nils-herrmann/clean_scirepeval huggingface dataset:
Stance Detection with Bidirectional Conditional Encoding
Venue: EMNLP

Stance detection is the task of classifying the attitude expressed in a text towards a target such as Hillary Clinton to be "positive" negative" or "neutral" Previous work has assumed that either the target is mentioned in the text or that training data for every target is given. This paper considers the more challenging version of this task, where targets are not always mentioned and no training data is available for the test targets. We experiment with conditional LSTM encoding, which builds a representation of the tweet that is dependent on the target, and demonstrate that it outperforms encoding the tweet and the target independently. Performance is improved further when the conditional model is augmented with bidirectional encoding. We evaluate our approach on the SemEval 2016 Task 6 Twitter Stance Detection corpus achieving performance second best only to a system trained on semi automatically labelled tweets for the test target. When such weak supervision is added, our approach achieves state of the art results.
MITRE at SemEval 2016 Task 6: Transfer Learning for Stance Detection
Venue: *SEMEVAL

We describe MITRE's submission to the SemEval 2016 Task 6, Detecting Stance in Tweets. This effort achieved the top score in Task A on supervised stance detection, producing an average F1 score of 67.8 when assessing whether a tweet author was in favor or against a topic. We employed a recurrent neural network initialized with features learned via distant supervision on two large unlabeled datasets. We trained embeddings of words and phrases with the word2vec skip gram method, then used those features to learn sentence representations via a hashtag prediction auxiliary task. These sentence vectors were then fine tuned for stance detection on several hundred labeled examples. The result was a high performing system that used transfer learning to maximize the value of the available training data.
pkudblab at SemEval 2016 Task 6 A Specific Convolutional Neural Network System for Effective Stance Detection
Venue: *SEMEVAL

In this paper, we develop a convolutional neural network for stance detection in tweets. According to the official results, our system ranks 1 on subtask B (among 9 teams) and ranks 2 on subtask A (among 19 teams) on the twitter test set of SemEval2016 Task 6. The main contribution of our work is as follows. We design a "vote scheme" for prediction instead of predicting when the accuracy of validation set reaches its maximum. Besides, we make some improvement on the specific subtasks. For subtask A, we separate datasets into five sub datasets according to their targets, and train and test five separate models. For subtask B, we establish a two class training dataset from the official domain corpus, and then modify the softmax layer to perform three class classification. Our system can be easily re implemented and optimized for other related tasks.
aptMTVL: Nailing Interactions in Multi Task Multi View Multi Label Learning using Adaptive basis Multilinear Factor Analyzers
Venue: CIKM

We investigate a new direction of multi task multi view learning where we have data sets with multiple tasks, multiple views and multiple labels. We call this problem a multi task multi view multi label learning problem or MTVL learning for short. There is a wide application of MTVL leaning where examples include Internet of Things, brain science, and document classification. In designing effective MTVL learning algorithms, we hypothesize that a key component is to "disentangle" interactions among tasks, views, and labels, or the Utask view label interactions. For that purpose we have developed an adaptive basis multilinear analyzers(aptMLFA) that utilizes a loading tensor to modulate interactions among multiple latent factors. With aptMLFA we designed a new MTVL learning algorithm, aptMTVL, and evaluated its performance on 3 real world data sets. The experimental results demonstrated the effectiveness of our proposed method as compared to the state of the art MTVL learning algorithm.
The Determinants of Bank Interest Margins: A Short term Funding Perspective

The net interest margins (NIM) is one of the key metrics for Banks, and for those who want to evaluate the performance of a banki s investments. In this paper, the determinants of net interest margins of Chinai s financial institutions are investigated, while taking into account the effects of short term funding and the presence of foreign banks. By distinguishing different shareholding structure and comparing the banksi net interest margins before and after the banking liberalization, we find that both short term funding and foreign bank presence have negative impacts on the net interest margins of Chinese domestic banks. The banking sector after the financial liberalization, especially the national wide banks, has better ability to manage the financial stress. The China Construction Banks(CCBs) and Credit Cooperatives suffer higher interest margin decline than State Owned commercial Banks(SOBs) and Joint Stock Banks(JSBs) The national wide banks need to accelerate the business diversification and increase the intermediary revenue.
Avoiding Missed Opportunities by Analyzing the Sensitivity of Our Decisions.
Venue: Journal of medicinal chemistry

