Miracle-dz/UniFAR-FTUs huggingface dataset:
An empirical investigation of missing data handling in cloud node failure prediction

Cloud computing systems have become increasingly popular in recent years. A typical cloud system utilizes millions of computing nodes as the basic infrastructure. Node failure has been identified as one of the most prevalent causes of cloud system downtime. To improve the reliability of cloud systems, many previous studies collected monitoring metrics from nodes and built models to predict node failures before the failures happen. However, based on our experience with large-scale real-world cloud systems in Microsoft, we find that the task of predicting node failure is severely hampered by missing data. There is a large amount of missing data, and the online latest data utilized for prediction is even worse. As a result, the real-time performance of the node prediction model is limited. In this paper, we first characterize the missing data problem for node failure prediction. Then, we evaluate several existing data interpolation approaches, and find that node dimension interpolation approaches outperform time dimension ones and deep learning based interpolation is the best for early prediction. Our findings can help academics and engineers address the missing data problem in cloud node failure prediction and other data-driven software engineering scenarios.
Design and rapid prototyping of printed graphene electrochemical biosensors for sensitive monitoring of pesticide levels for agricultural use

While the use of pesticides (herbicides and insecticides) are critically important to meet the current and future food demands (increases crop yield by up to 40%), their overuse has shown long-term detrimental impacts on the environment from polluting watersheds used for drinking water to eutrophic “dead zones”. Current pesticide soil measurement methods (chromatography) are costly, require trained technicians, and take days to analyze; thus, farmers are taking an “over-application approach” which is pollution the environment and waterways. A disposable pesticide soil sensor would provide farmers the opportunity of precisely regulating the application of pesticides in an independent and economical fashion. Electrochemical biosensors provide a unique ability to quickly detect analytes with a handheld device and low-cost sensors; however, the detection limit and sensitivity of these biosensors are inadequate for current applications. This dissertation addresses this issue with the following focus in mind: 1) Increasing the enzymatic efficiency of organophosphate hydrolase by strategically functionalizing to nanomaterials [e.g., 17-fold increase in Vmax when functionalized to gold nanoparticles vs free enzyme]. 2) Develop a low-cost, rapid, and high-resolution manufacturing method to pattern solution-phase graphene [i.e., inkjet maskless lithography (IML), line resolution ~20 μm, sheet resistance ~50 Ω/sq]. 3) Enhance the electroactive surface area by nano/microstructuring the graphene surface [3D petal-like graphene morphology] using laser annealing. 4) Increase the electrochemical surface area by incorporating macro and micro pores [2.2 times higher sensitivity with the inclusion of macropores] in the graphene surface. This work demonstrates the manufacturing of simple, low-cost electrochemical biosensors which is suitable for rapid in-field detection of organophosphates. The fabricated
FABRICATION OF NANOFIBRES BY ELECTROSPINNING USING KERATIN FROM WASTE CHICKEN FEATHERS, PVA AND AgNPs

Objective: To prepare and characterise keratin from chicken feathers (CF), collected from the slaughter house, and to blend with poly vinly alcohol (PVA) and biosynthesised silver nanoparticles (AgNPs) and to convert into nanofibers by an elctrospinning process. Methods: The extraction of keratin from chicken feathers was done by sodium m-bisulphite. The solution was subjected to ammonium sulphate precipitation to separate keratin. The nanoparticles was synthesised using tridax procumbens. The isolated keratin and PVA was mixed in the ration 0f 50:50 with 1 ml of biosynthesised nanoparticles was blended and made into nanofibres by electrospinning technique. Results: The precipitated protein was analysed using FT-IR analysis confirming the presence of β-keratin in the sample isolated from chicken feathers and the concentration of keratin was estimated to be 1.85 g/ml. PVA solution with 4% w/v had the best film forming ability. The solution containing keratin, PVA and silver nanoparticles was prepared in various proportions. These solutions when subjected to electrospinning, fibrous network was observed in 50:50 (PVA: Keratin) ratio with 1 ml of synthesised silver nanoparticle solution. Hydrogen bonding between keratin and PVA indicated in the XRD analysis showed successful film forming of the nanofiber, the DSC analysis also showed similar results as the obtained peak was at 214 °C which is in between the characteristic heat degradation temperature of both the keratin and PVA. The thermogravimetric analysis (TGA) showed high thermal stability as the complete degradation of the nanofiber was observed at 420 °C. Incorporation of metal nanoparticles by herbal approach using tridax procumbens in the nanofibers provided the antimicrobial properties. The nanofibres obtained by electrospinning process appeared stable and continous for solutions containing no more than 50% wt of CF. The average diameter of the nanofibres increased as the CF content increased. Conclusion: Keratin isolated from the waste chicken feathers impregnated with biosyntheised silver nanoparticles using tridax procumbens and PVA can be converted into nanofibers by electrospinning process. Thus, the biocomposite nano fibers are shown as a novel eco-friendly material that must be adequately applied in the development of green composites for the biomedical applications such as wound dressings.
Experimental validation of steel slag as thermal energy storage material in a 400 kWht prototype

