Reviewerly/exHarmony huggingface dataset:
Stochastic Computing Implementation of Chaotic Systems

An exploding demand for processing capabilities related to the emergence of the Internet of Things (IoT), Artificial Intelligence (AI), and big data, has led to the quest for increasingly efficient ways to expeditiously process the rapidly increasing amount of data. These ways include different approaches like improved devices capable of going further in the more Moore path but also new devices and architectures capable of going beyond Moore and getting more than Moore. Among the solutions being proposed, Stochastic Computing has positioned itself as a very reasonable alternative for low-power, low-area, low-speed, and adjustable precision calculations—four key-points beneficial to edge computing. On the other hand, chaotic circuits and systems appear to be an attractive solution for (low-power, green) secure data transmission in the frame of edge computing and IoT in general. Classical implementations of this class of circuits require intensive and precise calculations. This paper discusses the use of the Stochastic Computing (SC) framework for the implementation of nonlinear systems, showing that it can provide results comparable to those of classical integration, with much simpler hardware, paving the way for relevant applications.
A two-phase virtual machine placement policy for data-intensive applications in cloud

Cloud computing and big data are two technologies whose combination can yield many benefits and challenges. One of the most significant challenges is the traffic produced by data-intensive jobs within a data center. A possible way to manage the produced traffic is optimizing the placement of virtual machines (VM) on hosts. However, placing VMs compactly to reduce the communication cost can negatively impact on other aspects such as host utilization and load balancing. In this paper, we aim to make a balance between optimizing the host utilization and the communication cost while considering load balancing. We investigate the VM placement problem by modeling it as the minimum weight K-vertex-connected induced subgraph. We prove the NP-Hardness of the problem and propose a novel two-phase strategy for placing VMs on hosts. At first phase, in order to balance traffic and workload among racks, we rank all racks using a fuzzy inference system and select the best ones based on a linear programming model. At second phase, we introduce a novel greedy algorithm to assign each VM to a host regarding a proposed communication cost metric. We evaluate our approach using CloudSim simulator whose results show our two-phase strategy is able to make a balance between host utilization and network traffic. It keeps more than 80 percent of the traffic rack local while reduces the average network link saturation to almost 40 percent with a low variance. Besides, the number of used hosts increase linearly by increasing the number of VMs which leads to a higher host utilization.
aiTPR: Attribute Interaction-Tensor Product Representation for Image Caption

Region visual features enhance the generative capability of the machines based on features. However, they lack proper interaction-based attentional perceptions and end up with biased or uncorrelated sentences or pieces of misinformation. In this work, we propose Attribute Interaction-Tensor Product Representation (aiTPR), which is a convenient way of gathering more information through orthogonal combination and learning the interactions as physical entities (tensors) and improving the captions. Compared to previous works, where features add up to undefined feature spaces, TPR helps maintain sanity in combinations, and orthogonality helps define familiar spaces. We have introduced a new concept layer that defines the objects and their interactions that can play a crucial role in determining different descriptions. The interaction portions have contributed heavily to better caption quality and have out-performed various previous works on this domain and MSCOCO dataset. For the first time, we introduced the notion of combining regional image features and abstracted interaction likelihood embedding for image captioning.
Diminished Reality for Close Quarters Robotic Telemanipulation

In robot telemanipulation tasks, the robot can sometimes occlude a target object from the user's view. We investigate the potential of diminished reality to address this problem. Our method uses an optical see-through head-mounted display to create a diminished reality illusion that the robot is transparent, allowing users to see occluded areas behind the robot. To investigate benefits and drawbacks of robot transparency, we conducted a user study that examined diminished reality in a simple telemanipulation task involving both occluded and unoccluded targets. We discovered that while these visualizations show promise for reducing user effort, there are drawbacks in terms of task efficiency and user preference. We identified several friction points in user experiences with diminished reality interfaces. Finally, we describe several design trade-offs among different visualization options.
Efficient Oblivious Query Processing for Range and kNN Queries

