Reviewerly/exHarmony huggingface dataset:
Leveraging Bilinear Attention to Improve Spoken Language Understanding

Spoken language understanding system (SLU) typically includes two tasks: Intent detection (ID) and Slot filling (SF). Optimizing these two tasks in an interactive way with attention mechanism has been shown effective. However, previous attention-based works leveraged only the first order attention design, which is lacking in efficacy. To trigger more adequate information interaction between the input intent or slot features, we propose a novel framework with Bilinear attention, which can build the second order feature interactions. By stacking numerous Bilinear attention modules and equipping the Exponential Linear Unit activation, it can build higher and infinity order feature interactions. To demonstrate the effectiveness of the proposed framework, we conduct some experiments on two benchmark datasets, i.e., SNIPS and ATIS. And the experimental results show that our framework is more competitive than multiple baselines as well as the first order attention model.
MAG+: An Extended Multimodal Adaptation Gate for Multimodal Sentiment Analysis

Human multimodal sentiment analysis is a challenging task that devotes to extract and integrate information from multiple resources, such as language, acoustic and visual information. Recently, multimodal adaptation gate (MAG), an attachment to transformer-based pre-trained language representation models, such as BERT and XLNet, has shown state-of-the-art performance on multimodal sentiment analysis. MAG only uses a 1-layer network to fuse multimodal information directly, and does not pay attention to relationships among different modalities. In this paper, we propose an extended MAG, called MAG+, to reinforce multimodal fusion. MAG+ contains two modules: multi-layer MAGs with modality reinforcement (M3R) and Adaptive Layer Aggregation (ALA). In the MAG with modality reinforcement of M3R, each modality is reinforced by all other modalities via crossmodal attention at first, and then all modalities are fused via MAG. The ALA module leverages the multimodal representations at low and high levels as the final multimodal representation. Similar to MAG, MAG+ is also attached to BERT and XLNet. Experimental results on two widely used datasets demonstrate the efficacy of our proposed MAG+.
Generalized Lagrange Coded Computing: A Flexible Computation-Communication Tradeoff

We consider the problem of evaluating arbitrary multivariate polynomials over a massive dataset, in a distributed computing system with a master node and multiple worker nodes. Generalized Lagrange Coded Computing (GLCC) codes are proposed to provide robustness against stragglers who do not return computation results in time, adversarial workers who deliberately modify results for their benefit, and information-theoretic security of the dataset amidst possible collusion of workers. GLCC codes are constructed by first partitioning the dataset into multiple groups, and then encoding the dataset using carefully designed interpolation polynomials, such that interference computation results across groups can be eliminated at the master. Particularly, GLCC codes include the state-of-the-art Lagrange Coded Computing (LCC) codes as a special case, and achieve a more flexible tradeoff between communication and computation overheads in optimizing system efficiency.
Application of Computational Intelligence and Machine Learning to Conventional Operational Research Methods

Machine learning and computational intelligence are two methods for achieving this (CI); traditional operational research methods are combined with machine learning-based computational techniques (OR). Students can handle complex decision-making problems thanks to the synergy between those methods and techniques. This research’s primary goal is to present and demonstrate potential connections amid the two computational arenas. Using applications, we show how machine learning techniques like fuzzy logic, neural networks and reinforcement learning can be combined to provide a simpler solution to more complex problems than traditional OR methods., which is a research contribution in and of itself.
Petrography and mineralogy of rinded ferrous‐carbonate concretions in the Middle Eocene carbonate rocks: A case study from the Avanah Formation in north‐east Erbil City, northern Iraq

The spherical rinded concretions in the carbonate rocks of the Avanah Formation (Middle Eocene) in the Gomaspan section, north‐east Erbil City, northern Iraq have been studied for the first time. They occur within the grey to yellow thick‐bedded marly dolomitic limestone interbedded with thin beds of grey shale in the lower part of the formation. Their diameter varies between 1 and 3 cm, are mostly spherical in shape, and characterized by brownish to dark grey isopachous rinds or cortex and brownish‐red centre. The microscopic study shows a similarity between the concretions and their host rocks in their microfacies and fossils contained. The main microfacies is dolomitized lime mudstone and contains Valvulina sp. and pelecypods. The concretions are affected by selective diagenesis that resulted in the colour changes between the centre and outer rims of the concretions. X‐ray diffraction (XRD) analysis of concretions and host rocks revealed the presence of dolomite, calcite, quartz, illite and haematite, the latter exists only in the concretions. Meanwhile, the scanning electron microscopy (SEM) and EDX analyses supported the results of (XRD) and show that the iron oxide (haematite) mainly was concentrated in the outer orange to red cortex. While the dark black inner cortex is characterized by containing less amount of Fe as compared with Ca, O, C and Mg in addition to illite, which also exists in the carbonate host rocks. Field observations, petrographic and mineralogical analyses revealed that the studied concretions had undergone four stages of formation, which consequently include dissolution of semi‐rounded fossils such as pelecypods and forming the moulds during the deposition of the carbonate rocks of the Avanah Formation, precipitation of calcite minerals in the moulds and formation of calcite concretions syn‐depositionally, partial dolomitization of calcite in meteoric‐marine mixed zone and formation of rinded iron oxide concretion by selective diagenesis in meteoric water conditions by the action of acidic water to form haematite.
Classifying Natural Disaster Tweet using a Convolutional Neural Network and BERT Embedding

