nils-herrmann/clean_scirepeval huggingface dataset:
Adversarial Active Learning

Active learning is an area of machine learning examining strategies for allocation of finite resources, particularly human labeling efforts and to an extent feature extraction, in situations where available data exceeds available resources. In this open problem paper, we motivate the necessity of active learning in the security domain, identify problems caused by the application of present active learning techniques in adversarial settings, and propose a framework for experimentation and implementation of active learning systems in adversarial contexts. More than other contexts, adversarial contexts particularly need active learning as ongoing attempts to evade and confuse classifiers necessitate constant generation of labels for new content to keep pace with adversarial activity. Just as traditional machine learning algorithms are vulnerable to adversarial manipulation, we discuss assumptions specific to active learning that introduce additional vulnerabilities, as well as present vulnerabilities that are amplified in the active learning setting. Lastly, we present a software architecture, Security-oriented Active Learning Testbed (SALT), for the research and implementation of active learning applications in adversarial contexts.
Recursive Bayesian Recurrent Neural Networks for Time-Series Modeling

This paper develops a probabilistic approach to recursive second-order training of recurrent neural networks (RNNs) for improved time-series modeling. A general recursive Bayesian Levenberg-Marquardt algorithm is derived to sequentially update the weights and the covariance (Hessian) matrix. The main strengths of the approach are a principled handling of the regularization hyperparameters that leads to better generalization, and stable numerical performance. The framework involves the adaptation of a noise hyperparameter and local weight prior hyperparameters, which represent the noise in the data and the uncertainties in the model parameters. Experimental investigations using artificial and real-world data sets show that RNNs equipped with the proposed approach outperform standard real-time recurrent learning and extended Kalman training algorithms for recurrent networks, as well as other contemporary nonlinear neural models, on time-series modeling.
Convex Optimization Procedure for Clustering: Theoretical Revisit

In this paper, we present theoretical analysis of SON – a convex optimization procedure for clustering using a sum-of-norms (SON) regularization recently proposed in [8, 10, 11, 17]. In particular, we show if the samples are drawn from two cubes, each being one cluster, then SON can provably identify the cluster membership provided that the distance between the two cubes is larger than a threshold which (linearly) depends on the size of the cube and the ratio of numbers of samples in each cluster. To the best of our knowledge, this paper is the first to provide a rigorous analysis to understand why and when SON works. We believe this may provide important insights to develop novel convex optimization based algorithms for clustering.
Echo state networks are universal

This paper shows that echo state networks are universal uniform approximants in the context of discrete-time fading memory filters with uniformly bounded inputs defined on negative infinite times. This result guarantees that any fading memory input/output system in discrete time can be realized as a simple finite-dimensional neural network-type state-space model with a static linear readout map. This approximation is valid for infinite time intervals. The proof of this statement is based on fundamental results, also presented in this work, about the topological nature of the fading memory property and about reservoir computing systems generated by continuous reservoir maps.
Metric Regression Forests for Correspondence Estimation

We present a new method for inferring dense data to model correspondences, focusing on the application of human pose estimation from depth images. Recent work proposed the use of regression forests to quickly predict correspondences between depth pixels and points on a 3D human mesh model. That work, however, used a proxy forest training objective based on the classification of depth pixels to body parts. In contrast, we introduce Metric Space Information Gain (MSIG), a new decision forest training objective designed to directly minimize the entropy of distributions in a metric space. When applied to a model surface, viewed as a metric space defined by geodesic distances, MSIG aims to minimize image-to-model correspondence uncertainty. A naïve implementation of MSIG would scale quadratically with the number of training examples. As this is intractable for large datasets, we propose a method to compute MSIG in linear time. Our method is a principled generalization of the proxy classification objective, and does not require an extrinsic isometric embedding of the model surface in Euclidean space. Our experiments demonstrate that this leads to correspondences that are considerably more accurate than state of the art, using far fewer training images.
Interpreter Exploitation

As remote exploits further dwindle and perimeter defenses become the standard, remote client-side attacks are becoming the standard vector for attackers. Modern operating systems have quelled the explosion of client-side vulnerabilities using mitigation techniques such as data execution prevention (DEP) and address space layout randomization (ASLR). This work illustrates two novel techniques to bypass these mitigations. The two techniques leverage the attack surface exposed by the script interpreters commonly accessible within the browser. The first technique, pointer inference, is used to find the memory address of a string of shellcode within the Adobe Flash Player's ActionScript interpreter despite ASLR. The second technique, JIT spraying, is used to write shellcode to executable memory, bypassing DEP protections, by leveraging predictable behaviors of the ActionScript JIT compiler. Previous attacks are examined and future research directions are discussed.
Student Preferences on Gaming Aspects for a Serious Game in Pharmacy Practice Education: A Cross-Sectional Study

Serious games are motivating and provide a safe environment for students to learn from their mistakes without experiencing any negative consequences from their actions. However, little is known about students' gaming preferences and the types of serious games they like to play for education.



