nils-herrmann/clean_scirepeval huggingface dataset:
Identifying critical infrastructure sectors and their dependencies: An Indian scenario

Across the globe, critical infrastructures constantly face the risk of cyber and/or other attacks from hostile and malicious entities as well as damage inflicted by natural disasters. This paper seeks to identify the critical infrastructure sectors of a country, namely India, and to explore the dependencies existing among them. The research draws on the extant literature as well as expert opinion and judgments to identify the critical infrastructure sectors. Following this, the interpretive structural modeling (ISM) technique is employed to discover the relationships and dependencies existing among the identified critical infrastructure sectors. Next, cross-impact matrix multiplication applied to classification (MICMAC) analysis is used to categorize the critical infrastructure sectors into four subgroups based on their driving power and dependence on other sectors. Policy implications for government entities and businesses in India are also discussed. & 2014 Elsevier B.V. All rights reserved.
HearHere: smartphone based audio localization using time difference of arrival: demo

Recent advancements in audio recording on mobile devices have improved audio localization capabilities using phones. Previous research has shown that millimeter level accuracy is capable using an off-the-shelf smartphone. This work demonstrates that such valuable resources in smartphones are readily available for developing consumer applications. We develop a smartphone application called HearHere that utilizes geometric features of sound to categorize a tapping sound within a grid. The geometric features are based on the Time Difference of Arrival (TDoA) of the sound between two microphones. By using this classification, any off the shelf phone with stereo recording capability can be made into a music producing device. The application consists of three major components: system calibration, data collection, and audio localization engine based on TDoA. We demonstrate that we can accurately map the tapping sound six region on various solid surface and turn it into a MIDI controller with six different types of instruments.
Big Five factors of personality and replicated predictions of behavior.

Measures of the Big Five factors of personality were used to predict a variety of criterion variables thought to represent behaviors of some social and cultural significance (e.g., alcohol consumption, grade point average). Analyses focused on replicated predictions across 2 independent samples of participants (Ns = 276 and 142) with 3 different measures of the Big Five (the NEO Five-Factor Inventory, the Revised NEO Personality Inventory, and the Five-Factor Nonverbal Personality Questionnaire, the latter an experimental nonverbal personality inventory). The results indicated substantial consistency in behavior predictions across the different Big Five assessments. The data are interpreted as supporting both the construct validity of the personality measures used and the role of the Big Five factors as determinants of certain complex behaviors.
Personality Traits Recognition on Social Network - Facebook

For the natural and social interaction it is necessary to understand human behavior. Personality is one of the fundamental aspects, by which we can understand behavioral dispositions. It is evident that there is a strong correlation between users’ personality and the way they behave on online social network (e.g., Facebook). This paper presents automatic recognition of Big-5 personality traits on social network (Facebook) using users’ status text. For the automatic recognition we studied different classification methods such as SMO (Sequential Minimal Optimization for Support Vector Machine), Bayesian Logistic Regression (BLR) and Multinomial Naïve Bayes (MNB) sparse modeling. Performance of the systems had been measured using macro-averaged precision, recall and F1; weighted average accuracy (WA) and un-weighted average accuracy (UA). Our comparative study shows that MNB performs better than BLR and SMO for personality traits recognition on the social network data.
Use multiplexing to increase information in QR code

In this paper a novel approach to multiplex data in QR Code is presented. This technique can increase the amount of data, as the original information, in QR Code as well as keeping secret information. The original data for encoding is divided into smaller parts, each part will form QR Code pattern in its standard form. Each pattern is encoded or multiplexed and represented each module in QR Code with black and white special symbols. At the receiving end, this QR Code with special symbols (that was multiplexed) is decoded to give back the number of QR Code patterns that was multiplexed. These QR Code pattern can be read by the general QR Code reader and the data can be concatenated back to form its original information.
Empowerment-driven Exploration using Mutual Information Estimation

Exploration is a difficult challenge in reinforcement learning and is of prime importance in sparse reward environments. However, many of the state of the art deep reinforcement learning algorithms, that reply on epsilon-greedy exploration, fail on these environments. In such environments, empowerment can serve as an intrinsic reward signal to enable the agent to explore by maximizing the influence it has over the near future. We formulate empowerment as the channel capacity between actions and states and is calculated by estimating the mutual information between the actions and the following states. The mutual information is estimated using a Mutual Information Neural Estimator and a forward dynamics model. We demonstrate that an empowerment driven agent is able to improve significantly the score of a baseline DQN agent on the game of Montezuma’s Revenge.
16-element Vivaldi antenna array fed by a single ridge substrate integrated waveguide with over 75% bandwidth

