nils-herrmann/clean_scirepeval huggingface dataset:
Three tensions in participatory design for inclusion

One ideal of Participatory Design (PD) is active involvement by all stakeholders as co-designers. However, when PD is applied to real projects, certain compromises are unavoidable, no matter what stakeholders are involved. With this paper we want to shed light on some of the challenges in implementing "true" PD in the case of designing with children, in particular children with severe disabilities. We do this work to better understand challenges in an ongoing project, RHYME, and by doing so we hope to provide insight and inspiration for others.
A vulnerability in Google AdSense: Automatic extraction of links to ads

On the basis of the XSS (Cross Site Scripting) and Web Crawler techniques it is possible to go through the barriers of the Google Adsense advertising system by obtaining the validated links of the ads published on a website. Such method involves obtaining the source code built for the Google java applet for publishing and handling ads and for the final link retrieval. Once the links of the ads have been obtained, you can use the user sessions visiting other websites to load such links, in the background, by a simple re-direction, through a hidden iframe, so that the IP addresses clicking are different in each case.
Scalable distributed inference of dynamic user interests for behavioral targeting

Historical user activity is key for building user profiles to predict the user behavior and affinities in many web applications such as targeting of online advertising, content personalization and social recommendations. User profiles are temporal, and changes in a user's activity patterns are particularly useful for improved prediction and recommendation. For instance, an increased interest in car-related web pages may well suggest that the user might be shopping for a new vehicle.In this paper we present a comprehensive statistical framework for user profiling based on topic models which is able to capture such effects in a fully \emph{unsupervised} fashion. Our method models topical interests of a user dynamically where both the user association with the topics and the topics themselves are allowed to vary over time, thus ensuring that the profiles remain current.
 We describe a streaming, distributed inference algorithm which is able to handle tens of millions of users. Our results show that our model contributes towards improved behavioral targeting of display advertising relative to baseline models that do not incorporate topical and/or temporal dependencies. As a side-effect our model yields human-understandable results which can be used in an intuitive fashion by advertisers.
A method for extracting major workflow composed of ingredients, tools, and actions from cooking procedural text

In this paper, we propose a method for extracting a major workflow of cooking procedure from a Japanese recipe on the Web. It is utilized for various applications including recipe search, summarization, and visualization. This major workflow is represented as a tree in which each leaf corresponds to a food or a cooking tool, each intermediate node corresponds to a cooking action, and the root node corresponds to the final cooking action. We had already developed a method for converting a procedural text to a workflow as a directed acyclic graph, and the proposed method is based on the previous one. The results are evaluated by comparing with manually extracted major workflows as a ground truth.
Automatically characterizing places with opportunistic crowdsensing using smartphones

Automated and scalable approaches for understanding the semantics of places are critical to improving both existing and emerging mobile services. In this paper, we present CrowdSense@Place (CSP), a framework that exploits a previously untapped resource -- opportunistically captured images and audio clips from smartphones -- to link place visits with place categories (e.g., store, restaurant). CSP combines signals based on location and user trajectories (using WiFi/GPS) along with various visual and audio place "hints" mined from opportunistic sensor data. Place hints include words spoken by people, text written on signs or objects recognized in the environment. We evaluate CSP with a seven-week, 36-user experiment involving 1,241 places in five locations around the world. Our results show that CSP can classify places into a variety of categories with an overall accuracy of 69%, outperforming currently available alternative solutions.
A Machine Learning Framework for Resource Allocation Assisted by Cloud Computing

Conventionally, resource allocation is formulated as an optimization problem and solved online with instantaneous scenario information. Since most resource allocation problems are not convex, the optimal solutions are very difficult to obtain in real time. Lagrangian relaxation or greedy methods are then often employed, which results in performance loss. Therefore, the conventional methods of resource allocation are facing great challenges to meet the ever increasing QoS requirements of users with scarce radio resource. Assisted by cloud computing, a huge amount of historical data on scenarios can be collected for extracting similarities among scenarios using machine learning. Moreover, optimal or near-optimal solutions of historical scenarios can be searched offline and stored in advance. When the measured data of a scenario arrives, the current scenario is compared with historical scenarios to find the most similar one. Then the optimal or near-optimal solution in the most similar historical scenario is adopted to allocate the radio resources for the current scenario. To facilitate the application of new design philosophy, a machine learning framework is proposed for resource allocation assisted by cloud computing. An example of beam allocation in multi-user massive MIMO systems shows that the proposed machine-learning-based resource
Virtual garments : A Fully Geometric Approach for Clothing Design

