Miracle-dz/UniFAR-FTUs huggingface dataset:
Safety Helmet Wearing Detection Based on Image Processing and Deep Learning

The environment of the steel factory workshop is complex, and there may be a variety of unexpected potential dangers, so wearing a helmet to enter the workshop is a prerequisite for the factory. In order to supervise this situation, it is necessary for employees to wear helmets for testing, which is a key part of the overall intelligent monitoring system for steel plant personnel. In this paper, through the crawler to collect high-definition employees wearing helmets and no helmet pictures, using manual labeling, proposed a helmet detection framework based on computer vision deep learning detection framework Faster-RCNN. The actual testing results produce convincing experimental results, which proves the effectiveness and practicability of the proposed framework.
Assessment of Neuroplasticity Using EEG Signal in Rehabilitation of Brain Stem Stroke Patients

Robot-assisted motor training provides an efficient alternative to conventional rehabilitation methods used for poststroke patients. The re-learning of lost motor functions happens through neuroplasticity in the brain. Electroencephalogram (EEG) provides an effective method for assessing neuroplasticity. Movement-related cortical potential (MRCP), an EEG-derived time-domain pattern, indicates changes due to motor skills gained as a result of the training. This study aims to perform a two-stage robot-assisted rehabilitation program on brain stem stroke patients consisting of a total of 24 training sessions and to assess whether significant motor recovery and neuroplasticity induction are achieved after the first stage or after completing both stages of the designed rehabilitation program. Three brain stem stroke patients were recruited for hand motor training on AMADEO rehabilitation robot for 8 weeks consisting of two stages of 4 weeks each. Three assessments methods which include standard clinical tests, hand strength and range of movement measurements using AMADEO assessment tool, as well as EEG signal acquisition, were performed at the beginning of all the training sessions (week 0), after completion of the first stage of rehabilitation (week 4) and after completion of both stages of the training sessions (week 8). The experimental results demonstrate that all brain stem stroke patients show significant functional hand motor recovery, as indicated by clinical tests, hand strength, and range of movement measurements, after completing 8 weeks of the training. Moreover, MRCP signal negative peak showed a significant decrease in its amplitude when the patients completed two phases of rehabilitation training, indicating neuroplasticity induction.
HETSIM: Simulating Large-Scale Heterogeneous Systems using a Trace-driven, Synchronization and Dependency-Aware Framework

The rising complexity of large-scale heterogeneous architectures, such as those composed of off-the-shelf processors coupled with fixed-function logic, has imposed challenges for traditional simulation methodologies. While prior work has explored trace-based simulation techniques that offer good tradeoffs between simulation accuracy and speed, most such proposals are limited to simulating chip multiprocessors (CMPs) with up to hundreds of threads. There exists a gap for a framework that can flexibly and accurately model different heterogeneous systems, as well as scales to a larger number of cores. We implement a solution called HETSIM, a trace-driven, synchronization and dependency-aware framework for fast and accurate pre-silicon performance and power estimations for heterogeneous systems with up to thousands of cores. HETSIM operates in four stages: compilation, emulation, trace generation and trace replay. Given (i) a specification file, (ii) a multithreaded implementation of the target application, and (iii) an architectural and power model of the target hardware, HETSIM generates performance and power estimates with no further user intervention. HETSIM distinguishes itself from existing approaches through emulation of target hardware functionality as software primitives. HETSIM is packaged with primitives that are commonplace across many accelerator designs, and the framework can easily be extended to support custom primitives. We demonstrate the utility of HETSIM through design-space exploration on two recent target architectures: (i) a reconfigurable many-core accelerator, and (ii) a heterogeneous, domain-specific accelerator. Overall, HETSIM demonstrates simulation time speedups of 3.2×-10.4× (average 5.0×) over gem5 in syscall emulation mode, with average deviations in simulated time and power consumption of 15.1% and 10.9%, respectively. HETSIM is validated against silicon for the second target and estimates performance within a deviation of 25.5%, on average.
Identifying and Analyzing Data Model Requirements and Technology Potentials of Machine Learning Systems in the Manufacturing Industry of the Future

Although machine learning (ML) methods have already been well described in science, the transfer into manufacturing business practice is only slowly taking place. One of the reasons is that current research is lacking a comprehensive analysis of working ML methods and their characteristics. Therefore, this paper systematically analyzes successfully implemented ML solutions to facilitate the design process for future machine learning systems (MLS) implementations in manufacturing companies. First, a systematic literature review based on 18 scientific publications is conducted to confirm the lacks assumed. Second, 15 MLS approaches are analyzed based on a technology framework to solve the shortcomings identified and extract further findings.In total, we identified two general MLS design patterns. Furthermore, we extracted seven suitable data and data model requirements as well as technology potentials. The results show that theory-based ML approaches are often based on linear methods requiring low-dimensional data, e.g. in image recognition. This points towards the hypothesis, that the application of non-linear ML methods processing high-dimensional data could increase the number of possible use cases. Thus, further high technology potentials regarding the application of MLS in the manufacturing industry would arise.
Visual Detection of Productive Crop and Pasture Fields from Aerial Image Analysis

