nils-herrmann/clean_scirepeval huggingface dataset:
A fuzzy TOPSIS model via chi-square test for information source selection

The Information Source (IS) selection involves various aspects with different requirements under indeterminate conditions. It is such a complicated process pertaining to seeking for the most appropriate solution that how to resolve the constraint resources needs to be congruously considered. This paper proposes a Multi-Criteria Group Decision Making (MCGDM) model, which uniforms the quantitative and qualitative factual value of different attributes with trapezoidal fuzzy numbers. Analytic Hierarchy Process (AHP) and Entropy Weights (EW) are integrated to alleviate the conflicts by experts’ intuitions and provide the accurate weight vector in this model. Besides, the Euclidean Distance (ED) is substituted by the Value of Chi-Square Test (VCST) to refine the Relative Closeness (RC), which theoretically excluded the potential bias arising from relative importance of the two types of distances, in a revised Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The optimal recommendation compromises in a social decision making way. Finally, the software named ‘‘Evaluator’’, which is based on the presented model, is illustrated to show how it can be practically used for IS selection with comparative analysis.
Multichannel Decoded Local Binary Patterns for Content-Based Image Retrieval

Local binary pattern (LBP) is widely adopted for efficient image feature description and simplicity. To describe the color images, it is required to combine the LBPs from each channel of the image. The traditional way of binary combination is to simply concatenate the LBPs from each channel, but it increases the dimensionality of the pattern. In order to cope with this problem, this paper proposes a novel method for image description with multichannel decoded LBPs. We introduce adder- and decoder-based two schemas for the combination of the LBPs from more than one channel. Image retrieval experiments are performed to observe the effectiveness of the proposed approaches and compared with the existing ways of multichannel techniques. The experiments are performed over 12 benchmark natural scene and color texture image databases, such as Corel-1k, MIT-VisTex, USPTex, Colored Brodatz, and so on. It is observed that the introduced multichannel adder- and decoder-based LBPs significantly improve the retrieval performance over each database and outperform the other multichannel-based approaches in terms of the average retrieval precision and average retrieval rate.
Factors affecting adoption of mobile banking in Pakistan : Empirical Evidence

In this research paper we investigated the determinants likely to influence the adoption of mobile banking services, with a special focus on under banked/unbanked low-income population of Pakistan. The adoption of mobile banking services has been a strategic goal, both for banks and telcos. For this purpose, Technology Acceptance Model (TAM) was used, with additional determinants of perceived risk and social influence. Data was collected by surveying 372 respondents from the two largest cities (Karachi and Hyderabad) of the province Sindh, in Pakistan using judgement sampling method. This study empirically concluded that consumers’ intention to adopt mobile banking services was significantly influenced by social influence, perceived risk, perceived usefulness, and perceived ease of use. The most significant positive impact was of social influence on consumers’ intention to adopt mobile banking services. The paper concluded with discussion on results, and several business implications for the banking industry of Pakistan.
Identifying the Information Structure of Scientific Abstracts: An Investigation of Three Different Schemes

Many practical tasks require accessing specific types of information in scientific literature; e.g. information about the objective, methods, results or conclusions of the study in question. Several schemes have been developed to characterize such information in full journal papers. Yet many tasks focus on abstracts instead. We take three schemes of different type and granularity (those based on section names, argumentative zones and conceptual structure of documents) and investigate their applicability to biomedical abstracts. We show that even for the finest-grained of these schemes, the majority of categories appear in abstracts and can be identified relatively reliably using machine learning. We discuss the impact of our results and the need for subsequent task-based evaluation of the schemes.
A highly efficient power harvester with wide dynamic input power range for 900 MHz wireless power transfer applications

This work demonstrates the design of an adaptive reconfigurable rectifier to address the issue of early breakdown voltage in a conventional rectifier and extends the rectifier's operation for wide dynamic input power range. A depletion-mode field-effect transistor has been introduced to operate as a switch and compensate at low and high input power levels for rectifier. This design accomplishes 40% of RF-DC power conversion efficiency over a wide dynamic input power range from -10 dBm to 27 dBm, while exhibiting 78% of peak power efficiency at 22 dBm. The power harvester is designed to operate in the 900 MHz ISM band and suitable for Wireless Power Transfer applications.
Proxy Cryptography Revisited

