Reviewerly/exHarmony huggingface dataset:
Qualitative physics as a component in natural language semantics: A progress report

We propose that qualitative physics can provide an important component of natural language semantics. Specifically, we describe how qualitative process theory can be recast in terms of frame semantics, as used in the Berkeley FrameNet project. This reformulation is important because it could allow the techniques of qualitative reasoning to be harnessed for natural language understanding and it expands the range of phenomena that can be described in NL semantics. We show that these ideas can account for a large percentage of a small corpus of explanatory text, and that they support the construction of QP models from such texts.
Period Doubling as a Means of Representing Multiply Instantiated Entities

The problem of multiple instantiation is the ability to handle different instances of a unique object at the same time. For connectionist models that do not use a working area containing copies of items from a long-term knowledge base, the problem of multiple instantiation is a particularly difficult one. While people are able to deal with multiple instances, their performance when doing so is nonetheless poorer, which is not the case for symbolic models. A cognitive model should reflect competence, as well as its limits. Some connectionist solutions to the problem of multiple instantiation are mentioned in this paper. An new solution which makes use of semi-distributed representations is presented. This model dons not separate the long term knowledge base from a working area and has no recourse to copies. This solution limits the process of multiple instantiation in a way that should better reflect human data.
Building Placement Optimization in Real-Time Strategy Games

In this paper we propose using a Genetic Algorithm to optimize the placement of buildings in Real-Time Strategy games. Candidate solutions are evaluated by running base assault simulations. We present experimental results in SparCraft — a StarCraft combat simulator --- using battle setups extracted from human and bot StarCraft games. We show that our system is able to turn base assaults that are losses for the defenders into wins, as well as reduce the number of surviving attackers. Performance is heavily dependent on the quality of the prediction of the attacker army composition used for training, and its similarity to the army used for evaluation. These results apply to both human and bot games.
Seeking Optimum System Settings for Physical Activity Recognition on Smartwatches

Physical activity recognition using wearable devices can provide valued information regarding an individual’s degree of functional ability and lifestyle. Smartphone-based physical activity recognition is a well-studied area. However, research on smartwatch-based physical activity recognition, on the other hand, is still in its infancy. Through a large-scale exploratory study, this work aims to investigate the smartwatch-based physical activity recognition domain. A detailed analysis of various feature banks and classification methods are carried out to find the optimum system settings for the best performance of any smartwatch-based physical activity recognition system for both personal and impersonal models in real life scenarios. To further validate our hypothesis for both personal and impersonal models, we tested single subject out cross validation process for smartwatch-based physical activity recognition.
Artificial intelligence, big data and intellectual property: protecting computer generated works in the United Kingdom

Big data and its use by artificial intelligence (AI) is changing the way intellectual property is developed and granted. For decades, machines have been autonomously generating works which have traditionally been eligible for copyright and patent protection. Now, the growing sophistication of AI and the prevalence of big data is positioned to transform computer generated works (CGWs) into major contributors to the creative and inventive economies. However, intellectual property law is poorly prepared for this eventuality. The UK is one of the few nations, and perhaps the only EU member state, to explicitly provide copyright protection for CGWs. It is silent on patent protection for CGWs. The chapter makes several contributions to the literature. First, it provides an up to date review of UK, EU and international law. Second, it argues that patentability of CGWs is a matter of first impression in the UK, but that CGWs should be eligible for patent protection as a matter of policy. Finally, it argues that the definition of CGWs should be amended to reflect the fact that a computer can be an author or inventor in a joint work with a person.
A Model for Distributed Processing and Analyses of NGS Data under Map-Reduce Paradigm

Massively parallel sequencing technique, introduced by NGS technology, has resulted in an exponential growth of sequencing data, with greatly reduced cost and increased throughput. This huge explosion of data has introduced new challenges in regard to its storage, integration, processing, and analyses. In this paper, we have proposed a novel distributed model under Map-Reduce paradigm to address the NGS big data problem. The architecture of the model involves Map-Reduce based modularized approach involving three different phases that support various analytical pipelines. The first phase will generate detailed base level information of various individual genomes, by granulating the alignment data. The other two phases independently process this base level information in parallel. One of these two phases will provide an integrated DNA profile of multiple individuals, whereas the other phase will generate contigs with similar features in an individual. Each of these three phases will generate a repository of genomic information that will facilitate other analytical pipelines. A simulated and real experimental prototypes has been provided as results to show the effectiveness of the model and its superiority over a few existing popular models and tools. A detailed description of the scope of applications of this model is also included in this article.
Classical and quantum dissipative dynamics in Josephson junctions: An Arnold problem, bifurcation, and capture into resonance

