Reviewerly/exHarmony huggingface dataset:
A NoGAN approach for image and video restoration and compression artifact removal

Lossy image and video compression algorithms introduce several different types of visual artifacts that reduce the visual quality of the compressed media, and the higher the compression rate the higher is the strength of these artifacts. In this work, we describe an approach for visual quality improvement of compressed images and videos to be performed at presentation time, as to obtain the benefits of fast data transfer and reduced data storage, while enjoying a visual quality that could be obtained only reducing the compression rate. To obtain this result we propose to use a deep neural network trained using the NoGAN approach, adapting the popular DeOldify architecture used for colorization. We show how the proposed method can be applied both to image and video compression artifact removal and restoration.
Adversarial Reinforcement Learning in Dynamic Channel Access and Power Control

Deep reinforcement learning (DRL) has recently been used to perform efficient resource allocation in wireless communications. In this paper, the vulnerabilities of such DRL agents to adversarial attacks is studied. In particular, we consider multiple DRL agents that perform both dynamic channel access and power control in wireless interference channels. For these victim DRL agents, we design a jammer, which is also a DRL agent. We Propose an adversarial jamming attack scheme that utilizes a listening phase and significantly degrades the users' sum rate. Subsequently, we develop an ensemble policy defense strategy against such a jamming atta.ck.er by reloa.di.ng models (saved during retraining) that have minimum transition correlation.
An Investigation of End-to-End Models for Robust Speech Recognition

End-to-end models for robust automatic speech recognition (ASR) have not been sufficiently well-explored in prior work. With end-to-end models, one could choose to preprocess the input speech using speech enhancement techniques and train the model using enhanced speech. Another alternative is to pass the noisy speech as input and modify the model architecture to adapt to noisy speech. A systematic comparison of these two approaches for end-to-end robust ASR has not been attempted before. We address this gap and present a detailed comparison of speech enhancement-based techniques and three different model-based adaptation techniques covering data augmentation, multi-task learning, and adversarial learning for robust ASR. While adversarial learning is the best-performing technique on certain noise types, it comes at the cost of degrading clean speech WER. On other relatively stationary noise types, a new speech enhancement technique outperformed all the model-based adaptation techniques. This suggests that knowledge of the underlying noise type can meaningfully inform the choice of adaptation technique.
Making Sense of 3D Modelling and 3D Printing Activities of Young People

Physical space and materials we work with, as well as people we interact with affect the design and making processes. These aspects in relation to 3D designing in alignment with 3D printing need considerable exploration within Child-Computer interaction (CCI) research community. We conducted our case study by collecting 9-full-day-observation and interview data and examining 3D modeling and 3D printing activities, as work duties of 15-17-years-old summer trainees organized at the university. We identified, inspired by nexus analysis, different discourses circulating around these activities of novice young people and how the discourses are intermingled with the space, the materials, and the task at hand in complex ways, constructing and shaping the experience of the young participants. In our research and design implications, by signifying the impact of the people, challenges, tasks, spaces and tools, we provide recommendations for maintaining children's engagement in digital fabrication, significantly 3D designing and 3D printing, activities.
Hierarchical Speaker-Aware Sequence-to-Sequence Model for Dialogue Summarization

Traditional document summarization models cannot handle dialogue summarization tasks perfectly. In situations with multiple speakers and complex personal pronouns referential relationships in the conversation. The predicted summaries of these models are always full of personal pronoun confusion. In this paper, we propose a hierarchical transformer-based model for dialogue summarization. It encodes dialogues from words to utterances and distinguishes the relationships between speakers and their corresponding personal pronouns clearly. In such a from-coarse-to-fine procedure, our model can generate summaries more accurately and relieve the confusion of personal pronouns. Experiments are based on a dialogue summarization dataset SAMsum, and the results show that the proposed model achieved a comparable result against other strong baselines. Empirical experiments have shown that our method can relieve the confusion of personal pronouns in predicted summaries.
Hybrid Quantum Network for classification of finance and MNIST data

In the ongoing era of noisy intermediate scaled quantum computers, one of the possible applications to search for an advantage of quantum computing is machine learning. Here we report about an approach, where a hybrid quantumclassical network is applied to classify non-trivial datasets (finance and MNIST data). In comparison to a pure classical network, we find an advantage when looking at several performance measures. However, as in classical machine learning problems around overfitting the dataset arise. Therefore, we outline the path to future research, which has to be done in the field of (hybrid) quantum networks.
Assessment of the Simulator Sickness Questionnaire for Omnidirectional Videos

