Reviewerly/exHarmony huggingface dataset:
On the predictability of domain‐independent temporal planners

Abstract Temporal planning is a research discipline that addresses the problem of generating a totally or a partially ordered sequence of actions that transform the environment from some initial state to a desired goal state, while taking into account time constraints and actions' duration. For its ability to describe and address temporal constraints, temporal planning is of critical importance for a wide range of real‐world applications. Predicting the performance of temporal planners can lead to significant improvements in the area, as planners can then be combined in order to boost the performance on a given set of problem instances. This paper investigates the predictability of the state‐of‐the‐art temporal planners by introducing a new set of temporal‐specific features and exploiting them for generating classification and regression empirical performance models (EPMs) of considered planners. EPMs are also tested with regard to their ability to select the most promising planner for efficiently solving a given temporal planning problem. Our extensive empirical analysis indicates that the introduced set of features allows to generate EPMs that can effectively perform algorithm selection, and the use of EPMs is therefore a promising direction for improving the state of the art of temporal planning, hence fostering the use of planning in real‐world applications.
EPPADS: An Enhanced Phase-Based Performance-Aware Dynamic Scheduler for High Job Execution Performance in Large Scale Clusters

The way in which jobs are scheduled is critical to achieve high job processing performance in large scale data clusters. Most existing scheduling mechanism employs a First-In First-Out, serialized approach encompassed with task straggler hunting techniques which launches speculative tasks after detecting slow tasks. This is often achieved through the instrumentation of processing nodes. Such node instrumentation incurs frequent communication overheads as the number of processing nodes increase. Moreover the sequential scheduling of job tasks and the straggler hunting approach fails to meet optimal performance as they increase job waiting time in queue and incurs delayed speculative execution of straggling tasks respectively. In this paper we propose an Enhanced Phase based Performance Aware Dynamic Scheduler (EPPADS), which schedules job tasks without additional instrumentation modules. EPPADS uses a two staged scheduling approach, that is, the slow start phase (SSP) and accelerate phase (AccP). The SSP schedules the initial task in the queue in the normal FIFO way and records the initial execution times of the processing nodes. The AccP uses the initial execution times to compute the processing nodes task distribution ratio of the remaining tasks and schedules them using a single scheduling I/O. We implement EPPADS scheduler in Hadoop's MapReduce framework. Our evaluation shows that EPPADS can achieve a performance improvement on FIFO scheduler of 30%. Compared with existing Dynamic scheduling approach which uses node instrumentation, EPPADS achieves a better performance of 22%.
Incremental Learning Meets Reduced Precision Networks

Hardware accelerators for Deep Neural Networks (DNNs) that use reduced precision parameters are more energy efficient than the equivalent full precision networks. While many studies have focused on reduced precision training methods for supervised networks with the availability of large datasets, less work has been reported on incremental learning algorithms that adapt the network for new classes and the consequence of reduced precision has on these algorithms. This paper presents an empirical study of how reduced precision training methods impact the iCARL incremental learning algorithm. The incremental network accuracies on the CIFAR-100 image dataset show that weights can be quantized to 1 bit (2.39% drop in accuracy) but when activations are quantized to 1 bit, the accuracy drops much more (12.75%). Quantizing gradients from 32 to 8 bits only affects the accuracies of the trained network by less than 1%. These results are encouraging for hardware accelerators that support incremental learning algorithms.
Evolving MIMO multi-layered artificial neural networks using grammatical evolution

In this paper, we propose a scheme for evolving multiple-input-multiple-output (MIMO) artificial neural networks (ANNs) using grammatical evolution (GE). GE is a well-known technique for program evolution. While it has also been used for the evolution of ANN structures in the past, little work is reported on the evolution of MIMO ANNs.
Towards a fractionation-based verification

This work contributes to reduce the cost of the verification process of model-based systems, especially the ones designed as SysML activity diagrams, by relying on the concept of diagram calls defined in SysML standard. By exploiting the diagram calls concept, we propose a mechanism to transform a given diagram into its equivalent fractal (hierarchical) form. Further, we present an abstraction algorithm to reduce the size of the obtained SysML activity diagram by ignoring the irrelevant behaviors and by merging similar artifacts. To do verification, the abstracted diagram is transformed automatically into PRISM source code to check the system's requirements that are specified in the probabilistic temporal logic, PCTL. The soundness of the provided framework is presented for each step, and, its practical effectiveness is demonstrated on diagrams obtained by reversing the open source of OpenSAF middle-ware.
A novel approach for collecting and sharing software metrics data

