Reviewerly/exHarmony huggingface dataset:
A Commodity Review Sentiment Analysis Based on BERT-CNN Model

Sentiment analysis through the investigation on commodity reviews will be of great importance to commodity quality improvement of the seller and subsequent consumption choice of buyers. The accuracy of the existing sentiment analysis models remains to be further improved, so a BERT-CNN sentiment analysis model, an improvement of the original BERT model, was proposed in this paper in order to improve the accuracy of commodity sentiment analysis. Firstly, BERT model was constructed, and then a representation layer was input into the model to encode the review texts; after then, CNN semantic extraction layer was utilized to extract local features of the review text vectors, BERT semantic extraction layer to extract global features of the review text vectors and semantic connection layer to fuse features extracted by the two complementary models; in the end, a sentiment analysis of online commodity reviews was performed via the sentiment classification layer. The experimental results indicated that in comparison with BERT and CNN models, F1 value of BERTCNN model was elevated by about 14.4% and 17.4%, respectively.
Dense Video Captioning Using Graph-Based Sentence Summarization

Recently, dense video captioning has made attractive progress in detecting and captioning all events in a long untrimmed video. Despite promising results were achieved, most existing methods do not sufficiently explore the scene evolution within an event temporal proposal for captioning, and therefore perform less satisfactorily when the scenes and objects change over a relatively long proposal. To address this problem, we propose a graph-based partition-and-summarization (GPaS) framework for dense video captioning within two stages. For the “partition” stage, a whole event proposal is split into short video segments for captioning at a finer level. For the “summarization” stage, the generated sentences carrying rich description information for each segment are summarized into one sentence to describe the whole event. We particularly focus on the “summarization” stage, and propose a framework that effectively exploits the relationship between semantic words for summarization. We achieve this goal by treating semantic words as the nodes in a graph and learning their interactions by coupling Graph Convolutional Network (GCN) and Long Short Term Memory (LSTM), with the aid of visual cues. Two schemes of GCN-LSTM Interaction (GLI) modules are proposed for seamless integration of GCN and LSTM. The effectiveness of our approach is demonstrated via an extensive comparison with the state-of-the-arts methods on the two benchmarks ActivityNet Captions dataset and YouCook II dataset.
Usability Principles for Augmented Reality based Kindergarten Applications

Augmented Reality (AR) overlays digital information over user's real world. Designing and developing AR applications is a difficult task. It involves user interface designing, content generation according to the target audience so that the application is easy to understand, interactive, consistent and ease to use. To design these applications there are many design principles evidenced in existing literature such as learnability, affordance, simplicity etc. However, there are no studies in the literature which define most appropriate usability principles/guidelines for designing mobile based AR applications for kindergarten kids. Age is a significant factor to be considered in developing applications for kids especially when it comes to interaction techniques to be used in learning applications. The kids may not easily use and interact with the applications developed for the adults. Therefore, there is a need to identify the principles that should be kept in mind while developing AR applications according to the kids. The aim of our research is to develop usability principles for mobile based applications for kindergarten kids using AR. We proposed 23 usability principles by studying the existing literature on usability, design principles for AR applications, guidelines/principles for tangible interfaces. These principles would help other researcher and developers to create more interactive, learnable and easy to understand AR applications for kids solving the identified usability problems.
Evaluation Goals for Online Process Mining: A Concept Drift Perspective

Online process mining refers to a class of techniques for analyzing in real-time event streams generated by the execution 5 of business processes.These techniques are crucial in the reactive monitoring of business processes, timely resource allocation and 6 detection/prevention of dysfunctional behavior.Many interesting advances have been made by the research community in recent 7 years, but there is no consensus on the exact set of properties these techniques have to achieve.This article fills the gap by identifying 8 a set of evaluation goals for online process mining and examining their fulfillment in the state of the art.We discuss parameters and 9 techniques regulating the balance between conflicting goals and outline research needed for their improvement.Concept drift detection 10 is crucial in this sense but, as demonstrated by our experiments, it is only partially supported by current solutions.Q1 11
Adaptive level of autonomy for human-UAVs collaborative surveillance using situated fuzzy cognitive maps

