nils-herrmann/clean_scirepeval huggingface dataset:
The effects of Social Networking Site (SNS) use on college students' friendship and well-being

The present study was conducted with two goals in mind: (1) to examine the influence of using different types of SNS use on users’ well-being, and (2) to examine the mediating roles of online self-disclosure and friendship quality in the relationship between types of SNS use and well-being. Participants were from two large 4-year undergraduate universities in Southwestern China. The study was conducted during Spring semester, 2013, using advertisements that described the nature of the research and indicated that compensation for participation was U10 (about $1.5 U.S.). Of the 402 students approached, 337 completed the survey (i.e., response rate was 83.83%). Structural equation modeling showed that ‘‘social’’ type SNS use was positively related to users’ well-being, whereas ‘‘entertainment’’ type SNS use was not. In addition, online self-disclosure was a significant predictor of users’ friendship quality. However, there was an inverse relationship between ‘‘social’’ SNS use and online self-disclosure, and no relationship between friendship quality based on SNS use and well-being. It should be noted that generalizations of our findings should be made cautiously. The cross-section design and self-reported usage of SNS would also be limitations. Experimental and longitudinal studies should be conducted to provide stronger evidence of causal relations among variables examined in this study.
Multiplexed protein quantitation in Saccharomyces cerevisiae using amine-reactive isobaric tagging reagents.

We describe here a multiplexed protein quantitation strategy that provides relative and absolute measurements of proteins in complex mixtures. At the core of this methodology is a multiplexed set of isobaric reagents that yield amine-derivatized peptides. The derivatized peptides are indistinguishable in MS, but exhibit intense low-mass MS/MS signature ions that support quantitation. In this study, we have examined the global protein expression of a wild-type yeast strain and the isogenic upf1Delta and xrn1Delta mutant strains that are defective in the nonsense-mediated mRNA decay and the general 5' to 3' decay pathways, respectively. We also demonstrate the use of 4-fold multiplexing to enable relative protein measurements simultaneously with determination of absolute levels of a target protein using synthetic isobaric peptide standards. We find that inactivation of Upf1p and Xrn1p causes common as well as unique effects on protein expression.
A Fog-Based Digital Forensics Investigation Framework for IoT Systems

The increasing number of IoT devices is prompting the need to investigate digital forensic techniques that can be efficiently applied to solve computer-related crimes involving IoT devices. In digital forensics, it is common for forensic investigators to consider computing hardware and operating systems for forensic data acquisition. However, applying current forensic data acquisition techniques for further digital evidence analysis may not be applicable to some IoT devices. It is becoming increasingly challenging to determine what type of data should be collected from IoT devices and how traces from such devices can be leveraged by forensic investigators. In this paper, we introduce a fog-based IoT forensic framework (FoBI) that attempts to address the key challenges associated with digital IoT forensics. Throughout this paper, we discuss the overall architecture, use cases and implementation details of FoBI. We further use our FoBI framework to provide insights on improving the digital forensics processes involving IoT systems.
Novel Low-Density Signature for Synchronous CDMA Systems Over AWGN Channel

Novel low-density signature (LDS) structure is proposed for transmission and detection of symbol-synchronous communication over memoryless Gaussian channel. Given N as the processing gain, under this new arrangement, users' symbols are spread over N chips but virtually only dv < N chips that contain nonzero-values. The spread symbol is then so uniquely interleaved as the sampled, at chip rate, received signal contains the contribution from only dc < K number of users, where K denotes the total number of users in the system. Furthermore, a near-optimum chip-level iterative soft-in-soft-out (SISO) multiuser decoding (MUD), which is based on message passing algorithm (MPA) technique, is proposed to approximate optimum detection by efficiently exploiting the LDS structure. Given beta = K/N as the system loading, our simulation suggested that the proposed system alongside the proposed detection technique, in AWGN channel, can achieve an overall performance that is close to single-user performance, even when the system has 200% loading, i.e., when beta = 2. Its robustness against near-far effect and its performance behavior that is very similar to optimum detection are demonstrated in this paper. In addition, the complexity required for detection is now exponential to dc instead of K as in conventional code division multiple access (CDMA) structure employing optimum multiuser detector.
Lung nodules diagnosis based on evolutionary convolutional neural network

Lung cancer presents the highest cause of death among patients around the world, in addition of being one of the smallest survival rates after diagnosis. In this paper, we exploit a deep learning technique jointly with the genetic algorithm to classify lung nodules in whether malignant or benign, without computing the shape and texture features. The methodology was tested on computed tomography (CT) images from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), with the best sensitivity of 94.66%, specificity of 95.14%, accuracy of 94.78% and area under the ROC curve of 0.949.
Machine Teaching for Inverse Reinforcement Learning: Algorithms and Applications

