Reviewerly/exHarmony huggingface dataset:
CUI@IUI: Theoretical and Methodological Challenges in Intelligent Conversational User Interface Interactions

This workshop aims to bring together the Intelligent User Interface (IUI) and Conversational User Interface (CUI) communities to understand the theoretical and methodological challenges in designing, deploying and evaluating CUIs. CUIs have continued to prosper with the increased use and technological developments in both text-based chatbots and speech-based systems. However, challenges remain in creating established theoretical and methodological approaches for CUIs, and how these can be used with recent engineering advances. These include assessing the impact of interface design on user behaviours and perceptions, developing design guidelines, understanding the role of personalisation and issues of ethics and privacy. Our half-day multidisciplinary workshop brings together researchers and practitioners from the IUI and CUI communities in academia and industry. We aim to (1) identify and map out key focus areas and research challenges to address these critical theoretical and methodological gaps and (2) foster strong relationships between disciplines within and related to Artificial Intelligence (AI) and Human-Computer Interaction (HCI).
Self-supervised Adversarial Robustness for the Low-label, High-data Regime

Recent work discovered that training models to be invariant to adversarial perturbations requires substantially larger datasets than those required for standard classification. Perhaps more surprisingly, these larger datasets can be mostly unlabeled. Pseudo-labeling, a technique simultaneously pioneered by four separate and simultaneous works in 2019, has been proposed as a competitive alternative to labeled data for training adversarially robust models. However, when the amount of labeled data decreases, the performance of pseudo-labeling catastrophically drops, thus questioning the theoretical insights put forward by Uesato et al. (2019), which suggest that the sample complexity for learning an adversarially robust model from unlabeled data should match the fully supervised case. We introduce Bootstrap Your Own Robust Latents (BYORL), a self-supervised learning technique based on BYOL for training adversarially robust models. Our method enables us to train robust representations without any labels (reconciling practice with theory). This robust representation can be leveraged by a linear classifier to train adversarially robust models. We evaluate BYORL and pseudo-labeling on CIFAR-10 and demonstrate that BYORL achieves significantly higher robustness (i.e., models resulting from BYORL are up to two times more accurate). Experiments on CIFAR-10 against l2 and l∞ norm-bounded perturbations demonstrate that BYORL achieves near state-of-the-art robustness with as little as 500 labeled examples. We also note that against l2 norm-bounded perturbations of size ϵ=128/255, BYORL surpasses the known state-of-the-art with an accuracy under attack of 77.61% (against 72.91% for the prior art).
Analyzing of LAM-CIoT: Lightweight Authentication Mechanism in Cloud-based IoT Environment

Communicating smart entities in the Internet of Things (IoT) environment using cloud technology makes human daily life more convenient and brings benefits in various fields. After the occurrence of the novel coronavirus, the demand for remotely managed systems will significantly raise up to mitigate the harmful consequences of social distancing and to increase the systems' efficiency as well. In such systems, sharing relevant information among honest entities over public connection brings numerous security risks. In this context, in 2020, Wazid et al. proposed a new lightweight authentication mechanism for cloudbased IoT environment called LAM-CIoT. Their mechanism uses multi-factor authentication and provides real-time data access from the deployed IoT-sensors. Wazid et al. argued that the mechanism they proposed is stable, efficient and scalable. However, we find out that Wazid et al.'s mechanism shows weaknesses and is incomplete. In this paper, we briefly introduce Wazid et al.'s mechanism and expound that their mechanism is still unstable and not suitable to be applied in practice.
A Fast and Effective Multiple Kernel Clustering Method on Incomplete Data

Multiple kernel clustering is an unsupervised data analysis method that has been used in various scenarios where data is easy to be collected but hard to be labeled. However, multiple kernel clustering for incomplete data is a critical yet challenging task. Although the existing absent multiple kernel clustering methods have achieved remarkable performance on this task, they may fail when data has a high value-missing rate, and they may easily fall into a local optimum. To address these problems, in this paper, we propose an absent multiple kernel clustering (AMKC) method on incomplete data. The AMKC method first clusters the initialized incomplete data. Then, it constructs a new multiple-kernel-based data space, referred to as <i>K</i>-space, from multiple sources to learn kernel combination coefficients. Finally, it seamlessly integrates an incomplete-kernel-imputation objective, a multiple-kernel-learning objective, and a kernel-clustering objective in order to achieve absent multiple kernel clustering. The three stages in this process are carried out simultaneously until the convergence condition is met. Experiments on six datasets with various characteristics demonstrate that the kernel imputation and clustering performance of the proposed method is significantly better than state-of-the-art competitors. Meanwhile, the proposed method gains fast convergence speed.
Automated Silicon-Substrate Ultra-Microtome for Automating the Collection of Brain Sections in Array Tomography

