Reviewerly/exHarmony huggingface dataset:
Prediction of Two Year Survival Among Patients of Non-small Cell Lung Cancer

Lung cancer ranks as second most prevalent type of cancer. Still predictions for survival of lung cancer patients are not accurate. In this research, we try to create a prediction model, with the help of machine learning to accurately predict the survival of non-small cell lung cancer patients (NSCLC). Clinical data of 559 patients was taken for training and testing of models. We have developed multilevel perceptron model for survival prediction. Other models developed during this study were compared to measure performance of our model. Attributes that are found to be useful as biomarkers for prediction of survival analysis of NSCLC have also been computed and ranked accordingly for increase in accuracy of prediction model by implementing feature selection method. The final model included T stage, N stage, Modality, World Health Organization Performance status, Cumulative Total Tumor dose, tumor load, Overall treatment time as the variables. Two year survival was chosen as the prediction outcome. Neural Network was found as the best prediction model with area under Curve (AUC) of 0.75. By far to our knowledge Multilevel Neural Network is found to be the best model for predicting two-year survival among patients of non-small cell lung cancer.
Randomized cross-over study and a qualitative analysis comparing virtual microscopy and light microscopy for learning undergraduate histopathology

Virtual microscopy (VM) use in teaching and learning is increasing worldwide. However, there is a paucity of information comparing it to light microscopy (LM) in learning undergraduate histopathology. We investigated whether VM or LM had a higher impact on student learning and performance in histopathology. In addition, we investigated whether students preferred VM over LM, and whether VM use provided a platform to fulfill the Accreditation Council for Graduate Medical Education core competencies.We used a sequential exploratory mixed method study design. A qualitative phase inquiring about student preference for VM or LM was followed by a randomized cross-over study. Student preference was measured by an online survey based on a Likert scale. In the cross-over study, students were randomized to either the VM or the LM arm, and their mean scores in standardized exams were compared after using VM and LM.A total of 152 students completed the qualitative study and a total of 64 students participated in the cross-over study. Eighty-three percent (83%) of the students preferred to use VM over LM. Students who used VM scored significantly (P < 0.001) higher [(87.1% vs. 72.4%) and (85.3% vs. 76.1%)], respectively, in both phases of the cross-over study compared to those who used LM.Using VM to learn histopathology has significantly increased student learning and performance compared to using LM.
Using a systematic framework to critically analyze proposed smart card based two factor authentication schemes

The importance of smart card based two factor authentication can be gauged by the fact that many schemes have been proposed so far yet none of them used a systematic framework to critically analyze themselves to prove them practical and use worthy. This research however accomplishes exactly this by showing how using a criteria set can aid in highlighting the underlying hidden weaknesses in the design of the proposed schemes and what can be done to improve them by incorporating security features from the initial development stages of the protocols. This would also ensure only meaningful contribution in developing stronger schemes. Our research also lays the foundation stone by assessing the latest scheme proposed by Xie et al. and suggesting improvements in it. And finally, a novel scheme rating mechanism has been introduced to rate schemes thus helping in determining the actual goodness of the schemes and facilitating top managements of corporate sectors to make informed decisions.
A convex nonlocal total variation regularization algorithm for multiplicative noise removal

This study proposes a nonlocal total variation restoration method to address multiplicative noise removal problems. The strictly convex, objective, nonlocal, total variation effectively utilizes prior information about the multiplicative noise and uses the maximum a posteriori estimator (MAP). An efficient iterative multivariable minimization algorithm is then designed to optimize our proposed model. Finally, we provide a rigorous convergence analysis of the alternating multivariable minimization iteration. The experimental results demonstrate that our proposed model outperforms other currently related models both in terms of evaluation indices and image visual quality.
A Weight-Varying Ensemble Method for Short-term Forecasting PV Power Output

The photovoltaic (PV) power generation has randomicity and intermittence due to the influence by meteorological factors and it is of significance to establish a reasonable prediction model for PV power. Since it is tough to get a completely optimal prediction of PV power by a single model due to the existence of uncertainty, the paper considered proposing an ensemble method by integrating various individual models to achieve better accuracy. In this paper, a weight-varying ensemble (WVE) forecasting model is established to improve the precision of the short-term prediction of PV power. First, the extraction of feature vectors was implemented to find out the most important variables which also helps to improve the accuracy through data pre-processing. Second, the weather pattern was recognized and clustered based on a self-organizing feature map (SOM) method. Third, the five min-ahead prediction output was obtained using the WVE model that utilizes an integrated framework of Generalized Regression Neural Network (GRNN), Extreme Learning Machine Neural Network (ELMNN) and Elman Neural Network (ElmanNN), which were assembled by Genetic Algorithms optimized Back Propagation Neural Network (GA-BPNN). Results show that the WVE model achieved a higher accuracy with mean absolute percentage error (MAPE) of 5.17%, 5.26%, 5.49% and 5.82% for four types of data. The GA can be applied to optimization of other weight-varying ensemble problems.
How to Find Correlated Internet Failures