Inverse reinforcement learning (IRL) infers a reward function from demonstrations, allowing for policy improvement and generalization. However, despite much recent interest in IRL, little work has been done to understand of the minimum set of demonstrations needed to teach a specific sequential decisionmaking task. We formalize the problem of finding maximally informative demonstrations for IRL as a machine teaching problem where the goal is to find the minimum number of demonstrations needed to specify the reward equivalence class of the demonstrator. We extend previous work on algorithmic teaching for sequential decision-making tasks by showing a reduction to the set cover problem which enables an efficient approximation algorithm for determining the set of maximallyinformative demonstrations. We apply our proposed machine teaching algorithm to two novel applications: providing a lower bound on the number of queries needed to learn a policy using active IRL and developing a novel IRL algorithm that can learn more efficiently from informative demonstrations than a standard IRL approach.
Message in a Sealed Bottle: Privacy Preserving Friending in Social Networks

Many proximity-based mobile social networks are developed to facilitate connections between any two people, or to help a user to find people with a matched profile within a certain distance. A challenging task in these applications is to protect the privacy of the participants' profiles and personal interests. In this paper, we design novel mechanisms, when given a preference-profile submitted by a user, that search persons with matching-profile in decentralized multi-hop mobile social networks. Our mechanisms also establish a secure communication channel between the initiator and matching users at the time when the matching user is found. Our rigorous analysis shows that our mechanism is privacy-preserving (no participants' profile and the submitted preference-profile are exposed), verifiable (both the initiator and the unmatched user cannot cheat each other to pretend to be matched), and efficient in both communication and computation. Extensive evaluations using real social network data, and actual system implementation on smart phones show that our mechanisms are significantly more efficient than existing solutions.
Automatically Evading Classifiers: A Case Study on PDF Malware Classifiers

Machine learning is widely used to develop classifiers for security tasks. However, the robustness of these methods against motivated adversaries is uncertain. In this work, we propose a generic method to evaluate the robustness of classifiers under attack. The key idea is to stochastically manipulate a malicious sample to find a variant that preserves the malicious behavior but is classified as benign by the classifier. We present a general approach to search for evasive variants and report on results from experiments using our techniques against two PDF malware classifiers, PDFrate and Hidost. Our method is able to automatically find evasive variants for both classifiers for all of the 500 malicious seeds in our study. Our results suggest a general method for evaluating classifiers used in security applications, and raise serious doubts about the effectiveness of classifiers based on superficial features in the presence of adversaries.
Wideband Circular Polarization Reconfigurable Antenna

This communication introduces a polarization reconfigurable wideband circularly polarized (CP) antenna, which consists of four radiating arms connected to a reconfigurable feeding network. The four radiating arms excited by the feeding network are able to generate wideband CP waves with bidirectional radiation patterns in free space. In order to increase the gain and obtain a broadside radiation pattern, the proposed antenna is placed above a metallic reflector with the distance of quarter wavelength at the center frequency. In addition, polarization reconfigurability is realized by utilizing PIN diodes in the feeding transmission lines such that left-handed circular polarization (LHCP) and right-handed circular polarization (RHCP) modes can be selectively excited by controlling the on/off states of the PIN diodes. The proposed antenna exhibits a wide impedance bandwidth of 80% and an overlapped axial ratio (AR) bandwidth of 23.5% for both modes. The antenna gain is stable across the operating bandwidth with the peak gain of 4.8 dBic. The antenna has a wide AR beamwidth of 90°. The presented work is suitable for GPS, CNSS, or RFID applications.
Vaginal labiaplasty: current practices and a simplified classification system for labial protrusion.

Vaginal labiaplasty has been described for the management of functional and aesthetic problems associated with protrusion of the labia minora. Despite increasing numbers of procedures performed, there is a paucity of data to guide treatment paradigms. This systematic review aims to establish a simple, unifying classification scheme for labial protrusion and summarize current labiaplasty techniques and practices.



A systematic literature review was performed using the PubMed database. Additional articles were selected after reviewing references of identified articles.



The search returned 247 articles. After applying inclusion criteria to identify prospective and retrospective studies evaluating different techniques, outcomes, complications, and patient satisfaction, 19 articles were selected. Labiaplasty of the labia minora was described in 1949 patients. Seven different surgical techniques were used for labiaplasty, including deepithelialization, direct excision, W-shaped resection, wedge resection, composite reduction, Z-plasty, and laser excision. Patient satisfaction rates for each technique ranged from 94 to 100 percent. The most common postoperative complication for all techniques was wound dehiscence (4.7 percent). Key areas for perioperative patient management were defined.



