Implementation Choices Research Articles

A Compliance Return Method to Evaluate Different Approaches to Implementing Regulations: The Example of Food Hygiene Standards

Abstract We investigate how different approaches to the implementation of regulations affect compliance by evaluating the implementation of food hygiene standards in four English authorities. We draw upon regulatory theory in general together with specific literature on food hygiene standards to advance a new “compliance return” concept, which enables compliance outcomes and resource inputs to be considered jointly. We operationalize “compliance return” as part of a low-cost methodology for evaluating the relative effectiveness of different approaches to implementing food hygiene standards. We find that different approaches to implementation have significant impacts on food policy outcomes. Specifically, implementation ranged from a greater emphasis on deterrence to a greater emphasis on cooperation, with cooperation resulting in greater compliance and deterrence requiring less resource input. When compliance level is considered alongside resource input, to assess the overall “compliance return,” a stronger case emerges for a deterrence approach. The food policy implications are two-fold: (i) implementation affects policy outcomes, and (ii) the measure of “compliance return” developed and implemented in this article offers a low-cost approach to support the ex-post evaluation of implementation choices. The Compliance Return approach may be applicable more widely, especially where there is discretion regarding how regulations are to be implemented.

A provably masked implementation of BIKE Key Encapsulation Mechanism

BIKE is a post-quantum key encapsulation mechanism (KEM) selected for the 4th round of the NIST's standardization campaign. It relies on the hardness of the syndrome decoding problem for quasi-cyclic codes and on the indistinguishability of the public key from a random element, and provides the most competitive performance among round 4 candidates, which makes it relevant for future real-world use cases. Analyzing its side-channel resistance has been highly encouraged by the community and several works have already outlined various side-channel weaknesses and proposed ad-hoc countermeasures. However, in contrast to the well-documented research line on masking lattice-based algorithms, the possibility of generically protecting code-based algorithms by masking has only been marginally studied in a 2016 paper by Chen et al. in SAC 2015. At this stage of the standardization campaign, it is important to assess the possibility of fully masking BIKE scheme and the resulting cost in terms of performances. In this work, we provide the first high-order masked implementation of a code-based algorithm. We had to tackle many issues such as finding proper ways to handle large sparse polynomials, masking the key-generation algorithm or keeping the benefit of the bitslicing. In this paper, we present all the gadgets necessary to provide a fully masked implementation of BIKE, we discuss our different implementation choices and we propose a full proof of masking in the Ishai Sahai and Wagner (Crypto 2003) model. More practically, we also provide an open C-code masked implementation of the key-generation, encapsulation and decapsulation algorithms with extensive benchmarks. While the obtained performance is slower than existing masked lattice-based algorithms, we show that masking at order 1, 2, 3, 4 and 5 implies a performance penalty of x5.8, x14.2, x24.4, x38 and x55.6 compared to order 0 (unmasked and unoptimized BIKE). This scaling is encouraging and no Boolean to Arithmetic conversion has been used.

Research on Speech Emotion Recognition System Using Machine Learning

Speech Emotion Recognition (SER) remains a hot topic in the domain of affective computing, drawing considerable research interest. Its growing potential, advancements in algorithms, and real-world applications contribute to this interest. Human speech carries paralinguistic cues that can be quantitatively represented through features like pitch, intensity, and Mel-Frequency Cepstral Coefficients (MFCC). SER typically involves three main stages: data processing, feature extraction/selection, and classification based on emotional features present. These steps, tailored to the unique attributes of human speech, make machine learning methods a fitting choice for SER implementation. While recent studies in affective computing leverage various ML techniques for SER tasks, few delve into the techniques aiding these core steps. Moreover, the challenges within these stages and cutting-edge approaches addressing them often receive limited discussion or are overlooked in these works.This project introduces a pioneering Speech Emotion Recognition System leveraging BiLSTM Algorithm and Image Processing techniques, developed for implementation on the Raspberry Pi platform using Python. The system analyses recorded audio input, employing three LEDs for mood detection and interfacing with Raspberry Pi programming facilitated by an SD card. This abstract outlines an all-encompassing approach for real-time emotion recognition via audio analysis, catering to varied applications in emotional AI and human-computer interaction. KEYWORDS: Speech Emotion Recognition (SER), Machine Learning, Image Processing, Raspberry Pi.

