Reviewerly/exHarmony huggingface dataset:
On Domination and Control in Strategic Ability

We propose a technique to compare partial strategies for agents and coalitions in multi-agent environments with imperfect information. The proposed relation mimics the game-theoretic notion of strategic dominance: a stronger partial strategy can replace a weaker one in a full strategy, while preserving the enforceability of a given goal. Our version of dominance is based on a new notion of input/output characteristics for partial strategies. Intuitively, the inputs are the states where the strategy gains control over execution, and the outputs are the states where it returns the control to the environment. Moreover, we identify the sources of dependence between partial strategies, and show conditions under which they can be analysed in a fully independent way. We evaluate our approach on two scalable benchmarks from the literature. The experiments are carried out for the tasks of incremental synthesis of uniform strategies and optimization of a winning strategy, with very promising results.
A Neural Network Sparseness Algorithm Based on Relevance Dropout

Dropout is a regularization method that prevents model over-fitting by randomly deleting some neurons in the original network structure. With the increasing scale and complexity of neural networks, the number of linearly correlated neurons in the network will increase. Correlated neurons will not only form data redundancy, waste resources, but also it is not conducive to sparseness of neural networks, affecting the efficiency of algorithm execution. Based on this, this paper integrates the idea of relevance into Dropout, and improves the original Dropout, marked as R-Dropout (Relevance-Dropout). That is the key point of the R-Dropout method, to improve the sparseness of neural networks, by deleting some high correlation neurons with more probability. The experimental results on the public data sets show that R-Dropout regularization method not only improves the accuracy slightly, but also improves the convergence speed, and more efficient.
Filter Pruning for Efficient Transfer Learning in Deep Convolutional Neural Networks

Convolutional Neural Networks are extensively used in computer vision applications. Many convolutional models became famous after being widely adopted in a variety of computer vision tasks because o their high accuracy and great generality. Trough Transfer Learning, pre-trained versions of these models can be applied to a large number of different tasks and datasets without the need to train an entire large convolutional model. We aim at finding methods to prune convolutional filters from these pre-trained models in order to make inference more efficient for the new task. To achieve this we propose a genetic algorithms based method for pruning convolutional filters of pre-trained models applied to a different dataset than the one they were trained for. After transferring knowledge from an already trained model to a new task, genetic algorithms are used to find good solutions to the filter pruning problem through natural selection. We then evaluate the results of the proposed methods and compare with state-of-the-art pruning strategies for convolutional neural networks. Obtained experimental results show that the method is able to maintain network accuracy while producing networks with a significant reduction in Floating Point Operations (FLOPs).
IS Education Revisited: Reflections on a BSc Program in Business Information Systems Design

The presence of the information age has greatly influenced how learning in higher education has evolved. Information Systems (IS) curricula are particularly liable with this trend and their likelihood to remain effective depends on how they orchestrate the use of technology. In this study, we evaluate an IS program that was designed and implemented in 2010. We assess its effectiveness eight years after its inception on a qualitative basis with six semi-structured interviews with designers, implementors and quality-assurers. We found that the constructive feedback received in the form of criticism, have pushed us to rethink how IS programs should enhance progressive development, with the aim to prepare IS reflective practitioners for their future in the industry.
A First Approach on Big Data Missing Values Imputation

Albeit most techniques and algorithms assume that the data is accurate, measurements in our analogic world are far from being perfect. Since our capabilities of storing and processing data are growing everyday, these imperfections will accumulate, generating poorer decisions and hindering any knowledge extraction process carried out over the raw data. One of the most disturbing imperfections is the presence of missing values. Many inductive algorithms assume that the data is complete, thus if they face missing data they will not work properly or the quality of the knowledge extracted will be poorer. At this point there is no sophisticated missing values treatment implemented in any major Big Data framework. In this contribution, we present two novel imputation methods based on clustering that achieve better results than simply removing the faulty examples or filling-in the missing values with the mean that can be easily ported to Spark’s MLlib.
A Description Logic for Unifying Different Points of View

Multilevel modelling is the conceptual modelling problem of having concepts that could be instances of another concepts. It is a relevant problem for many areas and in particular for ontology design. We motivate our work by a realworld case study on the accounting domain in which the points of view of expert and operator users are conceptualized as two knowledge levels. In this paper we address theoretical aspects of extending the tableau algorithm for a description logic that enables unifiying different user perspectives in a multilevel knowledge modelling, following a Henkin semantics.
Crystalizing Effect of Simulated Annealing on Boltzmann Machine