Social media platforms have become a medium to find a vast source of information throughout the internet. Twitter has become one of the more popular microblogging platforms out there, and the more users there are in these platforms means the more various types of information can be sent out in a day. On Twitter users are able to write their expression in the form of tweets, this will then create a post on twitter's timeline and other users are able to see these tweets. If a tweet suddenly gets viral, Twitter will put the user's tweets into the trending page allowing even more users to view the said tweet. During an event of a natural disaster often a lot of the tweets that are being posted, have mention of the disaster making it a trending topic on Twitter. From this, a vast number of tweets about a disaster can be collected as data, but not always are the tweets containing information about the disaster. Often there are tweets that use natural disaster words but do not actually talk about the disaster itself, hence are not informative and can be classified as a non-disaster tweet. This research paper aims to propose a system to classify the disaster tweets and the non-disaster tweet during a disaster. The proposed method is based on Convolutional Neural Network (CNN), using a Bidirectional Encoder Representation from Transformers (BERT) as an Embedding. As a comparison, it will then be compared with another embedding method named Word2Vec. The Evaluation result after training and testing of the CNN with BERT embeddings gave the most consistent results attaining accuracy of 97.16% precision of 97.63%, a recall of 96.64, and an f1-score of 97.13% for the model classification.
Optimising the website accessibility conformance evaluation methodology

The Website Accessibility Conformance Evaluation Methodology (WCAG-EM) is a methodology that guides an auditor through evaluating a website's conformance to the Web Content Accessibility Guidelines (WCAG). In this paper, we raise some concerns present in the five stages of the WCAG-EM, including the evaluative scope, the non-probabilistic sampling approach, and the potential for bias within the selected sample. We propose a novel framework positioned parallel to the WCAG-EM, but with additional statistical-based metrics to address these concerns. These metrics consider the coverage, representativeness, complexity, popularity, freshness, and accessibility of a population of pages. We provide an overview of these metrics and discuss the possible use with three web page population-sourcing methods: server log files, breadth-first crawling, and depth-first crawling. Finally, we highlight the potential of utilising server log files as a population-sourcing method.
How Ready is Your Ready? Assessing the Usability of Incident Response Playbook Frameworks

Incident response playbooks provide step-by-step guidelines to help security operations personnel quickly respond to specific threat scenarios. Although playbooks are common in the security industry, they have not been empirically evaluated for effectiveness. This paper takes a first step toward measuring playbooks and the frameworks used to design them, using two studies conducted in an enterprise environment. In the first study, twelve security professionals created two playbooks each, using two standard playbook design frameworks; the resulting playbooks were evaluated by experts for accuracy. In the second, we observed five personnel using the created playbooks in no-notice threat exercises within a live security-operations center. We find that playbooks can help simplify and support incident response efforts. However, playbooks designed using the frameworks we examined often lack sufficient detail for real-world use, particularly for more junior technicians. We provide recommendations for improving playbooks, playbook frameworks, and organizational processes surrounding playbook use.
How AI-Based Training Affected the Performance of Professional Go Players

In this study, we analyzed how the performance of professional Go players has changed since the advent of AlphaGo, the first artificial intelligence (AI) application to defeat a human world Go champion. We interviewed and surveyed professional Go players and found that AI has been actively introduced into the Go training process since the advent of AlphaGo. The significant impact of AI-based training was confirmed in a subsequent analysis of 6,292 games in Korean Go tournaments and Elo rating data of 1,362 Go players worldwide. Overall, the tendency of players to make moves similar to those recommended by AI has sharply increased since 2017. The degree to which players’ expected win rates fluctuate during a game has also decreased significantly since 2017. We also found that AI-based training has provided more benefits to senior players and allowed them to achieve Elo ratings higher than those of junior players.
Automatically Generating and Improving Voice Command Interface from Operation Sequences on Smartphones

