Reviewerly/exHarmony huggingface dataset:
DÜŞÜK ÇÖZÜNÜRLÜK VE GRİ GÖRÜNTÜLERDE GERÇEK ZAMANLI CİLT SEGMENTASYONU

In this study, the skin segmentation problem, which is frequently used in the literature, will be detected on a real-time webcam with semantic segmentation deep learning algorithms. Skin segmentation generally requires high processing power to distinguish it from images with high resolution. The deep learning algorithm and the proposed image processing method used in this study provide real-time, very low CPU usage, and lag-free skin detection via webcam.
Automatic Summary Generation of Chinese News Text Based on Cross-Layer Parameter Shared Transformer-Encoder-PGN Model

To address the problems that the traditional neural Seq2Seq model is difficult to replicate factual details, cannot handle words out of word list, and the generated content is prone to self-duplication, a generative summary model based on Transformer Encoder-PGN with cross-layer parameter sharing is proposed for Chinese news texts. The contextual word embedding vector is first obtained using a Transformer Encoder pre-trained model with across layers shared parameters combined with word location features. Words are then copied from the original text using a copying mechanism through a pointer generator model, or extracted from a glossary to form a summary. Finally, the model uses the coverage mechanism to dynamically adjust the attention distribution to avoid repeated content generation. Experiments on the Chinese dataset of Weibo news, Rouge-l, Rouge-2 and Rouge-L reach 39.35%, 27.60% and 37.86%, respectively, which are 1.16%, 2.01% and 2.22% higher than the PGN model using LSTM-Attention, and the generated abstracts are more concise and accurate, indicating that the model has a more comprehensive and adequate understanding of the original text.
Fuzzy Clustering with Features Combination Optimization Forming Subspaces

Clustering algorithms have been widely used in the real world. The relationship between data features is crucial for mining data structures. Subspace clustering aims to partition all features in a data set to expand the possibility of exploring different data structures, thus maximizing the clustering capability. However, existing subspace clustering methods focus on mining consistency and complementary information while ignoring subspace partitioning, limiting the clustering performance. This paper proposes a fuzzy clustering with features combination optimization forming subspaces(FCOS) method addressing this aspect. We divide the data into different subsets and implement data subspace mining and information sharing with the help of the fuzzy clustering algorithm. Both steps are executed in an optimization framework based on the particle swarm algorithm. It is worth mentioning that during the optimization process, the data subsets are adaptively tuned with metrics related to the clustering performance to assist the clustering process further. We demonstrate the effectiveness of the method by experimenting with different methods in real available data.
Measuring the relationship between organizational culture and project success: A survey of Agile software development teams

Currently, many organizations have adopted agile software development (ASD) methods. Although ASD is widely known as a better way of developing software, its implementation also has challenges that need special attention. Therefore, some research revealed various dimensions of factors that can potentially maximize the implementation success of ASD methods. Still, none of them specifically examined the dimensions of organizational culture, even though the previous survey and several studies have discovered it as an essential factor determining the ASD Project Success. Consequently, this research tries to identify how the organizational culture affects the success of ASD projects. This study aims to provide a new research model regarding organizational culture and project success in ASD and its validation. The validation process uses the Partial Least Squares-Structural Equation Modeling (PLS-SEM) technique. The findings show that Rational Culture and Group Culture form positively impacts ASD Project Success. Meanwhile, Hierarchical Culture form negatively impacts the ASD Project Success. Particularly for Development Culture, there is a positive impact on the ASD Project Success only if there is mediation from the ASD Practices.
MorphNet Impacts on Neural Network Optimizers

Field of artificial intelligence have reported remarkable progress since the 2012. With this progress, computational requirement increases, along with power requirement and carbon footprint. MorphNet proposes solution to increase efficiency of neural network by proposing improved network design from existing network design. We study the impact of MorphNet on optimizer by comparing training process using six optimizers of Adam, AdaMax, AdaGrad, AdaDelta, RMSProp, and SGD. We constructed models according to proposed model from previous MorphNet research, and performed training using the optimizers. As the result, training time using selected optimizers for 20 epochs, on average reduced by 19%. Validation loss on average was increased by 19.65%. Validation accuracy on average was reduced by 1.20 percentage points. On training time, SGD optimizer was the most improved with reduction in training time of 29%. On validation loss, AdaGrad and AdaDelta was the most affected with increase of 33.6% and 33.7% respectively. On validation accuracy, the AdaGrad and AdaDelta was also the most affected with reduction of 1.97 and 4.97 percentage points respectively. However, we found that AdaMax performs the best on the smallest network size in terms of training time and accuracy.
Studying The Perception of Using Visualization Dashboard to Measure Cybersecurity Maturity Stage