This paper proposes a method to estimate the posterior distribution of a Boltzmann machine. Due to high feature extraction ability, a Boltzmann machine is often used for both of supervised and unsupervised learning. It is expected to be suitable for multimodal data because of its bi-directional connection property. However, it needs a sampling method to estimate the posterior distribution, which becomes a problem during an inference period because of the computation time and instability. Therefore, it is usually converted to feedforward neural networks, which means to lose its bi-directional property. To deal with these problems, this paper proposes a method to estimate the posterior distribution of a Boltzmann machine fast and stably without converting it to feedforward neural networks. The key idea of the proposed method is to estimate the posterior distribution using a simulated annealing on non-uniform temperature distribution. The advantage of the proposed method against Gibbs sampling and conventional simulated annealing is shown through experiments with artificial dataset and MNIST. Furthermore, this paper also gives the mathematical analysis of Boltzmann machine’s behaviour with regard to temperature distribution.
Knowledge Management: Factors Influencing Successful Innovation

The aim of this study is to find out the factors, which have a great impact on innovation. As innovation is the only way forward to the demands of the modern market. Knowledge management, knowledge economy and knowledge about consumer behaviors are the three most effective inputs for any type of innovation. The research is based on hypothesis testing in SPSS software. All of the three variables are found to have a positive and significant correlation with innovation. The results add a significant contribution to the body of knowledge regarding innovation. And the conceptual framework is applicable for any type of innovation and organization. Keywords: Knowledge Management, Knowledge Economy, Knowledge about Consumers Behavior and Innovation.
What just happened?

Recent work has attempted to enhance vector space representations using information from structured semantic resources. This process, dubbed retrofitting (Faruqui et al., 2015), has yielded improvements in word similarity performance. Research has largely focused on the retrofitting algorithm, or on the kind of structured semantic resources used, but little research has explored why some resources perform better than others. We conducted a fine-grained analysis of the original retrofitting process, and found that the utility of different lexical resources for retrofitting depends on two factors: the coverage of the resource and the evaluation metric. Our assessment suggests that the common practice of using correlation measures to evaluate increases in performance against full word similarity benchmarks 1) obscures the benefits offered by smaller resources, and 2) overlooks incremental gains in word similarity performance. We propose root-mean-square error (RMSE) as an alternative evaluation metric, and demonstrate that correlation measures and RMSE sometimes yield opposite conclusions concerning the efficacy of retrofitting. This point is illustrated by word vectors retrofitted with novel treatments of the FrameNet data (Fillmore and Baker, 2010).
A Method for Identification of Students’ States Using Kinect

In study classes, teachers pay attention to students to find out whether they are following the study contents or not. A system for automatic identification of learning states of students would be useful for teachers to grasp learning states of all students. To develop the system to identify learning states, a method for identification of learning states of a student was proposed in previous work. In the method, a gesture identification function to estimate an action from information of a human skeleton and Computer Vision API which returns words and sentences representing the situation in an image of a student captured during learning are utilized. In this study, to make the situation closer to real situations, learning data to generate identifiers and identifiers for the gestures are added. In order to confirm the effectiveness of these improvements, the identification experiments in this laboratory are conducted.
Non-parametric priors for generative adversarial networks

The advent of generative adversarial networks (GAN) has enabled new capabilities in synthesis, interpolation, and data augmentation heretofore considered very challenging. However, one of the common assumptions in most GAN architectures is the assumption of simple parametric latent-space distributions. While easy to implement, a simple latent-space distribution can be problematic for uses such as interpolation. This is due to distributional mismatches when samples are interpolated in the latent space. We present a straightforward formalization of this problem; using basic results from probability theory and off-the-shelf-optimization tools, we develop ways to arrive at appropriate non-parametric priors. The obtained prior exhibits unusual qualitative properties in terms of its shape, and quantitative benefits in terms of lower divergence with its mid-point distribution. We demonstrate that our designed prior helps improve image generation along any Euclidean straight line during interpolation, both qualitatively and quantitatively, without any additional training or architectural modifications. The proposed formulation is quite flexible, paving the way to impose newer constraints on the latent-space statistics.
Adaptive Visual-Depth Fusion Transfer

