nils-herrmann/clean_scirepeval huggingface dataset:
Towards Unsupervised Speech-to-Text Translation

We present a framework for building speech-to-text translation (ST) systems using only monolingual speech and text corpora, in other words, speech utterances from a source language and independent text from a target language. As opposed to traditional cascaded systems and end-to-end architectures, our system does not require any labeled data (i.e., transcribed source audio or parallel source and target text corpora) during training, making it especially applicable to language pairs with very few or even zero bilingual resources. The framework initializes the ST system with a cross-modal bilingual dictionary inferred from the monolingual corpora, that maps every source speech segment corresponding to a spoken word to its target text translation. For unseen source speech utterances, the system first performs word-by-word translation on each speech segment in the utterance. The translation is improved by leveraging a language model and a sequence denoising autoencoder to provide prior knowledge about the target language. Experimental results show that our unsupervised system achieves comparable BLEU scores to supervised end-to-end models despite the lack of supervision. We also provide an ablation analysis to examine the utility of each component in our system.
A secure distributed framework for achieving k-anonymity

k-anonymity provides a measure of privacy protection by preventing re-identification of data to fewer than a group of k data items. While algorithms exist for producing k-anonymous data, the model has been that of a single source wanting to publish data. Due to privacy issues, it is common that data from different sites cannot be shared directly. Therefore, this paper presents a two-party framework along with an application that generates k-anonymous data from two vertically partitioned sources without disclosing data from one site to the other. The framework is privacy preserving in the sense that it satisfies the secure definition commonly defined in the literature of Secure Multiparty Computation.
Gain-of-function mutations in SMAD4 cause a distinctive repertoire of cardiovascular phenotypes in patients with Myhre syndrome.

Myhre syndrome is a rare, distinctive syndrome due to specific gain-of-function mutations in SMAD4. The characteristic phenotype includes short stature, dysmorphic facial features, hearing loss, laryngotracheal anomalies, arthropathy, radiographic defects, intellectual disability, and a more recently appreciated spectrum of cardiovascular defects with a striking fibroproliferative response to surgical intervention. We report four newly described patients with typical features of Myhre syndrome who had (i) a mildly narrow descending aorta and restrictive cardiomyopathy; (ii) recurrent pericardial and pleural effusions; (iii) a large persistent ductus arteriosus with juxtaductal aortic coarctation; and (iv) restrictive pericardial disease requiring pericardiectomy. Additional information is provided about a fifth previously reported patient with fatal pericardial disease. A literature review of the cardiovascular features of Myhre syndrome was performed on 54 total patients, all with a SMAD4 mutation. Seventy percent had a cardiovascular abnormality including congenital heart defects (63%), pericardial disease (17%), restrictive cardiomyopathy (9%), and systemic hypertension (15%). Pericarditis and restrictive cardiomyopathy are associated with high mortality (three patients each among 10 deaths); one patient with restrictive cardiomyopathy also had epicarditis. Cardiomyopathy and pericardial abnormalities distinguish Myhre syndrome from other disorders caused by mutations in the TGF-β signaling cascade (Marfan, Loeys-Dietz, or Shprintzen-Goldberg syndromes). We hypothesize that the expanded spectrum of cardiovascular abnormalities relates to the ability of the SMAD4 protein to integrate diverse signaling pathways, including canonical TGF-β, BMP, and Activin signaling. The co-occurrence of congenital and acquired phenotypes demonstrates that the gene product of SMAD4 is required for both developmental and postnatal cardiovascular homeostasis.
High-bandwidth high-power gradient driver for magnetic resonance imaging with digital control

Gradient coils for magnetic resonance imaging (MRI) require large currents (> 500 A) for the gradient field strength, as well as high voltage (> 1600 V) for fast slew rates. Additionally, extremely high fidelity, reproducing the command signal, is critical for image quality. A new driver topology recently proposed can provide the high power and operate at high switching frequency allowing high bandwidth control. The paper presents additional improvements to the new driver architecture, and more importantly, describes the digital control design and implementation, crucial to achieve the required performance level. The power stage and control have been build and tested with the experimental results showing that the performance achieved with the new digital control capability, more than fulfills the system requirements
Center-Fed Unilateral and Pattern Reconfigurable Planar Antennas With Slotted Ground Plane