Towards technology neutrality in modular biosignal analysis and visualization: Extending the AcuWave Software Suite to transparently support multiple implementation choices

Towards technology neutrality in modular biosignal analysis and visualization: Extending the AcuWave Software Suite to transparently support multiple implementation choices

Standard log-capture differentials as performance metrics for deepwater wave power generation

As a support to performance assessment of deepwater wave energy converters, this work quantifies susceptibility of output power to short duration variations, independent of source wave statistics. Introduced as ς-metrics, the quantified measures may be a basis for technology comparisons, with possible convergence to a choice for implementation. The metrics further allow comparison between different versions of a specific converter technology, thereby helping in design revision and refinement.With power conversion represented in terms of a technology specific capture width function, the log-capture differential is defined relative to a hypothetical benchmark characterised by negligible power variability. Root-mean-square aggregation of the differential further leads to standard log-capture differentials as site independent ς-metrics for a capture-and-conversion mechanism.Study results are included in three phases. First, three established wave energy conversion technologies are analysed at respective power maximums, so as to illustrate applicability of log-capture differential and ς-metrics. Next, the metrics are analysed at fractional output power to realise converter performance at reduced variability. Finally for a given marine environment, ς-metrics are shown to be useful in projection of output power variability across technologies, together with the mean output power as fraction of rated maximum.

Development and Validation of a Natural Language Processing Algorithm to Pseudonymize Documents in the Context of a Clinical Data Warehouse.

The objective of this study is to address the critical issue of deidentification of clinical reports to allow access to data for research purposes, while ensuring patient privacy. The study highlights the difficulties faced in sharing tools and resources in this domain and presents the experience of the Greater Paris University Hospitals (AP-HP for Assistance Publique-Hôpitaux de Paris) in implementing a systematic pseudonymization of text documents from its Clinical Data Warehouse. We annotated a corpus of clinical documents according to 12 types of identifying entities and built a hybrid system, merging the results of a deep learning model as well as manual rules. Our results show an overall performance of 0.99 of F1-score. We discuss implementation choices and present experiments to better understand the effort involved in such a task, including dataset size, document types, language models, or rule addition. We share guidelines and code under a 3-Clause BSD license.

Performance profiling and design choices of an RDMA implementation using FPGA devices

RDMA communication is an efficient choice for many applications, such as data acquisition systems, data center networking and any other networking application, where high bandwidth and low latency are necessary. RDMA can be implemented using a large array of options, which need to be tailored to the needed use case, in order to get optimal results. Aspects such as the effects of using multiple simultaneous connections, using various transport functions such as RDMA Write and RDMA Send and communication models such as sending individual bursts or continuous streams of data will be investigated for implementing RDMA on FPGA devices.

ARSIP: Automated Robotic System for Industrial Painting

This manuscript addresses the critical need for precise paint application to ensure product durability and aesthetics. While manual work carries risks, robotic systems promise accuracy, yet programming diverse product trajectories remains a challenge. This study aims to develop an autonomous system capable of generating paint trajectories based on object geometries for user-defined spraying processes. By emphasizing energy efficiency, process time, and coating thickness on complex surfaces, a hybrid optimization technique enhances overall efficiency. Extensive hardware and software development results in a robust robotic system leveraging the Robot Operating System (ROS). Integrating a low-cost 3D scanner, calibrator, and trajectory optimizer creates an autonomous painting system. Hardware components, including sensors, motors, and actuators, are seamlessly integrated with a Python and ROS-based software framework, enabling the desired automation. A web-based GUI, powered by JavaScript, allows user control over two robots, facilitating trajectory dispatch, 3D scanning, and optimization. Specific nodes manage calibration, validation, process settings, and real-time video feeds. The use of open-source software and an ROS ecosystem makes it a good choice for industrial-scale implementation. The results indicate that the proposed system can achieve the desired automation, contingent upon surface geometries, spraying processes, and robot dynamics.