Collaborating with a squad of Unmanned Aerial Vehicles (UAVs) is challenging for a human operator in a cooperative surveillance task. In this paper, we propose a cognitive model that can dynamically adjust the Levels of Autonomy (LOA) of the human-UAVs team according to the changes in task complexity and human cognitive states. Specifically, we use the Situated Fuzzy Cognitive Map (SiFCM) to model the relations among tasks, situations, human states and LOA. A recurrent structure has been used to learn the strategy of adjusting the LOA, while the collaboration task is separated into a perception routine and a control routine. Experiment results have shown that the workload of the human operator is well balanced with the task efficiency.
Image super-resolution reconstruction based on sparse representation and deep learning

Super-resolution reconstruction technology has important scientific significance and application value in the field of image processing by performing image restoration processing on one or more low-resolution images to improve image spatial resolution. Based on the SCSR algorithm and VDSR network, in order to further improve the image reconstruction quality, an image super-resolution reconstruction algorithm combined with multi-residual network and multi-feature SCSR(MRMFSCSR) is proposed. Firstly, at the sparse reconstruction stage, according to the characteristics of image blocks, our algorithm extracts the contour features of non-flat blocks by NSCT transform, extracts the texture features of flat blocks by Gabor transform, then obtains the reconstructed high-resolution (HR) images by using sparse models. Secondly, according to improve the VDSR deep network and introduce the feature fusion idea, the multi-residual network structure (MR) is designed. The reconstructed HR image obtained by the sparse reconstruction stage is used as the input of the MR network structure to optimize the high-frequency detail residual information. Finally, we can obtain a higher quality super-resolution image compared with the SCSR algorithm and the VDSR algorithm.
Understanding the effect of data center resource disaggregation on production DBMSs

Resource disaggregation is a new architecture for data centers in which resources like memory and storage are decoupled from the CPU, managed independently, and connected through a high-speed network. Recent work has shown that although disaggregated data centers (DDCs) provide operational benefits, applications running on DDCs experience degraded performance due to extra network latency between the CPU and their working sets in main memory. DBMSs are an interesting case study for DDCs for two main reasons: (1) DBMSs normally process data-intensive workloads and require data movement between different resource components; and (2) disaggregation drastically changes the assumption that DBMSs can rely on their own internal resource management. We take the first step to thoroughly evaluate the query execution performance of production DBMSs in disaggregated data centers. We evaluate two popular open-source DBMSs (MonetDB and PostgreSQL) and test their performance with the TPC-H benchmark in a recently released operating system for resource disaggregation. We evaluate these DBMSs with various configurations and compare their performance with that of single-machine Linux with the same hardware resources. Our results confirm that significant performance degradation does occur, but, perhaps surprisingly, we also find settings in which the degradation is minor or where DDCs actually improve performance.
Variational Domain Adversarial Learning With Mutual Information Maximization for Speaker Verification

Domain mismatch is a common problem in speaker verification (SV) and often causes performance degradation. For the system relying on the Gaussian PLDA backend to suppress the channel variability, the performance would be further limited if there is no Gaussianity constraint on the learned embeddings. This paper proposes an information-maximized variational domain adversarial neural network (InfoVDANN) that incorporates an InfoVAE into domain adversarial training (DAT) to reduce domain mismatch and simultaneously meet the Gaussianity requirement of the PLDA backend. Specifically, DAT is applied to produce speaker discriminative and domain-invariant features, while the InfoVAE performs variational regularization on the embedded features so that they follow a Gaussian distribution. Another benefit of the InfoVAE is that it avoids posterior collapse in VAEs by preserving the mutual information between the embedded features and the training set so that extra speaker information can be retained in the features. Experiments on both SRE16 and SRE18-CMN2 show that the InfoVDANN outperforms the recent VDANN, which suggests that increasing the mutual information between the embedded features and input features enables the InfoVDANN to extract extra speaker information that is otherwise not possible.
IoT-Inspired Smart Toilet System for Home-Based Urine Infection Prediction

The healthcare industry is the premier domain that has been significantly influenced by incorporation of Internet of Things (IoT) technology resulting in smart healthcare application. Inspired by the enormous potential of IoT technology, this research provides a framework for an IoT-based smart toilet system, which enables home-based determination of Urinary Infection (UI) efficaciously. The overall system comprises a four-layered architecture for monitoring and predicting infection in urine. The layers include the Urine Acquisition, Urine Analyzation, Temporal Extraction, and Temporal Prediction layers, which enable an individual to monitor his or her health on daily basis and predict UI so that precautionary measures can be taken at early stages. Moreover, probabilistic quantification of urine infection in the form of Degree of Infectiousness (DoI) and Infection Index Value (IIV) were performed for infection prediction based on a temporal Artificial Neural Network. In addition, the presence of UI is displayed to the user based on a Self-Organized Mapping technique. For validation purposes, numerous experimental simulations were performed on four individuals for 60 days. Results were compared with different state-of-the-art techniques for measuring the overall efficiency of the proposed system.
An In-Depth Empirical Investigation of State-of-the-Art Scheduling Approaches for Cloud Computing