Software metrics collection is a fundamental activity required to perform almost any empirical software engineering study. However, even if a wide range of tools exists, the collection of such fundamental data is still very time consuming. Moreover, every researcher collects almost the same data (e.g., CK metrics, McCabe Cyclomatic Complexity, etc.) from almost the same projects (e.g., well-known open source projects). The amount of such duplicate work performed in the community reduces the effort that researchers can actually spend in the most valuable part of their research such as the development of new theories and models and their empirical evaluation.
Single Image Super Resolution Model Using Learnable Weight Factor in Residual Skip Connection

In single image super-resolution problems, the recent feed forward deep learning architectures use residual connections in order to preserve local features and carry them through the next layer. In a simple residual skip connection, all the features of the earlier layer are concatenated with the features of the current layer. A simple concatenation of the features does not exploit the fact that some features may be more useful than other features and vice versa. To overcome this limitation, we propose an extended architecture (baby neural network) which will have input as the features learned from the previous layer and output a multiplication factor. This multiplication factor will give importance to the given feature and thus help in training the current layer's features more accurately. The proposed model clearly outperforms the existing works.
Smart contracts in view of the civil code

Explicit legal regulation is still lacking in many countries for cryptocurrencies like Bitcoin, Ripple, Ethereum, and others. This is not unusual for a new technology. On top of such cryptocurrencies, another new technology, so-called smart contracts, has been established. Smart contracts are specialized computer code with the aim to automate the exchange of (digital) assets. In decentralized applications (Ðapps), parts of the business logic and the data storage of traditional web applications are replaced with smart contracts. They are credited with considerable potential for commercial use, especially in FinTech but also beyond. Smart contracts mostly lack explicit legal regulation as well. Hitherto there is little experience in applying current law to them. Building a business on uncertain terrain is at least risky. We seek a clarification of smart contracts in the legal context, especially against the background of the Civil Code.
Convexity properties of superpositions of degenerate bipartite eigenstates

The entanglement content of superpositions of pairs of degenerate eigenstates of a bipartite system are considered in the case that both are also eigenstates of the $z$ component of the total angular momentum. It is shown that the von Neumann entropy of the state that is obtained tracing out one of the parts of the system has a definite convexity (concavity) as a function of the superposition parameter and that its convexity (concavity) can be predicted using a quantity of information that measures the entropy shared by the states at the extremes of the superposition. Several examples of two particle system, whose eigenfunctions and density matrices can be obtained exactly, are analyzed thoroughly.
FRVRIT

Virtual reality (VR) games continue to grow in popularity with the advancement of commercial VR capabilities such as the inclusion of full body tracking. This means game developers can design for novel interactions involving a player's full body rather than solely relying on controller input. However, existing research on evaluating player interaction in VR games primarily focuses on game content and inputs from game controllers or player hands. Current approaches for evaluating player full body interactions are limited to simple qualitative observation which makes evaluation difficult and time-consuming. We present a Full Room Virtual Reality Investigation Tool (FRVRIT) which combines data recording and visualization to provide a quantitative solution for evaluating player movement and interaction in VR games. The tool facilitates objective data observation through multiple visualization methods that can be manipulated, allowing developers to better observe and record player movements in the VR space to improve and iterate on the desired interactions in their games.
Assessing the Feasibility of Speech-Based Activity Recognition in Dynamic Medical Settings

We describe an experiment conducted with three domain experts to understand how well they can recognize types and performance stages of activities using speech data transcribed from verbal communications during dynamic medical teamwork. The insights gained from this experiment will inform the design of an automatic activity recognition system to alert medical teams to process deviations in real time. We contribute to the literature by (1) characterizing how domain experts perceive the dynamics of activity-related speech, and (2) identifying the challenges associated with system design for speech-based activity recognition in complex team-based work settings.
A method for Representation the Knowledge of Functions and Operators and Application

Computational Objects Knowledge Base (COKB) model is very useful for building knowledge bases of intelligent systems, especially for designing intelligent problems solver systems. This model is constructed based on ontology and object-oriented approach. Objects in COKB model have the structure and behaviors to solve problems on themselves. This model had algorithms for solving general problems on it. However, the reasoning on the knowledge of functions and operators has not yet been researched completely. In this paper, we will present techniques for reasoning on the knowledge of functions and operators in COKB model. These results have been also applied to design an intelligent problems solver in Linear Algebra in the mathematic curriculum of the university.
Improving a 3-D Convolutional Neural Network Model Reinvented from VGG16 with Batch Normalization

