Reviewerly/exHarmony huggingface dataset:
CAE: Mechanism to Diminish the Class Imbalanced in SLU Slot Filling Task

Spoken Language Understanding (SLU) task is a wide application task in Natural Language Processing. In the success of the pre-trained BERT model, NLU is addressed by Intent Classification and Slot Filling task with significant improvement performance. However, classed imbalance problem in NLU has not been carefully investigated, while this problem in Semantic Parsing datasets is frequent. Therefore, this work focuses on diminishing this problem. We proposed a BERT-based architecture named JointBERT Classify Anonymous Entity (JointBERT-CAE) that improves the performance of the system on three Semantic Parsing datasets ATIS, Snips, ATIS Vietnamese, and a well-known Named Entity Recognize (NER) dataset CoNLL2003. In JointBERT-CAE architecture, we use multitask joint-learning to split conventional Slot Filling task into two sub-task, detect Anonymous Entity by Sequence tagging and Classify recognized anonymous entities tasks. The experimental results show the solid improvement of JointBERT-CAE when compared with BERT on all datasets, as well as the wide applicable capacity to other NLP tasks using the Sequence Tagging technique.
Textural Features Sensitivity to Scale and Illumination Variations

Visual scene recognition is predominantly based on visual textures representing an object's material properties. However, the single material texture varies in scale and illumination angles due to mapping an object's shape. We present a comparative study of the color histogram, Gabor, opponent Gabor, Local Binary Pattern (LBP), and wide-sense Markovian textural features concerning their sensitivity to simultaneous scale and illumination variations. Due to their application dominance, these textural features are selected from more than 50 published textural features. Markovian features are information preserving, and we demonstrate their superior performance for scale and illumination variable observation conditions over the standard alternative textural features. We bound the scale variation by double size, and illumination variation includes illumination spectra, acquisition devices, and 35 illumination directions spanned above a sample.
Dynamic Sparse Attention for Scalable Transformer Acceleration

Transformers are the mainstream of NLP applications and are becoming increasingly popular in other domains such as Computer Vision. Despite the improvements in model quality, the enormous computation costs make Transformers difficult at deployment, especially when the sequence length is large in emerging applications. Processing attention mechanism as the essential component of Transformer is the bottleneck of execution due to the quadratic complexity. Prior art explores sparse patterns in attention to support long sequence modeling, but those pieces of work are on static or fixed patterns. We demonstrate that the sparse patterns are dynamic, depending on input sequences. Thus, we propose the Dynamic Sparse Attention (DSA) that can efficiently exploit dynamic sparse patterns in attention. Compared with other methods, our approach can achieve better trade-offs between accuracy and model complexity. Moving forward, we identify challenges and provide solutions to implement DSA on existing hardware (GPUs) and specialized hardware in order to achieve practical speedup and efficiency improvements for Transformer execution.
Scheduling Management of Controllable Load Participating in Power Grid Enhanced by Double-Chain Structure

Smart grid has emerged as a successful application of cyber-physical systems in the electric power system. Among numerous key technologies of the smart grid, blockchain technology provides a promising solution to reduce the level of demand-side management by ensuring fair competition among all participating entities. However, it brings problems that it is difficult to balance decentralization and platform performance in the controllable scenario in the smart grid. In this paper, we propose a fair and efficient main/side chain framework by exploring the scalability of blockchain, integrating the operation of power entities, and building the decision-making model. First, we develop a main/side blockchain-based electric trading mechanism for controllable load. Then, we consider the operation and maintenance cost of power entities, and propose two decision-making model functions including controllable load and load agents. The optimization problem falls into the category of difference of convex programming and is solved by using the multi-objective evolutionary algorithm. Next, we propose the framework that the transaction process and power flow calculation process are deployed on the main chain and side chain respectively, which ensures the efficiency of the main chain. Finally, the performance of the proposed structure is validated via numerical results and theoretical analysis.
A Decentralized Solution for Epidemiological Surveillance in Campus Scenarios