We theoretically study the phase dynamics in Josephson junctions, which maps onto the oscillatory motion of a point-like particle in the washboard potential. Under appropriate driving and damping conditions, the Josephson phase undergoes intriguing bistable dynamics near a saddle point in the quasienergy landscape. The bifurcation mechanism plays a critical role in superconducting quantum circuits with relevance to non-demolition measurements such as high-fidelity readout of qubit states. We address the question `what is the probability of capture into either basin of attraction' and answer it concerning both classical and quantum dynamics. Consequently, we derive the Arnold probability and numerically analyze its implementation of the controlled dynamical switching between two steady states under the various nonequilibrium conditions.
Training recurrent neural networks via dynamical trajectory-based optimization

This paper introduces a new method to train recurrent neural networks using dynamical trajectory-based optimization. The optimization method utilizes a projected gradient system (PGS) and a quotient gradient system (QGS) to determine the feasible regions of an optimization problem and search the feasible regions for local minima. By exploring the feasible regions, local minima are identified and the local minimum with the lowest cost is chosen as the global minimum of the optimization problem. Lyapunov theory is used to prove the stability of the local minima and their stability in the presence of measurement errors. Numerical examples show that the new approach provides better results than genetic algorithm and error backpropagation (EBP) trained networks.
The characteristic polynomials of abelian varieties of higher dimension over finite fields

The characteristic polynomials of abelian varieties over the finite field Fq with q=pn elements have a lot of arithmetic and geometric information. They have been explicitly described for abelian varieties up to dimension 4, but little is known in higher dimension. In this paper, among other things, we obtain the following three results on the characteristic polynomial of abelian varieties. First, we prove a relation between n and e, where e is a certain multiplicity associated with a simple abelian variety of arbitrary dimension over Fq. Second, we explicitly describe the characteristic polynomials of simple abelian varieties of arbitrary dimension g, when e=g. Finally, we explicitly describe the coefficients of characteristic polynomials of abelian varieties of dimension 5 over Fq.
Correlators Exceeding One in Continuous Measurements of Superconducting Qubits

We consider the effect of phase backaction on the correlator $\langle I(t)\, I(t+\tau )\rangle$ for the output signal $I(t)$ from continuous measurement of a qubit. We demonstrate that the interplay between informational and phase backactions in the presence of Rabi oscillations can lead to the correlator becoming larger than 1, even though $|\langle I\rangle|\leq 1$. The correlators can be calculated using the generalized "collapse recipe" which we validate using the quantum Bayesian formalism. The recipe can be further generalized to the case of multi-time correlators and arbitrary number of detectors, measuring non-commuting qubit observables. The theory agrees well with experimental results for continuous measurement of a transmon qubit. The experimental correlator exceeds the bound of 1 for a sufficiently large angle between the amplified and informational quadratures, causing the phase backaction. The demonstrated effect can be used to calibrate the quadrature misalignment.
Blind Deblurring of Natural Stochastic Textures Using an Anisotropic Fractal Model and Phase Retrieval Algorithm

The challenging inverse problem of blind deblurring has been investigated thoroughly for natural images. Existing algorithms exploit edge-type structures, or similarity to smaller patches within the image, to estimate the correct blurring kernel. However, these methods do not perform well enough on natural stochastic textures (NSTs), which are mostly random and, in general, are not characterized by distinct edges and contours. In NST, even small kernels cause severe degradation to images. Restoration poses, therefore, an outstanding challenge. In this paper, we refine an existing method by implementing an anisotropic fractal model to estimate the blur kernel's power spectral density. The final kernel is then estimated via an adaptation of a phase retrieval algorithm, originally proposed for sparse signals. We further incorporate additional constraints that are specific to blur filters, to yield even better results. The latter are compared with results obtained by recently published blind deblurring methods.
Too trivial to test? An inverse view on defect prediction to identify methods with low fault risk