Labiaplasty is safe and carries a high satisfaction rate. However, current practices remain exceedingly diverse. The authors propose a simplified classification system based on the distance of the lateral edge of the labia minora from that of the labia majora, rather than from the introitus. Key areas for perioperative patient management include patient anesthesia, resection technique used, wound closure, and postoperative care. Further randomized studies using a standardized classification system are required to better compare different techniques and establish best practices.
Marketing malpractice: the cause and the cure.

Ted Levitt used to tell his Harvard Business School students, "People don't want a quarter-inch drill--they want a quarter-inch hole." But 35 years later, marketers are still thinking in terms of products and ever-finer demographic segments. The structure of a market, as seen from customers' point of view, is very simple. When people need to get a job done, they hire a product or service to do it for them. The marketer's task is to understand what jobs periodically arise in customers' lives for which they might hire products the company could make. One job, the "I-need-to-send-this-from-here-to-there-with-perfect-certainty-as-fast-as-possible"job, has existed practically forever. Federal Express designed a service to do precisely that--and do it wonderfully again and again. The FedEx brand began popping into people's minds whenever they needed to get that job done. Most of today's great brands--Crest, Starbucks, Kleenex, eBay, and Kodak, to name a few-started out as just this kind of purpose brand. When a purpose brand is extended to products that target different jobs, it becomes an endorser brand. But, over time, the power of an endorser brand will surely erode unless the company creates a new purpose brand for each new job, even as it leverages the endorser brand as an overall marker of quality. Different jobs demand different purpose brands. New growth markets are created when an innovating company designs a product and then positions its brand on a job for which no optimal product yet exists. In fact, companies that historically have segmented and measured markets by product categories generally find that when they instead segment by job, their market is much larger (and their current share much smaller) than they had thought. This is great news for smart companies hungry for growth.
Multi-Source Domain Adaptation Using Approximate Label Matching

Domain adaptation, and transfer learning more generally, seeks to remedy the problem created when training and testing datasets are generated by different distributions. In this work, we introduce a new unsupervised domain adaptation algorithm for when there are multiple sources available to a learner. Our technique assigns a rough labeling on the target samples, then uses it to learn a transformation that aligns the two datasets before final classification. In this article we give a convenient implementation of our method, show several experiments using it, and compare it to other methods commonly used in the field.
Zero-Knowledge Proof of Decryption for FHE Ciphertexts

Zero-knowledge proofs of knowledge and fully-homomorphic encryption are two areas that have seen considerable advances in recent years, and these two techniques are used in conjunction in the context of verifiable decryption. Existing solutions for verifiable decryption are aimed at the batch setting, however there are many applications in which there will only be one ciphertext that requires a proof of decryption. The purpose of this paper is to provide a zero-knowledge proof of correct decryption on an FHE ciphertext, which for instance could hold the result of a cryptographic election. We give two main contributions. Firstly, we present a bootstrapping-like protocol to switch from one FHE scheme to another. The first scheme has efficient homomorphic capabilities; the second admits a simple zero-knowledge protocol. To illustrate this, we use the Brakerski et al. (ITCS, 2012) scheme for the former, and Gentry’s original scheme  2009) for the latter. Secondly, we present a simple one-shot zero-knowledge protocol for verifiable decryption using Gentry’s original FHE scheme.
H.263+: video coding at low bit rates

In this tutorial paper, we discuss the ITU-T H.263+ (or H.263 Version 2) low-bit-rate video coding standard. We first describe, briefly, the H.263 standard including its optional modes. We then address the 12 new negotiable modes of H.263+. Next, we present experimental results for these modes, based on our public-domain implementation (see our Web site at http://spmg.ece.ubc.ca). Tradeoffs among compression performance, complexity, and memory requirements for the H.263+ optional modes are discussed. Finally, results for mode combinations are presented.
scikit-multilearn : A scikit-based Python environment for performing multi-label classification

scikit-multilearn is a Python library for performing multi-label classification. The library is compatible with the scikit/scipy ecosystem and uses sparse matrices for all internal operations. It provides native Python implementations of popular multi-label classification methods alongside novel framework for label space partitioning and division. It includes graph-based community detection methods that utilize the powerful igraph library for extracting label dependency information. In addition its code is well test covered and follows PEP8. Source code and documentation can be downloaded from http://scikit.ml and also via pip. The library follows scikit’s BSD licencing scheme.
Content analysis and thematic analysis: Implications for conducting a qualitative descriptive study.