Virtual Reality/360° videos provide an immersive experience to users. Besides this, 360° videos may lead to an undesirable effect when consumed with Head-Mounted Displays (HMDs), referred to as simulator sickness/cybersickness. The Simulator Sickness Questionnaire (SSQ) is the most widely used questionnaire for the assessment of simulator sickness. Since the SSQ with its 16 questions was not designed for 360° video related studies, our research hypothesis in this paper was that it may be simplified to enable more efficient testing for 360° video. Hence, we evaluate the SSQ to reduce the number of questions asked from subjects, based on six different previously conducted studies. We derive the reduced set of questions from the SSQ using Principal Component Analysis (PCA) for each test. Pearson Correlation is analysed to compare the relation of all obtained reduced questionnaires as well as two further variants of SSQ reported in the literature, namely Virtual Reality Sickness Questionnaire (VRSQ) and Cybersickness Questionnaire (CSQ). Our analysis suggests that a reduced questionnaire with 9 out of 16 questions yields the best agreement with the initial SSQ, with less than 44% of the initial questions. Exploratory Factor Analysis (EFA) shows that the nine symptom-related attributes determined as relevant by PCA also appear to be sufficient to represent the three dimensions resulting from EFA, namely, Uneasiness, Visual Discomfort and Loss of Balance. The simplified version of the SSQ has the potential to be more efficiently used than the initial SSQ for 360° video by focusing on the questions that are most relevant for individuals, shortening the required testing time.
LightTouch Gadgets: Extending Interactions on Capacitive Touchscreens by Converting Light Emission to Touch Inputs

We present LightTouch, a 3D-printed passive gadget to enhance touch interactions on unmodified capacitive touchscreens. The LightTouch gadgets simulate finger operations such as tapping, swiping, and multi-touch gestures by means of conductive materials and light-dependent resistors (LDR) embedded in the object. The touchscreen emits visible light and the LDR senses the level of this light, which changes its resistance value. By controlling the screen brightness, it intentionally connects or disconnects the path between the GND and the touchscreen, thus allowing the touch inputs to be controlled. In contrast to conventional physical extensions for touchscreens, our technique requires neither continuous finger contact on the conductive part nor the use of batteries. As such, it opens up new possibilities for touchscreen interactions beyond the simple automation of touch inputs, such as establishing a communication channel between devices, enhancing the trackability of tangibles, and inter-application operations.
Multi-Resolution Fusion and Multi-scale Input Priors Based Crowd Counting

Crowd counting in still images is a challenging problem in practice due to huge crowd-density variations, large perspective changes, severe occlusion, and variable lighting conditions. The state-of-the-art patch rescaling module (PRM) based approaches prove to be very effective in improving the crowd counting performance. However, the PRM module requires an additional and compromising crowd-density classification process. To address these issues and challenges, the paper proposes a new multi-resolution fusion based end-to-end crowd counting network. It employs three deep-layers based columns/branches, each catering the respective crowd-density scale. These columns regularly fuse (share) the information with each other. The network is divided into three phases with each phase containing one or more columns. Three input priors are introduced to serve as an efficient and effective alternative to the PRM module, without requiring any additional classification operations. Along with the final crowd count regression head, the network also contains three auxiliary crowd estimation regression heads, which are strategically placed at each phase end to boost the overall performance. Comprehensive experiments on three benchmark datasets demonstrate that the proposed approach outperforms all the state-of-the-art models under the RMSE evaluation metric. The proposed approach also has better generalization capability with the best results during the cross-dataset experiments.
Characterizing Duplicate Bugs: An Empirical Analysis

Bug handling is an essential part of the software development process. Ideally, in a bug tracking system, bugs are reported, fixed, verified, and closed. In some cases, bugs have to be reopened mostly due to an incorrect fix. However, instead of reopening the existing bug report, users may submit a new report on a previously reported bug, which causes duplicate bug reports. Additionally, users might report duplicate bugs if they are unable to reopen the previously reported bugs due to the bug being unresolved (i.e., in progress) and when they miss previously reported bug reports. These duplicate bug reports may cost extra maintenance efforts in triaging and fixing bugs.There have been several studies on characterizing reopened bugs and duplicate bug reports, however, to the best of our knowledge, there has been no prior work on understanding the dynamics of their intersection, which is missed reopen bugs. Our study is based on analyzing the differences between duplicate and non-duplicate bugs, and further categorizing the duplicated bugs. In this regard, we categorize duplicate bugs according to their creation time with respect to their master's resolution status as Master-Unresolved bugs and Master-Resolved (Missed Reopen bugs) to distinguish their properties. We compare these two different types of bugs in terms of various aspects such as their relationships to their master bugs, bug surface time, bug fix time, bug's severity, and the number of users involved. We perform case studies using the Eclipse and Mozilla projects' bug repositories that include more than 165,500 and 394,000 bug reports respectively.
The Impact of Focus and Context Visualization Techniques on Depth Perception in Optical See-Through Head-Mounted Displays