Background. Test resources are usually limited and therefore it is often not possible to completely test an application before a release. To cope with the problem of scarce resources, development teams can apply defect prediction to identify fault-prone code regions. However, defect prediction tends to low precision in cross-project prediction scenarios. Aims. We take an inverse view on defect prediction and aim to identify methods that can be deferred when testing because they contain hardly any faults due to their code being "trivial". We expect that characteristics of such methods might be project-independent, so that our approach could improve cross-project predictions. Method. We compute code metrics and apply association rule mining to create rules for identifying methods with low fault risk. We conduct an empirical study to assess our approach with six Java open-source projects containing precise fault data at the method level. Results. Our results show that inverse defect prediction can identify approx. 32-44% of the methods of a project to have a low fault risk; on average, they are about six times less likely to contain a fault than other methods. In cross-project predictions with larger, more diversified training sets, identified methods are even eleven times less likely to contain a fault. Conclusions. Inverse defect prediction supports the efficient allocation of test resources by identifying methods that can be treated with less priority in testing activities and is well applicable in cross-project prediction scenarios.
Combining Subgoal Graphs with Reinforcement Learning to Build a Rational Pathfinder

In this paper, we present a hierarchical path planning framework called SG-RL (subgoal graphs-reinforcement learning), to plan rational paths for agents maneuvering in continuous and uncertain environments. By "rational", we mean (1) efficient path planning to eliminate first-move lags; (2) collision-free and smooth for agents with kinematic constraints satisfied. SG-RL works in a two-level manner. At the first level, SG-RL uses a geometric path-planning method, i.e., Simple Subgoal Graphs (SSG), to efficiently find optimal abstract paths, also called subgoal sequences. At the second level, SG-RL uses an RL method, i.e., Least-Squares Policy Iteration (LSPI), to learn near-optimal motion-planning policies which can generate kinematically feasible and collision-free trajectories between adjacent subgoals. The first advantage of the proposed method is that SSG can solve the limitations of sparse reward and local minima trap for RL agents; thus, LSPI can be used to generate paths in complex environments. The second advantage is that, when the environment changes slightly (i.e., unexpected obstacles appearing), SG-RL does not need to reconstruct subgoal graphs and replan subgoal sequences using SSG, since LSPI can deal with uncertainties by exploiting its generalization ability to handle changes in environments. Simulation experiments in representative scenarios demonstrate that, compared with existing methods, SG-RL can work well on large-scale maps with relatively low action-switching frequencies and shorter path lengths, and SG-RL can deal with small changes in environments. We further demonstrate that the design of reward functions and the types of training environments are important factors for learning feasible policies.
Adopting the cloud: how it affects firm strategy

Purpose The purpose of this paper is to explore the role that cloud adoption plays in the strategies of large firms. As an innovative and transforming technology, cloud computing can have a significant impact on firms. It may propel them to adapt their existing strategies to survive in today’s unstable, rapidly expanding, digital environment. Methodology The relationship between cloud computing and company strategy was explored through a qualitative research study. The author conducted 35 semi-structured interviews with business and IT stakeholders working in large French organizations. Findings Analysis of the results demonstrates that large cloud-adopting firms are driven by three main needs: a need to maintain their place in a highly competitive market, a need to implement innovative solutions and a need to lower costs. Research limitations/implications The limitations of this research include a number of contingency factors, such as the location of these firms (France) and their size (large). However, the academic literature does not address how adopting cloud services will affect the strategies used by large firms. Given the complex structures of these firms, it seems inevitable that they will adapt their strategies. This is highlighted by this study, which sheds light on the importance of the relationship between cloud adoption and company strategies. Practical implications This exploratory research study stresses the importance of companies adapting their strategies and enables business professionals to focus on redirecting their strategies when adopting cloud services. In addition, this study provides examples of the behavior of large firms in the French market. Originality/value Companies need to develop effective business strategies to survive, especially in today’s modern world. This paper shows that cloud adoption is leading firms to alter their strategies, and has led to the emphasis of three types of strategy: competition, innovation and cost reduction.
Charts based on millions of fluid dynamics simulations provide a simple tool to estimate how far from its source a specific blood stain can be found

The bloodstain pattern analyst sometimes has to judge if a given stain could originate from a specific location. A wide range of values of the maximum distance that a blood drop can travel have been reported from experiments, ranging from less than one meter to more than 10 m. Here we formulate the problem in a fluid dynamics and data mining framework. The fluid dynamics is solved with Newton’s classical equation of motion coupled with well-established models for the gravity and drag forces that bend the trajectories of drops. The parameters screened are the drop size, initial velocity and launch angle, as well as the height of a blood source and the ceiling height. Combining a wide range of values of those five parameters commended the performance of more than 5 million fluid dynamic simulations. Results of these simulations have been searched and mined for parameters directly measurable on a crime scene, such as the stain size and stain ellipticity. The results are presented in simple, easy to use charts, which do not require any knowledge of fluid dynamics from the analyst.
Robust Visual Tracking Based on Adaptive Extraction and Enhancement of Correlation Filter