Throughout the various containment phases of a pandemic, such as Covid-19, digital tools and services prove to be essential measures to counteract its disrupting effects in social and working interactions. Within this scope, Nausica@DApp, the comprehensive solution proposed in this paper, is capable of facilitating a manageable co-existence of the teaching and research activities of a campus corporation with the organizational constraints posed by the pandemic, or even when the virus will exhibit an endemic behavior, throughout several intervention areas, such as personnel contact tracing, crowd gathering surveillance, and epidemiological monitoring. These operational requirements, in particular indirect contact tracing and overcrowd monitoring, call for the adoption of an absolute device localization paradigm, which, in the proposed solution, has been devised on top of the campus WiFi infrastructure and has proved to be encouragingly accurate in most cases. Absolute localization, on the other hand, imply a certain degree of server-based centralized operations, which might affect the preservation of user data privacy. The novelty of the proposed solution consists in maximizing confidentiality and integrity in sensitive personal information in spite of the centralized localization system, by means of a mostly decentralized approach, with mobile apps performing contact tracing matching operations locally to the portable devices on pseudonymized user data, whose authenticity is guaranteed by a blockchain which preserves their cryptographic hashes. Furthermore, the proposed novel solution includes enforcing privacy preservation by avoiding the use of the Bluetooth app-to-app channel for user data exchange from the overall design, conversely a typical approach of most contract tracing solutions. By means of a sensible use of the blockchain features, integrated into the solution’s microservice-based backend, a higher degree of operation transparency to rely on boosts the user’s level of trust. As a result of using the proposed solution, the availability and reliability of data about people gathering within the campus premises are enhanced; moreover, contact tracing is achieved while using the WiFi services, hence without forcing users to adopt new habits, and without compromising privacy. The overall system has been analysed in terms of performance and costs, and the experiments have shown that its adoption is viable and effective.
Data Mining and Machine Learning Methods for Robust Reliability Predictions on Automotive Components

The ability to deliver fast and accurate reliability predictions is a key factor in order to accelerate the design validation phase during product development.
A Blockchain-based Decentralized Framework for Carbon Accounting, Trading and Governance

This study proposes the relationship between carbon trading and blockchain and moves forward to find solutions to the major problem of reduction of carbon footprint on our planet. The “Kyoto Protocol '' of 1997 introduced the need for a mechanism to trade grants issued, allowing the restricted emissions of greenhouse gasses into the atmosphere, and creating an ecosystem for trading the surplus and deficiency of these credits/grants. This study aims at making this trade fraud free by using the powerful features of the blockchain like that of decentralization and a trustless environment by using different smart contracts that operate between multiple stakeholders in the ecosystem, the first contract creates a distributed ledger register for stakeholders, next quantifies CO2 consumptions and mints carbon tokens, third helps in verification of CO2 consumptions and the final contract is a DAO for voting on governance issues. Thus, this work proposes a blockchain based solution for trading of carbon credits to solve the problem of climate change.
Mario Plays on a Manifold: Generating Functional Content in Latent Space through Differential Geometry

Deep generative models can automatically create content of diverse types. However, there are no guarantees that such content will satisfy the criteria necessary to present it to end-users and be functional, e.g. the generated levels could be unsolvable or incoherent. In this paper we study this problem from a geometric perspective, and provide a method for reliable interpolation and random walks in the latent spaces of Categorical VAEs based on Riemannian geometry. We test our method with “Super Mario Bros” and “The Legend of Zelda” levels, and against simpler baselines inspired by current practice. Results show that the geometry we propose is better able to interpolate and sample, reliably staying closer to parts of the latent space that decode to playable content.
Mastering the Game of 3v3 Snakes with Rule-Enhanced Multi-Agent Reinforcement Learning

As a popular game around the world, Snakes has multiple modes with different settings. In this work, we are dedicated to the 3v3 Snakes, which is characterized by a complex mixture of competition and cooperation. To address this mode of Snakes, most existing AI agents adopt rule based methods, which achieve limited performance due to human’s oversight of some special circumstances. Inspired by the superiority of multi-agent reinforcement learning (MARL), we propose a rule-enhanced multi-agent reinforcement learning algorithm and build a 3v3 Snakes AI. Specifically, we introduce the territory matrix which is commonly utilized in rule based methods to the state features and mask the illegal actions through designed rules. The relationships of individual-team and friends-foes are also merged into reward design. Trained with Distributed PPO and self-play on a single GeForce RTX 2080 GPU for twenty-four hours, our AI achieves state-of-the-art performance and beats human players. On JIDI platform, our agent outperforms the other 132 participating agents and ranks the first for more than 20 consecutive days.
On Linking Level Segments

An increasingly common area of study in procedural content generation is the creation of level segments: short pieces that can be used to form larger levels. Previous work has used concatenation to form these larger levels. However, even if the segments themselves are completable and well-formed, concatenation can fail to produce levels that are completable and can cause broken in-game structures (e.g. malformed pipes in Mario). We show this with three tile-based games: a side-scrolling platformer, a vertical platformer, and a top-down roguelike. To address this, we present a Markov chain and a tree search algorithm that finds a link between two level segments, which uses filters to ensure completability and unbroken in-game structures in the linked segments. We further show that these links work well for multi-segment levels. We find that this method reliably finds links between segments and is customizable to meet a designer’s needs.
LevDoom: A Benchmark for Generalization on Level Difficulty in Reinforcement Learning

