Reviewerly/exHarmony huggingface dataset:
Learning the Influence between Partially Observable Processes using Scorebased Structure Learning

The difficulty of learning the underlying structure between processes is a common task found throughout the sciences, however not much work is dedicated towards this problem.In this paper, we attempt to use the language of structure learning to address learning the dynamic influence network between partially observable processes represented as dynamic Bayesian networks.The significance of learning an influence network is to promote knowledge discovery and improve on density estimation in the temporal space.We learn the influence network, defined by this paper, by learning the optimal structure for each process first, and thereafter apply redefined structure learning algorithms for temporal models.Our procedure builds on the language of probabilistic graphical model representation and learning.This paper provides the following contributions: we (a) provide a definition of influence between stochastic processes represented by dynamic Bayesian networks; (b) expand on the conventional structure learning literature by providing a structure score and learning procedure for temporal models; and (c) introduce the notion of a structural assemble which is used to associate two stochastic processes represented by dynamic Bayesian networks.
Second Life Application for Creativity in Art and Design Education

&lt;p style="text-align:justify"&gt;This research aims at revealing the effects of Second Life (SL) application used in virtual environment in art and design education of creativity. This research is a qualitative study conducted evaluating the results of a group of 16 students studying art and design at universities, for a period of 12 weeks. For SL, a 3D, online, multi-user virtual environment was chosen as the study area. The process of the research was evaluated by the researcher with a Structured Observation Form (SOF). Student designs were evaluated by four graduated artists/educators and researchers with a Rubric. The study group discovered SL during the application process. They made numerous trials, took risks and tried different solutions in SL. The design process that started with an avatar creation continued with 3D design, adding texture, colour, and script to objects and creating a notecard. According to the researcher’s feedback based on SOF, the group is considered “Good” in the design process. According to the Rubric for creativity, applied by the experts is evaluated as “Accomplished.” As for the rubric applied by the researcher, the group is evaluated “Good.” Based on these results, it can be considered that the inclusion of SL in the art and design education curriculum will contribute to 21st century skills and develop imagination and creativity of students.&lt;/p&gt;
Eliciting Sketched Expressions of Command Intentions in an IDE

Software engineers routinely use sketches (informal, ad-hoc drawings) to visualize and communicate complex ideas for colleagues or themselves. We hypothesize that sketching could also be used as a novel interaction modality in integrated software development environments (IDEs), allowing developers to express desired source code manipulations by sketching right on top of the IDE, rather than remembering keyboard shortcuts or using a mouse to navigate menus and dialogs. For an initial assessment of the viability of this idea, we conducted an elicitation study that prompted software developers to express a number of common IDE commands through sketches. For many of our task prompts, we observed considerable agreement in how developers would express the respective commands through sketches, suggesting that further research on a more formal sketch-based visual command language for IDEs would be worthwhile.
The Dynamic Geometry of Interaction Machine: A Token-Guided Graph Rewriter

In implementing evaluation strategies of the lambda-calculus, bothcorrectness and efficiency of implementation are valid concerns. While thenotion of correctness is determined by the evaluation strategy, regardingefficiency there is a larger design space that can be explored, in particularthe trade-off between space versus time efficiency. Aiming at a unifiedframework that would enable the study of this trade-off, we introduce anabstract machine, inspired by Girard's Geometry of Interaction (GoI), a machinecombining token passing and graph rewriting. We show soundness and completenessof our abstract machine, called the \emph{Dynamic GoI Machine} (DGoIM), withrespect to three evaluations: call-by-need, left-to-right call-by-value, andright-to-left call-by-value. Analysing time cost of its execution classifiesthe machine as ``efficient'' in Accattoli's taxonomy of abstract machines.
Adversarial Robustness Against Image Color Transformation within Parametric Filter Space.

We propose Adversarial Color Enhancement (ACE), a novel approach to generating non-suspicious adversarial images by optimizing a color transformation within a parametric filter space. The filter we use approximates human-understandable color curve adjustment, constraining ACE with a single, continuous function. This property gives rise to a principled adversarial action space explicitly controlled by filter parameters. Existing color transformation attacks are not guided by a parametric space, and, consequently, additional pixel-related constraints such as regularization and sampling are necessary. These constraints make methodical analysis difficult. In this paper, we carry out a systematic robustness analysis of ACE from both the attack and defense perspectives by varying the bound of the color filter parameters. We investigate a general formulation of ACE and also a variant targeting particularly appealing color styles, as achieved with popular image filters. From the attack perspective, we provide extensive experiments on the vulnerability of image classifiers, but also explore the vulnerability of segmentation and aesthetic quality assessment algorithms, in both the white-box and black-box scenarios. From the defense perspective, more experiments provide insight into the stability of ACE against input transformation-based defenses and show the potential of adversarial training for improving model robustness against ACE.
Prediction Model of Breast Cancer Based on mRMR Feature Selection

