nils-herrmann/clean_scirepeval huggingface dataset:
An Experimental Evaluation of DoS Attack and Its Impact on Throughput of IEEE 802.11 Wireless Networks

In recent years, wireless LAN (WLAN) has gained popularity in a variety of locations. This has lead to development of high level security protocols for WLAN. The newest protocol IEEE 802.11i ratified to provide strong data encryption but it can not prevent Denial of Service (DoS) attacks on WLAN. This paper in a testbed, conducts an experimental framework to implement and quantify common types of DoS attacks against WLAN throughput. The results of implementation of our experiments shows that how easily DoS attacks can be performed on WLAN which causes to reduce throughput of communication considerably to make inaccessible wireless connection for its authorized members.
Security and Privacy System Requirements for Adopting Cloud Computing in Healthcare Data Sharing Scenarios

The emerging cloud computing technology enables new essential scenarios in healthcare, in particular those of data sharing among practitioners. Nevertheless, their security and privacy concerns still impede the wide adoption of cloud computing in this area. Although there are numerous publications in the context of cloud computing in healthcare, we found no consistent typical security and privacy system requirements framework in this domain so far. Owing to the lack of those studies and preparing the ground for creating secure and privacy-friendly cloud architectures for healthcare, we survey security and privacy system requirements for cloud-based medical data sharing scenarios using two strategies. We base on a systematic design science approach following the literature-driven requirement elicitation strategy and apply an established security requirement elicitation methodology as part of the scenario-driven strategy. Finally, we evaluate and compare the two security and privacy system requirements elicitation strategies used in this paper.
Fast Local and Global Similarity Searches in Large Motion Capture Databases

Fast searching of content in large motion databases is essential for efficient motion analysis and synthesis. In this work we demonstrate that identifying locally similar regions in human motion data can be practical even for huge databases, if medium-dimensional (15--90 dimensional) feature sets are used for kd-tree-based nearest-neighbor-searches. On the basis of kd-tree-based local neighborhood searches we devise a novel fast method for global similarity searches. We show that knn-searches can be used efficiently within the problems of (a) "numerical and logical similarity searches", (b) reconstruction of motions from sparse marker sets, and (c) building so called "fat graphs", tasks for which previously algorithms with preprocessing time quadratic in the size of the database and thus only applicable to small collections of motions had been presented. We test our techniques on the two largest freely available motion capture databases, the CMU and HDM05 motion databases comprising more than 750 min of motion capture data proving that our approach is not only theoretically applicable but also solves the problem of fast similarity searches in huge motion databases in practice.
Semantic Web and Internet of Things Supporting Enhanced Learning

This paper outlines possible evolution trends of e-learning, supported by most recent advancements in the World Wide Web. Specifically, we consider a situation in which the Semantic Web technology and tools are widely adopted, and fully integrated within a context of applications exploiting the Internet of Things paradigm. Such a scenario will dramatically impact on learning activities, as well as on teaching strategies and instructional design methodology. In particular, the models characterized by learning pervasiveness and interactivity will be greatly empowered.
Real-Time Gaze Estimation Using a Kinect and a HD Webcam

In human-computer interaction, gaze orientation is an important and promising source of information to demonstrate the attention and focus of users. Gaze detection can also be an extremely useful metric for analysing human mood and affect. Furthermore, gaze can be used as an input method for human-computer interaction. However, currently real-time and accurate gaze estimation is still an open problem. In this paper, we propose a simple and novel estimation model of the real-time gaze direction of a user on a computer screen. This method utilises cheap capturing devices, a HD webcam and a Microsoft Kinect. We consider that the gaze motion from a user facing forwards is composed of the local gaze motion shifted by eye motion and the global gaze motion driven by face motion. We validate our proposed model of gaze estimation and provide experimental evaluation of the reliability and the precision of the method.
Effective Recommendation with Category Hierarchy

Although flat item category structure where categories are independent in a same level has been well studied to enhance recommendation performance, in many real applications, item category is often organized in hierarchies to reflect the inherent correlations among categories. In this paper, we propose a novel matrix factorization model by exploiting category hierarchy from the perspectives of users and items for effective recommendation. Specifically, a user (an item) can be influenced (characterized) by her preferred categories (the categories it belongs to) in the hierarchy. We incorporate how different categories in the hierarchy co-influence a user and an item. Empirical results show the superiority of our approach against other counterparts.
STRATEGIC ALLIANCES AND INTERFIRM KNOWLEDGE TRANSFER