Iñigo Ortega-Fernández, Yang Wang, Mikel Durán, Erika Garitaonandia, Lucía Unamunzaga, Daniel Bielsa and Elena Palomo Ph.D, Associate Engineer; Ph.D, Postdoctoral Researcher; Undergraduate Student; Dipl. -Eng., Technology Transfer Coordinator; Ph.D, Scientific Director. Address: CIC Energigune, Albert Einstein 48, 01510 Miñano (Álava), Spain. Phone: +34 945297108. E-mail: iortega@cicenergigune.com MSc. , Senior Researcher; MSc. , Environmental Group Coordinator. Address: IK4-Azterlan, Aliendalde Auzunea 6, 48200 Durango (Vizcaya), Spain.
Directed Self-Assembly using PS-b-PMMA Block Copolymers

project. A pattern of dense contact pillars of 16.8nm in size was achieved. To best utilize this process, a tone reversal process should be used to form contact holes. Future work for this project could include matching the resist/BCP film heights to assist in BCP formation, and the use of a randomly polymerized PS undercoat in place of the surface brush. Smaller trench CDs could also be explored to attempt to force the DSA pattern into a lamellar morphology. Thermal annealing could also be refined and explored as an alternative to the SVA used in this project.
Measurement of Impedance Values of Different Erythrocyte Suspensions

Aggregation give rise to erythrocyte surfaces parallel to each other and clustering in a special way. Erythrocyte aggregation occurs only in the suspension phase in the presence of a certain size and structure of macromolecules. This clustering does not occur when the same cells suspended in simple salt solutions. Factors affecting red blood cell (RBC) aggregation can be divided into intrinsic and extrinsic factors: (1) Extrinsic factors include levels of plasma proteins [exempli gratia (e.g.), fibrinogen, macroglobulins], hematocrit and shear rate; (2) Intrinsic factors include RBC shape, deformability and membrane surface properties (1-3)
ARCHIDNA: A GENREATIVE SYSTEM FOR SHAPE CONFIGURATONS

his thesis concerns a new generation process for shape configurations using a set of operations. The approach derives from analyzing a particular design style and programming them into a computer. It discusses how generative CAD software can be developed that embodies a style and how this software can serve in the architectural design process as a computational design tool. The thesis proposes a prototype software system, ArchiDNA, to demonstrate the use of operations to generate drawings in a specific design style. ArchiDNA employs a set of operations to produce design drawings of shape configuration in Peter Eisenman's style for the Biocentrum building plan in Frankfurt, Germany. The principles of form generation are defined as a set of operations. ArchiDNA generates 2D and 3D drawings similar to Eisenmans plan and model for the Biocentrum building. The extension system of ArchiDNA, called ArchiDNA++, supports designers in defining operations and generating shape configurations. Designers can enter and edit their own shapes for the generation process and also control the parameters and attributes for shape operations. Thus, designers can manage the generation process and explore using ArchiDNA++, to generate shape configurations that are consistent with their own drawing style.
RESTInfer: automated inferring parameter constraints from natural language RESTful API descriptions

RESTful APIs have been applied to provide cloud services by various notable companies. The quality assurance of RESTful API is critical. Several automatic RESTful API testing techniques have been proposed to tame this issue. By analyzing crashes caused by each test case, developers can find potential bugs in cloud services. However, it is difficult for automated tools to generate feasible parameters under complicating constraints randomly. Fortunately, RESTful API descriptions can be used to infer possible parameter constraints. Given parameter constraints, automated tools can further improve the efficiency of testing. In this paper, we propose RESTInfer, a two-phase approach to infer parameter constraints by natural language processing. The preliminary evaluation result shows that RESTInfer can achieve a high code coverage and bug finding.
Iterating the minimizing Delta debugging algorithm

Probably the most well-known solution to automated test case minimization is the minimizing Delta Debugging algorithm (DDMIN). It is widely used because it “just works” on any kind of input. In this paper, we focus on the fixed-point iteration of DDMIN (named DDMIN*), more specifically whether it can improve on the result of the original algorithm. We present a carefully crafted example where the output of DDMIN could be reduced further, and iterating the algorithm finds a new, smaller local optimum. Then, we evaluate the idea on a publicly available test suite. We have found that the output of DDMIN* was usually smaller than the output of DDMIN. Using characters as units of reduction, the output became smaller by 67.94% on average, and in the best case, fixed-point iteration could improve as much as 89.68% on the output size of the original algorithm.
Experimental and numerical study of wavy mechanical face seals operating under pressure inversions

