Reviewerly/exHarmony huggingface dataset:
Using Sentinel-2 data to estimate the concentration of heavy metals caused by industrial activities in Ust-Kamenogorsk, Northeastern Kazakhstan

This study aims to investigate the change in heavy metal concentration and evaluate pollution intensity using Sentinel-2 data. Sixty samples collected from the surface soil in the area were used to determine the concentration of lead, copper, and zinc using atomic absorption spectroscopy. Then, the step-by-step regression method was used in ArcGIS software to determine the relationship between the concentration of heavy metals and the ranking of the influential spectral bands of Sentinel-2 to monitor heavy metals in the relevant sampling points. According to the results, lead monitoring was effective through the blue channel, the ratio of green to near infrared-IV channels, and the ratio of short-wave infrared-III to near infrared-II channels. At the same time, copper was monitored through reflectance values in the red channel, the ratios of green to near infrared-IV channels, and the ratio of short-wave infrared-III to near infrared-II channels. The blue channel and the ratio of green to near infrared-IV channels the ratio of near infrared-II to near infrared-IV channels were efficient for zinc monitoring. Pollution Load Indices (PLI) and Geographical Accumulation Index (Igeo) were calculated to classify the contaminated soils of the region. The efficiency of each relationship obtained was evaluated using the root mean square error (RMSE) and Pearson's correlation coefficient (R). In summary, the copper, lead, and zinc equations had RMSE values of 1.8, 2.5, and 1.60 mg/kg, respectively. The Pearson correlation coefficients (R) for copper, lead, and zinc were 0.80, 0.76, and 0.72, respectively, which indicated good agreement between measured and estimated values.
What's Wrong With Giving Students Feedback?

This paper reviewed the extensive evidence on the effectiveness of feedback on learning.The research supported five claims about feedback.First, informational feedback is effective in domains with clear right or wrong answers when tested immediately after training.Second, when the same maximal feedback conditions are tested for retention or transfer, they are less effective than conditions with less feedback.Third, feedback can draw attention away from the learning task.Fourth, feedback apparently plays a minor role in actual classroom situations.Fifth, teaching students to provide their own feedback and explanation is an effective alternative.These findings suggest that instructors may be more effective if they put less effort into grading and commenting on students' products and more effort into structuring their courses to help students learn how to assess and reflect on their state of learning themselves.Two specific pedagogical strategies are suggested.First, giving students more assignments than the instructor could grade or comment on will provide more of the kinds of practice they need to develop expertise.Second, helping students to learn how to assess and reflect on their state of learning will help them learn how to provide their own feedback and thus help them to become independent life-long learners.
Integrating Drama Management into an Adventure Game

Often, video game designers must choose between creating a linear experience, and designing an open world with many different story lines that fail to form a tightly crafted narrative arc. A drama manager (DM) can provide a solution to this dilemma. ADM monitors an interactive experience, such as a computer game, and intervenes to shape the global experience so that it satisfies the author's expressive goals without decreasing a player's interactive agency. In this demo we present the first integration of declarative optimization-based drama management (DODM) into an adventure-style dungeon game called EMPath.
Active Stratified Sampling with Clustering-Based Type Systems for Predicting the Search Tree Size of Problems with Real-Valued Heuristics

In this paper we advance the line of research launched by Knuth which was later improved by Chen for predicting the size of the search tree expanded by heuristic search algorithms such as IDA*. Chen's Stratified Sampling (SS) uses a partition of the nodes in the search tree called type system to guide its sampling. Recent work has shown that SS using type systems based on integer-valued heuristic functions can be quite effective. However, type systems based on real-valued heuristic functions are often too large to be practical. We use the k-means clustering algorithm for creating effective type systems for domains with real-valued heuristics. Orthogonal to the type systems, another contribution of this paper is the introduction of an algorithm called Active SS. SS allocates the same number of samples for each type. Active SS is the application of the idea of active sampling to search trees. Active SS allocates more samples to the types with higher uncertainty. Our empirical results show that (i) SS using clustering-based type systems tends to produce better predictions than competing schemes that do not use a type system, and that (ii) Active SS can produce better predictions than the regular version of SS.
A Note on Hypothesis Testing with Random Sample Sizes and its Relationship to Bayes Factors

Frequentist and Bayesian hypothesis testing are often viewed as "two separate worlds" by practitioners.While theoretical relationships of course exist, our goal here is to demonstrate a practical example where one must be careful conducting frequentist hypothesis testing, and in that context illustrate a practical equivalence between Bayesian and frequentist testing.In particular, if the sample size is random (hardly unusual in practical problems where the sample size may be "all available experimental units"), then choosing an α level in advance such as 0.05 and using it for every possible sample size is inadmissible.In other words, one can find a different overall procedure which has the same overall type I error but greater power.Not coincidentally, this alternative procedure is based on Bayesian testing procedures.
Game Design for Classical AI