Increasingly, individuals and companies adopt a cloud service provider as a primary data and IT infrastructure platform. The remote access of the data inevitably brings the issue of trust. Data encryption is necessary to keep sensitive information secure and private on the cloud. Yet adversaries can still learn valuable information regarding encrypted data by observing data access patterns. To solve such problem, Oblivious RAMs (ORAMs) are proposed to completely hide access patterns. However, most ORAM constructions are expensive and not suitable to deploy in a database for supporting query processing over large data. Furthermore, an ORAM processes queries <i>synchronously</i> , hence, does not provide high throughput for <i>concurrent query processing</i> . In this article, we design a practical <i>oblivious query processing framework</i> to enable efficient query processing over a cloud database. In particular, we focus on processing multiple range and <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> NN queries <i>asynchronously and concurrently with high throughput</i> . The key idea is to integrate indices into ORAM which leverages a suite of optimization techniques (e.g., oblivious batch processing and caching). The effectiveness and efficiency of our oblivious query processing framework is demonstrated through extensive evaluations over large datasets. Our construction shows an order of magnitude speedup in comparison with other baselines.
Comparing Understanding and Memorization in Physicalization and VR Visualization

We investigate whether presenting data in a VR visualization or as a physicalization impacts understanding and recollection. Two equivalent representations of the same data set, one in physical form and one in VR, were created. Participants answered understanding questions while they had access to the model, and were subsequently asked about the data after the model was removed. We recorded time needed to answer understanding questions and correctness rates for recollection questions. The results favour the conclusion that the virtual representation and the technical VR setup significantly inhibit participants' ability to work with the data set. Reflecting on our study setup and participants' comments, we discuss recommendations for future studies aiming at a systematic and comprehensive comparison of the differences in interacting with purely virtual and with physical data representations.
Multi-input Functional Encryption: Efficient Applications from Symmetric Primitives

Functional Encryption (FE) allows users who hold a specific secret key (known as the functional key) to learn a specific function of encrypted data whilst learning nothing about the content of the underlying data. Considering this functionality and the fact that the field of FE is still in its infancy, we sought a route to apply this potent tool to design efficient applications. To this end, we first built a symmetric FE scheme for the l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> norm of a vector space, which allows us to compute the sum of the components of an encrypted vector. Then, we utilized our construction, to design an Order-Revealing Encryption (ORE) scheme and a privately encrypted database. While there is room for improvement in our schemes, this work is among the first attempts that seek to utilize FE for the solution of practical problems that can have a tangible effect on people's daily lives.
Identifying comparable entities from online question-answering contents

As an emerging platform, online question-answering (Q&A) communities are becoming valuable corpus sources that reflect the opinions of expert consumers on comparable entities. In this study, a novel method-Identifying Comparable entities from online Question-Answering contents (ICQA)-is proposed to effectively extract comparable entities from online Q&A communities. In ICQA, candidate entities are firstly extracted by utilizing the advantages of pattern-based methods and supervised learning-based methods. An entity comparison network is then built by considering the credibility difference and entity relatedness in Q&A contents to analyze competitiveness between entities and extract comparable entities from candidate entities accordingly. Thus, within the same method framework, comparable entities and their competitiveness ranks can be simultaneously identified for a given entity specified by managers or consumers. By taking the automotive industry as the experimental background, the effectiveness of ICQA is demonstrated with comprehensive experiments regarding comparable entity identification and comparable entity ranking. Experimental results demonstrate that compared with state-of-the-art methods, ICQA can identify more accurate and broader comparable entities, find novel entities ignored by other methods, and provide a more solid comparable entity rank-features that are deemed desirable and applicable for both managers and consumers in making rational decisions based on online Q&A contents.
The Body Beyond Movement: (Missed) Opportunities to Engage with Contemporary Dance in HCI

This paper argues that a significant paradigm change in contemporary dance can offer further opportunities for HCI researchers interested in embodied interaction and interactive system design. Based on the analysis of 42 HCI papers in our data set, resulting from searches in two computing research libraries, we suggest seven thematic categories that reflect how HCI researchers have been engaging with contemporary dance. Moreover, we propose a standardized usage of contemporary dance terminology in HCI literature, and discuss the current state of engagement with publications from the field of performance theory. We identify three opportunities for HCI, which can arise through further engagement with the knowledge produced in contemporary dance and performance: to engage with the field of embodied interaction from the perspective of performance research and theory; to employ contemporary dance methods and practices in HCI research; and to integrate contemporary dance choreographers and performers as researchers in interdisciplinary projects.
VIMA: Modeling and Visualization of High Dimensional Machine Sensor Data Leveraging Multiple Sources of Domain Knowledge

