nils-herrmann/clean_scirepeval huggingface dataset:
Browsing Zoomable Treemaps: Structure-Aware Multi-Scale Navigation Techniques

Treemaps provide an interesting solution for representing hierarchical data. However, most studies have mainly focused on layout algorithms and paid limited attention to the interaction with treemaps. This makes it difficult to explore large data sets and to get access to details, especially to those related to the leaves of the trees. We propose the notion of zoomable treemaps (ZTMs), an hybridization between treemaps and zoomable user interfaces that facilitates the navigation in large hierarchical data sets. By providing a consistent set of interaction techniques, ZTMs make it possible for users to browse through very large data sets (e.g., 700,000 nodes dispatched amongst 13 levels). These techniques use the structure of the displayed data to guide the interaction and provide a way to improve interactive navigation in treemaps.
Do You Feel What I Hear? Enabling Autonomous IoT Device Pairing Using Different Sensor Types

Context-based pairing solutions increase the usability of IoT device pairing by eliminating any human involvement in the pairing process. This is possible by utilizing on-board sensors (with same sensing modalities) to capture a common physical context (e.g., ambient sound via each device's microphone). However, in a smart home scenario, it is impractical to assume that all devices will share a common sensing modality. For example, a motion detector is only equipped with an infrared sensor while Amazon Echo only has microphones. In this paper, we develop a new context-based pairing mechanism called Perceptio that uses time as the common factor across differing sensor types. By focusing on the event timing, rather than the specific event sensor data, Perceptio creates event fingerprints that can be matched across a variety of IoT devices. We propose Perceptio based on the idea that devices co-located within a physically secure boundary (e.g., single family house) can observe more events in common over time, as opposed to devices outside. Devices make use of the observed contextual information to provide entropy for Perceptio's pairing protocol. We design and implement Perceptio, and evaluate its effectiveness as an autonomous secure pairing solution. Our implementation demonstrates the ability to sufficiently distinguish between legitimate devices (placed within the boundary) and attacker devices (placed outside) by imposing a threshold on fingerprint similarity. Perceptio demonstrates an average fingerprint similarity of 94.9% between legitimate devices while even a hypothetical impossibly well-performing attacker yields only 68.9% between itself and a valid device.
An Audit Model for ISO 9001 Traceability Requirements in Agile-XP Environments

Software organizations that develop their software products using the agile software processes such as Extreme Programming (XP) face a number of challenges in their effort to demonstrate that their process activities conform to ISO 9001 requirements, a major one being product traceability: software organizations must provide evidence of ISO 9001 conformity, and they need to develop their own procedures, tools, and methodologies to do so. This paper proposes an auditing model for ISO 9001 traceability requirements that is applicable in agile (XP) environments. The design of our model is based on evaluation theory, and includes the use of several auditing “yardsticks” derived from the principles of engineering design, the SWEBOK Guide, and the CMMI-DEV guidelines for requirement management and traceability for each yardstick. Finally, five approaches for agile-XP traceability approaches are audited based on the proposed audit model.
Inexperience and experience with online stores: the importance of TAM and trust

An e-vendor’s website inseparably embodies an interaction with the vendor and an interaction with the IT website interface. Accordingly, research has shown two sets of unrelated usage antecedents by customers: 1) customer trust in the e-vendor and 2) customer assessments of the IT itself, specifically the perceived usefulness and perceived ease-of-use of the website as depicted in the technology acceptance model (TAM). Research suggests, however, that the degree and impact of trust, perceived usefulness, and perceived ease of use change with experience. Using existing, validated scales, this study describes a free-simulation experiment that compares the degree and relative importance of customer trust in an e-vendor vis-à-vis TAM constructs of the website, between potential (i.e., new) customers and repeat (i.e., experienced) ones. The study found that repeat customers trusted the e-vendor more, perceived the website to be more useful and easier to use, and were more inclined to purchase from it. The data also show that while repeat customers’ purchase intentions were influenced by both their trust in the e-vendor and their perception that the website was useful, potential customers were not influenced by perceived usefulness, but only by their trust in the e-vendor. Implications of this apparent trust-barrier and guidelines for practice are discussed.
Few-Example Object Detection with Model Communication.

