Reviewerly/exHarmony huggingface dataset:
Comparing BERT against Traditional Machine Learning Models in Text Classification

The BERT model has arisen as a popular state-of-the-art machine learning model in the recent years that is able to cope with multiple NLP tasks such as supervised text classification without human supervision. Its flexibility to cope with any type of corpus delivering great results has make this approach very popular not only in academia but also in the industry. Although, there are lots of different approaches that have been used throughout the years with success. In this work, we first present BERT and include a little review on classical NLP approaches. Then, we empirically test with a suite of experiments dealing different scenarios the behaviour of BERT against the traditional TF-IDF vocabulary fed to machine learning algorithms. Our purpose of this work is to add empirical evidence to support or refuse the use of BERT as a default on NLP tasks. Experiments show the superiority of BERT and its independence of features of the NLP problem such as the language of the text adding empirical evidence to use BERT as a default technique to be used in NLP problems.
A Survey on Redactable Blockchain: Challenges and Opportunities

While immutability is an important property of blockchain and can be utilized to build a credible data publishing platform, the same property can also be abused to disseminate illicit content and may violate requirements under the General Data Protection Regulation (GDPR) as well as other privacy regulations. Hence, there has been increased focus on designing redactable blockchain-based solutions, which is the focus of this survey article. Specifically, we survey the extant literature on redactable blockchain, and discuss the existing limitations and challenges prior to outlining a number of future research opportunities which hopefully will benefit the broader research and practitioner communities.
SAM-Adapter: Adapting Segment Anything in Underperformed Scenes

The emergence of large models, also known as foundation models, has brought significant advancements to AI research. One such model is Segment Anything (SAM), which is designed for image segmentation tasks. However, as with other foundation models, our experimental findings suggest that SAM may fail or perform poorly in certain segmentation tasks, such as shadow detection and camouflaged object detection (concealed object detection). This study first paves the way for applying the large pre-trained image segmentation model SAM to these downstream tasks, even in situations where SAM performs poorly. Rather than fine-tuning the SAM network, we propose SAM-Adapter, which incorporates domain-specific information or visual prompts into the segmentation network by using simple yet effective adapters. By integrating task-specific knowledge with general knowledge learnt by the large model, SAM-Adapter can significantly elevate the performance of SAM in challenging tasks as shown in extensive experiments. We can even outperform task-specific network models and achieve state-of-the-art performance in the task we tested: camouflaged object detection, shadow detection. Our code of adapting SAM in downstream applications have been released publicly at https://github.com/tianrun-chen/SAM-Adapter-PyTorch/ and has benefited many researchers. We believe our work opens up opportunities for utilizing SAM in downstream tasks, with potential applications in various fields, including medical image processing, agriculture, remote sensing, and more.
On the Relation of Constraint Answer Set Programming Languages and Algorithms

Recently a logic programming language AC was proposed by Mellarkod et al. (2008) to integrate answer set programming (ASP) and constraint logic programming. Similarly, Gebser et al. (2009) proposed a CLINGCON language integrating ASP and finite domain constraints. These languages allow new efficient inference algorithms that combine traditional ASP procedures and other methods in constraint programming. In this paper we show that a transition system introduced by Nieuwenhuis et al. (2006) to model SAT solvers can be extended to model the "hybrid" Acsolver algorithm by Mellarkod et al. developed for simple AC programs and the Clingcon algorithm by Gebser et al. for clingcon programs. We define weakly-simple programs and show how the introduced transition systems generalize the Acsolver and Clingcon algorithms to such programs. Finally, we state the precise relation between AC and CLINGCON languages and the Acsolver and Clingcon algorithms.
Modelling Goal-Directed Players in Digital Games

The pursuit of a viable model of player behaviour has gained momentum in research in recent years, and it is beginning to attract the attention of the designers of next-generation digital games. In this paper, we present a novel enhancement to player modelling that is well-suited to the digital role-playing and puzzle game industries, titled Goal-Directed- Player Modelling, in which state abstraction based on a player's goals is used to improve the performance of a classifier for predicting player actions. We survey a set of related research, formally introduce a method for Goal-Directed-Player Modelling, and present empirical results which clearly show the ability of Goal-Directed-Player Modelling to greatly improve the accuracy of a simple, online, low cost incremental classi ier to a level near those of more advanced and complex of line methods.&lt;/p&gt;
Approximate Policy Iteration with Linear Action Models

