Reviewerly/exHarmony huggingface dataset:
Not rational, but not brutely causal either: A reply to Fodor on concept acquisition

Jerry Fodor has argued that concept acquisition cannot be a psychological or “rational-causal” process, but can only be a “brute-causal” process of acquisition. This position generates the “doorknob --&gt; DOORKNOB” problem: why are concepts typically acquired on the basis of experience with items in their extensions? I argue that Fodor’s taxonomy of causal processes needs supplementation, and characterize a third type: what I call “intelligible-causal processes.” Armed with this new category I present what I regard as a better response than Fodor’s to the doorknob --&gt; DOORKNOB problem.
Supporting Preservice Chemistry Teachers to Learn Islam and Science Using Moodle

Information and communication technology or ICT (Information and Communication Technology) is growing very rapidly.This has a tremendous impact on various sectors of life, one of which is education, then the application of ICT becomes a mandatory choice that must be applied.But the problem of technology still plays a small role in learning and teaching.This study aims to develop web-based learning media in Islamic and Science courses in the face of 21st-century challenges.The research and development method uses a 4D model by following the path of Thiagarajan et al. which has four stages, namely define, design, development, and disseminate, but researchers did not go through the disseminate stage.The assessment was done in the form of media and pedagogic validation, as well as limited trials by students of the 2015 chemistry education.Data analysis techniques on media and pedagogic validation using the Guttman scale, while limited trials using Likert scale scoring, with percentages interpreted according to Riduwan'scriteria .The results obtained in the media and pedagogic validation that the product developed is very good, then at the limited trial stage the percentage is 69% with good categories.
HOMMO: A Hierarchical Flow Management Framework for Multi-Objective Data Center Networks

In data center networks, flows with different objectives coexist and compete for limited resources. From the application-level perspective, it is hard to satisfy the demands of different flows without effective resource planning. To address the bandwidth allocation problem under multi-objective scenarios, we study a multi-objective network utility maximization (NUM) problem and propose our practical bandwidth allocation framework HOMMO, which consists of an upper layer algorithm (ULA) and a lower layer algorithm (LLA). This hierarchical design helps to strike a balance between accuracy and efficiency. Implemented in network switches, ULA is an online learning-based scheme that allocates bandwidth for aggregated flows with different performances objectives. Taking the outputs from ULA as capacity constraints, LLA acts as a fast scheduling method at packet-level. We extend NUMFabric by implementing the design of isolate queues and corresponding dequeue strategy in switches and make it a feasible solution of the LLA. Therefore, HOMMO achieves satisfactory isolation across flows with different objectives, which is equivalent to provide a network slicing solution. To evaluate the proposed framework, we implement it in ns-3 and verify the performance under various scenarios. The simulation results show that HOMMO not only quickly converges to a near-optimal solution of the multi-objective NUM problem but also guarantees a Pareto-optimal solution. Moreover, it outperforms the well-known transport protocol (i.e., DCTCP) with 2x increase on average bandwidth utilization and 1.96x improvement in global network utility at the aggregated flow level.
Model Konduktivitas Termal Nanofluida Berdasarkan Grup Tak-Berdimensi dengan Parameter Termofisika Kompleks

This article presents a model of thermal conductivity of nanofluids based on dimensionless-group methods. In addition to the thermal conductivity of base fluids and nanoparticles, nanoparticle diameter, temperature, and volume fraction of nanoparticles, the proposed model involves several thermophysical parameters such as specific heat, density, and viscosity. The reason for the development of the model requires complex thermophysical parameters because, based on the experiments, these parameters determine the thermal conductivity of nanofluids. Validation of the model through comparison of the model with the experimental results shows that the models that have non-linear correlation have good accuracy in predicting the thermal conductivity of nanofluids.
Proactive and reactive context reasoning architecture for smart web services

The web of things (WoT) uses web technologies to connect embedded objects to each other and to deliver services to stakeholders. The context of these interactions (situation) is a key source of information which can be sometimes uncertain. In this paper, we focus on the development of intelligent web services. The main requirements for intelligent service are to deal with context diversity, semantic context representation and the capacity to reason with uncertain information. From this perspective, we propose a framework for intelligent services to deal with various contexts, to reactively respond to real-time situations and proactively predict future situations. For the semantic representation of context, we use PR-OWL, a probabilistic ontology based on multi-entity Bayesian networks. PR-OWL is flexible enough to represent complex and uncertain contexts. We validate our framework with an intelligent plant watering use case to show its reasoning capabilities.
Physical and Augmented Reality As Elements of Visual Culture: Proportions in Educational Products for Persons with ASD

