Reviewerly/exHarmony huggingface dataset:
Multi-Task Multi-Head Attention Memory Network for Fine-Grained Sentiment Analysis

Sentiment analysis is widely applied in personalized recommendation, business reputation monitoring, and consumer-driven product design and quality improvement. Fine-grained sentiment analysis, aimed at directly predicting sentiment polarity for multiple pre-defined fine-grained categories in an end-to-end way without having to identify aspect words, is more flexible and effective for real world applications. Constructing high performance fine-grained sentiment analysis models requires the effective use of both shared document level features and category-specific features, which most existing multi-task models fail to accomplish. In this paper, we propose an effective multi-task neural network for fine-grained sentiment analysis, Multi-Task Multi-Head Attention Memory Network (MMAM). To make full use of the shared document level features and category-specific features, our framework adopts a multi-head document attention mechanism as the memory to encode shared document features, and a multi-task attention mechanism to extract category-specific features. Experiments on two Chinese language fine-grained sentiment analysis datasets in the Restaurant-domain and Automotive-domain demonstrate that our model consistently outperforms other compared fine-grained sentiment analysis models. We believe extracting and fully utilizing document level features to establish category-specific features is an effective approach to fine-grained sentiment analysis.
Universally Verifiable MPC and IRV Ballot Counting

We present a very simple universally verifiable MPC protocol. The first component is a threshold somewhat homomorphic cryptosystem that permits an arbitrary number of additions (in the source group), followed by a single multiplication, followed by an arbitrary number of additions in the target group. The second component is a black-box construction of universally verifiable distributed encryption switching between any public key encryption schemes supporting shared setup and key generation phases, as long as the schemes satisfy some natural additive-homomorphic properties. This allows us to switch back from the target group to the source group, and hence perform an arbitrary number of multiplications. The key generation algorithm of our prototypical cryptosystem, which is based upon concurrent verifiable secret sharing, permits robust re-construction of powers of a shared secret.
Refined Segmentation R-CNN: A Two-Stage Convolutional Neural Network for Punctate White Matter Lesion Segmentation in Preterm Infants

Accurate segmentation of punctate white matter lesion (PWML) in infantile brains by an automatic algorithm can reduce the potential risk of postnatal development. How to segment PWML effectively has become one of the active topics in medical image segmentation in recent years. In this paper, we construct an efficient two-stage PWML semantic segmentation network based on the characteristics of the lesion, called refined segmentation R-CNN (RS R-CNN). We propose a heuristic RPN (H-RPN) which can utilize surrounding information around the PWML for heuristic segmentation. Also, we design a lightweight segmentation network to segment the lesion in a fast way. Densely connected conditional random field (DCRF) is used to optimize the segmentation results. We only use T1w MRIs to segment PWMLs. The result shows that the lesion of ordinary size or even pixel size can be well segmented by our model. The Dice similarity coefficient reaches 0.6616, the sensitivity is 0.7069, the specificity is 0.9997, and the Hausdorff distance is 52.9130. The proposed method outperforms the state-of-the-art algorithm. (The code of this paper is available on https://github.com/YalongLiu/Refined-Segmentation-R-CNN ).
Class Imbalance Impact on the Prediction of Complications during Home Hospitalization: A Comparative Study

Home hospitalization (HH) is presented as a healthcare alternative capable of providing high standards of care when patients no longer need hospital facilities. Although HH seems to lower healthcare costs by shortening hospital stays and improving patient's quality of life, the lack of continuous observation at home may lead to complications in some patients. Since blood tests have been proven to provide relevant prognosis information in many diseases, this paper analyzes the impact of different sampling methods on the prediction of HH outcomes. After a first exploratory analysis, some variables extracted from routine blood tests performed at the moment of HH admission, such as hemoglobin, lymphocytes or creatinine, were found to unmask statistically significant differences between patients undergoing successful and unsucessful HH stays. Then, predictive models were built with these data, in order to identify unsuccessful cases eventually needing hospital facilities. However, since these hospital admissions during HH programs are rare, their identification through conventional machine-learning approaches is challenging. Thus, several sampling strategies designed to face class imbalance were herein overviewed and compared. Among the analyzed approaches, over-sampling strategies, such as ROSE (Random Over-Sampling Examples) and conventional random over-sampling, showed the best performances. Nevertheless, further improvements should be proposed in the future so as to better identify those patients not benefiting from HH.
Echoes of the Past: Recovering Blockchain Metrics from Merged Mining

