Reviewerly/exHarmony huggingface dataset:
Staying Hidden: An Analysis of Hiding Strategies in a 2D Level with Occlusions

The need to stay hidden from opponents is a common feature of many games. Defining algorithmic strategies for hiding, however, is difficult, and thus not usually a non-player character activity outside of very simple or scripted behaviours. In this work we explore several algorithmic approaches for ensuring a character can remain hidden with respect to another, moving agent. We compare these strategies with an upper-bound solution based on a known opponent path, giving us a mechanism for evaluating both relative efficacy and for understanding the different factors that affect success. Experimental evaluation considers multiple levels, including ones adapted from commercial games, and also examines the impact of relative movement speed and different observer movements. Our analysis shows that simple cost-effective approaches to hiding are feasible, but success strongly depends on level geometry, with a large gap remaining between heuristic and optimal performance.
Visualising medical evidence on sepsis treatment

Visual information can have a range of benefits for busy health practitioners. At The BMJ, we often use visuals to provide quick summaries of information from at times lengthy articles. This paper presents a case study of the design process for an interactive graphic on sepsis treatment, aiming to update doctors working in intensive care units of current evidence. Through explaining this design process, four major challenges for the use of interactive graphics in science publishing are highlighted: (1) how to identify suitable knowledge for visualisations; (2) how to select an appropriate depth of information for a particular project; (3) how interactive graphics can be a permanent part of the scientific record, and (4) how they can be deployed across a range of platforms and devices.
Distributed Data Center Bandwidth Allocation for Cloud-Based Streaming

Cloud-based video streaming systems such as YouTube and Netflix are usually supported by the content delivery networks and data centers that can consume many megawatts of power. Most existing work independently studies the issues of improving quality of experience (QoE) for viewers and reducing the cost and emissions associated with the enormous energy usage of data centers. By contrast, this paper addresses them both, and jointly optimizes the QoE, the energy cost and emissions by intelligently allocating data center bandwidth among different client groups. Specially, we propose a distributed algorithm to achieve the optimal bandwidth allocation, given the prediction of future workload. The algorithm novelly decomposes the optimization process into separate ones, which are solved iteratively across data centers and clients. Further, the algorithm has robust performance guarantee in terms of the variance of the prediction error. We demonstrate its convergence and robustness by both proofs using theoretical analysis and validation based on trace-driven simulations. The results further show that the proposed algorithm converges very fast and achieves much better QoE-cost balance than existing approaches.
Effective discovery of missing links in citation networks using citation relevancy check process

Effective dissemination of knowledge published by eminent authors in reputed journals and ensuring that the referred work is cited properly is the need of the hour. Citation analysis is about the similarity measures of articles or journals which are put forward to scaling as well as clustering procedures. A proper citation relevancy check (CRC) is required to avoid the missing links in the citation networks. Both similar and dissimilar references in the articles have important article citations. The purpose of this work is devise a method to find the most significant articles which can provide useful information to the journal editors and writers. The strategy presented in this paper can assist an author to incorporate most important articles and can help the editor in evaluating the quality of the references. The main benefit in detecting the missing articles is improvement in quality of research along with increased citation count.
Load Thresholds for Cuckoo Hashing with Overlapping Blocks

We consider a natural variation of cuckoo hashing proposed by Lehman and Panigrahy (2009). Each of cn objects is assigned k = 2 intervals of size ℓ in a linear hash table of size n and both starting points are chosen independently and uniformly at random. Each object must be placed into a table cell within its intervals, but each cell can only hold one object. Experiments suggested that this scheme outperforms the variant with blocks in which intervals are aligned at multiples of ℓ. In particular, the load threshold is higher, i.e., the load c that can be achieved with high probability. For instance, Lehman and Panigrahy (2009) empirically observed the threshold for ℓ = 2 to be around 96.5% as compared to roughly 89.7% using blocks. They pinned down the asymptotics of the thresholds for large ℓ, but the precise values resisted rigorous analysis. We establish a method to determine these load thresholds for all ℓ ≥ 2, and, in fact, for general k ≥ 2. For instance, for k = ℓ = 2, we get ≈ 96.4995%. We employ a theorem due to Leconte, Lelarge, and Massoulié (2013), which adapts methods from statistical physics to the world of hypergraph orientability. In effect, the orientability thresholds for our graph families are determined by belief propagation equations for certain graph limits. As a side note, we provide experimental evidence suggesting that placements can be constructed in linear time using an adapted version of an algorithm by Khosla (2013).
Teaching Communication Skills In Software Engineering Courses

