Reviewerly/exHarmony huggingface dataset:
Effective Source-Aware Domain Enhancement and Adaptation for CNN-Based Object Segmentation

In this paper, we propose an effective source-aware domain enhancement and adaptation (SDEA) approach to increase the accuracy of the existing convolutional neural network-based (CNN-based) object segmentation methods. We first scoop out the source elements, such as the falling-leaves, manhole covers, cirrus clouds, and advertisements, which often cause invalid object segmentation and make the existing object segmentation methods provide unreliable information to the ADAS (automatic driving assistance systems) applications. Secondly, we create a new GTA5-like (Grand Theft Auto V-like) dataset with the scenarios including these source elements. Furthermore, we perform a domain adaptation on the created GTA5-like dataset to generate a photo-realistic GTA5-like dataset, namely GTA5 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SDEA</sup> . Without the need to relabel the pixel-annotations for GTA5 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SDEA</sup> , we combine GTA5 <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SDEA</sup> with the realistic dataset, namely Camvid, to constitute a newly enhanced dataset. After retraining the existing CNN-based object segmentation methods by using our enhanced dataset, it can achieve substantial segmentation accuracy improvement. The comprehensive experimental results have demonstrated the clear accuracy improvement merit by applying our SDEA approach to the state-of-the-art object segmentation methods on FCN (Fully Convolutional Networks), SegNet-basic, AdaptSegNet, and Gated-AdaptSegNet, providing more reliable information to ADAS applications.
Towards an Inference Detection System Against Multi-database Attacks

Nowadays, users are permanently prompted to create web accounts when they buy online goods. This collected data gives an insight on the user, sometimes beyond the application scope. Inference attacks on databases represent an issue for data controllers when malicious processors attempt to guess sensitive data - to which they haven't access - by inferring them using legally accessed data. Several inference attack detection systems address this problem in case of a single targeted database. But the issue remains unsolved in case of several databases to which the same users might have submitted their data. In this paper, we propose a global model and its associated graph representation named Global Instance Graph (GIG) representing the probabilistic and semantic dependencies inside each database, enriched by the dependencies between the different databases. The graph is obtained using privacy-preserving record linkage techniques and serves as a knowledge input to the inference attack detection system. We validate the GIG creation feasibility thanks to a proof of concept. Despite the quadratic creation time, the performances when data is queried from the databases are not affected since the GIG creation is performed offline.
Penerapan Algoritma Klasifikasi Dengan Fitur Seleksi Weight By Information Gain Pada Pemodelan Prediksi Kelulusan Mahasiswa

The problem faced by institutions of higher education is not exactly of graduation.It is the task of institution of higher education, especially courses in the students academic monitoring.Today, there are many methods or ways to solve various problems of information technology either by machine learning at data mining.This study uses Naive Bayes.Linear Regression, dan Multi Layer Perceptron algorithm also Weight by Information Gain as Features Selection to optimize accuracy in predicting the time of students graduation.The dataset processing with certain attributes including registration line, origin school, origin city, and the semester grade point 1 to 4 in RapidMiner by implementing the three algorithms along with the selection feature produces relatively good accuracy.Naive Bayes produces an accuracy of 81.66% with an execution time of 1.16 seconds, Linear Regression of 80.70% in 2.44 seconds and Multi Layer Perceptron of 82.16% in 1 hour 57 minutes.
DPWord2Vec: Better Representation of Design Patterns in Semantics

With the plain text descriptions of design patterns, developers could better learn and understand the definitions and usage scenarios of design patterns. To facilitate the automatic usage of these descriptions, e.g., recommending design patterns by free-text queries, design patterns and natural languages should be adequately associated. Existing studies usually use texts in design pattern books as the representations of design patterns to calculate similarities with the queries. However, this way is problematic. Lots of information of design patterns may be absent from design pattern books and many words would be out of vocabulary due to the content limitation of these books. To overcome these issues, a more comprehensive method should be constructed to estimate the relatedness between design patterns and natural language words. Motivated by Word2Vec, in this study, we propose DPWord2Vec that embeds design patterns and natural language words into vectors simultaneously. We first build a corpus containing more than 400 thousand documents extracted from design pattern books, Wikipedia, and Stack Overflow. Next, we redefine the concept of context window to associate design patterns with words. Then, the design pattern and word vector representations are learnt by leveraging an advanced word embedding method. The learnt design pattern and word vectors can be universally used in textual description based design pattern tasks. An evaluation shows that DPWord2Vec outperforms the baseline algorithms by 24.2-120.9 percent in measuring the similarities between design patterns and words in terms of Spearman’s rank correlation coefficient. Moreover, we adopt DPWord2Vec on two typical design pattern tasks. In the design pattern tag recommendation task, the DPWord2Vec-based method outperforms two state-of-the-art algorithms by 6.6 and 32.7 percent respectively when considering <inline-formula><tex-math notation="LaTeX">$Recall@10$</tex-math></inline-formula> . In the design pattern selection task, DPWord2Vec improves the existing methods by 6.5-70.7 percent in terms of MRR.
Direct Adversarial Training for GANs

