nils-herrmann/clean_scirepeval huggingface dataset:
Sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network

This paper proposes a neural network architecture and training scheme to learn the start and end time of sound events (strong labels) in an audio recording given just the list of sound events existing in the audio without time information (weak labels). We achieve this by using a stacked convolutional and recurrent neural network with two prediction layers in sequence one for the strong followed by the weak label. The network is trained using frame-wise log melband energy as the input audio feature, and weak labels provided in the dataset as labels for the weak label prediction layer. Strong labels are generated by replicating the weak labels as many number of times as the frames in the input audio feature, and used for strong label layer during training. We propose to control what the network learns from the weak and strong labels by different weighting for the loss computed in the two prediction layers. The proposed method is evaluated on a publicly available dataset of 155 hours with 17 sound event classes. The method achieves the best error rate of 0.84 for strong labels and F-score of 43.3% for weak labels on the unseen test split.
Personalized search result diversification via structured learning

This paper is concerned with the problem of personalized diversification of search results, with the goal of enhancing the performance of both plain diversification and plain personalization algorithms. In previous work, the problem has mainly been tackled by means of unsupervised learning. To further enhance the performance, we propose a supervised learning strategy. Specifically, we set up a structured learning framework for conducting supervised personalized diversification, in which we add features extracted directly from the tokens of documents and those utilized by unsupervised personalized diversification algorithms, and, importantly, those generated from our proposed user-interest latent Dirichlet topic model. Based on our proposed topic model whether a document can cater to a user's interest can be estimated in our learning strategy. We also define two constraints in our structured learning framework to ensure that search results are both diversified and consistent with a user's interest. We conduct experiments on an open personalized diversification dataset and find that our supervised learning strategy outperforms unsupervised personalized diversification methods as well as other plain personalization and plain diversification methods.
NEON Crypto

NEON is a vector instruction set included in a large fraction of new ARM-based tablets and smartphones. This paper shows that NEON supports high-security cryptography at surprisingly high speeds; normally data arrives at lower speeds, giving the CPU time to handle tasks other than cryptography. In particular, this paper explains how to use a single 800MHz Cortex A8 core to compute the existing NaCl suite of high-security cryptographic primitives at the following speeds: 5.60 cycles per byte (1.14 Gbps) to encrypt using a shared secret key, 2.30 cycles per byte (2.78 Gbps) to authenticate using a shared secret key, 527102 cycles (1517/second) to compute a shared secret key for a new public key, 650102 cycles (1230/second) to verify a signature, and 368212 cycles (2172/second) to sign a message. These speeds make no use of secret branches and no use of secret memory addresses.
Canonical Correlation Analysis based on Hilbert-Schmidt Independence Criterion and Centered Kernel Target Alignment

Canonical correlation analysis (CCA) is a well established technique for identifying linear relationships among two variable sets. Kernel CCA (KCCA) is the most notable nonlinear extension but it lacks interpretability and robustness against irrelevant features. The aim of this article is to introduce two nonlinear CCA extensions that rely on the recently proposed Hilbert-Schmidt independence criterion and the centered kernel target alignment. These extensions determine linear projections that provide maximally dependent projected data pairs. The paper demonstrates that the use of linear projections allows removing irrelevant features, whilst extracting combinations of strongly associated features. This is exemplified through a simulation and the analysis of recorded data that are available in the literature.
Transaction Processing: Concepts and Techniques

In undergoing this life, many people always try to do and get the best. New knowledge, experience, lesson, and everything that can improve the life will be done. However, many people sometimes feel confused to get those things. Feeling the limited of experience and sources to be better is one of the lacks to own. However, there is a very simple thing that can be done. This is what your teacher always manoeuvres you to do this one. Yeah, reading is the answer. Reading a book as this transaction processing concepts and techniques and other references can enrich your life quality. How can it be?
EEG-based Speech Recognition - Impact of Temporal Effects

In this paper, we investigate the use of electroencephalograhic signals for the purpose of recognizing unspoken speech. The term unspoken speech refers to the process in which a subject imagines speaking a given word without moving any articulatory muscle or producing any audible sound. Early work by Wester (Wester, 2006) presented results which were initially interpreted to be related to brain activity patterns due to the imagination of pronouncing words. However, subsequent investigations lead to the hypothesis that the good recognition performance might instead have resulted from temporal correlated artifacts in the brainwaves since the words were presented in blocks. In order to further investigate this hypothesis, we run a study with 21 subjects, recording 16 EEG channels using a 128 cap montage. The vocabulary consists of 5 words, each of which is repeated 20 times during a recording session in order to train our HMM-based classifier. The words are presented in blockwise, sequential, and random order. We show that the block mode yields an average recognition rate of 45.50%, but it drops to chance level for all other modes. Our experiments suggest that temporal correlated artifacts were recognized instead of words in block recordings and back the above-mentioned hypothesis.
Visualization of Interfirm Relations in a Converging Mobile Ecosystem

