份: ，原创研究文献占比: 100.00%，自引率:25.00%， Gold OA占比: 0.00%

ACM SIGAPP Applied Computing Review

期刊引文格式

有 1 位以上作者的期刊

有 2 位作者的期刊

有 3 位作者的期刊

有 5 位以上作者的期刊

书籍引用格式

学位论文引用格式

网页引用格式

专利引用格式

手工熬夜修改参考文献？研飞自动匹配期刊，一键轻松成稿，支持 Word/WPS

投稿经验分享