Reasoning using expressive symbolic representations is a central theme of AI research, yet there are surprisingly few deployed games, even within the AIIDE research community, that use this sort of “classical” AI. This is partly due to practical and methodological issues, but also due to fundamental mismatches between current game genres and classical AI systems. I will argue that if we want to build games that leverage high-end classical AI techniques like commonsense reasoning and natural language processing, we will also have to develop new game genres and mechanics that better exploit those capabilities. I will also present a design sketch of a game that explores potential game mechanics for classical AI.
How To Tell Head From Tail in User-Generated Content Corpora

This paper asks whether unpopular tail items in user-generated content corpora are important, and how tail items differ from the popular head items. We develop a user-centric characterisation of the tail which shows that although the head receives a disproportionate share of interest, tail items collectively serve a large number of users. "Tail seekers," with more like's in the tail than the head, are shown to constitute more than half the user base. We then examine how interests in head and tail items differ. Temporally, head items are found to enjoy a sustained interest, whereas interest in tail items is short lived. Spatially, interest in tail items is more geographically diverse. Finally, from a social angle, interest in unpopular items appears to be more "viral" than non-viral. We discuss implications of these observations for the handling and distribution of user-generated content.
Rule Ensemble Learning Using Hierarchical Kernels in Structured Output Spaces

The goal in Rule Ensemble Learning (REL) is simultaneous discovery of a small set of simple rules and their optimal weights that lead to good generalization. Rules are assumed to be conjunctions of basic propositions concerning the values taken by the input features. It has been shown that rule ensembles for classification can be learnt optimally and efficiently using hierarchical kernel learning approaches that explore the exponentially large space of conjunctions by exploiting its hierarchical structure. The regularizer employed penalizes large features and thereby selects a small set of short features. In this paper, we generalize the rule ensemble learning using hierarchical kernels (RELHKL) framework to multi class structured output spaces. We build on the StructSVM model for sequence prediction problems and employ a ρ-norm hierarchical regularizer for observation features and a conventional 2-norm regularizer for state transition features. The exponentially large feature space is searched using an active set algorithm and the exponentially large set of constraints are handled using a cutting plane algorithm. The approach can be easily extended to other structured output problems. We perform experiments on activity recognition datasets which are prone to noise, sparseness and skewness. We demonstrate that our approach outperforms other approaches.
A technology-aided multi-modal training approach to assist abdominal palpation training and its assessment in medical education

Computer-assisted multimodal training is an effective way of learning complex motor skills in various applications. In particular disciplines (eg. healthcare) incompetency in performing dexterous hands-on examinations (clinical palpation) may result in misdiagnosis of symptoms, serious injuries or even death. Furthermore, a high quality clinical examination can help to exclude significant pathology, and reduce time and cost of diagnosis by eliminating the need for unnecessary medical imaging. Medical palpation is used regularly as an effective preliminary diagnosis method all around the world but years of training are required currently to achieve competency. This paper focuses on a multimodal palpation training system to teach and improve clinical examination skills in relation to the abdomen. It is our aim to shorten significantly the palpation training duration by increasing the frequency of rehearsals as well as providing essential augmented feedback on how to perform various abdominal palpation techniques which has been captured and modelled from medical experts. Twenty three first year medical students divided into a control group (n=8), a semi-visually trained group (n=8), and a fully visually trained group (n=7) were invited to perform three palpation tasks (superficial, deep and liver). The medical students performances were assessed using both computer-based and human-based methods where a positive correlation was shown between the generated scores, r=.62, p(one-tailed)<.05. The visually-trained group significantly outperformed the control group in which abstract visualisation of applied forces and their palmar locations were provided to the students during each palpation examination (p<.05). Moreover, a positive trend was observed between groups when visual feedback was presented, J=132, z=2.62, r=0.55.
Techniques for Defending from Buffer Overflow Vulnerability Security Exploits

A survey of techniques ranging from static analysis to hardware modification describes how various defensive approaches protect against buffer overflow, a vulnerability that represents a severe security threat.
AI Authoring for Virtual Characters in Conflict