Even as residential users increasingly rely upon the Internet, connectivity sometimes fails. Characterizing small-scale failures of last mile networks is essential to improving Internet reliability.
Robotic Leg Illusion: System Design and Human-in-the-Loop Evaluation

The question how humans integrate artificial lower limb devices into their body schema has distinct potential for engineering motion assistance systems, e.g., the design of robotic prostheses. Adding robotic technology to existing psychological experiments enables a deeper investigation of multisensory interaction between proprioceptive, visual, and tactile stimuli during motion. This paper reports the design and control of a robot to investigate embodiment with regard to the lower limbs. In an evaluation study, the rubber hand illusion is transferred to the whole leg for the first time. Participants performed knee bends according to three different conditions being imitated by a robotic leg. The occurrence of a robotic leg illusion was subjectively assessed by a questionnaire and objectively measured by the proprioceptive drift. Considering both metrics, the results show a successful integration of the robotic leg into the body schema. Motion synchronization appears to be a paramount factor, whereby the study indicates that acoustical stimulation might also be relevant. The interrelation between mechatronic design and control of the human-in-the-loop experiment and the factors influencing the illusion are discussed and alternative experimental setups are suggested.
Unsupervised Domain Adaptation of Contextualized Embeddings: A Case Study in Early Modern English.

Contextualized word embeddings such as ELMo and BERT provide a foundation for strong performance across a wide range of natural language processing tasks by pretraining on large corpora of unlabeled text. However, the applicability of this approach is unknown when the target domain varies substantially from the pretraining corpus. We are specifically interested in the scenario in which labeled data is available in only a canonical source domain such as newstext, and the target domain is distinct from both the labeled and pretraining texts. To address this scenario, we propose domain-adaptive fine-tuning, in which the contextualized embeddings are adapted by masked language modeling on text from the target domain. We test this approach on sequence labeling in two challenging domains: Early Modern English and Twitter. Both domains differ substantially from existing pretraining corpora, and domain-adaptive fine-tuning yields substantial improvements over strong BERT baselines, with particularly impressive results on out-of-vocabulary words. We conclude that domain-adaptive fine-tuning offers a simple and effective approach for the unsupervised adaptation of sequence labeling to difficult new domains.
Relationship Prediction in Dynamic Heterogeneous Information Networks

Most real-world information networks, such as social networks, are heterogeneous and as such, relationships in these networks can be of different types and hence carry differing semantics. Therefore techniques for link prediction in homogeneous networks cannot be directly applied on heterogeneous ones. On the other hand, works that investigate link prediction in heterogeneous networks do not necessarily consider network dynamism in sequential time intervals. In this work we propose a technique that leverages a combination of latent and topological features to predict a target relationship between two nodes in a dynamic heterogeneous information network. Our technique, called MetaDynaMix, effectively combines meta path-based topology features and inferred latent features that incorporate temporal network changes in order to capture network (1) heterogeneity and (2) temporal evolution, when making link predictions. Our experiment results on two real-world datasets show statistically significant improvement over AUCROC and prediction accuracy compared to the state of the art techniques.
Architectural Support for Containment-based Security

Software security techniques rely on correct execution by the hardware. Securing hardware components has been challenging due to their complexity and the proportionate attack surface they present during their design, manufacture, deployment, and operation. Recognizing that external communication represents one of the greatest threats to a system's security, this paper introduces the TrustGuard containment architecture. TrustGuard contains malicious and erroneous behavior using a relatively simple and pluggable gatekeeping hardware component called the Sentry. The Sentry bridges a physical gap between the untrusted system and its external interfaces. TrustGuard allows only communication that results from the correct execution of trusted software, thereby preventing the ill effects of actions by malicious hardware or software from leaving the system. The simplicity and pluggability of the Sentry, which is implemented in less than half the lines of code of a simple in-order processor, enables additional measures to secure this root of trust, including formal verification, supervised manufacture, and supply chain diversification with less than a 15% impact on performance.
Gradient-based Active Learning Query Strategy for End-to-end Speech Recognition

In this paper, we propose an effective active learning query strategy for an automatic speech recognition system with the aim of reducing the training cost. Generally, training a deep neural network with supervised learning requires a massive amount of labeled data to obtain excellent performance. However, labeling data is tedious and costly manual work. Active learning can solve this problem by choosing and only annotating informative instances, which presents better results even with less transcribed data. In this approach it is vitally important to accurately select informative samples. Based on the preliminary experiment results that true gradient length has the best performance in terms of measuring sample informativeness in ideal conditions, we propose utilizing both uncertainty and the expected gradient length criterion to approximate the true gradient length using a neural network. The experiment results show that our proposed method is superior to the conventional individual criterion when applied to a phoneme-based speech recognition system, and it has both a faster convergence speed and the greatest loss reduction in both clean and noisy conditions.
AT-GAN: A Generative Attack Model for Adversarial Transferring on Generative Adversarial Nets.