This study aims to determine the types of gaming aspects that students would like to play in a pharmacy-related serious game.



A cross-sectional study was conducted using a self-administered survey, which obtained students' responses on their preferences regarding various gaming aspects (reward systems, game settings, storylines, viewing perspectives, and gaming styles) and for a hypothetical gaming scenario (authentic simulation or post-apocalyptic fantasy). Descriptive statistics, chi-square, and Fisher's exact tests were used for statistical analyses.



Response rate was 72.7% (497/684 undergraduates). The most popular game reward systems were unlocking mechanisms (112/497, 22.5%) and experience points (90/497, 18.1%). Most students preferred fantasy/medieval/mythic (253/497, 50.9%) and modern (117/497, 23.5%) settings, but lower year undergraduates preferred modern settings less than upper year seniors (47/236, 19.9% vs 70/242, 28.9%, P=.022). Almost one-third (147/497, 29.6%) preferred an adventurer storyline or an authentic pharmacy-related plot (119/497, 23.9%), and a collaborative game style was most preferred by the students (182/497, 36.6%). Three-dimensional game perspectives (270/497, 54.3%) were more popular than two-dimensional perspectives (221/497, 44.5%), especially among males than females (126/185, 68.1% vs 142/303, 46.9%, P<.001). In terms of choice for a pharmacy-related serious game, a post-apocalyptic fantasy game (scenario B, 287/497, 57.7%) was more popular than an authentic simulation game (scenario A, 209/497, 42.1%). More males preferred the post-apocalyptic fantasy scenario than females (129/187, 69.0% vs 155/306, 50.7%, P<.001).



In general, students want a three-dimensional, fantasy/medieval/mythic post-apocalyptic game, based on an adventurer storyline with an unlocking mechanism reward system. A balance between real-life and fantasy elements needs to be struck in order for the game to cater students towards health care practices.
Thyroid hormones and cardiovascular disease

Myocardial and vascular endothelial tissues have receptors for thyroid hormones and are sensitive to changes in the concentrations of circulating thyroid hormones. The importance of thyroid hormones in maintaining cardiovascular homeostasis can be deduced from clinical and experimental data showing that even subtle changes in thyroid hormone concentrations — such as those observed in subclinical hypothyroidism or hyperthyroidism, and low triiodothyronine syndrome — adversely influence the cardiovascular system. Some potential mechanisms linking the two conditions are dyslipidaemia, endothelial dysfunction, blood pressure changes, and direct effects of thyroid hormones on the myocardium. Several interventional trials showed that treatment of subclinical thyroid diseases improves cardiovascular risk factors, which implies potential benefits for reducing cardiovascular events. Over the past 2 decades, accumulating evidence supports the association between abnormal thyroid function at the time of an acute myocardial infarction (MI) and subsequent adverse cardiovascular outcomes. Furthermore, experimental studies showed that thyroid hormones can have an important therapeutic role in reducing infarct size and improving myocardial function after acute MI. In this Review, we summarize the literature on thyroid function in cardiovascular diseases, both as a risk factor as well as in the setting of cardiovascular diseases such as heart failure or acute MI, and outline the effect of thyroid hormone replacement therapy for reducing the risk of cardiovascular disease.
Spatiotemporal urbanization processes in the megacity of Mumbai , India : A Markov chains-cellular automata urban growth model

Several factors contribute to on-going challenges of spatial planning and urban policy in megacities, including rapid population shifts, less organized urban areas, and a lack of data with which to monitor urban growth and land use change. To support Mumbai’s sustainable development, this research was conducted to examine past urban land use changes on the basis of remote sensing data collected between 1973 and 2010. An integrated Markov ChainseCellular Automata (MCeCA) urban growth model was implemented to predict the city’s expansion for the years 2020e2030. To consider the factors affecting urban growth, the MCeCA model was also connected to multi-criteria evaluation to generate transition probability maps. The results of the multi-temporal change detection show that the highest urban growth rates, 142% occurred between 1973 and 1990. In contrast, the growth rates decreased to 40% between 1990 and 2001 and decreased to 38% between 2001 and 2010. The areas most affected by this degradation were open land and croplands. The MCeCA model predicts that this trend will continue in the future. Compared to the reference year, 2010, increases in built-up areas of 26% by 2020 and 12% by 2030 are forecast. Strong evidence is provided for complex future urban growth, characterized by a mixture of growth patterns. The most pronounced of these is urban expansion toward the north along the main traffic infrastructure, linking the two currently non-affiliated main settlement ribbons. Additionally, urban infill developments are expected to emerge in the eastern areas, and these developments are expected to increase urban pressure.
Montessori-based activities for long-term care residents with advanced dementia: effects on engagement and affect.

