nils-herrmann/clean_scirepeval huggingface dataset:
Machine Learning and Knowledge Discovery in Databases

Deep learning has recently helped AI systems to achieve human-level performance in several domains, including speech recognition, object classification, and playing several types of games. The major benefit of deep learning is that it enables end-to-end learning of representations of the data on several levels of abstraction. However, the overall network architecture and the learning algorithms’ sensitive hyperparameters still need to be set manually by human experts. In this talk, I will discuss extensions of Bayesian optimization for handling this problem effectively, thereby paving the way to fully automated end-to-end learning. I will focus on speeding up Bayesian optimization by reasoning over data subsets and initial learning curves, sometimes resulting in 100-fold speedups in finding good hyperparameter settings. I will also show competition-winning practical systems for automated machine learning (AutoML) and briefly show related applications to the end-to-end optimization of algorithms for solving hard combinatorial problems. Bio. Frank Hutter is an Emmy Noether Research Group Lead (eq. Asst. Prof.) at the Computer Science Department of the University of Freiburg (Germany). He received his PhD from the University of British Columbia (2009). Frank’s main research interests span artificial intelligence, machine learning, combinatorial optimization, and automated algorithm design. He received a doctoral dissertation award from the Canadian Artificial Intelligence Association and, with his coauthors, several best paper awards (including from JAIR and IJCAI) and prizes in international competitions on machine learning, SAT solving, and AI planning. In 2016 he received an ERC Starting Grant for a project on automating deep learning based on Bayesian optimization, Bayesian neural networks, and deep reinforcement learning. Frontiers in Recurrent Neural Network Research
Audit-Free Cloud Storage via Deniable Attribute-Based Encryption

Cloud storage services have become increasingly popular. Because of the importance of privacy, many cloud storage encryption schemes have been proposed to protect data from those who do not have access. All such schemes assumed that cloud storage providers are safe and cannot be hacked; however, in practice, some authorities (i.e., coercers) may force cloud storage providers to reveal user secrets or confidential data on the cloud, thus altogether circumventing storage encryption schemes. In this paper, we present our design for a new cloud storage encryption scheme that enables cloud storage providers to create convincing fake user secrets to protect user privacy. Since coercers cannot tell if obtained secrets are true or not, the cloud storage providers ensure that user privacy is still securely protected.
Protect sensitive sites from phishing attacks using features extractable from inaccessible phishing URLs

Phishing is the third cyber-security threat globally and the first cyber-security threat in China. There were 61.69 million phishing victims in China alone from June 2011 to June 2012, with the total annual monetary loss more than 4.64 billion US dollars. These phishing attacks were highly concentrated in targeting at a few major Websites. Many phishing Webpages had a very short life span. In this paper, we assume the Websites to protect against phishing attacks are known, and study the effectiveness of machine learning based phishing detection using only lexical and domain features, which are available even when the phishing Webpages are inaccessible. We propose several novel highly effective features, and use the real phishing attack data against Taobao and Tencent, two main phishing targets in China, in studying the effectiveness of each feature, and each group of features. We then select an optimal set of features in our phishing detector, which has achieved a detection rate better than 98%, with a false positive rate of 0.64% or less. The detector is still effective when the distribution of phishing URLs changes.
Injuries to posterolateral corner of the knee: a comprehensive review from anatomy to surgical treatment☆

Although injuries to the posterolateral corner of the knee were previously considered to be a rare condition, they have been shown to be present in almost 16% of all knee injuries and are responsible for sustained instability and failure of concomitant reconstructions if not properly recognized. Although also once considered to be the "dark side of the knee", increased knowledge of the posterolateral corner anatomy and biomechanics has led to improved diagnostic ability with better understanding of physical and imaging examinations. The management of posterolateral corner injuries has also evolved and good outcomes have been reported after operative treatment following anatomical reconstruction principles.
Unpacking the Exploration-Exploitation Tradeoff : A Synthesis of Human and Animal Literatures

Many decisions in the lives of animals and humans require a fine balance between the exploration of different options and the exploitation of their rewards. Do you buy the advertised car, or do you testdrive different models? Do you continue feeding from the current patch of flowers, or do you fly off to another one? Do you marry your current partner, or try your luck with someone else? The balance required in these situations is commonly referred to as the exploration-exploitation tradeoff. It features prominently in a wide range of research traditions, including learning, foraging, and decisionmaking literatures. Here, we integrate findings from these and other often-isolated literatures in order to gain a better understanding of the possible tradeoffs between exploration and exploitation, and we propose new theoretical insights that might guide future research. Specifically, we explore how potential tradeoffs depend on (1) the conceptualization of exploration and exploitation; (2) the influencing environmental, social, and individual factors; (3) the scale at which exploration and exploitation are considered; (4) the relationship and types of transitions between the two behaviors; and (5) the goals of the decision maker. We conclude that exploration and exploitation are best conceptualized as points on a continuum, and that the extent to which an agent’s behavior can be interpreted as exploratory or exploitative depends upon the level of abstraction at which it is
Simultaneous Modeling of Multiple Complications for Risk Profiling in Diabetes Care