This article raises the question of the impossibility of standardized use of augmented reality technologies in the educational process. This approach changes the status of augmented reality and transforms it into a phenomenon of visual culture. The authors represent a hypothetical ratio of physical and augmented realities in educational products for people with autism spectrum disorders (ASD), considering their needs, behavioral patterns and states. The proportion is based on the authors’ advanced research.&#x0D; Keywords: augmented reality, visual culture, education, educational product, needs, autism spectrum disorders.
Akurasi Citra Data Foto Udara UAV Quadcopter Persimpangan Lalu Lintas Kota Kendari

The use of the Unnamed Aerial Vehicle (UAV) called “Drone” has been widely used in various areas of planning, one of them by mapping the highway traffic junction. The range of remote roaming, adjustable spatial resolution greatly gives flexibility in the effectiveness of field surveys. The mapping accuracy test conducted on 7 (seven) Unsignaling junction in Kendari City by Omisi and Komisi Equation method gives significant output and quite accurate to serve as further planning data. The difference between image and field measurement results, obtained accuracy of 96%, so that the use of UAV in supporting mapping, survey and field planning still prioritize effectiveness and accuracy of the measurement.
Image Super-Resolution Based on Sparse Coding with Multi-Class Dictionaries

Sparse coding-based single image super-resolution has attracted much interest. In this paper, a super-resolution reconstruction algorithm based on sparse coding with multi-class dictionaries is put forward. We propose a novel method for image patch classification, using the phase congruency information. A sub-dictionary is learned from patches in each category. For a given image patch, the sub-dictionary that belongs to the same category is selected adaptively. Since the given patch has similar pattern with the selected sub-dictionary, it can be better represented. Finally, iterative back-projection is used to enforce global reconstruction constraint. Experiments demonstrate that our approach can produce comparable or even better super-resolution reconstruction results with some existing algorithms, in both subjective visual quality and numerical measures.
Modern Educational Psychotechnologies for Professional Training of Future Psychologists

Society creates demands for changing the quality of training of psychologists who would be able to take new positions in the understanding of human and the transformational processes of the present as a whole. Implementation of the progress towards the restructuring of the educational process, in particular the professional training of professional psychologists, will allow for the creation of new educational psychotechnologies. The methodological basis of professional training of the future psychologists should be the value-personality approach. The formation of a value relation to the future profession provides individuals with the possibility of self-realization in it. The main goal of professional training of future specialists is seen in the focus on comprehension of socio-normative, personal, professional values and the development of self-consciousness in general. Implementation of this goal requires the development and implementation of innovative educational psychotechnologies aimed at the development of sustainable integrative personal and professional competencies. The formation of the integral of the professional value of a future specialist should become one of the priorities of professional training of a psychologist in higher education institutions. The presence of a formed integral of professional value as a dynamic, multifunctional, and relatively stable personal formation will provide an understanding of normative values of the profession, awareness of the value of the personal self as a psychologist, and transforming them into professional competence. Instead, the slowdown in the aim-level personality self-awareness of the above-mentioned transformational processes will indicate axiological and identity uncertainty, a low level of self-interest and self-understanding of the student. The result of the introduction of psychological technologies for professional training should be the integral of the professional value of the future psychologist. This will ensure its professional efficiency and competitiveness in the context of the modern challenges of society.
Prototype E-Order pada Restoran Bebek Goreng Haji Yogi Menggunakan Metode PIECES

Restoran Bebek Goreng Haji Yogi adalah suatu usaha restoran yang berkembang Bebek Goreng Haji Yogi Restaurant is a restaurant business that develops in the West Jakarta area. The fried haji yogi duck restaurant serves a variety of menus with the main ingredients of duck meat. During this time, the system that runs in restaurants still uses notes of paper on consumer orders. Losing sheet paper transactions will have an impact on not fulfilling consumer orders and will worsen service. The application of a computerized system at Bebek Goreng Haji Yogi Restaurant is needed to provide effective ordering services to customers and make it easier to make reports. The analytical method used uses the pieces and supporting software methods to design using PHP, MySQL and balsamiq programming languages. The aim is to increase the effectiveness of orders to customers. So that the E-Order service information system can simplify ordering and minimize customers in queuing for food orders so they can minimize data error in recording product sales transaction data.
Word2Cluster: A New Multi-Label Text Clustering Algorithm with an Adaptive Clusters Number

