Reviewerly/exHarmony huggingface dataset:
On Armstrong’s Difficulties with Adequate Truthmaking Restrictions

D. M. Armstrong rejects various ontologies that posit truths without truthmakers. But, lest proponents of such questionable ontologies postulate suspicious truthmakers in a bid to regain ontological respectability, Armstrong requires a plausible restriction on truthmaking that eliminates such ontologies. I discuss three different candidate restrictions: categorical, natural, and intrinsic difference-making. While the categorical and natural restrictions eliminate the questionable ontologies, they also eliminate Armstrong’s own ontology. The intrinsic difference-making restriction, on the other hand, fails to eliminate any of them. Thus Armstrong lacks a principled reason for rejecting such ontologies.
Self-education as a condition of professional and personal development of a future specialist

In the article, personal self-improvement is considered as the highest form of conscious self-development of the personality, as an opportunity for personal growth in a person and his independent desire to approach a certain ideal for acquiring personality traits and qualities; The content and significance of self-improvement of the personality of the future teacher as a factor of personality development are determined.Self-education in conditions of increasing information contributes to the rapid assimilation of the necessary relevant knowledge for the timely development of all the information received. It is important to comprehend the information flow in a timely manner and direct our own, including professional, efforts in the right direction with the goal of the least cost and greatest benefit for ourselves and society.
Open Tools for Publishing Education: A Workshop on Pedagogy and Practice

This presentation was part of a two-day course at the 2019 Force11 Scholarly Communication Institute designed to explore possible uses of open publishing platforms, including Pressbooks, Wordpress, OJS, and Omeka, to teach scholarly communication and publishing concepts, as well as practical skills, in libraries or higher education institutions.
Accounting Information System Credit To Customers Barupada Jalin Matra District Village Sentul Sumbersuko Lumajang

Interlace dimension as one of the government agencies that has a business sense, Because The most basic activities are buying money by collecting funds from the community and then selling the money that has been collected by channeling it back to the community through loans or loans. Problems with Accounting Information Systems giving credit as well as the strengths and weaknesses of the Accounting Information System giving credit to new customers in the capital of Sajanguko Sentul village sub-district Lumajang. The researcher used qualitative methods. The procedure for granting credit to the Sentul village capital, Sajanguko Lumajang sub-district, is already good enough and there are elements of internal control, this can be seen from the separation of functions in each section so that no part of the loan transaction from the beginning to the end.
A Hybrid Learning Algorithm in Automated Text Categorization of Legacy Data

The goal of this research is to develop an algorithm to automatically classify measurement types from NASA's airborne measurement data archive.The product has to meet specific metrics in term of accuracy, robustness and usability, as the initial decision-tree based development has shown limited applicability due to its resource intensive characteristics.We have developed an innovative solution that is much more efficient while offering comparable performance.Similar to many industrial applications, the data available are noisy and correlated; and there is a wide range of features that are associated with the type of measurement to be identified.The proposed algorithm uses a decision tree to select features and determine their weights.A weighted Naive Bayes is used due to the presence of highly correlated inputs.The development has been successfully deployed in an industrial scale, and the results show that the development is well-balanced in term of performance and resource requirements.
Query Classification Based on Automatic Learning Query Representation

Recently, both commercial websites and search engines require people to enter query keywords to get useful information. It is becoming increasingly important to understand the user's intentions. Query classification task is quite helpful to commercial websites and search engines, and its goal is to classify the query to predefined categories to better understand the needs of the user. In this paper, we use the supervised learning method to learn query vector representation automatically based on semantics feature in query classification task. Our experiments use the three different neural networks, respectively convolutional neural network, Long Short-Term Memory (LSTM) and two layers LSTM to model the query. The experiments results show that our automatic learning query vector model outperforms other models. The two layers LSTM model perform better than the other two models, and the F-score improves 4% compared to Logistic Regression.
Strong target constrained video saliency detection

Aiming at the problem of video saliency detection, a strong target constrained video saliency detection method is proposed in this paper. In order to detect the salient region fast and effectively, strong target constraints forcing by the location, scale and color model are introduced into the video saliency detection. First, a target locating strategy for obtaining the location and scale information is proposed by correcting the result of video tracking with the optical flow result and the segmentation result of the last frame. Second, the estimated color model of the target is also calculated by the obtained segmentation results. Finally, the strong target constraints are integrated into the saliency model in the way of extending the significance hypothesis, and a high quality saliency map is obtained, where segmentation is employed for constrained parameters updating. In details, Densecut is initialized by the obtained saliency map to calculate the segmentation result of the last frame. Compared with some state-of-art saliency detection methods, our proposed method performs outstandingly, and the results on DAVIS dataset are significantly improved in terms of accuracy and robustness.
An Energy Efficiency Optimization Method Based on Decoupling in Cloud Computing

