nils-herrmann/clean_scirepeval huggingface dataset:
Unsupervised Concept Categorization and Extraction from Scientific Document Titles

This paper studies the automated categorization and extraction of scientific concepts from titles of scientific articles, in order to gain a deeper understanding of their key contributions and facilitate the construction of a generic academic knowledgebase. Towards this goal, we propose an unsupervised, domain-independent, and scalable two-phase algorithm to type and extract key concept mentions into aspects of interest (e.g., Techniques, Applications, etc.). In the first phase of our algorithm we proposePhraseType, a probabilistic generative model which exploits textual features and limited POS tags to broadly segment text snippets into aspect-typed phrases. We extend this model to simultaneously learn aspect-specific features and identify academic domains in multi-domain corpora, since the two tasks mutually enhance each other. In the second phase, we propose an approach based on adaptor grammars to extract fine grained concept mentions from the aspect-typed phrases without the need for any external resources or human effort, in a purely data-driven manner. We apply our technique to study literature from diverse scientific domains and show significant gains over state-of-the-art concept extraction techniques. We also present a qualitative analysis of the results obtained.
Full 3-D Printed Microwave Termination: A Simple and Low-Cost Solution

This paper describes the realization and characterization of microwave 3-D printed loads in rectangular waveguide technology. Several commercial materials were characterized at X-band (8-12 GHz). Their dielectric properties were extracted through the use of a cavity-perturbation method and a transmission/reflection rectangular waveguide method. A lossy carbon-loaded Acrylonitrile Butadiene Styrene (ABS) polymer was selected to realize a matched load between 8 and 12 GHz. Two different types of terminations were realized by fused deposition modeling: a hybrid 3-D printed termination (metallic waveguide + pyramidal polymer absorber + metallic short circuit) and a full 3-D printed termination (self-consistent matched load). Voltage standing wave ratio of less than 1.075 and 1.025 were measured over X-band for the hybrid and full 3-D printed terminations, respectively. Power behavior of the full 3-D printed termination was investigated. A very linear evolution of reflected power as a function of incident power amplitude was observed at 10 GHz up to 11.5 W. These 3-D printed devices appear as a very low cost solution for the realization of microwave matched loads in rectangular waveguide technology.
A Hybrid Approach to Grapheme to Phoneme Conversion in Assamese

Assamese is one of the low resource Indian languages. This paper implements both rule-based and data-driven grapheme to phoneme (G2P) conversion systems for Assamese. The rule-based system is used as the baseline which yields a word error rate of 35.3%. The data-driven systems are implemented using state-of-the-art sequence learning techniques such as —i) Joint-Sequence Model (JSM), ii) Recurrent Neural Networks with LTSM cell (LSTM-RNN) and iii) bidirectional LSTM (BiLSTM). The BiLSTM yields the lowest WER i.e., 18.7%, which is an absolute 16.6% improvement on the baseline system. We additionally implement the rules of syllabification for Assamese. The surface output is generated in two forms namely i) phonemic sequence with syllable boundaries, and ii) only phonemic sequence. The output of BiLSTM is fed as an input to Hybrid system. The Hybrid system syllabifies the input phonemic sequences to apply the vowel harmony rules. It also applies the rules of schwa-deletion as well as some rules in which the consonants change their form in clusters. The accuracy of the Hybrid system is 17.3% which is an absolute 1.4% improvement over the BiLSTM based G2P.
Valuation of IT Investments Using Real Options Theory

Real Options Theory is often applied to the valuation of IT investments. The application of Real Options Theory is generally accompanied by a monetary valuation of real options through option pricing models which in turn are based on restrictive assumptions and thus subject to criticism. Therefore, this paper analyzes the application of option pricing models for the valuation of IT investments. A structured literature review reveals the types of IT investments which are valuated with Real Options Theory in scientific literature. These types of IT investments are further investigated and their main characteristics are compared to the restrictive assumptions of traditional option pricing models. This analysis serves as a basis for further discussion on how the identified papers address these assumptions. The results show that a lot of papers do not account for critical assumptions, although it is known that the assumptions are not fulfilled. Moreover, the type of IT investment determines the criticality of the assumptions. Additionally, several extensions or adaptions of traditional option pricing models can be found which provide the possibility of relaxing critical assumptions. Researchers can profit from the results derived in this paper in two ways: First, which assumptions can be critical for various types of IT investments are demonstrated. Second, extensions of option pricing models that relax critical assumptions are introduced.
Foot pressure distribution and contact duration pattern during walking at self-selected speed in young adults

