Reviewerly/exHarmony huggingface dataset:
Building Adversarial Defense with Non-invertible Data Transformations

Deep neural networks (DNN) have been recently shown to be susceptible to a particular type of attack possible through the generation of particular synthetic examples referred to as adversarial samples. These samples are constructed by manipulating real examples from the training data distribution in order to “fool” the original neural model, resulting in misclassification of previously correctly classified samples. Addressing this weakness is of utmost importance if DNN is to be applied to critical applications, such as those in cybersecurity. In this paper, we present an analysis of this fundamental flaw lurking in all neural architectures to uncover limitations of previously proposed defense mechanisms. More importantly, we present a unifying framework for protecting deep neural models using a non-invertible data transformation–developing two adversary-resistant DNNs utilizing both linear and nonlinear dimensionality reduction techniques. Empirical results indicate that our framework provides better robustness compared to state-of-art solutions while having negligible degradation in generalization accuracy.
Nomen Meum Earl: Yet Another Route to Intelligent Machine Behavior

A unifying machine learning algorithm is proposed, in which the same processes, data structures and memory management can be used simultaneously in divergent realms, including conversation, musical composition, and robotics. A central aspect of the project is setting up learning modules that can absorb and use relationships in the input – relationships that are neither analyzed and predicted nor perceived by the programmer. The data, internal objects, and actions available to the program exist as points in a quasi-Cartesian, multi-dimensional knowledge space. The geometry of this space is determined by semantic content. Within a combination of this space and a kind of production system one can take advantage of content-addressable memory to replace all search, one can implement table-driven program control that allows the program to change itself while running and distributed massively-parallel processing can be used.
Evaluating the Viability of Ciphertext-Policy Attribute-Based Encryption on Service-Oriented Health Information Exchange Systems

Achieving interoperability in healthcare leads to several benefits that affect consumers and other stakeholders. With this, several countries have already implemented a Health Information Exchange (HIE) to enable interoperability in their areas. OpenHIE [5] is an initiative that aims to address the interoperability challenges in underserved regions, such as third-world countries. OpenHIE provides a framework for implementing an HIE which is based on the Service-Oriented Architecture (SOA). Its design has been proven to work, and other countries and organizations have shown interest in adopting its components. Currently, OpenHIE can provide the de facto standard for securing health information systems. However, several essential security features concerning the privacy of health information have yet to be implemented. Here we explore the application of Ciphertext-Policy Attribute Based Encryption (CP-ABE) on OpenHIE to realize the aforementioned security features. Results show that applying CP-ABE will not only change the implementation of the functionalities of the HIE, but it will also add an overhead to its non-functional characteristics, such as the number of transactions per second, memory, and bandwidth usage.
Noise-Based Adversarial Training for Enhancing Agglutinative Neural Machine Translation

This study solves the problem of unknown(UNK) word in machine translation of agglutinative language in two ways. (1) a multi-granularity preprocessing based on morphological segmentation is used for the input of generative adversarial net. (2) a filtering mechanism is further used to identify the most suitable granularity for the current input sequence. The experimental results show that our approach has achieved significant improvement in the two representative agglutinative language machine translation tasks, including Mongolian $$\rightarrow $$ Chinese and Japanese $$\rightarrow $$ English.
Some Applications of the Formalization of the Pumping Lemma for Context-Free Languages

Context-free languages are highly important in computer language processing technology as well as in formal language theory. The Pumping Lemma for Context-Free Languages states a property that is valid for all context-free languages, which makes it a tool for showing the existence of non-context-free languages. This paper presents a formalization, extending the previously formalized Lemma, of the fact that several well-known languages are not context-free. Moreover, we build on those results to construct a formal proof of the well-known property that context-free languages are not closed under intersection. All the formalization has been mechanized in the Coq proof assistant.
Data Reduction Methods for Life-Logged Datasets

Life-logging utilises sensor technology to automate the capture of a person’s interaction with their environment. This produces useful information to assess wellbeing, but this information is often buried within the volume of data. In this chapter, we analyse a life-log comprising image data and contextual information and apply algorithms to collate, mine and categorise the data. Four approaches were investigated: (i) Self-reporting of important events by the person who collected the data; (ii) Clustering of images into location-based events using GPS metadata, (iii) Face detection within the images and (iv) Physiological monitoring using Galvanic Skin Response (GSR); as a way to identify more meaningful images. Using a bespoke wearable system, comprising a smartphone and smartwatch, six healthy participants recorded a life-log in the form of images of their surroundings coupled with metadata in the form of timestamps, GPS locations, accelerometer data and known social interactions. Following approximately 2.5 h of recording, the data reduction methodologies outlined above were applied to each participant’s dataset, yielding an 80–86% reduction in size which facilitates more realistic self-quantification. However, each approach has some shortcomings and the data reduction method used will need personalisation and depend on the intended application.
Design and Development of Reservoir Dynamic Analysis System Based on Web