So far, the topic of merged mining has mainly been considered in a security context, covering issues such as mining power centralization or cross-chain attack scenarios. In this work we show that key information for determining blockchain metrics such as the fork rate can be recovered through data extracted from merge mined cryptocurrencies. Specifically, we reconstruct a long-ranging view of forks and stale blocks in Bitcoin from its merge mined child chains, and compare our results to previous findings that were derived from live measurements. Thereby, we show that live monitoring alone is not sufficient to capture a large majority of these events, as we are able to identify a non-negligible portion of stale blocks that were previously unaccounted for. Their authenticity is ensured by cryptographic evidence regarding both, their position in the respective blockchain, as well as the Proof-of-Work difficulty. Furthermore, by applying this new technique to Litecoin and its child cryptocurrencies, we are able to provide the first extensive view and lower bound on the stale block and fork rate in the Litecoin network. Finally, we outline that a recovery of other important metrics and blockchain characteristics through merged mining may also be possible.
MLAttack: Fooling Semantic Segmentation Networks by Multi-layer Attacks

Despite the immense success of deep neural networks, their applicability is limited because they can be fooled by adversarial examples, which are generated by adding visually imperceptible and structured perturbations to the original image. Semantic segmentation is required in several visual recognition tasks, but unlike image classification, only a few studies are available for attacking semantic segmentation networks. The existing semantic segmentation adversarial attacks employ different gradient based loss functions which are defined using only the last layer of the network for gradient backpropogation. But some components of semantic segmentation networks implicitly mitigate several adversarial attacks (like multiscale analysis) due to which the existing attacks perform poorly. This provides us the motivation to introduce a new attack in this paper known as MLAttack, i.e., Multiple Layers Attack. It carefully selects several layers and use them to define a loss function for gradient based adversarial attack on semantic segmentation architectures. Experiments conducted on publicly available dataset using the state-of-the-art segmentation network architectures, demonstrate that MLAttack performs better than existing state-of-the-art semantic segmentation attacks.
Trend-Smooth: Accelerate Asynchronous SGD by Smoothing Parameters Using Parameter Trends

Stochastic gradient descent(SGD) is the fundamental sequential method in training large scale machine learning models. To accelerate the training process, researchers proposed to use the asynchronous stochastic gradient descent (A-SGD) method in model learning. However, due to the stale information when updating parameters, A-SGD converges more slowly than SGD in the same iteration number. Moreover, A-SGD often converges to a high loss value and results in lower model accuracy. In this paper, we propose a novel algorithm called Trend-Smooth which can be adapted to the asynchronous parallel environment to overcome the above problems. Specifically, Trend-Smooth makes use of the parameter trend during the training process to shrink the learning rate of some dimensions where the gradients' directions are opposite to the trends of parameters. Experiments on MNIST and CIFAR-10 datasets confirm that Trend-Smooth can accelerate the convergence speed in asynchronous training process. The test accuracy that Trend-Smooth achieves is shown to be higher than other asynchronous parallel baseline methods, and is very close to the SGD method. Moreover, Trend-Smooth can also be combined with other adaptive learning rate methods(like Momentum, RMSProp and Adam) in the asynchronous parallel environment to promote their performance.
Interactive Video Completion

We propose an interactive video completion method aiming for practical use in a digital production workplace. The results of earlier automatic solutions often require a considerable amount of manual modifications to make them usable in practice. To reduce such a laborious task, our method offers an efficient editing tool. Our iterative algorithm estimates the flow fields and colors in space-time holes in the video. As in earlier approaches, our algorithm uses an $L^1$L1 data term to estimate flow fields. However, we employ a novel $L^2$L2 data term to estimate temporally coherent color transitions. Our graphics processing unit implementation enables the user to interactively complete a video by drawing holes and immediately removes objects from the video. In addition, our method successfully interpolates sparse modifications initialized by the designer. According to our subjective evaluation, the videos completed with our method look significantly better than those with other state-of-the-art approaches.
Comparative study of several operation modes of AES algorithm for encryption ECG biomedical signal

