nils-herrmann/clean_scirepeval huggingface dataset:
Dependency Tree-based Sentiment Classification using CRFs with Hidden Variables

In this paper, we present a dependency treebased method for sentiment classification of Japanese and English subjective sentences using conditional random fields with hidden variables. Subjective sentences often contain words which reverse the sentiment polarities of other words. Therefore, interactions between words need to be considered in sentiment classification, which is difficult to be handled with simple bag-of-words approaches, and the syntactic dependency structures of subjective sentences are exploited in our method. In the method, the sentiment polarity of each dependency subtree in a sentence, which is not observable in training data, is represented by a hidden variable. The polarity of the whole sentence is calculated in consideration of interactions between the hidden variables. Sum-product belief propagation is used for inference. Experimental results of sentiment classification for Japanese and English subjective sentences showed that the method performs better than other methods based on bag-of-features.
Limiting factors for maximum oxygen uptake and determinants of endurance performance.

In the exercising human, maximal oxygen uptake (VO2max) is limited by the ability of the cardiorespiratory system to deliver oxygen to the exercising muscles. This is shown by three major lines of evidence: 1) when oxygen delivery is altered (by blood doping, hypoxia, or beta-blockade), VO2max changes accordingly; 2) the increase in VO2max with training results primarily from an increase in maximal cardiac output (not an increase in the a-v O2 difference); and 3) when a small muscle mass is overperfused during exercise, it has an extremely high capacity for consuming oxygen. Thus, O2 delivery, not skeletal muscle O2 extraction, is viewed as the primary limiting factor for VO2max in exercising humans. Metabolic adaptations in skeletal muscle are, however, critical for improving submaximal endurance performance. Endurance training causes an increase in mitochondrial enzyme activities, which improves performance by enhancing fat oxidation and decreasing lactic acid accumulation at a given VO2. VO2max is an important variable that sets the upper limit for endurance performance (an athlete cannot operate above 100% VO2max, for extended periods). Running economy and fractional utilization of VO2max also affect endurance performance. The speed at lactate threshold (LT) integrates all three of these variables and is the best physiological predictor of distance running performance.
Buffered Bloom Filters on Solid State Storage

Bloom Filters are widely used in many applications including database management systems. With a certain allowable error rate, this data structure provides an efficient solution for membership queries. The error rate is inversely proportional to the size of the Bloom filter. Currently, Bloom filters are stored in main memory because the low locality of operations makes them impractical on secondary storage. In multi-user database management systems, where there is a high contention for the shared memory heap, the limited memory available for allocating a Bloom filter may cause a high rate of false positives. In this paper we are proposing a technique to reduce the memory requirement for Bloom filters with the help of solid state storage devices (SSD). By using a limited memory space for buffering the read/write requests, we can afford a larger SSD space for the actual Bloom filter bit vector. In our experiments we show that with significantly less memory requirement and fewer hash functions the proposed technique reduces the false positive rate effectively. In addition, the proposed data structure runs faster than the traditional Bloom filters by grouping the inserted records with respect to their locality on the
Neural Networks for Language Modeling

Neural networks have become increasingly popular for the task of language modeling. Whereas feed-forward networks only exploit a fixed context length to predict the next word of a sequence, conceptually, standard recurrent neural networks can take into account all of the predecessor words. On the other hand, it is well known that recurrent networks are difficult to train and therefore are unlikely to show the full potential of recurrent models. These problems are addressed by a the Long Short-Term Memory neural network architecture. In this work, we analyze this type of network on an English and a large French language modeling task. Experiments show improvements of about 8 % relative in perplexity over standard recurrent neural network LMs. In addition, we gain considerable improvements in WER on top of a state-of-the-art speech recognition system.
Structured Document Segmentation and Representation by the Modified X-Y tree

In this paper we describe a top-down approach to the segmelltation alld representation of documents containing tabular structures. Examples of these documents are invoices and technical papers with tables. The segmentation is based on an extension of X-Y trees. where the regions are split b..... means of cuts along separators (e.g. lines). in addition lo ClItSalong white spaces. The leaves describe regions containing homogeneous information and cutting separators. Adjacency links among leaves of the tree describe local relationships between corresponding regions.
applied to renewable and sustainable energy : A review

