nils-herrmann/clean_scirepeval huggingface dataset:
Matrix Factorization+ for Movie Recommendation

We present a novel model for movie recommendations using additional visual features extracted from pictural data like posters and still frames, to better understand movies. In particular, several context-based methods for recommendation are shown to be special cases of our proposed framework. Unlike existing context-based approaches, our method can be used to incorporate visual features – features that are lacking in existing contextbased approaches for movie recommendations. In reality, movie posters and still frames provide us with rich knowledge for understanding movies as well as users’ preferences. For instance, user may want to watch a movie at the minute when she/he finds some released posters or still frames attractive. Unfortunately, such unique features cannot be revealed from rating data or other forms of context being used in most of existing methods. In this paper, we take a step forward in this direction and investigate both low-level and high-level visual features from the movie posters and still frames for further improvement of recommendation methods. Extensive experiments on real world datasets show that our approach leads to significant improvement over several state-of-the-art methods.
Adaptive Knee Joint Exoskeleton Based on Biological Geometries

This paper presents a relatively complete analytical model of a knee joint interacting with a two-link exoskeleton for investigating the effects of different exoskeleton designs on the internal joint forces/torque in the knee. The closed kinematic chain formed by the leg and exoskeleton has a significant effect on the joint forces/torque in the knee. A bio-joint model is used to capture this effect by relaxing a commonly made assumption that approximates a knee joint as a perfect engineering pin-joint in designing an exoskeleton. Based on the knowledge of a knee-joint kinematics, an adaptive knee-joint exoskeleton has been designed to eliminate negative effects associated with the closed leg-exoskeleton kinematic chain on a human knee. For experimental validation, the flexion motion of an artificial human knee is investigated comparing the performances of five exoskeleton designs against the case with no exoskeleton. Analytical results that estimate internal forces/torque using the kinematic and dynamic models (based on the properties of a knee joint) agree well with data obtained experimentally. This investigation illustrates the applications of the analytical model for designing an adaptive exoskeleton that minimizes internal joint forces due to a knee-exoskeleton interaction.
An Ontology for Developing e-Business Models

In this paper we demonstrate why executives and academics should consider thinking about e-business models. We show that the business model concept is an interesting tool for understanding, designing, sharing, measuring, changing and even simulating businesses. Based on an extensive review of e-business and business model literature we develop an e-business model ontology. This ontology defines the concepts in e-business models and the relationships between them and shall be the foundation for a variety of management tools that facilitate business decisions. Our e-business model ontology outlines what value a company offers to which customer segments. It describes the architecture of the firm and its network of partners for creating, marketing and delivering value and relationship capital, in order to generate profitable and sustainable revenue streams.
Cloud Computing : Types , Architecture , Applications , Concerns , Virtualization and Role of IT Governance in Cloud

Technology innovation and its adoption are two critical successful factors for any business/organization. Cloud computing is a recent technology paradigm that enables organizations or individuals to share various services in a seamless and cost-effective manner. This paper describes cloud computing, a computing platform for the next generation of the Internet. The paper defines clouds, types of cloud Provides, Comparison of Cloud Computing with Grid Computing, applications and concerns of Cloud Computing , Concept of Virtualization in Cloud Computing. Readers will also discover the working, Architecture and Role of I.T Governance in Cloud Computing.
Design and Analysis of a 21–29-GHz Ultra-Wideband Receiver Front-End in 0.18-$\mu$ m CMOS Technology

This paper reports the design and analysis of 21-29-GHz CMOS low-noise amplifier (LNA), balun and mixer in a standard 0.18-μm CMOS process for ultra-wideband automotive radar systems. To verify the proposed LNA, balun, and mixer architectures, a simplified receiver front-end comprising an LNA, a double-balanced Gilbert-cell-based mixer, and two Marchand baluns was implemented. The wideband Marchand baluns can convert the single RF and local oscillator (LO) signals to nearly perfect differential signals over the 21-29-GHz band. The performance of the mixer is improved with the current-bleeding technique and a parallel resonant inductor at the differential outputs of the RF transconductance stage. Over the 21-29-GHz band, the receiver front-end exhibits excellent noise figure of 4.6±0.5 dB, conversion gain of 23.7±1.4 dB, RF port reflection coefficient lower than -8.8 dB, LO-IF isolation lower than -47 dB, LO-RF isolation lower than -55 dB, and RF-IF isolation lower than -35.5 dB. The circuit occupies a chip area of 1.25×1.06 mm2, including the test pads. The dc power dissipation is only 39.2 mW.
53Gbps native GF(24)2 composite-field AES-encrypt/decrypt accelerator for content-protection in 45nm high-performance microprocessors

