Miracle-dz/UniFAR-FTUs huggingface dataset:
Drag and Drop Programming Experiences and Equity: Analysis of a Large Scale Middle School Student Motivation Survey

Drag and drop programming languages have been promoted for broadening participation and increasing motivation for middle grade students in computer science (CS), yet their impact on motivation and the implications for equity are not well documented. This raises questions about potential issues of equity. Despite considerations to increase participation and diversity, these tools have not been evaluated as to how they provide more equitable outcomes regarding student motivation in CS. This study analyzes data from a survey (n=9,573) to investigate middle school student motivation in CS when considering previous experiences with drag and drop programming. Student responses to motivational items are categorized into four factors based on exploratory and confirmatory factor analysis. Multiple regression interaction analysis shows that previous experience is associated with increases (p<0.01) in three motivational factors: general academic and computer use confidence, programming confidence, and interest in future CS courses. These associations hold regardless of race and gender although they are lower for demographics underrepresented in CS, female and/or non-White and non-Asian students. Previous experience was also associated with an increase in interest in the current course for White and Asian male students and a decrease for female underrepresented minority students. While it appears that drag and drop programming efforts could help increase future interest and confidence for all students, it does so to differing degrees for different demographics. This suggests that these initiatives are not addressing issues of equity since groups overrepresented in CS still report higher gains than underrepresented demographics after early programming experiences.
A Principled Approach to Designing a Computational Thinking Practices Assessment for Early Grades

In today's increasingly digital world, it is critical that all students learn to think computationally from an early age. Assessments of Computational Thinking (CT) are essential for capturing information about student learning and challenges. Several existing K-12 CT assessments focus on concepts like variables, iterations and conditionals without emphasizing practices like algorithmic thinking, reusing and remixing, and debugging. In this paper, we discuss the development of and results from a validated CT Practices assessment for 4th-6th grade students. The assessment tasks are multilingual, shifting the focus to CT practices, and making the assessment useful for students using different CS curricula and different programming languages. Results from an implementation of the assessment with about 15000 upper elementary students in Hong Kong indicate challenges with algorithm comparison given constraints, deciding when code can be reused, and choosing debugging test cases. These results point to the utility of our assessment as a curricular tool and the need for emphasizing CT practices in future curricular initiatives and teacher professional development.
YewPar: skeletons for exact combinatorial search

Combinatorial search is central to many applications, yet the huge irregular search trees and the need to respect search heuristics make it hard to parallelise. We aim to improve the reuse of intricate parallel search implementations by providing the first general purpose scalable parallel framework for exact combinatorial search, YewPar. We make the following contributions. (1) We present a novel formal model of parallel backtracking search, covering enumeration, decision, and optimisation search. (2) We introduce Lazy Node Generators as a uniform API for search tree generation. (3) We present the design and implementation of 12 widely applicable algorithmic skeletons for tree search on shared and distributed memory architectures. (4) Uniquely in the field we demonstrate how a wide range of parallel search applications can easily be constructed by composing Lazy Node Generators and the search skeletons. (5) We report a systematic performance analysis of all 12 YewPar skeletons on standard instances of 7 search applications, investigating skeleton overheads and scalability up to 255 workers on 17 distributed locations.
ELDA: LDA made efficient via algorithm-system codesign submission

Latent Dirichlet Allocation (LDA) is a statistical approach for topic modeling with a wide range of applications. In spite of the significance, we observe very few attempts from system track to improve LDA, let alone the algorithm and system codesigned efforts. To this end, we propose eLDA with an algorithm-system codesigned optimization. Particularly, we introduce a novel three-branch sampling mechanism to taking advantage of the convergence heterogeneity of various tokens in order to reduce redundant sampling task. Our evaluation shows that eLDA outperforms the state-of-the-arts.
LUXOR: An FPGA Logic Cell Architecture for Efficient Compressor Tree Implementations

We propose two tiers of modifications to FPGA logic cell architecture to deliver a variety of performance and utilization benefits with only minor area overheads. In the first tier, we augment existing commercial logic cell datapaths with a 6-input XOR gate in order to improve the expressiveness of each element, while maintaining backward compatibility. This new architecture is vendor-agnostic, and we refer to it as LUXOR. We also consider a secondary tier of vendor-specific modifications to both Xilinx and Intel FPGAs, which we refer to as X-LUXOR+ and I-LUXOR+ respectively. We demonstrate that compressor tree synthesis using generalized parallel counters (GPCs) is further improved with the proposed modifications. Using both the Intel adaptive logic module and the Xilinx slice at the 65nm technology node for a comparative study, it is shown that the silicon area overhead is less than 0.5% for LUXOR and 5-6% for LUXOR+, while the delay increments are 1-6% and 3-9% respectively. We demonstrate that LUXOR can deliver an average reduction of 13-19% in logic utilization on micro-benchmarks from a variety of domains. BNN benchmarks benefit the most with an average reduction of 37-47% in logic utilization, which is due to the highly-efficient mapping of the XnorPopcount operation on our proposed LUXOR+ logic cells.
GPGPU performance estimation for frequency scaling using cross-benchmarking

