Reviewerly/exHarmony huggingface dataset:
Fast Execute-Only Memory for Embedded Systems

Remote code disclosure attacks threaten embedded systems as they allow attackers to steal intellectual property or to find reusable code for use in control-flow hijacking attacks. Execute-only memory (XOM) prevents remote code disclosures, but existing XOM solutions either require a memory management unit that is not available on ARM embedded systems or incur significant overhead.We present PicoXOM: a fast and novel XOM system for ARMv7-M and ARMv8-M devices which leverages ARM's Data Watchpoint and Tracing unit along with the processor's simplified memory protection hardware. On average, PicoXOM incurs 0.33% performance overhead and 5.89% code size overhead on two benchmark suites and five real-world applications.
Democratizing the Production of Interactive Hardware

The development of new hardware can be split into two phases: prototyping and production. A wide variety of tools and techniques have empowered people to build prototypes during the first phase, but the transition to production is still complex, costly and prone to failure. This means the second phase often requires an up-front commitment to large volume production in order to be viable. I believe that new tools and techniques can democratize hardware production. Imagine "DevOps for hardware" - everything from circuit simulation tools to re-usable hardware test jig designs; and from test-driven development for hardware to telepresence for remote factory visits. Supporting low volume production and organic scaling in this way would spur innovation and increase consumer choice. I encourage the UIST community to join me in pursuit of this vision.
TOAD: Trace Ordering for Anomaly Detection

Outlier detection is one of the most important tasks to keep your processes in control. Unawareness of critical anomalies can lead to exhausting expenses, hence, it is highly beneficial to treat process failures as soon as possible. However, anomalies are difficult to detect due to their rarity though they occur too often to neglect the necessity of its detection. Even if the detection problem is solved, the treatment of singular anomalies and the adjustment of the process based on each abnormal trace is tedious and costly regarding time and money. To increase the efficiency of later anomaly treatment, we propose a novel strategy to detect collective anomalies. However, this is not equivalent to anomaly clustering as a post-processing step. TOAD orders process instances by similarity and detects abnormal accumulations of deviating cases. These collections are abnormal due to their aggregated behavior. Assuming that similar deviations are caused by the same reason, the treatment of such an anomaly is more cost-efficient than the handling of deviating singletons. Applying TOAD to an event log yields a ranking of significant, temporally abnormal trace collections, that provide a baseline for further analysis.
Feasible and economical scheme to entangle a polarized coherent state and a polarized photon

We design a scheme to generate a new type of hybrid entanglement between a macroscopic polarized coherent state and a single polarized photon. Concretely, a polarized Schrodinger cat state, an entangled polarized photon pair, and a polarized coherent state, which have been prepared off-line, are supplied as inputs to our scheme. Then, using conventional optical devices such as beam splitters, polarizing beam splitters, half-wave plates, and photon-number-resolving detectors, all the necessary operations can be effectively performed in-line. Conditioned on photon detection results, creation of the desired hybrid entanglement is heralded. The total success probability of our scheme is twice as high as that in existing schemes for unpolarized coherent states. Our scheme is not only feasible but also economical with regard to resource consumption because no active displacement operations must be utilized in-line. We also consider practical factors affecting the scheme performance, including decoherence of the input states, realistic resources, and device imperfections. Detailed analyses show that our scheme is robust against small imperfection effects and could be implemented with current technology. The proposed hybrid entanglement would serve as a vital resource for processing information encoded in superpositions of differently polarized coherent states which represent a new format of qubit.
Hybridization of K-means Clustering Using Different Distance Function to Find the Distance Among Dataset

Clustering using K-means algorithm is very common way to understand and analyze the obtained output data. This clustering is done by making a sample collection of similar objects in one slot and non-similar objects in another slot. The slot can have say let N number of data objects and say let K number of clusters (slot) are formed to make a bunch of common data where k < n. Each cluster has its own centroid, but we cannot predict how exactly cluster shall be based on data. Cluster formation has random occurence, i.e., if one object is dissimilar from rest of all other cluster objects, then the dissimilar object must have its own cluster. This is the main drawback of this approach, and we cannot get optimal result from this clustering approach. To overcome the drawback challenge, we are proposing a formula to find the clusters at the run time, so this approach can give us optimal results. The proposed approach uses Euclidian distance formula as well Mehlanobis to find the minimum distance between slots as technically we called as clusters, and we have also applied the same approach to ant colony optimization (ACO) algorithm which results in the production of two- and multi-dimensional matrix.
Deep Generative Positive-Unlabeled Learning under Selection Bias

