Miracle-dz/UniFAR-FTUs huggingface dataset:
Up or Down?: An Insight into Programmer's Acquisition of Iteration Skills

Iteration is one of the basic programming constructs taught in a CS1 course. Students learn to use loops by being exposed to a range of loop examples, initially with scalar values and then over composite data such as vectors. When writing a finite loop to iterate over a vector, we can iterate upwards or downwards. In many cases, the order does not change the outcome; however, for some tasks it is important to consider which direction is most suitable before writing a solution. For example, if we want to shift the elements of a vector one position to the right (left), we should choose a downward (upward) loop. However, when we asked undergraduate students to implement the shift right task, most of them used an upward loop. This outcome indicates the students didn't consider the loop direction before coding the iterative task. We hypothesise that this was in part due to course materials favouring upward loops. To confirm this hypothesis, we have analysed the examples presented in our local CS0/CS1 courses as well as four popular MOOCs. This analysis has corroborated an overwhelming bias towards upward loops. We have also found a lack of instruction regarding the possible impact of direction on code efficiency. Thus, in order to improve novice programmers' iteration skills, we propose CS1 examples that expose students to a more balanced mix of up/down loops and the explicit choice of direction.
A Feasibility Study on Mission-Critical Push-to-Talk: Standards and Implementation Perspectives

In this article, we study feasibility issues of mission-critical push-to-talk (MCPTT), which is an evolution of a group communication service for public safety use. Along with the overall standardization of the MCPTT in the Third Generation Partnership Project (3GPP), distinguishing features of MCPTT are investigated in comparison with Open Mobile Alliance (OMA) PTT over cellular (PoC). Based on the requirements of MCPTT, we elaborate on various technologies that need to be leveraged for minimizing end-to-end latency in terms of both communications and application processes. To validate MCPTT in practice, we show not only the realization of MCPTT with a practical testbed but also the verification with key performance indicators defined in the 3GPP specification as performance metrics. The results from our proof of concept reveal that MCPTT can be a reasonable candidate for group communication, guaranteeing a sufficient level of survivability and immediacy beyond its potential.
Dynamic model of online information quality perceptions and impacts: a literature review

ABSTRACT This paper reviews the literature related to the concept of information quality (IQ) in the context of the online environment to understand how it has been conceptualised in the Information Systems (IS) literature, to identify gaps in the literature, and to make recommendations for future research. This study also extends the Information Processing Model (IPM) and proposes a novel dynamic model of online IQ perceptions and impacts. To understand the online IQ construct, an in-depth review of the literature is conducted. Overall, we reviewed 508 journal articles published between 1995 and 2016. This study found that there is a substantial gap in the IS literature in regards to a full understanding of the online IQ construct and its relation with the IPM. More research is needed to understand the way individuals process and respond to online information. Further, although the importance of context in theory development has been emphasised in the literature, very few empirical articles have explored the impact of contextual factors on the IPM as well as perceived online IQ. The findings provide online vendors with important guidelines that should be considered to motivate individuals to acquire more information from their websites that could lead to more satisfied and loyal users.
Hybrid Genetic Grey Wolf Algorithm for Large-Scale Global Optimization

Most real-world optimization problems tackle a large number of decision variables, known as Large-Scale Global Optimization (LSGO) problems. In general, the metaheuristic algorithms for solving such problems often suffer from the “curse of dimensionality. ” In order to improve the disadvantage of Grey Wolf Optimizer when solving the LSGO problems, three genetic operators are embedded into the standard GWO and a Hybrid Genetic Grey Wolf Algorithm (HGGWA) is proposed. Firstly, the whole population using Opposition-Based Learning strategy is initialized. Secondly, the selection operation is performed by combining elite reservation strategy. Then, the whole population is divided into several subpopulations for cross-operation based on dimensionality reduction and population partition in order to increase the diversity of the population. Finally, the elite individuals in the population are mutated to prevent the algorithm from falling into local optimum. The performance of HGGWA is verified by ten benchmark functions, and the optimization results are compared with WOA, SSA, and ALO. On CEC’2008 LSGO problems, the performance of HGGWA is compared against several state-of-the-art algorithms, CCPSO2, DEwSAcc, MLCC, and EPUS-PSO. Simulation results show that the HGGWA has been greatly improved in convergence accuracy, which proves the effectiveness of HGGWA in solving LSGO problems.
An Open-Source Verification Framework for Open-Source Cores: A RISC-V Case Study

