Miracle-dz/UniFAR-FTUs huggingface dataset:
Logging statements' prediction based on source code clones

Log files are widely used to record runtime information of software systems, such as the time-stamp of an event, the unique ID of the source of the log, and a part of the state of task execution. The rich information of logs enables system operators to monitor the runtime behaviors of their systems and further track down system problems in production settings. Although logs are useful, there exists a trade-off between their benefit and cost, and it is a crucial problem to optimize the location and content of log messages in the source code, i.e., "where and what to log? " Prior research has analyzed logging statements in the source code and proposed ways to predict and suggest the location of log statements in order to partially automate log statement addition to the source code. However, there are gaps and unsolved problems in the literature to fully automate the logging process. Thus, in this research, we perform an experimental study on open-source Java projects and apply code-clone detection methods for log statements' prediction. Our work demonstrates the feasibility of logging automation by predicting the location of a log point in a code snippet based on the existence of a logging statement in its corresponding code clone pair. We propose a Log-Aware Code-Clone Detector (LACC) which achieves a higher accuracy of log prediction when compared to state-of-the-art general-purpose clone detectors. Our analysis shows that 98% of clone snippets match in their logging behavior, and LACC can predict the location of logging statements by the accuracy of 90+% for Apache Java projects.
On the nature of cooperative scheduling in active objects

Active objects interact via asynchronous messages which specify method invocations. In contrast to the run to completion mode of method execution, mechanisms for suspending the execution of a method allow an active object to schedule cooperatively its methods in a co-routine manner. In this paper, we show how cooperative scheduling can be reduced to a run to completion mode of execution. We do so by a formal translation using a guarded command language for describing the execution of method bodies.
Actions and impediments for technical debt prevention: results from a global family of industrial surveys

Background: Preventing the occurrence of technical debt (TD) in software projects can be cheaper than its payment. Prevention practices also help in catching inexperienced developers' 'not-so-good' solutions. However, little is known on how to prevent the occurrence of TD. Aims: To investigate, from the point of view of software practitioners, preventive actions that can be used to curb the occurrence of TD and the impediments that hamper the use of those actions. Method: We use data from the InsighTD Project, a family of industrial surveys specifically designed to study software engineering TD. We use a corpus of answers from 207 practitioners across different geographic locations to identify and analyze - both quantitatively and qualitatively - the TD preventive actions most used in practice. Results: We found that project planning, adoption of good practices, well-defined requirements, creating tests, and training are the most cited preventive actions that curb TD in software projects. We also identified seven preventive action categories and defined relationships among them and TD types. On the other hand, the main impediments to prevent TD are related to inappropriate project planning and lack of expertise of the team. Conclusions: Our list of preventive actions and impediments can help practitioners to implement policies for the sector and guide TD researches in a problem-driven way.
Optimized directed roadmap graph for multi-agent path finding using stochastic gradient descent

We present a novel approach called Optimized Directed Roadmap Graph (ODRM). It is a method to build a directed roadmap graph that allows for collision avoidance in multi-robot navigation. This is a highly relevant problem, for example for industrial autonomous guided vehicles. The core idea of ODRM is, that a directed roadmap can encode inherent properties of the environment which are useful when agents have to avoid each other in that same environment. Like Probabilistic Roadmaps (PRMs), ODRM's first step is generating samples from C-space. In a second step ODRM optimizes vertex positions and edge directions by Stochastic Gradient Descent (SGD). This leads to emergent properties like edges parallel to walls and patterns similar to two-lane streets or roundabouts. Agents can then navigate on this graph by searching their path independently and solving occurring agent-agent collisions at run-time. Using the graphs generated by ODRM compared to an non-optimized graph significantly fewer agent-agent collisions happen. We evaluate our roadmap with both, centralized and decentralized planners. Our experiments show that with ODRM even a simple centralized planner can solve problems with high numbers of agents that other multi agent planners can not solve. Additionally, we use simulated robots with decentralized planners and online collision avoidance to show how agents are a lot faster on our roadmap than on standard grid maps.
OVER: overhauling vulnerability detection for iot through an adaptable and automated static analysis framework