Communication skills are important to software engineers.Yet, this topic is sometimes overlooked in computer science and software engineering curricula.To address this problem, we attempted to explicitly teach communication skills in a software engineering course.We experimented with a number of approaches, including lectures by the instructor, student presentations, mini-lectures mixed with in-class discussions, and other in-class activities such as student-designed scenarios.The results of these approaches were mixed.There were approaches that clearly worked better than one or more other approaches; there were also approaches to which students with different backgrounds responded differently.Overall, after taking this course, students communicated better and were more self-confident in team environments.Our experience shows that with careful planning and innovative pedagogy, we can help our students become both technically competent software engineers or computer scientists, and good team players in the same time.
Composing with Microsound: An Approach to Structure and Form when Composing for Acoustic Instruments with Electronics

This chapter explores the implications of using microsound as an organizing principle when structuring composition for acoustic instruments and electronics. The ideas are presented in the context of a composition by the author for bass clarinet, flute, piano and electronics: The Sea Turns Sand to Stone (2015) (Winner of the William Mathias composition prize, Bangor New Music Festival 2015). After giving a definition of microsound, the compositional affordances of microsound are considered. Microsound is presented as an aesthetically rich tool for creating cohesion between acoustic and electroacoustic sounds, and different parameters for manipulating the sounds are presented. Issues of structure and form are discussed and the challenges of creating a coherent environment that uses both note-based and texture-based material are explored. The implications of applying different models of form to mixed compositions are considered. This leads to a discussion of the different relationships that exist between the acoustic and the electroacoustic parts of a composition. Extended instrumental techniques provide one way of creating perceptual links between the acoustic and the electroacoustic. Examples of the way such techniques have been used in conjunction with microsound to impose a structural framework on The Sea Turns Sand to Stone are given. Finally, the use of a pure sound/noise axis, mediated through the application of microsound, is presented as a viable organizing principle for structuring mixed compositions. The implications of such a model are explored, and the underlying structure of The Sea Turns Sand to Stone is presented as a practical example of the application of the process.
A New Universe of Piano Sound?

The starting point for this research is simply musical curiosity. I believe that there is a hidden treasure of extended piano sound which is at this moment locked away, waiting to be discovered. I have had odd, tantalising glimpses from time to time of a world beyond, yet have had neither the means nor the motivation till now to discover it. This research is to try to find the key to this treasure and to freely distribute its content to whoever may be interested. Julian Johnson (2002:129)1 has eloquently described the resulting possibility of life-enhancing developments: Not only does music offer the possibility of transcending daily life; it offers, in as many forms as there are musics, a reshaping of these categories. It doesn’t obliterate them in some narcotic emptiness, but reworks them and thus offers us new models of experience. And this has real power, because as we participate in this process of enactment, we experience new ways for ourselves. When we leave the musical work and return to daily life, we have tasted a different way of being, a different perception of the world. Potentially, this leaves us marked by the experience. It subsequently produces an altered perception of the world. There are two strands in my musical background which I hope can be brought together as a result of this research: extended piano techniques and electronics. Boulez (cited in Emmerson 1986:6)2 has succinctly summarised a fundamental paradox: As though by a defensive reflex, the greate
Finite Mixture of Birnbaum–Saunders Distributions Using the k-Bumps Algorithm

Mixture models have received a great deal of attention in statistics due to the wide range of applications found in recent years. This paper discusses a finite mixture model of Birnbaum–Saunders distributions with G components, which is an important supplement to that developed by Balakrishnan et al. (J Stat Plann Infer 141:2175–2190, 2011) who considered a model with two components. Our proposal enables the modeling of proper multimodal scenarios with greater flexibility for a model with two or more components, where a partitional clustering method, named k-bumps, is used as an initialization strategy in the proposed EM algorithm to the maximum likelihood estimates of the mixture parameters. Moreover, the empirical information matrix is derived analytically to account for standard error, and bootstrap procedures for testing hypotheses about the number of components in the mixture are implemented. Finally, we perform simulation studies to evaluate the results and analyze two real dataset to illustrate the usefulness of the proposed method.
Learning Structures

In this dissertation we discuss learning with structures, which appears frequently in both machine learning theories and applications. First we review existing structure learning algorithms, then we study several specifically interesting problems. The first problem we study is the structure learning of dynamic systems. We investigate using dynamic Bayesian networks to reconstruct functional cortical networks from the spike trains of neurons. Next we study structure learning from matrix factorization, which has been a popular research area in recent years. We propose an efficient non-negative matrix factorization algorithm which derives not only the membership assignments to the clusters but also the interaction strengths among the clusters. Following that we study the hierarchical and grouped structure in regularization. We propose a novel regularizer called group lasso which introduces competitions among variables in groups, and thus results in sparse solutions. Finally we study the sparse structure in a novel problem of online feature selection, and propose an online learning algorithm that only needs to sense a small number of attributes before the reliable decision can be made.
PENGEMBANGAN BAHAN AJAR IPS BERBASIS PENINGGALAN SEJARAH LOKAL PADA MATERI PENINGGALAN SEJARAH