Understanding the structure and working principle of brain neural networks requires three-dimensional reconstruction of brain tissue samples using array tomography method. In order to improve the reconstruction performance, the sequence of brain sections should be collected with silicon wafers for subsequent electron microscopic imaging. However, the current collection of brain sections based on silicon substrate involve mainly manual collection, which requires the involvement of automation techniques to increase collection efficiency. This paper presents the design of an automatic collection device for brain sections. First, a novel mechanism based on circular silicon substrates is proposed for collection of brain sections; second, an automatic collection system based on microscopic object detection and feedback control strategy is proposed. Experimental results verify the function of the proposed collection device. Three objects (brain section, left baffle, right baffle) can be detected from microscopic images by the proposed detection method. Collection efficiency can be further improved with position feedback of brain sections well. It has been experimentally verified that the proposed device can well fulfill the task of automatic collection of brain sections. With the help of the proposed automatic collection device, human operators can be partially liberated from the tedious manual collection process and collection efficiency can be improved.
Generating Visual and Semantic Explanations with Multi-task Network

Explaining deep models is desirable especially for improving the user trust and experience. Much progress has been done recently towards visually and semantically explaining deep models. However, establishing the most effective explanation is often human-dependent, which suffers from the bias of the annotators. To address this issue, we propose a multitask learning network (MTL-Net) that generates saliency-based visual explanation as well as attribute-based semantic explanation. Via an integrated evaluation mechanism, our model quantitatively evaluates the quality of the generated explanations. First, we introduce attributes to the image classification process and rank the attribute contribution with gradient weighted mapping, then generate semantic explanations with those attributes. Second, we propose a fusion classification mechanism (FCM) to evaluate three recent saliency-based visual explanation methods by their influence on the classification. Third, we conduct user studies, quantitative and qualitative evaluations. According to our results on three benchmark datasets with varying size and granularity, our attribute-based semantic explanations are not only helpful to the user but they also improve the classification accuracy of the model, and our ranking framework detects the best performing visual explanation method in agreement with the users.
Two-Level Lattice Neural Network Architectures for Control of Nonlinear Systems

In this paper, we consider the problem of automatically designing a Rectified Linear Unit (ReLU) Neural Network (NN) architecture (number of layers and number of neurons per layer) with the guarantee that it is sufficiently parametrized to control a nonlinear system. Whereas current state-of-the-art techniques are based on hand-picked architectures or heuristic-based search to find such NN architectures, our approach exploits a given model of the system to design an architecture; as a result, we provide a guarantee that the resulting NN architecture is sufficient to implement a controller that satisfies an achievable specification. Our approach exploits two basic ideas. First, we assume that the system can be controlled by a Lipschitz-continuous state-feedback controller that is unknown but whose Lipschitz constant is upper-bounded by a known constant; then using this assumption, we bound the number of affine functions needed to construct a Continuous Piecewise Affine (CPWA) function that can approximate the unknown Lipschitz-continuous controller. Second, we utilize the authors' recent results on the Two-Level Lattice (TLL) NN architecture, a novel NN architecture that was shown to be parameterized directly by the number of affine functions that comprise the CPWA function it realizes. We also evaluate our method by designing a NN architecture to control an inverted pendulum.
Image/video aesthetic quality management based on fusing 3D CNN and shallow features

Aiming at the problem that image/videos are the streaming data captured by different acquisition modality, each kind of which has its particular quality evaluation mode, this paper proposes a video quality evaluation (VQA) model based on 3D CNN correlated with image/video data. In order to achieve accurate VQA value, the model adopted a Bi-LSTM-based architecture to process video-related image/ text as auxiliary part to provide additional information for VQA architecture. Firstly, the auxiliary part constructs feature vector for each video-related text by the model originating from Bi-LSTM and Seq2Seq. Then, the feature vector was feed to a well-trained decoder to reconstruct the original input data. Then, the feature vector complied with the image/video data are inputted into end-to-end VQA modal based on the 3D CNN straightly. Experimental results on the image/video dataset from the Internet show that the proposed model reflects the subjective quality of medical image more comprehensively, and has better overall image/video VQA assessment performance than the other state-of-the-art non-reference methods.
MUSCLE: Strengthening Semi-Supervised Learning Via Concurrent Unsupervised Learning Using Mutual Information Maximization