Estimating the depth of virtual content has proven to be a challenging task in Augmented Reality (AR) applications. Existing studies have shown that the visual system makes use of multiple depth cues to infer the distance of objects, occlusion being one of the most important ones. The ability to generate appropriate occlusions becomes particularly important for AR applications that require the visualization of augmented objects placed below a real surface. Examples of these applications are medical scenarios in which the visualization of anatomical information needs to be observed within the patient's body. In this regard, existing works have proposed several focus and context (F+C) approaches to aid users in visualizing this content using Video See-Through (VST) Head-Mounted Displays (HMDs). However, the implementation of these approaches in Optical See-Through (OST) HMDs remains an open question due to the additive characteristics of the display technology. In this article, we, for the first time, design and conduct a user study that compares depth estimation between VST and OST HMDs using existing in-situ visualization methods. Our results show that these visualizations cannot be directly transferred to OST displays without increasing error in depth perception tasks. To tackle this gap, we perform a structured decomposition of the visual properties of AR F+C methods to find best-performing combinations. We propose the use of chromatic shadows and hatching approaches transferred from computer graphics. In a second study, we perform a factorized analysis of these combinations, showing that varying the shading type and using colored shadows can lead to better depth estimation when using OST HMDs.
A Method for Scaling Window Sidelobe Magnitude

Many types of windows have been designed in the past decades for various applications. Each window type has its own specific characteristics. In this letter, we present a general technique for trading off main lobe width against sidelobe magnitude for any arbitrary window while keeping the number of sidelobe peaks and their relative magnitudes unchanged although their exact locations and magnitudes are changed.
Analysis on data deduplication techniques of storage of big data in cloud

As nowadays, many devices are connected to the internet (Thing continuum), and many businesses deal with a huge amount of data, digital data growth is exponentially increased. Cloud computing is the optimal technology that provides many computing resources, especially storage for Big data. Cloud offers the best storage management to back up the big data from IoT, business, enterprise, or government. All data owners want the protection of their own data, so they encrypt the data before outsourcing data in clouds. As many users avail cloud storage, different users may outsource the same data with different encryption techniques, and it results in data duplication or data redundancy. Although cloud computing offers a huge amount of storage space, data duplication decreases the efficiency and performance of cloud storage, and also it results in poor data management and the requirement of high bandwidth. The deduplication technique is used to manage data duplication in clouds. Although there are some deduplication approaches used to avoid data redundancy, still they have lack efficiency. The main aim of this paper is to obtain sufficient knowledge and a good idea about deduplication techniques by surveying existing approaches and this work may help the researcher and practitioner for their future research in developing efficient cloud storage management techniques.
Exploring Bi-Directional Pinpointing Techniques for Cross-Reality Collaboration

Virtual Reality (VR) technology enables users to immerse themselves in artificial worlds. However, it isolates users from the outside world and impedes them from collaborating with other users who might be outside of the VR experience and vice-versa. We implemented two systems where we explore how such an external user in the real world can interact across realities with a user immersed in virtual reality, either locally or remotely, in order to to share pinpoint locations. In the first we investigate three cross-reality techniques for the external user to draw the attention of their VR counterpart on specific objects present in the virtual environment (Voice, Highlight, and Arrow). Participants performed better overall and preferred the Arrow technique, followed by the Highlight technique. In the second system we expand on these two techniques to explore an even starker cross-reality interaction between users in VR and users interacting via a tablet computer to direct each other to pinpoint objects in the scene. We adapted the previous two techniques and implemented two others (Vision cone, Pointing) that support bi-directional communication between users. When it comes to bi-directional pinpointing, VR users still showed preference for the Arrow technique (now described as Pointing in Giant mode), while mobile users were split between the Vision cone and the Highlight techniques.
MomentMeld: AI-augmented Mobile Photographic Memento towards Mutually Stimulatory Inter-generational Interaction