In this work we revisit and formally study the notion of proxy cryptography. Intuitively, various proxy functions allow two cooperating parties F (the “FBI”) and P (the “proxy”) to duplicate the functionality available to t he third party U (the “user”), without being able to perform this functionality on their own (without cooperation). The concept is closely related to the notion of threshold cryptography, except we deal with only two parties P andF , and place very strict restrictions on the way the operations are performed (which is done for the sake of efficiency, usability and scalability). For example, for decryption (res p. signature)P (F ) sends a single message to F (P ), after which the latter can decrypt (sign) the message. Our formal modeling of proxy cryptography significantly generalizes, simplifies and simultaneously clarifies the model of “atomic proxy” suggested by Blaze and Strauss . In particular, we define bidirectional and unidirectional var iants of our model 1, and show extremely simple generic solutions for proxy signature and encryption in these models. We also give more efficient solutions for several specific schemes. We conclude that proxy cryptography is a relatively simple concept to satisfy when looked from the correct and formal standpoint.
Analysis of classifiers’ robustness to adversarial perturbations

The goal of this paper is to analyze the intriguing instability of classifiers to adversarial perturbations (Szegedy et al.,  2014). We provide a theoretical framework for analyzing the robustness of classifiers to adversarial perturbations, and show fundamental upper bounds on the robustness of classifiers. Specifically, we establish a general upper bound on the robustness of classifiers to adversarial perturbations, and then illustrate the obtained upper bound on two practical classes of classifiers, namely the linear and quadratic classifiers. In both cases, our upper bound depends on a distinguishability measure that captures the notion of difficulty of the classification task. Our results for both classes imply that in tasks involving small distinguishability, no classifier in the considered set will be robust to adversarial perturbations, even if a good accuracy is achieved. Our theoretical framework moreover suggests that the phenomenon of adversarial instability is due to the low flexibility of classifiers, compared to the difficulty of the classification task (captured mathematically by the distinguishability measure). We further show the existence of a clear distinction between the robustness of a classifier to random noise and its robustness to adversarial perturbations. Specifically, the former is shown to be larger than the latter by a factor that is proportional to $$\sqrt{d}$$ d (with d being the signal dimension) for linear classifiers. This result gives a theoretical explanation for the discrepancy between the two robustness properties in high dimensional problems, which was empirically observed by Szegedy et al. in the context of neural networks. We finally show experimental results on controlled and real-world data that confirm the theoretical analysis and extend its spirit to more complex classification schemes.
A 45GHz Doherty power amplifier with 23% PAE and 18dBm output power, in 45nm SOI CMOS

A 45GHz Doherty power amplifier is implemented in 45nm SOI CMOS. Two-stack FET amplifiers are used as main and auxiliary amplifiers, allowing a supply voltage of 2.5V and high output power. The use of slow-wave coplanar waveguides (CPW) improves the PAE and gain by approximately 3% and 1dB, and reduces the die area by 20%. This amplifier exhibits more than 18dBm saturated output power, with peak power gain of 7dB. It occupies 0.64mm2 while achieving a peak PAE of 23%; at 6dB back-off the PAE is 17%.
Digital Fabrication Techniques for Cultural Heritage: A Survey

Digital fabrication devices exploit basic technologies in order to create tangible reproductions of 3D digital models. Although current 3D printing pipelines still suffer from several restrictions, accuracy in reproduction has reached an excellent level. The manufacturing industry has been the main domain of 3D printing applications over the last decade. Digital fabrication techniques have also been demonstrated to be effective in many other contexts, including the consumer domain. The Cultural Heritage is one of the new application contexts and is an ideal domain to test the flexibility and quality of this new technology. This survey overviews the various fabrication technologies, discussing their strengths, limitations, and costs. Various successful uses of 3D printing in the Cultural Heritage are analysed, which should also be useful for other application contexts. We review works that have attempted to extend fabrication technologies in order to deal with the specific issues in the use of digital fabrication in the Cultural Heritage. Finally, we also propose areas for future research.
Outlier Detection in High Dimension Data Based On Multimodality And Neighbourhood Size Using KNN Method

Outlier detection in high-dimensional data presents various challenges resulting from the “curse of dimensionality.” A prevailing view is that distance concentration, i.e., the tendency of distances in high-dimensional data to become indiscernible, hinders the detection of outliers by making distance-based methods label all points as almost equally good outliers. In this paper, we provide evidence supporting the opinion that such a view is too simple, by demonstrating that distance-based methods can produce more contrasting outlier scores in high-dimensional settings. Furthermore, we show that high dimensionality can have a different impact, by reexamining the notion of reverse nearest neighbors in the unsupervised outlier-detection context. Namely, it was recently observed that the distribution of points’ reverse-neighbor counts becomes skewed in high dimensions, resulting in the phenomenon known as hubness. We provide insight into how some points (antihubs) appear very infrequently in k-NN lists of other points, and explain the connection between antihubs, outliers, and existing unsupervised outlier-detection methods. By evaluating the classic k-NN method, the angle-based technique designed for high-dimensional data, the density-based local outlier factor and influenced outlierness methods, and antihub-based methods on various synthetic and real-world data sets, we offer novel insight into the usefulness of reverse neighbor counts in unsupervised outlier detection.
Automatic analysis of speech quality for aphasia treatment

