Miracle-dz/UniFAR-FTUs huggingface dataset:
The Influence of NMI against Modularity in Community Detection Problem: A Case Study for Unsigned and Signed Networks

Community detection is useful for better understanding the structure of complex networks. It aids in the extraction of the required information from such networks and has a vital role in different fields that range from healthcare to regional geography, economics, human interactions, and mobility. The method for detecting the structure of communities involves the partitioning of complex networks into groups of nodes, with extensive connections within community and sparse connections with other communities. In the literature, two main measures, namely the Modularity (Q) and Normalized Mutual Information (NMI) have been used for evaluating the validation and quality of the detected community structures. Although many optimization algorithms have been implemented to unfold the structures of communities, the influence of NMI on the Q, and vice versa, between a detected partition and the correct partition in signed and unsigned networks is unclear. For this reason, in this paper, we investigate the correlation between Q and NMI in signed and unsigned networks. The results show that there is no direct relationship between Q and NMI in both types of networks.
Multilevel Image Thresholding based on Kapur's Entropy Using Cuckoo Search Algorithm

In this paper, optimal thresholds for multi‐level thresholding in image segmentation are gained by maximizing Kapur's entropy using cuckoo search algorithm (CS). and the between‐class variance is taken as the object function to compared with Kapur's entropy. The performance assessment is carried using peak‐to‐signal ratio (PSNR). The experiment results show that the more threshold, the better the segmentation effect and the Kapur's entropy in image segmentation is better than the between‐class variance.
A Review of Emerging Trends in Cloud Computing

The emerging practice of using a network of remote servers for resources including and not limited to data storage, management, compute, instead of a local self managed server or a personal computer is known as Cloud Computing. Cloud computing has an ever increasing set of applications in today’s digital world, allowing organizations and individuals alike to outsource the management of all forms of computer resources and infrastructure, and to focus their time and talent on more important tasks. In this paper, we survey different state of art papers on cloud computing to examine recent trends and its impact on the industry.
Evaluation of Growth, Yield, and Biochemical Attributes of Bitter Gourd (Momordica charantia L.) Cultivars under Karaj Conditions in Iran

Vegetative and reproductive characteristics, fruit yield, and biochemical compounds of six bitter melon cultivars (Iranshahr, Mestisa, No. 486, Local Japanese, Isfahan, and Ilocano) were evaluated under Karaj conditions in Iran. The phytochemical properties of the cultivars were evaluated using both shade-dried and freeze-dried samples at three fruit developmental stages (unripe, semi-ripe, and ripe). There were significant differences in the vegetative and reproductive characteristics among cultivars, where cv. No. 486 was superior to most vegetative attributes. The fruit yield of cultivars varied from 2.98–5.22 kg/plant. The number of days to male and female flower appearance ranged from 19.00–25.33 and from 25–33 days, respectively. The leaf charantin content was in the range of 4.83–11.08 μg/g. Fruit charantin content varied with developmental stage, drying method, and cultivar. The highest charantin content (13.84 ± 3.55 µg/g) was observed at the semi-ripe fruit stage, and it was much higher in the freeze-dried samples than the shade-dried samples. Cultivar No. 486 had the highest (15.43 ± 2.4 µg/g) charantin content, whereas the lowest charantin content (8.51 ± 1.15 µg/g) was recorded in cultivar cv. Local Japanese. The highest total phenol content (25.17 ± 2.27 mg GAE/g) was recorded in freeze-dried samples of ripe fruits of cv. No. 486, whereas the lowest phenol content was detected in the shade-dried samples of semi-ripe fruits of Isfahan. cv. Flavonoid content was higher with the shade-drying method, irrespective of cultivar. In conclusion, considering the fruit yield and active biological compounds in the studied cultivars, cv. No. 486 should be grown commercially because of its higher yield and production of other secondary metabolites.
Distance Dependence of Exciton Dissociation at a Phthalocyanine–C60 Interface

