Reviewerly/exHarmony huggingface dataset:
Logic without metaphysics

Abstract Standard definitions of logical consequence for formal languages are atomistic. They take as their starting point a range of possible assignments of semantic values to the extralogical atomic constituents of the language, each of which generates a unique truth value for each sentence. In modal logic, these possible assignments of semantic values are generated by Kripke-style models involving possible worlds and an accessibility relation. In first-order logic, they involve the standard structures of model theory, as sets of objects from which the extralogical symbols of the language receive their denotations. I argue that there is an alternative, holistic, approach to the task of defining logical consequence for a formal language. It specifies necessary and sufficient conditions for an assignment of truth values to all the sentences of the language to be compatible with the intended interpretation of its logical constants. It achieves this without invoking possible assignments of semantic values to the extralogical atomic constituents of the language, or the formal resources that are employed to generate these. I show how this approach can be successfully applied to modal propositional logic and to first-order logic, modal as well as nonmodal. I show that the holistic definitions of logical consequence that I supply for these languages are equivalent to the standard atomistic definitions.
Research and Analysis of the Development Approaches in E-Business Mobile Application

With e-Business rapidly spreads out in recent years, there is a significant trend that more and more consumers are using mobile applications in engaging with e-Commerce. In order to reach out to maximum consumers across diverse mobile platforms, how to choose a suitable e-Commerce mobile application development approaches with reliable compatibility and lower cost becomes a challenging solution. This paper starts with describing an overview of various development approaches in the field of e-Commerce mobile applications. The research will focus on examining the performance of e-Business mobile applications in terms of access to mobile features, advanced graphics and compatibility are developed using such frameworks. By analyzing the research data on several platforms, this paper will list the pros and cons in each development approaches under different commercial criteria. Finally, the paper summarizes the contributions and concludes with some possible future e-Commerce mobile development directions.
Design Considerations for an Intelligent Video Surveillance System Using Cloud Computing

Recently, deep neural network and cloud computing based intelligent video surveillance technology are growing interests in the industrial and academia. The synergy with both technologies emerges as a key role of the public safety and video surveillance in the field. Reflecting these trends, we have been studying a cloud-based intelligent video analytic service using deep learning technology. INCUVAS (cloud-based INCUbating platform for Video Analytic Service) is a platform that continuously enhances the video analysis performance by updating real-time dataset with the deep neural network on a cloud environment. The goal of this cloud service can provide continuous performance enhancement and management using image dataset from the real environment. In this paper, we consider the design requirements for online deep learning based intelligent video analytics service.
On reconstruction of bandlimited signals from purely timing information

The well known Logan’s theorem asserts that, under some assumptions, a one octave bandpass signal is recoverable, modulo a multiplicative constant, from its zero crossings only. However, such recovery is numerically problematic and the theorem is not applicable to general bandlimited signals. In this paper, we demonstrate that the additional timing information sufficient for recovery of general band limited signals can be provided in the form of the zero crossings of several of its derivatives and propose novel numerically robust algorithms for such timing extraction and for signal reconstruction, both with high fidelity. We tested the proposed algorithms extensively, with both synthetic and audio signals. In particular, we numerically demonstrate that the timing of the zero crossings of a typical speech signal and of its first two derivatives are not sufficient for a numerically robust recovery of such a signal, but that a robust recovery becomes possible by adding the timing of the zero crossings of the third order derivative.
Model-Based Motion Artifact Correction in Digital Subtraction Angiography Using Optical-Flow

Digital subtraction angiography is an important method for obtaining an accurate visualization of contrast-enhanced blood vessels. The technique involves the digital subtraction of two X-ray images, one with contrast filled vessels (fill image) and one without (mask image). Unfortunately, artifacts that are introduced due to the subtraction of misaligned mask and fill images may potentially degrade the diagnostic value of an image. The techniques used for correcting such artifacts involve the use of affine image registration techniques for aligning the mask and fill images and image processing techniques for suppressing the artifacts. Although affine registration techniques often yield acceptable results, they may fail when the imaged object undergoes 3D transformations. The techniques used for suppressing artifacts may cause blurring, when a projection image can no longer be corrected using a globally uniform motion model. In this paper, we have introduced an optical-ow based local motion compensation approach, where pixel-wise deformation fields are computed based on an X-ray imaging model. A visual inspection of the results shows a significant improvement in the image quality due to a reduction in the artifacts caused by misregistrations.
An Experimental Evaluation of Bow-Tie Analysis for Cybersecurity Requirements

