Reviewerly/exHarmony huggingface dataset:
Capturing and Obscuring Ping-Pong Patterns to Mitigate Continuous Attacks

In this paper, we observed Continuous Attacks are one kind of common side channel attack scenarios, where an adversary frequently probes the same target cache lines in a short time. Continuous Attacks cause target cache lines to go through multiple load-evict processes, exhibiting Ping-Pong Patterns. Identifying and obscuring Ping-Pong Patterns effectively interferes with the attacker’s probe and mitigates Continuous Attacks. Based on the observations, this paper proposes Ping-Pong Regulator to identify multiple Ping-Pong Patterns and block them with different strategies (Preload or Lock). The Preload proactively loads target lines into the cache, causing the attacker to mistakenly infer that the victim has accessed these lines; the Lock fixes the attacked lines’ directory entries on the last level cache directory until they are evicted out of caches, making an attacker’s observation of the locked lines is always the L2 cache miss. The experimental evaluation demonstrates that the Ping-Pong Regulator efficiently identifies and secures attacked lines, induces negligible performance impacts and storage overhead, and does not require any software support.
IMPACT OF IT INFRASTRUCTURE AND KNOWLEDGE MANAGEMENT CAPABILITY ON ORGANISATIONAL PERFORMANCE OF STAR HOTELS IN SRI LANKA

Today, the organizational environment is changing rapidly and the competitive environment is very volatile, as a result, business firms face new challenges, especially, the competition increased with the changes environment in which they operate. One of the strategic philosophies assisting firms at this juncture to develop strategic capabilities dealing with uncertainty is knowledge management infrastructure capabilities. Through enhanced technological infrastructure and systematic implementation of knowledge management process organizations able to develop strategies that enable them to be proactive and adaptable to external changes and achieve better organizational performances. The main aim of this study is to analyse the impact of Information Technology (IT) infrastructure and Knowledge Management process capability on Organizational Performance of star rated Hotels in Sri Lanka. Empirical examination of the hypothesized relationships among variables is conducted by means of questionnaire surveys in Sri Lanka. The 233 responses received from respondents serving as managers in star hotels and the data analysed using AMOS version 23.0. According to the results of SEM analyses, the proposed measurement model satisfied the necessary fit conditions. The empirical evidence confirms that the technological infrastructure capability is correlated and it is an enabler of knowledge management process capability on organizational performance of star rated hotels in Sri Lanka. In addition to theoretical contributions, the study also attempts to provide a variety of practical recommendations for hotel managers, to be successful in utilizing IT resources on knowledge management to the attainment of better organizational performances.
In Search of Trustworthy and Transparent Intelligent Systems With Human-Like Cognitive and Reasoning Capabilities

At present we are witnessing a tremendous interest in Artificial Intelligence (AI), particularly in Deep Learning (DL) / Deep Neural Networks (DNNs). One of the reasons appears to be the unmatched performance achieved by such systems. This has resulted in an enormous hope on such techniques and often these are viewed as all - cure solutions. But most of these systems cannot explain why a particular decision is made (black box) and sometimes miserably fail in cases where other systems would not. Consequently, in critical applications such as healthcare and defence practitioners do not like to trust such systems. Although an AI system is often designed taking inspiration from the brain, there is not much attempt to exploit cues from the brain in true sense. In our opinion, to realize intelligent systems with human like reasoning ability, we need to exploit knowledge from the brain science. Here we discuss a few findings in brain science that can help designing intelligent systems. We explain the relevance of transparency, explainability, learning from a few examples, and the trustworthiness of an AI system. We also discuss a few ways that may help to achieve these attributes in a learning system.
The Nexus between Education and Economic Growth: Analyzing Empirically a Case of Middle-Income Countries

The purpose of this paper is to examine empirically the nexus between education accumulation and economic growth for a sample of middle-income countries through panel data regressions. The sample consists of 28 middle-income countries from various continents: North Africa and the Middle East (6 countries), sub-Saharan Africa (7 countries), Latin America and the Caribbean (8 countries), East Asia and the Pacific (3 countries), and Europe and Central Asia (4 countries). Education is measured by quantitative (average years of labour force study) and qualitative indicators (student scores on international assessments of educational achievements). To test the impact of education accumulation on GDP per capita growth, a static panel is used during the period of study from 1970 to 2014. A dynamic panel is also being developed to estimate the effect of the education stock on the growth rate of GDP per capita. The results confirm the positive and significant impact of the education quantity and quality on economic growth, both in level and variation. The stock of education and its increase are positively affecting the growth. Moreover, this paper’s original findings suggest that the quality of education is more significant than its quantity.
Distributionally Robust Deep Learning using Hardness Weighted Sampling