In real life, there are a lot of unbalanced data, and there are great differences in the data volume in category distribution, especially in the medical data where this problem is more prominent because of the prevalence rate. In this paper, the P-mRMR algorithm is proposed based on the mRMR algorithm to improve the feature selection process of unbalance data, and to process the attributes with more missing values and integrate the missing values into feature selection while selecting features specific to the characteristics of more missing values in the data set, so as to reduce the complexity of the data pre-processing. In the experiments, the AUC, confusion matrix and probability of missing value are used to compare the algorithms. The experiment shows that the features selected by the improved algorithm have better results in the classifiers.
Exploration Study of Skillsets Needed in Cyber Security Field

Demands for professionals in the field of cyber security is high. In Malaysia, there is a huge shortage of skilled and qualified cyber security professionals. Diverse skill sets are required to keep up with the rapidly changing threat landscape. In order to meet future industry, many higher learning institutions in Malaysia offer many undergraduate and postgraduate cyber security programs. However, literature has reported that the lack of attention given between academia and industry appears when investigating their views concerning the skills gap. With growing numbers of cyber security attacks, it is crucial to develop a model that would guide both main stakeholders, industry, and academia to identify and prioritize skill sets needed to prepare students for the cyber security job market. This study will identify the required cyber security skillsets in preparing students for cyber security job market and develop a hierarchy structure for cyber security competency. The job position that is being reviewed is Information Security Analyst because it is the top job position for cyber security according to the literature review. All of the skills for information security analyst career are compared, and there are two skills reviewed which is soft skills and technical skills, including the various type of professional security certifications. This study developed a structured model for cyber security competency using the analytical hierarchy process (AHP).
Micro-lasers for neuromorphic computing

Spiking micro-lasers are interesting neuromorphic building blocks to realize all-optical spiking neural networks. Optical spike-based computing offers speed and parallelism of optical technologies combined with a sparse way of representing information in spikes, thus with a potential for efficient brain-inspired computing. This article reviews some of the latest advances in this field using single and coupled semiconductor excitable micro-lasers.
Concrete Syntax with Black Box Parsers

Context: Meta programming consists for a large part of matching, analyzing, and transforming syntax trees. Many meta programming systems process abstract syntax trees, but this requires intimate knowledge of the structure of the data type describing the abstract syntax. As a result, meta programming is error-prone, and meta programs are not resilient to evolution of the structure of such ASTs, requiring invasive, fault-prone change to these programs. Inquiry: Concrete syntax patterns alleviate this problem by allowing the meta programmer to match and create syntax trees using the actual syntax of the object language. Systems supporting concrete syntax patterns, however, require a concrete grammar of the object language in their own formalism. Creating such grammars is a costly and error-prone process, especially for realistic languages such as Java and C++. Approach: In this paper we present Concretely, a technique to extend meta programming systems with pluggable concrete syntax patterns, based on external, black box parsers. We illustrate Concretely in the context of Rascal, an open-source meta programming system and language workbench, and show how to reuse existing parsers for Java, JavaScript, and C++. Furthermore, we propose Tympanic, a DSL to declaratively map external AST structures to Rascal's internal data structures. Tympanic allows implementors of Concretely to solve the impedance mismatch between object-oriented class hierarchies in Java and Rascal's algebraic data types. Both the algebraic data type and AST marshalling code is automatically generated. Knowledge: The conceptual architecture of Concretely and Tympanic supports the reuse of pre-existing, external parsers, and their AST representation in meta programming systems that feature concrete syntax patterns for matching and constructing syntax trees. As such this opens up concrete syntax pattern matching for a host of realistic languages for which writing a grammar from scratch is time consuming and error-prone, but for which industry-strength parsers exist in the wild. Grounding: We evaluate Concretely in terms of source lines of code (SLOC), relative to the size of the AST data type and marshalling code. We show that for real programming languages such as C++ and Java, adding support for concrete syntax patterns takes an effort only in the order of dozens of SLOC. Similarly, we evaluate Tympanic in terms of SLOC, showing an order of magnitude of reduction in SLOC compared to manual implementation of the AST data types and marshalling code. Importance: Meta programming has applications in reverse engineering, reengineering, source code analysis, static analysis, software renovation, domain-specific language engineering, and many others. Processing of syntax trees is central to all of these tasks. Concrete syntax patterns improve the practice of constructing meta programs. The combination of Concretely and Tympanic has the potential to make concrete syntax patterns available with very little effort, thereby improving and promoting the application of meta programming in the general software engineering context.
Federated Composite Optimization