A 16-element Vivaldi antenna array with a single ridge SIW feed network has been developed using a two-layer substrate. The single ridge substrate integrated waveguide (RSIW) structure is used to sustain the inherent wide bandwidth performance of the Vivaldi array. The array itself is fed by a novel compact and wide band RSIW eight way divider that is excited by a wideband GCPW to RSIW transition. Vivaldi elements are arranged in an elegant way to avoid wide spacing between the array elements to avoid grating lobes, which is typical for waveguide fed antenna arrays with large apertures. The 16-element uniform linear array was fabricated and its measured 10-dB bandwidth exceeded 75% with a 12-16 dB and 75% efficiency gain and efficiency respectively, over 6 to 11 GHz. Measured results are consistent with simulated ones.
Automatic text summarization of news articles

Text Summarization has always been an area of active interest in the academia. In recent times, even though several techniques have being developed for automatic text summarization, efficiency is still a concern. Given the increase in size and number of documents available online, an efficient automatic news summarizer is the need of the hour. In this paper, we propose a technique of text summarization which focuses on the problem of identifying the most important portions of the text and producing coherent summaries. In our methodology, we donot require full semantic interpretation of the text, instead we create a summary using a model of topic progression in the text derived from lexical chains. We present an optimized and efficient algorithm to generate text summary using lexical chains and using the WordNet thesaurus. Further, we also overcome the limitations of the lexical chain approach to generate a good summary by implementing pronoun resolution and by suggesting new scoring techniques to leverage the structure of news articles.
Design of an FFT/IFFT Processor for MIMO OFDM Systems

In this paper, we present a novel 128/64 point fast Fourier transform (FFT)/ inverse FFT (IFFT) processor for the applications in a multiple-input multiple-output orthogonal frequency-division multiplexing based IEEE 802.11n wireless local area network baseband processor. The unfolding mixed-radix multipath delay feedback FFT architecture is proposed to efficiently deal with multiple data sequences. The proposed processor not only supports the operation of FFT/IFFT in 128 points and 64 points but can also provide different throughput rates for 1-4 simultaneous data sequences to meet IEEE 802.11n requirements. Furthermore, less hardware complexity is needed in our design compared with traditional four-parallel approach. The proposed FFT/IFFT processor is designed in a 0.13-mum single-poly and eight-metal CMOS process. The core area is 660times2142 mum2 , including an FFT/IFFT processor and a test module. At the operation clock rate of 40 MHz, our proposed processor can calculate 128-point FFT with four independent data sequences within 3.2 mus meeting IEEE 802.11n standard requirements
Cross-cultural studies of personality traits and their relevance to psychiatry.

This article provides a brief review of recent cross-cultural research on personality traits at both individual and culture levels, highlighting the relevance of recent findings for psychiatry.



In most cultures around the world, personality traits can be clearly summarized by the five broad dimensions of the Five-Factor Model (FFM), which makes it feasible to compare cultures on personality and psychopathology.



Maturational patterns and sex differences in personality traits generally show cultural invariance, which generates the hypothesis that age of onset, clinical evolution, and sex differences in the prevalence of psychiatric disorders might follow similar universal patterns. The average personality profiles from 51 cultures show meaningful geographical distributions and associations with culture-level variables, but are clearly unrelated to national character stereotypes.



Aggregate personality scores can potentially be related to epidemiological data on psychiatric disorders, and dimensional personality models have implications for psychiatric diagnosis and treatment around the world.
An underactuated propeller for attitude control in micro air vehicles

Traditional coaxial helicopter micro air vehicles use a large propeller motor in conjunction with two small servomotors to control thrust, pitch, and roll forces and moments. Quadrotors similarly generate these necessary forces and moments through the coordinated control of multiple actuators. We present a novel propeller architecture which allows a single motor and rotor to express such control by modulating the torque applied to one passively hinged, underactuated propeller. Flight tests of a two-motor coaxial helicopter demonstrate that such a system can provide active stability and control in a real flight system.
Comparison of complex network analysis software: Citespace, SCI2 and Gephi