Qualitative content analysis and thematic analysis are two commonly used approaches in data analysis of nursing research, but boundaries between the two have not been clearly specified. In other words, they are being used interchangeably and it seems difficult for the researcher to choose between them. In this respect, this paper describes and discusses the boundaries between qualitative content analysis and thematic analysis and presents implications to improve the consistency between the purpose of related studies and the method of data analyses. This is a discussion paper, comprising an analytical overview and discussion of the definitions, aims, philosophical background, data gathering, and analysis of content analysis and thematic analysis, and addressing their methodological subtleties. It is concluded that in spite of many similarities between the approaches, including cutting across data and searching for patterns and themes, their main difference lies in the opportunity for quantification of data. It means that measuring the frequency of different categories and themes is possible in content analysis with caution as a proxy for significance.
Towards Internet of Things : Survey and Future Vision

Internet of things is a promising research due to its importance in many commerce, industry, and education applications. Recently, new applications and research challenges in numerous areas of Internet of things are fired. In this paper, we discuss the history of Internet of things, different proposed architectures of Internet of things, research challenges and open problems related to the Internet of things. We also introduce the concept of Internet of things database and discuss about the future vision of Internet of things. These are the manuscript preparation guidelines used as a standard template for all journal submissions. Author must follow these instructions while preparing/modifying these guidelines.
An Optimum Loop Gain Tracking All-Digital PLL Using Autocorrelation of Bang–Bang Phase-Frequency Detection

An all-digital phase-locked loop with a bang-bang phase-frequency detector (BBPFD) that tracks the optimum loop gain for minimum jitter is proposed. The autocorrelation of the output of BBPFD indicates whether the bang-bang PLL operates in the nonlinear regime or the random noise regime. An adaptive loop gain controller continuously evaluates the autocorrelation of the BBPFD output and adjusts the loop gain to make the autocorrelation zero. The digital loop filter operates at higher than the reference clock frequency to reduce the loop latency and to mitigate the resolution of the digitally controlled oscillator. The prototype chip has been fabricated in a 65-nm CMOS process. The core consumes 5 mW at 2.5 GHz and exhibits root-mean-square jitter of 1.72 ps.
Electric Vehicle Charging on Residential Distribution Systems: Impacts and Mitigations

This paper aims to understand, identify, and mitigate the impacts of residential electric vehicle (EV) charging on distribution system voltages. A thorough literature review on the impacts of residential EV charging is presented, followed by a proposed method for evaluating the impacts of EV loads on the distribution system voltage quality. Practical solutions to mitigate EV load impacts are discussed as well, including infrastructural changes and indirect controlled charging with time-of-use (TOU) pricing. An optimal TOU schedule is also presented, with the aim of maximizing both customer and utility benefits. This paper also presents a discussion on implementing smart charging algorithms to directly control EV charging rates and EV charging starting times. Finally, a controlled charging algorithm is proposed to improve the voltage quality at the EV load locations while avoiding customer inconvenience. The proposed method significantly decreases the impacts of EV load charging on system peak load demand and feeder voltages.
ANOMALY DETECTION OF ATTACKS (ADA) ON DNN CLASSIFIERS AT TEST TIME

A significant threat to wide deployment of machine learning-based classifiers is adversarial learning attacks, especially at test-time. Recently there has been significant development in defending against such attacks. Several such works seek to robustify the classifier to make "correct" decisions on perturbed patterns. We argue it is often operationally more important to detect the attack, rather than to "correctly classify" in the face of it (Classification can proceed if no attack is detected). We hypothesize that, even if human-imperceptible, adversarial perturbations are machine-detectable. We propose a purely unsupervised anomaly detector (AD), based on suitable (null hypothesis) density models for the different layers of a deep neural net and a novel decision statistic built upon the Kullback-Leibler divergence.  when is it appropriate to aim to "correctly classify" a perturbed pattern?; 2) What is a good AD detection statistic, one which exploits all likely sources of anomalousness associated with a test-time attack? 3) Where in a deep neural net (DNN) (in an early layer, a middle layer, or at the penultimate layer) will the most anomalous signature manifest? Tested on MNIST and CIFAR-10 image databases under three prominent attack strategies, our approach outperforms previous detection methods, achieving strong ROC AUC detection accuracy on two attacks and substantially better accuracy than previously reported on the third (strongest) attack.
Synaptic patterning and the timescales of cortical dynamics

Neocortical circuits, as large heterogeneous recurrent networks, can potentially operate and process signals at multiple timescales, but appear to be differentially tuned to operate within certain temporal receptive windows. The modular and hierarchical organization of this selectivity mirrors anatomical and physiological relations throughout the cortex and is likely determined by the regional electrochemical composition. Being consistently patterned and actively regulated, the expression of molecules involved in synaptic transmission constitutes the most significant source of laminar and regional variability. Due to their complex kinetics and adaptability, synapses form a natural primary candidate underlying this regional temporal selectivity. The ability of cortical networks to reflect the temporal structure of the sensory environment can thus be regulated by evolutionary and experience-dependent processes.