Federated Learning (FL) is a distributed learning paradigm that scales on-device learning collaboratively and privately. Standard FL algorithms such as FedAvg are primarily geared towards smooth unconstrained settings. In this paper, we study the Federated Composite Optimization (FCO) problem, in which the loss function contains a non-smooth regularizer. Such problems arise naturally in FL applications that involve sparsity, low-rank, monotonicity, or more general constraints. We first show that straightforward extensions of primal algorithms such as FedAvg are not well-suited for FCO since they suffer from the "curse of primal averaging," resulting in poor convergence. As a solution, we propose a new primal-dual algorithm, Federated Dual Averaging (FedDualAvg), which by employing a novel server dual averaging procedure circumvents the curse of primal averaging. Our theoretical analysis and empirical experiments demonstrate that FedDualAvg outperforms the other baselines.
A User's Guide to Calibrating Robotics Simulators

Simulators are a critical component of modern robotics research. Strategies for both perception and decision making can be studied in simulation first before deployed to real world systems, saving on time and costs. Despite significant progress on the development of sim-to-real algorithms, the analysis of different methods is still conducted in an ad-hoc manner, without a consistent set of tests and metrics for comparison. This paper fills this gap and proposes a set of benchmarks and a framework for the study of various algorithms aimed to transfer models and policies learnt in simulation to the real world. We conduct experiments on a wide range of well known simulated environments to characterize and offer insights into the performance of different algorithms. Our analysis can be useful for practitioners working in this area and can help make informed choices about the behavior and main properties of sim-to-real algorithms. We open-source the benchmark, training data, and trained models, which can be found at https://github.com/NVlabs/sim-parameter-estimation.
Agile Values and Practices

I often start my agile project management classes by asking students what they think of when they read or hear “agile” (I always think of a cheetah). Students typically respond with “fast, nimble, flexible, responsive.” I mention the cheetah in the context of chasing a gazelle: The cheetah’s goal is the gazelle, which is constantly moving and changing direction – much like the requirements and objectives of some projects. The cheetah must process the input of the gazelle’s changes in direction along with the changes in the landscape and respond to these changes in order to accomplish the goal of catching the gazelle.
Non-Euclidean Virtual Reality IV: Sol

This article presents virtual reality software designed to explore the Sol geometry. The simulation is available on 3-dimensional.space/sol.html
Deep Reinforcement Learning for Navigation in AAA Video Games

In video games, non-player characters (NPCs) are used to enhance the players' experience in a variety of ways, e.g., as enemies, allies, or innocent bystanders. A crucial component of NPCs is navigation, which allows them to move from one point to another on the map. The most popular approach for NPC navigation in the video game industry is to use a navigation mesh (NavMesh), which is a graph representation of the map, with nodes and edges indicating traversable areas. Unfortunately, complex navigation abilities that extend the character's capacity for movement, e.g., grappling hooks, jetpacks, teleportation, or double-jumps, increases the complexity of the NavMesh, making it intractable in many practical scenarios. Game designers are thus constrained to only add abilities that can be handled by a NavMesh if they want to have NPC navigation. As an alternative, we propose to use Deep Reinforcement Learning (Deep RL) to learn how to navigate 3D maps using any navigation ability. We test our approach on complex 3D environments in the Unity game engine that are notably an order of magnitude larger than maps typically used in the Deep RL literature. One of these maps is directly modeled after a Ubisoft AAA game. We find that our approach performs surprisingly well, achieving at least $90\%$ success rate on all tested scenarios. A video of our results is available at this https URL.
EFFICIENT SURFACE-AWARE SEMI-GLOBAL MATCHING WITH MULTI-VIEW PLANE-SWEEP SAMPLING

Abstract. Online augmentation of an oblique aerial image sequence with structural information is an essential aspect in the process of 3D scene interpretation and analysis. One key aspect in this is the efficient dense image matching and depth estimation. Here, the Semi-Global Matching (SGM) approach has proven to be one of the most widely used algorithms for efficient depth estimation, providing a good trade-off between accuracy and computational complexity. However, SGM only models a first-order smoothness assumption, thus favoring fronto-parallel surfaces. In this work, we present a hierarchical algorithm that allows for efficient depth and normal map estimation together with confidence measures for each estimate. Our algorithm relies on a plane-sweep multi-image matching followed by an extended SGM optimization that allows to incorporate local surface orientations, thus achieving more consistent and accurate estimates in areasmade up of slanted surfaces, inherent to oblique aerial imagery. We evaluate numerous configurations of our algorithm on two different datasets using an absolute and relative accuracy measure. In our evaluation, we show that the results of our approach are comparable to the ones achieved by refined Structure-from-Motion (SfM) pipelines, such as COLMAP, which are designed for offline processing. In contrast, however, our approach only considers a confined image bundle of an input sequence, thus allowing to perform an online and incremental computation at 1Hz–2Hz.
Spatio-temporal summarized visualization of smartX multi-view visibility in cloud-native edge boxes