Drug discovery is a multiparameter optimization process in which the goal of a project is to identify compounds that meet multiple property criteria required to achieve a therapeutic objective. However, once a profile of property criteria has been chosen, the impact of these criteria on the decisions made regarding progression of compounds or chemical series should be carefully considered. In some cases the decision is very sensitive to a specific property criterion, and such a criterion may artificially distort the direction of the project; any uncertainty in the "correct" value or the importance of this criterion may lead to valuable opportunities being missed. In this paper, we describe a method for analyzing the sensitivity of the prioritization of compounds to a multiparameter profile of property criteria. We show how the results can be easily interpreted and illustrate how this analysis can highlight new avenues for exploration.
Flawed logics: strategic nuclear arms control from Truman to Obama

security threat" (p. 7) loomed over the western world in the form of the Soviet Union. even in a cantankerous democracy, this threat provided a guiding star by which to organize efforts. in the absence of it, is arguable whether post 1989 Britain has really had a true national security strategy and certainly not a grand strategy. This observation is not offered as a lament; it would be perverse to wish upon ourselves an existential threat which would require one. in the face of numerous non existential threats, such strategy as there is consists of "risk management" and the development of "resilience" as Richards notes (p. 33) But the broader truth is that it is more the ebb and flow of external events say, a civil war in Libya, or a flood of migrants at Calais which determines the month to month allocation of priorities and resources. Short term politics and the "BBC effect" usually have more to do with this than what is written in strategy documents. This book would have benefited from greater emphasis on this dynamic. Part of the reason that this book cannot grapple fully with this question is timing. in 2012, when this book was published, it was arguably too early to judge either the implementation of the Uk national security review or the operation of the National Security Council. A book wanting to argue that there was something truly original in all of this would doubtless need to take a longer view. it is still too early to do so in this reviewer's opinion. But it seems at least as likely that the Coalition government will be remembered more for the remark attributed to Foreign Secretary Philip Hammond that there are "no votes in defence" than for a consequential reorganization of national security policy making. Furthermore, the outcome of the intervention in Libya which Richards says "appeared to be successful" (p. 176) as perhaps it did in 2012 looks much murkier today. it certainly calls into question the Uk government's claim to be acting strategically and in cognizance of the long term rather than in response to short term developments. in sum, Richards' book is a competent introduction to Uk national security policy which will provide a basis for future historians and analysts who are able to consider the record of the Coalition government in the round. in the meantime, it serves as a useful introduction to Uk national security policy making for students.
Ultrasonication pre treatment of combined effluents from palm oil, pulp and paper mills for improving photofermentative biohydrogen production

The improvement of batch photofermentative biohydrogen production was investigated using ultrasonication pre treatment on a combined effluent of palm oil and pulp and paper mills. The effects of the amplitude (30 90% and ultrasonication duration (5 60 min) were investigated in terms of their influences on the biohydrogen yield and chemical oxygen demand (COD) removal. The recommended ultrasonication parameters were found at the higher ranges of amplitude and duration (A70T45) Using A70T45 ultrasonication, the production of biohydrogen at 30 degC could be enhanced up to 8.72 mL H 2 /mL medium with a total COD removal of 36.9% During pre treatment at A70T45, an energy input of 775 J/mL was supplied to disintegrate complex compounds into simpler structures. As a result, an increase in the soluble organic matter concentration was achieved, which led to enhanced biohydrogen production. On the other hand, the lowest biohydrogen yield (4.67 mL H 2 /mL medium and total COD removal (28.8% were obtained in the control without pre treatment. The enthalpy of the photofermentation process was estimated to be 141.1 kJ/mol with a threshold temperature of 30.9 degC based on a modified Arrhenius approach.
Utilization of solid wastes/byproducts from paper mills in Controlled Low Strength Material (CLSM)

Due to increasing generation of solid wastes from papermaking industry, major concerns arise about the disposal of the solid wastes and their adverse impacts on environments, in which the disposal of paper sludge massively produced from paper mills has become one of the most serious solid waste problems worldwide. In this study, laboratory experiments were conducted to evaluate the feasibility and applicability of incorporating solid wastes from paper mills into controlled low strength material (CLSM) in which fly ash was used as a substitute for cement, bottom ash was added by partially replacing fine aggregate, and paper sludge was treated as a fibrous admixture. Results show that both fly ash and bottom ash could be effectively used in production of CLSM mixtures with desired performances. By limiting the amount of cement used in the mixture, the ultimate strength of CLSM could be controlled for easy excavation. Fly ash could effectively increase flowability of the mixture, thus improving self compactibility. Both bottom ash and paper sludge had an adverse effect on the flowability. The strength of CLSM was reduced when a higher content of fly ash was incorporated to replace cement, while the strength was increased when more river sand was replaced with bottom ash. Due to a high water absorption and poor dispersibility of paper sludge, the CLSM made with additional paper sludge exhibited a relatively low flowability and a reduction in strength. However, with proper mixture design, paper sludge could still be effectively used in CLSM.
Comparison of Multiple Measures of Noise Exposure in Paper Mills.
Venue: The Annals of occupational hygiene