Internet of Things (IoT) exposes various vulnerabilities at the software level. In this paper, we propose a static analysis framework for IoT. The proposed framework is designed for detecting security vulnerabilities such as Buffer Overflow, Memory Leaks, Code Injection, TOCTOU, Banned functions, and other code-related vulnerabilities. We consider end-to-end IoT software suite that includes kernels, protocol stacks, APKs, firmware, and others. In particular, we unpacked and analyzed over 21,000 IoT firmware, 628 IoT APKs and 50 IoT Open Source Software (OSS). Our framework is an adaptable and automated static analysis technique that begins with crawling the web for fetching the IoT related files and ends with report generation consisting of IoT Risk Rating. In total, we were able to raise 7 new CVEs and detected 342 existing CVEs and 894 vulnerable code clones in IoT OSS. We found over 70% of APKs vulnerable to SQL Injection and 56% APKs using weak cryptographic algorithms. Also, our framework found 3783 hard-coded passwords and archaic BusyBox versions in IoT firmware.
Guiding the Illumination Estimation Using the Attention Mechanism

Deep learning methods have achieved a large step forward in many computer vision applications. With mechanisms such as attention, deep models can now guide themselves to focus on parts of an image that are more significant for a given task. In computational color constancy, the most important step is to estimate the illumination vector as accurately as possible. Since illumination estimation algorithms can be sensitive to noise, such as ambiguous regions in the image, the ability to have a mechanism to look for specific regions in an image could be helpful. In this paper, a convolutional neural network with an attention mechanism is proposed. The attention mechanism helps the network to focus on regions that contain more content and to avoid regions where ambiguous estimations may occur. In the experimental results, it is shown that the attention mechanism does help the network to obtain more accurate estimations and puts the focus of the network on the regions in an image where gradients are high. The network with the attention mechanism achieves up to 10% increase in accuracy compared to the same network architecture without the attention mechanism.
Mining association rules in asymmetric data for territorial evolution modeling

This work starts from the hypothesis that spatial dynamics and the functions (cover or use) of geographical objects could be, partly, explained or anticipated by the history of their functions and co-localizations changes. Hence, an approach relying on association rules mining for the extraction of explicative/predictive models of territorial evolution is proposed. In order to deal with the asymmetry of the used learning data, we proposed to adapt the supports assignment process for the MSApriori and we also proposed a new multiple minimum support based algorithm called BERA. Applied on study cases from the Corine Land Cover database between 1990 and 2012, the proposed mining methods proved their worth in the management of data imbalance and the generated rules highlight realistic urban dynamics.
Access control method using electrocardiogram signal for fog computing security

Personal identification methods using conventional fog computing services are not secure and efficient enough. This paper suggests a method for controlling fog server access rights by using ECG signals. When a user asks personal identification to control the fog server, wearable devices are used to measure ECG signals, and personal identification is confirmed through matching by using a computing device. The public ECG data is used for the experiment by means of the suggested network in order to examine the potential method for controlling the suggested fog server access rights. The experiment results revealing recognition performance of 99.81% of 52 participants in the experiment supports the fact that the method for controlling fog server access rights by using ECG signals in this paper is demonstrated as an effective method.
Selecting suitable configurations for automated link discovery

Linking individuals in one dataset to other same individuals in existing datasets is a major problem known as link discovery. Existing automated link discovery techniques make users responsible for selecting suitable properties, distances and transformations, a.k. a. configurations, which is challenging for both researchers and practitioners. Furthermore, failing to provide suitable configurations dramatically increases the complexity of link discovery since many configurations need to be evaluated. Current approaches to help users select proper configurations assume datasets are not heterogeneous or require the existence of a schema or ontology, making them less appealing in the context of Linked Data. In this paper, we present an approach to help users select suitable configurations solely based on data, i.e., no schema or ontology is required. We rely on the concepts of universality and uniqueness, i.e., properties that are present in many individuals of the datasets to link (universality) and do not have repeated objects (uniqueness). We use the concept of singularity to focus on configurations in which only a few individuals are very similar while the rest are very dissimilar. We evaluate our approach using eight commonly-used scenarios, in which, on average, we only suggest 5% of all the possible configurations. Additionally, selected configurations consistently generate links achieving high precision and recall with respect to a ground truth. Finally, we provide a number of guidelines to apply our approach in additional scenarios.
Structural Basis for How Biologic Medicines Bind their Targets in Psoriasis Therapy