Dynamic Voltage and Frequency Scaling (D VFS) on General-Purpose Graphics Processing Units (GPGPUs) is now becoming one of the most significant techniques to balance computational performance and energy consumption. However, there are still few fast and accurate models for predicting GPU kernel execution time under different core and memory frequency settings, which is important to determine the best frequency configuration for energy saving. Accordingly, a novel GPGPU performance estimation model with both core and memory frequency scaling is herein proposed. We design a cross-benchmarking suite, which simulates kernels with a wide range of instruction distributions. The synthetic kernels generated by this suite can be used for model pre-training or as supplementary training samples. Then we apply two different machine learning algorithms, Support Vector Regression (SVR) and Gradient Boosting Decision Tree (GBDT), to study the correlation between kernel performance counters and kernel performance. The models trained only with our cross-benchmarking suite achieve satisfying accuracy (16%~22% mean absolute error) on 24 unseen real application kernels. Validated on three modern GPUs with a wide frequency scaling range, by using a collection of 24 real application kernels, the proposed model is able to achieve accurate results (5.1%, 2.8%, 6.5% mean absolute error) for the target GPUs (GTX 980, Titan X Pascal and Tesla P100).
Quantum Cryptography and Simulation: Tools and Techniques

The advancement of quantum mechanics has accelerated the quantum computer architecture and hardware. However, algorithms and implementations to take the full advantage of entanglements provided by quantum devices are still far behind. Quantum cryptography offers the possibility of theoretically perfect security based on the principles of quantum mechanics, ensuring that the presence of an eavesdropper will be detected before any sensitive information is transmitted. However, the relevant technology is still under development - hardware, though commercially available, is still in an immature state, and the protocols used to implement secure communications using that hardware may still be improved. The use of simulations is an important tool for studying quantum cryptography, as they can enable researchers to make valuable insights at a relatively low cost. The data garnered from working with simulations can provide direction for further research both in the development of new communications protocols and in the improvement of actual hardware systems.
Fun with AEDs: Examining the Effects of a Gamified Mobile Crowdsourcing Application

Crowdsourcing applications enable mass collaboration on problem solving by distributing tasks to individual users. However, the success of crowdsourcing relies on active participation of the users and hence requires an effective incentive mechanism. This study presents an application prototype which utilises mobile crowdsourcing for a nationwide automated external defibrillator (AED) database and explores gamification as the sole incentive strategy on it. Based on motivational needs identified in selfdetermination theory (SDT), we conducted a survey study of 160 participants by examining the effects of the gamified mobile crowdsourcing application against a non-gamified version. Results of regression analyses reveal that the gamification influenced how motivational factors, including perceived value, competence and relatedness, drove users' usage intention of the application.
DroidLight: Lightweight Anomaly-based Intrusion Detection System for Smartphone Devices

Smartphone malware attacks are increasing alongside the growth of smartphone applications in the market. Researchers have proposed techniques to detect malware attacks using various approaches, which broadly include signature and anomaly-based intrusion detection systems (IDSs). Anomaly-based IDSs usually require training machine learning models with datasets collected from running both benign and malware applications. This may result in low detection accuracy when detecting zero-day malwares, i.e. those not previously seen or recorded. In this paper, we propose DroidLight, a lightweight IDS which can detect zero-day malware efficiently and effectively. We designed an algorithm for DroidLight that is based on one class classification and probability distribution analysis. For each smartphone application, the classification model learns its normal CPU utilisation and network traffic pattern. The model flags an intrusion alert if there is any significant deviation from the normal pattern. By deploying three self-developed malwares we performed realistic evaluation of DroidLight, i.e. the evaluation was performed on a real device while a real user was interacting with it. Evaluation results demonstrate that DroidLight can detect smartphone malwares with accuracy ranging from 93.3% to 100% while imposing only 1.5% total overhead on device resources.
CORMORANT: On Implementing Risk-Aware Multi-Modal Biometric Cross-Device Authentication For Android

