nils-herrmann/clean_scirepeval huggingface dataset:
Modelling Public Sentiment in Twitter: Using Linguistic Patterns to Enhance Supervised Learning

This paper describes a Twitter sentiment analysis system that classifies a tweet as positive or negative based on its overall tweet-level polarity. Supervised learning classifiers often misclassify tweets containing conjunctions such as “but” and conditionals such as “if”, due to their special linguistic characteristics. These classifiers also assign a decision score very close to the decision boundary for a large number tweets, which suggests that they are simply unsure instead of being completely wrong about these tweets. To counter these two challenges, this paper proposes a system that enhances supervised learning for polarity classification by leveraging on linguistic rules and sentic computing resources. The proposed method is evaluated on two publicly available Twitter corpora to illustrate its effectiveness.
Effects of DMSO on the structure and function of polytene chromosomes of Chironomus

Dimethylsulfoxide (DMSO) controlled puff induction and repression (or non-induction) in larval polytene chromosomes of Chironomus tentans were studied for the case of the Balbiani rings (BR). A characteristic reaction pattern, involving BR 1, BR 2 and BR 3, all in salivary gland chromosome IV was found. In vivo exposure of 4th instar larvae (not prepupae) to 10% DMSO at 18° C first evokes an over-stimulation of BR 3 while DMSO-stimulation of puffing at BR 1 and BR 2 always follows that of BR 3. After removal of the drug, a rapid uniform collapse of all puffs occurs, thus more or less restoring the banding pattern of all previously decondensed chromosome segments. Recovery proceeds as BR's and other puffs reappear. By observing the restoration, one can locate the site from which a BR (puff) originates. BR 2, which is normally the most active non-ribosomal gene locus in untreated larvae, here serves as an example. As the sizes of BR 3, BR 1 and BR 2 change, so do the quantities of the transcriptional products in these gene loci (and vice versa), as estimated electron-microscopically in ultrathin sections and autoradiographically in squash preparations. In autoradiograms, the DMSO-stimulated BRs exhibit the most dense concentration of silver grains and therefore the highest rate of transcriptional activity. In DMSO-repressed BRs (and other puffs) the transcription of the locus specific genes is not completely shut off. In chromosomes from nuclei with high labelling intensities the repressed BRs (and other puffs) always exhibit a low level of 3H-uridine incorporation in vivo. The absence of cytologically visible BR (puff) formation therefore does not necessarily indicate complete transcriptional inactivity. Typically, before the stage of puff formation the 3H-uridine labelling first appears in the interband-like regions.
Glottal closure and opening instant detection from speech signals

This paper proposes a new procedure to detect Glottal Closure and Opening Instants (GCIs and GOIs) directly from speech waveforms. The procedure is divided into two successive steps. First a mean-based signal is computed, and intervals where speech events are expected to occur are extracted from it. Secondly, at each interval a precise position of the speech event is assigned by locating a discontinuity in the Linear Prediction residual. The proposed method is compared to the DYPSA algorithm on the CMU ARCTIC database. A significant improvement as well as a better noise robustness are reported. Besides, results of GOI identification accuracy are promising for the glottal source characterization.
Provable Data Possession of Resource-constrained Mobile Devices in Cloud Computing

Benefited from cloud storage services, users can save their cost of buying expensive storage and application servers, as well as deploying and maintaining applications. Meanwhile they lost the physical control of their data. So effective methods are needed to verify the correctness of the data stored at cloud servers, which are the research issues the Provable Data Possession (PDP) faced. The most important features in PDP are: 1) supporting for public, unlimited numbers of times of verification; 2) supporting for dynamic data update; 3) efficiency of storage space and computing. In mobile cloud computing, mobile end-users also need the PDP service. However, the computing workloads and storage burden of client in existing PDP schemes are too heavy to be directly used by the resource-constrained mobile devices. To solve this problem, with the integration of the trusted computing technology, this paper proposes a novel public PDP scheme, in which the trusted third-party agent (TPA) takes over most of the calculations from the mobile end-users. By using bilinear signature and Merkle hash tree (MHT), the scheme aggregates the verification tokens of the data file into one small signature to reduce communication and storage burden. MHT is also helpful to support dynamic data update. In our framework, the mobile terminal devices only need to generate some secret keys and random numbers with the help of trusted platform model (TPM) chips, and the needed computing workload and storage space is fit for mobile devices. Our scheme realizes provable secure storage service for resource-constrained mobile devices in mobile cloud computing.
Bag-of-Words Representation for Biomedical Time Series Classification

