Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of Wave Parameters Using CCTV Images-Based on Deep Learning Algorithm

ABSTRACT Yoon, J.; Kim, S. ; Kim, I. , and Song, D. , 2021. Detection of wave parameters using CCTV images-based on deep learning algorithm. In: Lee, J.L. ; Suh, K. -S. ; Lee, B. ; Shin, S. , Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 281–284. Coconut Creek (Florida), ISSN 0749-0208. The closed-circuit television (CCTV) systems have been used for various applications in oceanic research fields. CCTV is an effective installation method for offshore monitoring that can conveniently and continuously observe the coastal region using the visible or infrared light in the electromagnetic spectrum. However, in the case of wave-observing CCTV images, there is a problem: it is difficult to extract the corresponding feature points between the video frames, which causes of inaccurate measurement of wave parameters. Recently, in the field of machine learning technology, deep learning-based video analysis that does not require the detection of feature points has been proposed. In this paper, we analyze wave-observing CCTV images with deep learning method in particular convolution neural network, and find the optimal method of observing waves through CCTV by three different datasets. For training, the CCTV data from the Samcheok Beach in the Republic of Korea were used. Wave observation data, which were used as input for machine learning, were collected by an acoustic wave and current profiler (AWAC). Moreover, to make up for insufficient observation data, the data to enhance the prediction model through deep learning were augmented. The results of this study confirm that the deep learning algorithm can be utilized to detect wave period and wave height. Consequently, continuous CCTV images can be combined with deep learning algorithm to clarify the complex characteristics of wave distribution and its temporal changes.
Adenosquamous and squamous cell carcinoma of the pancreas: two histopathological variants of ductal adenocarcinoma

Abstract Introduction: Primary squamous cell carcinoma (SCC) and adenosquamous carcinoma of the pancreas are rare malignancies for which diagnostic and treatment strategy are challenging. In this paper we present a literature review of these tumors based on two case reports. Case presentation: In the first case, a 55-year-old male presented with an inoperable pancreatic head/body junction tumor. Endoscopic ultrasound-guided fine needle aspiration was practiced, and histopathological examination revealed a squamous cell carcinoma of the pancreas. After exclusion of any another tumor, the diagnosis of cT4N0M0-staged primary pancreatic SCC was made. The patient is under treatment with gemcitabine and oxaliplatin. The second case is represented by a 73-year-old patient in which imagistic examinations highlighted a cystic mass of the pancreatic body. Following coporeo-caudal splenic-pancreatectomy and histopathological-proved diagnosis of adenosquamous carcinoma, the patient started chemotherapy but died at 11 months after surgery. Both tumor components displayed positivity for markers which prove ductal (cytokeratin19, maspin) and squamous differentiation (p63, cytokeratin5/6) same as vimentin, as indicator of epithelial mesenchymal transition (EMT). Conclusions: SCC and adenosquamous carcinoma of the pancreas are aggressive malignancies which prognosis remains highly reserved. These tumors might be variants of ductal adenocarcinomas which are dedifferentiated through EMT phenomenon.
Biomass Waste Processing into Artificial Humic Substances

Abstract To address the biomass waste processing problem and transform it into refractory forms of carbon, recently suggested hydrothermal carbonization (HTC) of biomass in alkaline environment can be considered as a prospective and climate neutral approach. Biomass HTC provides possibilities to obtain form of biochar (hydrochar) and artificial humic substances, however the conditions of the HTC have not been much studied. Optimization by Response Surface Methodology of the HTC using microwave treatment with three experimental variables (treatment temperature, length of microwave treatment, amount of biomass per volume) provides the possibility to significantly increase the yield of humic substances at the same time reducing the duration of the treatment. However, with an increase in the yield of humic substances, the yield of hydrochar decreases, thus supporting possibilities to obtain most needed biomass waste processing product.
When triggers become tigers: taming the autonomic nervous system via sensory support system modulation

ABSTRACT Personalized recovery technologies may enable individuals with Substance Use Disorder (SUD) to monitor and manage acute craving and drug use urges in ways that improve drug-seeking decisions in real-time. Direct and indirect regulation of the autonomic nervous system through sensory input monitoring and modulation may enhance control over behavioral decisions and prevent relapse. A personalized sensory support system that monitors neurophysiological reactivity and offers non-pharmacological point-in-time personalized digital interventions may increase awareness of and control over craving reactivity. It is critical to be able to detect these warning signs and intervene early and effectively. The use of wearable technologies that assess point-in-time neurophysiological escalation and shape behavioral responses through personalized interventions could be transformative in allowing individuals to better manage their recovery as they transition out of institutions and move back into community settings.
Multi-layered parallel plate waveguide with electrically and magnetically biased graphene sheets