Big Data Analysis (BDA) has attracted considerable interest and curiosity from scientists of various fields recently. As big size and complexity of big data, it is pivotal to uncover hidden patterns, bursts of activity, correlations and laws of it. Complex network analysis could be effective method for this purpose, because of its powerful data organization and visualization ability. Besides the general complex software (Gephi), some bibliometrics software (Citespace and SCI2) could also be used for this analysis, due to their powerful data process and visualization functions. This paper presents a comparison of Citespace, SCI2 and Gephi from the following three aspects: (1) Data preprocessing. Citespace is time-consuming and laborious on merging synonyms. SCI2 is efficient and suitable for massive data. Both of them could remove duplicate records. However, Gephi lacks these. (2) Network extraction. Citespace pays more attention on temporal analysis, but SCI2 and Gephi pay less attention on it. Besides, Citespace and SCI2 could provide pruning algorithms highlight the main structure of social network, but Gephi lacks this. (3) Visualization. In Citespace, co-occurrence network could present time, frequency and betweenness centrality simultaneously; cluster view could label the clusters with phrases. However, SCI2 and Gephi provide more various layout algorithms to present network. Besides, they have a better edit-ability than Citespace on generated network.
Sampling theory for graph signals

We propose a sampling theory for finite-dimensional vectors with a generalized bandwidth restriction, which follows the same paradigm of the classical sampling theory. We use this general result to derive a sampling theorem for bandlimited graph signals in the framework of discrete signal processing on graphs. By imposing a specific structure on the graph, graph signals reduce to finite discrete-time or discrete-space signals, effectively ensuring that the proposed sampling theory works for such signals. The proposed sampling theory is applicable to both directed and undirected graphs, the assumption of perfect recovery is easy both to check and to satisfy, and, under that assumption, perfect recovery is guaranteed without any probability constraints or any approximation.
Additive manufacturing of microwave components: Different approaches and methodologies

In this paper, two different approaches for the additive manufacturing of microwave component are discussed. The first approach consists is the popular and cheap fused deposition modeling (FDM). In this paper it is shown that, by using different infill factor, FDM is suitable for the manufacturing of devices with controlled dielectric constant. The second approach is the Stereolithography (SLA). With this approach, better results can be obtained in terms of resolution. Furthermore a very easy way to copper plate the surface of microwave devices is shown and it effectiveness is demonstrated through the manufacturing and measurement of a two-pole filter with mushroom-shaped resonators.
Job insecurity and organizational citizenship behavior: exploring curvilinear and moderated relationships.

This article examined a curvilinear relationship between job insecurity and organizational citizenship behavior (OCB). Drawing from social exchange theory and research on personal control, we developed and tested an explanation for employees' reactions to job insecurity based on their conceptualization of their social exchange relationship with the organization at different levels of job insecurity. Using data from 244 Chinese employees and 102 supervisory ratings of OCB, we found support for a U-shaped relationship between job insecurity and OCB. Moreover, 2 factors--psychological capital and subordinate-supervisor guanxi--moderated the curvilinear relationship, such that the curvilinear relationship is more pronounced among those with lower psychological capital or less positive subordinate-supervisor guanxi.
Enhancing Electric Power Quality Using UPQC: A Comprehensive Overview

This paper presents a comprehensive review on the unified power quality conditioner (UPQC) to enhance the electric power quality at distribution levels. This is intended to present a broad overview on the different possible UPQC system configurations for single-phase (two-wire) and three-phase (three-wire and four-wire) networks, different compensation approaches, and recent developments in the field. It is noticed that several researchers have used different names for the UPQC based on the unique function, task, application, or topology under consideration. Therefore, an acronymic list is developed and presented to highlight the distinguishing feature offered by a particular UPQC. In all 12 acronyms are listed, namely, UPQC-D, UPQC-DG, UPQC-I, UPQC-L, UPQC-MC, UPQC-MD, UPQC-ML, UPQC-P, UPQC-Q, UPQC-R, UPQC-S, and UPQC-VA. More than 150 papers on the topic are rigorously studied and meticulously classified to form these acronyms and are discussed in the paper.
What is in that picture ? Visual Question Answering System

Visual Question Answering is a complex task which aims at answering a question about an image. This task requires multimodal representation learning for both images and text. To solve this problem, an image and text representation is required and high level interactions between the two must be carefully encoded into the model in order to provide the correct answer. The answers can be a word, a phrase, a yes/no answer, selection from multiple choice or fill in the blank . For this project, I decided to focus on building a model to answer open-ended questions. Answers to openended questions are specially challenging because it requires vast set of AI capabilities to answer. Some examples of the capabilities needed are object detection, activity recognition, knowledge base reasoning, and commonsense reasoning[2].
Adverse Drug Reactions and Interactions: Mechanisms, Risk Factors, Detection, Management and Prevention