An on-die, reconfigurable AES encrypt/decrypt hardware accelerator is fabricated in 45nm CMOS, targeted for content-protection in high-performance microprocessors. Compared to conventional AES implementations, this design computes the entire AES round in native GF(24)2 composite-field with one-time GF(28)-to-GF(24)2 mapping cost amortized over multiple AES iterations. This approach along with a fused Mix/InvMixColumns circuit and folded ShiftRow datapath results in 20% area savings and 67% reduction in worst-case interconnect length, enabling AES-128/192/256 ECB block throughput of 53/44/38Gbps, 125mW power measured at 1.1V, 50°C.
Analyzing Computer Vision Data — The Good, the Bad and the Ugly

In recent years, a great number of datasets were published to train and evaluate computer vision (CV) algorithms. These valuable contributions helped to push CV solutions to a level where they can be used for safety-relevant applications, such as autonomous driving. However, major questions concerning quality and usefulness of test data for CV evaluation are still unanswered. Researchers and engineers try to cover all test cases by using as much test data as possible. In this paper, we propose a different solution for this challenge. We introduce a method for dataset analysis which builds upon an improved version of the CV-HAZOP checklist, a list of potential hazards within the CV domain. Picking stereo vision as an example, we provide an extensive survey of 28 datasets covering the last two decades. We create a tailored checklist and apply it to the datasets Middlebury, KITTI, Sintel, Freiburg, and HCI to present a thorough characterization and quantitative comparison. We confirm the usability of our checklist for identification of challenging stereo situations by applying nine state-of-the-art stereo matching algorithms on the analyzed datasets, showing that hazard frames correlate with difficult frames. We show that challenging datasets still allow a meaningful algorithm evaluation even for small subsets. Finally, we provide a list of missing test cases that are still not covered by current datasets as inspiration for researchers who want to participate in future dataset creation.
Backtrack Programming Techniques

The purpose of this paper is twofold. First, a brief exposition of the general backtrack technique and its history is given. Second, it is shown how the use of macros can considerably shorten the computation time in many cases. In particular, this technique has allowed the solution of two previously open combinatorial problems, the computation of new terms in a well-known series, and the substantial reduction in computation time for the solution to another combinatorial problem.
An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition

This article provides an overview of the first BioASQ challenge, a competition on large-scale biomedical semantic indexing and question answering (QA), which took place between March and September 2013. BioASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies. The 2013 BioASQ competition comprised two tasks, Task 1a and Task 1b. In Task 1a participants were asked to automatically annotate new PubMed documents with MeSH headings. Twelve teams participated in Task 1a, with a total of 46 system runs submitted, and one of the teams performing consistently better than the MTI indexer used by NLM to suggest MeSH headings to curators. Task 1b used benchmark datasets containing 29 development and 282 test English questions, along with gold standard (reference) answers, prepared by a team of biomedical experts from around Europe and participants had to automatically produce answers. Three teams participated in Task 1b, with 11 system runs. The BioASQ infrastructure, including benchmark datasets, evaluation mechanisms, and the results of the participants and baseline methods, is publicly available. A publicly available evaluation infrastructure for biomedical semantic indexing and QA has been developed, which includes benchmark datasets, and can be used to evaluate systems that: assign MeSH headings to published articles or to English questions; retrieve relevant RDF triples from ontologies, relevant articles and snippets from PubMed Central; produce “exact” and paragraph-sized “ideal” answers (summaries). The results of the systems that participated in the 2013 BioASQ competition are promising. In Task 1a one of the systems performed consistently better from the NLM’s MTI indexer. In Task 1b the systems received high scores in the manual evaluation of the “ideal” answers; hence, they produced high quality summaries as answers. Overall, BioASQ helped obtain a unified view of how techniques from text classification, semantic indexing, document and passage retrieval, question answering, and text summarization can be combined to allow biomedical experts to obtain concise, user-understandable answers to questions reflecting their real information needs.
Evaluating and Developing Theories in the Information Systems Discipline

This paper articulates a framework and criteria that can be used to evaluate the quality of theories. While the framework and criteria have general applicability, my focus is the evaluation of theories within the information systems discipline. To illustrate the usefulness of the framework and criteria, I show how they can be employed to pinpoint the strengths and weaknesses of a theory which, based upon citation evidence, has had a significant impact on other researchers within the information systems discipline. Because the evaluation of existing theories often provides the basis for refining existing theories or building new theories, I also show how the framework and criteria can be used to inform the development of high-quality theory.
Fourier Analysis for Demand Forecasting in a Fashion Company

