Reviewerly/exHarmony huggingface dataset:
Aligning Architecture with Business Goals in the Automotive Domain

When designing complex automotive systems in practice, employed technologies and architectural decisions need to reflect business goals. While the software architecture community has acknowledged the need to align business goals with architectural decisions, there is a lack of practical approaches to achieve this alignment. In this paper, we intend to close this gap by providing a systematic approach for architecture-business alignment. The approach describes how to align architecture with business concerns by eliciting goals, identifying quality attributes, and deriving architectural tactics. We iteratively developed and evaluated the approach together with an international automotive manufacturer. We show the application of the proposed approach within our participating company leveraging a use case related to software-over-the-air technologies. The proposed approach is perceived as beneficial by our participants, since it provides a structured mechanism to align architecture and business goals by determining key architectural concerns as quality attributes and tactics.
Fast Game Content Adaptation Through Bayesian-based Player Modelling

In games, as well as many user-facing systems, adapting content to users' preferences and experience is an important challenge. This paper explores a novel method to realize this goal in the context of dynamic difficulty adjustment (DDA). Here the aim is to constantly adapt the content of a game to the skill level of the player, keeping them engaged by avoiding states that are either too difficult or too easy. Current systems for DDA rely on expensive data mining, or on handcrafted rules designed for particular domains, and usually adapts to keep players in the flow, leaving no room for the designer to present content that is purposefully easy or difficult. This paper presents Fast Bayesian Content Adaption (FBCA), a system for DDA that is agnostic to the domain and that can target particular difficulties. We deploy this framework in two different domains: the puzzle game Sudoku, and a simple Roguelike game. By modifying the acquisition function's optimization, we are reliably able to present a content with a bespoke difficulty for players with different skill levels in less than five iterations for Sudoku and fifteen iterations for the simple Roguelike. Our method significantly outperforms simpler DDA heuristics with the added benefit of maintaining a model of the user. These results point towards a promising alternative for content adaption in a variety of different domains.
Robust Graph-Filter Identification with Graph Denoising Regularization

When approaching graph signal processing tasks, graphs are usually assumed to be perfectly known. However, in many practical applications, the observed (inferred) network is prone to perturbations which, if ignored, will hinder performance. Tailored to those setups, this paper presents a robust formulation for the problem of graph-filter identification from input-output observations. Different from existing works, our approach consists in addressing the robust identification by formulating a joint graph denoising and graph-filter identification problem. Such a problem is formulated as a non-convex optimization, suitable relaxations are proposed, and graph-stationarity assumptions are incorporated to enhance performance. Finally, numerical experiments with synthetic and real-world graphs are used to assess the proposed schemes and compare them with existing (robust) alternatives.
Classification of Intestinal Gland Cell-Graphs Using Graph Neural Networks

We propose to classify intestinal glands as normal or dysplastic using cell-graphs and graph-based deep learning methods. Dysplastic intestinal glands can lead to colorectal cancer, which is one of the three most common cancer types in the world. In order to assess the cancer stage and thus the treatment of a patient, pathologists analyse tissue samples of affected patients. Among other factors, they look at the changes in morphology of different tissues, such as the intestinal glands. Cell-graphs have a high representational power and can describe topological and geometrical properties of intestinal glands. However, classical graph-based methods have a high computational complexity and there is only a limited range of machine learning methods available. In this paper, we propose Graph Neural Networks (GNNs) as an efficient learning-based approach to classify cell-graphs. We investigate different variants of so-called Message Passing Neural Networks and compare them with a classical graph-based approach based on approximated Graph Edit Distance and k-nearest neighbours classifier. A promising classification accuracy of 94.8% is achieved by the proposed method on the pT1 Gland Graph dataset, which is an increase of 11.5% over the baseline result.
Towards Efficiently Diversifying Dialogue Generation Via Embedding Augmentation

Dialogue generation models face the challenge of producing generic and repetitive responses. Unlike previous augmentation methods that mostly focus on token manipulation and ignore the essential variety within a single sample using hard labels, we propose to promote the generation diversity of the neural dialogue models via soft embedding augmentation along with soft labels in this paper. Particularly, we select some key input tokens and fuse their embeddings together with embeddings from their semantic-neighbor tokens. The new embeddings serve as the input of the model to replace the original one. Besides, soft labels are used in loss calculation, resulting in multi-target supervision for a given input. Our experimental results on two datasets illustrate that our proposed method is capable of generating more diverse responses than raw models while remains a similar n-gram accuracy that ensures the quality of generated responses.
Learning a Sparse Generative Non-Parametric Supervised Autoencoder

