nils-herrmann/clean_scirepeval huggingface dataset:
Two-factor authentication: too little, too late

T wo-factor authentication isn't our savior. It won't defend against phishing. It's not going to prevent identity theft. It's not going to secure online accounts from fraudulent transactions. It solves the security problems we had 10 years ago, not the security problems we have today. The problem with passwords is that it is too easy to lose control of them. People give their passwords to other people. People write them down, and other people read them. People send them in email, and that email is intercepted. People use them to log into remote servers, and their communications are eavesdropped on. Passwords are also easy to guess. And once any of that happens, the password no longer works as an authentication token because you can never be sure who is typing in that password. Two-factor authentication mitigates this problem. If your password includes a number that changes every minute, or a unique reply to a random challenge, then it's difficult for someone else to intercept. You can't write down the ever-changing part. An intercepted password won't be usable the next time it's needed. And a two-factor password is more difficult to guess. Sure, someone can always give his password and token to his secretary, but no solution is foolproof. These tokens have been around for at least two decades, but it's only recently that they have received mass-market attention. AOL is rolling them out. Some banks are issuing them to customers , and even more are talking about doing it. It seems that corporations are finally recognizing the fact that passwords don't provide adequate security , and are hoping that two-factor authentication will fix their problems. Unfortunately, the nature of attacks has changed over those two decades. Back then, the threats were all passive: eavesdropping and offline password guessing. Today, the threats are more active: phishing and Trojan horses. Two new active attacks we're starting to see include: Man-in-the-Middle Attack. An attacker puts up a fake bank Web site and entices a user to that Web site. The user types in his password, and the attacker in turn uses it to access the bank's real Web site. Done correctly, the user will never realize that he isn't at the bank's Web site. Then the attacker either disconnects the user and makes any fraudulent transactions he wants, or passes along the user's banking transactions while making his own transactions at the same …
Customer churn prediction for retail business

Customer churn happens when a customer discontinues his or her interaction with a company. In retail business, a customer is treated to be churned once his/her transactions outdate a particular amount of time. Once a customer becomes a churn, the loss incurred by the company is not just the lost revenue due to the lost customer but also the costs involved in additional marketing in order to attract new customer. Reducing customer churn is a key business goal of every online business. In this project, we have considered data set from UCI Machine Learning repository. This dataset contains records of transactions that occurred between December 1, 2010 and December 1, 2011. This is recorded from an online retail gift store based in United Kingdom. The customers of this company are mainly wholesalers. This data set is pre-processed by removing NAs, validating numerical values, removing erroneous data points. We then perform aggregations on the data to generate invoice based and customer based data sets. A variable churn is attached to each data point. This churn value is determined based on the customer's transactions. Three algorithms are run on this customer aggregated dataset to predict churn value. They are Random forest, Support vector machines and Extreme gradient boosting. A comparative study is done on these three algorithms.
An Analysis of Internal Environment of a Commercial-oriented Research Organization : Using Mckinsey 7 S Framework in a Ghanaian Context 1

The largest research organization in Ghana has of part of its mandate to raise internally generated funds through the commercialization of research products and services in order to supplement government’s budgetary allocation. How could its internal structures, vision and mission be strategically aligned for effective commercialization performance? The study utilizes the McKinsey 7 S Framework which is an analytical tool used to help in a variety of situations, including how to improve the performance of a company, and determine how best to implement a proposed strategy. Finally, the study makes some strategic recommendations for consideration by policy makers within the public sector.
SANE: System for Fine Grained Named Entity Typing on Textual Data

Assignment of fine-grained types to named entities is gaining popularity as one of the major Information Extraction tasks due to its applications in several areas of Natural Language Processing. Existing systems use huge knowledge bases to improve the accuracy of the fine-grained types. We designed and developed SANE, a system that uses Wikipedia categories to fine grain the type of the named entities recognized in the textual data. The main contribution of this work is building a named entity typing system without the use of knowledge bases. Through our experiments, 1) we establish the usefulness of Wikipedia categories to Named Entity Typing and 2) we show that SANE’s performance is on par with the state-ofthe-art.
Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks

