nils-herrmann/clean_scirepeval huggingface dataset:
Machine learning in virtual screening.

In this review, we highlight recent applications of machine learning to virtual screening, focusing on the use of supervised techniques to train statistical learning algorithms to prioritize databases of molecules as active against a particular protein target. Both ligand-based similarity searching and structure-based docking have benefited from machine learning algorithms, including naïve Bayesian classifiers, support vector machines, neural networks, and decision trees, as well as more traditional regression techniques. Effective application of these methodologies requires an appreciation of data preparation, validation, optimization, and search methodologies, and we also survey developments in these areas.
Semantic softmax loss for zero-shot learning

A typical pipeline for Zero-Shot Learning (ZSL) is to integrate the visual features and the class semantic descriptors into a multimodal framework with a linear or bilinear model. However, the visual features and the class semantic descriptors locate in different structural spaces, a linear or bilinear model can not capture the semantic interactions between different modalities well. In this letter, we propose a nonlinear approach to impose ZSL as a multi-class classification problem via a Semantic Softmax Loss by embedding the class semantic descriptors into the softmax layer of multi-class classification network. To narrow the structural differences between the visual features and semantic descriptors, we further use an L2 normalization constraint to the differences between the visual features and visual prototypes reconstructed with the semantic descriptors. The results on three benchmark datasets, i.e., AwA, CUB and SUN demonstrate the proposed approach can boost the performances steadily and achieve the state-of-the-art performance for both zero-shot classification and zero-shot retrieval.
Multi-Task Learning for Contextual Bandits

Contextual bandits are a form of multi-armed bandit in which the agent has access to predictive side information (known as the context) for each arm at each time step, and have been used to model personalized news recommendation, ad placement, and other applications. In this work, we propose a multi-task learning framework for contextual bandit problems. Like multi-task learning in the batch setting, the goal is to leverage similarities in contexts for different arms so as to improve the agent’s ability to predict rewards from contexts. We propose an upper confidence bound-based multi-task learning algorithm for contextual bandits, establish a corresponding regret bound, and interpret this bound to quantify the advantages of learning in the presence of high task (arm) similarity. We also describe an effective scheme for estimating task similarity from data, and demonstrate our algorithm’s performance on several data sets.
Medical Persona Classification in Social Media

Identifying medical persona from a social media post is of paramount importance for drug marketing and pharmacovigilance. In this work, we propose multiple approaches to infer the medical persona associated with a social media post. We pose this as a supervised multi-label text classification problem. The main challenge is to identify the hidden cues in a post that are indicative of a particular persona. We first propose a large set of manually engineered features for this task. Further, we propose multiple neural network based architectures to extract useful features from these posts using pre-trained word embeddings. Our experiments on thousands of blogs and tweets show that the proposed approach results in 7% and 5% gain in F-measure over manual feature engineering based approach for blogs and tweets respectively.
MsCGAN: Multi-scale Conditional Generative Adversarial Networks for Person Image Generation

To synthesize high quality person images with arbitrary poses is challenging. In this paper, we propose a novel Multi-scale Conditional Generative Adversarial Networks (MsCGAN), aiming to convert the input conditional person image to a synthetic image of any given target pose, whose appearance and the texture are consistent with the input image. MsCGAN is a multi-scale adversarial network consisting of two generators and two discriminators. One generator transforms the conditional person image into a coarse image of the target pose globally, and the other is to enhance the detailed quality of the synthetic person image through a local reinforcement network. The outputs of the two generators are then merged into a synthetic, discriminant and high-resolution image. On the other hand, the synthetic image is down-sampled to multiple resolutions as the input to multi-scale discriminator networks. The proposed multi-scale generators and discriminators handling different levels of visual features can benefit to synthesizing high resolution person images with realistic appearance and texture. Experiments are conducted on the Market-1501 and DeepFashion datasets to evaluate the proposed model, and both qualitative and quantitative results demonstrate superior performance of the
A Review of Solar Collectors and Thermal Energy Storage in Solar Thermal Applications

Thermal applications are drawing increasing attention in the solar energy research field, due to their high performance in energy storage density and energy conversion efficiency. In these applications, solar collectors and thermal energy storage systems are the two core components. This paper focuses on the latest developments and advances in solar thermal applications, providing a review of solar collectors and thermal energy storage systems. Various types of solar collectors are reviewed and discussed, including both non-concentrating collectors (low temperature applications) and concentrating collectors (high temperature applications). These are studied in terms of optical optimisation, heat loss reduction, heat recuperation enhancement and different suntracking mechanisms. Various types of thermal energy storage systems are also reviewed and discussed, including sensible heat storage, latent heat storage, chemical storage and cascaded storage. They are studied in terms of design criteria, material selection and different heat transfer enhancement technologies. Last but not least, existing and future solar power stations are overviewed.
Review Graph Based Online Store Review Spammer Detection