Deep neural networks are powerful, massively parameterized machine learning models that have been shown to perform well in supervised learning tasks. However, very large amounts of labeled data are usually needed to train deep neural networks. Several semi-supervised learning approaches have been proposed to train neural networks using smaller amounts of labeled data with a large amount of unlabeled data. The performance of these semisupervised methods significantly degrades as the size of labeled data decreases. We introduce Mutual-information-based Unsupervised & Semi-supervised Concurrent LEarning (MUSCLE), a hybrid learning approach that uses mutual information to combine both unsupervised and semisupervised learning. MUSCLE can be used as a standalone training scheme for neural networks, and can also be incorporated into other learning approaches. We show that the proposed hybrid model outperforms state of the art on several standard benchmarks, including CIFAR-10, CIFAR-100, and Mini-Imagenet. Furthermore, the performance gain consistently increases with the reduction in the amount of labeled data, as well as in the presence of bias. We also show that MUSCLE has the potential to boost the classification performance when used in the fine-tuning phase for a model pre-trained only on unlabeled data.
Development of Adaptive Sampling Based strategy for Human Activity Predictions Using Sequential Networks

The prediction of human activities dynamically is a formidable issue at the edge of computer vision and graphics. This paper presents an innovative idea of developing an adaptive sampling-based strategy to recognize and predict human activities dynamically. More specifically, we propose a new adaptive cost function to train a Gated Recurrence Unit (GRU) using Human 3.6M sequential data [1]. Subsequently, after getting trained on our proposed cost function, the deep neural network can predict human motions more accurately with a significant reduction of mean angel error than the state of the art models in this area. The adaptive sampling-based cost function is a bell-shaped function, and it takes input from its samples during training to make it capable of learning from its own mistakes during training. We develop and configure this function with several variations of the bandwidth. We analyze and compare our results with several models, such as Long Sh ort Term Memory (LSTM) with three layers, structural Recurrent Neural Network(RNN), Zero velocity, and Residual supervised models. In our model, the convergence of training loss and validation loss is much faster; the reasons for which and other details have been elaborated in this paper.
Student Activity Credit Framework (PSUCOIN)

Prince of Songkla University, Phuket Campus (PSU Phuket) has activities every semester. Students have to collect activity hours (as credits) for completing the condition of graduation. Sometimes, students who regularly participate in activities will receive enough activity credits. This prevents them from participating in the further activities. Gain more activity credits are not tempting anymore. For this reason, if there is a utility token that can represent as an activity credit, then students can use it for other purposes. It causes joining activity more attractive. The traditional activity credit system uses a simple spreadsheet file, which has several issues such as offline, errors and transparency because all information is stored at a PSU staff only. Thus, it leads us to propose a utility token, called PSUCOIN, changing from a centralized system to a blockchain-based system. Students receive PSUCOIN coins when they attend an activity organized by PSU campus. They will be more involved in activities since PSUCOIN coins can be exchanged for items or other future purposes as well. It can be used to motivate students who are not participating in activities.
OFDM Ensemble Autoencoder Using CNN and SPSA for End-to-End Learning Communication Systems

The fundamental objective of wireless communication systems is to reproduce message sent from transmitter, at receiver. Rigorous work has already been done in the domain of communication, where every part of communication system is studied and optimized to the near optimal solution, but optimizing communication modules individually does not ensures global optimality of the communication system. In this work a community-based autoencoder communication system with three encoders and three decoders is proposed to jointly optimize transmitter and receiver. Convolutional neural networks (CNN) are used to implement autoencoder to facilitate variable length inputs. Unavailability of exact channel model is dealt with the use of simultaneous perturbation stochastic approximation algorithm (SPSA) in place of backpropogation, and to ensure synchronization between transmitter and receiver OFDM technique is incorporated.
An Empirical Study of User Story Quality and Its Impact on Open Source Project Performance

When software development teams apply Agile Software Development practices, they commonly express their requirements as User Stories. We aim to study the quality of User Stories and its evolution over time. Firstly, we develop a method to automatically monitor the quality of User Stories. Secondly, we investigate the relationship between User Story quality and project performance measures such as the number of reported bugs and the occurrence of rework and delays. We measure User Story quality with the help of a recently published quality framework and tool, Automatic Quality User Story Artisan (AQUSA). For our empirical work, we use six agile open source software projects. We apply time series analysis and use the Windowed Time Lagged Cross Correlation (WTLCC) method. Our results indicate that automatic User Story quality monitoring is feasible and may result in various distinct dynamic evolution patterns. In addition, we found the following relationship patterns between User Story quality and the software development aspects. A decrease/increase in User Story quality scores is associated with (i) a decrease/increase of the number of bugs after 1–13 weeks in short-medium projects, and 12 weeks in longer ones, (ii) an increase in rework frequency after 18–28, 8–15, and 1–3 weeks for long, medium, and short projects, respectively, and (iii) an increase in delayed issues after 7–20, 8–11, and 1–3 weeks for long, medium, and short duration projects.
On the Computational Performance of IBM Quantum Devices Applied to Combinatorial Optimisation Problems