In this paper, we study object detection using a large pool of unlabeled images and only a few labeled images per category, named "few-example object detection". The key challenge consists in generating trustworthy training samples as many as possible from the pool. Using few training examples as seeds, our method iterates between model training and high-confidence sample selection. In training, easy samples are generated first and, then the poorly initialized model undergoes improvement. As the model becomes more discriminative, challenging but reliable samples are selected. After that, another round of model improvement takes place. To further improve the precision and recall of the generated training samples, we embed multiple detection models in our framework, which has proven to outperform the single model baseline and the model ensemble method. Experiments on PASCAL VOC'07, MS COCO'14, and ILSVRC'13 indicate that by using as few as three or four samples selected for each category, our method produces very competitive results when compared to the state-of-the-art weakly-supervised approaches using a large number of image-level labels.
Grid connected Converters for Photovoltaic, State of the Art, Ideas for Improvement of Transformerless Inverters

The paper presents a short overview of the state of the art for grid tied PV inverters at low and medium power level (1..100 kW), mainly intended for rooftop applications. The inverters are categorized according to the configuration of the PV system, the configuration of the conversion stages within the inverter and whether they use transformers or not , . After the introduction of the state of the art of inverters for PV systems with and without transformers, the paper focuses on some known problems and challenges for transformerless inverters. Topologies without transformers have big advantages like low weight, volume and cost. In addition they often reach higher efficiencies than topologies with transformers. Therefore they are important for future developments.
Cyberbullying: Youngsters' Experiences and Parental Perception

This paper describes a survey on the prevalence and nature of cyberbullying of and by youngsters and the parental perceptions. Two questionnaires, one for youngsters and one for their parents, were sent to 1,211 final-year pupils of primary schools and first-year pupils of all levels of secondary schools and their parents. Pupils completed the questionnaires in their classroom, and parents completed them at home. The results show that about 16% of the youngsters had engaged in bullying via the Internet and text messages, while about 23% of the youngsters had been victims of cyberbullying. Cyberbullying seems to be a rather anonymous, individualistic activity, which primarily takes place at home. The most frequently used nature of harassment were name-calling and gossiping. Youngsters mostly react to cyberbullying by pretending to ignore it, by really ignoring it, or by bullying the bully. Most parents set rules for their children about the way to they should use the Internet but are not really conscious of the harassments. They underestimate their own children's bullying behavior and have insufficient notion of their children as victims of bullying.
Feasibility, Acceptability, and Preliminary Effects of the COPE Online Cognitive-Behavioral Skill-Building Program on Mental Health Outcomes and Academic Performance in Freshmen College Students: A Randomized Controlled Pilot Study.

Despite the increasing prevalence of mental health disorders in university students, few receive needed evidence-based treatment.



The purpose of this study was to assess the feasibility and preliminary effects of a seven-session online cognitive-behavioral skill-building intervention (i.e., COPE, Creating Opportunities for Personal Empowerment) versus a comparison group on their anxiety, depressive symptoms, and grade performance.



A randomized controlled pilot study was conducted from September 2012 to May 2013 with 121 college freshmen enrolled in a required one credit survey course.



Although there were no significant differences in anxiety and depressive symptoms between the groups, only COPE students with an elevated level of anxiety at baseline had a significant decline in symptoms. Grade point average was higher in COPE versus comparison students. Evaluations indicated that COPE was a positive experience for students.



COPE is a promising brief intervention that can be integrated effectively into a required freshman course.
Linguistic-based Patterns for Figurative Language Processing: The Case of Humor Recognition and Irony Detection

Figurative language represents one of the most difficult tasks regarding natural language processing. Unlike literal language, figurative language takes advantage of linguistic devices such as irony, humor, sarcasm, metaphor, analogy, and so on, in order to communicate indirect meanings which, usually, are not interpretable by simply decoding syntactic or semantic information. Rather, figurative language reflects patterns of thought within a communicative and social framework that turns quite challenging its linguistic representation, as well as its computational processing. In this Ph. D. thesis we address the issue of developing a linguisticbased framework for figurative language processing. In particular, our efforts are focused on creating some models capable of automatically detecting instances of two independent figurative devices in social media texts: humor and irony. Our main hypothesis relies on the fact that language reflects patterns of thought; i.e. to study language is to study patterns of conceptualization. Thus, by analyzing two specific domains of figurative language, we aim to provide arguments concerning how people mentally conceive humor and irony, and how they verbalize each device in social media platforms. In this context, we focus on showing how fine-grained knowledge, which relies on shallow and deep linguistic layers, can be translated into valuable patterns to automatically identify figurative uses of language. Contrary to most researches that deal with figurative language, we do not support our arguments on prototypical examples neither of humor nor of irony. Rather, we try to find patterns in texts such as blogs, web comments, tweets, etc., whose intrinsic characteristics are quite different to the characteristics described in the specialized literature. Apart from providing a linguistic inventory for detecting humor and irony at textual level, in this investigation we stress out the importance of considering user-generated tags in order to automatically build resources for figurative language processing, such as ad hoc corpora in which human annotation is not necessary. Finally, each model is evaluated in terms of its relevance to properly identify instances of humor and irony, respectively. To this end, several experiments are carried out taking into consideration different data sets and applicability scenarios. Our findings point out that figurative language processing (especially humor and irony) can provide fine-grained knowledge in tasks as diverse as sentiment analysis, opinion mining, information retrieval, or trend discovery.
Software requirements as an application domain for natural language processing