This paper examines interfirm knowledge transfers within strategic alliances. Using a new measure of changes in alliance partners' technological capabilities, based on the citation patterns of their patent portfolios. we analyze changes in the extent to which partner firms' technological resources 'overlap' as a result of alliance participation. This measure allows us to test hypothesesfrom the literature on interfirm knowledge transfer in alliances, with interesting results: we find support for some elements of this 'received wisdom'-equity arrangements promote greater knowledge transfer, and 'absorptive capacity' helps explain the extent of technological capability transfer, at least in some alliances. But the results also suggest limits to the 'capabilities acquisition' view of strategic alliances. Consistent with the argument that alliance activity can promote increased specialization, we find that the capabilities of partner firms become more divergent in a substantial subset of alliances.
Development of a quad rotor tail-sitter VTOL UAV without control surfaces and experimental verification

This paper presents development of a quad rotor tail-sitter VTOL UAV (Vertical Takeoff and Landing Unmanned Aerial Vehicle) which is composed of four rotors and a fixed wing. The conventional VTOL UAVs have a drawback in the accuracy of the attitude control in stationary hovering because they were developed based on a fixed-wing aircraft and they used the control surfaces, such as aileron, elevator, and rudder for the attitude control. To overcome such a drawback, we developed a quad rotor tail-sitter VTOL UAV. The quad rotor tail-sitter VTOL UAV realizes high accuracy in the attitude control with four rotors like a quad rotor helicopter and achieves level flight like a fixed-wing airplane. The remarkable characteristic of the developed quad rotor tail-sitter VTOL UAV is that it does not use any control surfaces even in the level flight. This paper shows the design concept of the developed UAV and experimental verification of all flight modes including hovering, transition flight and level flight.
Social capital on facebook: differentiating uses and users

Though social network site use is often treated as a monolithic activity, in which all time is equally social and its impact the same for all users, we examine how Facebook affects social capital depending upon: (1) types of site activities, contrasting one-on-one communication, broadcasts to wider audiences, and passive consumption of social news, and (2) individual differences among users, including social communication skill and self-esteem. Longitudinal surveys matched to server logs from 415 Facebook users reveal that receiving messages from friends is associated with increases in bridging social capital, but that other uses are not. However, using the site to passively consume news assists those with lower social fluency draw value from their connections. The results inform site designers seeking to increase social connectedness and the value of those connections.
Watch Global, Cache Local : YouTube Network Traffic at a Campus Network -Measurements and Implications

User Generated Content has become very popular since the birth of web services such as YouTube allowing the distribution of such user-produced media content in an easy manner. YouTube-like services are different from existing traditional VoD services because the service provider has only limited control over the creation of new content. We analyze how the content distribution in YouTube is realized and then conduct a measurement study of YouTube traffic in a large university campus network. The analysis of the traffic shows that: (1) No strong correlation is observed between global and local popularity; (2) neither time scale nor user population has an impact on the local popularity distribution; (3) video clips of local interest have a high local popularity. Using our measurement data to drive trace-driven simulations, we also demonstrate the implications of alternative distribution infrastructures on the performance of a YouTube-like VoD service. The results of these simulations show that client-based local caching, P2P-based distribution, and proxy caching can reduce network traffic significantly and allow faster access to video clips.
Adaptive Kalman filtering and smoothing for gravitation tracking in mobile systems

This paper is concerned with inertial-sensor-based tracking of the gravitation direction in mobile devices such as smartphones. Although this tracking problem is a classical one, choosing a good state-space for this problem is not entirely trivial. Even though for many other orientation related tasks a quaternion-based representation tends to work well, for gravitation tracking their use is not always advisable. In this paper we present a convenient linear quaternion-free state-space model for gravitation tracking. We also discuss the efficient implementation of the Kalman filter and smoother for the model. Furthermore, we propose an adaption mechanism for the Kalman filter which is able to filter out shot-noises similarly as has been proposed in context of adaptive and robust Kalman filtering. We compare the proposed approach to other approaches using measurement data collected with a smartphone.
Software bertillonage: finding the provenance of an entity

