Miracle-dz/UniFAR-FTUs huggingface dataset:
An Initial Exploration of Machine Learning Techniques to Classify Source Code Comments in Real-time

Dr. Jean Mohammadi-Aragh is an assistant professor in the Department of Electrical and Computer Engineering at Mississippi State University. Dr. Mohammadi-Aragh investigates the use of digital systems to measure and support engineering education, speciﬁcally through learning analytics and the pedagogical uses of digital systems. She also investigates fundamental questions critical to improving undergraduate engineering degree pathways. She earned her Ph.D. in Engineering Education from Virginia Tech. In 2013, Dr. Mohammadi-Aragh was honored as a promising new engineering education researcher when she was selected as an ASEE Educational Research and Methods Division Apprentice Faculty. Abstract Providing real-time feedback to novice programmers is critical to their ability to learn to program. Higher enrollment in introductory computer science courses reduces the amount of time for individual student-instructor interaction. Reduced interaction time equates to a reduction in the time for and amount of instructor feedback. Building on our work involving manual classification and analysis of student source code comments, in this full paper we explore how machine learning techniques can be leveraged to provide automated feedback to students with regards to their computational thinking processes. This paper discusses the initial classification of student source code comments using supervised machine learning methods. In this phase of classification, we focus on whether a comment is sufficient or insufficient . The classification process is broken down into three steps: text processing, data exploration, and comment classification using the Multinomial Naïve Bayes Classifier and a Random Forest Classifier. We detail the text processing requirements, including how to prepare the raw student data using natural languages processing techniques such as stop word filtration, tokenization, and lemmatization. We also show how the data preparation process can affect the final classification outcome. Using Multinomial Naïve Bayes we achieved a precision rate of 82%. Using a Random Forest classifier and lemmatization we achieved a classification precision of 90%. We conclude with a description of how the current classification results can be used to provide real-time feedback to students while they are learning to program. Towards our ultimate goal of providing comprehensive real-time feedback to students, we describe future research plans, which include using unsupervised machine learning techniques to move beyond basic binary classification.
Gliricidia Leaf Meal in Broiler Chickens Diet: Effects on Performance, Carcass, and Haemato-biochemical Parameters

Aims: This study seeks to access the influence of Gliricidia leaf meal on broiler chickens’ growth performance, carcass traits, and haemato-biochemical parameters. Study Design: Completely randomised design. Place and Duration of Study: The 56-day feeding Methodology: Fresh Gliricidia sepium leaves were harvested, shade dried and milled to produce leaf meal. Three diets were formulated to contain Gliricidia sepium leaf meal (GLM) at 0 (control), 5 and 10% level. A total number of 144 day-old chicks of arbor acres were assigned to 3 dietary treatments of 3 replicates of 16 chicks per replicate in a completely randomised design experiment. Results: The total weight gain and total feed intake was lower (P<0.05) in broiler chicken fed 10% GLM inclusive diet (1890.48 g/b and 5188.90 g/b) compare to the total weight gain and total feed intake of those fed the control (2188.04 g/b and 5754.14 g/b) and 5% GLM inclusive diet (2154.31 g/b and 5579.04 g/b). The slaughter weight, dressed weight and dressed percentage of birds fed 10% GLM inclusive diet were lower (P<0.05) than those fed the control and 5% GLM inclusive diets. The liver, kidney and gizzard relative weights were higher (P<0.05) in the birds fed 10% GLM inclusive diet compared to those fed rest diets. The white blood cell count of birds fed control diet and 5% GLM inclusive diet were similar (P>0.05) but significantly (P<0.05) lower than white blood cell counts of birds fed 10% GLM inclusive diet. The cholesterol levels of birds fed GLM inclusive diets (5 and 10%) were lower (P<0.05) compare to those birds fed the control diet. Conclusion: The dietary gliricidia leaf meal at 5% inclusion level supports normal body weight gain, feed intake, carcass traits and normal health status of the broiler chickens.
Correct diagnostic conclusion in patients with chronic heart failure: a reality or a pipe dream?