This study aims to produce social studies based social studies teaching materials on historical relic materials. This type of research is development research with the Dick & Carey development model. The results showed that: (1) the learning outcomes obtained by the experimental class were higher than the learning outcomes obtained by the control class. This was seen in the results of the pre test and post test conducted by researchers. At the time of the pre-test of 31 students in class IV-A only 13 people (41.94%) completed, while another 18 people (58.06%) did not complete with RRK 60. While in class IV-B out of 34 students only 10 people (29.41%) who finished, as many as 24 more people (70.59%) had not finished with RRK 56.03. At the time of the post-test of class IV-A as many as 28 students (90.32%) had completed only 3 more people (9.68%) who did not complete with RRK 68.71. While from class IV-B as many as 33 people (97.06%) had been completed, the remaining 1 person (2.94%) who had not completed the RRK was 73.82. (2) The validity of the product also gets a very good value, it is proven from the values obtained from the four validators, namely: material validation score 3.62 (90.63%), language validation score 3.75 (93.75%), the media validation score was 3.69 (92.19%), and the learning design validation score was 4.00 (100%).
Planar content selection in images and videos using frontalness

This paper addresses the problem of selecting instances of a planar object in a video or from a set of images based on an evaluation of its “frontalness”. We introduce the idea of “evaluating the frontalness” by computing how close the object’s surface normal aligns with the optical axis of a camera. Unlike previous planar object pose estimation methods, our method does not require the true frontal image as a reference. The intuition is that a true frontal image can produce other non-frontal images by perspective projection, while the non-frontal images have limited ability to produce other non-frontal images. We show that this intuition of comparing ‘frontal’ and ‘non-frontal’ can be extended to comparing ‘more frontal’ and ‘less frontal’ images. Based on this observation, our method estimates the relative frontalness of an image by exploiting the objective space error. We also propose the use of K-invariant space to evaluate the frontalness even when the camera intrinsic parameters are unknown (e.g., images/videos from the web). Our method outperforms the homography decomposition-based method and does not require reference images. A qualitative evaluation is also carried out to show that our method can be applied in selecting the most frontal characters from a set of images captured in various viewpoints.
A Method of Collecting the IP Addresses of Hidden Server in Tor Networks

Nowadays, anonymity in the Internet has attracted attention from the viewpoint of privacy protection. The Onion Router (Tor) is one of the most popular software that can anonymize the Internet communication. Tor can provide "Hidden Service" that is a web service and that keeps the anonymity for both of users and servers. This paper analyzes the communication characteristics of hidden services. Based on the results of the analysis, this paper also proposes a method of collecting IP addresses of hidden services. Furthermore, the experiment shows effectiveness of the proposed method.
Feedforward Neural Networks

This extraordinary three-volume work, written in an engaging and rigorous style by a world authority in the field, provides an accessible, comprehensive introduction to the full spectrum of mathematical and statistical techniques underpinning contemporary methods in data-driven learning and inference. This final volume, Learning, builds on the foundational topics established in volume I to provide a thorough introduction to learning methods, addressing techniques such as least-squares methods, regularization, online learning, kernel methods, feedforward and recurrent neural networks, meta-learning, and adversarial attacks. A consistent structure and pedagogy is employed throughout this volume to reinforce student understanding, with over 350 end-of-chapter problems (including complete solutions for instructors), 280 figures, 100 solved examples, datasets and downloadable Matlab code. Supported by sister volumes Foundations and Inference, and unique in its scale and depth, this textbook sequence is ideal for early-career researchers and graduate students across many courses in signal processing, machine learning, data and inference.
The Brain-Mind-Computer Trichotomy

The unifying framework, that is, the brain-mind-computer trichotomy is suggested analyzed by using hermeneutic approach. While we argue that brain is a hermeneutic device, and hermeneutics is also necessary to understand situations and other's minds. Intentional dynamics is a possible approach to set a unifying framework.
Deep Error-Correcting Output Codes