Noise exposures are associated with a host of adverse health effects, yet these exposures remain inadequately characterized in many industrial operations, including paper mills. We assessed noise at four paper mills using three measures: (i) personal noise dosimetry, (ii) area noise measurements, and (iii) questionnaire items addressing several different aspects of perceived noise exposure.  We assessed exposures to noise characterized using the three measures and compared the relationships between them. We also estimated the validity of each of the three measures using a novel application of the Method of Triads, which does not appear to have been used previously in the occupational health literature.  We collected 209 valid dosimetry measurements and collected perceived noise exposure survey items from 170 workers, along with 100 area measurements. We identified exposures in excess of 85 dBA at all mills. The dosimetry and area noise measurements assigned to individual subjects generally showed good agreement, but for some operations within mill, large differences between the two measures were observed, and a substantial fraction of paired measures differed by >5 dB. Perceived noise exposures varied greatly between the mills, particularly for an item related to difficulty speaking in noise. One perceived noise exposure item related to difficulty hearing due to noise showed strong and significant correlations with both dosimetry and area measurements. The Method of Triads analysis showed that dosimetry measures had the highest estimated validity coefficient (0.70) and that the best performing perceived exposure measure had validity that exceeded that of area measurements (0.48 versus 0.40, respectively) CONCLUSIONS Workers in Swedish pulp mills have the potential for exposures to high levels of noise. Our results suggest that, while dosimetry remains the preferred approach to exposure assessment, perceived noise exposures can be used to evaluate potential exposures to noise in epidemiological studies.
Feasible process development and techno economic evaluation of paper sludge to bioethanol conversion: South African paper mills scenario

Paper sludge samples collected from recycling mills exhibited high ash content in the range of 54.59% 65.50% and glucose concentrations between 21.97% and 31.11% Washing the sludge reduced the total ash content to between 10.7% and 19.31% and increased the concentration of glucose, xylose and lignin. Samples were screened for ethanol production and fed batch simultaneous saccharification and fermentation (SSF) was optimised for the washed samples that resulted in highest and lowest ethanol concentrations. Maximum ethanol concentrations of 57.31 g/L and 47.72 g/L (94.07% and 85.34% of the maximum theoretical yield, respectively) was predicted for high and low fermentative potential samples, respectively, and was experimentally achieved with 1% deviation. A generic set of process conditions were established for the conversion of high ash containing paper sludge to ethanol. Techno economic analysis based on three different revenue scenarios, together with Monte Carlo analysis revealed 95% probability of achieving IRR values in excess of 25% at a paper sludge feed rate of 15 t/d. Feed rates of 30 t/d and 50 t/d exhibited a cumulative probability of 100% This study presents the technical feasibility and economic viability of paper mills expansion towards bioethanol production from paper sludge.
A NEW IMAGE ENCRYPTION AND DATA HIDING TECHNIQUE USING WAVELET TRANSFORM

In this paper a new image encryption and decryption is proposed by using wavelet Packet decomposition. Data hiding is also embedding in the encrypted image. In the process of encryption original image is wavelet decomposed; further security can be achieved by rotational transform of the pixels that is the pixels are rotated after decomposing using wavelet transform. The data to be hided is embedded after rotational transform of the image; the resultant image is encrypted image with data hiding .The data recovered from data hided image by using inverse data hiding after the decrypt the image by inverse rotational transform and inverse wavelet decomposition. This encryption and data hiding technique is efficient technique for secure image and data transmission.
Immunoprotective potential of in silico predicted Acinetobacter baumannii outer membrane nuclease, NucAb.
Venue: International journal of medical microbiology IJMM