The performance of a multi-layered parallel plate waveguide, supported by graphene plates, is evaluated here. The graphene layers were biased with both magnetic and electric fields. The graphene plates weremodelled as an anisotropic surface derived from the Kubo formula. Maxwell's equations were solved for these waveguides; and it is shown when both magnetic and electric biases were applied to the graphene, hybrid modes propagated inside the waveguides. The intensity of each TM and TE mode could be adjusted with an applied external field bias. This study of wave confinement has shown that, by varying the bias, full control can be exerted over the wave's propagation within a waveguide; whereby, increasing the bias causes added phase changes over the propagation length, while increasing the waveguide's attenuation. It must be emphasized that the presented procedure is also applicable to other guiding structures that have boundary with isotropic or anisotropic conductivities.
ALGORITHM TO CONSTRUCT INTEGRO SPLINES

Abstract We study some properties of integro splines. Using these properties, we design an algorithm to construct splines $S_{m+1}(x)$ of neighbouring degrees to the given spline $S_m(x)$ with degree m. A local integro-sextic spline is constructed with the proposed algorithm. The local integro splines work efficiently, that is, they have low computational complexity, and they are effective for use in real time. The construction of nonlocal integro splines usually leads to solving a system of linear equations with band matrices, which yields high computational costs.
CuART - a CUDA-based, scalable Radix-Tree lookup and update engine

In this work we present an optimized version of the Adaptive Radix Tree (ART) index structure for GPUs. We analyze an existing GPU implementation of ART (GRT), identify bottlenecks and present an optimized data structure and layout to improve the lookup and update performance. We show that our implementation outperforms the existing approach by a factor up to 2 times for lookups and up to 10 times for updates using the same GPU. We also show that the sequential memory layout presented here is beneficial for lookup-intensive workloads on the CPU, outperforming the ART by up to 10 times. We analyze the impact of the memory architecture of the GPU, where it becomes visible that traditional GDDR6(X) is beneficial for the index lookups due to the faster clock rates compared to High Bandwidth Memory (HBM).
Identifying Logging Practices in Open Source Python Containerized Application Projects

Despite the popularity of microservice architectures using container based virtualization, there are not many studies that describe how logging practices are being employed in this scenario. In this empirical study, we cloned 10,918 of the most stargazed GitHub Python repositories with a Docker/Kubernetes associated file. We were able to find 1,166 Python projects that used containers. A custom parser identified and saved log statements from Python source code. We discovered that certain licenses tend to have a higher log density; over 99% of projects use the built-in Python logging library; the repository age does not affect its log statements/LLOC ratio; logging verbosity levels debug and info are used almost twice as much as warning and error. We hope our study provides the community with useful data about this topic, possibly contributing to the improvement of techniques that stimulate its applications.
Evaluation of the application of gamification in the discipline of Software Engineering in times of pandemic

The new coronavirus pandemic promoted structural changes in society due to the need to elevate social isolation to the extreme. In this context, the majority of educational institutions have chosen to continue their activities remotely. One of the strategies used to engage students with academic activities is the use of gamification. Due to the new scenario, driven by the pandemic, this study aims to present and evaluate the application of gamification by comparing its use in the remote and in-person contexts, as well as the necessary adaptations for the new scenario. The comparison took place between two Software Engineering classes, one being in-person and the other in remote format. As initial results, a positive evaluation related to the application of the game was achieved, greater than 80% for the two teaching formats.
How Do Brazilian Software Development Teams Deal with Working From Home After a Year of the COVID-19 Pandemic?

The COVID-19 pandemic has lasted for over 1 year. During the pandemic, several software development companies migrated to working from home (WFH). Several studies have recorded an initial moment of adaptation to WFH. This study investigated how Brazilian software development teams dealt with WFH after 1 year of a pandemic and how this change in work was on the software development process. We applied a survey with 67 participants of software development teams and investigated aspects, as: work routine, collaboration, communication, productivity, financial assistance and software development process. We performed a quantitative and qualitative analysis of the survey results and compared them with previous studies. Our key findings are: (i) 56.71% of the participants said that it has an impact on the work routine related to work overload and exceeds the company’s standard hours; (ii) 92.54% of the participants consider their team to be collaborative; (iii) 82.70% are satisfied with the communication at WFH; (iv) 86.55% has meetings once or twice a day; (v) 59.7% had an improvement in productivity in WFH and 75% are satisfied with their productivity; (vi) 79.60% receive some assistance from the company for WFH; (vii) 82.1% said that the company intends or probably intends to continue in the WFH, and (viii) 55.2% said they had changed in the software development process due to WFH. The main positive changes in the process are related to the practices of: daily meetings, agile processes, code review, pair programming and code versioning.
Communication Avoiding All-Pairs Shortest Paths Algorithm for Sparse Graphs