Limiting failures of machine learning systems is of paramount importance for safety-critical applications. In order to improve the robustness of machine learning systems, Distributionally Robust Optimization (DRO) has been proposed as a generalization of Empirical Risk Minimization (ERM). However, its use in deep learning has been severely restricted due to the relative inefficiency of the optimizers available for DRO in comparison to the wide-spread variants of Stochastic Gradient Descent (SGD) optimizers for ERM.&lt;br&gt;We propose SGD with hardness weighted sampling, a principled and efficient optimization method for DRO in machine learning that is particularly suited in the context of deep learning. Similar to a hard example mining strategy in practice, the proposed algorithm is straightforward to implement and computationally as efficient as SGD-based optimizers used for deep learning, requiring minimal overhead computation. In contrast to typical ad hoc hard mining approaches, we prove the convergence of our DRO algorithm for over-parameterized deep learning networks with ReLU activation and finite number of layers and parameters.&lt;br&gt;Our experiments on fetal brain 3D MRI segmentation and brain tumor segmentation in MRI demonstrate the feasibility and the usefulness of our approach. Using our hardness weighted sampling for training a state-of-the-art deep learning pipeline leads to improved robustness to anatomical variabilities in automatic fetal brain 3D MRI segmentation using deep learning and to improved robustness to the image protocol variations in brain tumor segmentation.a decrease of 2% of the interquartile range of the Dice scores for the enhanced tumor and the tumor core regions.&lt;br&gt;Our code is available at &lt;a href='https://github.com/LucasFidon/HardnessWeightedSampler'&gt;https://github.com/LucasFidon/HardnessWeightedSampler&lt;/a&gt;
Consumer purchase patterns based on market basket analysis using apriori algorithms

Abstract Analyzing customer purchasing patterns can help minimarket expand marketing strategies by gaining insight into which items are often bought together by customers. Also, transaction data is a source of information available at the convenience store and one thing that can be used for business decision making. In this paper, we aim to use the Apriori algorithm method to obtain consumer purchasing patterns to analyze consumer purchasing patterns. This system uses a priori algorithm calculation method where the input data is consumer transaction data. Transaction data will be sorted and calculated by providing a minimum support value and a minimum confidence value. In this study, the authors conclude that the results of the analysis of information systems in determining consumer purchasing patterns can be as information to determine sales and in the application of Apriori algorithms can provide information in the form of a combination of consumer purchase patterns based on consumer transaction data with a minimum support value above 10% and the minimum confidence value is above 65%.
Information Technologies Significance in Higher Education in Context of Its Digitalization

The problem is that the education system does not meet the challenges of the digital economy. The digital economy requires competent personnel, to whose training is necessary to improve education system and specialized training, adapt didactic programs in accordance with digital challenges. The goal of the paper is to promote the introduction of information technologies in the framework of digitalization in the education system and professional training of students by improving the efficiency of the educational process. This is done by analyzing and identifying the distinctive features of the teacher of distance and traditional learning systems, revealing the essence of the main models of distance education, elements of the work of the distance learning teacher. The research methods included both empirical (observation, comparison, etc.) and theoretical (analysis, synthesis, modeling, etc.). The article considers the objective factors of integration of information technologies in the higher education system, and the relationship between the functions of information technologies and their methodological and communicative potential in education.
Black-Box Inference for Non-Linear Latent Force Models

Latent force models are systems whereby there is a mechanistic model describing the dynamics of the system state, with some unknown forcing term that is approximated with a Gaussian process. If such dynamics are non-linear, it can be difficult to estimate the posterior state and forcing term jointly, particularly when there are system parameters that also need estimating. This paper uses black-box variational inference to jointly estimate the posterior, designing a multivariate extension to local inverse autoregressive flows as a flexible approximater of the system. We compare estimates on systems where the posterior is known, demonstrating the effectiveness of the approximation, and apply to problems with non-linear dynamics, multi-output systems and models with non-Gaussian likelihoods.
Leakage-Resilient Secret Sharing in Non-Compartmentalized Models.