At the current stage, the complexity of digital threat has increased along with the exponential changes in the technology's advancement. Moreover, the infrastructure for technology continuously improves through the border of security measures well beyond controls of the organization. To resolve this concern, assessment of cybersecurity maturity on dashboard is proposed to facilitate cybersecurity implementation plan and make it easier for organization to improve their cybersecurity through visual cues. This article aims to study the Technology Acceptance Model on the acceptance of Maturity Dashboard and examine the proposed research model framework. Through the adaptation of TAM based questionnaire of Visual Dashboard for Cybersecurity Maturity, the research will be able to gauge the proper visualization dashboard design suitable for employees in MNC companies of ICT industry and thence, comes up with a visualization dashboard which caters to the requirements specified by the employees.
Tolerances Of Phasor Measurement Unit. How To Take Into Account And Decrease Them

Phasor measurements – is one of the most important technologies for measuring the electric power regime parameters, which is part of the Wide Area Monitoring, Protection and Control System complex. Formally, phasor measurements replace the existing systems of telemetry and distance measurements SCADA due to higher sampling rate, time synchronization and high-performance. Phasor measurement unit (PMU) having been used since early 2000s all around the world, and the potential of their application has not yet been fully disclosed. The issues of assessing the tolerances of PMU are considered in foreign literature. There is a standard [1] in Russia, which regulates the tolerances of PMU. According to the standard, class M PMU (for measurements) and class P PMU (for protection) have different permissible limits of tolerances. The standard also specifies the ranges of permissible tolerances for different operating conditions of PMU – static and dynamic. Moreover, there is a way of changing the input parameters of PMU for which the limits of permissible tolerances are specified for each of the conditions. The method of calculating the main types of tolerances (total vector error TVE, frequency measurement error FE and rate of frequency change error RFE) is also determined in standard. It is necessary to consider the reasons for the appearance of those tolerances in the conditions of changing the electric regime parameters in order to understand what determines the measurement tolerance of PMU on a real power grid.
The Role of Gear Grind Games in Creating a Motivating Learning Environment

Games have played an impactful life in our current generation brimming with technological advancement. Not only has it become the main source of entertainment for people to enjoy, but it has also transitioned to becoming a learning experience for others. With our current repetitive standards and methods of teaching and learning in schools, the mind of many students has dulled their creativities as well as developing the extrinsic motivation to study at all. However, the implementation of game learning can be developed into modern-day teaching and be used as a tool for students to grasp their potentials and be motivated to learn level more using games. From the mix mode research conducted, many tertiary students would rather replace their conventional studying methods with a game learning experience. The player will be able to understand, develop listening skills and verbal communication skills towards other people to understand when it is necessary to be assertive in an environment. Moreover, this game will tackle the understanding of real-life decision-making skills that mimic real-life situations for players to adapt to. With that said, JomGear Grind Games aims to help students learn various skills such as creative thinking and interpersonal skills.
Recognizing modern Japanese magazines by combining Deep Learning with language models

As one of the most culturally rich countries globally, Japan also has a rich history of magazines. In modern Japanese magazines, which were published during the 19-20th centuries, Japanese usage is similar to the current style of the contemporary Japanese language. However, most of those documents are not digitized but stored as images. Due to their importance to Japanese culture, history, and other socio-scientific topics, the problem of using computers to help identify these image-based modern magazines has been investigated from research and widely disseminated through the use of different methods in Deep Learning. However, these methods are still limited to achieve strong performance in recognizing handwriting images, especially uncommon Kanji characters. In this research, we address this problem by developing a deep learning-based language model and integrating it into the current OCR system for modern Japanese magazine documents. We also propose a combination strategy between the current Japanese OCR tool and our language model. The strategy will learn where the system should rely on OCR (e.g., Hiragana and Common kanji characters recognized correctly by OCR) or language model (uncommon Kanji characters are frequently recognized incorrectly by OCR, the system should rely on the language model). Our method enjoys visible improvement once experimented with real data.
Research on improvement of moisture proof technology for JIS control cabinet of 220kV substation based on big data

Abstract The outdoor control cabinet of intelligent substation includes the central control cabinet and the intelligent control cabinet. The internal integrated circuits and electronic components are easily affected by the environmental humidity. Through the operation and maintenance data statistics, it is found that the existing heating and dehumidification system in the control cabinet can not fully meet the humidity requirements in the cabinet. In view of this problem, this paper analyzes the factors leading to the increase of humidity in the cabinet, puts forward the research on the improvement of moisture-proof technology of JIS control cabinet in 220kV substation based on big data. In an environment where the air temperature changes rapidly, the test data verify that the moisture-proof technology of JIS control cabinet in 220kV substation based on big data can effectively reduce the humidity in the intelligent control cabinet, and effectively ensure the safe operation of electronic equipment in the intelligent control cabinet.
A Community Bus Location System Utilizing Two-Way Communication via Virtual Avatar