Mapping functional requirements first to specifications and then to code is one of the most challenging tasks in software development. Since requirements are commonly written in natural language, they can be prone to ambiguity, incompleteness and inconsistency. Structured semantic representations allow requirements to be translated to formal models, which can be used to detect problems at an early stage of the development process through validation. Storing and querying such models can also facilitate software reuse. Several approaches constrain the input format of requirements to produce specifications, however they usually require considerable human effort in order to adopt domain-specific heuristics and/or controlled languages. We propose a mechanism that automates the mapping of requirements to formal representations using semantic role labeling. We describe the first publicly available dataset for this task, employ a hierarchical framework that allows requirements concepts to be annotated, and discuss how semantic role labeling can be adapted for parsing software requirements.
More Electric Aircraft: Review, Challenges, and Opportunities for Commercial Transport Aircraft

Similar to the efforts to move toward electric vehicles, much research has focused on the idea of a more electric aircraft (MEA). The motivations for this research are similar to that for vehicles and include goals to reduce emissions and decrease fuel consumption. In traditional aircraft, multiple systems may use one type or a combination of types of energy, including electrical, hydraulic, mechanical, and pneumatic energy. However, all energy types have different drawbacks, including the sacrifice of total engine efficiency in the process of harvesting a particular energy, as with hydraulic and pneumatic systems. The goal for future aircraft is to replace most of the major systems currently utilizing nonelectric power, such as environmental controls and engine start, with new electrical systems to improve a variety of aircraft characteristics, such as efficiency, emissions, reliability, and maintenance costs. This paper provides an in-depth look into how the systems have-or will be-changed. Future aircraft capabilities such as electric taxi and gas-electric propulsion for aircraft are also included for discussion. Most recent commercial transport aircrafts are described as the current state-of-the-art electric aircraft system. Future goals, including those of NASA, are presented for future advances in MEA.
uPAR-targeted optical near-infrared (NIR) fluorescence imaging and PET for image-guided surgery in head and neck cancer: proof-of-concept in orthotopic xenograft model

Urokinase-like Plasminogen Activator Receptor (uPAR) is overexpressed in a variety of carcinoma types, and therefore represents an attractive imaging target. The aim of this study was to assess the feasibility of two uPAR-targeted probes for PET and fluorescence tumor imaging in a human xenograft tongue cancer model.



Tumor growth of tongue cancer was monitored by bioluminescence imaging (BLI) and MRI. Either ICG-Glu-Glu-AE105 (fluorescent agent) or 64Cu-DOTA-AE105 (PET agent) was injected systemically, and fluorescence imaging or PET/CT imaging was performed. Tissue was collected for micro-fluorescence imaging and histology. A clear fluorescent signal was detected in the primary tumor with a mean in vivo tumor-to-background ratio of 2.5. Real-time fluorescence-guided tumor resection was possible, and sub-millimeter tumor deposits could be localized. Histological analysis showed co-localization of the fluorescent signal, uPAR expression and tumor deposits. In addition, the feasibility of uPAR-guided robotic cancer surgery was demonstrated. Also, uPAR-PET imaging showed a clear and localized signal in the tongue tumors.