The highly integrated design of the electrified powertrain creates new challenges in the holistic testing of high-quality standards. Particularly test technicians face the challenge, that lots of machine sensor data is recorded during these tests that needs to be analyzed. We present VIMA, a VA system that processes high dimensional machine-sensor data to support test technicians with these analyses. VIMA makes use of the concept of interactive labeling to train machine learning models and the process model of knowledge creation in visual analytics to create new knowledge through the interaction with the system. Its usefulness is demonstrated in a qualitative user study with four test technicians. Results indicate that through VIMA, previously undetected abnormal parts, could be identified. Additionally, a model trained with labels generated through VIMA, was deployed on a test station, that outperforms the current testing procedure, in detecting increased backlashes and improved the test benches output by 15%.
Deep Domain Adaptation Approach for Classification of Disaster Images

In the last decade, emergency responders, government organizations and disaster response agencies have certainly acknowledged that microblogging platforms such as Twitter and Instagram can be an important source of actionable information at the time of disaster. However, researchers feel that analyzing social media data for disasters using supervised machine learning algorithms is still a challenge. During the first few hours of any disaster when labeled data is not available, the learning process gets much delayed. The first objective of this study is to explore the domain adaptation techniques by making use of labeled data of some previous disaster along with the abundance of unlabeled data that is available for the current disaster. The second objective is to apply these domain adaptation techniques on disaster-related imagery data from the microblogging platforms since imagery data has largely been unexplored as compared to textual content. To achieve these objectives, domain adaptation methods would be applied to classify the images of an ongoing disaster as informative versus non-informative. This study, for the first time, proposes a semi-supervised domain adaptation technique where the classifier is trained on three types of data, labeled data of the previous disaster, unlabeled data of current disaster and a small batch of labeled data of current disaster. Our experiments have been performed on Twitter images corresponding to three disasters. The experimental results show that there is an improvement in the accuracy of the classification model if a small batch of labeled target images is also added along with the unlabeled target images and labeled source images at the time of training. The experiment aims to make the best use of the labeled data of a previous disaster to analyze the current disaster without any delay for better response and recovery.
Joint User Scheduling and Resource Allocation for Federated Learning over Wireless Networks

Federated learning (FL) is a decentralized algorithm that can train a globally shared model without the requirement to send the raw data to a centralized server by user equipments (UEs). Consider the UEs with non-independently and identically distributed (non-IID) data, heterogeneous computational capabilities and wireless channel conditions, FL becomes unproductive over a wireless network. In this paper, we jointly optimize the user scheduling policy and resource allocation to achieve a tradeoff among the fairness of user scheduling, the accuracy of FL, training time, and energy consumption of UEs. The optimization problem is formulated as a Markov Decision Process considering the potential impact of current scheduling on subsequent training and available resources. To solve the problem, a policy network is trained based on an actor-critic deep reinforcement learning framework. Simulation results show that the proposed user scheduling and resource allocation policy reduces the time and energy cost of the training process while improving the freshness of local update and performance on the 20% worst UEs compared with random user selection and resource allocation policy.
Non-Factoid Answer Selection in Indonesian Science Question Answering System using Long Short-Term Memory (LSTM)

Understanding natural science has become vital since it has effects on various facets of daily necessity. However, the lack of comprehensive information sources has led to difficulties in finding information quickly and efficiently, emphasising the need to develop a question answering system. The type of question utilised for question answering systems that specifically deal with science topics is referred to as a non-factoid question. In this research, the question answering system utilised Long Short-Term Memory (LSTM) model for answer selection problems. Testing was done for the following LSTM hyper-parameters: dropouts, learning rates, number of hidden units, size of the answer pool, and margins. Data utilised in this study consisted of 400 pairs of questions and answers on science topics that were obtained from Wikipedia. The highest average values were 90.06% for Mean Reciprocal Rank (MRR) and 78.69% for Mean Average Precision (MAP), which were achieved when using a dropout value of 0.2, 50 hidden units, learning rate value of 0.05, a margin of 0.1, and an answer pool size of 20.
BVTNet: Multi-label Multi-class Fusion of Visible and Thermal Camera for Free Space and Pedestrian Segmentation

Deep learning-based visible camera semantic segmentation report state-of-the-art segmentation accuracy. However, this approach is limited by the visible camera’s susceptibility to varying illumination and environmental conditions. One approach to address this limitation is visible and thermal camera-based sensor fusion. Existing literature utilizes this sensor fusion approach for object segmentation, but the approach’s application to free space segmentation has not been reported. Here, a multi-label multi-class visible-thermal camera learning framework, termed as the BVTNet, is proposed for the semantic segmentation of pedestrians and the free space. The BVTNet estimates the pedestrians and free space in an individual multi-class output branch. Additionally, the network also separately estimates the free space and pedestrian boundaries in another multi-class output branch. The boundary semantic segmentation is integrated within the full semantic segmentation framework in a post-processing step. The proposed framework is validated on the public MFNet dataset. A comparative analysis with baseline algorithms and ablation studies with BVTNet variants show that the proposed framework report state-of-the-art segmentation accuracy in real-time in challenging environmental conditions.
Look and Listen: A Multi-modality Late Fusion Approach to Scene Classification for Autonomous Machines