The authors of this article have analyzed the problem of diagnostic conclusion unification in patients with chronic heart failure (CHF). The root of this problematic situation in which practitioners find themselves is that, despite the large number of different regulatory documents, there is no consensus on what is considered correct and what is wrong when formulating a diagnostic conclusion in a patient with CHF. The many-faced syndrome is designated differently: CHF, congestive heart failure, chronic circulatory failure. There are difficulties in determining the stage of CHF in patients receiving optimal drug therapy or in those who are in a state of compensation after a successful surgical correction. When assessing the functional status in a patient with CHF, a distinct subjectivity should be taken into account in determining which limited physical activity is slight or, conversely, marked, as well as what kind of physical exertion is normal for the patient. This subjectivity naturally leads to low reproducibility of the assessment results of the CHF functional class in the same patient by different doctors. CHF should also be classified according to the value of a left ventricular ejection fraction. The diagnosis should also take into account the state characteristics of a diastolic function of the left ventricle (especially in patients with CHF and preserved left ventricular ejection fraction). The authors give examples of diagnostic conclusions, including cases of comorbid pathology.
Simulation and Analysis of Random

Random fields are the d-dimensional analogues of the one-dimensional stochastic processes; they are used to model spatial data as observed in environmental , atmospheric, and geological sciences. They are traditionally needed in mining and exploration to model ore deposits, oil reservoirs, and related structures. The contributed package RandomFields allows for the simulation of Gaussian random fields defined on Euclidean spaces up to dimension 3. It includes some geostatistical tools and algorithms for the simulation of extreme-value random fields. In the following two sections we give an example of an application, and a summary of the features of RandomFields. A brief geostatistical analysis To demonstrate key features we consider soil moisture data collected by the Soil Physics Group at the University of Bayreuth (see Figure 1), and perform a simple geostatistical analysis.
Thermal modeling of wide bandgap semiconductor devices for high frequency power converters

The emergence of wide bandgap semiconductors has led to development of new generation semiconductor switches that are highly efficient and scalable. To exploit the advantages of GaNFETs in power converters, in terms of reduction in the size of heat sinks and filters, a thorough understanding of the thermal behavior of the device is essential. This paper aims to establish a thermal model for wideband gap semiconductor GaNFETs commercially available, which will enable power electronic designers to obtain the thermal characteristics of the device more effectively. The model parameters is obtained from the manufacturer’s data sheet by adopting an exponential curve fitting technique and the thermal model is validated using PSPICE simulations. The model was developed based on the parametric equivalence that exists between the thermal and electrical components, such that it responds for transient thermal stresses. A suitable power profile has been generated to evaluate the GaNFET model under different power dissipation scenarios. The results were compared with a Silicon MOSFETs to further highlight the advantages of the GaN devices. The proposed modeling approach can be extended for other GaN devices and can provide a platform for the thermal study and heat sink optimization.
Parents’ Experience in Coping with Sexuality Matters in Adolescents with Autism Spectrum Disorders

Parents are often challenged with the personal development of adolescents with autism especially in dealing with sexuality problems. The study explores the parents’ caring experience of adolescents with autism. The research focusing on the adjustment to the existing condition of the adolescents, especially in the transformation of puberty, and how to communicate about the sexuality matters. This research employed an exploratory, qualitative study. Participants in this study include three parents who predominantly support the adolescents boy with autism. An interview was applied in this research to collect the data. The data was analyzed using Braun and Clarke's thematic analysis. The result showed two major themes namely parent's experiences regarding sexual behavior in adolescents with ASD and strategies to cope with sexuality matters in adolescents with ASD. Some parents experience a challenging times when signs of puberty appear in their children. Parents state that there are behavioral changes in their children, such as the emergence of masturbation in boys. Parents coped this behavior by providing an understanding of sexuality education to their adolescents so that they can behave adaptively when arousal occurs.
Deriving a realistic workload model to simulate high-volume financial data feeds for performance benchmarking

Processing financial market data at scale and in real-time poses a set of unique challenges to event-driven architectures due to the volume, variety, velocity, and veracity of the enclosed information on top of other constraints. Reproducible stress tests at scale using configurable benchmarks are key to building and tuning suitable processing systems. Available benchmarks, however, lack realistic and configurable workload models for market data scenarios. In previous work we already addressed this gap by describing the specific challenges of processing financial data at scale and by introducing a modular open-source benchmarking framework. This paper makes two contributions to the ongoing challenge of building realistic benchmarks for the financial data processing domain by outlining: (a) a detailed statistical analysis of real-world financial market data feeds processed on a global scale by Infront Financial Technology GmbH; and (b) a simple workload model built on this analysis to simulate high-volume market data feeds with their distinctive characteristics to be used in benchmarks. We evaluate our model using the DEBS 2022 Grand Challenge data set Trading Data.
PermDroid: automatically testing permission-related behaviour of Android applications