This study demonstrated the feasibility of combining two uPAR-targeted probes in a preclinical head and neck cancer model. The PET modality provided preoperative non-invasive tumor imaging and the optical modality allowed for real-time fluorescence-guided tumor detection and resection. Clinical translation of this platform seems promising.
Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold Simplification

The latest deep learning approaches perform better than the state-of-the-art signal processing approaches in various image restoration tasks. However, if an image contains many patterns and structures, the performance of these CNNs is still inferior. To address this issue, here we propose a novel feature space deep residual learning algorithm that outperforms the existing residual learning. The main idea is originated from the observation that the performance of a learning algorithm can be improved if the input and/or label manifolds can be made topologically simpler by an analytic mapping to a feature space. Our extensive numerical studies using denoising experiments and NTIRE single-image super-resolution (SISR) competition demonstrate that the proposed feature space residual learning outperforms the existing state-of-the-art approaches. Moreover, our algorithm was ranked high in the NTIRE competition with 5-10 times faster computational time compared to the top ranked teams.
User Modeling for Adaptive News Access

We present a framework for adaptive news access, based on machine learning techniques specifically designed for this task. First, we focus on the system's general functionality and system architecture. We then describe the interface and design of two deployed news agents that are part of the described architecture. While the first agent provides personalized news through a web-based interface, the second system is geared towards wireless information devices such as PDAs (personal digital assistants) and cell phones. Based on implicit and explicit user feedback, our agents use a machine learning algorithm to induce individual user models. Motivated by general shortcomings of other user modeling systems for Information Retrieval applications, as well as the specific requirements of news classification, we propose the induction of hybrid user models that consist of separate models for short-term and long-term interests. Furthermore, we illustrate how the described algorithm can be used to address an important issue that has thus far received little attention in the Information Retrieval community: a user's information need changes as a direct result of interaction with information. We empirically evaluate the system's performance based on data collected from regular system users. The goal of the evaluation is not only to understand the performance contributions of the algorithm's individual components, but also to assess the overall utility of the proposed user modeling techniques from a user perspective. Our results provide empirical evidence for the utility of the hybrid user model, and suggest that effective personalization can be achieved without requiring any extra effort from the user.
An Empirical Analysis of Phishing Blacklists

In this paper, we study the effectiveness of phishing blacklists. We used 191 fresh phish that were less than 30 minutes old to conduct two tests on eight anti-phishing toolbars. We found that 63% of the phishing campaigns in our dataset lasted less than two hours. Blacklists were ineffective when protecting users initially, as most of them caught less than 20% of phish at hour zero. We also found that blacklists were updated at different speeds, and varied in coverage, as 47% 83% of phish appeared on blacklists 12 hours from the initial test. We found that two tools using heuristics to complement blacklists caught significantly more phish initially than those using only blacklists. However, it took a long time for phish detected by heuristics to appear on blacklists. Finally, we tested the toolbars on a set of 13,458 legitimate URLs for false positives, and did not find any instance of mislabeling for either blacklists or heuristics. We present these findings and discuss ways in which anti-phishing tools can be improved.
A low stand-by power start-up circuit for SMPS PWM controller

In this paper, a novel start-up circuit with a simple topology and low stand-by power during under voltage lockout (UVLO) mode is proposed for SMPS (switching mode power supplies) application. The proposed start-up circuit is designed using only a few MOSFETs, LDMOSs, and one JFET based on the analysis of the existing start-up circuits to address the power consumption and input voltage range issues of the conventional start-up. Simulated results using 0.35um BCDMOS process demonstrate that the leakage current of the proposed circuit is less than 1uA after UVLO signal turns on. Setting time is less than 1ms when the load current changes from 10mA to 20mA and vice versa
BDD100K: A Diverse Driving Video Database with Scalable Annotation Tooling

Datasets drive vision progress and autonomous driving is a critical vision application, yet existing driving datasets are impoverished in terms of visual content. Driving imagery is becoming plentiful, but annotation is slow and expensive, as annotation tools have not kept pace with the flood of data. Our first contribution is the design and implementation of a scalable annotation system that can provide a comprehensive set of image labels for large-scale driving datasets. Our second contribution is a new driving dataset, facilitated by our tooling, which is an order of magnitude larger than previous efforts, and is comprised of over 100K videos with diverse kinds of annotations including image level tagging, object bounding boxes, drivable areas, lane markings, and full-frame instance segmentation. The dataset possesses geographic, environmental, and weather diversity, which is useful for training models so that they are less likely to be surprised by new conditions. The dataset can be requested at http://bdd-data.berkeley.edu.
Recommendation Using Textual Opinions

Many web sites collect reviews of products and services and use them provide rankings of their quality. However, such rankings are not personalized. We investigate how the information in the reviews written by a particular user can be used to personalize the ranking she is shown. We propose a new technique, topic profile collaborative filtering, where we build user profiles from users’ review texts and use these profiles to filter other review texts with the eyes of this user. We verify on data from an actual review site that review texts and topic profiles indeed correlate with ratings, and show that topic profile collaborative filtering provides both a better mean average error when predicting ratings and a better approximation of user preference orders.