It is challenging to build and train a Convolutional Neural Network model that can achieve a high accuracy rate for the first time. There are many variables to consider such as initial parameters, learning rate, and batch size. Unsuccessfully training a model is one of the most inevitable problems. In some cases, the model struggles to find a lower Loss Function value which results in a poor performance. Batch Normalization is considered as a remedy to overcome this problem. In this paper, two models reinvented from VGG16 are created with and without using Batch Normalization to evaluate their model performance. It is clear that the model using Batch Normalization provides a better result in terms of Loss Function value and model accuracy, which also achieves a very high accuracy rate. It also reaches the saturation point of the highest model accuracy faster than the model without Batch Normalization. This paper also finds that the accuracy of 3D Convolutional Neural Network model reinvented from VGG16 with Batch Normalization is at 91.2% which can beat many benchmarking results on UCF101 such as IDT [6], Two-Stream [11], and Dynamic Image Networks IDT [4]. The technique introduced in this paper shows a fast, reliable and accurate estimation of human activity type and could be used in smart environments.
Pillar Networks: Combining parametric with non-parametric methods for action recognition

Image understanding using deep convolutional network has reached human-level performance, yet the closely related problem of video understanding, especially action recognition, has not reached the same required level of maturity. As a solution we propose two independent architectures for action recognition using meta-classifiers – the first is based on combining kernels of support-vector-machines (SVM) and the second is based on distributed Gaussian Processes (GP). Both receive features that are computed using a multi-stream deep convolutional neural network, enabling the achievement of state-of-the-art performance on a 51 and a 101-class action recognition problem (HMDB-51/UCF-101 dataset). We have named the resulting architecture ‘pillar networks’ as each (very) deep neural network acts as a pillar for the meta-classifiers. In addition, we illustrate that hand-crafted features such as the improved dense trajectories (iDT) and Multi-skip Feature Stacking (MIFS), when used as additional pillars, can further supplement the performance.
Agglomerative oversegmentation using dual similarity and entropy rate

Oversegmentation is a preprocessing step for many computer vision and remote sensing tasks, such as object recognition and image understanding. In this paper, a method called agglomerative oversegmentation (AOS) is proposed. AOS first designs a dual similarity of the connected pixels by considering both the pixel and region levels. The entropy rate of random walks is then employed on the image plane, and the gain of each pair of connected pixels is estimated. Finally, a fast agglomerative algorithm is developed to oversegment the image using an entropy rate gain function. Two challenging datasets are utilized in the experiments which show the promising performance of AOS. In contrast to the state-of-the-art algorithms, the oversegmentation of AOS is more consistent with the geometric structures of the target objects, especially for the objects with complex texture on remote sensing images.
Making Machines That Make

Personal fabrication has been touted as the killer application of digital fabrication. The history of the personal computer is invoked as a roadmap to ubiquitous, user-friendly, and inexpensive personal-scale manufacturing. But what will those machines of the future actually look like? Perhaps there is an error in the assumption that the personal digital fabrication machines look like smaller versions of their industrial counterparts. After all, the user profile of someone making one thing for themselves is very different from that of a machinist making many of the same parts in a factory. Perhaps their tools also look different. The precision of computer controlled machines enables reliable production of parts to tight tolerances. However, how do we harness the precision of machines without losing the creativity of individuals? My lab at the University of Washington is called Machine Agen
A Lexical Resource-Constrained Topic Model for Word Relatedness

Word relatedness computation is an important supporting technology for many tasks in natural language processing.Traditionally, there have been two distinct strategies for word relatedness measurement: one utilizes corpus-based models, whereas the other leverages external lexical resources.However, either solution has its strengths and weaknesses.In this paper, we propose a lexical resourceconstrained topic model to integrate the two complementary strategies effectively.Our model is an extension of probabilistic latent semantic analysis, which automatically learns word-level distributed representations forward relatedness measurement.Furthermore, we introduce generalized expectation maximization (GEM) algorithm for statistical estimation.The proposed model not merely inherit the advantage of conventional topic models in dimension reduction, but it also refines parameter estimation by using word pairs that are known to be related.The experimental results in different languages demonstrate the effectiveness of our model in topic extraction and word relatedness measurement.
Virtual Hand Representation and Motion Control for Smart Wheelchair with Touch-Based Extended Hand Projection