Abstract The cloud resource is becoming a key infrastructure carrying the various types of applications, and providing basic support for emerging areas such as artificial intelligence, big data, and internet of things. It is an important driving force for the development of science and technology. Cloud computing has laid the development direction and foundation for future computing. Because cloud is composed of large-scale distributed resources, resource allocation and optimization of computing efficiency in cloud computing is a very important task. In order to adapt to the complex use environment of multi-load parallel in cloud computing, this paper proposes a decoupling method in virtualization environment, which can optimize the energy allocation strategy of distributed systems and effectively improve the efficiency of cloud resources.
On the Semi-incremental Clustering Algorithm based on Kalman Filter and Bayes Approach

In this paper, Kalman filter is applied to clustering algorithms, and two new incremental clustering algorithms are proposed. A semi-supervised incremental clustering Algorithm based on Kalman and semi-incremental clustering is proposed to improve the precision of the existing algorithm. Compared with the existing algorithms, the accuracy of the algorithm is improved, and better experimental results are obtained.
GRALD: an Approach for Goal and Risk Analysis in the Development of Information Systems for the Web of Data

The publication of Linked Data on the Web regarding several application domains leads to new problems related to Requirements Engineering, which needs to take into account aspects related to new ways of developing systems and delivering information integrated with the Web of Data. Tasks such as (functional and non-functional) requirements elicitation and ontology-based conceptual modeling can be applied to the development of systems that publish Linked Data, in order to obtain a better shared conceptualization (i.e., a domain ontology) of the published data. The use of vocabularies is an intrinsic activity when publishing or consuming Linked Data and their choice can be supported by the elicited requirements and domain ontology. However, it is important to assess the risk when choosing external vocabularies, as their use can lead to problems, such as misinterpretation of meanings due to poor documentation, connection timeouts due to infrastructure problems, etc. Thus, risk identification, modeling and analysis techniques can be employed, in order to identify risks and their impacts on stakeholder goals. In this work, we propose GRALD: Goals and Risks Analysis for Linked Data, an approach for modeling goals and risks for information systems for the Web of Data.
The faster lattice sieving for SVP

Abstract The security of lattice-based cryptography is based on the hardness of the difficult problems on lattice, especially the famous shortest vector problem. There are many famous heuristic lattice sieving algorithms to solve SVP, such as the Gauss Sieve, NV Sieve, which deal the full-rank n -dimensional lattice from start. Inspired by the idea of rank reduction, in this paper we present new technique on lattice sieving to make the algorithm solve the SVP faster. We split the basis of “bigger” lattice into several blocks according to some rule until the sublattice is small enough. Then we recursively sieved on these sub-lattices to get short vector lists. With the short vector lists, we can find the shortest vector when the algorithm recoverd the original lattice. This lead to obviously speedup with the recursive procedure without extra space overhead. Compared with the Gauss Sieve, the new method converge faster, and achieved at least 70% acceleration.
Public Key Encryption with Equality Test for Heterogeneous Systems in Cloud Computing

Cloud computing provides a broad range of services like operating systems, hardware, software and resources.Availability of these services encourages data owners to outsource their intensive computations and massive data to the cloud.However, considering the untrusted nature of cloud server, it is essential to encrypt the data before outsourcing it to the cloud.Unfortunately, this leads to a challenge when it comes to providing search functionality for encrypted data located in the cloud.To address this challenge, this paper presents a public key encryption with equality test for heterogeneous systems (PKE-ET-HS).The PKE-ET-HS scheme simulates certificateless public encryption with equality test (CLE-ET) with the identity-based encryption with equality test (IBE-ET).This scheme provides the authorized cloud server the right to actuate the equivalence of two messages having their encryptions performed under heterogeneous systems.Basing on the random oracle model, we construct the security of our proposed scheme under the bilinear Diffie-Hellman (BDH) assumption.Eventually, we evaluate the size of storage, computation complexities, and properties with other related works and illustrations indicate good performance from our scheme.
Modeling the Placement of Charging Stations for All-Electric Vehicles

Abstract All-electric vehicles are attracting everyone’s eyes for its using clean energy rather than fossil energy. At the same time, Tesla, a rising and new born enterprise is famous for its futuristic design, which makes all-electric vehicles become more and more popular. However, the law that the development of supporting facilities is always likely to be relatively backward is confirmed in the progress of all-electric vehicles and charging stations.
A new kernel method for nonnegative matrix factorization