The Android runtime permission model allows users to grant and revoke permissions at runtime. To verify the robustness of apps, developers have to test the apps repeatedly under a wide range of permission combinations, which is time-consuming and unsuited for regression testing. Existing app testing techniques are of limited help in this context, as they seldom consider different permission combinations explicitly. To address this issue, we present PermDroid to automatically test the permission-related behaviour of apps with permissions granted/revoked dynamically. PermDroid first statically constructs a state transition graph (STG) for the app; it then utilizes the STG for the permission-directed exploration to test permission-related behaviour only under the combinations of the relevant permissions. The experimental results on 50 real-world Android apps demonstrate the effectiveness and efficiency of PermDroid: the average permission-related API invocation coverage achieves 72.38% in 10 minutes, and seven permission-related bugs are uncovered, six of which are not detected by the competitors.
A large-scale study of usability criteria addressed by static analysis tools

Static analysis tools support developers in detecting potential coding issues, such as bugs or vulnerabilities. Research on static analysis emphasizes its technical challenges but also mentions severe usability shortcomings. These shortcomings hinder the adoption of static analysis tools, and in some cases, user dissatisfaction even leads to tool abandonment. To comprehensively assess the current state of the art, this paper presents the first systematic usability evaluation in a wide range of static analysis tools. We derived a set of 36 relevant criteria from the scientific literature and gathered a collection of 46 static analysis tools complying with our inclusion and exclusion criteria - a representative set of mainly non-proprietary tools. Then, we evaluated how well these tools fulfill the aforementioned criteria. The evaluation shows that more than half of the considered tools offer poor warning messages, while about three-quarters of the tools provide hardly any fix support. Furthermore, the integration of user knowledge is strongly neglected, which could be used for improved handling of false positives and tuning the results for the corresponding developer. Finally, issues regarding workflow integration and specialized user interfaces are proved further. These findings should prove useful in guiding and focusing further research and development in the area of user experience for static code analyses.
Path-sensitive code embedding via contrastive learning for software vulnerability detection

Machine learning and its promising branch deep learning have shown success in a wide range of application domains. Recently, much effort has been expended on applying deep learning techniques (e.g., graph neural networks) to static vulnerability detection as an alternative to conventional bug detection methods. To obtain the structural information of code, current learning approaches typically abstract a program in the form of graphs (e.g., data-flow graphs, abstract syntax trees), and then train an underlying classification model based on the (sub)graphs of safe and vulnerable code fragments for vulnerability prediction. However, these models are still insufficient for precise bug detection, because the objective of these models is to produce classification results rather than comprehending the semantics of vulnerabilities, e.g., pinpoint bug triggering paths, which are essential for static bug detection. This paper presents ContraFlow, a selective yet precise contrastive value-flow embedding approach to statically detect software vulnerabilities. The novelty of ContraFlow lies in selecting and preserving feasible value-flow (aka program dependence) paths through a pretrained path embedding model using self-supervised contrastive learning, thus significantly reducing the amount of labeled data required for training expensive downstream models for path-based vulnerability detection. We evaluated ContraFlow using 288 real-world projects by comparing eight recent learning-based approaches. ContraFlow outperforms these eight baselines by up to 334.1%, 317.9%, 58.3% for informedness, markedness and F1 Score, and achieves up to 450.0%, 192.3%, 450.0% improvement for mean statement recall, mean statement precision and mean IoU respectively in terms of locating buggy statements.
Travel light: state shedding for efficient operator migration

Operator migration is a crucial concept to adapt event processing systems to dynamic changes. When the placement of a stateful operator changes, the operator state must be migrated to the new host. However, operator state size and time constraints can make it impossible to migrate the operator without severe Quality of Service (QoS) degradation. As a relief, we propose to perform state shedding in such a situation. The core idea of state shedding is to partition the operator state, assign a utility to each partial state, and use the utility and size of each partial state to identify the most useful partial states that can be migrated in a given time frame. Thus, state shedding can maintain a substantially higher QoS with a lower impact on query results than state-of-the-art solutions targeting consistent state at the old and new host. In this paper, we define this novel approach and in a simulation environment evaluate state shedding in migration scenarios with pattern-matching queries.
Infrared dim and small target detection based on total variation and multiple noise constraints modeling

To improve the ability of infrared dim small target detection algorithm based on traditional infrared patch-image (IPI) model, a new detection model based on total variation and multiple noise constraints is proposed. We firstly transform the original infrared image into an IPI, and then the total variational regularization constrains the background patch-image in order to reduce the noise on the target image. In the meantime, the edge information of the image can be preserved to avoid excessive smoothness of the restored background image. Additionally, considering the lack of noise distribution in the patch-image, the combined and norm are introduced to describe the noise more accurately. The experimental results show that the proposed method can suppress the background clutter better and improve detection performance effectively.
Almost correct invariants: synthesizing inductive invariants by fuzzing proofs