Bow-tie analysis includes a graphical representation for depicting threats and consequences related to unwanted events, and shows how preventive and reactive barriers can provide control over such situations. This kind of analysis has traditionally been used to elicit requirements for safety and reliability engineering, but as a consequence of the ever-increasing coupling between the cyber and physical world, security has become an additional concern. Through a controlled experiment, we provide evidence that the expressiveness of the bow-tie notation is suitable for this purpose as well. Our results show that a sample population of graduate students, inexperienced in security modelling, perform similarly as security experts when we have a well-defined scope and familiar target system/situation. We also demonstrate that misuse case diagrams should be regarded as more of a complementary than competing modelling technique.
Local Matching Networks for Engineering Diagram Search

Finding diagrams that contain a specific part or a similar part is important in many engineering tasks. In this search task, the query part is expected to match only a small region in a complex image. This paper investigates several local matching networks that explicitly model local region-to-region similarities. Deep convolutional neural networks extract local features and model local matching patterns. Spatial convolution is employed to cross-match local regions at different scale levels, addressing cases where the target part appears at a different scale, position, and/or angle. A gating network automatically learns region importance, removing noise from sparse areas and visual metadata in engineering diagrams.
Making as Research: An Interview with Kristina Andersen

Kristina Andersen designs objects and experiences to explore ideas and notions of the unknown. A central element of her practise is workshop-like experiences that expose everyday desires as drivers for ideas. They employ familiar, mundane materials—such as candy and cardboard—through which several planes collide: the possible, the unknown, the feared and the desired. These processes are aimed at allowing a broad range of knowledge to materialise as interdisciplinary knowledge, which belongs to no one. The outcomes range from requirement engineering, technology prototyping, to the making of work about technology, rather than of technology. She holds degrees in Industrial Design, Virtual Environments, and wrote her Ph.D. on “magic machines”. She was a researcher at STEIM (Studio for Electro-Instrumental Music) in Amsterdam for 15 years, and now works in the Future Everyday group at Industrial Design at TU Eindhoven as well as maintaining her own practice.
Unsupervised Domain Adaptation for Object Detection Using Distribution Matching in Various Feature Level

As the research on deep learning has become more active, the need for a lot of data has emerged. However, there are limitations in acquiring real data such as digital forensics, so domain adaptation technology is required to overcome this problem. This paper considers distribution matching in various feature level for unsupervised domain adaptation for object detection with a single stage detector. The object detection task assumes that training and test data are drawn from the same distribution; however, in a real environment, there is a domain gap between training and test data which leads to degrading performance significantly. Therefore, we aim to learn a model to generalize well in target domain of object detection by using maximum mean discrepancy (MMD) in various feature levels. We adjust MMD based on single shot multibox detector (SSD) model which is a single stage detector that learns to localize objects with various size using a multi-layer design of bounding box regression and infers object class simultaneously. The MMD loss on high-level features between source and target domain effectively reduces the domain discrepancy to learn a domain-invariant feature in SSD model. We evaluate the approaches using Syn2real object detection dataset. Experimental results show that reducing the domain shift in high-level features improves the cross-domain robustness of object detection, and domain adaptation works better with simple MMD method than complex method as GAN.
Improved High Capacity Spread Spectrum-Based Audio Watermarking by Hadamard Matrices

Traditional spread spectrum-based audio watermarking methods usually use randomly generated pseudonoise sequences for watermark embedding and extraction. In this paper, we use Hadamard sequences, which are rows of Hadamard matrices, to embed and extract watermarks instead of pseudonoise sequences. By exploiting the orthogonality of Hadamard sequences and a technique of sign change, we propose a new spread spectrum-based audio watermarking method. Experimental results show that, compared to the newly high embedding capacity spread spectrum-based audio watermarking method, our method achieves a better perceptual quality and a higher embedding capacity while maintaining almost equal strong robustness. We also provide a theoretical analysis of the security of our method.
Stereoscopic Image Saliency Detection Optimization: A Multi-Cue-Driven Approach