Automatic analysis of biomedical time series such as electroencephalogram (EEG) and electrocardiographic (ECG) signals has attracted great interest in the community of biomedical engineering due to its important applications in medicine. In this work, a simple yet effective bag-of-words representation that is able to capture both local and global structure similarity information is proposed for biomedical time series representation. In particular, similar to the bag-of-words model used in text document domain, the proposed method treats a time series as a text document and extracts local segments from the time series as words. The biomedical time series is then represented as a histogram of codewords, each entry of which is the count of a codeword appeared in the time series. Although the temporal order of the local segments is ignored, the bag-of-words representation is able to capture high-level structural information because both local and global structural information are well utilized. The performance of the bag-of-words model is validated on three datasets extracted from real EEG and ECG signals. The experimental results demonstrate that the proposed method is not only insensitive to parameters of the bag-of-words model such as local segment length and codebook size, but also robust to noise.
SWISS-MODEL: modelling protein tertiary and quaternary structure using evolutionary information

Protein structure homology modelling has become a routine technique to generate 3D models for proteins when experimental structures are not available. Fully automated servers such as SWISS-MODEL with user-friendly web interfaces generate reliable models without the need for complex software packages or downloading large databases. Here, we describe the latest version of the SWISS-MODEL expert system for protein structure modelling. The SWISS-MODEL template library provides annotation of quaternary structure and essential ligands and co-factors to allow for building of complete structural models, including their oligomeric structure. The improved SWISS-MODEL pipeline makes extensive use of model quality estimation for selection of the most suitable templates and provides estimates of the expected accuracy of the resulting models. The accuracy of the models generated by SWISS-MODEL is continuously evaluated by the CAMEO system. The new web site allows users to interactively search for templates, cluster them by sequence similarity, structurally compare alternative templates and select the ones to be used for model building. In cases where multiple alternative template structures are available for a protein of interest, a user-guided template selection step allows building models in different functional states. SWISS-MODEL is available at http://swissmodel.expasy.org/.
Query-driven context aware recommendation

Context aware recommender systems go beyond the traditional personalized recommendation models by incorporating a form of situational awareness. They provide recommendations that not only correspond to a user's preference profile, but that are also tailored to a given situation or context. We consider the setting in which contextual information is represented as a subset of an item feature space describing short-term interests or needs of a user in a given situation. This contextual information can be provided by the user in the form of an explicit query, or derived implicitly.
 We propose a unified probabilistic model that integrates user profiles, item representations, and contextual information. The resulting recommendation framework computes the conditional probability of each item given the user profile and the additional context. These probabilities are used as recommendation scores for ranking items. Our model is an extension of the Latent Dirichlet Allocation (LDA) model that provides the capability for joint modeling of users, items, and the meta-data associated with contexts. Each user profile is modeled as a mixture of the latent topics. The discovered latent topics enable our system to handle missing data in item features. We demonstrate the application of our framework for article and music recommendation. In the latter case, the set of popular tags from social tagging Web sites are used for context descriptions. Our evaluation results show that considering context can help improve the quality of recommendations.
A millimeter-wave perpendicular coax-to-microstrip transition

A novel transition from coaxial cable to microstrip is presented in which the coax connector is perpendicular to the substrate of the printed circuit. Such a right-angle transition has practical advantages over more common end-launch geometries in some situations. The design is compact, easy to fabricate, and provides repeatable performance of better than 14 dB return loss and 0.4 dB insertion loss from DC to 40 GHz.
Insider Trading in Credit Derivatives Viral

Insider trading in the credit derivatives market has become a significant concern for regulators and participants. This paper attempts to quantify the problem. Using news reflected in the stock market as a benchmark for public information, we report evidence of significant incremental information revelation in the credit default swap (CDS) market under circumstances consistent with the use of non-public information by informed banks. Specifically, the information revelation occurs only for negative credit news and for entities that subsequently experience adverse shocks. Moreover the degree of advance information revelation increases with the number of banks that have lending/monitoring relations with a given firm, and this effect is robust to controls for non-informational trade. We find no evidence, however, that the degree of asymmetric information adversely affects prices or liquidity in either the equity or credit markets. If anything, with regard to liquidity, the reverse appears to be true.
Free-Space Detection with Self-Supervised and Online Trained Fully Convolutional Networks

Recently, vision-based Advanced Driver Assist Systems have gained broad interest. In this work, we investigate free-space detection, for which we propose to employ a Fully Convolutional Network (FCN). We show that this FCN can be trained in a self-supervised manner and achieve similar results compared to training on manually annotated data, thereby reducing the need for large manually annotated training sets. To this end, our self-supervised training relies on a stereo-vision disparity system, to automatically generate (weak) training labels for the color-based FCN. Additionally, our self-supervised training facilitates online training of the FCN instead of offline. Consequently, given that the applied FCN is relatively small, the free-space analysis becomes highly adaptive to any traffic scene that the vehicle encounters. We have validated our algorithm using publicly available data and on a new challenging benchmark dataset that is released with this paper. Experiments show that the online training boosts performance with 5% when compared to offline training, both for Fmax and AP .
Hyaluronic acid gel ( Juvéderm™) preparations in the treatment of facial wrinkles and folds