Adverse drug reactions (ADRs), of which adverse interactions are a special case, are a major cause of morbidity in the community. They are also reported to account for up to 5 per cent of all medical admissions to hospital (Grahame-Smith and Aronson, 1992; Aronson and White, 1996) and they are occasionally fatal. In the USA, it has been suggested that they are between the fourth and sixth commonest cause of death in hospitalized patients (after heart disease, cancer and stroke and around the same frequency as pulmonary disease and roadtraffic accidents) (Lazarou et al., 1998). ADRs are often difficult to differentiate from non-drug-related disease. Many factors, such as concomitant treatment and disease, can cloud their identification, and there are few specific laboratory or clinical methods to confirm them. In clinical practice it is thus often difficult to separate adverse events from adverse reactions, particularly in the case of previously unrecognized reactions. Most doctors often rely on their own experience or the experience of others of similar problems before accepting that a drug might have caused the event. For these reasons, ADRs remain an important clinical and public health problem, although many ADRs are predictable and, therefore, potentially avoidable from the known pharmacology of the drug and the characteristics of the patient.
On Maximizing Sensor Network Lifetime by Energy Balancing

Many physical systems, such as water/electricity distribution networks, are monitored by battery-powered wireless-sensor networks (WSNs). Since battery replacement of sensor nodes is generally difficult, long-term monitoring can be only achieved if the operation of the WSN nodes contributes to long WSN lifetime. Two prominent techniques to long WSN lifetime are 1) optimal sensor activation and 2) efficient data gathering and forwarding based on compressive sensing. These techniques are feasible only if the activated sensor nodes establish a connected communication network (connectivity constraint), and satisfy a compressive sensing decoding constraint (cardinality constraint). These two constraints make the problem of maximizing network lifetime via sensor node activation and compressive sensing NP-hard. To overcome this difficulty, an alternative approach that iteratively solves energy balancing problems is proposed. However, understanding whether maximizing network lifetime and energy balancing problems are aligned objectives is a fundamental open issue. The analysis reveals that the two optimization problems give different solutions, but the difference between the lifetime achieved by the energy balancing approach and the maximum lifetime is small when the initial energy at sensor nodes is significantly larger than the energy consumed for a single transmission. The lifetime achieved by energy balancing is asymptotically optimal, and that the achievable network lifetime is at least 50% of the optimum. Analysis and numerical simulations quantify the efficiency of the proposed energy balancing approach.
Development of the SAIT single-port surgical access robot slave arm based on RCM Mechanism

An innovative single-port surgical robot has recently been developed by the Samsung Advanced Institute of Technology (SAIT). The robot can reach various surgical sites inside the abdominal cavity from a single incision on the body. It has two 7-DOF surgical tools, a 3-DOF endoscope, a flexible hyper-redundant 6-DOF guide tube, and a 6-DOF manipulator. This paper primarily focuses on the manipulator, called a slave arm, which is capable of setting the location of a Remote Center Motion (RCM) point. Because the surgical tools can explore the abdominal area through a small incision point when the RCM point is aligned with the incision area, the RCM mechanism is an integral part of the manipulator for single-port surgery. The mechanical feature, operational principle, control method, and the system architecture of the slave arm are introduced in this paper. In addition, manipulation experiments conducted validate its efficacy.
A Strategy for Ranking Optimization Methods using Multiple Criteria

An important component of a suitably automated machine learning process is the automation of the model selection which often contains some optimal selection of hyperparameters. The hyperparameter optimization process is often conducted with a black-box tool, but, because different tools may perform better in different circumstances, automating the machine learning workflow might involve choosing the appropriate optimization method for a given situation. This paper proposes a mechanism for comparing the performance of multiple optimization methods for multiple performance metrics across a range of optimization problems. Using nonparametric statistical tests to convert the metrics recorded for each problem into a partial ranking of optimization methods, results from each problem are then amalgamated through a voting mechanism to generate a final score for each optimization method. Mathematical analysis is provided to motivate decisions within this strategy, and sample results are provided to demonstrate the impact of certain design decisions.
Integrated path planning and dynamic steering control for autonomous vehicles

A method is presented for combining two previously proposed algorithms for path-planning and dynamic steering control into a computationally feasible scheme for real-time feedback control of autonomous vehicles in uncertain environments. In the proposed approach to vehicle guidance and control, Path Relaxation is used to compute critical points along a globally desirable path using a priori information and sensor data'. Generalized potential fields are then used for local feedback control to drive the vehicle along a collision-free path using the critical points as subgoals'. Simulation results are presented to demonstrate the control scheme.