Recently, Cloud computing has emerged as one of the widely used platforms to provide compute, storage and analytics services to end-users and organizations on a pay-as-you-use basis, with high agility, availability, scalability, and resiliency.This enables individuals and organizations to have access to a large pool of high processing resources without the need for establishing a high-performance computing (HPC) platform.From the past few years, task scheduling in Cloud computing is reckoned as eminent recourse for researchers.However, task scheduling is considered an NP-hard problem.In this research work, we investigate and empirically compare some of the most prominent state-of-the-art scheduling heuristics in terms of Makespan, Average resource utilization (ARUR), Throughput, and Energy consumption.The comparison is then extended by evaluating the approaches in terms of individual VM level load imbalance.After extensive simulation, the comparative analysis has revealed that Task Aware Scheduling Algorithm (TASA) and Proactive Simulation-based Scheduling and Load Balancing (PSSLB) outperformed as compared to the rest of the approaches and seems to be optimal choice keeping in view the trade-of between the complexities involved and the performance achieved concerning Makespan, Throughput, resource utilization, and Energy consumption.
IoT Services and Applications in Rehabilitation: An Interdisciplinary and Meta-Analysis Review

Internet of things (IoT) is a designation given to a technological system that can enhance possibilities of connectivity between people and things and has been showing to be an opportunity for developing and improving smart rehabilitation systems and helps in the e-Health area. Objective: to identify works involving IoT that deal with the development, architecture, application, implementation, use of technological equipment in the area of patient rehabilitation. Technology or Method: A systematic review based on Kitchenham's suggestions combined to the PRISMA protocol. The search strategy was carried out comprehensively in the IEEE Xplore Digital Library, Web of Science and Scopus databases with the data extraction method for assessment and analysis consist only of primary studies articles related to the IoT and Rehabilitation of patients. Results: We found 29 studies that addressed the research question, and all were classified based on scientific evidence. Conclusions: This systematic review presents the current state of the art on the IoT in health rehabilitation and identifies findings in interdisciplinary researches in different clinical cases with technological systems including wearable devices and cloud computing. The gaps in IoT for rehabilitation include the need for more clinical randomized controlled trials and longitudinal studies. Clinical Impact: This paper has an interdisciplinary feature and includes areas such as Internet of Things Information and Communication Technology with their application to the medical and rehabilitation domains.
NV-group

The advanced fabrics like NVIDIA NVLink are enabling the deployment of dense Graphics Processing Unit (GPU) systems such as DGX-2 and Summit. With the wide adoption of large-scale GPU-enabled systems for distributed deep learning (DL) training, it is vital to design efficient communication such as the Allreduce operation to achieve near-ideal speedup at scale. In this paper, we propose a link-efficient scheme through NVLink-aware cooperative reduction kernels to significantly accelerate Allreduce operations for distributed deep learning applications. By overlapping computation and communication and maximizing utilization of all available NVLinks between CPU and GPU, as well as among GPUs, we demonstrate 1.8X performance improvement of Allreduce on 1,536 GPUs compared to state-of-the-art GPU-Aware MPI and NVIDIA NCCL libraries. Finally, we demonstrate 93.9% and 89.7% scaling efficiency (i.e., 15X and 172X speedup) for training ResNet-50 models using TensorFlow on a 16-GPU DGX-2 node and on 192-GPUs of the Summit system, respectively. To the best of our knowledge, this is the first study that achieves near-ideal scaling efficiency for distributed DL training and deals with designs tailored for cutting-edge systems like DGX-2 and Summit clusters.
A Modified Lightweight PRESENT Cipher For IoT Security