Exciton dissociation at donor–acceptor (DA) interfaces is critical for the operation of organic photovoltaic (OPV) devices, yet a detailed physical understanding of this process is lacking. This work examines an important aspect of this process, namely the dependence of the exciton dissociation rate on distance from the DA interface. Time-resolved two-photon photoemission (TR-2PPE) measurements were performed on bilayer H2Pc\C60 heterojunctions fabricated using organic molecular beam epitaxy (MBE) with varying H2Pc thickness. In the measurements, the dynamics of the H2Pc S1 exciton population created with a 1.55 eV pump pulse were monitored via photoemission with a delayed UV probe pulse. The depth sensitivity of TR-2PPE, due to the short electron attenuation length, provides the means to follow excited state dynamics as a function of H2Pc thickness. Analysis of the S1 population decay as a function of H2Pc thickness revealed that the electron transfer rate for the first H2Pc layer, adjacent to C60, is kC.
Filamentation near Hill’s vortex

Abstract For the axi-symmetric incompressible Euler equations, we prove linear in time filamentation near Hill’s vortex: there exists an arbitrary small outward perturbation growing linearly for all times. This is based on combining the recent nonlinear orbital stability obtained by the first author with a dynamical bootstrapping scheme for particle trajectories. These results rigorously confirm numerical simulations by Pozrikidis in 1986.
IoTgo+Arts: The making of an Interactive Experience

IoTgo+Arts is a gamified toolkit specifically designed for artists to help them create interactive and IoT-enabled artwork. This toolkit was co-designed with the artists in workshops. This paper presents the first six artworks created with the help of the IoTgo+Arts toolkit, which together constitute the IoTgo+Arts Interactive Experience. All artworks were created in collaborative manner, with an artist paired with a technology expert. The use of the IoTgo+Arts toolkit helped in generating ideas that were feasible to implement with the technology available to the artists.
585-596 KSEEG-20-050.fm

포항지진은 포항지열발전소의 수리자극에 의한 촉발지진으로 조사되었으며, 수리자극을 위해 주입된 유체가 임계상태 에 도달한 지하단층을 재활성시킨것으로 알려져 있다. 하지만 포항지열발전소의 건설 이전, 포항지진 진앙지 인근에서 단층운동에 의한 제4기층 변형연구는 보고되지 않았다. 포항지진 이후 지표지질조사를 통해 진앙지로부터 약 4km 떨어 진 지점에서 대규모 물빠짐구조를 확인하였다. 마이오세 이암에에서 발생한 이 물빠짐 구조는 MIS 5에 형성된 상부 해 안퇴적층을 관입하고 있다. 이는 마이오세 퇴적층과 해안퇴적층의 부정합면을 따라 존재하는 지하수면과 마이오세 퇴적 층이 속성작용 완료되기 전에 융기된 영향으로 인해, 마이오세 퇴적층이 충분히 고화되지 않아 연질퇴적변형구조를 형 성할 수 있었음을 지시한다. 이 물빠짐구조는 미고화된 이암의 공극수압이 상부지층의 하중을 초과하여 발생한 구조로 서 지진에 의해 발생한 것으로 해석된다. 이러한 해석은 물빠짐구조로부터 약 400m 떨어진 지점에서 확인된 제4기 단 층의 존재, 한반도 남동부의 빠른 융기율, 포항인근 양산단층을 따라 보고된 제4기 단층과 역사지진 기록과도 잘 부합한 다. 따라서, 포항지진의 진앙지 일원은 제4기 동안 지구조운동과 이와 관련된 지표변형이 발생한 지점으로서 포항지진을 일으킨 단층 또한 지진발생 이전에 임계상태에 도달했을 것으로 추정된다.
Influence of Technical Expertise on Implementing Community Policing Programs in Nairobi City County, Kenya