Despite the recent success of deep reinforcement learning (RL), the generalization ability of RL agents remains an open problem for real-world applicability. RL agents trained on pixels may completely be derailed from achieving their objectives in unseen situations with different levels of visual changes. However, numerous existing RL suites do not address this as a primary objective or lack consistent level design of increased complexity. In this paper, we introduce the LevDoom benchmark, a suite containing semi-realistic 3D simulation environments with coherent levels of difficulty in the renowned video game Doom, designed to benchmark generalization in vision-based RL. We demonstrate how our benchmark reveals weaknesses of some popular Deep RL algorithms, which fail to prevail in modified environments. We further establish how our difficulty level design presents increasing complexity to these algorithms.
World of Bugs: A Platform for Automated Bug Detection in 3D Video Games

We present World of Bugs (WOB), an open platform that aims to support Automated Bug Detection (ABD) research in video games. We discuss some open problems in ABD and how they relate to the platform’s design, arguing that learning-based solutions are required if further progress is to be made. The platform’s key feature is a growing collection of common video game bugs that may be used for training and evaluating ABD approaches.
Fly My Little Dragon: Using AR to Learn Geometry

We present the gamified AR learning environment ARES. During the gameplay, learners guide the little dragon called “Ares” through dungeons. Using AR, ARES three-dimensionally integrates the dungeons in the real-world. Each dungeon represents a coordinate system with obstacles that ultimately create mathematical exercises, e.g. rotating a door to let the dragon fly through it. Overcoming such a challenge requires a learner to spatially analyze the exercise and apply the fundamental mathematical principles. ARES displays a debriefing screen at the end of a dungeon to further support the learning process. In a preliminary qualitative user study, pre-service and in-service teachers saw great potential in ARES for providing further practical and motivating exercises to deepen the knowledge in the classroom and at home.
EM Environment Adaptability Analysis of Airborne Radar Based on Complex Network Theory

This paper demonstrates the feasibility of using complex network theory to evaluate the adaptability of airborne radar in complex electromagnetic environment (EME). The technical and tactical characteristic indexes such as false alarm probability, detection probability, sensitivity and range of action are selected as indexes, an EME adaptability evaluation model is established based on complex network theory, and several test cases of airborne radar system are given, which proved the applicability of complex network theory in the assessment of EM suitability of airborne radar.
Universal approximation with neural networks on function spaces

Neural networks play a central role in the construction of learning models for artificial intelligence and machine learning. This is because neural networks are highly flexible and can approximate a wide variety of maps with high accuracy. The flexibility of neural networks is theoretically guaranteed by using universal approximation theorems. When the input and output spaces have finite dimensions, the universal approximation property of neural networks has been intensively investigated under various conditions of width, depth, and activation functions. However, these finite-dimensional results cannot be directly applied to settings in which an input or output space has infinite dimensions, such as functional data analysis and neural processes. This study provides a universal approximation theorem with neural networks for uniformly continuous maps between function spaces, whose dimensions can be infinite.
Quantitative evaluation of the influence of multiple MRI sequences and of pathological tissues on the registration of longitudinal data acquired during brain tumor treatment

Registration methods facilitate the comparison of multiparametric magnetic resonance images acquired at different stages of brain tumor treatments. Image-based registration solutions are influenced by the sequences chosen to compute the distance measure, and the lack of image correspondences due to the resection cavities and pathological tissues. Nonetheless, an evaluation of the impact of these input parameters on the registration of longitudinal data is still missing. This work evaluates the influence of multiple sequences, namely T1-weighted (T1), T2-weighted (T2), contrast enhanced T1-weighted (T1-CE), and T2 Fluid Attenuated Inversion Recovery (FLAIR), and the exclusion of the pathological tissues on the non-rigid registration of pre- and post-operative images. We here investigate two types of registration methods, an iterative approach and a convolutional neural network solution based on a 3D U-Net. We employ two test sets to compute the mean target registration error (mTRE) based on corresponding landmarks. In the first set, markers are positioned exclusively in the surroundings of the pathology. The methods employing T1-CE achieves the lowest mTREs, with a improvement up to 0.8 mm for the iterative solution. The results are higher than the baseline when using the FLAIR sequence. When excluding the pathology, lower mTREs are observable for most of the methods. In the second test set, corresponding landmarks are located in the entire brain volumes. Both solutions employing T1-CE obtain the lowest mTREs, with a decrease up to 1.16 mm for the iterative method, whereas the results worsen using the FLAIR. When excluding the pathology, an improvement is observable for the CNN method using T1-CE. Both approaches utilizing the T1-CE sequence obtain the best mTREs, whereas the FLAIR is the least informative to guide the registration process. Besides, the exclusion of pathology from the distance measure computation improves the registration of the brain tissues surrounding the tumor. Thus, this work provides the first numerical evaluation of the influence of these parameters on the registration of longitudinal magnetic resonance images, and it can be helpful for developing future algorithms.
The Effects of Knowledge Extraction Approaches on Cryptanalysis Studies and Analysis of the Success of Chaos-Based Countermeasures