In this paper we consider the problem of finding a good policy given some batch data.We propose a new approach, LAM-API, that first builds a so-called linear action model (LAM) from the data and then uses the learned model and the collected data in approximate policy iteration (API) to find a good policy.A natural choice for the policy evaluation step in this algorithm is to use least-squares temporal difference (LSTD) learning algorithm.Empirical results on three benchmark problems show that this particular instance of LAM-API performs competitively as compared with LSPI, both from the point of view of data and computational efficiency.
Anytime Truncated D* : Anytime Replanning with Truncation

Incremental heuristic searches reuse their previous search efforts to speed up the current search. Anytime search algorithms iteratively tune the solutions based on available search time. Anytime D* (AD*) is an incremental anytime search algorithm that combines these two approaches. AD* uses an inflated heuristic to produce bounded suboptimal solutions and improves the solution by iteratively decreasing the inflation factor. If the environment changes, AD* recomputes a new solution by propagating the new costs. Recently, a different approach to speed up replanning (TLPA*/TD* Lite) was proposed that relies on selective truncation of cost propagations instead of heuristic inflation. In this work, we present an algorithm called Anytime Truncated D* (ATD*) that combines heuristic inflation with truncation in an anytime fashion. We develop truncation rules that can work with an inflated heuristic without violating the completeness/suboptimality guarantees, and show how these rules can be applied in conjunction with heuristic inflation to iteratively refine the replanning solutions with minimal reexpansions. We explain ATD*, discuss its analytical properties and present experimental results for 2D and 3D (x, y, heading) path planning demonstrating its efficacy for anytime replanning.
Logical Agents for Language and Action

Game developers are faced with the difficult task of creating non-player characters with convincing behavior. This commonly involves an exhaustive specification of their actions in every conceivable situation that might arise. The process is laborious and the results do not apply in new and unforeseen circumstances. We present an agent architecture where game characters have a basic understanding of their environment that enables them to figure out what to do by themselves. Knowledge about facts and actions is encoded in a formal logic where automated reasoning technology can apply the knowledge to answer novel questions in dialog and to plan actions in new situations. A number of examples serve to demonstrate how the technology meets the challenges of application in a simple computer game prototype. We envision this technology being used to create more flexible game characters with a deeper understanding of the world they inhabit.
Support Vector Machines Classification on Class Imbalanced Data: A Case Study with Real Medical Data

Ranked set sampling and some of its variants have been applied successfully in different areas of applications such as industrial statistics, economics, environmental and ecological studies, biostatistics, and statistical genetics.Ranked set sampling is a sampling method that more efficient than simple random sampling.Also, it is well known that Fisher information of a ranked set sample (RSS) is larger than Fisher information of a simple random sample (SRS) of the same size about the unknown parameter of the underlying distribution in parametric inference.In this paper, we consider the Farlie-Gumbel-Morgenstern (FGM) family and study the information measures such as Shannon's entropy, Rényi entropy, mutual information, and Kullback-Leibler (KL) information of RSS data.Also, we investigate their properties and compare them with a SRS data.
Real-Time Collaborative Planning with the Crowd

Planning is vital to a wide range of domains, including robotics, military strategy, logistics, itinerary generation and more, that both humans and computers find difficult. Collaborative planning holds the promise of greatly improving performance on these tasks by leveraging the strengths of both humans and automated planners. However, this requires formalizing the problem domain and input, which must be done by hand, a priori, restricting its use in general real-world domains. We propose using a real-time crowd of workers to simultaneously solve the planning problem, formalize the domain, and train an automated system. As plans are developed, the system is able to learn the domain, and contribute larger segments of work.
The Future of Procedural Content Generation in Games

The future of procedural content generation (PCG) lies beyond the dominant motivations of “replayability” and creating large environments for players to explore. This paper explores both the past and potential future for PCG, identifying five major lenses through which we can view PCG and its role in a game: data vs. process intensiveness, the interactive extent of the content, who has control over the generator, how many players interact with it, and the aesthetic purpose for PCG being used in the game. Using these lenses, the paper proposes several new research directions for PCG that require both deep technical research and innovative game design.
Visual Saliency Map from Tensor Analysis