Foot load observed as pressure distribution is examined in relation to a foot and ankle functions within the gait cycle. This load defines the modes of healthy and pathological gait. Determination of the patterns of healthy, i.e. “normal” walk is the basis for classification of pathological modes. Eleven healthy participants were examined in this initial study. Participants walked over pressure plate barefoot at their self - selected speed. Maximal values of the pressure were recorded in the heel, the first, the second and the third metatarsal joints and in the hallux region. Largest contact duration is recorded in the metatarsal region.
Input feature selection for classification problems

Feature selection plays an important role in classifying systems such as neural networks (NNs). We use a set of attributes which are relevant, irrelevant or redundant and from the viewpoint of managing a dataset which can be huge, reducing the number of attributes by selecting only the relevant ones is desirable. In doing so, higher performances with lower computational effort is expected. In this paper, we propose two feature selection algorithms. The limitation of mutual information feature selector (MIFS) is analyzed and a method to overcome this limitation is studied. One of the proposed algorithms makes more considered use of mutual information between input attributes and output classes than the MIFS. What is demonstrated is that the proposed method can provide the performance of the ideal greedy selection algorithm when information is distributed uniformly. The computational load for this algorithm is nearly the same as that of MIFS. In addition, another feature selection algorithm using the Taguchi method is proposed. This is advanced as a solution to the question as to how to identify good features with as few experiments as possible. The proposed algorithms are applied to several classification problems and compared with MIFS. These two algorithms can be combined to complement each other's limitations. The combined algorithm performed well in several experiments and should prove to be a useful method in selecting features for classification problems.
7.6 1GB/s 2Tb NAND flash multi-chip package with frequency-boosting interface chip

NAND Flash-based solid-state drives (SSDs) have been adopted in enterprise storage applications that require high capacity and high-throughput performance. In recent years, a SATA interface supporting only up to 600MB/S throughput has hindered the accelerating performance growth of SSDs due to the host bandwidth limit. A PCI Express (PCIe) has emerged to close the limit because it can deliver 1GB/s throughput per lane and can be scaled to multi-lane to improve bandwidth. Accordingly, the SSD performance bottleneck has moved from the host interface to the NAND flash interface. In a memory system, a die-stacking technology in a NAND flash multi-chip package (MCP) effectively increases capacity and throughput performance in terms of PCB complexities and power consumption compared to a method increasing the number of channels. However, the multi-drop bus topology on NAND interfaces severely affects I/O speed degradations due to channel reflections and inter-symbol interference (ISI) resulting from large capacitive loadings. The undeniable paradox between larger storage capacity and higher I/O bandwidth has become a key challenge to reach enterprise-class SSDs. To overcome this issue, this paper presents a frequency-boosting interface chip (F-Chip) to boost I/O speeds while meeting capacity requirements. A 2Tb NAND flash MCP with 1GB/s toggle DDR interface is accomplished by incorporating the F-Chip into the NAND MCP including a 16-die stacked 128Gb NAND flash.
Microwave Doherty Power Amplifier for High Efficiency and Linearity

This paper explains the basic circuit configuration of microwave Doherty amplifier and additional design methods for wide bandwidth and high power applications. This paper also presents the recent experimental results of the group using the uneven power drive. Most of the previous works about microwave Doherty amplifier have employed identical input drives. In this works, two 1:1(2-way) and a 1:2(3-way) Doherty amplifiers are implemented at 2.14GHz center frequency using LDMOS with a peak envelope power of 180 watt and 285 watt, respectively. The 2-way Doherty amplifiers are optimized at 25 watt average output power for high efficiency and linearity and the 3-way Doherty amplifier at 16 watt average output power for high linearity. The amplifiers are tested using WCDMA 4FA signal. The performances of the 2-way Doherty amplifier are compared with that of the comparable class AB amplifier and the Doherty amplifier with even power drive. The performances of the 3-way Doherty amplifier are compared with that of the comparable class AB amplifier. The experimental results show the superior performances of our Doherty amplifiers
Supervised Adaptive Incremental Clustering for data stream of chunks

Many supervised clustering algorithms have been developed to find the optimal clusters for static datasets by presetting some parameters, but they are seldom suitable for dynamic datasets, such as the data stream of chunks. To find the optimal clusters of the data stream of chunks, a novel Supervised Adaptive Incremental Clustering (SAIC) algorithm is proposed. SAIC can cluster dynamic datasets of arbitrary shapes and sizes automatically. It includes learning and post-processing phases. In the learning phase, each cluster updates adaptively according to its learning rate that is calculated from its counter value. All data points are shuffled at each iteration in order to make SAIC insensitive to the input order of data points. In the post-processing phase, the outliers or boundary points are eliminated according to the counter value of each cluster and the number of iterations. Four synthetic datasets and fourteen UCI datasets are used to evaluate the performance of SAIC, respectively. The experiments on UCI datasets show that SAIC reaches to or outperforms some other supervised clustering algorithms and several unsupervised incremental clustering algorithms. In addition, three data stream of chunks are used to evaluate SAIC from different aspects, which shows SAIC has the scalability and incremental learning ability for the clustering of data streams of chunks.
Subject Content-Based Intelligent Cropping of Digital Photos