Using voice commands to automate smartphone tasks (e.g., making a video call) can effectively augment the interactivity of numerous mobile apps. However, creating voice command interfaces requires a tremendous amount of effort in labeling and compiling the graphical user interface (GUI) and the utterance data. In this paper, we propose AutoVCI, a novel approach to automatically generate voice command interface (VCI) from smartphone operation sequences. The generated voice command interface has two distinct features. First, it automatically maps a voice command to GUI operations and fills in parameters accordingly, leveraging the GUI data instead of corpus or hand-written rules. Second, it launches a complementary Q&A dialogue to confirm the intention in case of ambiguity. In addition, the generated voice command interface can learn and evolve from user interactions. It accumulates the history command understanding results to annotate the user’s input and improve its semantic understanding ability. We implemented this approach on Android devices and conducted a two-phase user study with 16 and 67 participants in each phase. Experimental results of the study demonstrated the practical feasibility of AutoVCI.
Tiny hole inspection of aircraft engine nacelle in 3D point cloud via robust statistical fitting

In the aircraft manufacturing industry, drilling hole inspection is a vital task for the noise reduction ability and the aircraft structure stability. Due to the complexity of the inner surface of the engine nacelle, inspecting drilling holes of a small size and a large number is fairly challenging. In this paper, we propose a framework for automatic hole inspection on composite flat parts. The raw data of traditional 3D laser scanning usually contain considerable noise and outliers, which has a great influence on tiny hole inspection. First, to perform measurement efficiently and to get raw data of good quality, we design a measurement platform to perform automatic measurement on a preset path. Instead of applying common boundary detection methods, we design a method to detect boundary points by evaluating the boundary possibility of each point, which is more likely to detect the points on a real hole boundary than noise. Based on the characteristics of laser scanning data, we then combine an effective algebraic circle fitting method with an iterative fitting strategy and develop a robust circle fitting method. The improved circle fitting method is capable of estimating a circle closest to the ground truth. Experiments demonstrate that our fitting method performs better than other common fitting methods for comparison under both synthetic and real scanning data.
Mouse Cusor Control Using Facial Movements - An HCI Application

Because of various limitations, some people are unable to operate computers. Cursor controls based on facial expression are extremely useful not just for the future of natural input, but also for the handicapped and disadvantaged. Furthermore, creating a control system allows them to operate computers without the assistance of a third party. While this is not an issue for a healthy person, it may be an insurmountable barrier for persons who have a limited range of motion in their limbs. In these instances, input techniques based on the region's greater skills, such as eye movements and facial expressions, would be preferable. A system was created that uses a low-cost way to permit alternative input methods like controlling a mouse cursor on a computer screen. Using a camera we can capture the image of the eye and head movement. The mouse cursor is controlled by head movement as moving face up, down, sidewise movements and mouse events such as right and left click are controlled through eye blinks and squinting. This system is mainly aimed at disabled people to have effective communication with the computer. It is quite beneficial to implement a controlling system that allows them to move without the assistance of another person.
Towards a Didactic Concept for Heterogeneous Target Groups in Digital Learning Environments—First Course Implementation

In the context of the ongoing digitization of interdisciplinary subjects, the need for digital literacy is increasing in all areas of everyday life. Furthermore, communication between science and society is facing new challenges, not least since the COVID-19 pandemic. In order to deal with these challenges and to provide target-oriented online teaching, new educational concepts for the transfer of knowledge to society are necessary. In the transfer project "Zukunftslabor Gesundheit" (ZLG), a didactic concept for the creation of E-Learning classes was developed. A key factor for the didactic concept is addressing heterogeneous target groups to reach the broadest possible spectrum of participants. The concept has already been used for the creation of the first ZLG E-Learning courses. This article outlines the central elements of the developed didactic concept and addresses the creation of the ZLG courses. The courses created so far appeal to different target groups and convey diverse types of knowledge at different levels of difficulty.
Learning to select goals in Automated Planning with Deep-Q Learning