Online reviews provide valuable information about products and services to consumers. However, spammers are joining the community trying to mislead readers by writing fake reviews. Previous attempts for spammer detection used reviewers' behaviors, text similarity, linguistics features and rating patterns. Those studies are able to identify certain types of spammers, e.g., those who post many similar reviews about one target entity. However, in reality, there are other kinds of spammers who can manipulate their behaviors to act just like genuine reviewers, and thus cannot be detected by the available techniques. In this paper, we propose a novel concept of a heterogeneous review graph to capture the relationships among reviewers, reviews and stores that the reviewers have reviewed. We explore how interactions between nodes in this graph can reveal the cause of spam and propose an iterative model to identify suspicious reviewers. This is the first time such intricate relationships have been identified for review spam detection. We also develop an effective computation method to quantify the trustiness of reviewers, the honesty of reviews, and the reliability of stores. Different from existing approaches, we don't use review text information. Our model is thus complementary to existing approaches and able to find more difficult and subtle spamming activities, which are agreed upon by human judges after they evaluate our results.
Artificial Intelligence for Information Retrieval

This article describes the most prominent approaches to apply artificial intelligence technologies to information retrieval (IR). Information retrieval is a key technology for knowledge management. It deals with the search for information and the representation, storage and organization of knowledge. Information retrieval is concerned with search processes in which a user needs to identify a subset of information which is relevant for his information need within a large amount of knowledge. The information seeker formulates a query trying to describe his information need. The query is compared to document representations which were extracted during an indexing phase. The representations of documents and queries are typically matched by a similarity function such as the Cosine. The most similar documents are presented to the users who can evaluate the relevance with respect to their problem (Belkin, 2000). The problem to properly represent documents and to match imprecise representations has soon led to the application of techniques developed within Artificial Intelligence to information retrieval.
What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?

The finding that very large networks can be trained efficiently and reliably has led to a paradigm shift in computer vision from engineered solutions to learning formulations. As a result, the research challenge shifts from devising algorithms to creating suitable and abundant training data for supervised learning. How to efficiently create such training data? The dominant data acquisition method in visual recognition is based on web data and manual annotation. Yet, for many computer vision problems, such as stereo or optical flow estimation, this approach is not feasible because humans cannot manually enter a pixel-accurate flow field. In this paper, we promote the use of synthetically generated data for the purpose of training deep networks on such tasks. We suggest multiple ways to generate such data and evaluate the influence of dataset properties on the performance and generalization properties of the resulting networks. We also demonstrate the benefit of learning schedules that use different types of data at selected stages of the training process.
Micronutrients and Nutritional Supplements and School Performance and / or Cognitive Ability * ( Continued from previous page )

This article reviews research from published studies on the association between nutrition among school-aged children and their performance in school and on tests of cognitive functioning. Each reviewed article is accompanied by a brief description of its research methodology and outcomes. Articles are separated into 4 categories: food insufficiency, iron deficiency and supplementation, deficiency and supplementation of micronutrients, and the importance of breakfast. Research shows that children with iron deficiencies sufficient to cause anemia are at a disadvantage academically. Their cognitive performance seems to improve with iron therapy. A similar association and improvement with therapy is not found with either zinc or iodine deficiency, according to the reviewed articles. There is no evidence that population-wide vitamin and mineral supplementation will lead to improved academic performance. Food insufficiency is a serious problem affecting children’s ability to learn, but its relevance to US populations needs to be better understood. Research indicates that school breakfast programs seem to improve attendance rates and decrease tardiness. Among severely undernourished populations, school breakfast programs seem to improve academic performance and cognitive functioning.
Unsupervised Sentence Representations as Word Information Series: Revisiting TF-IDF