Modeling dressed characters is known as a very tedious process. It usually requires specifying 2D fabric patterns, positioning and assembling them in 3D, and then performing a physically-based simulation. The latter accounts for gravity and collisions to compute the rest shape of the garment, with the adequate folds and wrinkles. This paper presents a more intuitive way to design virtual clothing. We start with a 2D sketching system in which the user draws the contours and seam-lines of the garment directly on a virtual mannequin. Our system then converts the sketch into an initial 3D surface using an existing method based on a precomputed distance field around the mannequin. The system then splits the created surface into different panels delimited by the seam-lines. The generated panels are typically not developable. However, the panels of a realistic garment must be developable, since each panel must unfold into a 2D sewing pattern. Therefore our system automatically approximates each panel with a developable surface, while keeping them assembled along the seams. This process allows us to output the corresponding sewing patterns. The last step of our method computes a natural rest shape for the 3D garment, including the folds due to the collisions with the body and gravity. The folds are generated using procedural modeling of the buckling phenomena observed in real fabric. The result of our algorithm consists of a realistic looking 3D mannequin dressed in the designed garment and the 2D patterns which can be used for distortion free texture mapping. As we demonstrate, the patterns we create allow us to sew real replicas of the virtual garments.
The Computable News project: Research in the Newsroom

We report on a four year academic research project to build a natural language processing platform in support of a large media company. The Computable News platform processes news stories, producing a layer of structured data that can be used to build rich applications. We describe the underlying platform and the research tasks that we explored building it. The platform supports a wide range of prototype applications designed to support different newsroom functions. We hope that this qualitative review provides some insight into the challenges involved in this type of project.
Cloud Log Forensics: Foundations, State of the Art, and Future Directions

Cloud log forensics (CLF) mitigates the investigation process by identifying the malicious behavior of attackers through profound cloud log analysis. However, the accessibility attributes of cloud logs obstruct accomplishment of the goal to investigate cloud logs for various susceptibilities. Accessibility involves the issues of cloud log access, selection of proper cloud log file, cloud log data integrity, and trustworthiness of cloud logs. Therefore, forensic investigators of cloud log files are dependent on cloud service providers (CSPs) to get access of different cloud logs. Accessing cloud logs from outside the cloud without depending on the CSP is a challenging research area, whereas the increase in cloud attacks has increased the need for CLF to investigate the malicious activities of attackers. This paper reviews the state of the art of CLF and highlights different challenges and issues involved in investigating cloud log data. The logging mode, the importance of CLF, and cloud log-as-a-service are introduced. Moreover, case studies related to CLF are explained to highlight the practical implementation of cloud log investigation for analyzing malicious behaviors. The CLF security requirements, vulnerability points, and challenges are identified to tolerate different cloud log susceptibilities. We identify and introduce challenges and future directions to highlight open research areas of CLF for motivating investigators, academicians, and researchers to investigate them.
Android Anti-forensics: Modifying CyanogenMod

Mobile devices implementing Android operating systems inherently create opportunities to present environments that are conducive to anti-forensic activities. Previous mobile forensics research focused on applications and data hiding anti-forensics solutions. In this work, a set of modifications were developed and implemented on a CyanogenMod community distribution of the Android operating system. The execution of these solutions successfully prevented data extractions, blocked the installation of forensic tools, created extraction delays and presented false data to industry accepted forensic analysis tools without impacting normal use of the device. The research contribution is an initial empirical analysis of the viability of operating system modifications in an anti-forensics context along with providing the foundation for future research.
HCI in Business, Government, and Organizations: Information Systems