This paper presents the design and open source implementation of Cormorant, an Android authentication framework able to increase usability and security of mobile authentication. It uses transparent behavioral and physiological biometrics like gait, face, voice, and keystrokes dynamics to continuously evaluate the user's identity without explicit interaction. Using signals like location, time of day, and nearby devices to assess the risk of unauthorized access, the required level of confidence in the user's identity is dynamically adjusted. Authentication results are shared securely, end-to-end encrypted using the Signal messaging protocol, with trusted devices to facilitate cross-device authentication for co-located devices, detected using Bluetooth low energy beacons. Cormorant is able to reduce the authentication overhead by up to 97% compared to conventional knowledge-based authentication whilst increasing security at the same time. We share our perspective on some of the successes and shortcomings we encountered implementing and evaluating Cormorant to hope to inform others working on similar projects.
MAADS: Mixed-Methods Approach for the Analysis of Debugging Sequences of Beginner Programmers

be especially problematic for beginners. While there has been some work trying to understand the difficulties that beginners face with debugging, investigating common mistakes or specific error types they struggle with, there is little work that focuses on in-depth analysis of how novice programmers approach debugging, and how it changes over time. In this paper, we present MAADS (Mixed-Methods Approach for the Analysis of Debugging Sequences), a scalable and generalizable approach that combines quantitative and qualitative methods by using a state/action representation and visualization to gain knowledge about the debugging process through a step by step analysis. To demonstrate the utility of MAADS, we analyzed the debugging processes of middle school students who developed code within May's Journey, a game designed to teach basic programming principles. The approach showed great utility in identifying differences in students' debugging techniques and learning paths.
Shallow Security: on the Creation of Adversarial Variants to Evade Machine Learning-Based Malware Detectors

The use of Machine Learning (ML) techniques for malware detection has been a trend in the last two decades. More recently, researchers started to investigate adversarial approaches to bypass these ML-based malware detectors. Adversarial attacks became so popular that a large Internet company has launched a public challenge to encourage researchers to bypass their (three) ML-based static malware detectors. Our research group teamed to participate in this challenge in August/2019, accomplishing the bypass of all 150 tests proposed by the company. To do so, we implemented an automatic exploitation method which moves the original malware binary sections to resources and includes new chunks of data to it to create adversarial samples that not only bypassed their ML detectors, but also real AV engines as well (with a lower detection rate than the original samples). In this paper, we detail our methodological approach to overcome the challenge and report our findings. With these results, we expect to contribute with the community and provide better understanding on ML-based detectors weaknesses. We also pinpoint future research directions toward the development of more robust malware detectors against adversarial machine learning.
Automated test generation for OpenCL kernels using fuzzing and constraint solving

Graphics Processing Units (GPUs) are massively parallel processors offering performance acceleration and energy efficiency unmatched by current processors (CPUs) in computers. These advantages along with recent advances in the programmability of GPUs have made them attractive for general-purpose computations. Despite the advances in programmability, GPU kernels are hard to code and analyse due to the high complexity of memory sharing patterns, striding patterns for memory accesses, implicit synchronisation, and combinatorial explosion of thread interleavings. Existing few techniques for testing GPU kernels use symbolic execution for test generation that incur a high overhead, have limited scalability and do not handle all data types. We propose a test generation technique for OpenCL kernels that combines mutation-based fuzzing and selective constraint solving with the goal of being fast, effective and scalable. Fuzz testing for GPU kernels has not been explored previously. Our approach for fuzz testing randomly mutates input kernel argument values with the goal of increasing branch coverage. When fuzz testing is unable to increase branch coverage with random mutations, we gather path constraints for uncovered branch conditions and invoke the Z3 constraint solver to generate tests for them. In addition to the test generator, we also present a schedule amplifier that simulates multiple work-group schedules, with which to execute each of the generated tests. The schedule amplifier is designed to help uncover inter work-group data races. We evaluate the effectiveness of the generated tests and schedule amplifier using 217 kernels from open source projects and industry standard benchmark suites measuring branch coverage and fault finding. We find our test generation technique achieves close to 100% coverage and mutation score for majority of the kernels. Overhead incurred in test generation is small (average of 0.8 seconds). We also confirmed our technique scales easily to large kernels, and can support all OpenCL data types, including complex data structures.
Trajectory of 10-Year Neurocognitive Functioning After Moderate–Severe Traumatic Brain Injury: Early Associations and Clinical Application