Despite the rapid development of adversarial machine learning, most adversarial attack and defense researches mainly focus on the perturbation-based adversarial examples, which is constrained by the input images. In comparison with existing works, we propose non-constrained adversarial examples, which are generated entirely from scratch without any constraint on the input. Unlike perturbation-based attacks, or the so-called unrestricted adversarial attack which is still constrained by the input noise, we aim to learn the distribution of adversarial examples to generate non-constrained but semantically meaningful adversarial examples. Following this spirit, we propose a novel attack framework called AT-GAN (Adversarial Transfer on Generative Adversarial Net). Specifically, we first develop a normal GAN model to learn the distribution of benign data, and then transfer the pre-trained GAN model to estimate the distribution of adversarial examples for the target model. In this way, AT-GAN can learn the distribution of adversarial examples that is very close to the distribution of real data. To our knowledge, this is the first work of building an adversarial generator model that could produce adversarial examples directly from any input noise. Extensive experiments and visualizations show that the proposed AT-GAN can very efficiently generate diverse adversarial examples that are more realistic to human perception. In addition, AT-GAN yields higher attack success rates against adversarially trained models under white-box attack setting and exhibits moderate transferability against black-box models.
LeGR: Filter Pruning via Learned Global Ranking.

Pruning convolutional filters has demonstrated its effectiveness in compressing ConvNets. Prior art in filter pruning requires users to specify a target model complexity (e.g., model size or FLOP count) for the resulting architecture. However, determining a target model complexity can be difficult for optimizing various embodied AI applications such as autonomous robots, drones, and user-facing applications. First, both the accuracy and the speed of ConvNets can affect the performance of the application. Second, the performance of the application can be hard to assess without evaluating ConvNets during inference. As a consequence, finding a sweet-spot between the accuracy and speed via filter pruning, which needs to be done in a trial-and-error fashion, can be time-consuming. This work takes a first step toward making this process more efficient by altering the goal of model compression to producing a set of ConvNets with various accuracy and latency trade-offs instead of producing one ConvNet targeting some pre-defined latency constraint. To this end, we propose to learn a global ranking of the filters across different layers of the ConvNet, which is used to obtain a set of ConvNet architectures that have different accuracy/latency trade-offs by pruning the bottom-ranked filters. Our proposed algorithm, LeGR, is shown to be 2x to 3x faster than prior work while having comparable or better performance when targeting seven pruned ResNet-56 with different accuracy/FLOPs profiles on the CIFAR-100 dataset. Additionally, we have evaluated LeGR on ImageNet and Bird-200 with ResNet-50 and MobileNetV2 to demonstrate its effectiveness. Code available at this https URL.
Real-time Inference in Multi-sentence Tasks with Deep Pretrained Transformers.

The use of deep pretrained bidirectional transformers has led to remarkable progress in learning multi-sentence representations for downstream language understanding tasks (Devlin et al., 2018). For tasks that make pairwise comparisons, e.g. matching a given context with a corresponding response, two approaches have permeated the literature. A Cross-encoder performs full self-attention over the pair; a Bi-encoder performs self-attention for each sequence separately, and the final representation is a function of the pair. While Cross-encoders nearly always outperform Bi-encoders on various tasks, both in our work and others' (Urbanek et al., 2019), they are orders of magnitude slower, which hampers their ability to perform real-time inference. In this work, we develop a new architecture, the Poly-encoder, that is designed to approach the performance of the Cross-encoder while maintaining reasonable computation time. Additionally, we explore two pretraining schemes with different datasets to determine how these affect the performance on our chosen dialogue tasks: ConvAI2 and DSTC7 Track 1. We show that our models achieve state-of-the-art results on both tasks; that the Poly-encoder is a suitable replacement for Bi-encoders and Cross-encoders; and that even better results can be obtained by pretraining on a large dialogue dataset.
Solving Minimum Cost Lifted Multicut Problems by Node Agglomeration