Sixteen residents in long-term care with advanced dementia (14 women; average age = 88) showed significantly more constructive engagement (defined as motor or verbal behaviors in response to an activity), less passive engagement (defined as passively observing an activity), and more pleasure while participating in Montessori-based programming than in regularly scheduled activities programming. Principles of Montessori-based programming, along with examples of such programming, are presented. Implications of the study and methods for expanding the use of Montessori-based dementia programming are discussed.
Clinical efficiency of 2% chlorhexidine gel in reducing intracanal bacteria.

This study evaluated the clinical efficacy of 2% chlorhexidine (CHX) gel on intracanal bacteria reduction during root canal instrumentation. The additional antibacterial effect of an intracanal dressing (Ca[OH](2) mixed with 2% CHX gel) was also assessed. Forty-three patients with apical periodontitis were recruited. Four patients with irreversible pulpitis were included as negative controls. Teeth were instrumented using rotary instruments and 2% CHX gel as the disinfectant. Bacterial samples were taken upon access (S1), after instrumentation (S2), and after 2 weeks of intracanal dressing (S3). Anaerobic culture was performed. Four samples showed no bacteria growth at S1, which were excluded from further analysis. Of the samples cultured positively at S1, 10.3% (4/39) and 8.3% (4/36) sampled bacteria at S2 and S3, respectively. A significant difference in the percentage of positive culture between S1 and S2 (p < 0.001) but not between S2 and S3 (p = 0.692) was found. These results suggest that 2% CHX gel is an effective root canal disinfectant and additional intracanal dressing did not significantly improve the bacteria reduction on the sampled root canals.
CacheZoom: How SGX Amplifies the Power of Cache Attacks

In modern computing environments, hardware resources are commonly shared, and parallel computation is widely used. Parallel tasks can cause privacy and security problems if proper isolation is not enforced. Intel proposed SGX to create a trusted execution environment within the processor. SGX relies on the hardware, and claims runtime protection even if the OS and other software components are malicious. However, SGX disregards sidechannel attacks. We introduce a powerful cache sidechannel attack that provides system adversaries a high resolution channel. Our attack tool named CacheZoom is able to virtually track all memory accesses of SGX enclaves with high spatial and temporal precision. As proof of concept, we demonstrate AES key recovery attacks on commonly used implementations including those that were believed to be resistant in previous scenarios. Our results show that SGX cannot protect critical data sensitive computations, and efficient AES key recovery is possible in a practical environment. In contrast to previous works which require hundreds of measurements, this is the first cache side-channel attack on a real system that can recover AES keys with a minimal number of measurements. We can successfully recover AES keys from T-Table based implementations with as few as ten
A hybrid collaborative filtering model for social influence prediction in event-based social networks

Event-based social networks (EBSNs) provide convenient online platforms for users to organize, attend and share social events. Understanding users’ social influences in social networks can benefit many applications, such as social recommendation and social marketing. In this paper, we focus on the problem of predicting users’ social influences on upcoming events in EBSNs. We formulate this prediction problem as the estimation of unobserved entries of the constructed user-event social influence matrix, where each entry represents the influence value of a user on an event. In particular, we define a user's social influence on a given event as the proportion of the user's friends who are influenced by him/her to attend the event. To solve this problem, we present a hybrid collaborative filtering model, namely, Matrix Factorization with Event-User Neighborhood (MF-EUN) model, by incorporating both event-based and user-based neighborhood methods into matrix factorization. Due to the fact that the constructed social influence matrix is very sparse and the overlap values in the matrix are few, it is challenging to find reliable similar neighbors using the widely adopted similarity measures (e.g., Pearson correlation and Cosine similarity). To address this challenge, we propose an additional information based neighborhood discovery (AID) method by considering both event-specific and user-specific features in EBSNs. The parameters of our MF-EUN model are determined by minimizing the associated regularized squared error function through stochastic gradient descent. We conduct a comprehensive performance evaluation on real-world datasets collected from DoubanEvent. Experimental results show that our proposed hybrid collaborative filtering model is superior than several alternatives, which provides excellent performance with RMSE and MAE reaching 0.248 and 0.1266 respectively in the 90% training data of 10 000
Microsoft Kinect Visual and Depth Sensors for Breathing and Heart Rate Analysis