Numerous stereoscopic image saliency detection algorithms have been presented to detect the salient objects in a stereoscopic image.However, they typically fail to uniformly highlight all the objects when the image contains multiple objects or complex backgrounds.In this paper, we propose a multi-cuedriven optimization (MCDO) for stereoscopic image saliency detection.MCDO leverages multiple cues, including depth, color, and spatial position to optimize the saliency maps generated by existing saliency detection algorithms.Fully connected conditional random field is used to integrate the depth, color, and spatial cues from the input stereoscopic image to ensure that pixels with similar depth, color, and/or spatial position have similar saliency values.Compared with original saliency maps, the optimized saliency maps have more uniformly highlighted salient objects, whose boundaries are more precise, and fewer incorrectly detected background regions.The experimental results on three datasets demonstrate that the proposed MCDO method can effectively improve the performance of stereoscopic and 2-D image saliency detection algorithms.
Teaming up with Artificial Intelligence: The Human in the Loop of Serious Game Pathfinding Algorithms

Serious games’ success depends on its capabilities to engage learners and to provide them with personalized gaming and learning experiences. Therefore, theoretically sound mechanisms for gaining a certain level of understanding of learning and gaming processes by the game is crucial. Consequently, AI and machine learning technologies increasingly enter the field. These technologies often fail, however, since serious games either pose highly complex problems (combining gaming and learning process) or do not provide the extensive data bases that would be required. One solution might be allowing human intelligence or intuition influence AI processes. In the present study, we investigated pathfinding algorithms with and without human interventions to the algorithms. As a testbed, we used a clone of the Travelling Salesman problem, the Travelling Snakesman game. We found some evidence that in this particular pathfinding problem human interventions result in superior results as the MAXMIN Ant System algorithm.
Cost Based Model for Secure Health Care Data Retrieval

In healthcare application, a huge amount of sensitive data is generated continuously which requires an efficient mechanism to retrieve them such that sensitive information is not leaked to unauthorized users. In this paper, we introduce a data retrieval strategy in linearly scalable high-performance NoSQL database like Cassandra and present a security analysis while retrieving the stored data. E-Health data is fragmented over multiple servers based on sensitive attributes and their sensitive association. It is required to make an extra layer of protection on actual data by representing it to users as chunks by introducing metadata concept. However, data retrieval requires authentication of users and the queries put forth by the user, for which a proper costing mechanism involving both user and query is introduced in this work using linear optimization concept.
Quantum Fisher Information Gap for Systems with Nonlinear Hamiltonians

Quantum Fisher information (QFI) gap characterizes the stability of QFI to space directions. We study the QFI distributions and QFI gap for quantum states generated from nonlinear Hamiltonians for both spin and bosonic systems. We find that the same spin-squeezing parameter (or principle squeezing parameter) corresponds to two different values QFI gap, and the locations of all extreme points of the QFI are explicitly given.
The Multilevel Approach to Learning: a Study Based on the Analysis of the Test Work of Younger Schoolchildren

The article substantiates the expediency of introducing multilevel training for a contingent of children who are not sufficiently prepared for school and demonstrate a reduced or low level of results when writing the final verification work. Particular attention is paid to the approved diagnostic tools in the form of a single set of verification work, which makes it possible to adequately assess the educational potential of younger students, and the conditions for its successful use are indicated. The author presents these studies, confirming the need for differentiation of the educational process, taking into account the real possibilities of schoolchildren, which allows to improve the system of primary school education.
Analysis of Pedagogy of Teacher's Capability to Transform Knowledge Into Practice Using Fuzzy Logic

Current literature and common practices suggest that there is no consistent method available to analyze the performance of teachers. Due to its inherent vagueness and uncertainty, this article analyzes the effectiveness of a teacher depending upon various factors using fuzzy logic. It explains various parameters influencing professional, interpersonal and personal behavior of teachers. Secondly, a fuzzy inference mechanism is developed to decide the possible quality of teachers. The article concludes by observing that the proposed fuzzy logic based system is consistent with that judged by the experts and can be used to predict the possible quality of teachers.
Processing Unstructured Databases Using a Quantum Approach

One of the most fundamental choices to store the big data it’s the use of unstructured databases. However, the classical algorithms used in NoSQL databases suffer from slow execution of orders, especially in search operations. In order to decrease the data time processing in general and more particularly the search period in unstructured databases, we suggest in this work the use of a quantum approach based on Grover’s algorithm.
New Construction of Maximum Distance Separable (MDS) Self-Dual Codes over Finite Fields