A major challenge in brain tumor treatment planning and quantit ative evaluation is determination of the tumor extent. The noninva s ve magnetic resonance imaging (MRI) technique has emerged as a front-line diag nostic tool for brain tumors without ionizing radiation. Manual segmentation of brain t umor extent from 3D MRI volumes is a very time-consuming task and the performance is highly relied on operator’s experience. In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent. In this study, w e propose a fully automatic method for brain tumor segmentation, which is develo ped using U-Net based deep convolutional networks. Our method was evalu ated on Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets, which contain 220 high-grade brain tumor and 54 low-grade tumor cases. Cro svalidation has shown that our method can obtain promising segmentation effi-
Novice user perception of e-services: A study in the Egyptian public sector

Egypt is investing in e-services as a driver for socio-economic development. Designing e-services that are conducive to novice users is central to their proliferation. This paper presents a study of novice user perception of e-services, based on a questionnaire derived from the e-Qual e-service quality dimensions, in an Egyptian government organization. Its main contribution is to show the difference in novice and experienced users' perception of two e-service designs. Novice users perceive both designs more highly in terms of usability and information quality, focusing primarily on attractiveness and ease of use and understanding. They find simpler interactions more appealing and easier to learn, but associate information quality with more complex interactions. Their perception of e-service reputation although affected by presentation and usability factors is dominated by the reputation of the organization. This is the first study to focus on novice user perceptions and its results have important implications for the design of e-services.
Effects of VDT and paper presentation on consumption and production of information: Psychological and physiological factors

Two experiments were performed to investigate the influence of VDT (video display terminals) and paper presentation of text on consumption of information (Study 1) measured in the form of convergent production and production of information (Study 2) measured in form of divergent production. The READ test of reading comprehension was used as the convergent task whereas the ‘‘Headlines’’ test was used as the divergent task. Several other factors pertaining to performance were also studied including the PANAS test of positive and negative affect, the STH test of stress, tiredness and hunger, the TRI (Technology Readiness Inventory) and the SE test of stress and energy. The results show that performance in the VDT presentation condition where inferior to that of the Paper presentation condition for both consumption and production of information. Concomitantly, participants in the VDT presentation condition of the consumption of information study reported higher levels of experienced stress and tiredness whereas the participants in the VDT presentation condition of production of information study reported only slightly higher levels of stress. Although the results are discussed inboth physiological andpsychological terms arguments are made that the incremental effects of VDT text presentation stem mainly from dual-task effects of fulfilling the assignment andworkingwith the computer resulting in a higher cognitiveworkload.
A Survey of Music Recommendation Systems and Future Perspectives

Along with the rapid expansion of digital music formats, managing and searching for songs has become significant. Though music information retrieval (MIR) techniques have been made successfully in last ten years, the development of music recommender systems is still at a very early stage. Therefore, this paper surveys a general framework and state-of-art approaches in recommending music. Two popular algorithms: collaborative filtering (CF) and content-based model (CBM), have been found to perform well. Due to the relatively poor experience in finding songs in long tail and the powerful emotional meanings in music, two user-centric approaches: context-based model and emotion-based model, have been paid increasing attention. In this paper, three key components in music recommender user modelling, item profiling, and match algorithms are discussed. Six recommendation models and four potential issues towards user experience, are explained. However, subjective music recommendation system has not been fully investigated. To this end, we propose a motivation-based model using the empirical studies of human behaviour, sports education, music psychology.
A roadmap of sentiment analysis and its research directions

The exponential growth of data on the websphere accelerated the need of extracting meaningful information from it. This information can be used for better decision making. The automatic generation of sentiments from the text is called as sentiment analysis (SA). It is a collaborative process of natural language processing and data mining. This paper tries to deeply analyse the existing research work in the area of SA. It presents work done till date and segregates it in terms of level of granularity. An ideal sentiment analyser should have the intellectual capability similar to a human being. This paper mentions a roadmap of the research directions to achieve the goal of ideal sentiment analyser. These research directions include SA based on temporal summarisation, multi-linguality, etc. This paper also mentions various research aspects to work on these research directions. Future research in this direction will also refine the performance of decision making in decision support system.
3D ActionSLAM: wearable person tracking in multi-floor environments