In recent years, correlation filter (CF)-based tracking methods have demonstrated competitive performance. However, conventional CF-based methods suffer from unwanted boundary effects because of the periodic assumption of the training and detection samples. A spatially regularized discriminative CF (SRDCF) has greatly alleviated boundary effects by proposing the spatial regularization weights, which penalize the CF coefficients during learning. However, the SRDCF utilizes a naive decaying exponential model to passively and fixedly update the CF from the previous results. Therefore, if the target meets with occlusion or is out of view, the SRDCF may encounter over-fitting to the recent polluted samples, which may lead to tracking drift and failure. In this paper, we present a novel CF-based tracking method to resolve this issue by dynamically and adaptively correcting the weights of learning CFs and fusing them together to promote a more robust tracking. Thus, if the recent samples are inaccurate in the case of occlusion or are out of view, our method will down-weight the corresponding CFs and vice versa. Moreover, in order to decrease computational complexity and ensure memory efficiency, we extract the key CFs from the previous frames to remove redundant CFs under the contiguous frame indexes constraint. Thus, we do not need to store all CFs and decrease computational burden. Benefiting from the extraction and enhancement of CF, our method improves the tracking precision on OTB-2015, VOT-2016 and UAV123 benchmarks and achieves a 56.0% relative gain in speed compared with the SRDCF. The extensive experimental results demonstrate that our method is competitive with the state-of-the-art algorithms.
Analog Value Associative Memory Using Restricted Boltzmann Machine

In this paper, we propose an analog value associative memory using Restricted Boltzmann Machine (AVAM). Research on treating knowledge is becoming more and more important such as in natural language processing and computer vision fields. Associative memory plays an important role to store knowledge. First, we obtain distributed representation of words with analog values using word2vec. Then the obtained distributed representation is learned in the proposed AVAM. In the evaluation experiments, we found simple but very important phenomenon in word2vec method: almost all of the values in the generated vectors are small values. By applying traditional normalization method for each word vector, the performance of the proposed AVAM is largely improved. Detailed experimental evaluations are carried out to show superior performance of the proposed AVAM.
Opportunistic pervasive computing: adaptive context recognition and interfaces

Pervasive computing is promising to radically change people’s life in several dimensions, including the way we work, travel, have leisure, and take care of ourselves. In order to realize the goal, pervasive technologies must be aware of people’s context and react accordingly by tailoring services and interfaces to the current situation. Unfortunately, today’s pervasive applications have a restricted scope: their visibility is limited to their specific application domain. Hence, they are very well suited to address their specific objective, but lack the overall landscape of the user’s context and goals. In this paper, we put forward the vision of opportunistic pervasive computing: pervasive technologies that can dynamically exploit the available data sources and heterogeneous reasoning services to opportunistically reconstruct the whole landscape of the users’ context and seamlessly adapt to their needs and expectations. We point out the research challenges involved in this vision, and we present a conceptual model and architecture to address these issues, that include solutions for interoperability, reasoning integration, and artificial intelligence tools. Moreover, we introduce technical solutions for opportunistic context data discovery, reasoning and integration, as well as a hybrid method for opportunistic context-aware adaptation. We have also developed a prototype implementation considering an adaptive healthcare application for self-administration of therapies, and a system for opportunistic recognition of different activities based on sensor data. Experimental results indicate the viability and effectiveness of our solution.
Metal‐Organic Frameworks: 2D Metal–Organic Framework Nanosheets with Time‐Dependent and Multilevel Memristive Switching (Adv. Funct. Mater. 3/2019)

In article number 1806637, Ye Zhou, Su-Ting Han, and co-workers demonstrate a 2D metal-organic framework nanosheet based memory device with a typical bipolar switching behaviour, superior retention, and high reliability. The devices show excellent multilevel information storage performance, and the percolated conducting paths model has been verified by the Weibull distribution.
JomNetwork: GLBP in Medium Size Enterprise

Nowadays network play a very important role, every company need network.It is because, there are a lot of work done by network based and need to monitor through network.Once network of a company down it will affect the whole company.Either lower down the productivity or the whole company have to stop working.Normally in a big company they can get the most expensive and good solution for their company because Big company have sufficient money to get the best solution but for medium size company they do not have much money.Therefore, in this paper will provide a best high availability solution for medium size enterprise.