Maximum distance separable (MDS) self-dual codes have useful properties due to their optimality with respect to the Singleton bound and its self-duality. MDS self-dual codes are completely determined by the length n , so the problem of constructing q-ary MDS self-dual codes with various lengths is a very interesting topic. Recently X. Fang et al. using a method given in previous research, where several classes of new MDS self-dual codes were constructed through (extended) generalized Reed-Solomon codes, in this paper, based on the method given in we achieve several classes of MDS self-dual codes.
Distributed Ledger Technology to Support Digital Evidence Integrity Verification Processes

This paper examines the way in which blockchain technology can be used to improve the integrity of the chain of evidence in digital forensics. A particular scalable method of verifying point-in-time existence of a piece of digital evidence, using the OpenTimestamps (OTS) service, is described, and tests are carried out to independently validate the claims made by the service. The results demonstrate that the OTS service is highly reliable, but not suitable for time-sensitive digital timestamping.
A Structured Listwise Approach to Learning to Rank for Image Tagging

With the growing quantity and diversity of publicly available image data, computer vision plays a crucial role in understanding and organizing visual information today. Image tagging models are very often used to make this data accessible and useful. Generating image labels and ranking them by their relevance to the visual content is still an open problem. In this work, we use a bilinear compatibility function inspired from zero-shot learning that allows us to rank tags according to their relevance to the image content. We propose a novel listwise structured loss formulation to learn it from data. We leverage captioned image data and propose different “tags from captions” schemes meant to capture user attention and intra-user agreement in a simple and effective manner. We evaluate our method on the COCO-Captions, PASCAL-sentences and MIRFlickr-25k datasets showing promising results.
Snooping Around a Fence: A Lesson from the Education Sector in a Software Service Ecosystem

Although the education sector has recognized the value of information technologies since the early 1990s, the advancement of education services is not clearly shown in the information technology era. This paper visualizes the trace of education services development in a software service ecosystem with real data about software services and their combinations resulting in composite services. Our graphical analysis results show that education services continuously emerge through reusing and recombining popular software services such as Google Maps and Facebook, although only a few education software services open their functions and data to the ecosystem. Moreover, our analysis results show that there no service groups that are built around education services. Our findings suggest that the education sector is immature within the software service ecosystem and that a software service sector has not been formed yet.
Teaching a generative model: Mathematical formulation and solution framework

Abstract We develop the mathematical formulation for teaching generative models to a learner whose learning processes and cognitive behaviors may be analytically intractable, but can be simulated by numerical processes. The model considers the learner’s bias (prior knowledge) or memory process by using stochastic models. We also present an optimization framework for solving the involved non-convex, stochastic optimization problems associated with machine teaching. The algorithm design and the conditions and analysis are discussed for local convergence properties of the proposed optimization algorithms. In the paper, we discuss a number of example cases to illustrate the algorithmic ideas and demonstrate their efficiency.
Ontology Modeling of the Estonian Traffic Act for Self-driving Buses

The development of self-driving cars is a major research area that has led to several still unresolved issues. One of them is the need to abide by the legal stipulations fixed by a traffic act concerning the territory of operation. An appropriate solution to make text understandable by machines is the use of ontologies. This paper presents a first approach where the Estonian Traffic Act is transformed from text into populated ontologies, so it can be understood by machines. The proposal is a (semi)-automatic ontology learning process that combines natural language processing (NLP) and ontology matching techniques with a deep learning model. The results show that 78% of the norms that have been considered valid can be modelled with the method described in the paper.
School Mediation Service: Development of Conflict-Free Educational Space

The article shows the role of school mediation for the formation and development of legal educational space. The article describes the basic requirements for the creation and functioning of the school mediation service in an educational organization, such as: training of teachers, students and their parents (legal representatives); interaction of all participants in educational relations for the prevention and settlement of the confl ict situation. It is noted that the appearance of the fi rst school mediation services in educational institutions of the city of Smolensk become active in the framework of the regional innovation platform “Development of a common legal educational space in the educational innovation cluster”. It is shown that the rational understanding of the conditions for building a mediation system in an educational organization is provided by the method of SWOT analysis. The necessity of using mediation approaches for the formation of confl ict-free educational space in the region is emphasized.