Abstract Objective: This study aimed to explore the 10-year trajectories of neurocognitive domains after moderate–severe traumatic brain injury (TBI), to identify factors related to long-term neurocognitive functioning, and to investigate whether performance remained stable or changed over time. Method: Seventy-nine patients with moderate–severe TBI between the ages of 16 and 55 years were assessed at 3 months, 1, 5, and 10 years postinjury using neuropsychological tests and functional outcomes. Three hierarchical linear models were used to investigate the relationships of domain-specific neurocognitive trajectories (Memory, Executive function, and Reasoning) with injury severity, demographics, functional outcome at 3 months (Glasgow Outcome Scale-Extended) and emotional distress at 1 year (Symptom Checklist 90-Revised). Results: Education, injury severity measures, functional outcome, and emotional distress were significantly associated with both Memory and Executive function. Education and emotional distress were related to Reasoning. The interaction effects between time and these predictors in predicting neurocognitive trajectories were nonsignificant. Among patients with data at 1 and 10 year follow-ups (n = 47), 94–96% exhibited stable scores on Executive function and Reasoning tasks, and 83% demonstrated stable scores on Memory tasks. Significant memory decline was presented in 11% of patients. Conclusions: The findings highlight the differential contribution of variables in their relationships with long-term neurocognitive functioning after moderate–severe TBI. Injury severity was important for Memory outcomes, whereas emotional distress influenced all neurocognitive domains. Reasoning (intellectual) abilities were relatively robust after TBI. While the majority of patients appeared to be cognitively stable beyond the first year, a small subset demonstrated a significant memory decline over time.
Characterization of network switching in disorder of consciousness at multiple time scales

Objective. Recent works have shown that flexible information processing is closely related to the reconfiguration of human brain networks underlying brain functions. However, the role of network switching for consciousness is poorly explored and whether such transition can indicate the behavioral performance of patients with disorders of consciousness (DOC) remains unknown. Here, we investigate the relationship between the switching of brain networks (states) over time and the consciousness levels. Approach. By applying multilayer network methods, we calculated time-resolved functional connectivity from source-level EEG data in different frequency bands. At various time scales, we explored how the human brain changes its community structure and traverses across defined network states (integrated and segregated states) in subjects with different consciousness levels. Main results. Network switching in the human brain is decreased with increasing time scale opposite to that in random systems. Transitions of community assignment (denoted by flexibility) are negatively correlated with the consciousness levels (particularly in the alpha band) at short time scales. At long time scales, the opposite trend is found. Compared to healthy controls, patients show a new balance between dynamic segregation and integration, with decreased proportion and mean duration of segregated state (contrary to those of integrated state) at small scales. Significance. These findings may contribute to the development of EEG-based network analysis and shed new light on the pathological mechanisms of neurological disorders like DOC.
Usefulness of Hybrid PET/MRI in Clinical Evaluation of Head and Neck Cancer Patients

(1) Background: The novel hybrid of positron emission tomography/magnetic resonance (PET/MR) examination has been introduced to clinical practice. The aim of our study was to evaluate PET/MR usefulness in preoperative staging of head and neck cancer (HNC) patients (pts); (2) Methods: Thirty eight pts underwent both computed tomography (CT) and PET/MR examination, of whom 21 pts underwent surgical treatment as first-line therapy and were further included in the present study. Postsurgical tissue material was subjected to routine histopathological (HP) examination with additional evaluation of p16, human papillomavirus (HPV), Epstein-Barr virus (EBV) and Ki67 status. Agreement of clinical and pathological T staging, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) of CT and PET/MR in metastatic lymph nodes detection were defined. The verification of dependences between standardized uptake value (SUV value), tumor geometrical parameters, number of metastatic lymph nodes in PET/MR and CT, biochemical parameters, Ki67 index, p16, HPV and EBV status was made with statistical analysis of obtained results; (3) Results: PET/MR is characterized by better agreement in T staging, higher specificity, sensitivity, PPV and NPV of lymph nodes evaluation than CT imaging. Significant correlations were observed between SUVmax and maximal tumor diameter from PET/MR, between SUVmean and CT tumor volume, PET/MR tumor volume, maximal tumor diameter assessed in PET/MR. Other correlations were weak and insignificant; (4) Conclusions: Hybrid PET/MR imaging is useful in preoperative staging of HNC. Further studies are needed.
Osteoarthritis and inflammation: a serious disease with overlapping phenotypic patterns

ABSTRACT Globally, osteoarthritis (OA) is the most prevalent arthritic condition in those aged over 60 years. OA has a high impact on patient disability and is associated with a significant economic burden. Pain is the most common first sign of disease and the leading cause of disability. Data demonstrating the increasing global prevalence of OA, together with a greater understanding of the burden of the disease, have led to a reassessment of the seriousness of OA and calls for the designation of OA as a serious disease in line with the diseases impact on comorbidity, disability, and mortality. While OA was traditionally seen as a prototypical ‘wear and tear’ disease, it is now more accurately thought of as a disease of the whole joint involving cartilage together with subchondral bone and synovium. As more has become known of the pathophysiology of OA, it has become increasingly common for it to be described using a number of overlapping phenotypes. Patients with OA will likely experience multiple phenotypes during their disease. This review focuses on what we feel are three key phenotypes: post-trauma, metabolic, and aging. A greater understanding of OA phenotypes, particularly at the early stages of disease, may be necessary to improve treatment outcomes. In the future, non-pharmacological and pharmacological treatments could be tailored to patients based on the key features of their phenotype and disease pathway.