A center-fed, single-layer, planar antenna with unilateral radiation patterns is investigated. The antenna consists of a turnstile-shaped patch and a slotted ground plane, which function as a vertical magnetic dipole and a horizontal electric dipole, respectively. By combining the two orthogonal dipoles with the same radiation intensities and antiphases, unilateral patterns with wide beamwidth and high front-to-back (F/B) ratio are achieved. As the unilateral radiation pattern can be easily steered in the horizontal plane by changing the slot location, a pattern reconfigurable antenna is further designed by using p-i-n diodes to control the connection states of the radial slots on the ground plane. Four steerable beams are obtained, capable of covering the entire azimuthal plane. For demonstration, both the unilateral and pattern reconfigurable antennas operating at 2.4 GHz WLAN band (2.40–2.48 GHz) were fabricated and measured. The measured overlapping bandwidths, with  $\vert S_{11}\vert <-10$  dB and F/B ratio >15 dB, are given by 7.0% (2.33–2.5 GHz) and 6.3% (2.32–2.47 GHz), respectively.
A case of severe stinging caused by venomous marine animal, "Portuguese man of war" (Physalia species) in all probability.

There are about 150 million jellyfish stings every year. "Portuguese man of war" is responsible for substantial proportion of stings worldwide. The biggest risk from a jellyfish stings may come from incorrect management. A 42-year-old woman was severely stung by venomous marine animal while bathing in waters of the Thai Gulf. It was most likely "Portuguese man of war". The patient didn't remember while being rescued. Looking at damages it seems that first aid was incorrect. Inappropriate and delayed management caused disfiguring scars. On the ground of this case, first aid for "Portuguese man of war" stings is reminded.
Unmanned Aerial Vehicle-based Automobile License Plate Recognition System for Institutional Parking Lots

Unmanned aerial vehicles (UAVs), also known as drones have many applications and they are a current trend across many industries. They can be used for delivery, sports, surveillance, professional photography, cinematography, military combat, natural disaster assistance, security, and the list grows every day. Programming opens an avenue to automate many processes of daily life and with the drone as aerial programmable eyes, security and surveillance can become more efficient and cost effective. At Barry University, parking is becoming an issue as the number of people visiting the school greatly outnumbers the convenient parking locations. This has caused a multitude of hazards in parking lots due to people illegally parking, as well as unregistered vehicles parking in reserved areas. In this paper, we explain how automated drone surveillance is utilized to detect unauthorized parking at Barry University. The automated process is incorporated into Java application and completed in three steps: collecting visual data, processing data automatically, and sending automated responses and queues to the operator of the system.
Non-rigid image registration using fully convolutional networks with deep self-supervision

We propose a novel non-rigid image registration algorithm that is built upon fully convolutional networks (FCNs) to optimize and learn spatial transformations between pairs of images to be registered. Different from most existing deep learning based image registration methods that learn spatial transformations from training data with known corresponding spatial transformations, our method directly estimates spatial transformations between pairs of images by maximizing an image-wise similarity metric between fixed and deformed moving images, similar to conventional image registration algorithms. At the same time, our method also learns FCNs for encoding the spatial transformations at the same spatial resolution of images to be registered, rather than learning coarse-grained spatial transformation information. The image registration is implemented in a multi-resolution image registration framework to jointly optimize and learn spatial transformations and FCNs at different resolutions with deep selfsupervision through typical feedforward and backpropagation computation. Since our method simultaneously optimizes and learns spatial transformations for the image registration, our method can be directly used to register a pair of images, and the registration of a set of images is also a training procedure for FCNs so that the trained FCNs can be directly adopted to register new images by feedforward computation of the learned FCNs without any optimization. The proposed method has been evaluated for registering 3D structural brain magnetic resonance (MR) images and obtained better performance than state-of-the-art image registration algorithms.
Detecting Web Attacks with End-to-End Deep Learning

Web applications are popular targets for cyber-attacks because they are network-accessible and often contain vulnerabilities. An intrusion detection system monitors web applications and issues alerts when an attack attempt is detected. Existing implementations of intrusion detection systems usually extract features from network packets or string characteristics of input that are manually selected as relevant to attack analysis. Manually selecting features, however, is time-consuming and requires in-depth security domain knowledge. Moreover, large amounts of labeled legitimate and attack request data are needed by supervised learning algorithms to classify normal and abnormal behaviors, which is often expensive and impractical to obtain for production web applications. This paper provides three contributions to the study of autonomic intrusion detection systems. First, we evaluate the feasibility of an unsupervised/semi-supervised approach for web attack detection based on the Robust Software Modeling Tool (RSMT), which autonomically monitors and characterizes the runtime behavior of web applications. Second, we describe how RSMT trains a stacked denoising autoencoder to encode and reconstruct the call graph for end-to-end deep learning, where a low-dimensional representation of the raw features with unlabeled request data is used to recognize anomalies by computing the reconstruction error of the request data. Third, we analyze the results of empirically testing RSMT on both synthetic datasets and production applications with intentional vulnerabilities. Our results show that the proposed approach can efficiently and accurately detect attacks, including SQL injection, cross-site scripting, and deserialization, with minimal domain knowledge and little labeled training data.
Implications for Training on Smartphone Medication Reminder App Use by Adults With Chronic Conditions: Pilot Study Applying the Technology Acceptance Model

The majority of middle-aged to older patients with chronic conditions report forgetting to take medications as prescribed. The promotion of patients' smartphone medication reminder app (SMRA) use shows promise as a feasible and cost-effective way to support their medication adherence. Providing training on SMRA use, guided by the technology acceptance model (TAM), could be a promising intervention to promote patients' app use.