&lt;span&gt;Biomedical signal processing provides a cross-disciplinary international forum through which research on signal and images measurement and analysis in clinical medicine as well as biological sciences is shared. Electrocardiography (ECG) signal is more frequently used for diagnosis of cardiovascular diseases. However, the ECG signals contain sensitive private health information as well as details that serve to individually distinguish patients. For this reason, the information must be encrypted prior to transmission across public media so as to prevent unauthorized access by adversaries. In this paper, the proposed the use of the Advanced Encryption Standard algorithm (AES), which is one of a symmetric key block cipher with lightweight properties for enhances confidentiality, integrity and authentication in ECG signal transmission. However, some of the challenges arising from the use of this algorithm are computational overhead and level of security, which occur when handling more complex.The AES algorithm has different operation modes using three different key sizes which can be utilized in encrypting the whole sample of ECG biomedical signal in electronic healthcare. The experiments in this research, exhibit comparative study of using five modes of operation in AES algorithm, which are coupled with three key sizes based on the execution time and security level for the encryption of ECG biomedical signals in electronic healthcare application. Thus, we reported that the CBC mode of the AES algorithm is suitable to be applied of security purpose.&lt;/span&gt;
A study of data pre-processing techniques for imbalanced biomedical data classification

Biomedical data are widely accepted in developing prediction models for identifying a specific tumour, drug discovery and human cancers detection. However, previous studies usually focused on different classifiers, and overlook the class imbalance problem in real-world biomedical datasets. This paper mainly focuses on reviewing and evaluating some popular and recently developed resampling and feature selection (FS) methods for class imbalance learning with data distribution being considered. Experimental results show that: 1) resampling and FS techniques exhibit better performance using support vector machine (SVM) classifier; 2) techniques such as random undersampling and FS perform better than other data pre-processing methods with T location-scale distribution when using SVM and K-nearest neighbours (KNN) classifiers. Random oversampling outperforms other methods on negative binomial distribution using Random Forest with lower level of imbalance ratio; 3) FS outperforms other data pre-processing methods in most cases, thus, FS with SVM classifier is the best choice for imbalanced biomedical data learning.
Graceful Interactions and Social Support as Motivational Design Strategies to Encourage Women in Exercising

Increasingly aware of the importance of active lifestyles, many people intend to exercise more. Yet the main challenge remains to translate these intentions into action. Wearable devices supporting exercise regrettably tend to adopt a one-size-fits-all approach by monitoring activity through quantified data as a motivation strategy. Since certain individuals are driven by social motives to exercise, less addressed through quantification, the impact of these devices on user experience and motivation is questionable. We contribute to the field by defining interaction attributes of graceful interactions in product design. We then embedded these in designing Grace, a piece of jewelry enabling women to share exercise intentions with friends to encourage social support. Instead of focusing on quantification, we rely on a qualitative approach using graceful interaction. Through this we extend the design space of sport-related wearables for women and inform how to design for exercise motivation through social support and graceful interactions.
On the Limits of Learning to Actively Learn Semantic Representations

One of the goals of natural language understanding is to develop models that map sentences into meaning representations. However, training such models requires expensive annotation of complex structures, which hinders their adoption. Learning to actively-learn(LTAL) is a recent paradigm for reducing the amount of labeled data by learning a policy that selects which samples should be labeled. In this work, we examine LTAL for learning semantic representations, such as QA-SRL. We show that even an oracle policy that is allowed to pick examples that maximize performance on the test set (and constitutes an upper bound on the potential of LTAL), does not substantially improve performance compared to a random policy. We investigate factors that could explain this finding and show that a distinguishing characteristic of successful applications of LTAL is the interaction between optimization and the oracle policy selection process. In successful applications of LTAL, the examples selected by the oracle policy do not substantially depend on the optimization procedure, while in our setup the stochastic nature of optimization strongly affects the examples selected by the oracle. We conclude that the current applicability of LTAL for improving data efficiency in learning semantic meaning representations is limited.
Shifting perspectives in pictorial narratives

We propose an extension of DRT for analyzing pictorial narratives. We test drive our PicDRT framework by analyzing the way authors represent characters’ mental states and perception in comics. Our investigation goes beyond Abusch and Rooth (2017) in handling not just free perception sequences, but also a form of apparent perspective blending somewhat reminiscent of free indirect discourse.
Processing of recycled waste PET (polyethylene terephthalate) plastics bottle into for the lightweight and reinforcement bricks

Abstract PET (polyethylene terephthalate) waste is a plastic bottle waste that difficult to recycle in large scale, so it was causing environmental pollution. In this research, PET waste used as a main material of bricks by using the mix design method. The mix design method used includes cutting of PET waste, melting of PET waste, mixing with other materials, and molding the materials. This research used four variations of PET waste added by cement and sand to find the ideal composition that mentioned by SNI (Indonesia National Standard). The ideal composition of brick is solid plastic: cement: sand volume ratio is 66:22:12. The result of this research showed that brick quality is ideal, which was characterized by FT-IR and SEM. This composition has 0% of water absorbency, the compressive strength is 75 Kg/cm 2 , water absorbency of SNI brick is under 20%, and the compressive strength is between 60-100 Kg/cm 2 .
Visible Light Communication by Using LED Array for Automatic Wheelchair Control in Hospitals