Ensemble learning, online learning and deep learning are very effective and versatile in a wide spectrum of problem domains, such as feature extraction, multi-class classification and retrieval. In this paper, combining the ideas of ensemble learning, online learning and deep learning, we propose a novel deep learning method called deep error-correcting output codes (DeepECOCs). DeepECOCs are composed of multiple layers of the ECOC module, which combines several incremental support vector machines (incremental SVMs) as base classifiers. In this novel deep architecture, each ECOC module can be considered as two successive layers of the network, while the incremental SVMs can be viewed as weighted links between two successive layers. In the pre-training procedure, supervisory information, i.e., class labels, can be used during the network initialization. The incremental SVMs lead this procedure to be very efficient, especially for large-scale applications. We have conducted extensive experiments to compare DeepECOCs with traditional ECOC, feature learning and deep learning algorithms. The results demonstrate that DeepECOCs perform, not only better than existing ECOC and feature learning algorithms, but also related to deep learning ones in most cases.
Generating integer polynomials from <i>X</i><sup>2</sup> and <i>X</i><sup>3</sup> using function composition: a study of subnearrings of (ℤ[<i>X</i>], +, ◦)

Which integer polynomials can we write down if the only exponent to be used is 3? Such problems can be considered as instances of the subnearring generation problem. We show that the nearring (ℤ[x], +, ◦) of integer polynomials, where the nearring multiplication is the composition of polynomials, has uncountably many subnearrings, and we give an explicit description of those nearrings that are generated by subsets of {1, x, x2, x3 }.
Probability and Statistics

Statistics and probability both fall within the broader scope of the theory of random phenomena. The first deals with providing probability distributions adaptable to the various real random phenomena, and the second deals very often with a random sample to describe its properties or infer to the underlying probabilistic model and the estimation of its parameters. The chapter tries to show this connection by reporting examples that are more or less known but that are characterized by being unconventional. Other objects could have been taken into consideration but those chosen are characterized by a closer link with the calculation of probabilities.
Univalent Foundations and the UniMath Library

We give a concise presentation of the Univalent Foundations of mathematics outlining the main ideas, followed by a discussion of the UniMath library of formalized mathematics implementing the ideas of the Univalent Foundations (section 1), and the challenges one faces in attempting to design a large-scale library of formalized mathematics (section 2). This leads us to a general discussion about the links between architecture and mathematics where a meeting of minds is revealed between architects and mathematicians (section 3). On the way our odyssey from the foundations to the "horizon" of mathematics will lead us to meet the mathematicians David Hilbert and Nicolas Bourbaki as well as the architect Christopher Alexander.
Computation on Encrypted Data Using Dataflow Authentication

Encrypting data before sending it to the cloud ensures data confidentiality but requires the cloud to compute on encrypted data. Trusted execution environments, such as Intel SGX enclaves, promise to provide a secure environment in which data can be decrypted and then processed. However, vulnerabilities in the executed program give attackers ample opportunities to execute arbitrary code inside the enclave. This code can modify the dataflow of the program and leak secrets via SGX side channels. Fully homomorphic encryption would be an alternative to compute on encrypted data without data leaks. However, due to its high computational complexity, its applicability to general-purpose computing remains limited. Researchers have made several proposals for transforming programs to perform encrypted computations on less powerful encryption schemes. Yet current approaches do not support programs making control-flow decisions based on encrypted data. We introduce the concept of dataflow authentication (DFAuth) to enable such programs. DFAuth prevents an adversary from arbitrarily deviating from the dataflow of a program. Our technique hence offers protections against the side-channel attacks described previously. We implemented two flavors of DFAuth, a Java bytecode-to-bytecode compiler, and an SGX enclave running a small and program-independent trusted code base. We applied DFAuth to a neural network performing machine learning on sensitive medical data and a smart charging scheduler for electric vehicles. Our transformation yields a neural network with encrypted weights, which can be evaluated on encrypted inputs in \( 12.55 \,\mathrm{m}\mathrm{s} \) . Our protected scheduler is capable of updating the encrypted charging plan in approximately 1.06 seconds.
Infrastructures for High-Performance Computing: Cloud Computing Development Environments

This article describes some of the most representative cloud computing development environments classified into four types. Integrated development environments are used to code, debug, deploy and monitor cloud applications that are executed on a cloud infrastructure. Parallel-processing development environments are used to define parallel applications for processing large amount of data thatrun on a cluster of virtual machines provided by a cloud infrastructure. Workflow development environments are used to define workflow-based applications that are executed on a cloud infrastructure. Data-analytics development environments are used to define data analysis applications through machine learning and data mining tools provided by a cloud infrastructure.
Half-term fun at Sandwell libraries

From magic to virtual reality, film clubs to yoga sessions, there is plenty to do at your local library this half-term.