Deployed software systems are typically composed of many pieces, not all of which may have been created by the main development team. Often, the provenance of included components -- such as external libraries or cloned source code -- is not clearly stated, and this uncertainty can introduce technical and ethical concerns that make it difficult for system owners and other stakeholders to manage their software assets. In this work, we motivate the need for the recovery of the provenance of software entities by a broad set of techniques that could include signature matching, source code fact extraction, software clone detection, call flow graph matching, string matching, historical analyses, and other techniques. We liken our provenance goals to that of Bertillonage, a simple and approximate forensic analysis technique based on bio-metrics that was developed in 19th century France before the advent of fingerprints. As an example, we have developed a fast, simple, and approximate technique called anchored signature matching for identifying library version information within a given Java application. This technique involves a type of structured signature matching performed against a database of candidates drawn from the Maven2 repository, a 150GB collection of open source Java libraries. An exploratory case study using a proprietary e-commerce Java application illustrates that the approach is both feasible and effective.
Static facial expression analysis in tough conditions: Data, evaluation protocol and benchmark

Quality data recorded in varied realistic environments is vital for effective human face related research. Currently available datasets for human facial expression analysis have been generated in highly controlled lab environments. We present a new static facial expression database Static Facial Expressions in the Wild (SFEW) extracted from a temporal facial expressions database Acted Facial Expressions in the Wild (AFEW) , which we have extracted from movies. In the past, many robust methods have been reported in the literature. However, these methods have been experimented on different databases or using different protocols within the same databases. The lack of a standard protocol makes it difficult to compare systems and acts as a hindrance in the progress of the field. Therefore, we propose a person independent training and testing protocol for expression recognition as part of the BEFIT workshop. Further, we compare our dataset with the JAFFE and Multi-PIE datasets and provide baseline results.
Conversational Chat Circles: Being All Here Without Having to Hear It All

Live streaming services are a growing form of social media. Most live streaming platforms allow viewers to communicate with each other and the broadcaster via a text chat. However, interaction in a text chat does not work well with too many users. Existing techniques to make text chat work with a larger number of participants often limit who can participate or how much users can participate. In this paper, we describe a new design for a text chat system that allows more people to participate without overwhelming users with too many messages. Our design strategically limits the number of messages a user sees based on the concept of neighborhoods, and emphasizes important messages through upvoting. We present a study comparing our system to a chat system similar to those found in commercial streaming services. Results of the study indicate that the Conversational Circle system is easier to understand and interact with, while supporting community among viewers and highlighting important content for the streamer.
In-Band Device-to-Device Communication in OFDMA Cellular Networks: A Survey and Challenges

Direct communication between two or more devices without the intervention of a base station, known as device-to-device (D2D) communication, is a promising way to improve performance of cellular networks in terms of spectral and energy efficiency. The D2D communication paradigm has been largely exploited in non-cellular technologies such as Bluetooth or Wi-Fi but it has not yet been fully incorporated into existing cellular networks. In this regard, a new proposal focusing on the integration of D2D communication into LTE-A has been recently approved by the 3GPP standardization community as discussed in this paper. In cellular networks, D2D communication introduces several critical issues, such as interference management and decisions on whether devices should communicate directly or not. In this survey, we provide a thorough overview of the state of the art focusing on D2D communication, especially within 3GPP LTE/LTE-A. First, we provide in-depth classification of papers looking at D2D from several perspectives. Then, papers addressing all major problems and areas related to D2D are presented and approaches proposed in the papers are compared according to selected criteria. On the basis of the surveyed papers, we highlight areas not satisfactorily addressed so far and outline major challenges for future work regarding efficient integration of D2D in cellular networks.
LOW POWER HIGH SPEED I/O INTERFACES IN 0.18um CMOS

The design and implementation of a low power high speed differential signaling input/output (I/O) interface in 0.18um CMOS technology is presented. The motivations for smaller signal swings in transmission are discussed. The prototype chip supports 4 Gbps data rate with less than 10mA current at 1.8V supply according to Cadence Spectre post-layout simulations. Performance comparisons between the proposed device and other signaling technologies reported recently are given.
Infrastructuring and the formation of publics in participatory design

Of late, there has been a renewed and reinvigorated exchange of ideas across science and technology studies and participatory design, emerging from a shared interest in ‘publics’. In this article, we explore the role of participatory design in constituting publics, drawing together recent scholarship in both science and technology studies and participatory design. To frame our discussion, we present two case studies of community-based participatory design as empirical examples. From these examples and the literature, we discuss the ways in which the concepts of infrastructuring and attachments are central to the constitution of publics. Finally, through an analysis of our case studies, we consider the differences between the practices of enabling participation and infrastructuring, calling attention to the ways that constituting publics foregrounds an engagement with authority structures and unknown futures through the participatory design process.
AgileEVM - earned value management in Scrum Projects