Modeling visual saliency map of an image provides important information for image semantic understanding in many applications. Most existing computational visual saliency models follow a bottom-up framework that generates independent saliency map in each selected visual feature space and combines them in a proper way. Two big challenges to be addressed explicitly in these methods are (1) which features should be extracted for all pixels of the input image and (2) how to dynamically determine importance of the saliency map generated in each feature space. In order to address these problems, we present a novel saliency map computational model based on tensor decomposition and reconstruction. Tensor representation and analysis not only explicitly represent image's color values but also imply two important relationships inherent to color image. One is reflecting spatial correlations between pixels and the other one is representing interplay between color channels. Therefore, saliency map generator based on the proposed model can adaptively find the most suitable features and their combinational coefficients for each pixel. Experiments on a synthetic image set and a real image set show that our method is superior or comparable to other prevailing saliency map models.
The Compressed Differential Heuristic

The differential heuristic (DH) is an effective memory-based heuristic for explicit state spaces. In this paper, we aim to improve its performance and memory usage. We introduce a compression method for DHs which stores only a portion of the original uncompressed DH, while preserving enough information to enable efficient search. Compressed DHs (CDH) can be tuned to fit any size of memory, even smaller than the size of the state space.Experimental results across different domains show that, for a given amount of memory, a CDH significantly outperforms an uncompress
An example of genre shift in the medicinal product information genre system

Medical writers and translators need information of three kinds: conceptual, contextual and textual. When in possession of this information, they can progressively improve their efficiency. Textual genre has proved to be useful to linguistic mediators of professional texts, providing them with the communicative and textual competence required. I suggest that the scope of textual genre can usefully be widened to include the notion of genre systems. This notion can be particularly useful for obtaining contextual information and understanding complex communicative activities in professional groups and can help enhance the professional competence of medical writers and translators. This paper has a two-fold objective: to describe the dynamic continuum of medical communication that operates in the genre system constituted by product information genres in the pharmaceutical sector also considering the restraints, genre conventions and sequence imposed by metagenres; and to illustrate the intralinguistic genre shift translation process that takes place between the summary of product characteristics and the package leaflet.
Towards Generic Models of Player Experience

Context personalisation is a flourishing area of research with many applications. Context personalisation systems usually employ a user model to predict the appeal of the context to a particular user given a history of interactions. Most of the models used are context-dependent and their applicability is usually limited to the system and the data used for model construction. Establishing models of user experience that are highly scalable while maintaing the performance constitutes an important research direction. In this paper, we propose generic models of user experience in the computer games domain. We employ two datasets collected from players interactions with two games from different genres where accurate models of players experience were previously built. We take the approach one step further by investigating the modelling mechanism ability to generalise over the two datasets. We further examine whether generic features of player behaviour can be defined and used to boost the modelling performance. The accuracies obtained in both experiments indicate a promise for the proposed approach and suggest that game-independent player experience models can be built.
The American English spontaneous speech minicorpus. Architecture and comparability

This paper presents the American English (AE) minicorpus, a spontaneous speech resource created within the auspices of the C-ORAL-BRASIL project consisting of texts selected from the Santa Barbara Corpus of Spoken American English. We focus on the sampling strategy that guided the selection of texts, the transcription criteria that were implemented and the prosodic and informational annotation carried on the AE minicorpus. The minicorpus was designed to be comparable to the minicorpora of the C-ORAL projects for Italian and Brazilian Portuguese, which were conceived to allow the study of information structure in spontaneous speech in accordance with the principles of the Language into Act Theory. This theory comprises a pragmatic framework for the study of spontaneous speech and it integrates the IPO approach into its prosodic model. The IPO approach consists of a perception-based model for the study of intonation, providing an apparatus for the description and classification of melodic contours observed in spontaneous speech.
Photo Crowdsourcing Based Privacy-Protected Healthcare

In this paper, the concept of crowdsourcing is applied to the medical field and a health monitoring mechanism based on photo crowdsourcing is proposed. Specifically, with photo crowdsourcing by many participators, the routine circumstances of users may be represented. However, these photos may include other people than the user, such as the visibility requestor, the invisibility requestor, and the passerby. The visibility and invisibility requestor are the participators in the system, whose identity can be set as visible or invisible, while the passerbys do not participate in the system. Hence, a privacy protection mechanism is proposed for this system, which includes two categories: i) The image fuzzy processing is provided for the invisibility requestor, while the original image is reserved for the visibility requestor. ii) The passerby's image is directly fuzzy processed for privacy protection.
Using Tablet PCs and Associated Technologies To Reveal Undergraduate and Graduate Student Thinking