Real-life programs contain multiple operations whose semantics are unavailable to verification engines, like third-party library calls, inline assembly and SIMD instructions, special compiler-provided primitives, and queries to uninterpretable machine learning models. Even with the exceptional success story of program verification, synthesis of inductive invariants for such "open" programs has remained a challenge. Currently, this problem is handled by manually "closing" the program---by providing hand-written stubs that attempt to capture the behavior of the unmodelled operations; writing stubs is not only difficult and tedious, but the stubs are often incorrect---raising serious questions on the whole endeavor. In this work, we propose Almost Correct Invariants as an automated strategy for synthesizing inductive invariants for such "open" programs. We adopt an active learning strategy where a data-driven learner proposes candidate invariants. In deviation from prior work that attempt to verify invariants, we attempt to falsify the invariants: we reduce the falsification problem to a set of reachability checks on non-deterministic programs; we ride on the success of modern fuzzers to answer these reachability queries. Our tool, Achar, automatically synthesizes inductive invariants that are sufficient to prove the correctness of the target programs. We compare Achar with a state-of-the-art invariant synthesis tool that employs theorem proving on formulae built over the program source. Though Achar is without strong soundness guarantees, our experiments show that even when we provide almost no access to the program source, Achar outperforms the state-of-the-art invariant generator that has complete access to the source. We also evaluate Achar on programs that current invariant synthesis engines cannot handle---programs that invoke external library calls, inline assembly, and queries to convolution neural networks; Achar successfully infers the necessary inductive invariants within a reasonable time.
Toward reducing cross-shard transaction overhead in sharded blockchains

Sharding is a promising approach to high-performance blockchains and has been extensively explored in academia recently. However, sharding also introduces cross-shard transactions, which require expensive inter-shard coordination to ensure state consistency. Such transactions significantly limit the performance of sharded blockchains. To reduce cross-shard transactions in UTXO-based sharded blockchains, we propose Rooted Graph Placement, which identifies the most appropriate shard for a transaction based on the interaction between the transaction and historical transactions. In conjunction with the placement algorithm, we also devise two techniques to lessen the system performance impact of the remaining cross-shard transactions. One technique parallelizes dependent transaction verification with the atomic commit protocol, and the other consolidates the atomic commit protocol. Combining all the three techniques, we can improve the maximum system throughput by 118% when compared with a state-of-the-art transaction placement algorithm.
Development of Evaluation Tools Using Quizizz Application In Indonesian Lessons

Evaluation is one of the activities carried out to determine the level of success of a program, including educational programs. Technological developments in the use of evaluation tools have had a good impact during the COVID-19 pandemic. An evaluation tool that is able to generate student motivation is very much needed in elementary schools.The purpose of this study was to determine how the feasibility of the evaluation tool using the Quizizz application and how students responded to the evaluation tool to be developed, the research method used was the Research and Development (R&D) method by adopting the development model according to Sugiyono with the stages of Potential and Problems, Gathering Information , product design, Design Validation, Design Improvement, Product Trial, Product Revision, Usage Trial, Product Revision, and Mass Production. The research instrument used in the form of tests and questionnaires. The student's assessment response to the evaluation tool using the quizizz application got a percentage of 84.67%, very interesting information. It was concluded that the evaluation tool in the form of an online test using the quizizz application on Indonesian subjects was feasible to use.
The Facilities and Infrastructure Management of Adiwiyata School at MTs Negeri Sleman

The Adiwiyata program is implemented by the Government of Indonesia to strengthen the character of the next generation to care about the environment. One component of the Adiwiyata program is the management of facilities and infrastructure that prioritizes environmental friendliness. Some schools are still experiencing problems in managing facilities and infrastructure. This research aims to describe deeply how the management of facilities and infrastructure in Adiwiyata School of MTs Negeri Sleman. This research used qualitative approach with descriptive research type involving the principal, the person in charge of the Adiwiyata program and the deputy head of the facilities and infrastructure division. Data collection techniques were in the form of observation, interviews, and document analysis. Then, the collected data was analyzed using Miles and Huberman in 2014 qualitative analysis. The results of this research indicated that the management of facilities and infrastructure was in accordance with the implementation of the Adiwiyata program, namely using the mechanisms of planning, supplying, inventorying, maintaining, and eliminating. Furthermore, this finding brought practical benefits because it could contribute to the current literature on environmental education in Indonesia, especially in presenting the practice of problems and challenges faced by schools in managing facilities and infrastructure to support the Adiwiyata program.