An aging population and the attendant growth in the need to care for people with serious chronic illnesses has created a demand for online support systems that can assist older adults to self-manage their illnesses. This could play a role in relieving some of the load on the healthcare system. Determining user-centered requirements of older adults for such systems is different from usual requirements analysis because older adults have particular needs, depending upon their chronic illnesses, their ability to manage technology, their access to appropriate technologies, and their cognitive abilities. This paper discusses in detail the use of the persona-scenario approach to elicit these needs from outpatients, informal care givers, and physicians. It proposes several suitable interface designs, depending on outpatient ability to deal with the proposed systems.
Performance Analysis of Engineering Students for Recruitment Using Classification Data Mining Techniques

-Data Mining is a powerful tool for academic intervention. Mining in education environment is called Educational Data Mining. Educational Data Mining is concerned with developing new methods to discover knowledge from educational database and can used for decision making in educational system. In our work, we collected the student’s data from engineering institute that have different information about their previous and current academics records like students S.No., Name, Branch, 10, 12 , B.Tech passing percentage and final grade & then apply different classification algorithm using Data Mining tools (WEKA) for analysis the students academics performance for Training & placement department or company executives. This paper deals with a comparative study of various classification data mining algorithms for the performance analysis of the student’s academic records and check which algorithm is optimal for classifying students’ based on their final grade. This analysis also classifies the performance of Students into Excellent, Good and Average categories.  Data Mining, Discover knowledge, Technical Education, Educational Data, Mining, Classification, WEKA, Classifiers.
Sensor networks for medical care

Sensor networks have the potential to greatly impact many aspects of medical care. By outfitting patients with wireless, wearable vital sign sensors, collecting detailed real-time data on physiological status can be greatly simplified. However, there is a significant gap between existing sensor network systems and the needs of medical care. In particular, medical sensor networks must support multicast routing topologies, node mobility, a wide range of data rates and high degrees of reliability, and security. This paper describes our experiences with developing a combined hardware and software platform for medical sensor networks, called CodeBlue. CodeBlue provides protocols for device discovery and publish/subscribe multihop routing, as well as a simple query interface that is tailored for medical monitoring. We have developed several medical sensors based on the popular MicaZ and Telos mote designs, including a pulse oximeter, EKG and motion-activity sensor. We also describe a new, miniaturized sensor mote designed for medical use. We present initial results for the CodeBlue prototype demonstrating the integration of our medical sensors with the publish/subscribe routing substrate. We have experimentally validated the prototype on our 30-node sensor network testbed, demonstrating its scalability and robustness as the number of simultaneous queries, data rates, and transmitting sensors are varied. We also study the effect of node mobility, fairness across multiple simultaneous paths, and patterns of packet loss, confirming the system’s ability to maintain stable routes despite variations in node location and
Judgment of information quality and cognitive authority in the Web

In the Web, making judgments of information quality and authority is a difficult task for most users because overall, there is no quality control mechanism. This study examines the problem of the judgment of information quality and cognitive authority by observing people’s searching behavior in the Web. Its purpose is to understand the various factors that influence people’s judgment of quality and authority in the Web, and the effects of those judgments on selection behaviors. Fifteen scholars from diverse disciplines participated, and data were collected combining verbal protocols during the searches, search logs, and postsearch interviews. It was found that the subjects made two distinct kinds of judgment: predictive judgment, and evaluative judgment. The factors influencing each judgment of quality and authority were identified in terms of characteristics of information objects, characteristics of sources, knowledge, situation, ranking in search output, and general assumption. Implications for Web design that will effectively support people’s judgments of quality and authority are also discussed.
Graph-based Dependency Parsing with Bidirectional LSTM

In this paper, we propose a neural network model for graph-based dependency parsing which utilizes Bidirectional LSTM (BLSTM) to capture richer contextual information instead of using high-order factorization, and enable our model to use much fewer features than previous work. In addition, we propose an effective way to learn sentence segment embedding on sentence-level based on an extra forward LSTM network. Although our model uses only first-order factorization, experiments on English Peen Treebank and Chinese Penn Treebank show that our model could be competitive with previous higher-order graph-based dependency parsing models and state-of-the-art models.
Securing a UAV using individual characteristics from an EEG signal