Image cropping is one of the most important operations performed to enhance photographs. The direct benefit is improved image composition in terms of better subject location, better subject magnification, and reduced background clutter. Indirectly, cropping can also help image and subject rendering because unwanted distractions are eliminated before image enhancement. We present a robust and efficient solution to this challenging problem for consumer snapshot photos. First, a main subject detection algorithm is employed to produce a belief map probabilistically indicating the subject content. Facilitated by the use of an integral image, a globally optimum cropping window is located to maximize the subject content within the cropping window while satisfying multiple constraints. Extensive evaluation using multiple judges has shown its overwhelming advantage over a commercially used cropping scheme.
MRR: an unsupervised algorithm to rank reviews by relevance

The automatic detection of relevant reviews plays a major role in tasks such as opinion summarization, opinion-based recommendation, and opinion retrieval. Supervised approaches for ranking reviews by relevance rely on the existence of a significant, domain-dependent training data set. In this work, we propose MRR (Most Relevant Reviews), a new unsupervised algorithm that identifies relevant revisions based on the concept of graph centrality. The intuition behind MRR is that central reviews highlight aspects of a product that many other reviews frequently mention, with similar opinions, as expressed in terms of ratings. MRR constructs a graph where nodes represent reviews, which are connected by edges when a minimum similarity between a pair of reviews is observed, and then employs PageRank to compute the centrality. The minimum similarity is graph-specific, and takes into account how reviews are written in specific domains. The similarity function does not require extensive pre-processing, thus reducing the computational cost. Using reviews from books and electronics products, our approach has outperformed the two unsupervised baselines and shown a comparable performance with two supervised regression models in a specific setting. MRR has also achieved a significantly superior run-time performance in a comparison with the unsupervised baselines.
Collaborative analytics for data silos

As a great deal of data has been accumulated in various disciplines, the need for the integrative analysis of separate but relevant data sources is becoming more important. Combining data sources can provide global insight that is otherwise difficult to obtain from individual sources. Because of privacy, regulations, and other issues, many large-scale data repositories remain closed off from the outside, raising what has been termed the data silo issue. The huge volume of today's big data often leads to computational challenges, adding another layer of complexity to the solution. In this paper, we propose a novel method called collaborative analytics by ensemble learning (CABEL), which attempts to resolve the main hurdles regarding the silo issue: accuracy, privacy, and computational efficiency. CABEL represents the data stored in each silo as a compact aggregate of samples called the silo signature. The compact representation provides computational efficiency and privacy preservation but makes it challenging to produce accurate analytics. To resolve this challenge, we formulate the problem of attribute domain sampling and reconstruction, and propose a solution called the Chebyshev subset. To model collaborative efforts to analyze semantically linked but structurally disconnected databases, CABEL utilizes a new ensemble learning technique termed the weighted bagging of base classifiers. We demonstrate the effectiveness of CABEL by testing with a nationwide health-insurance data set containing approximately 4,182,000,000 records collected from the entire population of an Organisation for Economic Co-operation and Development (OECD) country in 2012. In our binary classification tests, CABEL achieved median recall, precision, and F-measure values of 89%, 64%, and 76%, respectively, although only 0.001-0.00001% of the original data was used for model construction, while maintaining data privacy and computational efficiency.
Cognitive advantages and disadvantages in early and late bilinguals.

Previous research has documented advantages and disadvantages of early bilinguals, defined as learning a 2nd language by school age and using both languages since that time. Relative to monolinguals, early bilinguals manifest deficits in lexical access but benefits in executive function. We investigated whether becoming bilingual after childhood (late bilinguals) can produce the cognitive advantages and disadvantages typical of early bilinguals. Participants were 30 monolingual English speakers, 30 late English-Spanish bilinguals, and 30 early Spanish-English bilinguals who completed a picture naming task (lexical access) and an attentional network task (executive function). Late and early bilinguals manifested equivalent cognitive effects in both tasks, demonstrating lexical access deficits and executive function benefits. These findings provide support for the hypothesis that cognitive effects associated with bilingualism arise as the result of proficient, habitual use of 2 languages and not of developmental changes associated with becoming bilingual during childhood.
Smart RDF Data Storage in Graph Databases