Text clustering has been widely used in many Natural Language Processing (NLP) applications such as text summarization and news recommendation. However, most of the current algorithms need to predefine a clustering number, which is difficult to obtain. Moreover, the mutli-label clustering is useful in multiple clustering tasks in many applications, but related works are rarely available. Although several studies have attempted to solve above two problems, there is a need for methods that can solve the two issues simultaneously. Therefore, we propose a new text clustering algorithm called Word2Cluster. Word2Cluster can automatically generate an adaptive number of clusters and support multi-label clustering. To test the performance of Wrod2Cluster, we build a Chinese text dataset, Hotline, according to real world applications. To evaluate the clustering results better, we propose an improved evaluation method based on basic accuracy, precision and recall for multi-label text clustering. Experimental results on a Chinese text dataset (Hotline) and a public English text dataset (Reuters) demonstrate that our algorithm can achieve better F1-measure and runs faster than the state-of- the-art baselines.
Feature selected cost-sensitive twin SVM for imbalanced data

In this paper, we propose a cost-sensitive twin SVM (cs-tsvm) and apply it to imbalanced data. A weight is added to each instance according to its cost of misclassification which is related to its position. In preprocessing part, features are selected by their difference of majority and minority classes. The feature is selected when its difference value is higher than average one. The experiment is conducted on UCI datasets and G-mean, AUC and accuracy are evaluation metrics. The experimental results show that Feature selection with CS-TWSVM is useful for datasets with high dimension.
A Secure, Robust and Effective Cryptoscheme using Data Obfuscation of Entity Detection and Replacement &amp; Encryption for Ensuring Data Privacy in Private Cloud

Cloud has been emerging, popular and very demanding technology now a day.Cloud has got wide popularity with its sophisticated features such as internet access, more storage, easy setup, automatic updates, low cost and resource provisioning based on "pay as you go" policy.In spite of advantages, security is considered to be more important and drew the attention of many researchers.The data storage is becoming an indispensable measurement in cloud and most of the times cloud does not guarantee that information/data that has been stored is secured from illegitimate access.Many researchers are working to ensure information security in cloud but unfortunately they do not provide adequate security.This paper is aiming to propose an efficient algorithm with obfuscation and cryptography for unstructured data.The algorithm works to preserve confidentiality of data stored in cloud at two levels.At the fist level the algorithm obfuscate the document by replacing the key words (obfuscation), and at the second level obfuscated document is encrypted using traditional RSA algorithm for better security.Experimental results shows that the proposed algorithm yields good results.
A method for constructing permutations, involutions and orthomorphisms with strong cryptographic properties

S-Boxes are crucial components in the design of many symmetric ciphers.To construct permutations having strong cryptographic properties is not a trivial task.In this work, we propose a new scheme based on the well-known Lai-Massey structure for generating permutations of dimension n = 2k, k 2. The main cores of our constructions are: the inversion in GF(2 k ), an arbitrary k-bit non-bijective function (which has no pre-image for 0) and any k-bit permutation.Combining these components with the finite field multiplication, we provide new 8-bit permutations without fixed points possessing a very good combination for nonlinearity, differential uniformity and minimum degree (104; 6; 7) which can be described by a system of polynomial equations with degree 3. Also, we show that our approach can be used for constructing involutions and orthomorphisms with strong cryptographic properties.
Students Performance in Board Examination Analysis using Naïve Bayes and C4.5 Algorithms

In this generation, where the educational system is being added with new policies and requirements, the higher education institutes (HEIs) use data mining tools and methods for scholastic improvement of student performance.The authors gathered information from the Bachelor of Secondary Education (BSEd) graduates of Davao del Norte State College (DNSC), thru the Institute of Education (IEd) and other offices concerned.The information is comprised of six ( 6) attributes for analysis that are as follows: stanine, general weighted average (GWA), honors received, the type of scholarship that the specified respondents have, review center admission, and Licensure Examination for Teachers (LET) remarks.After the tools were performed on the datasets, it is found out that the general weighted average (GWA) is the most influential with the attribute's value of 0.171 and followed by review center admission with the attribute's value of 0.132.The performance of the models Naïve Bayes and C4.5 in predicting the students' performance in the Licensure Examination for Teachers were compared in terms of accuracy to identify which among the two classification algorithms performs best.Results show that Naïve Bayes has 85.14% and 90% prediction accuracy using both 10-fold cross-validation and 70% training and 30% testing, while C4.5 accumulated 86.13% and 86.66% accuracy for 10-fold cross-validation and 70-30 percentage split, respectively.Using Naive Bayes algorithm, the AUC results to 0.8507 and 0.8333 when using the C4.5 algorithm.Therefore, it is construed that Naive Bayes results are more consistent, suitable and reliable when testing the data compared to C4.5.
ANALISIS SISTEM PENGENDALIAN INTERNAL PERSEDIAAN BARANG DAGANGAN PADA PT. DAYA ANUGRAH MANDIRI CABANG MANADO