This paper concerns the supervised generative non parametric autoencoder. Classical methods are based on variational non supervised autoencoders (VAE). Variational autoencoders encourage the latent space to fit a prior distribution, like a Gaussian. However, they tend to draw stronger assumptions for the data, often leading to higher asymptotic bias when the model is wrong.In this paper, we relax the parametric distribution assumption in the latent space and we propose to learn a non-parametric data distribution of the clusters in the latent space. The network encourages the latent space to fit a distribution learned with the labels instead of the parametric prior assumptions. We have built a network architecture that uses the labels to compute the latent space. Thus we define a global criterion combining classification and reconstruction loss. In addition, we have proposed a ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1,1</inf> regularization which has the advantage of sparsifying the network and improving the clustering. Finally we propose a tailored algorithm to minimize the criterion with constraint. We demonstrate the effectiveness of our method using the popular image dataset MNIST and two biological datasets.
A Method for Assigning Probability Distributions in Attack Simulation Languages

Cyber attacks on IT and OT systems can have severe consequences for individuals and organizations, from water or energy distribution systems to online banking services. To respond to these threats, attack simulations can be used to assess the cyber security of systems to foster a higher degree of resilience against cyber attacks; the steps taken by an attacker to compromise sensitive system assets can be traced, and a time estimate can be computed from the initial step to the compromise of assets of interest. Previously, the Meta Attack Language (MAL) was introduced as a framework to develop security-oriented domain-specific languages. It allows attack simulations on modeled systems and analyzes weaknesses related to known attacks. To produce more realistic simulation results, probability distributions can be assigned to attack steps and defenses to describe the efforts required for attackers to exploit certain attack steps. However, research on assessing such probability distributions is scarce, and we often rely on security experts to model attackers’ efforts. To address this gap, we propose a method to assign probability distributions to the attack steps and defenses of MAL-based languages. We demonstrate the proposed method by assigning probability distributions to a MAL-based language. Finally, the resulting language is evaluated by modeling and simulating a known cyber attack.
Preparation and verification of tensor network states

We consider a family of tensor network states defined on regular lattices that come with a natural definition of an adiabatic path to prepare them. This family comprises relevant classes of states, such as injective Matrix Product and Projected Entangled-Pair States, and some corresponding to classical spin models. We show how uniform lower bounds to the gap of the parent Hamiltonian along the adiabatic trajectory can be efficiently computed using semi-definite programming. This allows one to check whether the adiabatic preparation can be performed efficiently with a scalable effort. We also derive a set of observables whose expectation values can be easily determined and that form a complete set, in the sense that they uniquely characterize the state. We identify a subset of those observables which can be efficiently computed if one has access to the quantum state and local measurements, and analyze how they can be used in verification procedures.
A multi-critic deep deterministic policy gradient UAV path planning

Deep Deterministic Policy Gradient is a reinforcement learning method, which is widely used in unmanned aerial vehicle (UAV) for path planning. In order to solve the environmental sensitivity in path planning, we present an improved deep deterministic policy gradient for UAV path planning. Simulation results demonstrate that the algorithm improves the convergence speed, convergence effect and stability. The UAV can learn more knowledge from the complex environment.
A Gated and Bifurcated Stacked U-Net Module for Document Image Dewarping

Capturing images of documents is one of the easiest and most used methods of recording them. These images however, being captured with the help of handheld devices, often lead to undesirable distortions that are hard to remove. We propose a supervised Gated and Bifurcated Stacked U-Net module to predict a dewarping grid and create a distortion free image from the input. While the network is trained on synthetically warped document images, results are calculated on the basis of real world images. The novelty in our methods exists not only in a bifurcation of the U-Net to help eliminate the intermingling of the grid coordinates, but also in the use of a gated network which adds boundary and other minute line level details to the model. The end-to-end pipeline proposed by us achieves state-of-the-art performance on the DocUnet dataset after being trained on just 8 percent of the data used in previous methods.
Light-TTS: Lightweight Multi-Speaker Multi-Lingual Text-to-Speech