In the fashion industry, demand forecasting is particularly complex: companies operate with a large variety of short lifecycle products, deeply influenced by seasonal sales, promotional events, weather conditions, advertising and marketing campaigns, on top of festivities and socio-economic factors. At the same time, shelf-out-of-stock phenomena must be avoided at all costs. Given the strong seasonal nature of the products that characterize the fashion sector, this paper aims to highlight how the Fourier method can represent an easy and more effective forecasting method compared to other widespread heuristics normally used. For this purpose, a comparison between the fast Fourier transform algorithm and another two techniques based on moving average and exponential smoothing was carried out on a set of 4year historical sales data of a €60+ million turnover mediumto large-sized Italian fashion company, which operates in the women’s textiles apparel and clothing sectors. The entire analysis was performed on a common spreadsheet, in order to demonstrate that accurate results exploiting advanced numerical computation techniques can be carried out without necessarily using expensive software.
A Survey on Application Layer Protocols for the Internet of Things

It has been more than fifteen years since the term Internet of Things (IoT) was introduced to the public. However, despite the efforts of research groups and innovative corporations, still today it is not possible to say that IoT is upon us. This is mainly due to the fact that a unified IoT architecture has not been yet clearly defined and there is no common agreement in defining protocols and standards for all IoT parts. The framework that current IoT platforms use consists mostly in technologies that partially fulfill some of the IoT requirements. While developers employ existing technologies to build the IoT, research groups are working on adapting protocols to the IoT in order to optimize communications. In this paper, we present and compare existing IoT application layer protocols as well as protocols that are utilized to connect the things but also end-user applications to the Internet. We highlight IETFs CoAP, IBMs MQTT, HTML 5s Web socket among others, and we argue their suitability for the IoT by considering reliability, security, and energy consumption aspects. Finally, we provide our conclusions for the IoT application layer communications based on the study that we have conducted.
Learning in Natural Language

Statistics-based classifiers in natural language are developed typically by assuming a generative model for the data, estimating its parameters from training data and then using Bayes rule to obtain a classifier. For many problems the assumptions made by the generative models are evidently wrong, leaving open the question of why these approaches work. This paper presents a learning theory account of the major statistical approaches to learning in natural language. A class of Linear Statistical Queries (LSQ) hypotheses is defined and learning with it is shown to exhibit some robustness properties. Many statistical learners used in natural language, including naive Bayes, Markov Models and Maximum Entropy models are shown to be LSQ hypotheses, explaining the robustness of these predictors even when the underlying probabilistic assumptions do not hold. This coherent view of when and why learning approaches work in this context may help to develop better learning methods and an understanding of the role of learning in natural language inferences.
Emotion and autonomic nervous system activity in the Minangkabau of west Sumatra.

Physiology and emotional experience were studied in the Minangkabau of West Sumatra, a matrilineal, Moslem, agrarian culture with strong proscriptions against public displays of negative emotion. Forty-six Minangkabau men were instructed to contract facial muscles into prototypical configurations of 5 emotions. In comparison with a group of 62 Ss from the United States, cross-cultural consistencies were found in (a) autonomic nervous system (ANS) differences between emotions and (b) high configuration quality being associated with increased ANS differentiation and increased report of emotional experience. These findings provide the first evidence that these patterns of emotion-specific ANS activity and the capacity of voluntary facial action to generate this activity are not unique to American culture.
Alteration of the platelet serotonin transporter in romantic love.

The evolutionary consequences of love are so important that there must be some long-established biological process regulating it. Recent findings suggest that the serotonin (5-HT) transporter might be linked to both neuroticism and sexual behaviour as well as to obsessive-compulsive disorder (OCD). The similarities between an overvalued idea, such as that typical of subjects in the early phase of a love relationship, and obsession, prompted us to explore the possibility that the two conditions might share alterations at the level of the 5-HT transporter.



Twenty subjects who had recently (within the previous 6 months) fallen in love, 20 unmedicated OCD patients and 20 normal controls, were included in the study. The 5-HT transporter was evaluated with the specific binding of 3H-paroxetine (3H-Par) to platelet membranes.



The results showed that the density of 3H-Par binding sites was significantly lower in subjects who had recently fallen in love and in OCD patients than in controls.