This paper investigates the impact of the face waviness and pressure inversions on the leakage and on the outer fluid entry of mechanical face seals using a numerical model and an experimental setup. The numerical model couples a transient Reynolds equation, an analytical contact model, a force balance solver, and a solver for the thermo-mechanical deformations. The experimental tests on a face seal with low waviness and on a face seal with high waviness provide leakage and outer fluid entry data, which are reproduced by the model. Contrary to the face seal with low waviness, the face seal with high waviness has poor performance and the pressure inversions increase significantly the ingression of outer fluid. The parametric study shows a decrease of leakage with increasing spring force, and an increase of leakage and outer fluid entry with increasing values of waviness amplitude. The higher leakage observed for wavy seals is shown to be due to the higher average film thickness, and to some extent due to the mechanisms associated with waviness: hydrodynamic pressure generation, film squeeze and stretching.
Security code smells in apps: are we getting better?

Users increasingly rely on mobile apps for everyday tasks, including security- and privacy-sensitive tasks such as online banking, e-health, and e-government. Additionally, a wealth of sensors captures the movements and habits of the users for fitness tracking and convenience. Despite legal regulations imposing requirements and limits on the processing of privacy-sensitive data, users must still trust the app developers to apply suffcient protections. In this paper, we investigate the state of security in Android apps and how security-related code smells have evolved since the introduction of the Android operating system. With an analysis of 300 apps per year over 12 years between 2010 and 2021 from the Google Play Store, we find that the number of code scanner findings per thousand lines of code decreases over time. Still, this development is offset by the increase in code size. Apps have more and more findings, suggesting that the overall security level decreases. This trend is driven by flaws in the use of cryptography, insecure compiler flags, insecure uses of WebView components, and insecure uses of language features such as reflection. Based on our data, we argue for stricter controls on apps before admission to the store.
Towards a neuroscience-based theory of personality: within-subjects dissociation of human brain activity during pursuit and goal conflict

Abstract As demonstrated by neuroimaging data, the human brain contains systems that control responses to threat. The revised Reinforcement Sensitivity Theory of personality predicts that individual differences in the reactivity of these brain systems produce anxiety and fear-related personality traits. Here we discuss some of the challenges in testing this theory and, as an example, present a pilot study that aimed to dissociate brain activity during pursuit by threat and goal conflict. We did this by translating the Mouse Defense Test Battery for human fMRI use. In this version, dubbed the Joystick Operated Runway Task (JORT), we repeatedly exposed 24 participants to pursuit and goal conflict, with and without threat of electric shock. The runway design of JORT allowed the effect of threat distance on brain activation to be evaluated independently of context. Goal conflict plus threat of electric shock caused deactivation in a network of brain areas that included the fusiform and middle temporal gyri, as well as the default mode network core, including medial frontal regions, precuneus and posterior cingulate gyrus, and laterally the inferior parietal and angular gyri. Consistent with earlier research, we also found that imminent threat activated the midbrain and that this effect was significantly stronger during the simple pursuit condition than during goal conflict. Also consistent with earlier research, we found significantly greater hippocampal activation during goal conflict than pursuit by imminent threat. In conclusion, our results contribute knowledge to theories linking anxiety disorders to altered functioning in defensive brain systems and also highlight challenges in this research domain.
You see what I want you to see: poisoning vulnerabilities in neural code search

Searching and reusing code snippets from open-source software repositories based on natural-language queries can greatly improve programming productivity.Recently, deep-learning-based approaches have become increasingly popular for code search. Despite substantial progress in training accurate models of code search, the robustness of these models has received little attention so far. In this paper, we aim to study and understand the security and robustness of code search models by answering the following question: Can we inject backdoors into deep-learning-based code search models? If so, can we detect poisoned data and remove these backdoors? This work studies and develops a series of backdoor attacks on the deep-learning-based models for code search, through data poisoning. We first show that existing models are vulnerable to data-poisoning-based backdoor attacks. We then introduce a simple yet effective attack on neural code search models by poisoning their corresponding training dataset. Moreover, we demonstrate that attacks can also influence the ranking of the code search results by adding a few specially-crafted source code files to the training corpus. We show that this type of backdoor attack is effective for several representative deep-learning-based code search systems, and can successfully manipulate the ranking list of searching results. Taking the bidirectional RNN-based code search system as an example, the normalized ranking of the target candidate can be significantly raised from top 50% to top 4.43%, given a query containing an attacker targeted word, e.g., file. To defend a model against such attack, we empirically examine an existing popular defense strategy and evaluate its performance. Our results show the explored defense strategy is not yet effective in our proposed backdoor attack for code search systems.
Cross-language Android permission specification