Type 2 diabetes mellitus (T2DM) is a chronic disease that oen results in multiple complications. Risk prediction and proling of T2DM complications is critical for healthcare professionals to design personalized treatment plans for patients in diabetes care for improved outcomes. In this paper, we study the risk of developing complications aer the initial T2DM diagnosis from longitudinal patient records. We propose a novel multi-task learning approach to simultaneously model multiple complications where each task corresponds to the risk modeling of one complication. Specically, the proposed method strategically captures the relationships (1) between the risks of multiple T2DM complications, (2) between the dierent risk factors, and (3) between the risk factor selection paerns. e method uses coecient shrinkage to identify an informative subset of risk factors from high-dimensional data, and uses a hierarchical Bayesian framework to allow domain knowledge to be incorporated as priors. e proposed method is favorable for healthcare applications because in additional to improved prediction performance, relationships among the dierent risks and risk factors are also identied. Extensive experimental results on a large electronic medical claims database show that the proposed method outperforms state-of-the-art models by a signicant margin. Furthermore, we show that the risk associations learned and the risk factors identied lead to meaningful clinical insights. CCS CONCEPTS •Information systems→ Data mining; •Applied computing → Health informatics;
Design of patch array antennas for future 5G applications

The purpose of the paper is to investigate the design of rectangular patch antenna arrays fed by miscrostrip and coaxial lines at 28 GHz for future 5G applications. Our objective is to design a four element antenna array with a bandwidth higher than 1 GHz and a maximum radiation gain. The performances of the rectangular 4∗1 and 2∗2 patch antenna arrays designed on Rogers RT/Duroid 5880 substrate were optimized and the simulation results reveal that the performance of 4∗1 antenna array fed by microstrip line is better than 2∗2 antenna array fed by coaxial cable. We obtained for the topology of 4∗1 rectangular patch array antenna a bandwidth of 2.15 GHz and 1.3 GHz respectively with almost similar gains of the order of 13.3 dBi.
A Review of Non-Isolated High Step-Up DC/DC Converters in Renewable Energy Applications

With the shortage of the energy and ever increasing of the oil price, research on the renewable and green energy sources, especially the solar arrays and the fuel cells, becomes more and more important. How to achieve high step-up and high efficiency DC/DC converters is the major consideration in the renewable grid-connected power applications due to the low voltage of PV arrays and fuel cells. The topology study with high step-up conversion is concentrated and most topologies recently proposed in these applications are covered and classified. The advantages and disadvantages of these converters are discussed and the major challenges of high step-up converters in renewable energy applications are summarized. This paper would like to make a clear picture on the general law and framework for the next generation non-isolated high step-up DC/DC converters.
Learning Shared Representations in Multi-task Reinforcement Learning

We investigate a paradigm in multi-task reinforcement learning (MT-RL) in which an agent is placed in an environment and needs to learn to perform a series of tasks, within this space. Since the environment does not change, there is potentially a lot of common ground amongst tasks and learning to solve them individually seems extremely wasteful. In this paper, we explicitly model and learn this shared structure as it arises in the state-action value space. We will show how one can jointly learn optimal value-functions by modifying the popular valueiteration and policy-iteration procedures to accommodate this shared representation assumption and leverage the power of multi-task supervised learning. Finally, we demonstrate that the proposed model and training procedures, are able to infer good value functions, even under low samples regimes. In addition to data efficiency, we will show in our analysis, that learning abstractions of the state space jointly across tasks leads to more robust, transferable representations with the potential for better generalization.
New multimedia archiving technique using multiple quick-response codes