Energy is a vital input for social and economic development. As a result of the generalization of agricultural, industrial and domestic activities the demand for energy has increased remarkably, especially in emergent countries. This has meant rapid grower in the level of greenhouse gas emissions and the increase in fuel prices, which are the main driving forces behind efforts to utilize renewable energy sources more effectively, i.e. energy which comes from natural resources and is also naturally replenished. Despite the obvious advantages of renewable energy, it presents important drawbacks, such as the discontinuity of ulti-criteria decision analysis
Radio propagation path loss models for 5G cellular networks in the 28 GHZ and 38 GHZ millimeter-wave bands

This article presents empirically-based large-scale propagation path loss models for fifth-generation cellular network planning in the millimeter-wave spectrum, based on real-world measurements at 28 GHz and 38 GHz in New York City and Austin, Texas, respectively. We consider industry-standard path loss models used for today's microwave bands, and modify them to fit the propagation data measured in these millimeter-wave bands for cellular planning. Network simulations with the proposed models using a commercial planning tool show that roughly three times more base stations are required to accommodate 5G networks (cell radii up to 200 m) compared to existing 3G and 4G systems (cell radii of 500 m to 1 km) when performing path loss simulations based on arbitrary pointing angles of directional antennas. However, when directional antennas are pointed in the single best directions at the base station and mobile, coverage range is substantially improved with little increase in interference, thereby reducing the required number of 5G base stations. Capacity gains for random pointing angles are shown to be 20 times greater than today's fourth-generation Long Term Evolution networks, and can be further improved when using directional antennas pointed in the strongest transmit and receive directions with the help of beam combining techniques.
Estimating Fundamental Frequencies of Tall Buildings

Empirical estimates of the fundamental frequency of tall buildings vary inversely with their height, a dependency not exhibited by the various familiar models of beam behavior. This paper examines and explains this apparent discrepancy by analyzing the consequences of using two models to estimate such natural frequencies: A two-beam model that couples the bending of a classical cantilever to that of a shear beam by imposing a displacement constraint; and a Timoshenko beam in which the Euler–Bernoulli beam model is extended by adding a shear-displacement term to the classical bending deflection. A comparison of the two beam models suggests that the Timoshenko model is appropriate for describing the behavior of shear-wall buildings, while the coupled two-beam model is appropriate for shear-wall–frame e.g., tube-and-core buildings, and that the coupled-beam model comes much closer to replicating the parametric dependence of building frequency on height.
LSTMs Exploit Linguistic Attributes of Data

While recurrent neural networks have found success in a variety of natural language processing applications, they are general models of sequential data. We investigate how the properties of natural language data affect an LSTM’s ability to learn a nonlinguistic task: recalling elements from its input. We find that models trained on natural language data are able to recall tokens from much longer sequences than models trained on non-language sequential data. Furthermore, we show that the LSTM learns to solve the memorization task by explicitly using a subset of its neurons to count timesteps in the input. We hypothesize that the patterns and structure in natural language data enable LSTMs to learn by providing approximate ways of reducing loss, but understanding the effect of different training data on the learnability of LSTMs remains an open question.
Aligning Users across Social Networks Using Network Embedding

In this paper, we adopt the representation learning approach to align users across multiple social networks where the social structures of the users are exploited. In particular, we propose to learn a network embedding with the followership/followee-ship of each user explicitly modeled as input/output context vector representations so as to preserve the proximity of users with “similar” followers/followees in the embedded space. For the alignment, we add both known and potential anchor users across the networks to facilitate the transfer of context information across networks. We solve both the network embedding problem and the user alignment problem simultaneously under a unified optimization framework. The stochastic gradient descent and negative sampling algorithms are used to address scalability issues. Extensive experiments on real social network datasets demonstrate the effectiveness and efficiency of the proposed approach compared with several state-of-the-art methods.
A Study on Sarcasm Detection Algorithms