Leakage-resilient secret sharing has mostly been studied in the compartmentalized models, where a leakage oracle can arbitrarily leak bounded number of bits from all shares, provided that the oracle only has access to a bounded number of shares when the leakage is taking place. We start a systematic study of leakage-resilient secret sharing against global leakage, where the leakage oracle can access the full set of shares simultaneously, but the access is restricted to a special class of leakage functions. More concretely, the adversary can corrupt several players and obtain their shares, as well as applying a leakage function from a specific class to the full share vector. We explicitly construct such leakage-resilient secret sharing with respect to affine leakage functions and low-degree multi-variate polynomial leakage functions, respectively. For affine leakage functions, we obtain schemes with threshold access structure that are leakage-resilient as long as there is a substantial difference between the total amount of information obtained by the adversary, through corrupting individual players and leaking from the full share vector, and the amount that the reconstruction algorithm requires for reconstructing the secret. Furthermore, if we assume the adversary is non-adaptive, we can even make the secret length asymptotically equal to the difference, as the share length grows. Specifically, we have a threshold scheme with parameters similar to Shamir’s scheme and is leakage-resilient against affine leakage. For multi-variate polynomial leakage functions with degree bigger than one, our constructions here only yield ramp schemes that are leakage-resilient against such leakage. Finally, as a result of independent interest, we show that our approach to leakage-resilient secret sharing also yields a competitive scheme compared with the state-of-the-art construction in the compartmentalized models.
A multivariate method for matching soil classification systems, with an Australian example

Differences between local systems of soil classification hinder the communication between pedologists from different countries. The FAO–UNESCO Soil Map of the World, as a fruit of world-wide collaboration between innumerable soil scientists, is recognised internationally. Ideally, pedologists should be able to match whole classes in their local systems to those in an international soil classification system. The Australian Soil Classification (ASC) system, created specifically for Australian soil, is widely used in Australia, and Australian pedologists wish to translate the orders they recognise into the FAO soil units when writing for readers elsewhere. We explored the feasibility of matching soil orders in the ASC to units in the FAO legend using a multivariate analysis. Twenty soil properties, variates, of 4927 profiles were estimated from their visible–near infrared reflectance (vis–NIR) spectra. We arranged the profiles in a Euclidean 20-dimensional orthogonal vector space defined by standardised variates. Class centroids were computed in that space, and the Euclidean distances between the centroids of the ASC orders and units in the FAO scheme were also computed. The shortest distance between a centroid of any ASC order and one of units in the FAO classification was treated as a best match. With only one exception the best matches were those that an experienced pedologist might expect. Second and third nearest neighbours in the vector space provided additional insight. We conclude that vis–NIR spectra represent sufficiently well the essential characters of the soil and so spectra could form the basis for the development of a universal soil classification system. In our case, we could assign with confidence the orders of the ASC to the units of the FAO scheme. A similar approach could be applied to link other national classification systems to one or other international systems of soil classification.
The New Rewriting Engine of Dedukti (System Description)

Dedukti is a type-checker for the λΠ-calculus modulo rewriting, an extension of Edinburgh’s logical framework LF where functions and type symbols can be defined by rewrite rules. It therefore contains an engine for rewriting LF terms and types according to the rewrite rules given by the user. A key component of this engine is the matching algorithm to find which rules can be fired. In this paper, we describe the class of rewrite rules supported by Dedukti and the new implementation of the matching algorithm. Dedukti supports non-linear rewrite rules on terms with binders using higher-order pattern-matching as in Combinatory Reduction Systems (CRS). The new matching algorithm extends the technique of decision trees introduced by Luc Maranget in the OCaml compiler to this more general context.
Sequential Unsupervised Domain Adaptation through Prototypical Distributions.

We develop an algorithm for unsupervised domain adaptation (UDA) of a classifier from a labeled source domain to an unlabeled target domain in a sequential learning setting. UDA has been studied extensively recently but the vast majority of the existing methods consider a joint learning setting where the model is trained on the source domain and the target domain data simultaneously. We consider a more practical setting, where the model has been trained on the labeled source domain data and then needs to be adapted to the unlabeled source domain, without having access to the source domain training data. We tackle this problem by aligning the distributions of the source and the target domain in a discriminative embedding space. To overcome the challenges of learning in a sequential setting, we learn an intermediate prototypical distribution from the source labeled data and then use this distribution for knowledge transfer to the target domain. We provide theoretical justification for the proposed algorithm by showing that it optimizes an upper-bound for the expected risk in the target domain. We also conduct extensive experiments with several standard benchmarks and demonstrate the competitiveness of the proposed method compared to existing joint learning UDA algorithms.
Secure Sharing Scheme of Sensitive Data in the Precision Medicine System