Quick Response (QR) code is a two-dimensional bar-code, which is quite popular due to its excellent storage capacity and error resilience. Generally, QR codes are widely used to store text information such as URL links, geographical coordinates, name cards, inventory information, authorship, etc. To reach the current limit of QR codes, in this paper, we would like to propose an innovative multimedia archiving technique, which is built upon the advanced signal processing scheme to tackle the multiple QR codes all at once. The multimedia data often include texts, images, audio data, special-purpose codes, etc. The recently proposed software-defined multiplexing code (SDMC) can be applied to combine all of them even though each type of data would have the individual data format different from others. Our proposed new archiving technique involves two phases, namely multimedia-amalgamation (MA) and multimedia-detachment (MD). In the MA phase, the multimedia data, regardless of their data format, can be converted to the binary streams; then the SDMC will be employed to aggregate them together in a longer binary stream; such an ultimate long binary stream will be converted to the QR codes. The QR codes, protected by the inherent error-correction mechanism, will thus be placed on a sheet or multiple sheets with a uniform spacing. The sheet(s) containing multiple QR codes can thus be archived in soft (PDF) or hard (print-out) copies. In the MD (recovery) phase, one can scan these PDF files and employ our designed signal processing algorithms to separate the individual QR codes; then the corresponding QR decoder can convert these QR codes back to the original binary stream. Finally the unstuffing algorithm in the SDMC can detach the individual data from the long binary stream composed by the multimedia mixture.
UnDeepVO: Monocular Visual Odometry Through Unsupervised Deep Learning

We propose a novel monocular visual odometry (VO) system called UnDeepVO in this paper. UnDeepVO is able to estimate the 6-DoF pose of a monocular camera and the depth of its view by using deep neural networks. There are two salient features of the proposed UnDeepVo:one is the unsupervised deep learning scheme, and the other is the absolute scale recovery. Specifically, we train UnDeepVoby using stereo image pairs to recover the scale but test it by using consecutive monocular images. Thus, UnDeepVO is a monocular system. The loss function defined for training the networks is based on spatial and temporal dense information. A system overview is shown in Fig. 1. The experiments on KITTI dataset show our UnDeepVO achieves good performance in terms of pose accuracy.
Effects of mindfulness training on body awareness to sexual stimuli: implications for female sexual dysfunction.

Treatments of female sexual dysfunction have been largely unsuccessful because they do not address the psychological factors that underlie female sexuality. Negative self-evaluative processes interfere with the ability to attend and register physiological changes (interoceptive awareness). This study explores the effect of mindfulness meditation training on interoceptive awareness and the three categories of known barriers to healthy sexual functioning: attention, self-judgment, and clinical symptoms.



Forty-four college students (30 women) participated in either a 12-week course containing a "meditation laboratory" or an active control course with similar content or laboratory format. Interoceptive awareness was measured by reaction time in rating physiological response to sexual stimuli. Psychological barriers were assessed with self-reported measures of mindfulness and psychological well-being.



Women who participated in the meditation training became significantly faster at registering their physiological responses (interoceptive awareness) to sexual stimuli compared with active controls (F(1,28) = 5.45, p = .03, η(p)(2) = 0.15). Female meditators also improved their scores on attention (t = 4.42, df = 11, p = .001), self-judgment, (t = 3.1, df = 11, p = .01), and symptoms of anxiety (t = -3.17, df = 11, p = .009) and depression (t = -2.13, df = 11, p < .05). Improvements in interoceptive awareness were correlated with improvements in the psychological barriers to healthy sexual functioning (r = -0.44 for attention, r = -0.42 for self-judgment, and r = 0.49 for anxiety; all p < .05).



Mindfulness-based improvements in interoceptive awareness highlight the potential of mindfulness training as a treatment of female sexual dysfunction.
Sclerocornea in a patient with van den Ende-Gupta syndrome homozygous for a SCARF2 microdeletion.

Van den Ende-Gupta Syndrome (VDEGS) is an autosomal recessive disorder characterized by blepharophimosis, distinctive nose, hypoplastic maxilla, and skeletal abnormalities. Using homozygosity mapping in four VDEGS patients from three consanguineous families, Anastacio et al.  Because sclerocornea had been described in DiGeorge-velo-cardio-facial syndrome but not in VDEGS, they suggested that the ocular abnormalities were caused by the 22q11.2 microdeletion. We report on a 23-year-old male who presented with bilateral sclerocornea and the VDGEGS phenotype who was subsequently found to be homozygous for a 17 bp deletion in exon 4 of SCARF2. The occurrence of bilateral sclerocornea in our patient together with that of Bedeschi et al., suggests that the full VDEGS phenotype may include sclerocornea resulting from homozygosity or compound heterozygosity for loss of function variants in SCARF2.
Recent advances in oncology acupuncture and safety considerations in practice.