Community policing was introduced in April 2005 with the aim of improving relations between community and the Police Service and help in fighting increasing crime and terrorism in Nairobi City County. The use of “NyumbaKumi” initiative was adopted after its successful implementation in the neighboring country of Tanzania. Monitoring and evaluation techniques have been used to improve the success rate in project management and their application in community policing programs hold a promising future for the sustainability of such projects. The main purpose of the study was to assess the impact of technical expertise on the implementation of community policing programs in Nairobi City County.The target population comprised community leaders, National Police Service, citizens, religious leaders, business community and households that have been impacted by the community policing programs. The study made use of a descriptive survey research design. The study made use of both primary and secondary data. Primary data was collected using a questionnaire. The data was then analyzed using SPSS software. Statistical measures and frequencies were utilized to provide insights about the data. A regression model was also run to be able to provide information about the relationship between the dependent and the independent variables. The study established that technical expertise had a positive and significant effect on implementing community policing programs in Nairobi City County, Kenya. The study concluded that enables the employees gain self-confidence that they have the knowledge and competence to perform their daily tasks to the best of their ability for effective implementation of projects. The study recommended that the project managers involved in the implementation of community policing must be highly organized and able to keep meticulous track of every aspect of every project.
Rolling Bearing Fault Diagnosis Method Based on Multisynchrosqueezing S Transform and Faster Dictionary Learning

Addressing the problem that it is difficult to extract the features of vibration signal and diagnose the fault of rolling bearing, we propose a novel diagnosis method combining multisynchrosqueezing S transform and faster dictionary learning (MSSST-FDL). Firstly, MSSST is adopted to transform vibration signals into high-resolution time-frequency images. Then, the local binary pattern (LBP) operator is introduced to extract the low-dimensional texture features of time-frequency images, which improves the speed of fault recognition. Finally, nonnegative matrix factorization (NMF) with only one hyperparameter and nonnegative linear equation are used to solve the dictionary learning and feature coding, respectively. The feature coding is input into the classifier for training and recognition. Experiments show that our method performs well on the rolling bearing dataset of Case Western Reserve University (CWRU) and the Society for Machinery Failure Prevention Technology (MFPT). Further, the proposed method is applied to the loudspeaker pure-tone detection dataset, and the loudspeaker anomaly diagnosis is achieved. The diagnosis results verify that our method can meet the needs of practical engineering.
Risk indicators in tapentadol use: opportunities to improve outcomes with a personal pharmaceutical care approach in community pharmacies

Opioids have historically been the pharmacological choice in the treatment of moderate to severe pain of oncological and chronic origin but they have also become a growing and controversial tool in the treatment of non-oncological pain that requires a risk/ benefit evaluation. The present study focuses on the opioid tapentadol, the newest opioid considered highly effective in treating cancer and non-cancer pain. According to the WHO Collaborating Center for Drugs Statistics Methodology, the stated Tapendatol Defined Daily Dose (DDD) for oral administration is 400 mg but the prescribed doses range from 100 mg/day in patients without previous opioid treatments up to a maximum of 600 mg/ day. However, the DDD is a unit of measurement that does not have to reflect the prescribed daily dose (PDD) because therapeutic doses usually differ from DDDs by taking into account individual patient considerations and pharmacokinetic conditions. This retrospective observational study in a community pharmacy on
Tolerance of Seven Woody Ornamentals to Soil-Applied Sodium Chloride

Two-year-old seedlings of seven woody ornamental species ranging in reported salt tolerance from good to poor were salinized with 250 ml of 0.25 N NaCl applied daily to the soil. Elaeagnus angustifolia and Rosa rugosa were not injured and contained the lowest leaf levels of Na and Cl. Elaeagnus umbellatus and Prunus tomentosa showed the greatest injury while Acer platanoides, Prunus besseyl, and Rhus glabra were intermediate in tolerance. Leaf Na contents did not reflect the degree of injury among Elaeagnus umbellatus, Prunus tomentosa, Acer platanoides, Prunus besseyl, and Rhus glabra. However, leaf Cl content in all injured species was greater than 2.5 percent and more accurately reflected the severity of injury although the most seriously injured species did not contain the greatest leaf CI levels.
Communication Complexity of Byzantine Broadcast