The mobile ecosystem is characterized by a large and complex network of companies interacting with each other, directly and indirectly, to provide a broad array of mobile products and services to end-customers. With the convergence of enabling technologies, the complexity of the mobile ecosystem is increasing multifold as new actors are emerging, new relations are formed, and the traditional distribution of power is shifted. Drawing on theories of complex systems, interfirm relationships, and the creative art and science of network visualization, this paper identifies key catalysts and develops a method to effectively map the complex structure and dynamics of over 7,000 global companies and 18,000 relationships in the mobile ecosystem. Our visual approach enables decision makers to explore the complexity of interfirm relations in the mobile ecosystem, understand their firmpsilas competitive position in a network context, and identify patterns that may influence their choice of innovation strategy or business models.
Exploiting diversity and synthetic biology for the production of algal biofuels

Modern life is intimately linked to the availability of fossil fuels, which continue to meet the world's growing energy needs even though their use drives climate change, exhausts finite reserves and contributes to global political strife. Biofuels made from renewable resources could be a more sustainable alternative, particularly if sourced from organisms, such as algae, that can be farmed without using valuable arable land. Strain development and process engineering are needed to make algal biofuels practical and economically viable.
Semantic Modelling with Long-Short-Term Memory for Information Retrieval

In this paper we address the following problem in web document and information retrieval (IR): How can we use long-term context information to gain better IR performance? Unlike common IR methods that use bag of words representation for queries and documents, we treat them as a sequence of words and use long short term memory (LSTM) to capture contextual dependencies. To the best of our knowledge, this is the first time that LSTM is applied to information retrieval tasks. Unlike training traditional LSTMs, the training strategy is different due to the special nature of information retrieval problem. Experimental evaluation on an IR task derived from the Bing web search demonstrates the ability of the proposed method in addressing both lexical mismatch and long-term context modelling issues, thereby, significantly outperforming existing state of the art methods for web document retrieval task.
Gamification design framework based on SDT for student motivation

Gamification is a new technique in instructional design that gains more and more attention in the elearning field. The implementation of gamification systems varies on different fields, such as sales, web design, marketing, education etc. There are many examples of applied gamification, such as international companies that use game elements in their applications in order to increase the users' motivation through a gamified process. In this paper we outline the design of a gamified scenario with the use of structural and content gamification to increase student intrinsic motivation based on the Self-Determination Theory. Besides, the use of game elements, thus game-design techniques, make the learning process more “fun” and maintain the realistic purpose of its situation. However, the process of increasing motivation has a level of complexity, since rewards and other game elements trigger extrinsic motivation. As such this study investigates students' intrinsic motivation by designing and implementing an eClass through practical usage of gamification based on the core components of Self Determination Theory (SDT), so as to improve student engagement in class. According to the findings the use of gamification in learning and instructional design can improve student motivation. This gamified elearning course can be used as an instructional tool to increase the levels of motivation and achieve all the learning outcomes and goals with the higher success rate in online education. Further research is required to confirm these theoretical findings.
AllergyBot: A Chatbot Technology Intervention for Young Adults with Food Allergies Dining Out

Dining out is one of the biggest challenges people with food allergies face. For young adults, especially, the fear of having an allergic reaction when dining out impairs social aspects of their life. The exhausting process of searching online and communicating with restaurants also increases their anxiety.
 To improve the quality of life for young adults with food allergies, we present AllergyBot, an intelligent and humane Chatbot that provides restaurants' allergy accommodation information based on users' allergens. We use established instant messaging platforms to create a form of conversation that young adults are familiar with. AllergyBot aims to reduce the users' inquiry overload, improve their overall dining out experiences, and support their social life.
Serum zinc levels in patients with iron deficiency anemia and its association with symptoms of iron deficiency anemia