Despite its complexity, the minimum cost lifted multicut problem has found a wide range of applications in recent years, such as image and mesh decomposition or multiple object tracking. Its solutions are decompositions of a graph into an optimal number of segments which are optimized w.r.t. a cost function defined on a superset of the edge set. While the currently available solvers for this problem provide high quality solutions in terms of the task to be solved, they can have long computation times for more difficult problem instances. Here, we propose two variants of a heuristic solver (primal feasible heuristic), which greedily generate solutions within a bounded amount of time. Evaluations on image and mesh segmentation benchmarks show the high quality of these solutions.
A Work Efficient Parallel Algorithm for Exact Euclidean Distance Transform

A fully-parallelized work-time optimal algorithm is presented for computing the exact Euclidean Distance Transform (EDT) of a 2D binary image with the size of n × n. Unlike existing PRAM (Parallel Random Access Machine) and other algorithms, this algorithm is suitable for implementation on modern SIMD (Single Instruction Multiple Data) architectures such as GPUs. As a fundamental operation of 2D EDT, 1D EDT is efficiently parallelized first. Specifically, the GPU algorithm for the 1D EDT, which uses CUDA (Compute Unified Device Architecture) binary functions, such as ballotO, ffs(), clzO, and shflO, runs in O(log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">32</sub> n) time and performs O(n) work. Using the 1D EDT as a fundamental operation, the fully-parallelized work-time optimal 2D EDT algorithm is designed. This algorithm consists of three steps. Step 1 of the algorithm runs in O(log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">32</sub> n) time and performs O(N) (N= <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n2</sup> ) of total work on GPU. Step 2 performs O(N) of total work and has an expected time complexity of O(logn) on GPU. Step 3 runs in O(log <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">32</sub> n) time and performs O(N) of total work on GPU. As far as we know, this algorithm is the first fully-parallelized and realized work-time optimal algorithm for GPUs. The experimental results show that this algorithm outperforms the prior state-of-the-art GPU algorithms.
Strong Post-Compromise Secure Proxy Re-Encryption

Proxy Re-Encryption (PRE) allows a ciphertext encrypted using a key $$\mathsf {pk}_{i}$$ to be re-encrypted by a third party so that it is an encryption of the same message under a new key $$\mathsf {pk}_{j}$$ , without revealing the message. We define Post-Compromise Security (PCS) in the context of PRE. This ensures that an adversary cannot distinguish which of two adversarially chosen ciphertexts a re-encryption was created from even when given the old secret key and the update token used to perform the re-encryption. We give separating examples demonstrating how PCS is stronger than existing security definitions for PRE achieving similar goals, before showing that PCS can be achieved using a combination of existing security properties from the literature. In doing so, we show there are existing PRE schemes satisfying PCS. Finally, we give a construction demonstrating that natural modifications of practical PRE schemes provably have PCS directly, without incurring overheads from the security reductions we have shown, and from weaker assumptions than existing schemes.
Technically white: Emoji skin-tone modifiers as American technoculture

The inclusion of skin-tone modifiers into the standard emoji set marked a shift from the default white racialization of emoji towards explicit attempts to expand racial representation in the human emoji characters. This study explores the racial logics of emoji as culturally-situated artifacts that rely on linked understandings of race and technology. We conduct an interface analysis of emoji skin-tone modifiers, coupled with user discourse analysis, to explore the design and user interpretations of skin-tone modifiers. Our findings suggest that though the skin-tone modifiers were introduced as an intervention into the lack of racial representation in emoji, they continue to technically center whiteness in the emoji set as an extension of American technoculture.
A Clicked-URL Feature for Transactional Query Identification

Understanding query intents can help search engines to effectively improve their search quality. Click-through data has proven to be a valuable resource for query classification. In this paper, we propose a novel Clicked-URL (CURL) feature that uses semantic information extracted from user clicked URLs in the search results, and compare with the key word list to identify transnational query type. Experiments show that we can obtain relatively high accuracy in transnational query identification with CURL, and achieve an improved performance in query classification combing with other features.
Design of a low-filling-factor and polarization-sensitive superconducting nanowire single photon detector with high detection efficiency

We designed a low-filling-factor and polarization-sensitive superconducting nanowire single photon detector (SNSPD), which can achieve a high absorption efficiency and counting rate simultaneously. Numerical simulations show that high absorption efficiency can be achieved by low-filling-factor SNSPDs with a silicon slot and silver reflector. The absorptance of the NbN nanowire for a transverse magnetic (TM) wave at the wavelength of 1550 nm can be 84.4% when the filling factor is only 16.5%, and the corresponding polarization extinction ratio (PER) is 562.9; the absorptance of the NbN nanowire for a transverse electric (TE) wave can be 67.0% when the filling factor is only 11.7%, and the PER is 7.4.