The prevalence of NP-Hard Combinatorial Optimisation Problems (COP) requires a continuous search for powerful heuristic techniques to efficiently solve problems of this nature, due to the exponentially growing CPU times faced by deterministic methods. This paper focuses on two COP, namely the Travelling Salesmen Problem (TSP) and the Quadratic Assignment Problem (QAP). In particular, the computational performance of classical optimisation techniques, Branch and Bound (BNB) and Simulated Annealing (SA), are compared to the Variational Quantum Eigensolver (VQE) algorithm. These algorithms are executed respectively on classical and Noisy Intermediate-Scale Quantum (NISQ) computers, in this case, a host of IBM quantum devices. The experimental results resemble and extend on previously reported results found in the literature. Furthermore, this research critically analyses, compares and comments on the performance of quantum computing techniques to that of classical techniques. The results highlight the current shortcomings of NISQ devices, as classical optimisation techniques significantly outperform the VQE algorithm when run on state of the art IBM Quantum devices.
Comparison of ACR Methods for 360° Video Quality Assessment Subject to Participants’ Experience with Immersive Media

Watching 360° videos on head-mounted displays (HMDs) has become more popular in recent years. As such, subjective quality assessment of 360° videos on HMDs is needed to obtain a ground truth on the quality as perceived by users. Due to the lack of standardized methods, the absolute category rating (ACR) with hidden reference (ACR-HR) method for conventional videos has also been used for subjective quality assessment of 360° videos on HMDs. A modified ACR (M-ACR) method tailored for assessing 360° videos on HMDs has recently been proposed. In this paper, we compare the ACR-HR and M-ACR with hidden reference (M-ACR-HR) method regarding subjective quality assessment of 360° videos on HMDs. The comparison is conducted subject to participants' experience with watching immersive media on HMDs. The findings include: 1) Average rating times to cast opinion scores is lower for the ACR-HR method irrespective of participants' experience, 2) Mean opinion scores are similar for both methods irrespective of participants' experience, 3) The M-ACR-HR method is more reliable, especially with experts and participants with no experience, 4) Discomfort is significantly lower for the ACR-HR method, especially, for participants with no experience.
Spam Detection by designing Machine Learning approach in Twitter Stream

As we all are aware of the Online social networking sites that has been rapidly growing very fast in today's world as there are many popular sites one of them is Twitter, on twitter users tweet in the form of short text messages to their added friends account. Many of the peoples are available on twitter and it is commonly in today's world, by this the attacks on this sites is also been varying day by day, as twitter has been affected by this attack where fake user spreads tweets as hash tags of the website or links and some other online services, this all tweets in different form becomes dangerous for the original users on twitter. We need to be aware from this type of attack thus by collecting twitter dataset and working on tweets to find the spam tweets that has been posted on twitter by spammers, in this there are different types of public tweets have been collected. To deal with the problem of Spam messages on twitter this can be solved by using the machine learning algorithm like that of Random forest algorithm this includes classification model by which we can easily classify different types of tweets.
Prediction simulation of sports injury based on embedded system and neural network

Sports injury prediction is one of the most important parts of the challenge of prevention and harm challenging in motion. Sports injury contemplated wherein a simplified view of the phenomenon to study the reduction unit's cause. A linear analysis is viewed as the unidirectional manner a substantial portion of, and causality. This reduction method depends on the correlation and regression analysis. Despite the extensive efforts to predict sports injuries, the existing method is the inability to identify the predictors that were and have been limited. The risk of the very important element for sports players' injury when developing prevention and risk mitigation strategies for work-related accidents. Some signs can be used in many ways to identify risk factors for injury. However, it can be made from the data and lead to incorrect inferences and difficulty understanding the nuances of different statistical methods. The proposed Neural Network (NN) and the embedded system classify the sports player's injury prediction to solve the problem. The proposed Neural Network (NN) and the embedded system have attracted a simple calculation and interpretation of the reliable results for sports injury prediction. The simulation results show the high performance compared to other existing methods.