In this paper, we propose a parallel algorithm for computing all-pairs shortest paths (APSP) for sparse graphs on the distributed memory system with p processors. To exploit the graph sparsity, we first preprocess the graph by utilizing several known algorithmic techniques in linear algebra such as fill-in reducing ordering and elimination tree parallelism. Then we map the preprocessed graph on the distributed memory system for both load balancing and communication reduction. Finally, we design a new scheduling strategy to minimize the communication cost. The bandwidth cost (communication volume) and the latency cost (number of messages) of our algorithm are and O(log 2p), respectively, where S is a minimal vertex separator that partitions the graph into two components of roughly equal size. Compared with the state-of-the-art result for dense graphs where the bandwidth and latency costs are and ), respectively, our algorithm reduces the latency cost by a factor of , and reduces the bandwidth cost by a factor of for sparse graphs with . We also present the bandwidth and latency costs lower bounds for computing APSP on sparse graphs, which are and Ω(log 2p), respectively. This implies that the bandwidth cost of our algorithm is nearly optimal and the latency cost is optimal.
NewroMap: Mapping CNNs to NoC-interconnected Self-Contained Data-Flow Accelerators for Edge-AI

Conventional AI accelerators are limited by von-Neumann bottlenecks for edge workloads. Domain-specific accelerators (often neuromorphic) solve this by applying near/in-memory computing, NoC-interconnected massive-multicore setups, and data-flow computation. This requires an effective mapping of neural networks (i.e, an assignment of network layers to cores) to balance resources/memory, computation, and NoC traffic. Here, we introduce a mapping called Snake for the predominant convolutional neural networks (CNNs). It utilizes the feed-forward nature of CNNs by folding layers to spatially adjacent cores. We achieve a total NoC bandwidth improvement of up to 3.8× for MobileNet and ResNet vs. random mappings. Furthermore, NewroMap is proposed that continues to optimize Snake mapping through a meta-heuristic; it also simulates the NoC traffic and can work with TensorFlow models. The communication is further optimized with up to 22.52% latency improvement vs. pure snake mapping shown in simulations.
Who are Vulnerability Reporters?: A Large-scale Empirical Study on FLOSS

(Background) Software vulnerabilities pose a serious threat to the security of computer systems. Hence, there is a constant race for defenders to find and patch them before attackers are able to exploit them. Measuring different aspects of this process is important in order to better understand it and improve the odds for defenders. (Aims) The human factor of the vulnerability discovery and patching process has received limited attention. Better knowledge of the characteristics of the people and organizations who discover and report security vulnerabilities can considerably enhance our understanding of the process, provide insights regarding the expended effort in vulnerability hunting, contribute to better security metrics, and help guide practical decisions regarding the strategy of projects to attract vulnerability researchers. (Method) In this paper, we present what is, to the best of our knowledge, the first large-scale empirical study on the people and organizations who report vulnerabilities in popular FLOSS projects. Collecting data from a multitude of publicly available sources (NVD, bug-tracking platforms, vendor advisories, source code repositories), we create a dataset of reporter information for 2193 unique reporting entities of 4756 CVEs affecting the Mozilla suite, Apache httpd, the PHP interpreter, and the Linux kernel. We use the dataset to investigate several aspects of the vulnerability discovery process, specifically regarding the distribution of contributions, their temporal characteristics, and the motivations of reporters. (Results) Among our results: around 80% of reports come from 20% of reporters; first time reporters are significant contributors to the yearly total in all 4 projects; productive reporters are specialized w.r. t. the project and vulnerability types; around half of all reports come from reporters acknowledging an affiliation. (Conclusions) Projects depend both on a core of dedicated and productive reporters, and on small contributions from a large number of community reporters. The generalized Pareto principle (the (1 - p)/p law) can be used as a metric for the concentration of contributions in the vulnerability-reporting ecosystem of a project.
ADA: An Application-Conscious Data Acquirer for Visual Molecular Dynamics

Visual molecular dynamics (VMD) has been widely used by numerous molecular dynamics (MD) applications to animate and analyze the trajectory of an MD simulation. One challenge faced by domain scientists, however, is how to filter out inactive data (i.e., data irrelevant to the subject) from the enormous output of an MD simulation. To solve it, we propose ADA (application-conscious data acquirer), a light-weight file system middleware that can perform an application-conscious data pre-processing. It provides host CPUs with only the data needed instead of an entire raw dataset. Next, we implement an ADA prototype, which is then integrated into three computing platforms: an SSD server, a nine-node OrangeFS storage cluster, and a fat-node server with 1 TB memory. Further, we evaluate ADA by running a computational biology application on the three platforms. Our experimental results show that compared to a traditional file system an ADA-assisted file system improves data processing turnaround time by up to 13.4x and reduces memory usage for data rendering by up to 2.5x. Besides, ADA allows the 1TB memory server to render more than 2x the VMD graphs while cutting energy consumption by 3x.
Parallel Multi-split Extendible Hashing for Persistent Memory