The use of unmanned aerial vehicles (UAV) is expanding rapidly throughout the world. Nowadays, it is common to find some useful applications that use them in both urban and rural environments. Especially in the second case, the UAV is promoting significant changes in traditional agricultural activities. Thus, current technologies have been incorporated into inspection, surveillance, and agricultural management. In this study, we investigated some practical uses of aerial images in rural areas. A new method for allowing a UAV to understand and interpret visual information from static imagery is presented. Tree detection and shadow segmentation are essential requirements for navigation and visual examination purposes. Therefore, our method deals with these tasks to be a starting point to enable a machine to perform visual inspections in production fields. The proposed method uses computer vision techniques such as visual color enhancement, morphological operations, and segmentation approaches. We performed an evaluation of our system based on a dataset with different types of crop areas and pasture lands. Moreover, we assessed our approach to verify tree canopy and shadow detection. We also verified delineating agricultural fields, and segmentation of sunlight exposed vegetation, as well as vegetation areas covered by shadows. Results demonstrate that our approach provides an exciting and robust approach to be adopted in the field.
A Novel Detection and Localization Approach for Nonlinear Faults in Two-Dimensional Structures

In this study, a novel detection and localization approach for nonlinear faults (loosening bolts or fatigue cracks) in two-dimensional structures is proposed and verified. In this new approach, a two-dimensional discrete multi-degree-of-freedom (MDOF) model, which simulates nonlinear faults as nonlinear damper-spring elements between each mass, is applied to characterize the dynamic behavior of two dimensional structures. With the nonlinear output spectrum and multi-position excitation method, damage features correspond to properties of substructures are derived, and local damage indexes are defined for nonlinear fault detection and localization in substructures. Numerical results of case study demonstrate that the proposed approach is realizable and sensitive to faults in the substructure under diagnosis even though there are faults in other substructures.
Accelerating Number Theoretic Transformations for Bootstrappable Homomorphic Encryption on GPUs

Homomorphic encryption (HE) draws huge attention as it provides a way of privacy-preserving computations on encrypted messages. Number Theoretic Transform (NTT), a specialized form of Discrete Fourier Transform (DFT) in the finite field of integers, is the key algorithm that enables fast computation on encrypted ciphertexts in HE. Prior works have accelerated NTT and its inverse transformation on a popular parallel processing platform, GPU, by leveraging DFT optimization techniques. However, these GPU-based studies lack a comprehensive analysis of the primary differences between NTT and DFT or only consider small HE parameters that have tight constraints in the number of arithmetic operations that can be performed without decryption. In this paper, we analyze the algorithmic characteristics of NTT and DFT and assess the performance of NTT when we apply the optimizations that are commonly applicable to both DFT and NTT on modern GPUs. From the analysis, we identify that NTT suffers from severe main-memory bandwidth bottleneck on large HE parameter sets. To tackle the main-memory bandwidth issue, we propose a novel NTT-specific on-the-fly root generation scheme dubbed on-the-fly twiddling (OT). Compared to the baseline radix-2 NTT implementation, after applying all the optimizations, including OT, we achieve 4.2⨯ speedup on a modern GPU.
Identification and Characterization of Challenges in the Future of Manufacturing for the Application of Machine Learning

In an increasingly dynamic and complex environment, manufacturing systems must respond quickly to changes in order to remain productive. Hence, existing tasks and decisions in manufacturing have to be aligned in an ever more complex system of connected and dependent machines and devices. Various data-enabled assistance systems that help to coordinate tasks and support decisions are already existent. However, due to the volatile, uncertain, complex, and ambiguous (VUCA) environment, further demands for the assistance systems emerge. Increasing availability of data and decreased costs for computing such as storage and computing capacities for the use of machine learning (ML) indicate promising potential to address the ever-changing conditions securing productivity and thus remain competitive. But the promising potential widely remains untapped. The challenges faced by manufacturing companies especially lie in the identification of attractive application areas and the recognition of the associated learning tasks. Therefore, the aim of this paper is to derive and systematize challenges in the future VUCA-submissive manufacturing landscape to effectively design ML applications. The results provide a target system of objectives in which challenges can be positioned, such as an application navigator for archetypical challenges to be addressed by ML.
Measurement and Analysis of Small Cell Splitting in a Real-world LTE-A HetNet