Conversational Agents for Energy Awareness and Efficiency: A Survey

The need to reduce greenhouse gas emissions and promote energy efficiency is crucial to achieve the energy transition and sustainable development goals. The availability of tools that provide clear information on energy consumption plays a key role in this transition, enabling users to monitor, manage, and optimize their energy use. This process, commonly referred to as energy feedback or eco-feedback, involves delivering information regarding energy usage and potentially suggesting more sustainable practices. Within the range of available tools, conversational agents can represent a valuable channel to receive detailed information about energy consumption and tailored advice for improving energy efficiency. The aim of this article is thus to explore the application of conversational agents, focusing on eco-feedback, as these tools are primarily devised to foster user awareness of energy usage and enhance more participatory conservation strategies. To this end, we conducted a keyword-based search of major scientific article databases, applying strict criteria to select relevant studies. The results of the collection showed that there is a very diverse landscape with respect to this topic. The surveyed works exhibit a high versatility in feedback goals. Furthermore, while predominantly applied domestically, they also show potential in commercial and industrial settings. Implementation choices also vary to a great extent, while evaluation practices lack a systematic approach and highlight the need for greater consistency. In light of these remarks, we also outline possible future extensions of this type of application, exploring in particular the emerging challenges associated with the increased use of renewable sources and the rise of local decentralized energy communities.

Multi-criteria Optimization of Real-time DAGs on Heterogeneous Platforms under P-EDF

This article tackles the problem of optimal placement of complex real-time embedded applications on heterogeneous platforms. Applications are composed of directed acyclic graphs of tasks, with each directed-acyclic-graph (DAG) having a minimum inter-arrival period for its activation requests and an end-to-end deadline within which all of the computations need to terminate since each activation. The platforms of interest are heterogeneous power-aware multi-core platforms with Dynamic Voltage and Frequency Scaling (DVFS) capabilities, including big.LITTLE Arm architectures and platforms with GPU or FPGA hardware accelerators with Dynamic Partial Reconfiguration capabilities. Tasks can be deployed on CPUs using partitioned EDF-based scheduling. Additionally, some of the tasks may have an alternate implementation available for one of the accelerators on the target platform, which are assumed to serve requests in non-preemptive FIFO order. The system can be optimized by minimizing power consumption, respecting precise timing constraints, maximizing the applications’ slack, respecting given power consumption constraints, or even a combination of these, in a multi-objective formulation. We propose an off-line optimization of the mentioned problem based on mixed-integer quadratic constraint programming (MIQCP). The optimization provides the DVFS configuration of all the CPUs (or accelerators) capable of frequency switching and the placement to be followed by each task in the DAGs, including the software-vs.-hardware implementation choice for tasks that can be hardware accelerated. For relatively big problems, we developed heuristic solvers capable of providing suboptimal solutions in a significantly reduced time compared to the MIQCP strategy, thus widening the applicability of the proposed framework. We validate the approach by running a set of randomly generated DAGs on Linux under SCHED_DEADLINE, deployed onto two real boards, one with Arm big.LITTLE architecture, the other with FPGA acceleration, verifying that the experimental runs meet the theoretical expectations in terms of timing and power optimization goals.