The aim of this pilot study was to (1) assess the feasibility of an SMRA training session designed to increase patients' intention to use the app through targeting perceived usefulness of app, perceived ease of app use, and positive subjective norm regarding app use and (2) understand the ways to improve the design and implementation of the training session in a hospital setting.



A two-group design was employed. A total of 11 patients older than 40 years (median=58, SD=9.55) and taking 3 or more prescribed medications took part in the study on one of two different dates as participants in either the training group (n=5) or nontraining group (n=6). The training group received an approximately 2-hour intervention training session designed to target TAM variables regarding one popular SMRA, the Medisafe app. The nontraining group received an approximately 2-hour control training session where the participants individually explored Medisafe app features. Each training session was concluded with a one-time survey and a one-time focus group.



Mann-Whitney U tests revealed that the level of perceived ease of use (P=.13) and the level of intention to use an SMRA (P=.33) were higher in the training group (median=7.00, median=6.67, respectively) than in the nontraining group (median=6.25, median=5.83). However, the level of perceived usefulness (U=4.50, Z=-1.99, P=.05) and the level of positive subjective norm (P=.25) were lower in the training group (median=6.50, median=4.29) than in the nontraining group (median=6.92, median=4.50). Focus groups revealed the following participants' perceptions of SMRA use in the real-world setting that the intervention training session would need to emphasize in targeting perceived usefulness and positive subjective norm: (1) the participants would find an SMRA to be useful if they thought the app could help address specific struggles in medication adherence in their lives and (2) the participants think that their family members (or health care providers) might view positively the participants' SMRA use in primary care settings (or during routine medical checkups).



Intervention training session, guided by TAM, appeared feasible in targeting patients' perceived ease of use and, thereby, increasing intention to use an SMRA. Emphasizing the real-world utility of SMRA, the training session could better target patients' perceived usefulness and positive subjective norm that are also important in increasing their intention to use the app.
Technical Report 2014-001 An Approach to Stack Overflow Counter-Measures Using Kernel Properties

The computer security became recently a subject of general concern for the main public, for governments, and for private companies alike. Indeed, the latest news involving stuxnet, flame, and so many other viruses made clear to everybody that privilege escalation and security threats in general deserve a greater attention. The range of buffer overflow exploitations in particular is large, ranging from data leaks to taking over a complete computing system. These are all dangerous, and most of the time the possibilities of exploitation are only limited by the skills of the attacking hacker. Our work contributes to this investigation by analyzing the hacking techniques for exploiting buffer overflows in order to understand the existing counter measures, and eventually find a more accurate way to prevent the exploitation of buffer overflows. Our working platform is the GNU/Linux family of operating systems. Our work is at the highest privilege levels and in the safest part of a GNU/Linux system, namely the kernel. We provide a system that allows the kernel to detect overflows and prevent their exploitation. Our system allows the kernel to inject at launch time some (minimal) code into the binary being run, and subsequently use this code to monitor the execution of that program with respect to its stack use, thus detecting stack overflows. The system stands alone in the sense that it does not need any hardware support; it also works on any program, no matter how that program was conceived or compiled. Beside the theoretical concepts we also present a proof-of-concept patch to the kernel supporting our idea. Overall we effectively show that guarding against buffer overflows at run time is not only possible but also feasible, and we also take the first steps toward implementing such a defense.
Real time control of ball and beam system with model reference adaptive control strategy using MIT rule

During real time control of a dynamic system, one needs to design control systems with advanced control strategies to handle inherent nonlinearities and disturbances. This paper deals with the designing of a model reference adaptive control system with the use of MIT rule for real time control of a ball and beam system. This paper uses the gradient theory to develop MIT rule in which one or more parameters of adaptive controller needs to be adjusted so that the plant could track the reference model. A linearized model of ball and beam system is used in this paper to design the controller on MATLAB and the designed controller is then applied for real time control of ball and beam system. Simulations carried on SIMULINK and MATLAB show good performance of the designed adaptive controller in real time.
Treatments for schizophrenia: a critical review of pharmacology and mechanisms of action of antipsychotic drugs