The Android system manages access to sensitive APIs by permission enforcement. An application (app) must declare proper permissions before invoking specific Android APIs. However, there is no official documentation providing the complete list of permission-protected APIs and the corresponding permissions to date. Researchers have spent significant efforts extracting such API protection mapping from the Android API framework, which leverages static code analysis to determine if specific permissions are required before accessing an API. Nevertheless, none of them has attempted to analyze the protection mapping in the native library (i.e., code written in C and C++), an essential component of the Android framework that handles communication with the lower-level hardware, such as cameras and sensors. While the protection mapping can be utilized to detect various security vulnerabilities in Android apps, such as permission over-privilege, imprecise mapping will lead to false results in detecting such security vulnerabilities. To fill this gap, we thereby propose to construct the protection mapping involved in the native libraries of the Android framework to present a complete and accurate specification of Android API protection. We develop a prototype system, named NatiDroid, to facilitate the cross-language static analysis and compare its performance with two state-of-the-practice tools, termed Axplorer and Arcade. We evaluate NatiDroid on more than 11,000 Android apps, including system apps from custom Android ROMs and third-party apps from the Google Play. Our NatiDroid can identify up to 464 new API-permission mappings, in contrast to the worst-case results derived from both Axplorer and Arcade, where approximately 71% apps have at least one false positive in permission over-privilege. We have disclosed all the potential vulnerabilities detected to the stakeholders.
Improved vertex finding in the MINERvA passive target region with convolutional neural networks and Deep Adversarial Neural Network

The precise understanding of the nuclear dependence of the neutrino-nucleus cross section is very important in its own right, and in turn helps with the accurate measurement of the neutrino oscillation parameters. We study these issues by examining the event kinematics and cross section ratios between different passive targets at MINERvA. There is an ongoing study of the A dependent nuclear effects in MINERvA; this measurement hinges on identification of the target nucleus, thereby demanding accurate reconstruction of the event vertex. Vertex reconstruction is usually done with tracking-based algorithms. However, the performance of this method suffers when there are tracks created by secondary interactions or decays, or when significant shower activity occurs near the vertex region. Here, we present an alternative vertex finding method in the nuclear target region using machine learning. We use an application of deep learning based on convolutional neural networks (CNNs). Additionally, we explore the use of Deep Adversarial Neural Networks (DANNs) in minimizing possible biases coming from the use of simulated data for the algorithm's training sets.
Detecting mistakes in a domain model

Domain models are a fundamental part of software engineering, and it is important for every software engineer to be taught the principles of domain modeling. Instructors play a vital role in teaching students the skills required to understand and design domain models. Instructors check models created by students for mistakes by comparing them with a correct solution. While this did not use to be an overwhelming task, this is not the case anymore nowadays due to a rapid increase in the number of students wanting to become software engineers, leading to larger class sizes. Hence, students may need to wait for a longer time to get feedback on their solutions and the feedback may be more superficial due to time constraints. In this paper, we propose a mistake detection system (MDS) that aims to automate the manual approach of checking student solutions and help save both students' and instructors' time. MDS automatically indicates the exact location and the type of the mistake to the student. At present, MDS accurately detects 83 out of 97 identified different types of mistakes that may exist in a student solution. A prototype tool verifies the feasibility of the proposed approach. When synonyms are considered by MDS, recall of 0.93 and precision of 0.79 are achieved based on the results for real student solutions. The proposed MDS takes us one step closer to automating the existing manual approach, freeing up instructor time and helping students learn domain modeling more effectively.
Towards realizing test conditions for automated vehicles

The verification and validation of Automated Driving Systems pose a challenge to the deployment of automated vehicles onto the roads. Current research considers a scenario-based approach to control the traffic situations an automated vehicle is tested in. One of the several open challenges in this field is how test conditions for the self-driving vehicle under test (VUT) can be realized. This is because the test designer probably cannot tailor the test script to the VUT's actual behaviour due to unknown knowledge of the complex system logic and therefore the VUT might not reach the situation of interest during a test. In this paper, the problem of bringing the VUT and the environment in the desired initial states for a test, respectively a scenario, is described. Furthermore, the idea of a reinforcement learning approach of controlling traffic agents in the environment of the VUT is presented and under development in the corresponding doctoral project with focus on virtual testing.