Learning in the positive-unlabeled (PU) setting is prevalent in real world applications. Many previous works depend upon theSelected Completely At Random (SCAR) assumption to utilize unlabeled data, but the SCAR assumption is not often applicable to the real world due to selection bias in label observations. This paper is the first generative PU learning model without the SCAR assumption. Specifically, we derive the PU risk function without the SCAR assumption, and we generate a set of virtual PU examples to train the classifier. Although our PU risk function is more generalizable, the function requires PU instances that do not exist in the observations. Therefore, we introduce the VAE-PU, which is a variant of variational autoencoders to separate two latent variables that generate either features or observation indicators. The separated latent information enables the model to generate virtual PU instances. We test the VAE-PU on benchmark datasets with and without the SCAR assumption. The results indicate that the VAE-PU is superior when selection bias exists, and the VAE-PU is also competent under the SCAR assumption. The results also emphasize that the VAE-PU is effective when there are few positive-labeled instances due to modeling on selection bias.
An Efficient Robust Lightweight Remote User Authentication Protocol using Extended Chaotic Maps

Rapid development in networking and mobile technology has resulted in new networking hypotheses such as the Internet of Things, enabling clients to link via different devices to an unsafe public communication channel and access remote server services and data. Remote user authentication process and key agreement process for wireless, and IoT communications therefore have become more challenging. In the present study, we propose an efficient robust lightweight remote user authentication protocol using extended chaotic maps. The presented protocol is secure against various attacks.
Side Channel Resistance at a Cost: A Comparison of ARX-Based Authenticated Encryption

Lightweight cryptography offers viable security solutions for resource constrained Internet of Things (IoT) devices. However, IoT devices have implementation vulnerabilities such as side channel attacks (SCA), where observation of physical phenomena associated with device operations can reveal sensitive internal contents. The U.S. National Institute of Standards and Technology has called for lightweight cryptographic solutions to process authenticated encryption with associated data (AEAD), and is evaluating candidates for suitability in a Lightweight Cryptography (LWC) Standardization Process. Two Round 2 candidate variants, COMET-CHAM and SCHWAEMM, use Addition-Rotation-XOR (ARX) primitives. However, ARX ciphers are known to be costly to protect against certain SCA. In this work we implement side channel protected versions of COMET-CHAM and SCHWAEMM using register transfer level design. Identical protection schemes consisting of a threshold implementation (TI)-protected Kogge-Stone adder are adopted. Resistance to power side channel analysis is verified on an Artix-7 FPGA target device. Implementations comply with the Hardware API for Lightweight Cryptography, and use a custom-designed extension of the Development Package for the Hardware API for Lightweight Cryptography which enables test and evaluation of side channel resistant designs. We compare side channel protection costs of the two candidates against each other, against their unprotected counterparts, and against previous side channel protected AEAD implementations. COMET-CHAM is shown to consume less area and power, while SCHWAEMM has higher throughput and throughput to area ratio, and is more energy efficient. On average, the costs of protecting these ciphers against SCA are 32% more in area and 38% more in power, compared to the average protection costs for a large selection of previously-evaluated ciphers of similar implementation. Our results highlight the costs involved in implementing side channel protected ARX-ciphers, and help to inform NIST LWC late round and final portfolio selections.
A Logic for Multi-Agent Conformant Planning Over Transition Systems

Reasoning about actions and information is one of the most active areas of research in artificial intelligence. In this article, we study the reasoning about actions and information in multi-agent conformant planning, which is an essential branch of artificial intelligence. We introduce a semantic-driven formalism to capture the information change during the execution of actions. We then propose a dynamic epistemic logic for multi-agent conformant planning. Moreover, we give an alternative Kripke semantics and provide a complete axiomatization.
Augmented Reality Based Learning Media as Interactive Learning Innovation to Enhanced Vocational School Learning Outcomes

Progress in the industrial world has reached industry 4.0, in this progress it has led to the development of education as well as entering the era of education 4.0. One of the advances in the era of education 4.0 is the Augmented Reality learning media. The purpose of the study was to test the effectiveness of the effects of Augmented Reality Learning media that are in line with the needs of current and future students, the ARTorque application product as an interactive learning innovation to improve the learning outcomes of SMK students. The Quasi Experiment research design with steps: Determination of the experimental and control class, Instrument Testing, The experimental class was given the treatment of Augmented Reality learning media and the control class was not given treatment and used conventional learning. Output results obtained are normality test, homogeneity test and t test results.
Metagame Autobalancing for Competitive Multiplayer Games