The How People Learn framework 1, 2 was applied to redesign the courses Food Chemistry (IA-332) and Advanced Food Chemistry (IA-530).Our goal was to improve undergraduate and graduate food chemistry teaching and learning by creating high-quality learning environments that promote an interactive classroom while integrating formative assessments into classroom practices by means of Tablet PCs and associated technologies 3,4 .We utilized InkSurvey, a web-based tool to pose open-ended questions to students during class and receive real-time student responses 5 .Furthermore, we identified classroom assessment techniques appropriate to the course and adapted them into a Tablet PC/Classroom Presenter environment to gauge student learning in real time, provide immediate feedback, and make realtime pedagogical adjustments as needed 6 .
PlotShot: Generating Discourse-Constrained Stories Around Photos

Story generators typically adopt a pipelined model of generation wherein fabula structure is decided independently and prior to discourse structure. In this paper, we propose a novel story generator, PlotShot, capable of reasoning over discourse materials during fabula generation such that these materials meaningfully constrain the development of a causally and intentionally coherent story. PlotShot incorporates user-supplied photographs as optional story states through an oversubscription planning paradigm. Further, to leverage existing work on planning-based models of generation, we present a technique to compile the photo story planning problem to classical narrative planning. Our system attempts to maximize quality of an illustrated story by analyzing the affinity between a photo and the action it is meant to depict. An evaluation of generated artifacts shows advantage over heuristic baseline techniques.
Co-Creative Drawing Agent with Object Recognition

This paper describes an updated version of a co-creative drawing system called the Drawing Apprentice. The system collaborates with users by analyzing their drawn input and responding in a real time dialogical and improvisational interaction. The current system includes an object recognition module that employs deep learning to classify sketched objects. The system architecture and implementation are described along with its evaluation during a public demonstration during which artists, non-artists, and designers provided feedback about the experience interacting with the system.
A Generic Method for Classification of Player Behavior

Player classification allows for considerable improvements on both game analytics and game adaptivity. With this paper we aim at reversing the ad-hoc tendency in player classification methods, by proposing an approach to player classification that can be integrated across different games and genres and is particularly suited to be used by game designers. This paper describes our generic method of interaction-based player classification, which consists of three components: (i) intercepting player interactions, (ii) finding player types through fuzzy cluster analysis and (iii) classification using Hidden Markov Models (HMM). To showcase our method we developed Blindmaze, a simple web-based hidden maze game publicly available, featuring a bounded set of interactions. All data collected from a game is interaction-based, requiring minimal implementation effort from the game developers. It is concluded that our method makes player classification even more available by making it generic and re-usable across different games.
Intelligent Information Processing

Intellligent information processing is the study on establishing the theory, algorithm, and systematic method and technology to deal with complex system information and its uncertainty. It has broad application prospects in complex system modeling, system analysis, decision, control, optimization, and design. It is an interdisciplinary subject in computer science, which involves neural network, fuzzy system, evolutionary computation, chaos dynamics, classification theory, wavelet transform, artificial intelligence, and so on. From information carrier to information processing, human intelligence is widely simulated to process various kinds of information. The combination of artificial intelligence and cognitive science will further promote the ability of human self-understanding and control. Studying the theory and method of intelligent information processing with cognitive mechanism, exploring the mechanism of cognition, building an available computing model, and developing applications may bring a breakthrough in the future information processing technology
Device-Free Activity Recognition Based on Coherence Histogram

Device-free activity recognition (DFAR) is a promising technique that detects the activity of a target by analyzing the influence of its existence on surrounding wireless links. It realizes target sensing without the participation or even awareness of the target. The key question of DFAR is how to characterize the influence of the target on wireless links. Existing works mostly utilize statistical features, such as mean and variance in time-domain, and energy as well as entropy in frequency-domain, to characterize the influenced signals. However, statistical features provide only partial information. This paper explores the method on how to characterize the distribution of the signal as a whole. Specifically, we present a novel coherence histogram, which leverages the spatial structural characteristics to better characterize the distribution of the wireless signal. The coherence histogram captures not only the occurrence probability of received signal strength (RSS) measurements, but also the spatial relationship between adjacent RSS measurements as well. Experimental results show that our coherence histogram-based DFAR system could achieve an accuracy of more than 96%, which significantly outperforms other state-of-the-art DFAR systems remarkably.