With the development of deep learning, end-to-end neural text-to-speech (TTS) systems have achieved significant improvements in high-quality speech synthesis. However, most of these systems are attention-based autoregressive models, resulting in slow synthesis speed and large model parameters. In addition, speech in different languages is usually synthesized using different models, which increases the complexity of the speech synthesis systems. In this paper, we propose a new lightweight multi-speaker multi-lingual speech synthesis system, named LightTTS, which can quickly synthesize the Chinese, English or code-switch speech of multiple speakers in a non-autoregressive generation manner using only one model. Moreover, compared to FastSpeech with the same number of neural network layers and nodes, our LightTTS achieves a 2.50x Mel-spectrum generation acceleration on CPU, and the parameters are compressed by 12.83x.
Quality Assurance for Automated Commit Message Generation

Many automated commit message generation (CMG) approaches have been proposed for facilitating the understanding of software changes. They are shown to be promising and can generate commit messages that are semantically relevant to the reference messages for a number of commits. However, a large proportion (over 50%) of semantically irrelevant commit messages are also generated simultaneously. Such messages may mislead developers, require additional efforts of developers to confirm and filter out, and hinder the application of existing CMG approaches in practice. For tackling this problem, prior work mainly focuses on proposing new methods to improve the generation accuracy. However, another promising way for bridging the gap between CMG approaches and the practice has not been well investigated, which is: can we automatically assure the semantic relevance of the generated messages?To that end, in this work, we propose an automated Quality A ssurance framework for commit message generation (QAcom). QAcom can assure the quality of generated commit messages by automatically filtering out the semantically-irrelevant generated messages and preserving the semantically-relevant ones as many as possible. In particular, QAcom consists of a Collaborative-Filtering-based (CF) component and a Retrieval-based (RE) component. Given a commit message generated by a CMG approach, QAcom estimates whether this generated message is semantically relevant to its ground truth, which is unknown when estimating, based on both the collaborative filtering algorithm and the similarity between this commit and historical commits. We evaluate the effectiveness of QAcom by "plugging" it in three state-of-the-art CMG approaches. Experimental results on three public datasets show that QAcom can effectively filter out semantically-irrelevant generated messages and preserve semantically-relevant ones.
Continuous vs. Discontinuous (Teleport) Locomotion in VR: How Implications can Provide both Benefits and Disadvantages

In this short paper we provide a comparative overview of the implications of using continuous vs. discontinuous (teleport) locomotion methods in VR, and argue how many of these implications can provide both benefits and disadvantages depending on the overall goal, desired user experiences, application scenario/context, as well as individual preferences and one's propensity for cybersickness.
The Future of Human-Food Interaction

There is an increasing interest in food within the HCI discipline, with many interactive prototypes emerging that augment, extend and challenge the various ways people engage with food, ranging from growing plants, cooking ingredients, serving dishes and eating together. Grounding theory is also emerging that in particular draws from embodied interactions, highlighting the need to consider not only instrumental, but also experiential factors specific to human-food interactions. Considering this, we are provided with an opportunity to extend human-food interactions through knowledge gained from designing novel systems emerging through technical advances. This workshop aims to explore the possibility of bringing practitioners, researchers and theorists together to discuss the future of human-food interaction with a particular highlight on the design of experiential aspects of human-food interactions beyond the instrumental. This workshop extends prior community building efforts in this area and hence explicitly invites submissions concerning the empirically-informed knowledge of how technologies can enrich eating experiences. In doing so, people will benefit not only from new technologies around food, but also incorporate the many rich benefits that are associated with eating, especially when eating with others.
Real time Machine Learning Approach for a Smart Door Unlocking Using Face recognition System

Abstract This proposed framework goes about as a home security framework for both Person location and gives security to entryway access control by utilizing facial acknowledgment for the home climate. The more peculiar’s face is distinguished inside a home climate accomplished by catching a picture from a web camera and handling will be done on caught pictures. The web camera catches the arrangement of pictures when the individual comes before the entryway. The caught picture is analyzed utilizing our face acknowledgment calculation model. In the event that the individual is the approved one, at that point the entryway is opened. Then again, if the individual isn’t the approved one. The picture of the individual is shipped off the power in the event that he perceives the individual and wants to open the entryway. He can distantly get to the entryway and permit the individual in.
Unsupervised Neural Adaptation Model Based on Optimal Transport for Spoken Language Identification

Due to the mismatch of statistical distributions of acoustic speech between training and testing sets, the performance of spoken language identification (SLID) could be drastically degraded. In this paper, we propose an unsupervised neural adaptation model to deal with the distribution mismatch problem for SLID. In our model, we explicitly formulate the adaptation as to reduce the distribution discrepancy on both feature and classifier for training and testing data sets. Moreover, inspired by the strong power of the optimal transport (OT) to measure distribution discrepancy, a Wasserstein distance metric is designed in the adaptation loss. By minimizing the classification loss on the training data set with the adaptation loss on both training and testing data sets, the statistical distribution difference between training and testing domains is reduced. We carried out SLID experiments on the oriental language recognition (OLR) challenge data corpus where the training and testing data sets were collected from different conditions. Our results showed that significant improvements were achieved on the cross domain test tasks.
Introducing a Tool for Concurrent Argumentation