Soft tissue augmentation with temporary dermal fillers is a continuously growing field, supported by the ongoing development and advances in technology and biocompatibility of the products marketed. The longer lasting, less immunogenic and thus more convenient hyaluronic acid (HA) fillers are encompassing by far the biggest share of the temporary dermal filler market. Since the approval of the first HA filler, Restylane, there are at least 10 HA fillers that have been approved by the FDA. Not all of the approved HA fillers are available on the market, and many more are coming. The Juvéderm product line (Allergan, Irvine, CA), consisting of Juvéderm Plus and Juvéderm Ultra Plus, was approved by the FDA in 2006. Juvéderm is a bacterium-derived nonanimal stabilized HA. Juvéderm Ultra and Ultra Plus are smooth, malleable gels with a homologous consistency that use a new technology called "Hylacross technology". They have a high concentration of cross-linked HAs, which accounts for its longevity. Juvéderm Ultra Plus is used for volumizing and correcting deeper folds, whereas Juvéderm Ultra is best for contouring and volumizing medium depth facial wrinkles and lip augmentation. Various studies have shown the superiority of the HA filler products compared with collagen fillers for duration, volume needed, and patient satisfaction. Restylane, Perlane, and Juvéderm are currently the most popular dermal fillers used in the United States.
Specification-based IDS for securing RPL from topology attacks

This paper focuses on the security aspect of RPL (Routing Protocol for Low-power and lossy network) by introducing a new type of threat — the topology attack, which changes the node operation for breaking the optimised network topology, and designing a specification-based IDS for detecting it. We present two novel RPL attacks of this type: the rank attack and local repair attack. We also propose an IDS architecture using network monitor backbone, and describe its monitoring mechanisms through a RPL finite state machine implemented in each monitor node. We show that our system can effectively detect these routing operation threats with a reasonable overhead.
Learning from Sensory and Reward Prediction Errors during Motor Adaptation

Voluntary motor commands produce two kinds of consequences. Initially, a sensory consequence is observed in terms of activity in our primary sensory organs (e.g., vision, proprioception). Subsequently, the brain evaluates the sensory feedback and produces a subjective measure of utility or usefulness of the motor commands (e.g., reward). As a result, comparisons between predicted and observed consequences of motor commands produce two forms of prediction error. How do these errors contribute to changes in motor commands? Here, we considered a reach adaptation protocol and found that when high quality sensory feedback was available, adaptation of motor commands was driven almost exclusively by sensory prediction errors. This form of learning had a distinct signature: as motor commands adapted, the subjects altered their predictions regarding sensory consequences of motor commands, and generalized this learning broadly to neighboring motor commands. In contrast, as the quality of the sensory feedback degraded, adaptation of motor commands became more dependent on reward prediction errors. Reward prediction errors produced comparable changes in the motor commands, but produced no change in the predicted sensory consequences of motor commands, and generalized only locally. Because we found that there was a within subject correlation between generalization patterns and sensory remapping, it is plausible that during adaptation an individual's relative reliance on sensory vs. reward prediction errors could be inferred. We suggest that while motor commands change because of sensory and reward prediction errors, only sensory prediction errors produce a change in the neural system that predicts sensory consequences of motor commands.
A Pattern-Based Approach for Multi-Class Sentiment Analysis in Twitter

Sentiment analysis and opinion mining in social networks present nowadays a hot topic of research. However, most of the state of the art works and researches on the automatic sentiment analysis and opinion mining of texts collected from social networks and microblogging websites are oriented toward the binary classification (i.e., classification into “positive” and “negative”) or the ternary classification (i.e., classification into “positive,” “negative,” and “neutral”) of texts. In this paper, we propose a novel approach that, in addition to the aforementioned tasks of binary and ternary classifications, goes deeper in the classification of texts collected from Twitter and classifies these texts into multiple sentiment classes. While in this paper, we limit our scope to seven different sentiment classes, the proposed approach is scalable and can be run to classify texts into more classes. We first introduce SENTA, our tool built to help users select out of a wide variety of features the ones that fit the most for their application, to run the classification, through an easy-to-use graphical user interface. We then use SENTA to run our own experiments of multi-class classification. Our experiments show that the proposed approach can reach up to 60.2% accuracy on the multi-class classification. Nevertheless, the approach proves to be very accurate in binary classification and ternary classification: in the former case, we reach an accuracy of 81.3% for the same data set used after removing neutral tweets, and in the latter case, we reached an accuracy of classification equal to 70.1%.
Smart Environment as a Service: Three Factor Cloud Based User Authentication for Telecare Medical Information System