Iron deficiency anemia (IDA) is a major public health problem especially in underdeveloped and developing countries. Zinc is the co-factor of several enzymes and plays a role in iron metabolism, so zinc deficiency is associated with IDA. In this study, it was aimed to investigate the relationship of symptoms of IDA and zinc deficiency in adult IDA patients. The study included 43 IDA patients and 43 healthy control subjects. All patients were asked to provide a detailed history and were subjected to a physical examination. The hematological parameters evaluated included hemoglobin (Hb); hematocrit (Ht); red blood cell (erythrocyte) count (RBC); and red cell indices mean corpuscular volume (MCV), mean corpuscular hemoglobin (МСН), mean corpuscular hemoglobin concentration (МСНС), and red cell distribution width (RDW). Anemia was defined according to the criteria defined by the World Health Organization (WHO). Serum zinc levels were measured in the flame unit of atomic absorption spectrophotometer. Symptoms attributed to iron deficiency or depletion, defined as fatigue, cardiopulmonary symptoms, mental manifestations, epithelial manifestations, and neuromuscular symptoms, were also recorded and categorized. Serum zinc levels were lower in anemic patients (103.51 ± 34.64 μ/dL) than in the control subjects (256.92 ± 88.54 μ/dL; <0.001). Patients with zinc level <99 μ/dL had significantly more frequent mental manifestations (p < 0.001), cardiopulmonary symptoms (p = 0.004), restless leg syndrome (p = 0.016), and epithelial manifestations (p < 0.001) than patients with zinc level > 100 μ/dL. When the serum zinc level was compared with pica, no statistically significant correlation was found (p = 0.742). Zinc is a trace element that functions in several processes in the body, and zinc deficiency aggravates IDA symptoms. Measurement of zinc levels and supplementation if necessary should be considered for IDA patients.
Ontology based classification for multi-label image annotation

Image annotation has been an important task for visual information retrieval. It usually involves a multi-class multi-label classification problem. To solve this problem, many researches have been conducted during last two decades, although most of the proposed methods rely on the training data with the ground truth. To prepare such a ground truth is an expensive and laborious task that cannot be easily scaled, and “semantic gaps” between low-level visual features and high-level semantics still remain. In this paper, we propose a novel approach, ontology based supervised learning for multi-label image annotation, where classifiers' training is conducted using easily gathered Web data. Moreover, it takes advantage of both low-level visual features and high-level semantic information of given images. Experimental results using 0.507 million Web images database show effectiveness of the proposed framework over existing method.
Design, implementation and security analysis of Bluetooth pairing protocol in NS2

Secure Simple Pairing (SSP), a characteristic of the Bluetooth Core Version 2.1 specification was build to address two foremost concerns amongst the Bluetooth user community: security and simplicity of the pairing process. It utilizes Elliptic Curve Diffie-Hellmen (ECDH) protocol for generating keys for the first time in Bluetooth pairing. It provides the security properties known session key security, forward security, resistance to key-compromise impersonation attack and to unknown key-share attack, key control. This paper presents the simulation and security analysis of Bluetooth pairing protocol for numeric comparison using ECDH in NS2. The protocol also employs SAGEMATH for cryptographic functions.
Site-Level Web Template Extraction Based on DOM Analysis

One of the main development resources for website engineers are Web templates. Templates allow them to increase productivity by plugin content into already formatted and prepared pagelets. For the final user templates are also useful, because they provide uniformity and a common look and feel for all webpages. However, from the point of view of crawlers and indexers, templates are an important problem, because templates usually contain irrelevant information such as advertisements, menus, and banners. Processing and storing this information leads to a waste of resources (storage space, bandwidth, etc.). It has been measured that templates represent between 40% and 50% of data on the Web. Therefore, identifying templates is essential for indexing tasks. In this work we propose a novel method for automatic web template extraction that is based on similarity analysis between the DOM trees of a collection of webpages that are detected using an hyperlink analysis. Our implementation and experiments demonstrate the usefulness of the technique.
An Ancient, Unified Mechanism for Metformin Growth Inhibition in C. elegans and Cancer

Metformin has utility in cancer prevention and treatment, though the mechanisms for these effects remain elusive. Through genetic screening in C. elegans, we uncover two metformin response elements: the nuclear pore complex (NPC) and acyl-CoA dehydrogenase family member-10 (ACAD10). We demonstrate that biguanides inhibit growth by inhibiting mitochondrial respiratory capacity, which restrains transit of the RagA-RagC GTPase heterodimer through the NPC. Nuclear exclusion renders RagC incapable of gaining the GDP-bound state necessary to stimulate mTORC1. Biguanide-induced inactivation of mTORC1 subsequently inhibits growth through transcriptional induction of ACAD10. This ancient metformin response pathway is conserved from worms to humans. Both restricted nuclear pore transit and upregulation of ACAD10 are required for biguanides to reduce viability in melanoma and pancreatic cancer cells, and to extend C. elegans lifespan. This pathway provides a unified mechanism by which metformin kills cancer cells and extends lifespan, and illuminates potential cancer targets. PAPERCLIP.
Wide-angle scanning cavity antenna element for mobile Satcom applications at Ka band