This paper is devoted to a new method of using Microsoft (MS) Kinect sensors for non-contact monitoring of breathing and heart rate estimation to detect possible medical and neurological disorders. Video sequences of facial features and thorax movements are recorded by MS Kinect image, depth and infrared sensors to enable their time analysis in selected regions of interest. The proposed methodology includes the use of computational methods and functional transforms for data selection, as well as their denoising, spectral analysis and visualization, in order to determine specific biomedical features. The results that were obtained verify the correspondence between the evaluation of the breathing frequency that was obtained from the image and infrared data of the mouth area and from the thorax movement that was recorded by the depth sensor. Spectral analysis of the time evolution of the mouth area video frames was also used for heart rate estimation. Results estimated from the image and infrared data of the mouth area were compared with those obtained by contact measurements by Garmin sensors (www.garmin.com). The study proves that simple image and depth sensors can be used to efficiently record biomedical multidimensional data with sufficient accuracy to detect selected biomedical features using specific methods of computational intelligence. The achieved accuracy for non-contact detection of breathing rate was 0.26% and the accuracy of heart rate estimation was 1.47% for the infrared sensor. The following results show how video frames with depth data can be used to differentiate different kinds of breathing. The proposed method enables us to obtain and analyse data for diagnostic purposes in the home environment or during physical activities, enabling efficient human-machine interaction.
Text Alignment for Real-Time Crowd Captioning

The primary way of providing real-time captioning for deaf and hard of hearing people is to employ expensive professional stenographers who can type as fast as natural speaking rates. Recent work has shown that a feasible alternative is to combine the partial captions of ordinary typists, each of whom types part of what they hear. In this paper, we describe an improved method for combining partial captions into a final output based on weighted A search and multiple sequence alignment (MSA). In contrast to prior work, our method allows the tradeoff between accuracy and speed to be tuned, and provides formal error bounds. Our method outperforms the current state-of-the-art on Word Error Rate (WER) (29.6%), BLEU Score (41.4%), and F-measure (36.9%). The end goal is for these captions to be used by people, and so we also compare how these metrics correlate with the judgments of 50 study participants, which may assist others looking to make further progress on this problem.
Elotuzumab Therapy for Relapsed or Refractory Multiple Myeloma.

Elotuzumab, an immunostimulatory monoclonal antibody targeting signaling lymphocytic activation molecule F7 (SLAMF7), showed activity in combination with lenalidomide and dexamethasone in a phase 1b-2 study in patients with relapsed or refractory multiple myeloma.



In this phase 3 study, we randomly assigned patients to receive either elotuzumab plus lenalidomide and dexamethasone (elotuzumab group) or lenalidomide and dexamethasone alone (control group). Coprimary end points were progression-free survival and the overall response rate. Final results for the coprimary end points are reported on the basis of a planned interim analysis of progression-free survival.



Overall, 321 patients were assigned to the elotuzumab group and 325 to the control group. After a median follow-up of 24.5 months, the rate of progression-free survival at 1 year in the elotuzumab group was 68%, as compared with 57% in the control group; at 2 years, the rates were 41% and 27%, respectively. Median progression-free survival in the elotuzumab group was 19.4 months, versus 14.9 months in the control group (hazard ratio for progression or death in the elotuzumab group, 0.70; 95% confidence interval, 0.57 to 0.85; P<0.001). The overall response rate in the elotuzumab group was 79%, versus 66% in the control group (P<0.001). Common grade 3 or 4 adverse events in the two groups were lymphocytopenia, neutropenia, fatigue, and pneumonia. Infusion reactions occurred in 33 patients (10%) in the elotuzumab group and were grade 1 or 2 in 29 patients.



Patients with relapsed or refractory multiple myeloma who received a combination of elotuzumab, lenalidomide, and dexamethasone had a significant relative reduction of 30% in the risk of disease progression or death. (Funded by Bristol-Myers Squibb and AbbVie Biotherapeutics; ELOQUENT-2 ClinicalTrials.gov number, NCT01239797.).
[Sciatica due to unusual causes: Tarlov cysts and nerve roots anomalies].

Tarlov cysts and nerve roots anomalies usually involve lumbosacral roots and are often asymptomatic. MRI has enabled recognition of many conditions that used to be missed by CT or myelography investigations performed for back and leg pain. However, even without additional compressive impingement (disc hernia, spondylolisthesis or lumbar canal stenosis) these anomalies can be responsible for sciatica, motor deficit and bladder sphincter dysfunction. Tarlov cysts are perinervous dilatations of the dorsal root ganglion. CT and especially MRI can reveal these cysts and their precise relations with the neighboring structures. Delayed filling of the cysts can be visualized on the myelogram. MRI is more sensitive than CT myelography for a positive diagnosis of nerve root anomalies, a differential diagnosis with disc hernia and classification of these anomalies. Surgical treatment is indicated for symptomatic Tarlov cysts and nerve root anomalies resistant to conservative treatment. Better outcome is observed in patients with an additional compressive impingement component. We report two cases of sciatica: one caused by Tarlov cysts diagnosed by MRI and the other by nerve root anomalies diagnosed by CT myelography. In both cases, conservative treatment was undertaken. The clinical, radiological and therapeutic aspects of these disorders are discussed.