Each new approach and scientific progress provides many advantages and innovations from theory to practice yet, its disadvantages should be evaluated comprehensively from different perspectives. In this study, the effects of knowledge extraction approaches on the cryptanalysis process have been evaluated. It is known that the secret key of the encryption algorithm can be obtained by using knowledge extraction approaches, especially with application oriented attacks such as side channel analysis. It is known that chaotic masks can be used as a countermeasure to prevent the success of these attacks. In this study, side-channel analyzes of the chaos-based substitution box structures that have been proposed in recent years have been carried out. The side channel analysis results of the existing studies in the literature have been carried out for the first time in this study. The results obtained showed that the approaches in the field of knowledge extraction should be handled more comprehensively in the cryptanalysis processes, and also pointed out that a new evaluation criterion should be analyzed for future studies for chaos-based design studies.
Hybrid Approach to Designating Ontology Attribute Semantics

In our previous works, we have developed a novel approach to ontologies that provides a notion of attributes semantics. We have noticed that attributes by themselves contain no explicit meaning besides being a carrier of values, and gain such only if included within a concept. The biggest difficulty while creating and maintaining ontologies using our formal framework is asserting the consistency of vocabulary used to express the aforementioned semantics. The following article presents the method for automatic designating of attributes semantics in ontologies. We extend our previous work where a semi-automatic method has been proposed. The new approach utilizes Word2Vec similarity (incorporating the Genism library) and the WordNet lexical database. The experiments confirmed the usefulness of the framework by comparing attribute semantics created by experts manually and using the proposed solution.
A Recommendation System for Job Providers Using a Big Data Approach

In recent times, Job seekers has dramatically increased. Hence, it has become very strenuous to search for potential candidates. Recently recommendation systems have become essential in many areas and newly for human resources. As a recruiter they can identifying profiles to be recruited and this through using online professional social networks such as LinkedIn. For this reason, we present, a generic and simple recommendation system to optimize the recruitment process. In this study, To create the matching between offer made by a recruiter and profiles and to identifies the most suitable candidate we need a important dataset size. We use content based recommendation techniques and a distributed big data processing framework (Apache Spark) and machine learning (ML) libraries. We use then a data augmentation algorithm to improve our results. According to the experiment, the use of our method can result a high precision of 0.936.
Data incarnations: Nesting complex inherited and learned behaviours

What happens when humans and birds engage each other through a collaboration-as-fantasy mediated by computers? Could such an exercise be modelled in a way that helps us to transcend the techno-ocularcentric fetishes for precision and certainty which demarcate our time? From Edgar Wind’s notion of 'incarnation' – as the place where empirical experience and metaphysical foundation meet in the single cognitive and experiential act – this article bridges the analogue with the digital, navigating nature’s strategies to embody inherited and learned complex behaviours in the design of nests, in what I call data-nests.
Nest-works

Two years ago, a nest box outside my window held a pair of Violet-Green Swallow. I counted six swallows fledge from the box and take their first flights in the July rain. Leaving the roof of the nest box, they flew in little loops out over the water, trying out their wings. I watched them from the dock, their bodies suspended in the air between the raindrops. This experience was the inspiration for what I call ‘nest-works’ – for poetic wilding of all-too-human spaces. Nest-works began with an experimental panel for the 2021 College Art Association Conference , called ‘Co-Making this World’. The experimental session was modelled after the nest of a bird, a Black-Capped Chickadee. As this cavity-nester builds a home of disparate materials, the panel of artist-researchers built a session of disparate theories and practices, as we considered relationships with world-systems that are in the process of making (such as the nest of the chickadee). For Technoetic Arts , we weave a new nest-work of research material, as we consider new models for knowledge and creative production. This nest-work is an entanglement of short essays made by artists working with a common pattern, framing eco-poetics on collaborative and participatory processes with the non-human/more-than-human.
Blockchain technology, foundations, protocols and aesthetic considerations

This article aims to outline the fundamental concepts that characterize blockchain technology in order to allow for a better understanding of how it is structured within the protocols which govern the internet, but also to portray the devices which allow its re-appropriation by capitalist culture. The theoretical foundations of this article are supported by a medio-archaeological position that allows us to acquire a technical look at the blockchain, but also to weave historical and aesthetic parallels in order to understand the NFT phenomenon, at the core of current economic issues and speculations.