Graph Database Management Systems (GDBMs) provide an effective and efficient solution to data storage in current scenarios where data are more and more connected, graph models are widely used, and systems need to scale to large data sets. In particular, the conversion of the persistent layer of an application from a RDF to a graph data store can be convenient but it is usually an hard task for database administrators. In this paper we propose a methodology to convert a RDF data store to a graph database by exploiting the ontology and the constraints of the source. We provide experimental results that show the feasibility of our solution and the efficiency of query answering over the target database.
Miniaturized Circularly Polarized Patch Antenna With Low Back Radiation for GPS Satellite Communications

A new size-reduction technique for circularly polarized (CP) patch antenna with the use of parasitic shorting strips is presented. The operating frequency of the CP patch antenna can be controlled by those parasitic shorting strips. Rectangular, U-shaped, and meandering shorting strips are studied. It is found that the size and the back radiation of the patch antenna can be reduced simultaneously. Good impedance matching and CP radiation characteristics can be achieved with different shapes of the shorting strips. The proposed antenna with meandering shorting strip is measured. Good agreement between analytical and experimental results is obtained.
Mobile technology and the digitization of healthcare.

The convergence of science and technology in our dynamic digital era has resulted in the development of innovative digital health devices that allow easy and accurate characterization in health and disease. Technological advancements and the miniaturization of diagnostic instruments to modern smartphone-connected and mobile health (mHealth) devices such as the iECG, handheld ultrasound, and lab-on-a-chip technologies have led to increasing enthusiasm for patient care with promises to decrease healthcare costs and to improve outcomes. This 'hype' for mHealth has recently intersected with the 'real world' and is providing important insights into how patients and practitioners are utilizing digital health technologies. It is also raising important questions regarding the evidence supporting widespread device use. In this state-of-the-art review, we assess the current literature of mHealth and aim to provide a framework for the advances in mHealth by understanding the various device, patient, and clinical factors as they relate to digital health from device designs and patient engagement, to clinical workflow and device regulation. We also outline new strategies for generation and analysis of mHealth data at the individual and population-based levels.
Design, Implementation and Performance Analysis of Highly Efficient Algorithms for AES Key Retrieval in Access-driven Cache-based Side Channel Attacks

Leakage of information between two processes sharing the same processor cache has been exploited in many novel approaches targeting various cryptographic algorithms. The software implementation of AES is an especially attractive target since it makes extensive use of cache-resident table lookups. We consider two attack scenarios where either the plaintext or ciphertext is known. We employ a multi-threaded spy process and ensure that each time slice provided to the victim (running AES) is small enough so that it makes a very limited number of table accesses. We design and implement a suite of algorithms to deduce the 128-bit AES key using as input the set of (unordered) cache line numbers captured by the spy threads in an access-driven cache-based side channel attack. Our algorithms are expressed using simple relational algebraic operations and run in under a minute. Above all, our attack is highly efficient – we demonstrate recovery of the full AES key given only about 6–7 blocks of plaintext or ciphertext (theoretically even a single block would suffice). This is a substantial improvement over previous cache-based side channel attacks that require between 100 and a million encryptions. Moreover, our attack supports varying cache hit/miss observation granularities, does not need frequent interruptions of the victim and will work even if the victim makes up to 60 cache accesses before being interrupted. Finally, we develop analytic models to estimate the number of encryptions/decryptions required as a function of access granularity and compare model results with those obtained from our experiments.
The role of academic emotions in the relationship between perceived academic control and self-regulated learning in online learning

Self-regulated learning is recognized as a critical factor for successful online learning, and students’ perceived academic control and academic emotions are important antecedents of self-regulated learning. Because emotions and cognition are interrelated, investigating the joint relationship between perceived academic control and academic emotions on self-regulated learning would be valuable to understanding the process of self-regulated learning. Therefore, this study examined the role of academic emotions (enjoyment, anxiety, and boredom) in the relationship between perceived academic control and selfregulated learning in online learning. The path model was proposed to test the mediating and moderating effects of academic emotions. Data were collected from 426 Korean college students registered in online courses, and a path analysis was conducted. The results demonstrated that enjoyment mediated the relationship between perceived academic control and self-regulated learning, but the moderating effect of enjoyment was not significant. Boredom and anxiety did not have significant mediating effects on self-regulated learning, whereas they showed significant moderating effects in the relationship between perceived academic control and self-regulated learning. The role of academic emotions in learning and their implications for facilitating students’ self-regulated learning in online learning were discussed based on the findings.