There is an interesting discovery that several neural networks are vulnerable to adversarial examples. That is, many machines learning models misclassify the samples with only a little change which will not be noticed by human eyes. Generative adversarial networks (GANs) are the most popular models for image generation by jointly optimizing discriminator and generator. With stability train, some regularization and normalization have been used to let the discriminator satisfy Lipschitz consistency. In this paper, we have analyzed that the generator may produce adversarial examples for discriminator during the training process, which may cause the unstable training of GANs. For this reason, we propose a direct adversarial training method for GANs. At the same time, we prove that this direct adversarial training can limit the lipschitz constant of the discriminator and accelerate the convergence of the generator. We have verified the advanced performs of the method on multiple baseline networks, such as DCGAN, WGAN, WGAN-GP, and WGAN-LP.
Female Graduate Students' Degree of Satisfaction with Blackboard System and its Use in their Emergency Remote Teaching in Light of DeLone and McLean's Information System Success Model

The study aimed to investigate the degree of satisfaction of graduate female students in Educational Technology specialization about the Blackboard system and its use in their emergency remote teaching in the light of DeLone and McLean's Information System Success Model.The study was applied after the end of the second semester of the academic year (1441 AH -2020 AD) on a sample consisting of (30) female students.A questionnaire prepared by the researcher was used, based on DeLone and McLean's Information System Success Model, and it included six factors: (system quality, information quality, service quality, user satisfaction, system use, net benefits).The results of the study have shown that system quality, information quality, service quality, and net benefit positively affected the user's satisfaction and the use of the system, and that system use affected the user's satisfaction positively.
Syntax Splitting for Iterated Contractions

Parikh developed the notion of syntax splitting to describe belief sets with independent parts. He also formulated a postulate demanding that belief revisions respect syntax splittings in belief sets. The concept of syntax splitting was later transferred to epistemic states with total preorders and ranking functions by Kern-Isberner and Brewka along with corresponding postulates for belief revisions. Besides revision, contraction is also a central operation in the field of general belief change. In this paper, we analyse belief contractions with respect to syntax splitting. Based on the work on syntax splitting for revision, we develop syntax splitting postulates for contractions on ranking functions, on epistemic states with total preorder, and on belief sets. Finally, we evaluate different contractions from the literature, namely moderate contraction, natural contraction, lexicographic contraction, and c-contractions with respect to the newly developed contraction postulates.
Towards a Logic of Meta-Analysis

We currently have access to a plethora of statistical analyses based on sampling limited parts of a population. Meta-analysis is the task of combining several statistical results to obtain a more precise and reliable picture of the population. By the nature of sampling, all these results are uncertain, and difficult to combine with other knowledge. In this position paper, we propose a first approach for automated reasoning in meta-analyses.
EXPLOITATION OF SENTINEL-2 IMAGES FOR LONG-TERM VEGETATION MONITORING AT A FORMER ORE PROCESSING SITE

Abstract. Excess metals in the soil or in plant tissues tend to have negative effects on plant health, growth, and biomass accumulation. The search for stressed or unusual growth patterns in cover vegetation has been enhanced by the use of vegetation index in the context of excessive exposure to heavy metals in the soil. This study aims to improve the monitoring of phyto-stabilized and natural vegetation of an ore processing site for several years after its closure by using multiple Sentinel-2 images. The time series is made up of 13 images, one image per season for four years. NDVI (Normalized Difference Vegetation Index), the most widely known and used vegetation index in the scientific literature, is used in combination with other spectral indexes identifying built-up areas and bare soils in order to enhance vegetation. A change detection technique based on absolute difference of vegetation maps is applied to detect abrupt changes related to meteorological conditions and significant environmental changes.
Mixing Deep Visual and Textual Features for Image Regression