As biologic therapies become first line treatments for many inflammatory disorders, it becomes increasingly important for the practicing physician to be familiar with how these drugs function at the molecular level. This information is useful in making therapeutic decisions and helping patients understand their treatment options. It is critical to patient safety and clinical response that the molecular differences between these drugs inform prescribing practices. To this end, we present and analyze the available structural biology information about the biologics used in the treatment of psoriasis including inhibitors of tumor necrosis factor alpha (TNFα), interleukin-17 (IL-17), and interleukin-23 (IL-23). We describe and analyze the molecular surface character of known binding epitopes for medications in these classes, showing that significant differences exist in epitope location, hydrophobicity, and charge. Some of these differences can be correlated with clinical data, but our analysis ultimately points to the need for more structural information to allow for a better understanding of the structure-function relationship of biologic therapies.
The lncRNA Rhno1/miR-6979-5p/BMP2 Axis Modulates Osteoblast Differentiation

The roles of long non-coding RNAs (lncRNAs) and micro RNAs (miRNAs) as regulators of mRNA expression in various diseases have recently been reported. Osteoblast differentiation is the vital process which mediates bone formation and fracture healing. In present study, we found microRNA-6979-5p (miR-6979-5p) to be the most differentially expressed miRNA between normal bone and calluses of mice, and overexpression of miR-6979-5p was negatively associated with osteoblast differentiation. Through luciferase assays, we found evidence that bone morphogenetic protein 2 (BMP2) is a miR-6979-5p target gene that positively regulates osteoblast differentiation. We further identified the lncRNA Rhno1 as a competing endogenous RNA (ceRNA) of miR-6979-5p, and we verified that it was able to influence osteoblast differentiation both in vitro and in vivo. In summary, our data indicates that the lncRNA Rhno1/miR-6979-5p/BMP2 axis is a significant regulatory mechanism controlling osteoblast differentiation, and it may thus offer a novel therapeutic strategy for fracture healing.
Potential therapeutic targets of the nuclear division cycle 80 (NDC80) complexes genes in lung adenocarcinoma

Background: Lung cancer is the most common cancer worldwide, both in terms of the incidence and mortality. NDC80 complex comprising of NDC80, NUF2, SPC24, and SPC25 is a heterotetrameric protein complex located in the outer layer of the kinetochore and plays a critical role in mitosis. This study focuses on the effects of NDC80 complex genes on clinical features and prognosis in lung adenocarcinoma (LUAD). Materials and methods: Expression of NDC80 complex in LUAD and related clinical information was extracted from the TCGA website. NDC80 complex gene functional analysis and correlation analysis was conducted by using DAVID, BiNGO, Gene MANIA, STRING and GSEA. Survival probability was predicted by nomogram. Statistical analysis was used to predict NDC80 complex gene expression on clinical features and prognosis in patients with LUAD. Results: Expression of NDC80, NUF2, SPC24 and SPC25 was significantly elevated in LUAD tumors compared with normal tissues (P < 0.05). These genes showed diagnostic values for LUAD (P < 0.001 for each; area under the curve (AUC), 0.958, 0.968, 0.951, and 0.932 respectively); combinatorial analysis of these genes was more advantageous than single analysis alone (P < 0.001; AUC > 0.900 for each). Expression of both NDC80 and SPC25 correlated with the prognosis of LUAD (P < 0.001; AUC > 0.600 for each). Higher expression of NDC80, NUF2, SPC24 and SPC25 was associated with low overall survival (OS) in univariate analysis. Higher expression of NDC80 and SPC25 was associated with low OS in multivariate analysis. High expression of NDC80 combined with high expression of SPC25 was predictive of poor OS in LUAD in joint analysis. Conclusion: NDC80 complex gene might be an early indicator of diagnosis and prognosis of LUAD. The combined detection of NDC80, NUF2, SPC24 and SPC25 may become a new research direction in LUAD diagnosis and a new target for tumor targeted gene therapy.
Crystal Structure Features of CsPbBr3 Perovskite Prepared by Mechanochemical Synthesis