Network densification is an important topic which has been studied during the past decades in the 4G heterogeneous networks (HetNets). Deployment of small cells and cell-splitting technique are aimed to increase network capacity, cell coverage, and total cell throughput in HetNets. However, most published literature is about theoretical analysis. In this paper, extensive measurements are conducted in a real-world LTE-A HetNet environment. The cell-splitting strategy is applied in a real-world LTE-A HetNet. Four directional antennas operate as one cell and two cells respectively in an indoor gymnasium in the University of Regina. Optimization techniques such as ABS (Almost Blank Subframe) are utilized to mitigate interference and increase UE (user equipment) SINR inside the gymnasium. Users' (both static users and moving users) average SINR and system cell throughput are used to evaluate the performance of the tests. Our results show that operating the small cells from one cell to three cells for the whole building, the SINR inside the gymnasium decreased from 29 dB to 5 dB, and cell throughput decreased from 140 Mbps to 88Mbps. Even though the throughput performance of cells inside the gymnasium is slightly lowered, the overall network capacity of the building is enhanced.
Phases désordonnées dans des gaz d'atomes froids de basse dimensionnalité

Cette these aborde deux problemes ayant trait a la physique des gaz quantiques de basse dimensionnalite. Le premier systeme etudie est un melange unidimensionnel de bosons et de fermions sans spin soumis a un potentiel aleatoire. Nous commencons par ecrire un Hamiltonien de basse energie et abordons la question de la localisation du point de vue de l'accrochage des ondes de densite par un desordre faible. En utilisant le Groupe de Renormalisation et une methode variationnelle dans l'espace des repliques, le diagramme de phase peut etre trace en fonctions de deux parametres : la force des interactions Bose-Bose et Bose-Fermi. La position et les proprietes des phases dependent d'un parametre additionnel, le rapport des vitesses du son de chaque composante du gaz. Quelque soit la valeur de ce rapport nous trouvons trois phases, (i) une phase totalement delocalisee, le liquide de Luttinger a deux composantes, (ii) une phase totalement localisee ou les deux composantes sont accrochees par le desordre et (iii) une phase intermediaire ou seuls les fermions sont localises. Le deuxieme systeme est un gaz de bosons de cœur dur sur un reseau en echelle. Trois parametres en controlent la physique : les amplitudes de saut transverse et longitudinale, et le remplissage. En utilisant plusieurs methodes analytiques (theorie des perturbations, bosonisation et RG) nous proposons une interpretation de resultats numeriques nouveaux obtenus par nos collaborateurs, notamment sur le parametre de Luttinger du mode symetrique. Nous en deduisons un diagramme de phase en presence de desordre faible.
Improvement of information System Audit to Deal With Network Information Security

With the rapid development of information technology and the increasing popularity of information and communication technology, the information age has come. Enterprises must adapt to changes in the times, introduce network and computer technologies in a timely manner, and establish more efficient and reasonable information systems and platforms. Large-scale information system construction is inseparable from related audit work, and network security risks have become an important part of information system audit concerns. This paper analyzes the objectives and contents of information system audits under the background of network information security through theoretical analysis, and on this basis, proposes how the IS audit work will be carried out.
The Effect of Real-World Capstone Project in an Acquisition of Soft Skills among Software Engineering Students

Research suggests that employers value various soft skills highly when recruiting software engineering (SE) graduates in the software industry; however, SE graduate students tend to lack those soft skills. This study examines the effects of a capstone course with a problem/project-based approach in the acquisition of soft skills. In this capstone, the graduate students worked closely on a real-world project with the customers from Finnish public and private organizations. Moreover, the study maps out the identified soft skills from the capstone course to the desired requirements of soft skills in various roles in the software industry. Thematic data analysis of reflections given by 48 students identified fifteen soft skill categories that are mainly acknowledged by the students after the completion of the course. In particular, we have shown that the majority of identified soft skills in this course correspond to the desirable soft skills in the software industry. To conclude, the approach taken in this course is a powerful means to improve the soft skills of the graduate students to meet the needs of employers, and thus increase their employment prospects.
Variance Reduced Diffusion Adaptation For Online Learning Over Networks

The stochastic variance reduced gradient (SVRG) algorithm has shown its effectiveness in accelerating the convergence of stochastic gradient algorithms. Considering the emergent applications of distributed estimation, it is interesting to investigate the way to adapt this algorithm to distributed learning with streaming data. For this purpose, in this work we first propose a time-averaging SVRG algorithm that fits into the context of streaming data processing. Then, we integrate this algorithm with the diffusion adaptation to enhance the performance of distributed estimation over networks. Theoretical analysis of the resulted algorithm is conducted to characterize its stability. We also provide the simulation results to illustrate its favorable performance.
Electroanalysis: Theory and Applications in Aqueous and Non-Aqueous Media and in Automated Chemical Control