Aphasia is a common language disorder which can severely affect an individual's ability to communicate with others. Aphasia rehabilitation requires intensive practice accompanied by appropriate feedback, the latter of which is difficult to satisfy outside of therapy. In this paper we take a first step towards developing an intelligent system capable of providing feedback to patients with aphasia through the automation of two typical therapeutic exercises, sentence building and picture description. We describe the natural speech corpus collected from our interaction with clients in the University of Michigan Aphasia Program (UMAP). We develop classifiers to automatically estimate speech quality based on human perceptual judgment. Our automatic prediction yields accuracies comparable to the average human evaluator. Our feature selection process gives insights into the factors that influence human evaluation. The results presented in this work provide support for the feasibility of this type of system.
LLparse and LRparse: visual and interactive tools for parsing

This paper describes instructional tools, LLparse and LRparse, for visualizing and interacting with small examples of LL and LR parsing. These tools can be used to understand the process of constructing LL(1) and LR(1) parse tables through a series of steps in which users receive feedback on the correctness of each step before moving on to the next step. For example, in LRparse, the user initially enters an LR(1) grammar, calculates FIRST and FOLLOW sets, graphically constructs a deterministic finite automaton of item sets, and finally constructs the LR(1) parsing table. Upon completion of the constructed table, the user can observe a visualization of the parsing of input strings. These tools can be used to provide problem solving feedback in courses on automata theory or compiler design.
AdJail: Practical Enforcement of Confidentiality and Integrity Policies on Web Advertisements

Web publishers frequently integrate third-party advertisements into web pages that also contain sensitive publisher data and end-user personal data. This practice exposes sensitive page content to confidentiality and integrity attacks launched by advertisements. In this paper, we propose a novel framework for addressing security threats posed by third-party advertisements. The heart of our framework is an innovative isolation mechanism that enables publishers to transparently interpose between advertisements and end users. The mechanism supports finegrained policy specification and enforcement, and does not affect the user experience of interactive ads. Evaluation of our framework suggests compatibility with several mainstream ad networks, security from many threats from advertisements and acceptable performance overheads.
Merging What's Cracked, Cracking What's Merged: Adaptive Indexing in Main-Memory Column-Stores

Adaptive indexing is characterized by the partial creation and refinement of the index as side effects of query execution. Dynamic or shifting workloads may benefit from preliminary index structures focused on the columns and specific key ranges actually queried — without incurring the cost of full index construction. The costs and benefits of adaptive indexing techniques should therefore be compared in terms of initialization costs, the overhead imposed upon queries, and the rate at which the index converges to a state that is fully-refined for a particular workload component. Based on an examination of database cracking and adaptive merging, which are two techniques for adaptive indexing, we seek a hybrid technique that has a low initialization cost and also converges rapidly. We find the strengths and weaknesses of database cracking and adaptive merging complementary. One has a relatively high initialization cost but converges rapidly. The other has a low initialization cost but converges relatively slowly. We analyze the sources of their respective strengths and explore the space of hybrid techniques. We have designed and implemented a family of hybrid algorithms in the context of a column-store database system. Our experiments compare their behavior against database cracking and adaptive merging, as well as against both traditional full index lookup and scan of unordered data. We show that the new hybrids significantly improve over past methods while at least two of the hybrids come very close to the “ideal performance” in terms of both overhead per query and convergence to a final state.
Transformation of formants for voice conversion using artificial neural networks

In this paper we propose a scheme for developing a voice conversion system that converts the speech signal uttered by a source speaker to a speech signal having the voice characteristics of the target speaker. In particular, we address the issue of transformation of the vocal tract system features from one speaker to another. Formants are used to represent the vocal tract system features and a formant vocoder is used for synthesis. The scheme consists of a formant analysis phase, followed by a learning phase in which the implicit formant transformation is captured by a neural network. The transformed formants together with the pitch contour modified to suit the average pitch of the target speaker are used to synthesize speech with the desired vocal tract system characteristics.
Modeling and analysis of electric field and electrostatic adhesion force generated by interdigital electrodes for wall climbing robots