In this work we propose a planning and acting architecture endowed with a module which learns to select subgoals with Deep Q-Learning. This allows us to decrease the load of a planner when faced with scenarios with real-time restrictions. We have trained this architecture on a video game environment used as a standard test-bed for intelligent systems applications, testing it on different levels of the same game to evaluate its generalization abilities. We have measured the performance of our approach as more training data is made available, as well as compared it with both a state-of-the-art, classical planner and the standard Deep Q-Learning algorithm. The results obtained show our model performs better than the alternative methods considered, when both plan quality (plan length) and time requirements are taken into account. On the one hand, it is more sample-efficient than standard Deep Q-Learning, and it is able to generalize better across levels. On the other hand, it reduces problem-solving time when compared with a state-of-the-art automated planner, at the expense of obtaining plans with only 9% more actions. • Deep Q-Learning is used by a planning agent to learn to select subgoals. • Our approach reduces planning time in online execution. • Our approach generalizes better than standard Deep Q-Learning. • Our approach is more sample-efficient than standard Deep Q-Learning.
Visual Delegate Generalization Frame – Evaluating Impact of Visual Effects and Elements on Player and User Experiences in Video Games and Interactive Virtual Environments

Visual effects and elements in video games and interactive virtual environments can be applied to transfer (or delegate) non-visual perceptions (e.g., proprioception, presence, pain) to players and users, thus increasing perceptual diversity via the visual modality. Such elements or effects are referred to as visual delegates (VDs). Current findings on the experiences that VDs can elicit relate to specific VDs, not to VDs in general. Deductive and comprehensive VD evaluation frameworks are lacking. We analyzed VDs in video games to generalize VDs in terms of their visual properties. We conducted a systematic paper analysis to explore player and user experiences observed in association with specific VDs in user studies. We conducted semi-structured interviews with expert players to determine their preferences and the impact of VD properties. The resulting VD framework (VD-frame) contributes to a more strategic approach to identifying the impact of VDs on player and user experiences.
Slabforge: Design Software for Slab-Based Ceramics

Slab-based ceramics are constructed by rolling out flat sheets of clay, cutting out a pattern, and then folding the cut clay to build a three-dimensional design. Slabforge is an open-source web-based software application that supports slab-based ceramics. It enables users to design a range of simple 3D forms and then generate flat patterns and matching 3D-printable slump molds that support the construction of those forms. This paper discusses the development of the software in the context of our own ceramics practice and then describes the results of a study in which students in an introductory ceramics course used Slabforge to create tea sets. We use both of these experiences to motivate a critical reflection on the relationships between materials, craft, digital fabrication, and software, introducing three themes of friction that we encountered during the course of this project.
Putting scientific results in perspective: Improving the communication of standardized effect sizes

How do people form impressions of effect size when reading scientific results? We present a series of studies on how people perceive treatment effectiveness when scientific results are summarized in various ways. We first show that a prevalent form of summarizing results—presenting mean differences between conditions—can lead to significant overestimation of treatment effectiveness, and that including confidence intervals can exacerbate the problem. We attempt to remedy potential misperceptions by displaying information about variability in individual outcomes in different formats: statements about variance, a quantitative measure of standardized effect size, and analogies that compare the treatment with more familiar effects (e.g., height differences by age). We find that all of these formats substantially reduce potential misperceptions and that analogies can be as helpful as more precise quantitative statements of standardized effect size. These findings can be applied by scientists in HCI and beyond to improve the communication of results to laypeople.
Visualizing Instructions for Physical Training: Exploring Visual Cues to Support Movement Learning from Instructional Videos

Instructional videos for physical training have gained popularity in recent years among sport and fitness practitioners, due to the proliferation of affordable and ubiquitous forms of online training. Yet, learning movement this way poses challenges: lack of feedback and personalised instructions, and having to rely on personal imitation capacity to learn movements. We address some of these challenges by exploring visual cues' potential to help people imitate movements from instructional videos. With a Research through Design approach, focused on strength training, we augmented an instructional video with different sets of visual cues: directional cues, body highlights, and metaphorical visualizations. We tested each set with ten practitioners over three recorded sessions, with follow-up interviews. Through thematic analysis, we derived insights on the effect of each set of cues for supporting movement learning. Finally, we generated design takeaways to inform future HCI work on visual cues for instructional training videos.
“I Didn’t Catch That, But I’ll Try My Best”: Anticipatory Error Handling in a Voice Controlled Game

Advances in speech recognition, language processing and natural interaction have led to an increased industrial and academic interest. While the robustness and usability of such systems are steadily increasing, speech-based systems are still susceptible to recognition errors. This makes intelligent error handling of utmost importance for the success of those systems. In this work, we integrated anticipatory error handling for a voice-controlled video game where the game would perform a locally optimized action in respect to goal completion and obstacle avoidance, when a command is not recognized. We evaluated the user experience of our approach versus traditional, repetition-based error handling (). Our results indicate that implementing anticipatory error handling can improve the usability of a system, if it follows the intention of the user. Otherwise, it impairs the user experience, even when deciding for technically optimal decisions.