A. Electroanalysis (Systematic Treatment). Introduction. Electrochemical cells. Non-faradaic methods of electrochemical analysis. Conductometric analysis. Potentiometric analysis. Faradaic methods of electrochemical analysis. Pragmatic treatment of the theory of electrolysis. Kinetic treatment of the theory of electrolysis. Voltammetry. Electrogravimetry. Coulometry. B. Electroanalysis in Non-Aqueous Media. Introduction. Theory of electrochemistry in non-aqueous media. Practice of electroanalysis in non-aqueous media. C. Electroanalysis in Automated Chemical Control. Introduction. Nature of chemical control. Character and degree of automation in chemical control. Role of electroanalysis in automated chemical control. Automated electroanalysis in laboratory control Automated electroanalysis in plant control. Automated electroanalysis in environment control. (References complete each chapter). Appendix. Author Index. Subject Index.
Histologic transformation from lung squamous cell carcinoma in primary lesion to small cell lung cancer in metastatic lymph node lesion following lobectomy and adjuvant chemotherapy: a case report

Introduction: Lung cancer is the leading cause of cancer death worldwide. Histologic transformation from lung squamous cell carcinoma to small cell lung cancer (SCLC) has become more and more, and its treatment is challenging because of unknown pathogenesis. Presentation of Case: A 62-year-old male patient presented with histologic transformation from primary lesion to metastatic recurrence lesion within 3 years following primary lobectomy and adjuvant chemotherapy. The patient had the symptom of cough at the time of initial illness and recurrence. The primary lesion at the root segment of left superior lobe was diagnosed as lung squamous cell carcinoma without gene mutation (including EGFR, ALK, BRAF V600E, etc.), and recurrence lesion at the swollen lymph nodes in mediastinum (4L group) was diagnosed as SCLC. Up to now, the patient was alive for nearly 4 years after first surgery. Discussion: Surgical resection is the main treatment for lung cancer patient in early stage. Lymph node metastasis is a big threat for the patients in early stage. Recently, the research reports focused on the mechanism and cases of histologic transformation of lung cancer have become more and more. In this case, histologic transformation from squamous cell carcinoma in primary focus to SCLC in metastatic lymph node lesion following lobectomy and adjuvant chemotherapy. However, there was no gene mutation. This is a very unusual report. Conclusion: We analyzed a lung cancer patient with histologic transformation from primary focus to lymphatic metastasis by clinical symptoms, radiologic features, laboratory tests, gene detection, histopathologic characteristics, and immunohistochemical testing. It is helpful to understand the pathogenesis of histologic transformation and metastatic progression for optimizing treatment measures to lung cancer.
Synthesis and Antifungal Properties of Some Benzimidazole Derivatives

Life-threatening infections caused by pathogenic fungi are becoming increasingly common, especially in individuals with suppressed immune systems such as cancer chemotherapy or AIDS patients. However, there are only a limited number of antifungal compounds available for such infections, which leads to a strong need to develop new classes of compounds having antifungal activities. In particular, Candidiasis is the fungal infection most frequently associated with HIV-positive patients, and Cryptococcosis, which is the cause of morbidity and mortality in AIDS patients, is caused by Cryptococcus neoformans. Compounds bearing benzimidazole moiety are reported to possess a number of interesting biological activities such as antitubercular, anticancer, anthelmintic, antiallergic, antioxidant7−9, antihistaminic and antimicrobial11−17. In addition, some thiourea derivatives have been reported as antimycobacterial and antimicrobial. In our previous studies we reported the synthesis and antimicrobial20−23 activities of a large series of benzimidazole derivatives. On the basis of these reports and as a continuation of our research program on benzimidazole derivatives, we report here the synthesis of novel benzimidazole derivatives to evaluate their antifungal properties.
Artificial intelligence in the diagnosis of pediatric allergic diseases

Artificial intelligence (AI) is a field of data science pertaining to advanced computing machines capable of learning from data and interacting with the human world. Early diagnosis and diagnostics, self‐care, prevention and wellness, clinical decision support, care delivery, and chronic care management have been identified within the healthcare areas that could benefit from introducing AI. In pediatric allergy research, the recent developments in AI approach provided new perspectives for characterizing the heterogeneity of allergic diseases among patients. Moreover, the increasing use of electronic health records and personal healthcare records highlighted the relevance of AI in improving data quality and processing and setting‐up advanced algorithms to interpret the data. This review aimed to summarize current knowledge about AI and discuss its impact on the diagnostic framework of pediatric allergic diseases such as eczema, food allergy, and respiratory allergy, along with the future opportunities that AI research can offer in this medical area.