Sentence representation at the semantic level is a challenging task for Natural Language Processing and Artificial Intelligence. Despite the advances in word embeddings (i.e. word vector representations), capturing sentence meaning is an open question due to complexities of semantic interactions among words. In this paper, we present an embedding method, which is aimed at learning unsupervised sentence representations from unlabeled text. We propose an unsupervised method that models a sentence as a weighted series of word embeddings. The weights of the word embeddings are fitted by using Shannon’s word entropies provided by the Term Frequency–Inverse Document Frequency (TF–IDF) transform. The hyperparameters of the model can be selected according to the properties of data (e.g. sentence length and textual gender). Hyperparameter selection involves word embedding methods and dimensionalities, as well as weighting schemata. Our method offers advantages over existing methods: identifiable modules, short-term training, online inference of (unseen) sentence representations, as well as independence from domain, external knowledge and language resources. Results showed that our model outperformed the state of the art in well-known Semantic Textual Similarity (STS) benchmarks. Moreover, our model reached state-of-the-art performance when compared to supervised and knowledge-based STS systems. Corresponding author  (Ignacio Arroyo-Fernández), cmendezc@ccg.unam.mx (Carlos-Francisco Méndez-Cruz), gsierram@ii.unam.mx (Gerardo Sierra), juan-manuel.torres@univ-avignon.fr (Juan-Manuel Torres-Moreno), sidorov@cic.ipn.mx (Grigori Sidorov) 1Av. Universidad s/n  Ciudad universitaria, Coyoacán 04510, Ciudad de México 3Université d’Avignon et des Pays de Vaucluse. 339 chemin des Meinajaries 84911, Avignon cedex 9, France 4Instituto Politécnico Nacional. Av. Juan de Dios Bátiz, Esq. Miguel Othón de Mendizábal,  2017
SGX-Shield: Enabling Address Space Layout Randomization for SGX Programs

Traditional execution environments deploy Address Space Layout Randomization (ASLR) to defend against memory corruption attacks. However, Intel Software Guard Extension (SGX), a new trusted execution environment designed to serve security-critical applications on the cloud, lacks such an effective, well-studied feature. In fact, we find that applying ASLR to SGX programs raises non-trivial issues beyond simple engineering for a number of reasons: 1) SGX is designed to defeat a stronger adversary than the traditional model, which requires the address space layout to be hidden from the kernel; 2) the limited memory uses in SGX programs present a new challenge in providing a sufficient degree of entropy; 3) remote attestation conflicts with the dynamic relocation required for ASLR; and 4) the SGX specification relies on known and fixed addresses for key data structures that cannot be randomized. This paper presents SGX-Shield, a new ASLR scheme designed for SGX environments. SGX-Shield is built on a secure in-enclave loader to secretly bootstrap the memory space layout with a finer-grained randomization. To be compatible with SGX hardware (e.g., remote attestation, fixed addresses), SGX-Shield is designed with a software-based data execution protection mechanism through an LLVM-based compiler. We implement SGX-Shield and thoroughly evaluate it on real SGX hardware. It shows a high degree of randomness in memory layouts and stops memory corruption attacks with a high probability. SGX-Shield shows 7.61% performance overhead in running common microbenchmarks and 2.25% overhead in running a more realistic workload of an HTTPS server.
Surface modification of biodegradable magnesium and its alloys for biomedical applications

Magnesium and its alloys are being paid much attention recently as temporary implants, such as orthopedic implants and cardiovascular stents. However, the rapid degradation of them in physiological environment is a major obstacle preventing their wide applications to date, which will result in rapid mechanical integrity loss or even collapse of magnesium-based implants before injured tissues heal. Moreover, rapid degradation of the magnesium-based implants will also cause some adverse effects to their surrounding environment, such as local gas cavity around the implant, local alkalization and magnesium ion enrichment, which will reduce the integration between implant and tissue. So, in order to obtain better performance of magnesium-based implants in clinical trials, special alloy designs and surface modifications are prerequisite. Actually, when a magnesium-based implant is inserted in vivo, corrosion firstly happens at the implant-tissue interface and the biological response to implant is also determined by the interaction at this interface. So the surface properties, such as corrosion resistance, hemocompatibility and cytocompatibility of the implant, are critical for their in vivo performance. Compared with alloy designs, surface modification is less costly, flexible to construct multi-functional surface and can prevent addition of toxic alloying elements. In this review, we would like to summarize the current investigations of surface modifications of magnesium and its alloys for biomedical application. The advantages/disadvantages of different surface modification methods are also discussed as a suggestion for their utilization.
Manipulating the surface coating of ultra-small Gd2O3 nanoparticles for improved T1-weighted MR imaging.