We present 3D ActionSLAM, a stand-alone wearable system that can track people in previously unknown multi-floor environments with sub-room accuracy. ActionSLAM stands for action-based simultaneous localization and mapping: It fuses dead reckoning data from a foot-mounted inertial measurement unit with the recognition of location-related actions to build and update a local landmark map. Simultaneously, this map compensates for position drift errors that accumulate in open-loop tracking by means of a particle filter. To evaluate the system performance, we analyzed 23 tracks with a total walked distance of 6,489 m in buildings with up to three floors. The algorithm robustly (93 % of runs converged) mapped the areas with a mean landmark positioning error of 0.59 m. As ActionSLAM is fully stand-alone and not dependent on external infrastructure, it is well suited for patient tracking in remote health care applications. The algorithm is computationally light-weight and runs in real-time on a Samsung Galaxy S4, enabling immediate location-aware feedback. Finally, we propose visualization techniques to facilitate the interpretation of tracking data acquired with 3D ActionSLAM.
ℓ<ce:inf loc=post>1</ce:inf>-penalized linear mixed-effects models for high dimensional data with application to BCI

Recently, a novel statistical model has been proposed to estimate population effects and individual variability between subgroups simultaneously, by extending Lasso methods. We will for the first time apply this so-called ℓ(1)-penalized linear regression mixed-effects model for a large scale real world problem: we study a large set of brain computer interface data and through the novel estimator are able to obtain a subject-independent classifier that compares favorably with prior zero-training algorithms. This unifying model inherently compensates shifts in the input space attributed to the individuality of a subject. In particular we are now for the first time able to differentiate within-subject and between-subject variability. Thus a deeper understanding both of the underlying statistical and physiological structures of the data is gained.
THE KTH-TIPS database

This document provides a brief Users’ Guide to the KTH-TIPS image database (KTH is the abbreviation of our university, and TIPS stands for Textures under varying Illumination, Pose and Scale). The guide describes which materials are contained in the database (Section 2), how images were acquired (Section 3) and subsequently cropped to remove the background (Section 4), and we also discuss some non-ideal artifacts, like poor focus, in some pictures (Section 5). This document concludes by outlining how we intend to extend the database in the future (Section 6).
Survey on UAANET Routing Protocols and Network Security Challenges

UAV Ad hoc Networks (UAANETs) is a subset of the well-known mobile ad hoc network (MANET) paradigm. It refers to the deployment of a swarm of small Unmanned Aerial Vehicles (UAVs) and Ground Control Stations (GCS). These UAVs collaborate in order to relay data (command and control traffic and remotely sensed data) between each other and to the Ground Control Station (GCS). Compared to other types of ad hoc networks, UAANETs have some unique features and bring several major challenges to the research community. One of these is the design of UAANET routing protocol. It must establish an efficient route between UAVs and adjust in real time to the rapidly changing topology. It must also be secured to protect the integrity of the network against malicious attackers. Security of routing protocols has been widely investigated in wired networks and MANETs, but as far as we are aware, there is no previous research paper dealing with the security features of UAANET routing protocols. This paper focuses on characteristics of UAANETs and provides a review of the literature on associated routing protocols. We also highlight the analysis of the security features of these protocols. Security requirements, potential threats and countermeasures are all described.
Segmentation and reconstruction of vascular structures for 3D real-time simulation

We propose a technique to obtain accurate and smooth surfaces of patient specific vascular structures, using two steps: segmentation and reconstruction. The first step provides accurate and smooth centerlines of the vessels, together with cross section orientations and cross section fitting. The initial centerlines are obtained from a homotopic thinning of the vessels segmented using a level set method. In addition to circle fitting, an iterative scheme fitting ellipses to the cross sections and correcting the centerline positions is proposed, leading to a strong improvement of the cross section orientations and of the location of the centerlines. The second step consists of reconstructing the surface based on this data, by generating a set of topologically preserved quadrilateral patches of branching tubular structures. It improves Felkel's meshing method (Felkel et al., 2004) by: allowing a vessel to have multiple parents and children, reducing undersampling artifacts, and adapting the cross section distribution. Experiments, on phantom and real datasets, show that the proposed technique reaches a good balance in terms of smoothness, number of triangles, and distance error. This technique can be applied in interventional radiology simulations, virtual endoscopy and in reconstruction of smooth and accurate three-dimensional models for use in simulation.
A Multi-Domain Analysis of Explanation-Based Recommendation using User-Generated Reviews