Reservoir development involves a wide variety of information and a huge amount of information. In the process of reservoir development, a large amount of information including various geological maps, development maps and surface engineering maps has been accumulated. It is becoming more and more important to use computer application software and modern equipment to comprehensively manage and apply these graphics and image information. After years of research and development, reservoir engineering dynamic analysis software has initially formed a software product, which has great prospects for promotion. This paper focuses on the reservoir network information system and the efficient management of oilfields, implements project management, and adopts project form.
Distributed Software Switching for Scalable Network Functions

Network packets have traditionally been processed by purpose built, fixed-function appliances using proprietary hardware and software designs. Today, these designs are transforming into collections of flexible software blocks running on general purpose computing hardware, with expectations of high performance and scalability. Among these pieces, software packet switching (i.e. vSwitch) plays a fundamental role, as all packets need to traverse the switch on a given platform, and quite often repeatedly. In this paper, we demonstrate that certain performance and scaling deficiencies of packet switching in software can be attributed to the centralized design and deployment of the switching software threads. While multithreading is crucial for scaling-out software performance, improper use of the multi-processor resources can lead to undesired impacts on performance, due to fixed-partitioning, sub-optimal scheduling and complex cache coherency protocol interactions involving non-negligible data-access latencies. In this study, we take advantage of symmetric multi-processor threading (SMT) and lay out principles for better software packet switching with a distributed approach. With proof of concept implementations based on the very popular Open vSwitch project and DPDK, we show distributed software switching can easily scale with the network function workload threads, and achieve more than 2× throughput by exploiting the data locality within distributed nature of the CPU cache-subsystem.
Research on Machine Learning Methods for Intelligent Decision Problems

In order to improve the accuracy, objectivity and scientificity of decision-making, people propose to use machine learning to automatically learn data and extract complex patterns from it, so as to make objective and fair decisions. Machine learning provides a new way to effectively solve decision support problems in many fields by virtue of its powerful ability to automatically learn data and extract complex patterns from them to make intelligent decisions. This paper mainly studies the construction of a supervisory classification model with strong generalization ability to solve complex decision-making problems such as disease diagnosis and financial risk prediction. Supervised classification learning methods such as SVM and FKNN are mainly studied. Diagnostic decision support methods based on combination RS and SVM, combined LFDA and SVM are proposed.
A Construction of Hybrid Structural Thai Treebank

It is possible to include complicated structures into an individual syntactic tree, to enhance the usefulness of parsed text corpus. In this part, existing works on Thai treebank construction have been developed in order to address the lack of high-level syntactic resources. However, it has yet to be sufficient for Thai Natural Language Processing. Furthermore, Thai treebanks have either syntactic or dependency structure only. This paper presents a construction of hybrid structural Thai treebank which includes both syntactic/dependency structure, a tool for conversion between constituency and dependency parse tree, and a web-based GUI for parse tree visualization. Towards the hybrid treebank construction, hundreds of constituent tree are manually annotated with predicate header to each phrase. Once the set of annotated constituent trees are obtained, the conversion procedure will be performed by determining the annotated head and its dependents. As our experiments, features of hybrid treebank are extracted and illustrated. Finally, difficulties and issues in constructing the hybrid Thai treebank are discussed.
Radiography calibration marker detection using Hough transformation

In this paper we analyse the possibility of simple detection of circular radiography markers.To detect the marker, we utilised the Hough transform.Two approaches were analysed: with detecting image edges and without image edge detection where pixel gradient was used in the Hough voting process, i.e. to increase the accumulator values.Approaches were evaluated on 13 clinical radiography images.It was shown that the approach that detects image edges spatially matches the reference circles only 0.22 % less than manual annotation values, whereas the approach that uses just the gradient magnitudes spatially matches the reference circles 3.2 % less than manual annotations.
Rational Inference Patterns