The complexity and heterogeneity of digital devices used in embedded systems is increasing everyday and delivering a bug-free design is still a very complex task. The interest for open-source hardware in real products is demanding for tools and advanced methodologies for verification to provide high reliability to open and free IPs. In this work, an open-source evolutionary optimizer has been used to create functional test programs that improve the verification test set for an open-source microprocessor, enhancing in this way, the verification level of the device. The verification programs are generated to optimize code coverage metrics and are tested against a high-level model to find device incorrectnesses during the generation time. A perturbation mechanism has been included in the verification framework to cover parts of the device under verification not reachable with only software stimuli such as interrupts or memory stalls. The proposed methodology uncovered 10 bugs still present in the RTL description of the analyzed device and demonstrated the effectiveness of open-source verification tools for the next generation of open-source RISC-V microprocessors.
Hidden Figures: Comparative Latency Analysis of Cellular Networks with Fine-grained State Machine Models

Comparative latency analysis of cellular network control planes is an intuitive and effective method used to determine the superiority/inferiority of one cellular network over others. However, operational policies and network configurations vary across different networks, making it difficult to conduct fine-grained latency comparisons. We present a novel diagnostic method for the comparison of the latencies in processing control plane messages among cellular networks. For each cellular network, we automatically build a fine-grained state machine based on control plane signaling messages collected from user smartphones. From the state machines of multiple network operators, we identify common state transitions consisting of signaling messages. We then compare the latencies in the message intervals for each identified common transition. We discovered 38 bottleneck intervals from three representative control plane procedures by analyzing the state machines of five major operators. Our promising preliminary analysis deserves further research.
ADeLe: Rapid Architectural Simulation for Approximate Hardware

Recent research has introduced approximate hardware units that produce incorrect outputs deterministically or probabilistically for some small subset of inputs but allow significantly higher throughput or lower power than their errorfree counterparts. The integration, validation, and evaluation of these approximate units in architectures and processors, however, remains challenging. In this paper, we introduce ADeLe, a high-level language for the description, configuration, and integration of approximate hardware units into processors. ADeLe reduces the design effort for approximate hardware by modeling approximations at a high level of abstraction and automatically injecting them into a processor model for architectural simulation. Approximations in ADeLe may modify or completely replace the functional behavior of instructions according to user-defined policies. Instructions may be approximated deterministically or probabilistically (e.g., based on operating voltage and frequency). To allow for controlled testing, approximations may be enabled and disabled from software. Energy is automatically accounted based on customizable models that consider the potential power savings of the approximations that are enabled in the system. ADeLe provides designers with a generic and flexible verification framework, allowing them to easily evaluate the energy-quality trade-offs of their designs in applications. We demonstrate the language and corresponding framework by introducing different approximation techniques into a processor model, on top of which we run selected applications. We demonstrate ADeLe using 6 approximate designs with 4 image processing and 2 floating point applications. Our experiments show how ADeLe may be used to generate approximate CPUs and to evaluate energy-quality trade-offs for different applications with reduced effort.
50 Years of CS1 at SIGCSE: A Review of the Evolution of Introductory Programming Education Research

The SIGCSE Technical Symposium is celebrating its 50th year, and a constant theme throughout this history has been to better understand how novices learn to program. In this paper, we present a perspective on the evolution of introductory programming education research at the Symposium over these 50 years. We also situate the Symposium's impact in the context of the wider literature on introductory programming research. Applying a systematic approach to collecting papers presented at the Symposium that focus on novice programming / CS1, we categorized hundreds of papers according to their main focus, revealing important introductory programming topics and their trends from 1970 to 2018. Some of these topics have faded from prominence and are less relevant today while others, including many topics focused on students, such as making learning programming more appropriate from gender, diversity, accessibility and inclusion standpoints, have garnered significant attention more recently. We present discussions on these trends and in doing so, we provide a checkpoint for introductory programming research. This may provide insights for future research on how we teach novices and how they learn to program.
A Collaboration Platform for Effective Task and Data Reporter Selection in Crowdsourcing Network

Due to the huge number of objects/things connected to the Internet of Things (IoT) which are embedded with electronics, software, and sensors, the IoT creates many exciting applications such as smart grids, smart homes, and smart cities. In the IoT, the sensing and control of objects/things can be abstracted as a task, in which many sensing devices sense and collect data. However, the substantial case studies show that by simply connecting them without further collaboration among the objects/things will lead to the bad performance of the system. With the number of sensing devices connected to the IoT increases, the collaboration for completing the task is becoming more and more urgent. In this paper, a Collaborative Multi-Tasks Data Collection Scheme (CMDCS) is proposed to solve the problem by constructing a collaborative platform for task publisher and data reporter. The main contribution of CMDCS includes the following two aspects: (1) a Task Unit Bid-based task selection strategy is proposed to select the task which can bring higher profits to the system, in which the Task Unit Bid is the ratio of task bid to the amount of data which are needed to collect sensing tasks; and (2) a greedy contributed density-based data collector set selection method is proposed to reduce the cost of data collection so as to maximize system profit, in which the contribution density is used to measure the contribution of a single data collector to a specific sensing task. A large number of experiments have been carried out to verify the effectiveness of our proposed strategy. The experiments show that compared to the traditional data collection strategy Random Task selection with Coverage First Reporter selection, in which the Task Unit Bid and Contribution Density are not used, the profit of the system is improved by 92.08%.
DReAM: Deep Recursive Attentive Model for Anomaly Detection in Kernel Events