The existing data summarization (and archival) techniques are generic and are not designed to leverage the unique characteristics of the spatio-temporal visualization at multi-resource level. In this paper, we propose and explore a family of data summaries that take advantage of the multiple layers i.e. physical/virtual resources with temporal and spatial correlation among distributed edge boxes. Significant challenges in measuring spatio-temporal data, however, contribute to both a tendency towards identifying efficient metrics with summarizing functionalities and effective verification methods. In this paper, we present our idea of maintaining summarized spatio-temporal data and verify through visualization of gathered operational data.
PBS-Calculus: A Graphical Language for Coherent Control of Quantum Computations

We introduce the PBS-calculus to represent and reason on quantum computations involving coherent control of quantum operations. Coherent control, and in particular indefinite causal order, is known to enable multiple computational and communication advantages over classically ordered models like quantum circuits. The PBS-calculus is inspired by quantum optics, in particular the polarising beam splitter (PBS for short). We formalise the syntax and the semantics of the PBS-diagrams, and we equip the language with an equational theory, which is proved to be sound and complete: two diagrams are representing the same quantum evolution if and only if one can be transformed into the other using the rules of the PBS-calculus. Moreover, we show that the equational theory is minimal. Finally, we consider applications like the implementation of controlled permutations and the unrolling of loops.
From Cowry Shells to Cryptos

Currency, the specific instruments of money used as a medium of exchange, has come in many forms—grain, beads, cowry shells, peppercorns, cigarettes, cans of mackerel and potatoes. While digital money comes in many forms—bank account records, foreign exchange, commercial currencies run by platform companies, cryptocurrencies—they share the common characteristic of compressing time-space through technologies. Blockchain-based cryptocurrencies are fundamentally predicated on a logic of algorithmic proofs, rather than centralized human governance. To achieve workable decentralized systems, cryptocurrencies rely on three key technologies: distributed databases, for recording interactions; networks of computer nodes; and encryption protocols. Cryptocurrencies have the standard characteristics expected from currency—readily divisible into smaller parts, easily transportable—and offer some new protections against fraud, counterfeiting, or seizure by a centralized authority. The design of Bitcoin and subsequent cryptocurrencies emerges from, and is embedded in, a particular segment of society working to mainstream some rather fringe ideas about money and currency.
Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment

Distribution alignment has many applications in deep learning, including domain adaptation and unsupervised image-to-image translation. Most prior work on unsupervised distribution alignment relies either on minimizing simple non-parametric statistical distances such as maximum mean discrepancy or on adversarial alignment. However, the former fails to capture the structure of complex real-world distributions, while the latter is difficult to train and does not provide any universal convergence guarantees or automatic quantitative validation procedures. In this paper, we propose a new distribution alignment method based on a log-likelihood ratio statistic and normalizing flows. We show that, under certain assumptions, this combination yields a deep neural likelihood-based minimization objective that attains a known lower bound upon convergence. We experimentally verify that minimizing the resulting objective results in domain alignment that preserves the local structure of input domains.
Exploiting ergodicity of the logistic map using deep-zoom to improve security of chaos-based cryptosystems

This paper explores the deep-zoom properties of the chaotic [Formula: see text]-logistic map, in order to propose an improved chaos-based cryptosystem. This map was shown to enhance the random features of the Logistic map, while at the same time reducing the predictability about its orbits. We incorporate its strengths to security into a previously published cryptosystem to provide an optimal pseudo-random number generator (PRNG) as its core operation. The result is a reliable method that does not have the weaknesses previously reported about the original cryptosystem.
Heisenberg-scaling angular displacement estimation with tunable squeezed Bell states

We demonstrate an angular momentum-enhanced protocol that permits an angular displacement estimation by using tunable squeezed Bell state and parity detection.We consider the resolution and the sensitivity, super-resolution is presented along with Heisenberg scaling sensitivity for arbitrary tunable factor, the tunable factor which can optimize the sensitivity is also discussed.Additionally, we analyze the advantages of using angular momentum via considering and comparing simulation results.Under the situation of the optimal tunable factor, the Heisenberg-limited sensitivity and 2 ( + 1)-fold super-resolution peak with quantum number are achieved.