A model is presented for the analysis of the electric field and electrostatic adhesion force produced by interdigital electrodes. Assuming that the potential varies linearly with distance in inter-electrode gaps, the potential distribution on the electrode plane is obtained by taking the first-order Taylor series approximation. The expressions of electric field components are then derived by solving the Laplace equation for the electrical potential in each subregion. The electrostatic adhesion force is calculated using the Maxwell stress tensor formulation. The dynamic properties of the electric field and electrostatic adhesion force are assessed by evaluating the transient response of the field and force under a step in applied voltages. To verify the model developed, an experimental study is carried out in conjunction with the theoretical analysis to evaluate the adhesion performance of an electrode panel on a glass pane. A double tracked wall climbing robot is designed and tested on various wall surfaces. The limit of the approximation method of the inter-electrode potential is discussed. It is found that vacuum suction force is involved in the adhesion. The influence of this vacuum suction force on electrostatic adhesion is also discussed. The results of this work would provide support for theoretical guidelines and system optimization for the electrostatic adhesion technology applied to wall climbing robots.
Usable security and privacy: a case study of developing privacy management tools

Privacy is a concept which received relatively little attention during the rapid growth and spread of information technology through the 1980's and 1990's. Design to make information easily accessible, without particular attention to issues such as whether an individual had a desire or right to control access to and use of particular information was seen as the more pressing goal. We believe that there will be an increasing awareness of a fundamental need to address privacy concerns in information technology, and that doing so will require an understanding of policies that govern information use as well as the development of technologies that can implement such policies. The research reported here describes our efforts to design a privacy management workbench which facilitates privacy policy authoring, implementation, and compliance monitoring. This case study highlights the work of identifying organizational privacy requirements, analyzing existing technology, on-going research to identify approaches that address these requirements, and iteratively designing and validating a prototype with target users for flexible privacy technologies.
Virtual Embodiment: A Scalable Long-Term Strategy for Artificial Intelligence Research

Meaning has been called the “holy grail” of a variety of scientific disciplines, ranging from linguistics to philosophy, psychology and the neurosciences . The field of Artifical Intelligence (AI) is very much a part of that list: the development of sophisticated natural language semantics is a sine qua non for achieving a level of intelligence comparable to humans. Embodiment theories in cognitive science hold that human semantic representation depends on sensori-motor experience ; the abundant evidence that human meaning representation is grounded in the perception of physical reality leads to the conclusion that meaning must depend on a fusion of multiple (perceptual) modalities . Despite this, AI research in general, and its subdisciplines such as computational linguistics and computer vision in particular, have focused primarily on tasks that involve a single modality. Here, we propose virtual embodiment as an alternative, long-term strategy for AI research that is multi-modal in nature and that allows for the kind of scalability required to develop the field coherently and incrementally, in an ethically responsible fashion.
Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among U.S. young adults

While increased time spent on social media (TSSM) has been associated with depression and anxiety, the independent role of using multiple social media (SM) platforms is unclear.  We surveyed a nationally-representative sample of 1787 U.S. young adults ages 19e32. Depression and anxiety symptoms were measured using the Patient-Reported Outcomes Measurement Information System (PROMIS). We assessed use of multiple SM platforms with an adapted Pew Internet Research scale. We used ordered logistic regression models to assess associations between use of multiple SM platforms and mental health outcomes while controlling for eight covariates, including overall TSSM.  Compared to those who used 0e2 social media platforms, participants who used 7e11 social media platforms had substantially higher odds of having increased levels of both depression (Adjusted Odds Ratio [AOR] 1⁄4 3.0, 95% CI 1⁄4 1.9e4.8) and anxiety symptoms (AOR 1⁄4 3.2, 95% CI 1⁄4 2.0e5.1). Associations were linear (p < 0.001 for all) and robust to all sensitivity analyses.  Use of multiple SM platforms is independently associated with symptoms of depression and anxiety, even when controlling for overall TSSM. These associations are strong enough that it may be valuable for clinicians to ask individuals with depression and anxiety about multiple platform use and to counsel regarding this potential contributing factor.
Automated Identification of Diabetic Retinopathy Stages Using Digital Fundus Images

Diabetic retinopathy (DR) is caused by damage to the small blood vessels of the retina in the posterior part of the eye of the diabetic patient. The main stages of diabetic retinopathy are non-proliferate diabetes retinopathy (NPDR) and proliferate diabetes retinopathy (PDR). The retinal fundus photographs are widely used in the diagnosis and treatment of various eye diseases in clinics. It is also one of the main resources for mass screening of diabetic retinopathy. In this work, we have proposed a computer-based approach for the detection of diabetic retinopathy stage using fundus images. Image preprocessing, morphological processing techniques and texture analysis methods are applied on the fundus images to detect the features such as area of hard exudates, area of the blood vessels and the contrast. Our protocol uses total of 140 subjects consisting of two stages of DR and normal. Our extracted features are statistically significant (p < 0.0001) with distinct mean ± SD as shown in Table 1. These features are then used as an input to the artificial neural network (ANN) for an automatic classification. The detection results are validated by comparing it with expert ophthalmologists. We demonstrated a classification accuracy of 93%, sensitivity of 90% and specificity of 100%.