System logs and traces contain information that reflects the state of the system and serves as a rich source of knowledge for system monitoring from the application to the kernel layer. Moreover, logging of traces as a tool for monitoring the operation of a cyber-physical system is recommended by most safety standard organizations. However, because the data can be overwhelmingly huge within a short space of time, the use of models that do not rely only on known signatures for online anomaly detection becomes difficult to use due to the challenge of processing such an enormous amount of data at runtime. Hence, most practitioners resort to the use of signature-based tools. In this paper, we introduce an anomaly detection model that uses intra-trace and inter-trace context vectors with long short-term memory networks to overcome the challenge of online anomaly detection in cyber-physical systems. We test the performance of the model with publicly available datasets that reflect the internal and external control flow of an embedded application and our model demonstrates both the effectiveness and robustness in detecting an anomalous sequence in a system call stream.
How Cyber Stalking and Cyber Bullying Affect Students’ Open Learning

This paper aims to bridge the literature gap concerning the use of social media to conduct collaboration learning and explore its effect on student performance through cyberstalking (CS) and cyberbullying (CB). To achieve the study objective, this study employed a questionnaire as the main data collection method and distributed it to 538 university students based on both the technology acceptance model and constructivism theory, all of whom use social media. The findings were obtained via a quantitative research method, structural equation modeling. This study found a significant relationship between perceived usefulness, perceived ease of use, and perceived enjoyment with social media use for open learning. However, this study found a negative relationship from social media use on open learning that was dampened by CB, which is considered a dampening factor. Also, open learning was reported to be negatively influenced by perceived usefulness as CS was found to dampen the relationship with open learning.
Green Start-Ups' Attitudes towards Nature When Complying with the Corporate Law

This paper examines how Spanish green start-ups develop improved attitudes towards nature. Despite the prevalence of theories based on green entrepreneurs, very little research has been conducted on how green attributes influence nature in the framework of the new Spanish Criminal Code in what concerns corporate compliance. One hundred and fifty-two start-ups were interviewed in 2018. Smart PLS Path Modelling has been used to build an interaction model among variables. The results obtained have a significant theoretical and practical implication since they add new findings to the current literature on how start-ups incorporate the recent Criminal Code in their environmental decisions. The model reveals a strong predictive power (R-squared = 77.9%). There are grounds to say that start-ups should implement effective monitoring systems and new organisational standards, in addition to developing surveillance measures to avoid unexpected sanctions.
Paradoxical Simulations to Enhance Education in Mathematics

The subject of probability and statistics is easily dismissed by students as assemblages of formulae to be rote-memorized. We propose here an integration of simulation-based activities with certain mathematical paradoxes using patchwork assessment to first-year undergraduates, such that they can better appreciate the real-world context of probability and statistics. The proposed examples alongside various facilitation skills for the instructor are discussed. We also provide an original spreadsheet simulation program in Excel and Visual Basic for Applications to reproduce the numerical experiments. This program is capable of running Monte Carlo simulations for all three seminal Parrondo’s paradox variants, and can be easily used by students and instructors; moreover, the computed datasets and code are fully-transparent, thereby allowing interactive discussions, modifications and extensions, and further analyses. Our findings suggest that the proposed teaching strategy is useful, and we hope that this work will initiate the significant adoption of paradoxical simulations in teaching practice. The interactive program is freely available on open science framework.
Development and Preliminary Investigation of a Semiautonomous Socially Assistive Robot (SAR) Designed to Elicit Communication, Motor Skills, Emotion, and Visual Regard (Engagement) from Young Children with Complex Cerebral Palsy: A Pilot Comparative Trial

Through play, typically developing children manipulate objects and interact with peers to establish and develop physical, cognitive, language, and social skills. However, children with complex disabilities and/or developmental delays have limited play experiences, thus compromising the quality of play and acquisition of skills. Assistive technologies have been developed to increase opportunities and level of interaction for children with disabilities to facilitate learning and development. One type of technology, Socially Assistive Robotics, is designed to assist the human user through social interaction while creating measurable growth in learning and rehabilitation. The investigators in this study designed, developed, and validated a semiautonomous Socially Assistive Robot to compare with a switch-adapted toy to determine robot effectiveness in quantity of, changes in, and differences in engagement. After interacting with both systems for three sessions each, five of the eight subjects showed a greater level of positive engagement with the robot than the switch-adapted toy, while the remaining three subjects showed slightly higher positive engagement with the toy. The preliminary results of the study suggest that Socially Assistive Robots specifically designed for children with complex cerebral palsy should be further researched and utilized to enrich play interactions and skill development for this population.
Densitometric Radiographic Imaging With Contour Sensors