Unmanned aerial vehicles (UAVs) have been applied for both civilian and military applications; scientific research involving UAVs has encompassed a wide range of scientific study. However, communication with unmanned vehicles are subject to attack and compromise. Such attacks have been reported as early as 2009, when a Predator UAV's video stream was compromised. Since UAVs extensively utilize autonomous behavior, it is important to develop an autopilot system that is robust to potential cyber-attack. In this work, we present a biometric system to encrypt communication between a UAV and a computerized base station. This is accomplished by generating a key derived from the Beta component of a user's EEG. When communication with a UAV is attacked, a safety mechanism directs the UAV to a safe ‘home’ location. This system has been validated on a commercial UAV under malicious attack conditions.
Live acquisition of main memory data from Android smartphones and smartwatches

Recent research in Android device forensics has largely focused on evidence recovery from NAND flash memory. However, pervasive deployment of NAND flash encryption technologies and the increase in malware infections which reside only in main memory have motivated an urgent need for the forensic study of main memory. Existing Android main memory forensics techniques are hardly being adopted in practical forensic investigations because they often require solving several usability constraints, such as requiring root privilege escalation, custom kernel replacement, or screen lock bypass. Moreover, there are still no commercially available tools for acquiring the main memory data of smart devices. To address these problems, we have developed an automated tool, called AMD, which is capable of acquiring the entire content of main memory from a range of Android smartphones and smartwatches. In developing AMD, we analyzed the firmware update protocols of these devices by reverse engineering the Android bootloader. Based on this study, we have devised a method that allows access to main memory data through the firmware update protocols. Our experimental results show that AMD overcomes the usability constraints of previous main memory acquisition approaches and that the acquired main memory data of a smartphone or smartwatch can be accurately used in forensic
Gesture recognition using kinect for sign language translation

Sign Language is a widely used method of communication among the community of deaf-mute people. It contains some series of body gestures, which enables a person to interact without the need of spoken words. Although the use of sign language is very popular among the deaf-mute people but the other communities don't even try to learn it, this creates a gulf of communication and hence becomes a cause of the isolation of physically impaired people. The problem creates a requirement of a system which can facilitate a way of communication between these two communities. This paper presents a novel method for identification of an isolated sign language gesture using Microsoft Kinect. This paper presents the way of extracting some highly robust features from the depth image provided by Kinect and to use them in creating a robust and accurate gesture recognition system, for the purpose of sign language translation. Apart from translation, the proposed system also opens the doors of endless applications in the field of Human Computer Interaction. The proposed algorithm helps in translating a sign language gesture performed by a user, which in-turn can be used as an input for different types of applications.
A circularly polarized short backfire antenna excited by an unbalance-fed cross aperture

An unbalance-fed cross aperture is developed to excite a short backfire antenna (SBA) for circular polarization. The cross aperture consists of two orthogonal H-shaped slots with a pair of capacitive stubs and is fed by a single probe that forms an unbalanced feed with a shorting pin. It is demonstrated that the cross-aperture-excited SBA can achieve an axial ratio (les 3 dB) bandwidth of 4.2% with a voltage standing wave ratio (VSWR) bandwidth of 6.5% (VSWR<1.2) and a gain of 14 dBi. The antenna structure is described and the simulation and experimental results are presented. The mechanisms for impedance matching and circular-polarization production are analyzed
OWL rules: A proposal and prototype implementation

Although the OWL Web Ontology Language adds considerable expressive power to the Semantic Web it does have expressive limitations, particularly with respect to what can be said about properties. We present SWRL (the Semantic Web Rules Language), a Horn clause rules extension to OWL that overcomes many of these limitations. SWRL extends OWL in a syntactically and semantically coherent manner: the basic syntax for SWRL rules is an extension of the abstract syntax for OWL DL and OWL Lite; SWRL rules are given formal meaning via an extension of the OWL DL model-theoretic semantics; SWRL rules are given an XML syntax based on the OWL XML presentation syntax; and a mapping from SWRL rules to RDF graphs is given based on the OWL RDF/XML exchange syntax. We discuss the expressive power of SWRL, showing that the ontology consistency problem is undecidable, provide several examples of SWRL usage, and discuss a prototype implementation of reasoning support for SWRL.