Aging often comes with declining social interaction, a known adversarial factor impacting the life satisfaction of senior population. Such decline appears even in family–a permanent social circle, as their adult children eventually go independent. We present MomentMeld, an AI-powered, cloud-backed mobile application that blends with everyday routine and naturally encourages rich and frequent inter-generational interactions in a family, especially those between the senior generation and their adult children. Firstly, we design a photographic interaction aid called mutually stimulatory memento, which is a cross-generational juxtaposition of semantically related photos to bring natural arousal of context-specific inter-generational empathy and reminiscence. Secondly, we build comprehensive ensemble AI models consisting of various deep neural networks and a runtime system that automates the creation of mutually stimulatory memento on top of the user’s usual photo-taking routines. We deploy MomentMeld in-the-wild with six families for an eight-week period, and discuss the key findings and further implications.
Deep Learning assisted Cross-Family Profiled Side-Channel Attacks using Transfer Learning

Side-channel analysis (SCA) utilizing the power consumption of a device has proved to be an efficient technique for recovering secret keys exploiting the implementation vulnerability of mathematically secure cryptographic algorithms. Recently, Deep Learning-based profiled SCA (DL-SCA) has gained popularity, where an adversary trains a deep learning model using profiled traces obtained from a dummy device (a device that is similar to the target device) and uses the trained model to retrieve the secret key from the target device. However, for efficient key recovery from the target device, training of such a model requires a large number of profiled traces from the dummy device and extensive training time. In this paper, we propose TranSCA, a new DL-SCA strategy that tries to address the issue. TranSCA works in three steps - an adversary (1) performs a one-time training of a base model using profiled traces from any device, (2) fine-tunes the parameters of the base model using significantly less profiled traces from a dummy device with the aid of transfer learning strategy in lesser time than training from scratch, and (3) uses the fine-tuned model to attack the target device. We validate TranSCA on simulated power traces created to represent different FPGA families. Experimental results show that the transfer learning strategy makes it possible to attack a new device from the knowledge of another device even if the new device belongs to a different family. Also, TranSCA requires very few power traces from the dummy device compared to when applying DL-SCA without any previous knowledge.
Design and Development of Dual Axis Solar Panel Tracking System for Normalized Performance Enhancement of Solar Panel

In today’s era, our life is dependent on energy. Nation’s development is somehow associated with the availability of energy. Solar energy is the most eminent and renewable & cleanest sources of energy. It can be easily harnessed with the help of solar photovoltaic (PV) panels. But, we mainly observe that most of the solar panels are positioned at fixed angles. In order to maximize the amount of solar radiation collected by a solar panel, we use solar tracking device whose function is to follow the sun orthogonally throughout the day which enhances the energy capacity of the system. This paper comprises of development and design of dual axis solar panel tracking system & experimental study of dual axis solar tracker compared to fix position solar panel in terms of performance enhancement. The tracking mechanism of the sun requires light dependent resistor (LDR) as sensor to sense the maximum light availability & two servomotor for two axis movement (i.e., vertical and horizontal) to direct the position of solar panel. The software part is done by the help of written code using an Arduino Uno controller.
Semantic-Aware Context Aggregation for Image Inpainting

Recent attention-based image inpainting methods have made inspiring progress by propagating distant contextual information into holes. However, they tend to generate blurry contents since the propagation process is always misled by preliminarily-recovered holes features which are not well-inferred. To handle this problem, we propose a novel semantic-aware context aggregation module (SACA) that aggregates distant contextual information from a semantic perspective by exploiting the internal semantic similarity of the input feature map. Compared with existing attention mechanisms that model the relation of all pixel-pairs, SACA can suppress the impact of misleading holes features in context aggregation and significantly reduce computation burden by learning the relation between pixels and semantics. Also, we apply SACA to both high-level and low-level feature maps in our model for generating both semantically and visually plausible results. Extensive experiments on Outdoor Scenes, CelebA and Paris StreetView datasets validate the superiority of our method compared with existing methods.
Comparison of Image Encoder Architectures for Image Captioning

Image captioning is a fascinating and challenging task combining two interesting fields in computer science - Natural Language Processing and Computer Vision. Several approaches have been tried for this task. Many of these approaches are based on a combination of Convolutional Neural Networks(CNN’s) as encoders and Recurrent Neural Networks(RNN’s) as decoders. This paper compares two CNN-based encoders – (Vector Geometry Group) VGG16 and InceptionV3, which are used as encoders for encoding the input image features. The proposed research work uses the popular Flickr8k dataset and also compares among two different loss functions used with each of the above architectures- Categorical Cross Entropy loss function and Kullback-Leibler Divergence. Our main goal is to study the effect of the image encoder and the loss function on the image captioning task while keeping all other parameters the same.