We present the technical/physical foundations of a new imaging technique that combines ordinary radiographic information (generated by conventional X-ray settings) with the patient’s volume to derive <italic>densitometric images</italic>. Traditionally, these images provide quantitative information about tissues densities. In our approach, they graphically enhance either soft or bony regions. After measuring the patient’s volume with contour recognition devices, the physical traversed lengths within it (as the Roentgen beam intersects the patient) are calculated and pixel-wise associated with the original radiograph (<inline-formula> <tex-math notation="LaTeX">$\mathcal {X}$ </tex-math></inline-formula>). In order to derive this map of lengths (<inline-formula> <tex-math notation="LaTeX">$\mathcal {L}$ </tex-math></inline-formula>), the camera equations of the X-ray system and the contour sensor are determined. The patient’s surface is also translated to the point-of-view of the X-ray beam and all its entrance/exit points are sought with the help of ray-casting methods. The derived <inline-formula> <tex-math notation="LaTeX">$\mathcal {L}$ </tex-math></inline-formula> is applied to <inline-formula> <tex-math notation="LaTeX">$\mathcal {X}$ </tex-math></inline-formula> as a physical operation (subtraction), obtaining soft tissue- (<inline-formula> <tex-math notation="LaTeX">$\mathcal {D}_{S}$ </tex-math></inline-formula>) or bone-enhanced (<inline-formula> <tex-math notation="LaTeX">$\mathcal {D}'_{B}$ </tex-math></inline-formula>) figures. In the <inline-formula> <tex-math notation="LaTeX">$\mathcal {D}_{S}$ </tex-math></inline-formula> type, the contained graphical information can be linearly mapped to the average electronic density (traversed by the X-ray beam). This feature represents an interesting proof-of-concept of associating density data to radiographs, but most important, their intensity histogram is objectively compressed, i.e., the dynamic range is more shrunk (compared against the corresponding <inline-formula> <tex-math notation="LaTeX">$\mathcal {X}$ </tex-math></inline-formula>). This leads to other advantages: improvement in the visibility of border/edge areas (high gradient), extended manual window level/width manipulations during screening, and immediate correction of underexposed <inline-formula> <tex-math notation="LaTeX">$\mathcal {X}$ </tex-math></inline-formula> instances. In the <inline-formula> <tex-math notation="LaTeX">$\mathcal {D}'_{B}$ </tex-math></inline-formula> type, high-density elements are highlighted and easier to discern. All these results can be achieved with low-energy beam exposures, saving costs and dose. Future work will deepen this clinical side of our research. In contrast with other image-based modifiers, the proposed method is grounded on the measurement of a physical entity: the span of the X-ray beam within a body while undertaking a radiographic examination.
A Novel Double-Strand DNA Genetic Algorithm for Multi-Objective Optimization

Multi-objective optimization is important for many businesses, science, and engineering applications. Existing evolutionary algorithms for multi-objective optimization problems based on single chain encoding still have difficulties in obtaining high-quality results. This paper presents a new DNA genetic algorithm that uses a novel double-strand DNA encoding, a set of new genetic operators, and two new ranking criteria to obtain solutions that closely approximate the Pareto-optimal front. The extensive experiments were performed using a set of comprehensive benchmark bi-objective and tri-objective test problems. The experimental results show that this algorithm outperforms a set of the state-of-the-art evolutionary algorithms on several well-accepted performance metrics.
We Learn in the Form of Stories: How Digital Storytelling Supports Critical Digital Literacy for Pre-Service Teachers

This article discusses the use of digital storytelling to enable pre-service teachers to develop critical digital literacy skills. Digital storytelling and new literacies share the same set of abilities: 1) finding, evaluating and consuming digital content; 2) exploring identity and cultural landscapes; 3) creating new digital materials drawing from multiple media; and 4) communicating what was found or created with an audience. The study is based on the essays of pre-service teachers enrolled in Cultural Foundations of Education, who produced two digital stories. Responses showed that they believed the assignments helped them become more reflective, culturally aware and media savvy, and led them to a critical and practical knowledge of multimodal content production. Digital storytelling, then, utilizes almost all of the critical digital skills and literacies pre-service teachers are expected to bring to diverse, globalized, and technology-infused 21st century classrooms.