Deep learning has been widely applied in the regression problem. However, little work addressed both visual and textual features in one unit frame. In this paper, we are the first to consider the deep feature, shallow convolutional neural network (CNN) feature, and textual feature in one unit deep neural network. Specifically, we propose a mixing deep visual and textual features model (MVTs) to combine all three features in one architecture, which enables the model to predict the house price. To train our model, we also collected large scale data from Los Angeles of California state, USA, which contains both visual images and textual attributes of 1000 houses. Extensive experiments show that our model achieves higher performance than state of the art.
Pesma Apps as Android-based Integrated Applications for Mahasantri Pesma KH Mas Mansur UMS

Pesantren Mahasiswa Internasional KH Mas Mansur (Pesma) has a shuttle facility for everyone who lives there. However, the use of the shuttle is not optimal because the ordering procedure still uses manual techniques. It contrasts with the rapid development of industry 4.0 and the mission of Pesma to get digitalization. This paper describes the effort to improve Pesma shuttle bookings by developing an application. The application is built upon the Android Studio 3.5.1 platform and uses Firebase real-time database. The development method implements the System Development Life Cycle (SDLC), Waterfall. The research results in an application called “Pesma Apps” that can be used by staff and mahasantri. Testing the Pesma apps, we obtained sufficiently good results where the black box testing proves that all functions work well. A usability testing using SUS with 30 respondents produces a good result at the level of 72.6, which suggests that the application is accepted.
Revision by Conditionals: From Hook to Arrow

The belief revision literature has largely focussed on the issue of how to revise one’s beliefs in the light of information regarding matters of fact. Here we turn to an important but comparatively neglected issue: How to model agents capable of acquiring information regarding which rules of inference (‘Ramsey Test conditionals’) they ought to use in reasoning about these facts. Our approach to this second question of so-called ‘conditional revision’ is distinctive insofar as it abstracts from the controversial details of how the address the ﬁrst. We introduce a ‘plug and play’ method for uniquely extending any iterated belief revision operator to the conditional case. The ﬂexibility of our approach is achieved by having the result of a conditional revision by a Ramsey Test conditional (‘arrow’) determined by that of a plain revision by its corresponding material conditional (‘hook’). It is shown to satisfy a number of new constraints that are of independent interest.
Prediksi Risiko Perjalanan Transportasi Online Dari Data Telematik Menggunakan Algoritma Support Vector Machine

The ride-hailing service is now booming because it has been helped by internet technology, therefore many call this service online transportation. The magnitude of the potential for growth in online transportation service users also increases the risk of user satisfaction which could have declined therefore the company is increasing in its service. Both in terms of application and services provided by partners/drivers of the company. During each trip, the online transportation application will record device movement data and send it to the server. This data set is usually called telematic data. This telematics data if processed can have enormous benefits. In this study, an analysis will be conducted to predict the risk of online transportation trips using the Support Vector Machine (SVM) algorithm based on the obtained telematic data. The data obtained is telematic data so it must be processed first using feature engineering to obtain 51 features, then trained using the SVM algorithm with RBF kernel and modified C values. Every C value that is changed will be used K-Fold cross-validation first to separate the testing data and training data. The specified k value is 5. The results for each trial obtained accuracy, Receiver Operating Characteristic (ROC) and Area Under the Curves (AUC), for the best that is at C = 100 while the worst at C = 0.001.
Latent Factor Analysis of Gaussian Distributions under Graphical Constraints

We explore the algebraic structure of the solution space of convex optimization problem Constrained Minimum Trace Factor Analysis (CMTFA), when the population covariance matrix Σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> has an additional latent graphical constraint, namely, a latent star topology. In particular, we have shown that CMTFA can have either a rank 1 or a rank n - 1 solution and nothing in between. The special case of a rank 1 solution, corresponds to the case where just one latent variable captures all the dependencies among the observables, giving rise to a star topology. We found explicit conditions for both rank 1 and rank n - 1 solutions for CMTFA solution of Σ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">x</sub> . As a basic attempt towards building a more general Gaussian tree, we have found a necessary and a sufficient condition for multiple clusters each having rank 1 CMTFA solution to satisfy a minimum probability, to combine together to build a Gaussian tree.
The Concept Of Pedagogical Skills, Its Role And Importance In Teaching