In Japan, there is a shortage of caretakers due to the increase of aging population. As a solution for this issue, an automated wheelchair driving system is proposed for the hospitals to reduce the workload of caretakers. In this system, the communication between the wheelchair and the external environment is realized by visible light communication which has no negative influence on the human body like other wave based communication methods. An LED matrix and a camera are used to achieve this communication. The wheelchair is guided by an LED blinking pattern which is recognized by a camera installed in the wheelchair. In this paper, the communication side of the system is studied. A set of experiments were carried out to validate the proposed system and their results emphasize the effectiveness of the system.
Bootstrapping Knowledge Graphs From Images and Text

The problem of generating structured Knowledge Graphs (KGs) is difficult and open but relevant to a range of tasks related to decision making and information augmentation. A promising approach is to study generating KGs as a relational representation of inputs (e.g., textual paragraphs or natural images), where nodes represent the entities and edges represent the relations. This procedure is naturally a mixture of two phases: extracting primary relations from input, and completing the KG with reasoning. In this paper, we propose a hybrid KG builder that combines these two phases in a unified framework and generates KGs from scratch. Specifically, we employ a neural relation extractor resolving primary relations from input and a differentiable inductive logic programming (ILP) model that iteratively completes the KG. We evaluate our framework in both textual and visual domains and achieve comparable performance on relation extraction datasets based on Wikidata and the Visual Genome. The framework surpasses neural baselines by a noticeable gap in reasoning out dense KGs and overall performs particularly well for rare relations.
Action Units: Directing User Attention in 360-degree Video based VR

A key challenge to effective storytelling using Virtual Reality (VR), such as with 360-degree videos, is how to direct user attention to important content without taking away user agency for free exploration. In this paper, we introduce the notion of an Action Unit system, composed of social cues such as head and arm movements, as a way of directing users to focus on content important for the given narrative. We applied this idea to a 360-degree VR tour, and evaluated its effects on memory, engagement, enjoyment, and cyber-sickness. The results indicate that the levels of engagement and enjoyment increased when these Action Units were applied. Users also preferred the Action Units for their diegetic aspects.
Digital Earth in Russia

Abstract A brief overview of the history of Digital Earth in Russia, its current status and prospects for further development are proposed and discussed in this chapter. The anticipation of the concept of Digital Earth in Russian culture is demonstrated and explained. Conclusions about the specificity of the development of the concept of Digital Earth in Russia due to its geographical, historical and cultural characteristics are drawn, and development factors are revealed. The vital need for the concept in ensuring the effective governance and sustainable development of the country is emphasized. Theoretical and applied results achieved by the Russian Digital Earth community are presented. Special attention is paid to the outreach of the Digital Earth vision to state governance, business, society and education. The key importance of international cooperation for the successful implementation of Digital Earth in Russia is explained.
Business architecture planning with TOGAF framework

Abstract Enterprise TOGAF architecture development methodology. TOGAF is a detailed method in its stages to build a new business process architecture and information system. The results in welcoming the era of industrial revolution, education in Indonesia especially higher education, one of which is obtained at STIKES strives to follow the development of information technology. One of the existing STIKES in East Java is a college that has a vision and mission according to the development of information technology. but in the implementation has not followed the rules of an enterprise, STIKES XYZ not have information systems, especially in the management of employees and Process/flow that occurs is still a manual process. Business process flow that exists requires improvement of business processes in order to improve quality and optimize the human resources information system with the support of a good and efficient. This study refers to the of this research are the blueprint new business processes in the form of a recommendation, which consists of documents that would explain what kind of information systems required by STIKES. The expected results of this research can be used as a guide in the procurement and development of information systems at STIKES XYZ.
Saccader: Improving Accuracy of Hard Attention Models for Vision

Although deep convolutional neural networks achieve state-of-the-art performance across nearly all image classification tasks, their decisions are difficult to interpret. One approach that offers some level of interpretability by design is \textit{hard attention}, which uses only relevant portions of the image. However, training hard attention models with only class label supervision is challenging, and hard attention has proved difficult to scale to complex datasets. Here, we propose a novel hard attention model, which we term Saccader. Key to Saccader is a pretraining step that requires only class labels and provides initial attention locations for policy gradient optimization. Our best models narrow the gap to common ImageNet baselines, achieving $75\%$ top-1 and $91\%$ top-5 while attending to less than one-third of the image.