Emerging persistent memory (PM) is a promising technology that provides near-DRAM performance and disk-like durability. However, many data structures designed based on DRAM, such as hash table and B-tree, are sub-optimal for PM. Prior studies have shown that the scalability of hash tables on Intel Optane DC Persistent Memory Modules (DCPMM) degrades significantly due to expensive lock-based concurrency control and massive data movements during rehashing. This paper proposes a lock-free parallel multi-split extendible hashing scheme (PMEH), which eliminates the lock contention overhead, reduces data movements during rehashing, and ensures data consistency. Under the widely used YCSB workloads, the evaluation results show that compared to other state-of-the-art hashing schemes, PMEH is up to 1.38x faster for insertion and up to 1.9x faster for deletion, while reducing 52% extra writes. In addition, PMEH can ensure instant recovery regardless of data size.
A Survey on the Use of UML in the Brazilian Industry

Although UML modeling has been used in the Brazilian industry in the last decade, little is known about the factors, perceptions of professionals and practices that end up affecting the UML use in real-world projects. This paper, therefore, reports on an exploratory survey focused on investigating how UML is used in practice in the Brazilian software industry. In total, 314 practitioners from 180 information technology companies answered an online questionnaire about the usage-affecting factors, difficulty and frequency of use, perceived benefits, and adoption-preventi ng contextual factors of UML models. Moreover, 20 practitioners participated in a semi-structured interview. The results show that the participants recognize the usefulness of the UML models, e.g., allowing an improved understanding of the integration among enterprise applications. However, 74.8% of the participants did not use UML models due to some factors such as continuous delivery practices, time constraints, lack of knowledge about modeling, company culture, and the ever-present difficulty of keeping the models updated and synchronized.
Automatic Code Generation and Optimization of Large-scale Stencil Computation on Many-core Processors

Stencil computation is an indispensable building block of many scientific applications and is widely used by the numerical solvers of partial differential equations (PDEs). Due to the complex computation patterns of different stencils and the various hardware targets (e.g., many-core processors), many domain-specific languages (DSLs) have been proposed to optimize stencil computation. However, existing stencil DSLs mostly focus on the performance optimizations on homogeneous many-core processors such as CPUs and GPUs, and fail to embrace emerging heterogeneous many-core processors such as Sunway. In addition, few of them can support expressing stencil with multiple time dependencies and optimizations from both spatial and temporal dimensions. Moreover, most stencil DSLs are unable to generate codes that can run efficiently in large scale, which limits their practical applicability. In this paper, we propose MSC, a new stencil DSL designed to express stencil computation in both spatial and temporal dimensions. It can generate high-performance stencil codes for large-scale execution on emerging many-core processors. Specially, we design several optimization primitives for improving parallelism and data locality, and a communication library for efficient halo exchange in large scale execution. The experiment results show that our MSC achieves better performance compared to the state-of-the-art stencil DSLs.
Towards the Development of an Adaptive E-learning System with Chatbot Using Personalized E-learning Model

Although there is no distinctive header, this is the abstract. This submission template allows authors to submit their papers E-learning has become one of the most used electronic systems in the field of education. Although it is beneficial, there are still some lacking capabilities and considerations that can negatively affect the performance of the students. This leads to the innovation that makes e-learning systems adaptive to the users’ personality, knowledge, behavior, interest, or preferences, the system is called personalized e-learning system. This survey paper aims to provide the general parameters in creating a personalized e-learning system based on the 150 research papers collected, and a timespan of 2016 to 2020 as a condition. Through a series of literature reviews of research papers published in the last five years, also related to personalized e-learning systems, this paper presents the common components, tools and algorithms, and learning model that are generally used in developing a personalized e-learning system to help as reference in developing more effective personalized e-learning systems. Moreover, considering the findings of this study, this paper has proposed developing a hybrid e-learning system with a chatbot.
Analysis on the Influence of Educational Attainment on Criminal Behavior

It is believed that there is a positive relationship between education and crime. Therefore, the author intends to analyze the influence of educational attainment on criminal behavior to support this hypothesis. In this paper, the effect of educational attainment on criminal behavior is discussed in detail. The government expenditure on higher education per person is equipped as the instrumental variable to avoid the simultaneous effect between education and crime. Based on the data from Statistics Canada, it is found that there is a significantly negative relation between educational attainment and the probability to practice crime through OLS and TSLS estimation. Evidence from both the violent crime severity index and the non-violent crime severity index suggests that the effect of education on crime is strong and significant.