In this paper, we propose a right kernel nonnegative matrix factorization (RKNMF) model. Different from the kernel NMF (KNMF) model which implements the kernel method at the left hand of the factorization, RKNMF applies the kernel method at the right hand of the factorization. KNMF lifts the original features to a higher dimension space by a nonlinear map to excavate the structures hidden in the data, while RKNMF hopes to excavate the nonlinear structures between the bases and the reduced dimensional representation. Moreover, we present a double kernel NMF (DKNMF) method which is a combination of KNMF and RKNMF. In addition, practical algorithms are presented for RKNMF and DKNMF. The numerical experiments show that RKNMF outperforms NMF on the task of image reconstruction, and RKNMF and DKNMF outperform NMF and KNMF on the task of extracting features.
A modified Laplacian phase unwrapping algorithm

Phase unwrapping is a general problem in many measurement fields, and plenty mature methods have been put up with. Among them, Laplacian operator combined Fast Fourier-based phase unwrapping method is widely used because of its high speed and relative robustness. However, in some applications such as structured-light 3D reconstruction, there always appear shadows of object in the object-edge regions. These shadows and discontinuity regions might cause big errors in unwrapping results, which will be even more serious for the Fast Fourier-based method. Therefore, in this paper, we analyzed the problem theoretically and present a modified algorithm based on the reference-phase mask. With a designed phase mask, this practical algorithm can remove errors effectively and reach a more precise result. The phase mask will be produced automatically based on the edge detection of the problematic regions combined with an empirical mode decomposition algorithm. The whole procedure will be complemented without manual intervention. Comparison experiments show that our method has great improvement than the original methods in the aspects of accuracy speed, and robustness. This enables the Laplacian based unwrapping algorithm to have compatibility with more optical, physical, medical and engineering occasions.
AN ENHANCED INTRUSION DETECTION SYSTEM USING HONEYPOT AND CAPTCHA TECHNIQUES

Internet is no doubt inevitable as it has a tremendous impact in our lives. Despite its importance, internet comes with many challenges, among which is security. From the literature, several attempts have been made to develop secure and user-friendly spam detection technique. But these attempts linger between these two fundamental issues: the robustness and the usability in CAPTCHA system, passiveness of Intrusion Detection System (IDS), which failed to detect some forms of novel attacks, flexibility to attacks and not efficient to users. In this work, honeyCAPTCHA, an enhanced intrusion detection framework is designed to solve the above problems as it is capable of detecting crawlers’ attacks, resilient and efficient to users. The system is mainly considered as an option to a CAPTCHA-BASED IDS model, which suffers the above problems. The system outperforms the existing system considering its performance measure based on the proposed metrics that includes detection rate (DR) of 76%, 1.7 times the detection rate of the existing system with false positive rate (FPR) of 10% against the existing system that have 36% FPR, which proved that the system is more robust compared to the existing system. The usability of the system measured using BDR and BNR is 1.5 times that of the existing system, which shows how efficient the system is to users when compared to the existing system. Both systems were compared based on standard IDS evaluation metrics CID which proves that the system is 2.26 times better than the existing system.
Aplikasi Kompresi GZip Pada Sistem Pemberkasan Digital Dokumen PKM Berbasis PHP

Documents of research and community dedication have become an inseparable part of a higher education institution. The documents usually stored digitally on a dedicated server or on several public servers or even stored locally. The number of digital documents will continue to increase over time so that the need for greater capacity will continue to emerge from time to time. To reduce the need of capacity requirements that continue to increase, it is necessary to have a compression feature that can be used to reduce document size so as to reduce storage requirements. In this study, a digital archieving system for research and community dedication documents was developed to that was not only used to manage these documents but also applied the Gzip compression module from the PHP library to help reduce storage consumption. Testing the implementation of Gzip on the compression of digital documents stored results in a compression ratio reaching 17.35% in docx documents and 12.02% in pdf documents.&#x0D; &#x0D; Keywords : Document, Digital, Compression, Gzip, PHP.
Designing Interactive Intelligent Systems for Human Learning, Creativity, and Sensemaking

The focus of intelligent systems is on "making things easy'' through automation. However, for many cognitive tasks---such as learning, creativity, or sensemaking---there is such a thing as too easy or too automated. Current human-AI design principles, as well as general usability guidelines, prioritize automation, and efficient task execution over human effort. However, this type of advice may not be suitable for designing systems that need to balance automation with other cognitive goals. In these cases, designers lack the necessary tools that will allow them to consider the trade-offs between automation, AI assistance, and human-effort. My dissertation looks at using models from cognitive psychology to inform the design of intelligent systems. The first system, Florum, looks at automation after human-effort as a strategy to facilitate learning from science text. The second system, TakeToons, explores automation as a complementary strategy to human-effort to support creative animation tasks. A third set, SmartCues and Affinity Lens use AI as a last-mile optimization strategy for human sensemaking tasks. Based on these systems, I am looking to develop a design framework that (1) classifies threats across different levels of design including automation, user interface, expectations from AI, and cognition and (2) offers ways to validate design decisions.
PENGELOMPOKAN PENDISTRIBUSIAN PRODUK OLAHAN AYAM MENGGUNAKAN ALGORITMA K-MEANS