Understanding, formalizing and modelling human reasoning is a core topic of artificial intelligence. In psychology, numerous fallacies and paradoxes have shown that classical logic is not a suitable logical framework for this. In a recent paper, Eichhorn, Kern-Isberner, and Ragni have succeeded in resolving paradoxes and modelling human reasoning consistently in a non-monotonic resp. conditional logic environment with so-called inference patterns. For further studies using inference patterns, however, it is mandatory to understand better how inference patterns are triggered by the characteristics of specific examples used in the empirical tests. The goal of this paper is to categorize empirical tasks by formal inference patterns and then find crucial features of the corresponding reasoning tasks in such a way that they can be used to predict the reasoning of human subjects according to the task. To this end a large amount of psychological studies dealing with human reasoning from the literature were investigated and classified according to the observed inference patterns. From this classification, we learnt a decision tree revealing which features of empirical tasks lead to which inference pattern in most cases. These results provide insights into the reasoning modes of humans which is important for choosing the right formal model, and help setting up proper tasks for testing inference patterns.
Multi-peak Algorithmic Trading Strategies Using Grey Wolf Optimizer

In this paper, we propose a new method of algorithmic trading for short term investors in the financial markets, by applying swarm intelligence. We apply a well known meta-heuristic, known as Grey Wolf Optimizer (GWO), and find multi-peak optimisation solutions having different expected risk and return ratios, to propose 3 automated trading strategies. The novelty of our method is how we leverage three best swarm agents to construct multi-peak solutions that are best suited for the stochastic nature of financial markets. We utilise the variance between the positions of swarm agents in GWO to construct different algorithmic approaches to day trading, with an aim to diversify expected portfolio volatility. Our research showcases how the three best swarms of GWO are best suited to predict stochastic time series problems, as we typically find in the field of finance. Our experiments demonstrate the capability of our model compared to industry benchmark indices and evaluates the effectiveness of the proposed strategies.
Model‐Based Product Line Engineering to Plan and Track Submarine Configuration

ABSTRACT One can measure the life of a class of submarines in decades. As such, the operational demands and expectations change both strategically and tactically over its lifetime. Coupling this adaptability with the length of time submarines take to design, build, and maintain, no two as‐built submarines in a class will ever be the same, even when constructed/maintained to the same build‐to design. Traditionally, we have accepted this as the standard, and in most cases the configuration management team managed the full information set at a class or batch level, but not at an individual submarine level. The team managed the configuration of an individual submarine in terms of agreed changes against the class or batch baseline. Advances in technology (hardware performance, software tools, and standards) now give us the opportunity to not only manage the full information set related to individual submarine system configuration baselines as they change over time, but also undertake rigorous model‐based trade‐off studies to plan the manner in which we can modify a class, a batch, an individual submarine (variant), or any combination thereof over time. This paper will explore the use of model‐based systems engineering (MBSE) coupled with recent developments of product line engineering (PLE)/orthogonal variability modelling (OVM) to provide a means to plan, track, manage, and evaluate an individual submarine's configuration over time in the context of the class, whilst simultaneously highlighting the wider application in the submarine enterprise and beyond.
Enterprise Rights Management System Based on RBAC Model

According to the design idea of RBAC model, the SSM framework based on JAVA language is combined with Maven to implement the background function module. The front-end technology such as AJAX and Bootstrap is used to design the WEB page. The MySQL database is used to store data and Tomcat is used as the server. The design of this system provides a good condition for the authority management of the enterprise.
Double JPEG compression detection based on noise-free DCT coefficients histograms

With the widespread usage of sophisticated image editing software, digital image tampering becomes very convenient and easy, which makes the detection of image authenticity significant. Among various image forensic tools, double JPEG image compression detector, which is not sensitive to specific tampering operation, has received large attention. In this paper, we propose an improved double JPEG compression detection method based on noise-free DCT coefficients histograms. Specifically, we eliminate the quantization noise which introduced by decompression and rounding operation before estimating the periodicity of the quantized DCT coefficients histograms. Then, a block-wise posterior probability map can be obtained from the estimated periodicity to expose the suspect regions. Extensive experimental results in both quantitative and qualitative terms prove the superior of our proposed method when compared with the related methods.
Dense coding and quantum teleportation using type II1 von Neumann algebras

Quantum states with perfect correlation of continuous variable systems are investigated. On the basis of the property of perfect correlation, the local observable algebras are assumed to be type II1 factor von Neumann algebras. Consequently, finite dimensional matrices may be represented using elements of local observable algebras. Therefore, the dense coding and teleportation processes are identical in performance to those in the finite dimensional cases.
Composing Proof Terms