The article provides a comprehensive analysis of the fact that the success of the pedagogical activity depends in many ways on his or her personality, character, and interaction with students.But in general, their impact on the educational process seems insignificant.However, an experienced educator will focus on how his or her behavior has affected students, making the necessary adjustments and improvements.The inspiring challenge of such teachers is to instill in students a sense of confidence that their desire to learn will enable them to do better.In this case, the methods of teaching and upbringing become a means of realizing the qualities embodied in the personality of the teacher and the transfer of moral qualities from him to the student.
Efficient Load Balancing for Cloud Computing by Using Content Analysis

Nowadays, computer networks have grown rapidly due to the demand for information technology management and facilitation of greater functionality. The service provided based on a single machine cannot accommodate large databases. Therefore, single servers must be combined for server group services. The problem in grouping server service is that it is very hard to manage many devices which have different hardware. Cloud computing is an extensive scalable computing infrastructure that shares existing resources. It is a popular option for people and businesses for a number of reasons including cost savings and security. This paper aimed to propose an efficient technique of load balance control by using HA Proxy in cloud computing with the objective of receiving and distributing the workload to the computer server to share the processing resources. The proposed technique applied round-robin scheduling for an efficient resource management of the cloud storage systems that focused on an effective workload balancing and a dynamic replication strategy. The evaluation approach was based on the benchmark data from requests per second and failed requests. The results showed that the proposed technique could improve performance of load balancing by 1,000 request /6.31 sec in cloud computing and generate fewer false alarms
QR Code as a Tool of the Marketing Library Activity

The article analyzes the scientific comprehension state of QR-coding technology in the domestic and foreign scientific literature. It has been revealed in which industries the QR-technology is most widely used. Two basic types of QR-codes are explored – dynamic and static, and the differences between them are identified. The possibilities of providing information services using QR-codes are outlined. The types of information and data that can be encoded (text and multimedia) are analyzed. Mobile applications of the new format (“smart” QR-code generators) have been considered to provide additional connectivity between smartphones, QR-codes, and content. The dynamics of the use of QR-codes and their popularity in foreign countries have been revealed.The advantages of QR-coding technology, which are easy to use and financially affordable due to the availability of free online services, are verified. Such advantages of QR-codes as versatility, flexibility, economy, environmental friendliness, tracking and analytics, user involvement are emphasized. It has been found that in order to get effective marketing results using QR-coding technology, certain recommendations are required, such as pre-testing QR-code readings, checking of the expediency of selecting their location, the relevance and importance of encoded content.On the basis of the experience of using QR-codes in library work, the prospect of QR-coding technology as a type of mobile service in library institutions from the point of view of modern marketing means of communication is substantiated. It has been established that today QR-codes in the library can be considered as a component of a mobile service, which makes it possible to increase the level of library service, as an effective way to provide additional information for the reader, to promote the books, authors and the library itself.
Moving towards Digitalization in Small and Medium Enterprises in Least Developed Countries, Review of the case of Yemen

Small businesses play a crucial role in the growth and development of economies worldwide.However, they face competition while focusing on the local markets, this puts a tremendous amount of pressure on SMEs to adopt new technology and transform towards digitalization in order to achieve competitive advantages and realize the desired economic growth and development.The need for such role is more needed in the least developed countries (LDC), due to the resources gap they are experiencing.This paper attempts to gain insight into the digitalization of the business process in Yemen through reviewing the literature for the potentials of the SMEs sector to digitalize in a sufficient manner that leads to achieving economic growth and development.The findings indicate that the sector of SMEs in Yemen has its ups and downs which can be attributed to the recent economic and political instability, moreover, initiatives are continuously put into practice which lack the required support in order to thrive into development and growth achievements.
Ecological Finance Theory: New Foundations

This paper puts forth Ecological Finance Theory as a valuable new vision for financial economics. Ecological Finance Theory is a mission-driven proposition seeking to re-embed financial systems within social and ecological constraints in order to ensure social resilience. Ecological Finance theory acknowledges the insufficiency of the neoclassical model to account for and respond to the socio-economic and biophysical realities of a post pandemic world. It introduces a sound ontological and epistemological framework taking into account the complex interactions between the financial, socio-economic and biophysical realms, and the impact of financial models in shaping reality. It draws on biological theory to provide insight into new metaphors and concepts for the study of financial systems. This paper paves the way for future work within this theory. We first describe its meta-theoretical hypotheses. We then introduce an initial set of metaphors. We finally develop a puzzle-solving application with a new stock-flow consistent model permitting to analyze ways to upscale the transformative power of local complementary currencies.