Byzantine Broadcast is a fundamental problem in distributed computing, with communication complexity being an important aspect of Byzantine Broadcast protocols. In Byzantine Broadcast, a designated leader must ensure that all honest users in a distributed system reach a consensus, even in the presence of some dishonest users. Previous works have shown an O ( n 2 ) lower bound on communication complexity, as well as protocols with O ( n 2 ) communication complexity for the honest majority scenario. In this paper, we review the previous work and provide various methods and intuition towards a possible O ( n 3 ) communication complexity lower bound for dishonest majority Byzantine Broadcast.
Enhancement in Security of Backup and Recovery Based System

The development of data backup and recovery technology guarantees the reliability and availability of the data. In any case, just reliability and availability cannot meet the security needs of data backup and recovery in significant zones of military, national defense and other high security areas of usage. In this paper, a security layer is proposed over the backup and recovery system. This security layer is designed through the improved basic access authentication, token-based authentication, single sign on authentication and password guessing attack prevention for ensuring that only the authentic users to have access of backup and recovery services. The framework is able to understand and recognize the authenticity of data which is backed up, and ensures prevention from data leak by forbidding the operators to back up data on a illegal destination or restore data on an illegal target machine.
Assessment of Soil and Groundwater Quality around Major Dumpsites in Rivers State Using Index Models Approach

Soil and groundwater quality assessment was conducted in order to determine the level and extent of contamination imposed from surface dumpsites cited within residential areas in Rivers State, Nigeria. Thirty soil and groundwater samples were obtained from dumpsite centers and surrounding residential areas for physico-chemical, heavy metals and petroleum hydrocarbons analysis. Sampling was carried out in the wet and dry seasons to reflect seasonal variations in soil and groundwater quality. The samples were analyzed using AAS and standard laboratory procedural guidelines. Pollution load index and water quality index were used to address soil and groundwater quality status in the area. The results of soil heavy metal pollution load ranged from 0.65 to 1.45 in the wet season and 0.74 to 1.55 in the dry season. Soil metal pollution load revealed all dump centres have deteriorated soil quality, while 20% and 87% of soils in residential areas have deteriorated quality for wet and dry seasons respectively. Groundwater across the entire area is very acidic in both wet and dry season with pH values < 5.0. Water quality index revealed only a single borehole in Igwuruta has unsuitable water quality, all other boreholes showed good to excellent quality across both wet and dry seasons. The study recommends groundwater in the area is treated with lime or carbonates to reduce its acidity before subsequent consumption. African Scholar Publications & Research International VOL. 18 NO. 4 ISSN: 2896-1783 SEPTEMBER, 2020 African Scholar Journal of Env. Design & Construction Mgt. (JECM-4)
A review of optics-based methods for thickness and surface characterization of two-dimensional materials

Two-dimensional (2D) materials have attracted great attention because of their unique physical properties and versatile applications in electronics and photonics. Following the trends of large-area 2D materials-based devices and systems implementation, large-area, high-throughput thickness and surface characterization techniques are required. Optics-based thin film characterization techniques have promising advantages in fast characterization speed, contactless large-area probing, and highly accurate measurement results. In this review, we overview optics-based methods for thickness and surface characterization of various 2D materials, including the use of optical reflection contrast, Raman spectroscopy, photoluminescence, optical interference effects, phase-shifting interferometry, nonlinear optical harmonic generations, and spectroscopic ellipsometry.
A Few-shot Learning algorithm based on attention adaptive mechanism

Deep learning has achieved remarkable results in image recognition, semantic segmentation and other fields. However, the primary premise of training deep learning model is the support of a large number of data sets and labeled samples. Therefore, deep learning is still faced with difficulties in medical, military and other areas where high-quality samples are scarce. Based on the image classification task as the application background, this paper improves a measure learning algorithm based on covariance representation. In the original Few-shot Learning algorithm based on measure learning, Firstly, from the perspective of second-order statistics, the covariance matrix between the feature vectors of each sample is constructed to realize the class representation; Then, an attention adaptive module is introduced to adjust the feature vectors of the class representation and query samples to make them closer to the class representation of the corresponding class. Finally, the experiment is carried out on the public data set(Omnight) to verify the effectiveness of the model. The experimental results show that the classification accuracy of the model designed in this paper is 6% higher than that of the Few-shot learning algorithm based on metric learning.
An Access Control Scheme Supporting Privacy Protection Based on Blockchain and Attribute