We present a mechanochemical procedure, with solvent-free, green-chemistry credentials, to grow all-inorganic CsPbBr3 perovskite. The crystal structure of this perovskite and its correlations with the physicochemical properties have been studied. Synchrotron X-ray diffraction (SXRD) and neutron powder diffraction (NPD) allowed us to follow the crystallographic behavior from 4 to 773 K. Unreported features like the observed negative thermal expansion of the b unit-cell parameter stem from octahedral distortions in the 4–100 K temperature range. The mechanochemical synthesis was designed to reduce the impact energy during the milling process, leading to a defect-free, well-crystallized sample characterized by a minimum unit-cell volume and octahedral tilting angles in the low-temperature orthorhombic perovskite framework, defined in the Pbnm space group. The UV–vis diffuse reflectance spectrum shows a reduced band gap of 2.22(3) eV, and the photocurrent characterization in a photodetector reveals excellent properties with potential applications of this material in optoelectronic devices.
PET Imaging of Liposomal Glucocorticoids using 89Zr-oxine: Theranostic Applications in Inflammatory Arthritis

The encapsulation of Glucocorticoids (GCs) into long-circulating liposomes (LCLs) is a proven strategy to reduce the side effects of glucocorticoids and improve the treatment of inflammatory diseases, such as rheumatoid arthritis (RA). With the aim of supporting the development of GC-loaded LCLs, and potentially predict patient response to therapy clinically, we evaluated a direct PET imaging radiolabelling approach for preformed GC-LCLs in an animal model of human inflammatory arthritis. Methods: A preformed PEGylated liposomal methylprednisolone hemisuccinate (NSSL-MPS) nanomedicine was radiolabelled using [89Zr]Zr(oxinate)4 (89Zr-oxine), characterised and tracked in vivo using PET imaging in a K/BxN serum-transfer arthritis (STA) mouse model of inflammatory arthritis and non-inflamed controls. Histology and joint size measurements were used to confirm inflammation. The biodistribution of 89Zr-NSSL-MPS was compared to that of free 89Zr in the same model. A therapeutic study using NSSL-MPS using the same time points as the PET/CT imaging was carried out. Results: The radiolabelling efficiency of NSSL-MPS with [89Zr]Zr(oxinate)4 was 69 ± 8 %. PET/CT imaging of 89Zr-NSSL-MPS showed high uptake (3.6 ± 1.5 % ID; 17.4 ± 9.3 % ID/mL) at inflamed joints, with low activity present in non-inflamed joints (0.5 ± 0.1 % ID; 2.7 ± 1.1 % ID/mL). Importantly, a clear correlation between joint swelling and high 89Zr-NSSL-MPS uptake was observed, which was not observed with free 89Zr. STA mice receiving a therapeutic dose of NSSL-MPS showed a reduction in inflammation at the time points used for the PET/CT imaging compared with the control group. Conclusions: PET imaging was used for the first time to track a liposomal glucocorticoid, showing high uptake at visible and occult inflamed sites and a good correlation with the degree of inflammation. A subsequent therapeutic response matching imaging time points in the same model demonstrated the potential of this radiolabeling method as a theranostic tool for the prediction of therapeutic response - with NSSL-MPS and similar nanomedicines - in the treatment of inflammatory diseases
A Single Clinical Experience in a Nursing Home Improves Physiotherapy Students' Attitudes Towards, and Confidence to Communicate With, Older People