Sarcasm detection refers to the identification of the use of sarcasma humorous literary device in text. The use of sarcasm can be misleading and often hinders accurate comprehension of sentences. This paper aims at presenting the challenges in detection and ideas inculcated in existing literature to overcome these challenges. This study reveals that sarcasm detection is an entity of ongoing research and that more novel techniques are required to improve its efficiency.  According to Cambridge’s dictionary, the term “sarcasm” means the use of remarks that clearly mean the opposite of what they say, made in order to hurt someone's feelings or to criticize someone/something in a humorous way. Sarcasm detection is the process of identifying whether a given piece of text is to be comprehended using its literal meaning, or as its opposite meaning. If a piece of text is to be comprehended opposite to its literal meaning, then it contains sarcasm. CHALLENGES FACED Natural Language Processing systems such as Online customer review summarization systems, dialogue systems or monitoring systems for brands face difficulty in identifying sarcasm. Typical Sentimental Analysis Systems do not support sarcasm detection. The results obtained from Sentimental analysis may provide a result opposite to what was intended. This adversely affects businesses employing this technology. Datasets used to perform sarcasm detection are usually derived from online review sites, social media posts and comments and micro-blogging sites. The structure of these sentences isn’t clearly defined, and they are short in nature, thus leading to ambiguity. Apart from these issues, there exist certain specific challenges in sarcasm detection as follows. •The datasets used for the detection process pose a problem due to the ambiguity and unnecessary plethora of details mentioned in the text. It is quite tough to extract useful information from the huge volumes of crude data. • It is an easier task to detect whether a person is using sarcasm in speech. The use of sarcasm can be detected from the tone of speech, body language and facial expressions. However, identifying sarcasm from a piece of text poses certain difficulties for many Natural Language Processing Systems.. • Sarcasm detection demands certain prerequisites such as being well informed regarding the current issues and trends. e.g. “Yet, another mind boggling performance by the Australian cricket team in England.” • Sarcasm detection, at times, requires the use of common sensical knowledge • Sometimes sarcasm uses the hyperbole. Hyperbole is the use of exaggeration i.e. use of words belonging to superlative degree. Eg. “They ran like greased lightning.”
HAHE: Hierarchical Attentive Heterogeneous Information Network Embedding

Given the intractability of large scale HIN, network embedding which learns low dimensional proximity-preserved representations for nodes in the new space becomes a natural way to analyse HIN. However, two challenges arise in HIN embedding. (1) Dierent HIN structures (also known as meta-paths) with dierent semantic meanings play dierent roles in capturing relationships among nodes in HIN, how can we learn personalized preferences over dierent meta-paths for each individual node in HIN? (2) With the number of large scale HIN increasing dramatically in various web services, how can we update the embedding information of new nodes in an ecient way? Existing HIN embedding methods only learn a global preference rather than personalized preference over each meta-path, therefore can not capture the personalized importance of meta-path to each node. Neither can they eciently compute the embedding for each new node without traversing the whole HIN. To tackle these challenges, we propose a Hierarchical Aentive Heterogeneous information network Embedding (HAHE ) model which is capable of learning personalized meta-path preferences for each node as well as updating the embedding information for each new node eciently with only its neighbor node information. e proposed HAHEmodel extracts the semantic relationships among nodes in the semantic space based on dierent meta-paths and adopts a neighborhood aention layer to conduct weighted aggregations of neighborhood structure features for each node, enabling the embedding information of each new node to be updated eciently. Besides, a meta-path aention layer is also employed to learn the personalized meta-path preferences for each individual node. Extensive experiments on several real-world datasets show that our proposed HAHEmodel signicantly outperforms the state-of-the-art methods in terms of various evaluation metrics.   Hierarchical Aentive Heterogeneous Information Network Embedding.  2017 (Conference’17), 12 pages.
Internet-of-Things Security and Vulnerabilities: Taxonomy, Challenges, and Practice

Recent years have seen rapid development and deployment of Internet-of-Things (IoT) applications in a diversity of application domains. This has resulted in creation of new applications (e.g., vehicle networking, smart grid, and wearables) as well as advancement, consolidation, and transformation of various traditional domains (e.g., medical and automotive). One upshot of this scale and diversity of applications is the emergence of new and critical threats to security and privacy: it is getting increasingly easier for an adversary to break into an application, make it unusable, or steal sensitive information and data. This paper provides a summary of IoT security attacks and develops a taxonomy and classification based on the application domain and underlying system architecture. We also discuss some key characteristics of IoT that make it difficult to develop robust security architectures for IoT applications.
DeepDTA: deep drug–target binding affinity prediction

The identification of novel drug-target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT pair interacts or not. However, protein-ligand interactions assume a continuum of binding strength values, also called binding affinity and predicting this value still remains a challenge. The increase in the affinity data available in DT knowledge-bases allows the use of advanced learning techniques such as deep learning architectures in the prediction of binding affinities. In this study, we propose a deep-learning based model that uses only sequence information of both targets and drugs to predict DT interaction binding affinities. The few studies that focus on DT binding affinity prediction use either 3D structures of protein-ligand complexes or 2D features of compounds. One novel approach used in this work is the modeling of protein sequences and compound 1D representations with convolutional neural networks (CNNs).