While RGB-D classification task has been actively researched in recent years, most existing methods focus on the RGB-D source to target transfer task. The application of such methods cannot address the real-world scenario where the paired depth images are not hold. This paper focuses on a more flexible task that recognizes RGB test images by transferring them into the depth domain. Such a scenario retains high performance due to gaining auxiliary information but reduces the cost of pairing RGB with depth sensors at test time. Existing methods suffer from two challenges: the utilization of the additional depth features, and the domain shifting problem due to the different mechanisms between conventional RGB cameras and depth sensors. As a step towards bridging the gap, we propose a novel method called adaptive Visual-Depth Fusion Transfer (aVDFT) which can take advantage of the depth information and handle the domain distribution mismatch simultaneously. Our key novelties are: (1) a global visual-depth metric construction algorithm that can effectively align RGB and depth data structure; (2) adaptive transformed component extraction for target domain that conditioned on invariant transfer on location, scale and depth measurement. To demonstrate the effectiveness of aVDFT, we conduct comprehensive experiments on six pairs of RGB-D datasets for object recognition, scene classification and gender recognition and demonstrate state-of-the-art performance.
Autonomous Motion Tracking for Dynamic Objects Using a Temporal Quad-Tree Algorithm

Tracking can be defined as calculating the trajectory of a moving object through a video. Tracking a moving object is an example of an automated process that needs to be done in real-time. Various tools are utilized for detecting moving objects such as sonar, laser, and computer vision. In recent years, with the massive improvements in the processing power of modern computers, computer vision techniques became a widely used tool for its tremendous advantages. In the proposed work, an object tracking technique named the Temporal Quad-Tree technique TQT is implemented to track a moving object within a series of images (a video). The proposed technique aims to enhance the computational time to detect a moving object. This technique will be implemented on a surveillance system, where time is an important issue. The system should track moving objects in an environment autonomously, especially when the presence of objects may be occluded for a certain period of time.
SpecFuzz: Bringing Spectre-type vulnerabilities to the surface

SpecFuzz is the first tool that enables dynamic testing for speculative execution vulnerabilities (e.g., Spectre). The key is a novel concept of speculation exposure: The program is instrumented to simulate speculative execution in software by forcefully executing the code paths that could be triggered due to mispredictions, thereby making the speculative memory accesses visible to integrity checkers (e.g., AddressSanitizer). Combined with the conventional fuzzing techniques, speculation exposure enables more precise identification of potential vulnerabilities compared to state-of-the-art static analyzers. Our prototype for detecting Spectre V1 vulnerabilities successfully identifies all known variations of Spectre V1 and decreases the mitigation overheads across the evaluated applications, reducing the amount of instrumented branches by up to 77% given a sufficient test coverage.
Privacy-Preserving Federated Data Sharing

Consider a set of agents with sensitive datasets who are interested in the same prediction task and would like to share their datasets without revealing private information. For instance, the agents may be medical centers with their own historical databases and the task may be the diagnosis of a rare form of a disease. This paper investigates whether sharing privacy-preserving versions of these datasets may improve the agent predictions. It proposes a Privacy-preserving Federated Data Sharing (PFDS) protocol that each agent can run locally to produce a privacy-preserving version of its original dataset. The PFDS protocol is evaluated on several standard prediction tasks and experimental results demonstrate the potential of sharing privacy- preserving datasets to produce accurate predictors.
Local Name Authorities and Linked Data: The Landscape

Name authority control—the process of selecting and maintaining a set of headings or access points for named entities—brings consistency to names in online systems and can help users locate needed items and identify related materials. Linked data provides the opportunity to extend name authority data sets and make them available to wider audiences. This column seeks to provide an overview of the landscape of local name authority projects using linked data, along with practices, challenges, and other considerations in this area.
Chinese Texts Classification System

In this article, we designed an automatic Chinese text classification system aiming to implement a system for classifying news texts. We propose two improved classification algorithms as two different choices for users to choose and then our system uses the chosen method for the obtaining of the classified result of the input text. There are two improved algorithms, one is k-Bayes using hierarchy conception based on NB method in machine learning field and another one adds attention layer to the convolutional neural network in deep learning field. Through experiments, our results showed that improved classification algorithms had better accuracy than based algorithms and our system is useful for making classifying news texts more reasonably and effectively.
Forget and Diversify: Regularized Refinement for Weakly Supervised Object Detection