Acinetobacter baumannii is an emerging multi drug resistant pathogen causing significant mortality in hospitalized ICU patients which demands developing new methods for prevention and treatment. A. baumannii 19606 proteome was analysed in silico through the online tool Vaxign for finding potential vaccine candidates. The selected nuclease (NucAb) was predicted to possess all the attributes of a promising vaccine candidate like outer membrane localization, high adhesin probability (0.53) one transmembrane helix only, non homology to human proteins and presence of B cell and T cell epitopes binding with high affinity (percentile rank<=1) to HLA alleles prevalent at high frequency in North Indian populations. nucAb gene was highly prevalent (100% among the clinical isolates (40/40) and conserved >98% among NCBI sequenced Acinetobacter strains. It was cloned in pET28a, purified and its immunoprotective potential was validated in murine pneumonia model. Immunization of BALB/c mice with recombinant NucAb (25mg) elicited high antibody titre (1 5x10(5) which reduced bacterial load by 5 log cycles in lungs of mice challenged with optimized lethal dose (10(8)CFU) Lung histopathology revealed marked suppression of inflammation. Pro inflammatory cytokines (TNF a and IL 6) levels were reduced significantly and anti inflammatory (IL 10) cytokine increased in lungs and serum leading to decreased severity and slow progression of disease. Though active immunization showed low survival rate (20% passive immunization improved the survival (40% This is the first study reporting an outer membrane nuclease as a vaccine candidate in Gram negative bacterium, A. baumannii through reverse vaccinology approach.
Reverse Vaccinology: Developing Vaccine Against MDR Acinetobacter baumannii

Emergence of resistance against drugs viz. carbapenems, fluoroquinolones, aminoglycosides is the major problem associated with Acinetobacter baumannii infections thus making it imperative to develop a suitable vaccine as an effective treatment option. Previous studies reveal that outer membrane proteins of A. baumannii could serve as vaccine candidates and provide partial or complete immunity against lethal doses in various mouse models. Recently, we have shown reverse vaccinology as a powerful tool to identify vaccine candidates. The immunoprotective efficacy of an outer membrane, putative pilus assembly protein, FilF, identified as a potential vaccine candidate was validated in A. baumannii associated murine pneumonia model and was found to provide 50% survival against A. baumannii lethal doses. NucAb, an outer membrane nuclease identified in silico as A. baumannii vaccine candidate, provided 20% survival on active and 40% survival on passive immunization.
Implementing a Volunteer Notification System Into a Scalable, Analytical Realtime Data Processing Environment
Venue: eTELEMED 2016

The pace at which next generation Internet of Things networks, consisting of wirelessly distributed sensors and devices, are being developed is speeding up. More and more devices produce data in automated manners and the demand of smartphones and wearable devices is continuously increasing. With respect to volunteer notification systems (VNS) the resulting vast amounts of data can be utilized for profiling and predicting the whereabouts of people that, combined with machine learning algorithms, complement artificial intelligence (AI) based decision systems. Hence, VNS benefit from keeping pace with the current developments by using the corresponding data streams in order to improve decision making during the volunteer selection process. In emergency scenarios, the velocity, low latency and reaction times of the system are essential, which results in the need of online stream processing and real time computational solutions. This paper will focus on a basic concept for implementing a VNS approach into a scalable, fault tolerant environment that uses state of the art analytical tools to process information streams in real time as well as on demand, and applies machine learning algorithms for an AI based volunteer selection. This work concentrates on leveraging open source Big Data technologies with the aim to deliver a robust, secure and highly available enterprise class Big Data platform. Within the given context, this work will furthermore give an insight on state of the art proprietary solutions for Big Data processing that are currently available.
Guest Editorial Special Issue on Cloud Computing for IoT
Venue: IEEE Internet Things J.

In recent years, there has been a growing interest in the ability of embedded devices, sensors, and actuators to communicate, and create a ubiquitous cyber physical world. The growth of the notion of the Internet of Things (IoT) and the rapid development of technologies such as short range mobile communication and improved energy efficiency is expected to create a pervasive connection of "things." This will inevitably result in the generation of enormous amount of data, which have to be stored, processed, and accessed. Cloud computing has long been recognized as a paradigm for big data storage and analytics. The combination of cloud computing and IoT can enable ubiquitous sensing services and powerful processing of sensing data streams beyond the capability of individual things, thus stimulating innovations in both fields. For example, cloud platforms allow the sensing data to be stored and used intelligently for smart monitoring and actuation with the smart devices. Novel data fusion algorithms, machine learning methods, and artificial intelligence techniques can be implemented and run centralized or distributed on the cloud to achieve automated decision making. These will boost the development of new applications, such as smart cities, grids, and transportation systems. New challenges, however, arise when IoT meets cloud there is an urgent need for novel network architectures that seamlessly integrate them, and protocols that facilitate big data streaming from IoT to the cloud. QoS and QoE, as well as data security, privacy, and reliability, are critical concerns during the integration.