Disentangling organizational levers and economic benefits in transitional care programs: a systematic review and configurational analysis

BackgroundPromoting safe and efficient transitions of care is critical to reducing readmission rates and associated costs and improving the quality of patient care. A growing body of literature suggests that transitional care (TC) programs are effective in improving quality of life and reducing unplanned readmissions for several patient groups. TC programs are highly complex and multidimensional, requiring evidence on how specific practices and system characteristics influence their effectiveness in patient care, readmission reduction and costs.MethodsUsing a systematic review and a configurational approach, the study examines the role played by system characteristics (size, ownership, professional skills, technology used), the organizational components implemented, analyzing their combinations, and the potential economic impact of TC programs.ResultsThe more organizational components are implemented, the greater the likelihood that a TC program will be successful in reducing readmission rates. Not all components have the same effect. The results show that certain components, ‘post-discharge symptom monitoring and management’ and ‘discharge planning’, are necessary but not sufficient to achieve the outcome. The results indicate the existence of two different combinations of components that can be considered sufficient for the reduction of readmissions. Furthermore, while system characteristics are underexplored, the study shows different ways of incorporating the skill mix of professionals and their mode of coordination in TC programs. Four organizational models emerge: the health-based monocentric, the social-based monocentric, the multidisciplinary team and the mono-specialist team. The economic impact of the programs is generally positive. Despite an increase in patient management costs, there is an overall reduction in all post-intervention costs, particularly those related to readmissions.ConclusionsThe results underline the importance of examining in depth the role of system characteristics and organizational factors in facilitating the creation of a successful TC program. The work gives preliminary insights into how to systematize organizational practices and different coordination modes for facilitating decision-makers’ choices in TC implementation. While there is evidence that TC programs also have economic benefits, the quality of economic evaluations is relatively low and needs further study.

On the effectiveness of developer features in code smell prioritization: A replication study

Code smells are sub-optimal design and implementation choices that hinder software maintainability. Although significant progress has been achieved in code smell detection, numerous results are perceived as trivial by developers. In response, a code smell prioritization approach capturing developer features has been proposed by a prior study (MSR’20), and it outperformed a code metric baseline (KBS). The conclusion was validated on a dataset collected from original developers, which includes their comments on code smell priority. However, the low presence of developer aspects in the comments is inconsistent with the performance improvement after involving such features. To explain the inconsistency, we replicate the two studies by exploiting different feature selection methods and a model explanation technique called SHAP. Our major findings are: (i) Correlation-based Feature Selection should not be used as a default method since it could harm Krippendoff’s Alpha by up to 72%, (ii) if better feature selection is applied, pure code metrics from KBS outperform the MSR features in 3 smells by up to 45% in Alpha, and (iii) the behavior of code metrics based models have more agreement with developers’ comments. We suggest exploiting different feature selection methods and using code metrics to prioritize the 3 change-insensitive smells.

Assessing the use of HL7 FHIR for implementing the FAIR guiding principles: a case study of the MIMIC-IV Emergency Department module.

To provide a real-world example on how and to what extent Health Level Seven Fast Healthcare Interoperability Resources (FHIR) implements the Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles for scientific data. Additionally, presents a list of FAIR implementation choices for supporting future FAIR implementations that use FHIR. A case study was conducted on the Medical Information Mart for Intensive Care-IV Emergency Department (MIMIC-ED) dataset, a deidentified clinical dataset converted into FHIR. The FAIRness of this dataset was assessed using a set of common FAIR assessment indicators. The FHIR distribution of MIMIC-ED, comprising an implementation guide and demo data, was more FAIR compared to the non-FHIR distribution. The FAIRness score increased from 60 to 82 out of 95 points, a relative improvement of 37%. The most notable improvements were observed in interoperability, with a score increase from 5 to 19 out of 19 points, and reusability, with a score increase from 8 to 14 out of 24 points. A total of 14 FAIR implementation choices were identified. Our work examined how and to what extent the FHIR standard contributes to FAIR data. Challenges arose from interpreting the FAIR assessment indicators. This study stands out for providing a real-world example of a dataset that was made more FAIR using FHIR. To the best of our knowledge, this is the first study that formally assessed the conformance of a FHIR dataset to the FAIR principles. FHIR improved the accessibility, interoperability, and reusability of MIMIC-ED. Future research should focus on implementing FHIR in research data infrastructures.