A novel dual linearly polarized cavity antenna with excellent scan performance up to 60° in both E- and H-plane is presented. The highly integrated antenna element for Ka band Satcom on the move applications is operating in the uplink band at 30 GHz. Vertical stubs incorporated into the 3-D architecture are proposed to compensate parasitic effects limiting the scan volume of planar phased array antennas. To verify this approach, the vertical transition used in the design, and the entire antenna structure in single element configuration were fabricated and measured successfully.
Lactose-fermenting, multiple drug-resistant Salmonella typhi strains isolated from a patient with postoperative typhoid fever.

Two lactose-fermenting Salmonella typhi strains were isolated from bile and blood specimens of a typhoid fever patient who underwent a cholecystectomy due to cholelithiasis. One lactose-fermenting S. typhi strain was also isolated from a pus specimen which was obtained at the tip of the T-shaped tube withdrawn from the operative wound of the common bile duct of the patient. These three lactose-fermenting isolates: GIFU 11924 from bile, GIFU 11926 from pus, and GIFU 11927 from blood, were phenotypically identical to the type strain (GIFU 11801 = ATCC 19430 = NCTC 8385) of S. typhi, except that the three strains fermented lactose and failed to blacken the butt of Kligler iron agar or triple sugar iron agar medium. All three lactose-fermenting strains were resistant to chloramphenicol, ampicillin, sulfomethoxazole, trimethoprim, gentamicin, cephaloridine, and four other antimicrobial agents. The type strain was uniformly susceptible to these 10 drugs. The strain GIFU 11925, a lactose-negative dissociant from strain GIFU 11926, was also susceptible to these drugs, with the sole exception of chloramphenicol (minimal inhibitory concentration, 100 micrograms/ml).
Semantic Image Inpainting with Progressive Generative Networks

Recently, image inpainting task has revived with the help of deep learning techniques. Deep neural networks, especially the generative adversarial networks~(GANs) make it possible to recover the missing details in images. Due to the lack of sufficient context information, most existing methods fail to get satisfactory inpainting results. This work investigates a more challenging problem, e.g., the newly-emerging semantic image inpainting - a task to fill in large holes in natural images. In this paper, we propose an end-to-end framework named progressive generative networks~(PGN), which regards the semantic image inpainting task as a curriculum learning problem. Specifically, we divide the hole filling process into several different phases and each phase aims to finish a course of the entire curriculum. After that, an LSTM framework is used to string all the phases together. By introducing this learning strategy, our approach is able to progressively shrink the large corrupted regions in natural images and yields promising inpainting results. Moreover, the proposed approach is quite fast to evaluate as the entire hole filling is performed in a single forward pass. Extensive experiments on Paris Street View and ImageNet dataset clearly demonstrate the superiority of our approach. Code for our models is available at https://github.com/crashmoon/Progressive-Generative-Networks.
Multi-Organ Segmentation with Missing Organs in Abdominal CT Images

Currently, multi-organ segmentation (MOS) in abdominal CT can fail to handle clinical patient population with missing organs due to surgical resection. In order to enable the state-of-the-art MOS for these clinically important cases, we propose (1) automatic missing organ detection (MOD) by testing abnormality of post-surgical organ motion and organ-specific intensity homogeneity, and (2) atlas-based MOS of 10 abdominal organs that handles missing organs automatically. The proposed methods are validated with 44 abdominal CT scans including 9 diseased cases with surgical organ resections, resulting in 93.3% accuracy for MOD and improved overall segmentation accuracy by the proposed MOS method when tested on difficult diseased cases,
Integrated electricity and heat active network management

Integration of electricity and heat is one of the most promising options to achieve a sustainable low-carbon energy system in presence of Renewable Energy Sources (RES). In this respect, there are ongoing studies worldwide with the aim of assessing the performance of fully-integrated energy systems. However, while they are encouraging in terms of results, the planning is generally made without considering electrical network constraints, missing to capture the real technical and economic performance of integrated systems. In this paper, a new model for integrated electricity and heat active network management based on a dual-horizon Dynamic AC OPF is presented. The model is capable to plan and optimise operations for different multi-energy technologies considering both electrical network and associated inter-temporal constraints and uncertainties, allowing assessing the potential benefits and available flexibility brought by energy integration.