The treatment of schizophrenia has evolved over the past half century primarily in the context of antipsychotic drug development. Although there has been significant progress resulting in the availability and use of numerous medications, these reflect three basic classes of medications (conventional (typical), atypical and dopamine partial agonist antipsychotics) all of which, despite working by varying mechanisms of actions, act principally on dopamine systems. Many of the second-generation (atypical and dopamine partial agonist) antipsychotics are believed to offer advantages over first-generation agents in the treatment for schizophrenia. However, the pharmacological properties that confer the different therapeutic effects of the new generation of antipsychotic drugs have remained elusive, and certain side effects can still impact patient health and quality of life. Moreover, the efficacy of antipsychotic drugs is limited prompting the clinical use of adjunctive pharmacy to augment the effects of treatment. In addition, the search for novel and nondopaminergic antipsychotic drugs has not been successful to date, though numerous development strategies continue to be pursued, guided by various pathophysiologic hypotheses. This article provides a brief review and critique of the current therapeutic armamentarium for treating schizophrenia and drug development strategies and theories of mechanisms of action of antipsychotics, and focuses on novel targets for therapeutic agents for future drug development.
PDDL+ Planning via Constraint Answer Set Programming

PDDL+ is an extension of PDDL that enables modelling planning domains with mixed discrete-continuous dynamics. In this paper we present a new approach to PDDL+ planning based on Constraint Answer Set Programming (CASP), i.e. ASP rules plus numerical constraints. To the best of our knowledge, ours is the first attempt to link PDDL+ planning and logic programming. We provide an encoding of PDDL+ models into CASP problems. The encoding can handle non-linear hybrid domains, and represents a solid basis for applying logic programming to PDDL+ planning. As a case study, we consider the EZCSP CASP solver and obtain promising results on a set of PDDL+ benchmark problems.
Aligning Superintelligence with Human Interests : A Technical Research Agenda

The property that has given humans a dominant advantage over other species is not strength or speed, but intelligence. If progress in artificial intelligence continues unabated, AI systems will eventually exceed humans in general reasoning ability. A system that is “superintelligent” in the sense of being “smarter than the best human brains in practically every field” could have an enormous impact upon humanity (Bostrom 2014). Just as human intelligence has allowed us to develop tools and strategies for controlling our environment, a superintelligent system would likely be capable of developing its own tools and strategies for exerting control (Muehlhauser and Salamon 2012). In light of this potential, it is essential to use caution when developing AI systems that can exceed human levels of general intelligence, or that can facilitate the creation of such systems.
Defining metrics for continuous delivery and deployment pipeline

Continuous delivery is a software development practice where new features are made available to end users as soon as they have been implemented and tested. In such a setting, a key technical piece of infrastructure is the development pipeline that consists of various tools and databases, where features flow from development to deployment and then further to use. Metrics, unlike those conventionally used in software development, are needed to help define the performance of the development pipeline. In this paper, we address metrics that are suited for supporting continuous delivery and deployment through a descriptive and exploratory single case study on a project of a mid-sized Finnish software company, Solita Plc. As concrete data, we use data from project ”Lupapiste”, a web site for managing municipal authorizations and permissions.
A Random Decision Tree Framework for Privacy-Preserving Data Mining

Distributed data is ubiquitous in modern information driven applications. With multiple sources of data, the natural challenge is to determine how to collaborate effectively across proprietary organizational boundaries while maximizing the utility of collected information. Since using only local data gives suboptimal utility, techniques for privacy-preserving collaborative knowledge discovery must be developed. Existing cryptography-based work for privacy-preserving data mining is still too slow to be effective for large scale data sets to face today's big data challenge. Previous work on random decision trees (RDT) shows that it is possible to generate equivalent and accurate models with much smaller cost. We exploit the fact that RDTs can naturally fit into a parallel and fully distributed architecture, and develop protocols to implement privacy-preserving RDTs that enable general and efficient distributed privacy-preserving knowledge discovery.
Influence of life stress on depression: moderation by a polymorphism in the 5-HTT gene.

In a prospective-longitudinal study of a representative birth cohort, we tested why stressful experiences lead to depression in some people but not in others. A functional polymorphism in the promoter region of the serotonin transporter (5-HT T) gene was found to moderate the influence of stressful life events on depression. Individuals with one or two copies of the short allele of the 5-HT T promoter polymorphism exhibited more depressive symptoms, diagnosable depression, and suicidality in relation to stressful life events than individuals homozygous for the long allele. This epidemiological study thus provides evidence of a gene-by-environment interaction, in which an individual's response to environmental insults is moderated by his or her genetic makeup.