The novelty of this study consists in a multi-modality approach to scene classification, where image and audio complement each other in a process of deep late fusion. The approach is demonstrated on a difficult classification problem, consisting of two synchronised and balanced datasets of 16,000 data objects, encompassing 4.4 hours of video of 8 environments with varying degrees of similarity. We first extract video frames and accompanying audio at one second intervals. The image and the audio datasets are first classified independently, using a fine-tuned VGG16 and an evolutionary optimised deep neural network, with accuracies of 89.27% and 93.72%, respectively. This is followed by late fusion of the two neural networks to enable a higher order function, leading to accuracy of 96.81% in this multi-modality classifier with synchronised video frames and audio clips. The tertiary neural network implemented for late fusion outperforms classical state-of-the-art classifiers by around 3% when the two primary networks are considered as feature generators. We show that situations where a single-modality may be confused by anomalous data points are now corrected through an emerging higher order integration. Prominent examples include a water feature in a city misclassified as a river by the audio classifier alone and a densely crowded street misclassified as a forest by the image classifier alone. Both are examples which are correctly classified by our multi-modality approach.
Auto Whole Heart Segmentation from CT images Using an Improved Unet-GAN

Abstract The development of deep learning is rapid, and convolutional neural network especially U-Net plays an important role in the medical image segmentation tasks, which is lack of data. Lots of models and methods are proposed to segment cardiac CT images. In this paper, we proposed a new network architecture. The network architecture is based on a traditional architecture called conditional generative adversarial network (cGAN), where R2U-Net acts as the generative network and FCN as the discriminative network. The performance of this model running on the dataset from MICCAI 2017 Multi-Modality Whole Heart Segmentation Challenge (MM-WHS 2017) is good.
Research on Image Recognition Based on Improved ResNet

With the increasingly easy access to data, the increasing amount of data and the improvement of computer performance have promoted the development of deep learning. To improve the performance, the neural network has been continuously deepened, the proposal of residual network makes great contribution to the breakthrough of bottleneck of traditional network. Subsequently, many improvements have been made to the internal structure of the residual network, such as wide residual network, pre-activated residual network, etc. However, there are some problems such as network, redundancy, poor performance on small data sets, and high requirement on computer performance. For these problems, this paper use to replace the convolution in the residual network, reduce the number of model parameters, increases the number of shortcut connection, and improves the flow of network information. Moreover, experiments are conducted on cifar-10 and cifar-100. The experiment shows that the error rate drops to 4.89% and 23.63%, which proves that our modification gets better results.
Reliability of eXplainable Artificial Intelligence in Adversarial Perturbation Scenarios

Nowadays, Deep Neural Networks (DNNs) are widely adopted in several fields, including critical systems, medicine, self-guided vehicles etc. Among the reasons sustaining this spread there are the higher generalisation ability and performance levels that DNNs usually obtain when compared to classical machine learning models. Nonetheless, their black-box nature raises ethical and judicial concerns that lead to a lack of confidence in their use in sensitive applications. For this reason, recently there has been a growing interest in eXplainable Artificial Intelligence (XAI), a field providing tools, techniques and algorithms designed to generate interpretable explanations, comprehensible to humans, for the decisions made by a machine learning model. However, it has been demonstrated that DNNs are susceptible to Adversarial Perturbations (APs), namely procedures intended to mislead a target model by means of an almost imperceptible noise. The relation existing between XAI and AP is extremely of interest since it can help improve trustworthiness in AI-based systems. To this aim, it is important to increase awareness of the risks associated with the use of XAI in critical contexts, in a world where APs are present and easy to perform. On this line, we quantitatively analyse the impact that APs have on XAI in terms of differences in the explainability maps. Since this work wants to be just an intuitive proof-of-concept, the aforementioned experiments are run in a fashion easy to understand and to quantify, by using publicly available dataset and algorithms. Results show that AP can strongly affect the XAI outcomes, even in the case of a failed attack, highlighting the need for further research in this field.