Oncology acupuncture is a new and emerging field of research. Recent advances from published clinical trials have added evidence to support the use of acupuncture for symptom management in cancer patients. Recent new developments include (1) pain and dysfunction after neck dissection; (2) radiation-induced xerostomia in head and neck cancer; (3) aromatase inhibitor-associated arthralgia in breast cancer; (4) hot flashes in breast cancer and prostate cancer; and (5) chemotherapy-induced neutropenia in ovarian cancer. Some interventions are becoming a non-pharmaceutical option for cancer patients, while others still require further validation and confirmation. Meanwhile, owing to the rapid development of the field and increased demands from cancer patients, safety issues concerning oncology acupuncture practice have become imperative. Patients with cancer may be at higher risk developing adverse reactions from acupuncture. Practical strategies for enhancing safety measures are discussed and recommended.
Technology adoption decisions in the household: A seven-model comparison

We identified 7 theoretical models that have been used to explain technology adoption and use. We then examined the boundary conditions of these models of technology adoption when applied to the household context using longitudinal empirical data from households regarding their purchase and use decisions related to household technologies. We conducted 2 studies and collected 1,247 responses from U.S. households for the first study and 2,064 responses from U.S. households for the second study. Those households that had adopted household technologies were surveyed regarding their use behavior. Potential adopters (i.e., those who had currently not adopted) were surveyed regarding their purchase intentions. This allowed us to identify the most influential factors affecting a household’s decision to adopt and use technologies. The results show that the model of adoption of technology in the household provided the richest explanation and explained best why households purchase and use technologies.
Antiproliferative activities of parthenolide and golden feverfew extract against three human cancer cell lines.

The medicinal herb feverfew [Tanacetum parthenium (L.) Schultz-Bip.] has long been used as a folk remedy for the treatment of migraine and arthritis. Parthenolide, a sesquiterpene lactone, is considered to be the primary bioactive compound in feverfew having anti-migraine, anti-tumor, and anti-inflammatory properties. In this study we determined, through in vitro bioassays, the inhibitory activity of parthenolide and golden feverfew extract against two human breast cancer cell lines (Hs605T and MCF-7) and one human cervical cancer cell line (SiHa). Feverfew ethanolic extract inhibited the growth of all three types of cancer cells with a half-effective concentration (EC50) of 1.5 mg/mL against Hs605T, 2.1 mg/mL against MCF-7, and 0.6 mg/mL against SiHa. Among the tested constituents of feverfew (i.e., parthenolide, camphor, luteolin, and apigenin), parthenolide showed the highest inhibitory effect with an EC50 against Hs605T, MCF-7, and SiHa of 2.6 microg/mL, 2.8 microg/mL, and 2.7 microg/mL, respectively. Interactions between parthenolide and flavonoids (apigenin and luteolin) in feverfew extract also were investigated to elucidate possible synergistic or antagonistic effects. The results revealed that apigenin and luteolin might have moderate to weak synergistic effects with parthenolide on the inhibition of cancer cell growth of Hs605T, MCF-7, and SiHa.
Analysis of buck converters for on-chip integration with a dual supply voltage microprocessor

An analysis of an on-chip buck converter is presented in this paper. A high switching frequency is the key design parameter that simultaneously permits monolithic integration and high efficiency. A model of the parasitic impedances of a buck converter is developed. With this model, a design space is determined that allows integration of active and passive devices on the same die for a target technology. An efficiency of 88.4% at a switching frequency of 477 MHz is demonstrated for a voltage conversion from 1.2–0.9 volts while supplying 9.5 A average current. The area occupied by the buck converter is 12.6 mm assuming an 80-nm CMOS technology. An estimate of the efficiency is shown to be within 2.4% of simulation at the target design point. Full integration of a high-efficiency buck converter on the same die with a dualmicroprocessor is demonstrated to be feasible.
A technical review on text recognition from images

Text recognition in images is an active research area which attempts to develop a computer application with the ability to automatically read the text from images. Nowadays there is a huge demand of storing the information available on paper documents in to a computer readable form for later use. One simple way to store information from these paper documents in to computer system is to first scan the documents and then store them as images. However to reuse this information it is very difficult to read the individual contents and searching the contents form these documents line-by-line and word-by-word. The challenges involved are: font characteristics of the characters in paper documents and quality of the images. Due to these challenges, computer is unable to recognize the characters while reading them. Thus, there is a need of character recognition mechanisms to perform document image analysis which transforms documents in paper format to electronic format. In this paper, we have reviewed and analyzed different methods for text recognition from images. The objective of this review paper is to summarize the well-known methods for better understanding of the reader.
Survey on Recent Progresses of Semantic Image Segmentation with CNNs

Convolutional neural networks (CNNs) have been the mainstream in many computer vision tasks, such as image classification, object detection, face recognition and so on. We survey the state-of-the-art results on Pascal VOC 2012 semantic segmentation challenge which has made great progresses in 2015. We investigate the effectiveness of the new layers, structures and strategies behind these results proposed to produce more refined segmentation. Their main contributions focus on utilizing more structures and contextual information in the image or feature spaces. Most of these approaches serve for several independent stages in semantic image segmentation. In this paper, we discuss possible architectures to incorporate existing structures and strategies. Finally possible directions on enhancing CNNs to segment given semantic objects are proposed.