With the large-scale application of cloud storage systems, lots of attribute-based access control (ABAC) schemes have been introduced to protect data and user security in this insecure environment. ABAC can make data owners control their own data and protect data security and privacy. However, there are two serious privacy leakage problems, namely user attribute privacy and policy privacy, should be solved in the process of constructing ABAC schemes. In this paper, an ABAC scheme supporting privacy protection is constructed, which can solve privacy leakage problems of user attribute privacy and policy privacy in large universe attribute system. Furthermore, a framework is proposed. The framework combines our ABAC scheme, the Ethereum blockchain and blockchain-based storage system. In this framework, the security characteristics of blockchain technology are used to realize decentralization, tamper-resistant and avoiding single point of failure. Besides, the problems of attribute revocation and policy updating are solved by smart contract on the Ethereum blockchain. Finally, we established an initial implementation on Linux and Rinkeby test network, and the experimental results show that our scheme is feasible.
Application Research on Combat Simulation Automation based on Intelligent Speech Interaction

Combat simulation test is the main means of evaluating command information systems. In order to improve the intelligence and efficiency of simulation experiment, the method of applying intelligent speech interaction technology (ISIT) to the simulation experiment automation process is proposed. The application processes of speech instruction recognition, instruction element analysis, data instruction generation and speech synthesis feedback are designed based on human-machine ISIT. Meanwhile, taking the air combat command and guidance scene an example, the command and guidance voice instruction database is constructed, and the speech interaction simulation experiment is designed and verified. The results show that ISIT can effectively reduce the number of commanders and operators in air combat simulation test, and improve test efficiency.
Quantum noise reduction in Advanced Virgo

In order to detect the small distance variations induced by gravitational waves, very sensitive devices must be used. Gravitational wave detectors are sophisticated interferometers sensitive even to vacuum fluctuations. These latter are responsible for quantum noise. Due to the frequency-dependent response of gravitational wave interferometers, quantum noise manifests itself as radiation pressure noise for frequencies below 100 Hz, while as shot noise for higher frequencies. The solution that has been adopted in order to reduce quantum noise is the injection, through the interferometer output port, of vacuum states, called squeezed, whose amplitude and phase uncertainties are correlated. A frequency-independent squeezing technique, as a method for the reduction of the quantum noise, has been already demonstrated in long-arm interferometers. Radiation pressure noise does not limit the sensitivity of the present interferometers, being this completely covered by other noises. But, in the near future, these noises will be reduced and also this quantum noise component will be relevant. The adopted solution to have a broad-band quantum noise reduction is a frequency-dependent squeezing technique. In this paper the results obtained in Advanced Virgo using the frequency-independent squeezing technique will be shown. Moreover the conceptual design for the implementation of the frequency-dependent squeezing will be presented.
Research of semantic role labeling based on long short-term memory neural networks

Semantic Role Labeling (SRL) is a shallow semantic analysis in the field of NLP, and a relatively basic and important step. Traditionally, SRL has been performed based on the results of syntactic analysis and has problems such as over-reliance on feature engineering. With the development of deep learning, many neural network models for NLP have been proposed and SRL tasks can be performed well by neural networks. Among these, long short-term memory networks form a very good fit with the SRL task by virtue of their sequence-based features. In this paper, in order, we first analyze the SRL task based on grammatical analysis and neural networks, then the SRL task based on LSTM and its improved models, then the dataset and model evaluation of the SRL task, and finally conclude and look forward.
Adversarial Response Generation Against Topic Relevance

In recent years, generative adversarial networks have performed well in the field of dialogue generation to improve the information diversity of dialogue responses. Often overlooked, however, is that the query and response are not relevant on the topic. In order to improve the topic relevance of chat conversation, the paper proposed a topic-relevance adversarial response generation model, TR-ARG, which is composed of generator G, discriminator D and topic classifier T. The experiment was evaluated on OpenSubtitles, an open dialog dataset, and compared with the current baseline models SEQ2SEQ and GAN-AEL. The results show that our model can effectively improve the topic relevance of generated responses.