ABSTRACT Aims: To evaluate how an early, single clinical experience in a nursing home influences physiotherapy students' attitudes towards older people and self-perceived confidence in communication. Methods: 102 Bachelor and 32 Master pre-registration physiotherapy students participated in a single visit to a nursing home, during which they interviewed (with educator support) an older person. Students completed surveys addressing their attitudes towards older people (Geriatrics Attitudes Scale), self-perceived confidence in communication (pre- and post-visit), and satisfaction with the innovation. Results: Bachelor students reported a positive change in attitudes towards older people for 3 Geriatrics Attitudes Scale items (P < 0.035). Bachelor and Master students demonstrated greater perceived confidence in their ability to perform several communication tasks with older people (P < 0.018) post-intervention, and reported high satisfaction towards the learning innovation. Conclusions: A single, early exposure to a nursing home can positively influence aspects of physiotherapy students' attitudes and perceived communication skills related to gerontology.
Adaptive partitioning of real-time tasks on multiple processors

This paper presents a new algorithm for scheduling real-time tasks on multiprocessor/multicore systems. This new algorithm is based on combining EDF scheduling with a migration strategy that moves tasks only when needed. It has been evaluated through an extensive set of simulations that showed good performance when compared with global or partitioned EDF: a worst-case utilisation bound similar to partitioned EDF for hard real-time tasks, and a tardiness bound similar to global EDF for soft real-time tasks. Therefore, the proposed scheduler is effective for dealing with both soft and hard real-time workloads.
Understanding emotions in SNS images from posters' perspectives

As the popularity of media-based social networking services (SNS), such as Instagram and Snapchat, has increased significantly, a growing body of research has analyzed SNS images in relation to emotional analysis and classification model development. However, these prior studies were based on relatively small amounts of data, where the emotions of images were labeled from viewers' perspectives, not posters' perspectives. Consequently, we analyze 120K images that reflect poster's emotion. We develop color- and content-based classification models by considering: (1) the dynamics of SNS, in terms of the volume and variety of images shared, and (2) the fact that people express their emotions through colors and objects. We demonstrate the comparable performance of our model with models proposed in prior studies and discuss the applications.
A secure network coding-enabled approach for a confidential cluster-based routing in wireless sensor networks

Wireless sensor networks (WSNs) face some security issues when transmitting data to the base station as these networks involve resources scarcity that needs to be tackled in the routing protocols. More specifically, collected and distributed data are only profitable if their confidentiality is guaranteed. Lately, some approaches have used network coding properties to comply with confidentiality issues. This paper proposes a Secure Network Coding-enabled approach for a confidential cluster-based Routing (SNCR) protocol in wireless sensor networks. It aims to thwart eavesdropping attacks and minimize energy dissipation within a clustered network. Our scheme uses the transmission of a single digit instead of all encoding coefficients, which reduces the communication overheads in the network. Confidentiality investigations and protocol discussions show that SNCR provides a countermeasure to eavesdropping attacks and ensures a compromise between security and the energy efficiency of data transmission in a multi-hop routing scheme.
AppSpeedXray: a mobile application performance measurement tool

The response time or speed of a mobile app is critical in the aspect of user experience. In the web environment, it is well known that WebPageTest and Google Lighthouse projects present web performance metrics measured through web browsers. However, there is no open-source mobile app performance measurement tool that automates performance tests for the metrics of the mobile app without its source code. In this paper, we propose a mobile app speed measurement tool, AppSpeedXray, that collects mobile app performance data of packet trace, XML log, execution video file, and CPU utilization from the mobile app binary file, and that computes performance metrics. For mobile app-specific performance metrics, we define speed index, first paint time, layout load time, CPU stable time, rendering time after data download, and the aggregated mobile app performance score. Our tool consists of a mobile app crawler to gather Android APK files, a UI automator to generate the user inputs for testing, and a performance analyzer to calculate the performance metrics.