Game developers strive to have engaging believable characters in their work. One of the elements that has been pointed out as contributing to believability is social behavior. A category of social behavior is interpersonal conflict. In our current research we compare two AI approaches to model NPC conflict resolution strategies: one using the reactive planning language ABL and another using the AI framework FAtiMA. We identify the following metrics to evaluate social behavior modeling: mapping theory, emotion, model checking, variability, policy change. In our analysis we found it was easier to map conflict concepts in ABL and the model checking process was faster. FAtiMA had better support for emotion and other emergent attributes.
Conformative Filtering for Implicit Feedback Data

Implicit feedback is the simplest form of user feedback that can be used for item recommendation. It is easy to collect and is domain independent. However, there is a lack of negative examples. Previous work tackles this problem by assuming that users are not interested or not as much interested in the unconsumed items. Those assumptions are often severely violated since non-consumption can be due to factors like unawareness or lack of resources. Therefore, non-consumption by a user does not always mean disinterest or irrelevance. In this paper, we propose a novel method called Conformative Filtering (CoF) to address the issue. The motivating observation is that if there is a large group of users who share the same taste and none of them have consumed an item before, then it is likely that the item is not of interest to the group. We perform multidimensional clustering on implicit feedback data using hierarchical latent tree analysis (HLTA) to identify user `tastes' groups and make recommendations for a user based on her memberships in the groups and on the past behavior of the groups. Experiments on two real-world datasets from different domains show that CoF has superior performance compared to several common baselines.
Fast and Diverse Narrative Planning through Novelty Pruning

Novelty pruning is a simple enhancement that can be added to most planners. A node is removed unless it is possible to find a set of n literals which are true in the current state and have never all been true in any of that plan's previous states. Expanding on the success of the Iterated Width algorithm in classical planning and general game playing, we apply this technique to narrative planning. Using a suite of 8 benchmark narrative planning problems, we demonstrate that novelty pruning can be used with breadth-first search to solve smaller problems optimally and combined with heuristic search to solve larger problems faster. We also demonstrate that when many solutions to the same problem are generated, novelty pruning can produce a wider variety of solutions in some domains.
Teleportation-assisted optical controlled-sign gates

Reliable entangling gates for qubits encoded in single-photon states represent a major challenge on the road to scalable quantum computing architectures based on linear optics. In this work, we present two approaches to develop high-fidelity, near-deterministic controlled-sign-shift gates based on the techniques of quantum gate teleportation. On the one hand, teleportation in a discrete-variable setting, i.e., for qubits, offers unit-fidelity operations but suffers from low success probabilities. Here, we apply recent results on advanced linear optical Bell measurements to reach a near-deterministic regime. On the other hand, in the setting of continuous variables, associated with coherent states, squeezing, and, typically, Gaussian states, teleportation can be performed in a deterministic fashion, but the finite amount of squeezing implies an inevitable deformation of a teleported single-mode state. Using a new generalized form of the nonlinear-sign-shift gate for gate teleportation, we are able to achieve fidelities of the resulting controlled-sign gate above $90%$. A special focus is also put on a comparison of the two approaches, not only with respect to fidelity and success probability, but also in terms of resource consumption.
The use of adversaries for optimal neural network training

B-decay data from the Belle experiment at the KEKB collider have a substantial background from $e^{+}e^{-}\to q \bar{q}$ events. To suppress this we employ deep neural network algorithms. These provide improved signal from background discrimination. However, the deep neural network develops a substantial correlation with the $\Delta E$ kinematic variable used to distinguish signal from background in the final fit due to its relationship with input variables. The effect of this correlation is reduced by deploying an adversarial neural network. Overall the adversarial deep neural network performs better than a Boosted Decision Tree algorithimn and a commercial package, NeuroBayes, which employs a neural net with a single hidden layer.
Involving users in energy conservation: a case study in scientific clouds

Services offered by cloud computing are convenient to users for reasons such as their ease of use, flexibility, and financial model. Yet data centres used for their execution are known to consume massive amounts of energy. The growing resource utilisation following the cloud success highlights the importance of the reduction of its energy consumption. This paper investigates a way to reduce the footprint of HPC cloud users by varying the size of the virtual resources they request. We analyse the influence of concurrent applications with different resources sizes on the system energy consumption. Simulation results show that resources with larger size are more energy consuming regardless of faster completion of applications. Although smaller-sized resources offer energy savings, it is not always favourable in terms of energy to reduce too much the size. High energy savings depend on the user profiles' distribution.
Connecting Legendre with Kummer and Edwards