We study weakly supervised learning for object detectors, where training images have image-level class labels only. This problem is often addressed by multiple instance learning, where pseudo-labels of proposals are constructed from image-level weak labels and detectors are learned from the potentially noisy labels. Since existing methods train models in a discriminative manner, they typically suffer from collapsing into salient parts and also fail in localizing multiple instances within an image. To alleviate such limitations, we propose simple yet effective regularization techniques, weight reinitialization and labeling perturbations, which prevent overfitting to noisy labels by forgetting biased weights. We also introduce a graph-based mode-seeking technique that identifies multiple object instances in a principled way. The combination of the two proposed techniques reduces overfitting observed frequently in weakly supervised setting, and greatly improves object localization performance in standard benchmarks.
PARTIAL DIFFERENTIAL EQUATION-BASED SHARPENING ALGORITHM FOR IMAGES TAKEN IN FOGGY ENVIRONMENT

Images taken in natural environment will have information loss, i.e., image blurring, limited by the shooting technology and equipment. Especially in foggy weather, image blurring is more serious, which has a negative impact on some fields which requires certain clarity of images. In order to make images taken in foggy weather clearer, image enhancement algorithm of fractional partial differential equation model was used and the defogging and enhancement processing was carried out on images taken in foggy environment on MATLAB in this study. Moreover, it was compared with the dark channel prior algorithm and the integer order partial differential equation model algorithm. The results showed that the three algorithms could effectively remove most of the fog. The comparison of the processed images and the average gradient, peak signal-to-noise ratio and information entropy of the images suggested that the fractional partial differential equation algorithm had the best defogging and enhancement performance, but it took more time in processing images than the other two algorithms.
Design for Guidance Method of UAV Autonomous Landing on Mobile Platform Based on Prediction of Intersection Points

Abstract The development of the technology for UVAs (Unmanned Aerial Vehicle) autonomous landing on mobile platform is introduced in this article. And the existing problems of UAVs autonomous landing on mobile platforms are also analysed. In order to solve these problems, a guidance method of UAV autonomous landing on mobile platform based on prediction of intersection points is proposed in this paper. In this method, the intersection point of the trajectory of UAV and the trajectory of the mobile platform is predicted based on the speed plan for UAV and the motion state of mobile platform. Then, the UAV will be guided to the intersection point by proportional guidance law. And at each time of the landing of the UAV, the location of the intersection point will be corrected in real time. In order to verify this method, a mathematical simulation experiment was designed. The result of this experiment shows that UAVs are able to land more accurately on the mobile platform with less overload using this method.
Steve Reich’s Counterpoints and Computers

This chapter re-evaluates the role of Steve Reich’s 1980s <italic>Counterpoint</italic> series in the context of his reinvention as a venerated member of New York’s new music establishment. It aims to show how Reich's re-engagement with past compositional interests—now expressed in more conventional terminology—formed a significant step in facilitating his gradual transformation from outsider to insider. Running in parallel with Reich’s transition toward tradition was a significant change in the composer’s working methods through the use of computer technology, as found in works such as <italic>The Four Sections</italic> and <italic>Electric Counterpoint</italic>. An investigation into this important new development offers insights into how Reich has since then pragmatically incorporated digital compositional habits alongside previous analog ones, all while maintaining a secure foothold in the Western classical canon.
Research and Implementation of Security Vulnerability Detection in Application System of WEB Static Source Code Analysis Based on JAVA

With the rapid development of Internet technology, Web applications are widely used in all walks of life, and their security requirements are increasing. Unfortunately, at present, the development of Web security technology still lags behind the development of Web application technology itself. The Web application itself and its operating environment are still relatively fragile, and its operating environment is easily forged or modified, making Web applications gradually become malicious. The object of the attack is frequently attacked. This paper investigates and analyzes the common vulnerabilities in web applications, deeply studies the basic characteristics of these vulnerabilities, and understands the principles and solutions of vulnerabilities. The static analysis method is used to analyze the vulnerabilities, and the static analysis methods are used to solve the security vulnerabilities in the Java web project.
Image Watermarking for Tamper Detection and Content Recovery Applications Based on a Preservative Bi-Level Moment Approach with Shuffling

In this paper, a modified color image watermarking is presented to be used for tamper detection and content recovery applications. The proposed technique is based on the bi-level moment-preserving approach where the developed technique maps the image blocks using a shuffling approach to improve resistance to attacks. Simulations have shown that the proposed technique exhibits better performance when compared to the classical bi-level moment-preserving technique in terms of the watermarking imperceptibility and recovery quality.