The Telecare Medical Information System (TMIS) provides a set of different medical services to the patient and medical practitioner. The patients and medical practitioners can easily connect to the services remotely from their own premises. There are several studies carried out to enhance and authenticate smartcard-based remote user authentication protocols for TMIS system. In this article, we propose a set of enhanced and authentic Three Factor (3FA) remote user authentication protocols utilizing a smartphone capability over a dynamic Cloud Computing (CC) environment. A user can access the TMIS services presented in the form of CC services using his smart device e.g. smartphone. Our framework transforms a smartphone to act as a unique and only identity required to access the TMIS system remotely.  Protocols and Authentication techniques are proposed followed by security analysis and a performance analysis with the two recent authentication protocols proposed for the healthcare TMIS system.
Interference coordination and cancellation for 4G networks

This article provides an overview of contemporary and forward looking inter-cell interference coordination techniques for 4G OFDM systems with a specific emphasis on implementations for LTE. Viable approaches include the use of power control, opportunistic spectrum access, intra and inter-base station interference cancellation, adaptive fractional frequency reuse, spatial antenna techniques such as MIMO and SDMA, and adaptive beamforming, as well as recent innovations in decoding algorithms. The applicability, complexity, and performance gains possible with each of these techniques based on simulations and empirical measurements will be highlighted for specific cellular topologies relevant to LTE macro, pico, and femto deployments for both standalone and overlay networks.
RFID-Enabled Physical Object Tracking in Process Flow Based on an Enhanced Graphical Deduction Modeling Method

The purpose of this paper is to develop an enhanced radio frequency identification (RFID)-enabled graphical deduction model (rfid-GDM) for tracking the time-sensitive state, position, and other attributes of RFID-tagged objects in process flow. Concepts and definitions related to processes and RFID applications are first clarified, and enhanced state blocks are proposed to depict four kinds of RFID application scenarios. The implementation framework of rfid-GDM and its five steps are further addressed. Both mathematical formalization and graphical description of each step are involved. Finally, a case is studied to verify the feasibility of rfid-GDM. It is expected that rfid-GDM will provide instructions for modeling and tracking RFID-enabled process flows in diverse fields.
Arduino based automatic wireless weather station with remote graphical application and alerts

In this paper we have proposed, developed and tested a hardware module based on Arduino Uno Board and Zigbee wireless technology, which measures the meteorological data, including air temperature, dew point temperature, barometric pressure, relative humidity, wind speed and wind direction. This information is received by a specially designed application interface running on a PC connected through Zigbee wireless link. The proposed system is also a mathematical model capable of generating short time local alerts based on the current weather parameters. It gives an on line and real time effect. We have also compared the data results of the proposed system with the data values of Meteorological Station Chandigarh and Snow & Avalanche Study Establishment Chandigarh Laboratory. The results have come out to be very precise. The idea behind to this work is to monitor the weather parameters, weather forecasting, condition mapping and warn the people from its disastrous effects.
Ontology Construction for Information Selection

Technology in the field of digital media generates huge amounts of textual information. The potential for exchange and retrieval of information is vast and daunting. The key problem in achieving efficient and user-friendly retrieval is the development of a search mechanism to guarantee delivery of minimal irrelevant information (high precision) while insuring relevant information is not overlooked (high recall). The traditional solution employs keyword-based search. The only documents retrieved are those containing user specified keywords. But many documents convey desired semantic information without containing these keywords. One can overcome this problem by indexing documents according to meanings rather than words, although this will entail a way of converting words to meanings and the creation of ontologies. We have solved the problem of an index structure through the design and implementation of a concept-based model using domain-dependent ontologies. Ontology is a collection of concepts and their interrelationships, which provide an abstract view of an application domain. We propose a new mechanism that can generate ontologies automatically in order to make our approach scalable. For this we modify the existing self-organizing tree algorithm (SOTA) that constructs a hierarchy. Furthermore, in order to find an appropriate concept for each node in the hierarchy we propose an automatic concept selection algorithm from WordNet, a linguistic ontology. To illustrate the effectiveness of our automatic ontology construction method, we have explored our ontology construction in text documents. The Reuters21578 text document corpus has been used. We have observed that our modified SOTA outperforms hierarchical agglomerative clustering (HAC).