In this report, monodispersed ultra-small Gd2O3 nanoparticles capped with hydrophobic oleic acid (OA) were synthesized with average particle size of 2.9 nm. Two methods were introduced to modify the surface coating to hydrophilic for bio-applications. With a hydrophilic coating, the polyvinyl pyrrolidone (PVP) coated Gd2O3 nanoparticles (Gd2O3-PVP) showed a reduced longitudinal T1 relaxation time compared with OA and cetyltrimethylammonium bromide (CTAB) co-coated Gd2O3 (Gd2O3-OA-CTAB) in the relaxation study. The Gd2O3-PVP was thus chosen for its further application study in MRI with an improved longitudinal relaxivity r1 of 12.1 mM(-1) s(-1) at 7 T, which is around 3 times as that of commercial contrast agent Magnevist(®). In vitro cell viability in HK-2 cell indicated negligible cytotoxicity of Gd2O3-PVP within preclinical dosage. In vivo MR imaging study of Gd2O3-PVP nanoparticles demonstrated considerable signal enhancement in the liver and kidney with a long blood circulation time. Notably, the OA capping agent was replaced by PVP through ligand exchange on the Gd2O3 nanoparticle surface. The hydrophilic PVP grants the Gd2O3 nanoparticles with a polar surface for bio-application, and the obtained Gd2O3-PVP could be used as an in vivo indicator of reticuloendothelial activity.
A KINEMATIC ANALYSIS AND SIMULATION BASED ON ADAMS FOR EGGPLANT PICKING ROBOT / 基于 ADAMS 的茄子采摘机器人运动学分析与仿真

Eggplant picking robot is a type of complex optical-mechanical-electrical equipment in greenhouse environment. Its structure and control are more exigent than traditional industrial robot. Optimization design method was utilized for the design of the eggplant picking robot body structure parameters in accordance with the eggplant growth and distribution space. In order to determine the spatial position relationship between the eggplant picking robot components and the end effector, the theoretical model of robot was established by virtue of DenavitHartenberg approach and the positive solution of the kinematic equation is obtained. Premultiplication decoupling of 1  i A and matrix 4 T were adopted to solve inverse kinematic solution with the help of Matlab software. Pro/E software was used to establish 3-D simulation model, and ADAMS (Automatic Dynamic Analysis of Mechanical Systems) simulation software was imported for the kinematics simulation analysis. It was indicated by the simulation results that the kinematic model established by DH approach reflects the real motion conditions of the robot, and both the positive and inverse kinematic solutions are correct. Structure of four degrees freedom eggplant picking robot was reasonable, it could meet the requirements of eggplant picking in the greenhouse cultivation pattern.
Addressing the Rare Word Problem in Neural Machine Translation

Neural Machine Translation (NMT) has recently attracted a l ot of attention due to the very high performance achieved by deep neural network s in other domains. An inherent weakness in existing NMT systems is their inabil ity to correctly translate rare words: end-to-end NMTs tend to have relatively sma ll vocabularies with a single “unknown-word” symbol representing every possibl e out-of-vocabulary (OOV) word. In this paper, we propose and implement a simple t echnique to address this problem. We train an NMT system on data that is augm ented by the output of a word alignment algorithm, allowing the NMT syste m to output, for each OOV word in the target sentence, its corresponding word in the source sentence. This information is later utilized in a post-process ing step that translates every OOV word using a dictionary. Our experiments on the WMT ’14 English to French translation task show that this simple method prov ides a substantial improvement over an equivalent NMT system that does not use thi technique. The performance of our system achieves a BLEU score of 36.9, whic h improves the previous best end-to-end NMT by 2.1 points. Our model matche s t performance of the state-of-the-art system while using three times less data.
K-Means Clustering Approach to Analyze NSL-KDD Intrusion Detection Dataset

1 Abstract— Clustering is the most acceptable technique to analyze the raw data. Clustering can help detect intrusions when our training data is unlabeled, as well as for detecting new and unknown types of intrusions. In this paper we are trying to analyze the NSL-KDD dataset using Simple K-Means clustering algorithm. We tried to cluster the dataset into normal and four of the major attack categories i.e. DoS, Probe, R2L, U2R. Experiments are performed in WEKA environment. Results are verified and validated using test dataset. Our main objective is to provide the complete analysis of NSL-KDD intrusion detection dataset.
Coordination in co-located agile software development projects

Agile software development provides a way to organise the complex task of multi-participant software development while accommodating constant project change. Agile software development is well accepted in the practitioner community but there is little understanding of how such projects achieve effective coordination, which is known to be critical in successful software projects. A theoretical model of coordination in the agile software development context is presented based on empirical data from three cases of co-located agile software development. Many practices in these projects act as coordination mechanisms, which together form a coordination strategy. Coordination strategy in this context has three components: synchronisation, structure, and boundary spanning. Coordination effectiveness has two components: implicit and explicit. The theoretical model of coordination in agile software development projects proposes that an agile coordination strategy increases coordination effectiveness. This model has application for practitioners who want to select appropriate practices from agile methods to ensure they achieve coordination coverage in their project. For the field of information systems development, this theory contributes to knowledge of coordination and coordination effectiveness in the context of agile software development.