Agent-based modelling languages naturally implement concurrency for handling complex interactions between communicating agents. On the other hand, the field of Argumentation Theory lacks of instruments to explicitly model concurrent behaviours. In this paper we introduce a tool for dealing with concurrent argumentation processes and that can be used, for instance, to model agents debating, negotiating and persuading. The tool implements operations as expansion, contraction and revision. We also provide a web interface exposing the functionalities of the tool and allowing for a more careful study of concurrent processes.
Real-Time Radio Modulation Classification With An LSTM Auto-Encoder

Identifying modulation type of a received radio signal is a challenging problem encountered in many applications including radio interference mitigation and spectrum allocation. This problem is rendered challenging by the existence of a large number of modulation schemes and numerous sources of interference. Existing methods for monitoring spectrum readily collect large amounts of radio signals. However, existing state-of-the-art approaches to modulation classification struggle to reach desired levels of accuracy with computational efficiency practically feasible for implementation on low-cost computational platforms. To this end, we propose a learning framework based on an LSTM denoising autoencoder designed to extract robust and stable features from the noisy received signals, and detect the underlying modulation scheme. The method uses a compact architecture that may be implemented on low-cost computational devices while achieving or exceeding state-of-the-art classification accuracy. Experimental results on realistic synthetic and over-the-air radio data show that the proposed framework reliably and efficiently classifies radio signals, and often significantly outperform state-of-the-art approaches.
Design of Cyber Food:

With a growing interest in HCI around food, there is a trend to combine computing technology and food to facilitate novel eating experiences. However, most current systems tend to superimpose the technology over food rather than consider food itself as a focal interaction material. This paper proposes a more direct computational food integration by conceptualizing the notion of “Cyber Food”, accentuating “food as computational artifact”, where food embodies digital computation that can be ultimately consumed and digested by the human body. With this work, we attempt to open a new pathway to enrich human-food interactions beyond the traditional boundaries between the physical (edible) and digital realms.
Improving Continuous Hand Gesture Detection and Recognition from Depth Using Convolutional Neural Networks

Hand gestures are becoming more and more efficient and intuitive means of communication between human and machine. While many proposed methods aim at increasing performance of recognition from spotted gestures segments, it lacks efficient solutions for both detection and recognition gesture from continuous video streams for practical application. In this paper, we approach by using a simple CNN detector to detect gesture candidates and a more precise and complicated CNN classifier to recognize gesture categories. We first deploy a method recently proposed in []. However, we improve that method by adjusting another condition for gesture decision making to avoid detection missing due to the detector performance. Our improved algorithm is compared with the original one, showing an improvement in term of overall accuracy (from 73.9% to 79.3%) on the same dataset.
“They don’t always think about that”: Translational Needs in the Design of Personal Health Informatics Applications

Personal health informatics continues to grow in both research and practice, revealing many challenges of designing applications that address people's needs in their health, everyday lives, and collaborations with clinicians. Research suggests strategies to address such challenges, but has struggled to translate these strategies into design practice. This study examines translation of insights from personal health informatics research into resources to support designers. Informed by a review of relevant literature, we present our development of a prototype set of design cards intended to support designers in re-thinking potential assumptions about personal health informatics. We examined our design cards in semi-structured interviews, first with 12 student designers and then with 12 health-focused professional designers and researchers. Our results and discussion reveal tensions and barriers designers encounter, the potential for translational resources to inform the design of health-related technologies, and a need to support designers in addressing challenges of knowledge, advocacy, and evidence in designing for health.
Competence-based approach in the training of physicists

Abstract Competence is the ability of an individual to solve various practical tasks, a set of knowledge, skills and abilities required to perform a specific practical activity. The use of intersubject connections during the training of physicists, in our opinion, will make it possible to prepare specialists with a broad Outlook and good qualifications. The knowledge acquired in this way will allow future physicists to compete more successfully in the labor market. This may be important if, due to certain circumstances, it is not possible to work in a previously acquired specialty, this will allow you to choose a new profession in which you will be able to maximize the use of previously acquired competencies, as well as make the choice of a new profession more consciously.