Scrum is an agile project management framework. This framework specifically focuses on maximizing return on investment (ROI). Scrum, however, does not define how to manage and track costs to evaluate actual ROI against the vision. A reasonable cost measurement that integrates with Scrum would help provide an additional feedback loop. We adapted earned value management (EVM), using values defined in Scrum. The result is called AgileEVM (agile earned value management) and is a simplified set of earned value calculations. From the values in Scrum, we derived a release date estimate using mean velocity and from this equation, generated an equivalent equation using traditional EVM techniques, thus establishing the validity of using EVM with the Scrum framework. Finally, we used this technique on two projects to further test our hypothesis. This investigation also helped us determine the utility of
Experiments with Convolutional Neural Network Models for Answer Selection

In recent years, neural networks have been applied to many text processing problems. One example is learning a similarity function between pairs of text, which has applications to paraphrase extraction, plagiarism detection, question answering, and ad hoc retrieval. Within the information retrieval community, the convolutional neural network model proposed by Severyn and Moschitti in a SIGIR 2015 paper has gained prominence. This paper focuses on the problem of answer selection for question answering: we attempt to replicate the results of Severyn and Moschitti using their open-source code as well as to reproduce their results via a de novo (i.e., from scratch) implementation using a completely different deep learning toolkit. Our de novo implementation is instructive in ascertaining whether reported results generalize across toolkits, each of which have their idiosyncrasies. We were able to successfully replicate and reproduce the reported results of Severyn and Moschitti, albeit with minor differences in effectiveness, but affirming the overall design of their model. Additional ablation experiments break down the components of the model to show their contributions to overall effectiveness. Interestingly, we find that removing one component actually increases effectiveness and that a simplified model with only four word overlap features performs surprisingly well, even better than convolution feature maps alone.
Smart home energy management system using IEEE 802.15.4 and zigbee

Wireless personal area network and wireless sensor networks are rapidly gaining popularity, and the IEEE 802.15 Wireless Personal Area Working Group has defined no less than different standards so as to cater to the requirements of different applications. The ubiquitous home network has gained widespread attentions due to its seamless integration into everyday life. This innovative system transparently unifies various home appliances, smart sensors and energy technologies. The smart energy market requires two types of ZigBee networks for device control and energy management. Today, organizations use IEEE 802.15.4 and ZigBee to effectively deliver solutions for a variety of areas including consumer electronic device control, energy management and efficiency, home and commercial building automation as well as industrial plant management. We present the design of a multi-sensing, heating and airconditioning system and actuation application - the home users: a sensor network-based smart light control system for smart home and energy control production. This paper designs smart home device descriptions and standard practices for demand response and load management "Smart Energy" applications needed in a smart energy based residential or light commercial environment. The control application domains included in this initial version are sensing device control, pricing and demand response and load control applications. This paper introduces smart home interfaces and device definitions to allow interoperability among ZigBee devices produced by various manufacturers of electrical equipment, meters, and smart energy enabling products. We introduced the proposed home energy control systems design that provides intelligent services for users and we demonstrate its implementation using a real testbad.
Lifelong Learning CRF for Supervised Aspect Extraction

This paper makes a focused contribution to supervised aspect extraction. It shows that if the system has performed aspect extraction from many past domains and retained their results as knowledge, Conditional Random Fields (CRF) can leverage this knowledge in a lifelong learning manner to extract in a new domain markedly better than the traditional CRF without using this prior knowledge. The key innovation is that even after CRF training, the model can still improve its extraction with experiences in its applications.
Improving user profile with personality traits predicted from social media content

Existing studies indicate that there exists strong correlation between personality and personal preference, thus personality could potentially be used to build more personalized recommender system. Personality traits are mainly measured by psychological questionnaires, and it is hard to obtain personality traits of large amount of users in real-world scenes.In this paper, we propose a new approach to automatically identify personality traits with Social Media contents in Chinese language environments. Social Media content features were extracted from 1766 Sina micro blog users, and the predicting model is trained with machine learning algorithms.The experimental results demonstrate that users' personality traits could be predicted from Social Media contents with acceptable Pearson Correlation, which makes it possible to develop user profiles for recommender system. In future, user profiles with predicted personality traits would be used to enhance the performance of existing personalized recommendation systems.