There is an increasing need for AI systems to justify and explain their decisions to end-users, as institutions such as the EU roll out regulations that contain a right to explanation for its citizens. While such regulations will introduce significant challenges for an increasingly data-driven algorithmic world, they also motivate interesting questions and present new opportunities for AI research. In this paper, we consider the importance of explanations in recommender systems. Researchers have long understood the importance of explanations to justify these recommendations. Early work often focused on different styles of explanation interfaces – how explanations should be presented and how users perceive them (Herlocker, Konstan, and Riedl 2000) – intending to improving transparency, persuasiveness, and trust. Recently, (Musto et al. 2016) generated natural language explanations using information from the Linked Open Data, and (Chang, Harper, and Terveen 2016) employed a reviewbased approach to explain movie recommendations. We will argue in favour of deeper explanations, which more authentically convey the true reasons behind recommendations. More relevant here is research by (Muhammad et al. 2015; Muhammad, Lawlor, and Smyth 2016) which has argued for a more intimate connection between recommendation and explanation, by using explanations to rank recommendations. Specifically, we extend recent work in this area by comparing a number of different approaches to such explanation-based recommendation and evaluating their effectiveness and comparing their relative performance in three different recommendation domains. Our findings show explanation-based recommendation to be more effective than conventional content-based and collaborative filtering approaches, but the benefits observed depend on certain domain characteristics and review properties.
All-Solid-State Repetitive Pulsed-Power Generator Using IGBT and Magnetic Compression Switches

By utilizing power semiconductor switches, especially high-voltage insulated gate bipolar transistors (IGBTs), as main switches, Marx modulators have demonstrated many advantages such as variable pulse length and pulse-repetition frequency, snubberless operation, and inherent redundancy. However, the relatively slow turn-on speed of the IGBT influences the pulse rise time of the Marx modulator. In this paper, a newly developed all-solid-state pulsed-power generator is proposed. This generator consists of a Marx modulator based on discrete IGBTs and a magnetic pulse-sharpening circuit, which is employed to compress the rising edge of the Marx output pulse. The experimental results are presented with a maximum voltage of 20 kV, a rise time of 200 ns, a pulse duration of 500 ns (full-width at half-maximum), and a repetition rate of 5 kHz on a 285- resistive load. The output power of the generator is 2.5 kW, and the average power in one pulse is 1 MW. The design methods of the IGBT drive circuits and the parameter calculation of the magnetic pulse-sharpening circuit are introduced in detail in this paper. The factors influencing the performance of the generator are also discussed.
Linear Model Selection by Cross-Validation

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. American Statistical Association is collaborating with JSTOR to digitize, preserve and extend access to Journal of the American Statistical Association. We consider the problem of selecting a model having the best predictive ability among a class of linear models. The popular leave-one-out cross-validation method, which is asymptotically equivalent to many other model selection methods such as the Akaike information criterion (AIC), the Cp, and the bootstrap, is asymptotically inconsistent in the sense that the probability of selecting the model with the best predictive ability does not converge to 1 as the total number of observations n-s o. We show that the inconsistency of the leave-one-out cross-validation can be rectified by using a leave-n,-out cross-validation with nv, the number of observations reserved for validation, satisfying no/n-1 I as n s* xoo. This is a somewhat shocking discovery, because ne/n-* 1 is totally opposite to the popular leave-one-out recipe in cross-validation. Motivations, justifications, and discussions of some practical aspects of the use of the leave-n,-out cross-validation method are provided, and results from a simulation study are presented.
Three-dimensional Facial Anatomy: Structure and Function as It Relates to Injectable Neuromodulators and Soft Tissue Fillers

The advent of new neuromodulators and soft-tissue fillers continues to expand the nonsurgical repertoire of the core cosmetic physician.



The authors review relevant facial anatomy as it relates to facial aging and the resultant structural changes that occur. These changes are important for the cosmetic physician to identify and understand in order to perform both safe and effective placement of neuromodulators and soft-tissue fillers. In this anatomic review, the authors review key structures that serve as landmarks for successful treatment using both neuromodulators and fillers.



Knowledge of key facial anatomical structures allows the cosmetic physician to perform injection of neuromodulators and soft-tissue fillers in a reproducible manner to optimizing patient safety and results.
CS 221 Project Final Report Community Detection based on Music Listening Habits

Music plays a big role in our lives and forms a billion dollar industry. A lot of research goes into music recommendation and prediction especially with so many internet radio services coming up like Spotify, Pandora etc. Even though community detection, in general, is a well tackled problem in social and information flow networks, an interesting and fairly unsolved problem is that of identifying communities within the music listening population. We want to tackle this problem as it can provide various insights into listening habits of people. As this is a unsupervised learning problem, we also build a recommendation system to suggest songs to our users based on the clustering. This helps us in evaluating our clustering model.