The main finding of the present study is that subjects who were in the early romantic phase of a love relationship were not different from OCD patients in terms of the density of the platelet 5-HT transporter, which proved to be significantly lower than in the normal controls. This would suggest common neurochemical changes involving the 5-HT system, linked to psychological dimensions shared by the two conditions, perhaps at an ideational level.
Passive radar from history to future

The history of passive radar dates back to the early days of radar in 1935 when the Daventry experiment was conducted in the UK. It continues in WW II with the German Klein Heidelberg passive radar and receives new interest today, as passive covert radar (PCR) systems like Silent Sentry and Homeland Alerter 100 are ready for operation. The future of PCR will strongly depend on the availability of transmitters of opportunity such as FM-radio and digital broadcast networks.
ERP SYSTEM IMPLEMENTATION ISSUES AND CHALLENGES IN DEVELOPING NATIONS

In current years there has been enhancement in exercising on Enterprise Resource Planning (ERP) systems in big concerns and government firms mostly in urbanized nations, whereas extensive acceptance of ERP systems in developing nations lags far away. It has been observed that there are many instances where ERP systems prove to be a failure either in the design or its implementation. A number of reasons contribute in the success or failure of an ERP systems. Success or failure of the system depends upon the initial stage of adoption and implementation of the system. Consideration of all issues and challenges at the initial stage may furnish a sound base for a successful ERP system implementation in an organisation. There is a significant need for considering ERP system implementation issues and challenges in developing nations, as ERP systems are still in their premature stages in these nations. Literature review on successful ERP system implementation disclose that there are many studies whispered by researchers, but very few have listed the success issues and challenges of ERP system implementation focusing on developing countries. Issues and challenges of ERP system implementation in developing nations differ from those of developed nations. The main objective of this study is to unearth the issues and challenges of successful ERP system implementation in developing nations like India. Data are collected by having an extensive literature review of about past fifteen years. Analysis and synthesis of collected data reveals that clear scope of implementation, top management commitment, proper implementation strategy, proper vendor selection, project management, user training & education, end user involvement and lack of experienced implementers are the important issues and challenges. Understanding of issues and challenges will help organisations to adopt appropriate implementation strategies leading to success of ERP system in an organisation.
A Functional MRI Study of Happy and Sad Emotions in Music with and without Lyrics

Musical emotions, such as happiness and sadness, have been investigated using instrumental music devoid of linguistic content. However, pop and rock, the most common musical genres, utilize lyrics for conveying emotions. Using participants' self-selected musical excerpts, we studied their behavior and brain responses to elucidate how lyrics interact with musical emotion processing, as reflected by emotion recognition and activation of limbic areas involved in affective experience. We extracted samples from subjects' selections of sad and happy pieces and sorted them according to the presence of lyrics. Acoustic feature analysis showed that music with lyrics differed from music without lyrics in spectral centroid, a feature related to perceptual brightness, whereas sad music with lyrics did not diverge from happy music without lyrics, indicating the role of other factors in emotion classification. Behavioral ratings revealed that happy music without lyrics induced stronger positive emotions than happy music with lyrics. We also acquired functional magnetic resonance imaging data while subjects performed affective tasks regarding the music. First, using ecological and acoustically variable stimuli, we broadened previous findings about the brain processing of musical emotions and of songs versus instrumental music. Additionally, contrasts between sad music with versus without lyrics recruited the parahippocampal gyrus, the amygdala, the claustrum, the putamen, the precentral gyrus, the medial and inferior frontal gyri (including Broca's area), and the auditory cortex, while the reverse contrast produced no activations. Happy music without lyrics activated structures of the limbic system and the right pars opercularis of the inferior frontal gyrus, whereas auditory regions alone responded to happy music with lyrics. These findings point to the role of acoustic cues for the experience of happiness in music and to the importance of lyrics for sad musical emotions.
Kathará: A container-based framework for implementing network function virtualization and software defined networks

Network Function Virtualization (NFV) and Software-Defined Networking (SDN) are deeply changing the networking field by introducing software at any level, aiming at decoupling the logic from the hardware. Together, they bring several benefits, mostly in terms of scalability and flexibility. Up to now, SDN has been used to support NFV from the routing and the architectural point of view. In this paper we present Kathará, a framework based on con­tainers, that allows network operators to deploy Virtual Network Functions (VNFs) through the adoption of emerging data-plane programmable capabilities, such as P4-compliant switches. It also supports the coexistence of SDN and traditional routing protocols in order to set up arbitrarily complex networks. As a side effect, thanks to Kathará, we demonstrate that implementing NFV by means of specific-purpose equipment is feasible and it provides a gain in performance while preserving the benefits of NFV. We measure the resource consumption of Kathará and we show that it performs better than frameworks that implement virtual networks using virtual machines by several orders of magnitude.