The results show that the proposed deep learning based model that uses the 1D representations of targets and drugs is an effective approach for drug target binding affinity prediction. The model in which high-level representations of a drug and a target are constructed via CNNs achieved the best Concordance Index (CI) performance in one of our larger benchmark datasets, outperforming the KronRLS algorithm and SimBoost, a state-of-the-art method for DT binding affinity prediction.


Availability and implementation
https://github.com/hkmztrk/DeepDTA.



Supplementary data are available at Bioinformatics online.
A hybrid approach for spam detection for Twitter

Online social networks (OSNs) are becoming extremely popular among Internet users as they spend significant amount of time on popular social networking sites like Facebook, Twitter and Google+. These sites are turning out to be fundamentally pervasive and are developing a communication channel for billions of users. Online community use them to find new friends, update their existing friends list with their latest thoughts and activities. Huge information available on these sites attracts the interest of cyber criminals who misuse these sites to exploit vulnerabilities for their illicit benefits such as advertising some product or to attract victims to click on malicious links or infecting users system just for the purpose of making money. Spam detection is one of the major problems these days in social networking sites such as twitter. Most previous techniques use different set of features to classify spam and non-spam users. In this paper, we proposed a hybrid technique which uses content-based as well as graph-based features for identification of spammers on twitter platform. We have analysed the proposed technique on real Twitter dataset with 11k uses and more than 400k tweets approximately. Our results show that the detection rate of our proposed technique is much higher than any of the existing techniques.
Studying Interdependencies of E-government Challenges in Tanzania along a Pestel Analysis

E-government challenges are well researched in literature and well known by governments. However, being aware of the challenges of e-government implementation is not sufficient, as challenges may interrelate and impact each other. Therefore, a systematic analysis of the challenges and their interrelationships contributes to providing a better understanding of how to tackle the challenges and how to develop sustainable solutions. This paper aims to investigate existing challenges of e-government and their interdependencies in Tanzania. The collection of e-government challenges in Tanzania is implemented through interviews, desk research and observations of actors in their job. In total, 32 challenges are identified. The subsequent PESTEL analysis studied interrelationships of challenges and identified 34 interrelationships. The analysis of the interrelationships informs policy decision makers of issues to focus on along the planning of successfully implementing the existing e-government strategy in Tanzania. The study also identified future research needs in evaluating the findings through quantitative analysis.
Information systems as a strategic partner in organizational performance

Initially seen as a support function, Information Systems (IS) department’s importance has increased as the business environment has grown more dynamic and the power to collect, assess, and disseminate information has expanded. Properly implemented information systems have become an even more valuable strategic resource – one that any organization can use to improve its competitive advantage. IS departments are rapidly becoming strategic partners with other business functions and integral to the general success of the organization. This work summarizes key issues related to the changing role of IS in the business environment for senior practitioners and strategic planners focusing on legal, marketing, HR and corporate governance.
Subscription fraud prevention in telecommunications using fuzzy rules and neural networks

A system to prevent subscription fraud in fixed telecommunications with high impact on long-distance carriers is proposed. The system consists of a classification module and a prediction module. The classification module classifies subscribers according to their previous historical behavior into four different categories: subscription fraudulent, otherwise fraudulent, insolvent and normal. The prediction module allows us to identify potential fraudulent customers at the time of subscription. The classification module was implemented using fuzzy rules. It was applied to a database containing information of over 10,000 real subscribers of a major telecom company in Chile. In this database, a subscription fraud prevalence of 2.2% was found. The prediction module was implemented as a multilayer perceptron neural network. It was able to identify 56.2% of the true fraudsters, screening only 3.5% of all the subscribers in the test set. This study shows the feasibility of significantly preventing subscription fraud in telecommunications by analyzing the application information and the customer antecedents at the time of application.
Projected-Capacitive Touch Technology

in a seismic change in the touch-screen business. Projected capacitive (pro-cap), the touch technology used in the iPhone touch screen, has become the first choice for many small-to-medium (<10-in.) touch-equipped products now in development. The technology is not just Apple-trendy but incorporates some of the best characteristics of competing touch technologies. The three most important advantages of pro-cap technology are as follows:
Artificial Intelligence : Potential Benefits and Ethical Considerations

 However, like all powerful technologies, great care must be taken in its development and deployment. To reap the societal benefits of AI systems, we will first need to trust them and make sure that they follow the same ethical principles, moral values, professional codes, and social norms that we humans would follow in the same scenario. Research and educational efforts, as well as carefully designed regulations, must be put in place to achieve this goal.