Scalar multiplication on suitable Legendre form elliptic curves can be speeded up in two ways. One can perform the bulk of the computation either on the associated Kummer line or on an appropriate twisted Edwards form elliptic curve. This paper provides details of moving to and from between Legendre form elliptic curves and associated Kummer line and moving to and from between Legendre form elliptic curves and related twisted Edwards form elliptic curves. Further, concrete twisted Edwards form elliptic curves are identified which correspond to known Kummer lines at the 128-bit security level which provide very fast scalar multiplication on modern architectures supporting SIMD operations.
Object Activity Scene Description, Construction, and Recognition

Action recognition is a critical task for social robots to meaningfully engage with their environment. 3-D human skeleton-based action recognition has been an attractive research area in recent years. Although the existing approaches are good at action recognition, it is a great challenge to recognize a group of actions in an activity scene. To tackle this problem, at first, we partition the scene into several primitive actions (PAs)-based upon motion attention mechanism. Then, the PAs are described by the trajectory vectors of the corresponding joints. After that, motivated by text classification based on word embedding, we employ a convolutional neural network (CNN) to recognize activity scenes by considering motion of joints as "word" of activity. The experimental results on the dataset of human activity scenes show the efficiency of the proposed approach.
High-fidelity Greenberger-Horne-Zeilinger state generation within nearby nodes

Generating entanglement in a distributed scenario is a fundamental task for implementing the quantum network of the future. We here report a protocol that uses only linear optics for generating Greenberger-Horne-Zeilinger states with high fidelities in a nearby node configuration. Moreover, we analytically show that the scheme is optimal for certain initial states in providing the highest success probability for sequential protocols. Finally, we give some estimates for the generation rate in a real scenario.
Surrogate outcomes and transportability

Identification of causal effects is one of the most fundamental tasks of causal inference. We consider an identifiability problem where some experimental and observational data are available but neither data alone is sufficient for the identification of the causal effect of interest. Instead of the outcome of interest, surrogate outcomes are measured in the experiments. This problem is a generalization of identifiability using surrogate experiments [1] and we label it as surrogate outcome identifiability. We show that the concept of transportability [2] provides a sufficient criteria for determining surrogate outcome identifiability for a large class of queries.
An Iterative Multilayer Unsupervised Learning Approach for Sensory Data Reliability Evaluation

This paper investigates the problem of extracting actionable patterns/models from unlabeled and potentially erroneous datasets in an unsupervised way. To address the need for both model extraction and data reliability evaluation, we propose a novel iterative multilayer micro-macro (IM3) method that defines data reliability, learns micro-macro models, and iteratively refines learned models. The IM3 method includes a general data reliability definition to evaluate the reliability level of each sample, a micro-macro model complexity determination, and an iterative data reliability and model complexity update mechanism to overcome the underfitting and overfitting issue. In particular, we propose a consistency-index-based approach to address underfitting and overfitting in an unsupervised way. The refinement of the learned models is enabled via dropping the most unreliable data until the data reliability is above a given threshold. The sensitivity of the proposed IM3 method with respect to the reliability threshold selection is further quantified via false alarm and missdetection to facilitate the selection of an appropriate reliability threshold. Evaluation of the proposed method and quantitative analysis of its sensitivity are provided on a polynomial regression problem via Monte Carlo simulations.
Integrating a local CRIS with the PTCRIS synchronization ecosystem

The scientific community, in particular, and society, in general, should have easy access to the innovation and knowledge generated by scientific research. However, the multiplicity of existing science information systems and institutional repositories does not allow for a simple and fast propagation of the results of scientific activities. In that context, and under the dictum “Add once, reuse multiple times”, the PTCRIS initiative was started in 2014 with the goal of creating an integrated ecosystem for the propagation and synchronization of scientific information amongst multiple parties. This ecosystem, which includes the development of the PTCRSync framework, allows for different information systems to share and synchronize information amongst them, thus avoiding the duplication of efforts of the researchers that used to have to deposit the same information in several different systems. This paper describes the integration of the PTCRISync synchronization mechanism with the current research information system of ISCTE-Instituto Universitário de Lisboa, Ciência-IUL. The results and the impact of the implemented approach in terms of the quantified benefits for researchers and institutions are also presented.
A Study on the Convolutional Neural Algorithm of Image Style Transfer

Recently, deep convolutional neural networks have resulted in noticeable improvements in image classification and have been used to transfer artistic style of images. Gatys et al. proposed the use of a learned Convolutional Neural Network (CNN) architecture VGG to transfer image style, but problems occur during the back propagation process because there is a heavy computational load. This paper solves these problems, including the simplification of the computation of chains of derivatives, accelerating the computation of adjustments, and efficiently choosing weights for different energy functions. The experimental results show that the proposed solutions improve the computational efficiency and render the adjustment of weights for energy functions easier.