Inventory is one of the important assets of the company. Therefore, internal control over inventory is needed to protect against the risk of theft, damage and fraud. This study aims to determine the internal control system for merchandise inventory implemented by PT. Daya Anugrah Mandiri Manado Branch in terms of the internal control component according to the coso (committee of sponsoring organizations). The method of analysis in this study uses qualitative methods by obtaining data through interviews and documentation and then conclusions are drawn. The results showed that the internal control system for merchandise inventory applied by PT. The overall grace of the Manado branch of Anugrah is already good. Companies should further enhance the security of inventory in the warehouse by installing cctv camera so that security is more secure, and the need for an accounting department that is specifically tasked with carrying out financial records and reporting.
The Effect of Eye-Hand Coordination Training on Accuracy of Service in Volleyball Players

This study aims to determine the effect of eyehand coordination exercises on the accuracy of top service for volleyball players.The research method used is the preexperiment design method.The samples in this study were 24 volleyball players in SMA Negeri 1 Angsana, Tanah Bumbu Indonesia, which were divided into two groups: the treatment group was twelve people and the control group was 12 people.The samples were selected using saturated sampling techniques.The instrument used to measure the accuracy of service for volleyball with a service test for volleyball (Nurhasan, 2007: 225).Data analysis techniques using the Hypothesis Test (average comparison analysis) paired-samples T-test (C.Trihendradi, 2013: 119).The results showed that the pretest data on the service accuracy of the volleyball players at SMAN 1 Angsana, Tanah Bumbu, obtained an average of 11.83 with a standard deviation = 5.540.In the post-test data on service accuracy, the average is 21.17 with a standard deviation = 7.445.While obtained t-count = 6.642> t-table (11; 0.025) = 2.201, with Sig.(2-tailed) = 0,000; it turns out that Sig.(2-tailed) <0.05 thus the t-count is significant.The conclusion is that there is an influence of eye-hand coordination exercises on the accuracy of top service.It implies that to improve the top serviceability of volleyball players can be conducted by implementing eye-hand coordination exercise.
Error Analysis of Atmospheric Visibility Measurements Based on an Image Brightness Contrast Method

The method of image visibility detection is one of the methods closest to human vision. It is based on the principle of human eye observation but avoids the errors resulting from human subjectivity. However, the background and stray light and weather conditions seriously impact the detection. Furthermore, the inherent brightness contrast of the artificial target plate has a large error due to the reflection of the surface structure, which hinders the widespread application of this method. In this study, the effects of the gain and zero-drift errors and the measurement error from inherent brightness contrast on the measurement range and accuracy of atmospheric visibility are analyzed in detail. The detection baseline length is adaptively adjusted according to these errors to achieve the best detection.
WEB BASED PHISHING EMAIL IDENTIFIER USING SENDER ADDRESS AND RETURNING ADDRESS

Phishing emails are the emails that contains attachment(s) such as URL(s) or any file attachment(s) and messages that convinces user to access the URL or the attachment file whereby the URL redirects user to a fake website and prompt user for inputs of confidential while file attachment might contain any malicious software that can infected the users device. The target victims of these attacks are ranging from regular webmail users to professionals and the victims are usually targeted randomly as well as other well-planned attacks. Therefore, general webmail users should know the basic of phishing email identification based on the existing information included in inbox mails. In this paper, researcher analyzed different algorithms proposed and how will the algorithms contribute to phishing emails detection. Based on the result, researcher realized the importance for general webmail users to have the knowledge in identifying phishing emails. The Web Based Phishing Email Identifier is developed to detect phishing emails in basic level as well as educating the users on characteristics of phishing emails.
Weakly Supervised Conditional Random Fields Model for Semantic Segmentation with Image Patches

Image semantic segmentation (ISS) is used to segment an image into regions with differently labeled semantic category. Most of the existing ISS methods are based on fully supervised learning, which requires pixel-level labeling for training the model. As a result, it is often very time-consuming and labor-intensive, yet still subject to manual errors and subjective inconsistency. To tackle such difficulties, a weakly supervised ISS approach is proposed, in which the challenging problem of label inference from image-level to pixel-level will be particularly addressed, using image patches and conditional random fields (CRF). An improved simple linear iterative cluster (SLIC) algorithm is employed to extract superpixels. for image segmentation. Specifically, it generates various numbers of superpixels according to different images, which can be used to guide the process of image patch extraction based on the image-level labeled information. Based on the extracted image patches, the CRF model is constructed for inferring semantic class labels, which uses the potential energy function to map from the image-level to pixel-level image labels. Finally, patch based CRF (PBCRF) model is used to accomplish the weakly supervised ISS. Experiments conducted on two publicly available benchmark datasets, MSRC and PASCAL VOC 2012, have demonstrated that our proposed algorithm can yield very promising results compared to quite a few state-of-the-art ISS methods, including some deep learning-based models.