Methodological recommendations on the practical application of tools for ensuring safe information interaction of cluster members

Based on the conducted research, the article presents methodological recommendations on the choice of implementation of the coordination organizational and management body of the cluster in the aspect of its information and communication interaction, depending on the type of cluster. It also describes proposals for flexible adaptation of cluster employees based on change management, aimed at overcoming resistance to innovations. The recommendations described in the article will make it possible to effectively implement in Russian clusters tools for automating the processing of requests to determine the authenticity of the subject, extraction and parallel processing of the targeted information pool while maintaining its speed, while optimally revealing their advantages in each practical case, realizing synergetic and multiplicative effects, as well as obtaining opportunities to consolidate the result and subsequent replication of such experience.

Design and Development of a CCSDS 131.2-B Software-Defined Radio Receiver Based on Graphics Processing Unit Accelerators

In recent years, the number of Earth Observation missions has been exponentially increasing. Satellites dedicated to these missions usually embark with payloads that produce large amount of data and that need to be transmitted towards ground stations, in time-limited windows. Moreover, the noisy nature of the link between satellites and ground stations makes it hard to achieve reliable communication. To address these problems, a standard for a flexible advanced coding and modulation scheme for high-rate telemetry applications has been defined by the Consultative Committee for Space Data Systems (CCSDS). The defined standard, referred to as CCSDS 131.2-B, makes use of Serially Concatenated Convolutional Codes (SCCC) based on 27 ModCods to optimize transmission quality. A limiting factor in the adoption of this standard is represented by the complexity and the cost of the hardware required for developing high-performance receivers. In the last decade, the performance of software has grown due to the advancement of general-purpose processing hardware, leading to the development of many high-performance software systems even in the telecommunication sector. These are commonly referred to as Software-Defined Radio (SDR), indicating a radio communication system in which components that are usually implemented in hardware, by means of FPGAs or ASICs, are instead implemented in software, offering many advantages such as flexibility, modularity, extensibility, cheaper maintenance, and cost saving. This paper proposes the development of an SDR based on NVIDIA Graphics Processing Units (GPU) for implementing the receiver end of the CCSDS 131.2-B standard. At first, a brief description of the CCSDS 131.2-B standard is given, focusing on the architecture of the transmitter and receiver sides. Then, the receiver architecture is shown, giving an overview of its functional blocks and of the implementation choices made to optimize the processing of the signal, especially for the SCCC Decoder. Finally, the performance of the system is analyzed in terms of data-rate and error correction and compared with other SW systems to highlight the achieved improvements. The presented system has been demonstrated to be a perfect solution for CCSDS 131.2-B-compliant device testing and for its use in science missions, providing a valid low-cost alternative with respect to the state-of-the-art HW receivers.

Code Smell Detection using Machine Learning Classification Algorithm

Code smell indicates a poor implementation choice that affects software quality attributes (Pérez, 2013). Fowler (1999) also describes it as an internal code-level problem where the code becomes complex, the design broken, and eventually worsens software quality. Jose (2020) has reported that most applied existing approaches for code smells detection are search-based (30.1%), metric-based (24.1%), and symptom-based approaches (19.3%). However, these existing approaches can only apply to simpler detection; the greater the complexity of code smell, the lower the results for code smell detection (Mantyla M, 2004). Kessentini (2014) also has reported that detecting the problems of code smell is difficult and the performance is not effective using the existing approaches such as search-based, symptom-based, visualization-based, probabilistic, cooperative-based, manual, metrics-based, and rule-based. As a result, many of these approaches extend to the application of machine learning classifiers in software code smell detection. Fontana (2016) reported that a supervised machine learning strategy can be used to forecast the value of the dependent variable using machine learning classifiers to address the problem. In this project, we propose a machine learning supervised Gaussian processes algorithm for JAVA open-source code smell detection. The Gaussian process is a highly interpretable supervised machine learning algorithm used in regression testing to quantify prediction uncertainty. A code smell detection application prototype will be developed to implement the proposed work. The effectiveness of the proposed work in terms of detection accuracy will be evaluated further.