Increasing population growth in Indonesia has triggered a large number of food product demand, so the higher the level of innovation of the company to produce processed food. PT Charoen Pokphand Indonesia Food Division is one of the companies engaged in processed food made from chicken meat. However, companies currently have not been able to process distribution data and rely solely on information from sales data to find out the number of product sales with which brands are sold the most each year. In fact, it is not enough to increase income and to be able to compete with other similar companies. From the existing data stack, it must be processed to find out new information from these data. Data processing methods like this are referred to as data mining. the results of cluster 3, again - it is seen that consumer characteristics in cluster 3 are also dominated by consumers who are more interested in Okey's processed chicken products, which is as much as 5615.67 Pack / Box. Whereas for enthusiasts in processed chicken products with the Fiesta brand as many as 4614.67 Pack / Box. and enthusiasts of processed chicken products under the Champ brand 1792.67 Pack / Box. So, from all clusters, it can be concluded that cluster 3 consists of cities with the lowest sales level.
Adversarial Anomaly Detection for Marked Spatio-Temporal Streaming Data

We consider the sequential anomaly detection problem in the one-class setting when only the anomalous sequences are available and propose an adversarial sequential detector by solving a minimax problem to find an optimal detector against the worst-case sequences from a generator. The generator captures the dependence in sequential events using the marked point process model. The detector sequentially evaluates the likelihood of a test sequence and compares it with a time-varying threshold, also learned from data through the minimax problem. We demonstrate our proposed method's good performance using numerical experiments on simulations and proprietary large-scale credit card fraud datasets. The proposed method can generally apply to detecting anomalous sequences.
Photonic Reservoir Computing Based on the Random Interaction of Transverse Optical Modes in Large Cross-Section Waveguides

A recent, pivotal advance in the domain of neuromoprhic engineering, is the realization of reservoir computing (RC) schemes based on passive photonic components, thus merging RC's efficiency with high bandwidth and negligible power consumption [1]. Following this direction, we demonstrate an RC scheme that relies on a phase-to-intensity mapping process, offered by the superposition of transverse modes of a large-core polymer optical fiber (POF). Randomness is provided by in-fiber defects that allow mode-coupling, whereas nonlinearity is introduced through the square-law of photodiodes (PDs) at the output. The proposed scheme is similar to [2, 3], with the exception that integrated waveguides are replaced by optical modes, while defects are operationally equivalent to couplers. Taking into consideration that defects are of wavelength-size (Mie scattering) and the number of modes can be tailored to reach 106, the proposed RC can facilitate an elevated number of nodes and synapses, whereas it is also equipped with fading memory, allowing it to classify high-speed image streams.
Large-scale quantum photonic processors: photonics for AI and AI for photonics (Conference Presentation)

Photons play a central role in many areas of quantum information science, either as qubit themselves or to mediate interactions between long-lived matter based qubits. Techniques for (1) high-fidelity generation, (2) precise manipulation and (3) ultra-efficient detection of quantum states of light are therefore a prerequisite for virtually all quantum technologies. A quantum photonics processor is the union of these three core technologies into a single system, and, bolstered by advances in integrated photonics, promises to be a versatile platform for quantum information science. In this talk we present recent progress towards large-scale quantum photonic processors, leveraging the platform of silicon photonics. We demonstrate how quantum photonic processors can accelerate both quantum and classical machine learning, and how optimization techniques can enhance large-scale quantum control and provide a new path towards efficient verification of near-term quantum processors.
Inside SQL Server Containers

When I thought about how I would approach this chapter, I thought about only talking about what is new for containers for SQL Server 2019. While that alone would be worth a chapter, I decided to go bigger and be more inclusive to talk about the concept of containers, why it is important as a new way of deploying SQL server, and of course what are new capabilities for containers with SQL Server 2019. I could write an entire book on containers so you will not walk away from this chapter with an exhaustive knowledge on the subject. There are many great resources on containers (one I still find value is still !) to supplement this chapter. My goal for this chapter is to give you enough information about containers to see how SQL Server containers work and why you should consider using them. I do promise you will leave this chapter understanding how to deploy, manage, and use SQL Server containers.