The objective of this research is to apply a feasible study on deploying “community MaaS”, which is a mechanism for participating users to actively participate in MaaS based on the concept of “making friends with a mobility”. The cutting-edge network technologies are continuing to evolve at a remarkable speed and compelling environment in which we carry out various activities transparently and not limited to fixed locations. We conducted a proof-of-concept experiment on the university campus for the improvement of user experience by aggregating voice dialogue, human behavior, and demand through smartphones.
Fractal dimension algorithm for automatic detection of gold mineralization

Synthetic aperture radar (SAR) images are required and accurate algorithms used in the automatic detection of ore potential zones. Conventional SAR data involve single-polarized SAR systems, while polarimetric SAR data, such AirSAR, PI-SAR-1/2, E/F-SAR, CV-580 SAR, SIR-C/X-SAR, ALOS-PALSAR1/2, Radarsat-2, TerraSAR-X, and TanDEM-X are contained multipolarized SAR systems. However, this chapter is devoted to operating with single-polarized SAR data for ore mineral deposits such as gold. To this end, gold potential zones and geological characteristics are addressed to develop an accurate algorithm for their automatic detection in SAR images. This chapter answers the critical question: How can conventional SAR systems be utilized as toolsets for the automatic detection of gold mining and discriminate it from the surrounding geological environment?
Using proximal sensors to assess pedogenetic development of Inceptisols and Oxisols in Brazil

Proximal sensors like portable the X-ray fluorescence (pXRF) spectrometer, visible and near-infrared spectrometer (Vis-NIR) and magnetic susceptibilimeter have been increasingly used for the acquisition of complementary data to investigate chemical, physical and mineralogical properties of soils. Through the association between these sensors, this study aims to differentiate classes of soils with similar pedogenetic development, but developed from different parent materials. Four soil profiles were described and sampled in Lavras, Minas Gerais, Brazil: Oxic Dystrustept and Anionic Acrudox, developed from gabbro and Typic Dystrustept and Rhodic Hapludox, developed from gneiss. Samples from the A, B and C horizons were subjected to analyses by pXRF, Vis-NIR, and magnetic susceptibility, in additon to their physicochemical characterization and determination of their mineralogy via X-ray diffraction (XRD). The contents of Si, Fe and Ti obtained by pXRF as well as the MS values were decisive in distinguishing soil classes according to the parent material. The spectral curves generated by Vis-NIR showed different spectral responses for the same soil class due to their differential mineralogical constitution. Soils developed from gabbro had higher contents of iron oxides and a low reflectance in relation to the soils developed from gneiss. The proximal sensors contributed to the differentialtion of soil properties both within soils developed from the same parent material, mainly expressing their different weathering degrees, and within soils of the same taxonomic order, but developed from distinct parent materials. These proximal sensors can improve the characterization of soils and evaluations about their genesis at reduced time and costs.
Multi-label Image Recognition Based on Multi-modal Graph Convolutional Networks Using Captioning Features

This paper presents a multi-label image recognition model based on multi-modal Graph Convolutional Networks (GCN) using captioning features. GCN have been frequently exploited to model label dependencies in recent studies of multi-label image recognition. However, the dependencies are built on information directly from the images. In this paper, we imports captioning features into the GCN-based multi-label image recognition model to use both image and text information. By introducing image captioning into the GCN model, the accuracy of multi-label image recognition can be improved. The experiment results show the effectiveness of the proposed method.
Towards playing AIs for 7 Wonders: main patterns and strategies for 3-player games

Artificial Intelligence (AI) in games may be greatly supported by a robust set of patterns, rules, and strategies that contribute to securing a win. In this work, we made a Knowledge Discovery pipeline - involving data selection, association, classification, and clustering - to successfully identify important factors that help to achieve a win in the board game 7 Wonders, focusing on 3-player matches. Our results show strong patterns and main strategies used by the best players in the world. This knowledge narrows the search for a Nash equilibrium for the game, getting us closer to create a top-tier AI.
Machine Learning Aided KREONET for Advanced Scientific Research Support

Korea research environment open network (KREONET) has been supporting advanced scientific researches in South Korea, by providing optimized network infrastructure as well as cutting edge technologies to the specific research groups. This paper proposes a concept of machine learning based scientific research support in KREONET. By considering practical network properties of scientific research groups of KREONET in year 2021, SVM models with diverse kernel functions and parameters calculate optimal boundaries which can be extended as a criterion for scientific research support of KREONET in the future.
The Improvement needs in Blockchain Technology