Of late, the massive use of pervasive devices in the electronics field has raised the concerns about security. In embedded applications or IoT domain implementing a full-fledged cryptographic environment using conventional encryption algorithms would not be practical because of the constraints like power dissipation, area and speed. To overcome such barriers the focus is on lightweight cryptography. In this paper a new lightweight PRESENT cipher has been proposed which has modified the original PRESENT cipher by reducing encryption round, modifying the Key Register updating technique and adding a new layer in between S-box layer and P-layer of the existing encryption-decryption process. The key register is updated by encrypting its value by adding delta value function of TEA (Tiny encryption algorithm), which is another lightweight cipher. The addition of extra layer helps us to reduce the PRESENT round from 31 to 25 which is the minimum round required for security. The efficiency of the proposed algorithm is increased by encrypting the key register. The proposed algorithm proves its superiority by analyzing different software parameter analysis like N-gram, Non-Homogeneity, Frequency Distribution graph and Histogram.
Evaluating Creativity Support Tools in HCI Research

The design and development of Creativity Support Tools (CSTs) is of growing interest in research at the intersection of creativity and Human-Computer Interaction, and has been identified as a 'grand challenge for HCI'. While creativity research and HCI each have had long-standing discussions about---and rich toolboxes of---evaluation methodologies, the nascent field of CST evaluation has so far received little attention. We contribute a survey of 113 research papers that present and evaluate CSTs, and we offer recommendations for future CST evaluation. We center our discussion around six major points that researchers might consider: 1) Clearly define the goal of the CST; 2) link to theory to further understanding of usage of CSTs; 3) recruit domain experts, if applicable and feasible; 4) consider longitudinal, in-situ studies; 5) distinguish and decide whether to evaluate usability or creativity; and 6) as a community, help develop a toolbox for CST evaluation.
JeL

Bio-responsive immersive Virtual Reality can transform our interactions to bring awareness to our physiological rhythms fostering connection with our bodies, each other and nature. JeL is an immersive installation that aims to foster a feeling of connection through the process of breathing synchronization. Two immersants synchronize their breathing to fuel the growth of a coral-like structure that, together with the interactions of others, populates an initially empty coral reef. JeL is designed to support an intimate connection between users and with nature, sending a message about our collective capacity to care for the environment. JeL is an installation and research platform for exploring breathing synchronization and its effect on the feeling of connection. It was well received at a digital art festival where participants were able to relax and synchronize using the installation. Reflection on our design process and observations provides insights for the development of systems that promote connection.
Resolution Invariant Face Recognition Using a Distillation Approach

Modern face recognition systems extract face representations using deep neural networks (DNNs) and give excellent identification and verification results, when tested on high resolution (HR) images. However, the performance of such an algorithm degrades significantly for low resolution (LR) images. A straight forward solution could be to train a DNN, using simultaneously, high and low resolution face images. This approach yields a definite improvement at lower resolutions but suffers a performance degradation for high resolution images. To overcome this shortcoming, we propose to train a network using both HR and LR images under the guidance of a fixed network, pretrained on HR face images. The guidance is provided by minimising the KL-divergence between the output Softmax probabilities of the pretrained (i.e., Teacher) and trainable (i.e., Student) network as well as by sharing the Softmax weights between the two networks. The resulting solution is tested on down-sampled images from FaceScrub and MegaFace datasets and shows a consistent performance improvement across various resolutions. We also tested our proposed solution on standard LR benchmarks such as TinyFace and SCFace. Our algorithm consistently outperforms the state-of-the-art methods on these datasets, confirming the effectiveness and merits of the proposed method.
Generating post hoc review-based natural language justifications for recommender systems

In this article, we present a framework to build post hoc natural language justifications that supports the suggestions generated by a recommendation algorithm. Our methodology is based on the intuition that reviews’ excerpts contain much relevant information that can be used to justify a recommendation; thus, we propose a black-box explanation strategy that takes as input a recommended item and a set of reviews and builds as output a post hoc natural language justification which is completely independent of the underlying recommendation model. To validate our claims, we also introduce three different implementations of our conceptual framework: the first one uses natural language processing and sentiment analysis techniques to identify relevant and distinguishing aspects discussed in the reviews and combines reviews’ excerpts mentioning these aspects in a natural language justification which is presented to the target user. The second implementation extends the first one by introducing automatic aspect extraction and text summarization, which are exploited to generate a unique synthesis presenting the main characteristics of the item that is used as justification. Finally, the third implementation tackles the problem of generating a context-aware justification, that is to say, a justification that differs on varying of the different contextual situations, by automatically learning a lexicon for each contextual setting and by using such a lexicon to diversify the justifications. In the experimental evaluation, we carried out three user studies in different domains, and the results showed that our methodology is able to make the recommendation process more transparent, engaging and trustful for the users, thus confirming the validity of the intuitions behind this work.