Analysis of 5G and LTE Signals for Opportunistic Navigation and Time Holdover.

The purpose of this study was to evaluate the stability and therefore suitability of available fifth generation (5G) and long-term evolution (LTE) signals for positioning navigation and timing (PNT) purposes with particular focus on answering questions around the time-scale-dependent stability of these sources, which, to our knowledge, has not been addressed in the context of the numerous publications within the PNT community to date. The methodology used directly measured the over-the-air signal phase stability to one or more of the cellular signal sources that were visible from the lab environment simultaneously while using a local atomic clock or differential measurements to isolate the time stability of the observable cellular downlink signals. This approach was taken since it does not require subscription or association with the networks under test. Instead, it exploits a 'signal of opportunity' (SoP) approach to signal use for PNT purposes. The somewhat surprising result is that the time domain instability of the sources was highly variable, dependent on the implementation choices of the operator, and that the stability of even the modernized towers was generally best at interrogation intervals of approximately 0.01 s, which indicates that the existing exploitation of these signals within the PNT community has substantial room for improvement through simple changes to the selected update rate used.

The human factor in phishing: Collecting and analyzing user behavior when reading emails

Phishing emails are constantly increasing their sophistication, and typical countermeasures struggle at addressing them. Attackers target our cognitive vulnerabilities with a varied set of techniques, and each of us, not trained enough or simply in the wrong moment, can be deceived and put an entire organization in trouble. To date, no study has evaluated the behavior of users when confronted with phishing emails characterized by a diverse set of features and attack strategies. We created a system called Spamley from these observations, which has the main aim of collecting and sharing data regarding such user behavior. Spamley is also meant to disseminate awareness among users. To reach these goals, we firstly analyzed the wide scientific literature on phishing. Our analysis shows that the focus of studies on phishing has more and more shifted from technical to human-oriented aspects. Building on this analysis we designed, implemented, and deployed a system comprising a web application to test user awareness about phishing, featuring a survey to identify the most interesting characteristics of users, and fueled by a large and varied set of test emails engineered to solicit the several possible cognitive vulnerabilities we all have. We describe in details the design and implementation choices, the lessons we learned, and the way we filled the gap in the available related work. Finally, we use real data from our first 500 users to show how data collected can be used for several important analyses, including which characteristics of the emails are more relevant for which cognitive vulnerability of specific groups of users. Results obtained can guide the development of novel email clients as well as tailored training programmes. Data collected is available to the scientific community for conducting further studies on the important and still unsolved issue of email phishing.

Feedback on Students’ Performance: Possible Ways of Enhancing Students’ Success with Formative Assessment

Feedback has become as the main performance metric that’s why meaningful and thoughtful feedback is important for improving students' learning path. It is provided by teachers to close the students’ knowledge gap. Positive outcomes are determined by informative, goal-oriented and friendly delivered feedback. However, students and teachers lack knowledge about the role of feedback and its contribution in learners’ progress. This article delves the significance of feedback on students’ performance and its role for students’ enhanced learning. The paper presents different types of formative feedback and their effectiveness for students’ performance improvement. It also shows how effective feedback works in the classroom and strategic choices for feedback implementation. Feedback is presented as an essential tool for improving quality of teaching and education in general.

Multicriteria Evaluation of Randomized Response Techniques for Population Mean

Increasing demand in surveys focused on quantitative characteristics (population mean among others) of controversial issues like corruption, tax evasion, drugs consumption of sensitive variables like spending on drugs or illegal sources of income which lead to lively research, are of randomized response techniques for quantitative variables. Therefore, we propose complex multicriteria evaluation methodology for randomized response techniques for population mean. Based on extensive review in the literature, following ranges of criteria were proposed: statistical properties of estimator, implementation and parameter choice, respondent burden and credibility and confidentiality protection of respondents’ data. Finally, we evaluate in this setting standard techniques using scramble variables and recently proposed techniques of dichotomous question.