Bitcoin's attack in finance has caused a new wind to blow in the stock market and with the emergence of many new crypto currencies, the crypto currency market has become a new financial area.Although Blockchain technology is the technological infrastructure of Bitcoin, awareness is not as high as Bitcoin.Despite it was found in 1992, its first use was in the shadow of Bitcoin, influenced by the fact that it was with Bitcoin in 2008.However, due to the features that it provides; Without Blockchain technology, the Bitcoin system would not work.As the dazzling offer of Bitcoin; through the decentralized structure, buyers and sellers can meet directly on a platform and make their purchases securely, without involvement of any third party.Verification in the system can only be done by approving by more than 50% of the participants.Thus, besides of no need for a central authority, it became almost impossible for any cyber attack to be successful.The continued success of Blockchain technology is vital for Bitcoin and other cryptographic currencies survival.Beside of all these advantages, there are some issues that need to be addressed for Blockchain technology.These can be listed as throughput, latency in processing, size and bandwidth, some security vulnerabilities, resource waste for adding a new block to chains, usability, and privacy.In this article, we will discuss these issues that need to be addressed for Blockchain technology.
Semantic Systems After 30 Years

Semantic systems are sytems with an inherent semantics. An example would be systems showing intrinsic intentionality: if a system is genuinely intentional, it must be able to define its own meanings. Searle was a forerunner of the modern idea of semantic systems in his oft-cited “Chinese Room” paper in 1980. The current author has approached the problem from a different angle 30 years ago in his book Self-Modifying Systems (Pergamon), claiming that minds can define their own meanings by virtue of being “material”, in the sense of freely generating new properties on the fly, as do material objects. In the course of the process, meaning should arise because syntax (and therefore any computation that is entirely syntactical) does not fully describe the rich functioning of such systems, so goes the argument. While still attractive, the author has in the meantime abandoned the idea, for at least two reasons. One, fundamentals-seeking has itself been largely abandoned, because the study of questions like “is the mind computational”? has turned out to be non-productive. Two, brute force approaches such as ANN-based deep learning have been “unreasonably” successful, and this fact puts aside arguments about the limits of computational systems. The paper provides an overview of these developments.
Investigating accuracy disparities for gender classification using convolutional neural networks

Automatic gender recognition (AGR) is a subfield of facial recognition that has recently been scrutinized for bias in the form of misgendering and erasure against various identity groups in our society. Recent studies have found that several commercial AGR classifiers (from Microsoft, IMB, Face++) are biased against women and darker-skinned people as well as gender non-binary people [8, 11]. In this work, we investigate and quantify AGR classifier bias against transgender people by developing and evaluating three different convolutional neural networks (CNN): using images of cisgender individuals, using images of transgender individuals, and using images of both cisgender and transgender individuals. We find that the cisgender trained classifier is 91.7% accurate when evaluated on cisgender people, but only 68.9% accurate when evaluated on transgender people, with the worst performance of 38.6% precision for transgender men. We investigate this low precision further by performing additional experiments where various parts of the face are obscured. We end with recommendations for commercial classifiers based upon our findings.
Stochastic Convergence

Abstract The modes of convergence introduced in Chapter 12 are studied in detail. Conditions for almost‐sure convergence are derived via the Borel–Cantelli lemma. Convergence in probability is contrasted, and then a number of results for convergence of transformed series are given. Convergence in LP‐norm is introduced as a sufficient condition for convergence in probability. Examples are given, and the chapter concludes with a preliminary look at the laws of large numbers.
A method for Forecasting Available Spectrum Resources with Location-Dependent Error Margin for Dynamic Spectrum Access

In higher frequency bands such as the millimeterwave band, which is expected to be utilized in the future, the received power fluctuates significantly due to changes in the propagation environment. Therefore, the spatial and temporal spectrum sharing between different wireless systems will be more practical. Some methods have been proposed to predict future spectrum usage, but prediction errors are unavoidable. There is a trade-off between the detection rate of future available spectrum resources and the false detection rate caused by prediction errors. Still, there is no established method for balancing these two factors. One conservative prediction to reduce false detections is to set a certain margin. However, the fluctuation tendency depends on the location, which results in an excessive margin in some sites, resulting in a lower detection rate. In this paper, we propose a method to set a location-dependent margin by considering the error characteristics of the constructed prediction model of received power. The proposed method enables us to increase the detection rate while reducing the false detection rate. The evaluation results by simulation in an urban area show that the proposed method can improve the detection rate by 10.6% while keeping the false detection rate, compared to when using a constant margin.