Automated game balancing has often focused on single-agent scenarios. In this paper we present a tool for balancing multi-player games during game design. Our approach requires a designer to construct an intuitive graphical representation of their meta-game target, representing the relative scores that high-level strategies (or decks, or character types) should experience. This permits more sophisticated balance targets to be defined beyond a simple requirement of equal win chances. We then find a parameterization of the game that meets this target using simulation-based optimization to minimize the distance to the target graph. We show the capabilities of this tool on examples inheriting from Rock-Paper-Scissors, and on a more complex asymmetric fighting game.
The evolution from plate margin to intraplate mineral systems in the Capricorn Orogen, links to prospectivity

Opening up greenfields regions for mineral exploration programs is facilitated through understanding their mineral prospectivity. The Capricorn Orogen in Western Australia can be considered such a greenfields-dominated region, that hosts several mineral occurrences or deposits, but only a few have been mined extensively. Mineralisation in much of the Capricorn Orogen can be related to basin development, inversion and orogenesis during the Paleoproterozoic. Extension, on a regional scale, dominates the tectonic processes observed in the area through time and is interspersed by periods of contractional orogenesis, including the 2005–1950 Ma Glenburgh and 1830–1780 Ma Capricorn orogenies. Prospectivity models for base metals and gold in the northern and southern parts of the Capricorn Orogen suggest there is a spatial link between potentially prospective zones and the Archean basement, but also between the base metal and gold mineral systems. One of these links is tied to the control of the deep crustal-scale tectonic architecture over basin development and subsequent inversion during orogenesis. This early architecture contributed towards the preservation of mineralisation by protecting the area from tectonothermal processes such as deep burial and metamorphism. In addition, the spatial concurrence of zones prospective for the c. 2 Ga Au-Cu volcanic massive sulphide and c. 1.8 Ga orogenic Au style mineralisation in the northern part of the Bryah Sub-basin suggests a spatial link between the two mineral systems, while supporting the hypothesis that deposition of different ore styles is driven by long-lasting, regional geodynamic processes. In the Capricorn Orogen, these changing conditions reflect the transition from a plate-margin setting to an intraplate setting.
Dynamical Gaussian Process Latent Variable Model for Representation Learning from Longitudinal Data

Many real-world applications involve longitudinal data, consisting of observations of several variables, where different subsets of variables are sampled at irregularly spaced time points. We introduce the Longitudinal Gaussian Process Latent Variable Model (L-GPLVM), a variant of the Gaussian Process Latent Variable Model, for learning compact representations of such data. L-GPLVM overcomes a key limitation of the Dynamic Gaussian Process Latent Variable Model and its variants, which rely on the assumption that the data are fully observed over all of the sampled time points. We describe an effective approach to learning the parameters of L-GPLVM from sparse observations, by coupling the dynamical model with a Multitask Gaussian Process model for sampling of the missing observations at each step of the gradient-based optimization of the variational lower bound. We further show the advantage of the Sparse Process Convolution framework to learn the latent representation of sparsely and irregularly sampled longitudinal data with minimal computational overhead relative to a standard Latent Variable Model. We demonstrated experiments with synthetic data as well as variants of MOCAP data with varying degrees of sparsity of observations that show that L-GPLVM substantially and consistently outperforms the state-of-the-art alternatives in recovering the missing observations even when the available data exhibits a high degree of sparsity. The compact representations of irregularly sampled and sparse longitudinal data can be used to perform a variety of machine learning tasks, including clustering, classification, and regression.
Bayesian Automatic Relevance Determination for Utility Function Specification in Discrete Choice Models

Specifying utility functions is a key step towards applying the discrete choice framework for understanding the behaviour processes that govern user choices. However, identifying the utility function specifications that best model and explain the observed choices can be a very challenging and time-consuming task. This paper seeks to help modellers by leveraging the Bayesian framework and the concept of automatic relevance determination (ARD), in order to automatically determine an optimal utility function specification from an exponentially large set of possible specifications in a purely data-driven manner. Based on recent advances in approximate Bayesian inference, a doubly stochastic variational inference is developed, which allows the proposed MNL-ARD model to scale to very large and high-dimensional datasets. Using semi-artificial choice data, the proposed approach is shown to be able to accurately recover the true utility function specifications that govern the observed choices. Moreover, when applied to real choice data, MNL-ARD is able discover high quality specifications that can outperform previous ones from the literature according to multiple criteria, thereby demonstrating its practical applicability.
Click-Through Rate Prediction Combining Mutual Information Feature Weighting and Feature Interaction