Abstract Proof terms are a useful concept for comparing computations in term rewriting. We analyze proof terms with composition, with an eye towards automation. We revisit permutation equivalence and projection equivalence, two key notions presented in the literature. We report on the integration of proof terms with composition into ProTeM, a tool for manipulating proof terms.
Multi-stream Convolutional Networks for Indoor Scene Recognition

Convolutional neural networks (CNNs) have recently achieved outstanding results for various vision tasks, including indoor scene understanding. The de facto practice employed by state-of-the-art indoor scene recognition approaches is to use RGB pixel values as input to CNN models that are trained on large amounts of labeled data (ImageNet or Places). Here, we investigate CNN architectures by augmenting RGB images with estimated depth and texture information, as multiple streams, for monocular indoor scene recognition. First, we exploit the recent advancements in the field of depth estimation from monocular images and use the estimated depth information to train a CNN model for learning deep depth features. Second, we train a CNN model to exploit the successful Local Binary Patterns (LBP) by using mapped coded images with explicit LBP encoding to capture texture information available in indoor scenes. We further investigate different fusion strategies to combine the learned deep depth and texture streams with the traditional RGB stream. Comprehensive experiments are performed on three indoor scene classification benchmarks: MIT-67, OCIS and SUN-397. The proposed multi-stream network significantly outperforms the standard RGB network by achieving an absolute gain of 9.3%, 4.7%, 7.3% on the MIT-67, OCIS and SUN-397 datasets respectively.
A model based method for stereo face reconstruction

Face reconstruction is a long-standing and extremely challenging problem. In order to solve this problem more accurately and efficiently, In this paper, we propose a model based stereo method to reconstruct fine detailed 3D face with the calibrated face stereo images captured by stereo camera. Our model based stereo method can avoid the limited power of expressiveness in model based reconstruction and the mismatch appeared in stereo reconstruction. In the proposed method, the sparse landmarks detected from stereo images are employed to reconstruct coarse shape and posture parameters with 3d morphable model(3DMM). The detailed face will be gotten by per vertex deformation based on coarse shape according to illumination value, gradient and surface smooth terms, and the direction of per vertex deformation follows the direction of the vertex’s normal, for which is the fastest changing direction of local mesh area. The experimental results show that our proposed method achieve high performance compared with the E-eos method.
On Distinguishing Sets of Structures by First-Order Sentences of Minimal Quantifier Rank

We investigate the distinguishability of sets of relational structures concerning a class of structures in the following sense: for a fixed class of structures, given two sets of structures in this class, find a first-order formula of minimal quantifier rank that distinguishes one set from the other. We consider the following classes of structures: monadic structures, equivalence structures, and disjoint unions of linear orders. We use results of the Ehrenfeucht–Fraïssé game on these classes of structures in order to design an algorithm to find such a sentence. For these classes of structures, the problem of determining if the Duplicator has a winning strategy in an Ehrenfeucht–Fraïssé game is solved in polynomial time. We also introduce the distinguishability sentences which are sentences that distinguish between two given structures. We define the distinguishability sentences based on necessary and sufficient conditions for a winning strategy in an Ehrenfeucht–Fraïssé game. Our algorithm returns a boolean combination of such sentences. We also show that any first-order sentence is equivalent to a boolean combination of distinguishability sentences. Finally, we also show that our algorithm's running time is polynomial in the size of the input.
Phuket Mangrove Gastronomy Information System Development

Nowadays, data can be compared as a weapon. Any agency or organization has information that is well managed and be able to transform that data into use like having a weapon that can fight. However, the data must have reliable and up-to-date and meet the needs of users. Management Information System is a collection of data, processing, and creation of an information system to help with the decision-making; coordination and control. It allows administrators to provide data for the benefit of operation or planning analysis of effective information system management in the organization. This paper presents the development of the Phuket Mangrove Gastronomy Information System by software engineering and using Power Query in the collection of mangrove plants data on the use of mangrove plant components in cooking from data that has been published online or in other database systems as well as in the form of files stored on the computer to be the main data of the system. The purpose of developing this system is for the collection of mangrove plants data from various published sources to analyze the data in the form of Management Information System as a resource for food scientists or nutritionists to be able to use information from the system to invent new food menus using components of mangrove plants as raw materials because some components of mangrove plants have properties in the treatment of diseases or prevention of various diseases.