Numerous industries, especially the medical industry, are likely to exhibit significant developments in the future. Ever since the announcement of the precision medicine initiative by the United States in 2015, interest i... | Find, read and cite all the research you need on Tech Science Press
3D Multilayered Turtle Shell Models for Image Steganography

By embedding secret data into cover images, image steganography can produce non-discriminable stego-images. The turtle shell model for data hiding is an excellent method that uses a reference matrix to make a good balance between image quality and embedding capacity. However, increasing the embedding capacity by extending the area of basic structures of the turtle shell model usually leads to severe degradation of image quality. In this research, we innovatively extend the basic structure of the turtle shell model into a three-dimensional (3D) space. Some intrinsic properties of the original turtle shell model are well preserved in the 3D version. Theoretic analysis shows that the new proposed models have good performance both in the image quality and in the complexity of the reference matrix. Our experimental results justify the theoretic conclusions.
Improving Text Recognition in Tor darknet with Rectification and Super-Resolution techniques

Text recognition can be used to retrieve textual information embedded in images. This task can be complex due to the low-resolution of the images and the orientation of the text, which are problems commonly found in Tor darknet images. In this work, we combine three different super-resolution algorithms, together with a rectification network, to increase the performance in the text recognition step. We evaluated these combinations in four state-of-the-art datasets, and in TOICO-1K, a Tor-based image dataset which was semi-automatically labelled for the task of Text Spotting in Tor darknet. We obtained the highest performance increase in ICDAR 2015 dataset, with an improvement of 3.77% when combining Residual Dense and the rectification networks. In TOICO-1K, we achieved a 3.41% of improvement when we combined Deep CNN and the rectification network. Our conclusion is that rectification performs slightly better than super-resolution when they are applied standalone, while their combination obtains the best results in the datasets evaluated.
Holistic cyber education

This chapter argues that the permeation of cyber into all aspects of our lives has induced a moral imperative for integrating a holistic cyber education approach into all levels of education, so that individuals are equipped with a level of cyber education proportionate to their role in society. Using the U.S. Military Academy (USMA) at West Point as a guide, the chapter demonstrates the holistic cyber education approach constitutes three elements: in-class curricula, experiential outside-the-classroom activities and a pervasive cyber-aware environment. Regarding in-class curricula, the chapter describes how the technical and non-technical components of cyber can be integrated into various levels of general cyber education, disciplinary and interdisciplinary cyber electives, cyber threads, minors and majors. Extracurricular cyber activities include internships, part-time-jobs, student clubs, conferences, and competitive events. Constructing a pervasive cyber environment involves exposing students to cyber professionals from whom they can receive mentorship, career guidance and inspiration. While the chapter primarily uses examples from undergraduate education, it argues that the three tenets of holistic cyber education––in-class curricula, extracurricular activities, and a pervasive cyber environment––can be extended to junior or post-graduate education.
Quadrature Squeezing and Geometric-Phase Oscillations in Nano-Optics

The geometric phase, as well as the familiar dynamical phase, occurs in the evolution of a squeezed state in nano-optics as an extra phase. The outcome of the geometric phase in that state is somewhat intricate: its time behavior exhibits a combination of a linear increase and periodic oscillations. We focus in this work on the periodic oscillations of the geometric phase, which are novel and interesting. We confirm that such oscillations are due purely to the effects of squeezing in the quantum states, whereas the oscillation disappears when we remove the squeezing. As the degree of squeezing increases in q-quadrature, the amplitude of the geometric-phase oscillation becomes large. This implies that we can adjust the strength of such an oscillation by tuning the squeezing parameters. We also investigate geometric-phase oscillations for the case of a more general optical phenomenon where the squeezed state undergoes one-photon processes. It is shown that the geometric phase in this case exhibits additional intricate oscillations with small amplitudes, besides the principal oscillation. Such a sub-oscillation exhibits a beating-like behavior in time. The effects of geometric-phase oscillations are crucial in a wide range of wave interferences which are accompanied by rich physical phenomena such as Aharonov–Bohm oscillations, conductance fluctuations, antilocalizations, and nondissipative current flows.
Satellite-based measurement-device-independent quantum key distribution

Abstract A satellite-based measurement-device-independent quantum key distribution model is proposed and its performance is analyzed through numerical simulations. The simulation result shows that the key rate increases almost linearly as transmittance increases and the higher the key rate is, the lower its probability may be. More significantly, as the transmission distance increases, the optimal intensities of the signal states decrease while those of the decoy states increase. The optimal values in the daytime are slightly larger than those at night. This work may provide some important parameters for the relevant experiment on satellite-based quantum communication network.