Click-through rate (CTR) prediction is an important issue in online advertising and recommendation systems.It is used to estimate the likelihood that a user will click on ads.The method used in the traditional CTR prediction task is to improve the prediction effect through a large number of feature engineering.However, these methods are time-consuming and laborious, and the construction process is not universal.Due to the sparse and high-dimensional characteristics of data features, it is necessary to measure the importance of sparse features and obtain efficient feature interactions.In this paper, a novel CTR model based on Mutual Information and Feature Interaction (MiFiNN) is proposed.First, the mutual information of each sparse feature and the click result is calculated as the weight of each sparse feature.Subsequently, an interactive method combining the outer product and inner product is constructed to carry out the feature interaction.Then, the resulting feature interactions and the dense features set of the original input are taken as DNN inputs.We verify the proposed model on four datasets.In addition, several widely known models are introduced for comparison.The experimental results indicate the superiority of the model.
Human Quality Evaluation of Machine-Translated Poetry

The quality of literary translation was from the beginning of literacy an important factor in publishing and, as a consequence, in research and education. The quality of literary text translation is of utmost significance for researchers and students, especially in higher education. Only complete and high-standard translations are believed to be necessary for the use in the evaluation and study of style and concepts of a given author or a literary genre. This quality verification applies even more to machine translation in general, due to the fact that such translations are deemed subpar and unsuitable for further dissemination and examination. The need for human quality evaluation of machine-translated text is therefore highly emphasised, since human translations are considered to be the “gold standard” and reference translations in the machine translation process. The aim of this paper is to explore, on the example of a data set consisting of poems written by a relevant contemporary Croatian poet, the effectiveness of applying machine translation on the Croatian-German language pair in the domain of poetry, with regard to human judgment of machine translation quality. Human evaluation in this paper is conducted by taking into account two machine translation quality criteria - adequacy and fluency, after which an inter-rater agreement analysis is performed.
SQL Injection Teaching Based on SQLi-labs

SQL injection attack is one of the most common methods of hacker attack, which is very harmful. In the course of information system security in our school, SQL injection attack and prevention is one of the most important and difficult contents in teaching. SQLi-labs is a teaching assistant software with various SQL injection vulnerabilities. With the help of this software, teacher can carry out SQL injection attacks in class, which help students better master the principle of SQL injection attack and prevention. This paper takes Union SQL injection as an example to introduce the teaching implementation process based on SQLi-labs. Teaching practice shows that this teaching mode increases the interest of students' learning and achieves good learning effect.
Key Frames Extraction Based on Optical-flow and Mutual Information Entropy

Abstract Key frames provide a suitable video summary and framework for video indexing, browsing and retrieval, and how to extract key frames is the core in the field of video retrieval. In order to solve the problem of redundancy and missing selection in key frame extraction, this paper proposed a key frame extraction algorithm based on optical flow and mutual information entropy. This algorithm integrates mutual information entropy and optical flow characteristics to extract key frames. First, the optical flow method calculated the total optical flow of each frame of the image, and selected the video frame with extreme optical flow difference in the neighbourhood as the key frame, and the other video frames were put into the candidate key frame set; by calculating the mutual information entropy of the key frame set, the minimum mutual information entropy is taken as the threshold; Then, The mutual information entropy of the candidate key frame set was calculated, and the image frames larger than the threshold value were put into the key frame set; Finally, redundant key frames are deleted through the measurement of inter-frame similarity, and the remaining key frames are the key frames to be extracted. The experimental results show that compared with other methods, the accuracy of extracted key frames by this approach is significantly improved on precision, recall and F-score, and the extracted key frames can reflect video content more effectively.
An SKOS-Based Vocabulary on the Swift Programming Language

Domain ontologies about one or several programming languages have been created in various occasions, mostly in the context of Technology Enhanced Learning (TEL). Their benefits range from modeling learning outcomes, over organization and annotation of learning material, to providing scaffolding support in programming labs by integrating relevant learning resources. The Swift programming language, introduced in 2014, is currently gaining momentum in different fields of application. Both its powerful syntax as well as the provided type safety make it a good language for first-year computer science students. However, it has not yet been the subject of a domain ontology. In this paper, we present an SKOS-based vocabulary on the Swift programming language, aiming at enabling the benefits of previous research for this particular language. After reviewing existing ontologies on other programming languages, we present the modeling process of the Swift vocabulary, its integration into the LOD Cloud and list all of its resources available to the research community. Finally, we showcase how it is being used in different TEL tools.