In this paper, we present a new smart wheelchair which utilizes projection-based mixed reality techniques. The wheelchair enables a user to interact distant objects by user's hand directly. A touch-based MR interface allows the user to extend his/her hand and arm onto the real environment by projecting the user's hand graphics from an onboard projector, In order to realize the higher controllability of wheelchair motion, the unified touch gesture-based interface controls both the extended virtual hand and the electric wheelchair seamlessly. This paper introduces a feasible framework of touch control of wheelchair, control of extended body, learning-based automatic operation switching, and graphical representation of projected hand image. We confirmed the accuracy of operation switching and the suitable projection of the virtual hand through experiments.
ML, Visibly Pushdown Class Memory Automata, and Extended Branching Vector Addition Systems with States

We prove that the observational equivalence problem for a finitary fragment of the programming langauge ML is recursively equivalent to the reachability problem for extended branching vector addition systems with states (EBVASS). This result has two natural and independent parts. We first prove that the observational equivalence problem is equivalent to the emptiness problem for a new class of class memory automata equipped with a visibly pushdown stack, called Visibly Pushdown Class Memory Automata (VPCMA). Our proof uses the fully abstract game semantics of the language. We then prove that the VPCMA emptiness problem is equivalent to the reachability problem for EBVASS. The results of this article complete our programme to give an automata classification of the ML types with respect to the observational equivalence problem for closed terms.
Estimating finite mixture of continuous trees using penalized mutual information

In this paper we introduce continuous tree mixture model that is the mixture of undirected graphical models with tree structured graphs and is considered as multivariate analysis with a non parametric approach. We estimate its parameters, the component edge sets and mixture proportions through regularized maximum likalihood procedure. Our new algorithm, which uses expectation maximization algorithm and the modified version of Kruskal algorithm, simultaneosly estimates and prunes the mixture component trees. Simulation studies indicate this method performs better than the alternative Gaussian graphical mixture model. The proposed method is also applied to water-level data set and is compared with the results of Gaussian mixture model.
A Noval Collaborative Filtering Technique Approach for Recommender System

Recommender frameworks are systems to interact with huge data to predict ratings. There have been numerous approaches for the assortment of issues tended to such systems utilized in addition to its useful applications. Recommender frameworks has consolidated a wide assortment of man-made reasoning strategies including machine learning, information mining, client demonstrating, case-based thinking, and imperative fulfillment. Customized suggestions are a vital piece of numerous web-based business applications such as Amazon and Netflix. The motivation behind the article in this exceptional issue is to assess the flow scene of recommender frameworks, then examine and recognize bearing the field that is currently customized. This paper gives an outline of the current condition of the field and presents the different approach for collaborative processing on uncommon issues.
Efficient non-Gaussianity measure for single-mode fields by kurtosis of a Gaussian operator

We propose a method to faithfully probe and quantify the non-Gaussianity of single-mode fields in the coherent-state representations based on the averaged-cumulant technique. In comparison with the previous measure, S H Xiang et al (2018 Phys. Rev. A 97 042303), we characterize the non-Gaussianity of quantum states using the kurtosis of a Gaussian operator, rather than that of a quadrature operator. As applications, we investigate the non-Gaussianities of three different non-Gaussian states: a mixture of vacuum and Fock states, Schrodinger cat states, and photon-added coherent states. It is proven that such a kurtosis is a powerful and effective measure in appraising non-Gaussianity.
Exploring the Relationship between the Structural and the Actual Similarities of Automata

One of the most common representations of formal languages is deterministic finite-state automata. In recent years, a bunch of papers dedicated to the problem of synthesizing such an automaton by a list of positive and negative examples of some formal language was published. Many of proposed models try to construct candidate automaton, which is believed to somehow approximate the target one. In this paper, we exploring the relationship between the structural similarity of candidate and target finite-state automata and the percentage of common words in their formal languages. We formalize two different notions of automata similarity, and present the results of their comparison and draw conclusions regarding the applicability of these definitions to the problem of grammar inference.
Cognitive Learning

Writing is a fundamental task in our daily life. Existing writing improvement tools mostly focus on low-level grammar error correction, rather than enhancing users' writing styles at the cognitive level. In this work, we present a computational approach that allows learners to have fast but effective learning experience with the help of automatic style transfer, visual stylometry analytics, machine teaching and practice. Our system provides a perfect fusion of vividly visualized style features and principles along with informative examples, which together can shape and drive personalized cognitive learning experience. We demonstrate the effectiveness of our system in a scenario of learning from William Shakespeare.