nils-herrmann/clean_scirepeval huggingface dataset:
Older Adults and Emerging Digital Service Delivery: A Mixed Methods Study on Information and Communications Technology Use, Skills, and Attitudes.

Based on data from a survey (n = 3291) and 14 qualitative interviews among Danish older adults, this study investigated the use of, and attitudes toward, information communications technology (ICT) and the digital delivery of public services. While age, gender, and socioeconomic status were associated with use of ICT, these determinants lost their explanatory power when we controlled for attitudes and experiences. We identified three segments that differed in their use of ICT and attitudes toward digital service delivery. As nonuse of ICT often results from the lack of willingness to use it rather than from material or cognitive deficiencies, policy measures for bridging the digital divide should focus on skills and confidence rather than on access or ability.
Expanding Domain Sentiment Lexicon through Double Propagation

Constraint programming is a promising technique for solving many difficult combinatorial problems. Since real-life constraints can be difficult to describe in symbolic expressions, or provide very weak propagation from their symbolic representation, they are sometimes represented in the form of the sets of solutions or sets of nogoods. This adhoc representation provides strong propagation through domain (generalized arc) consistency techniques. However, the adhoc representation is expensive in terms of memory and computation, when the adhoc constraint is large. So there is interest in determining less expensive methods for building propagators for adhoc constraints [Frühwirth, 1998; Apt and Monfroy, 1999; Abdennadher and Rigotti, 2000; Barták, 2001; Dao et al., 2002]. In this paper, we propose a new language-independent representation for adhoc constraints, the box constraint collection. The idea is to break up an adhoc constraint into pieces and cover these pieces using box constraints as tiles. This can be done automatically with a greedy algorithm. With the aid of constructive disjunction and a suitable choice of constraint templates in the collection, our new representation achieves domain consistency.
Nasal reconstruction with the paramedian forehead flap--details for success.

This article is focused on technical details for successfully reconstructing the nasal skin cover in parts or totally. Nasal reconstruction is based on the successful reconstruction of the inner lining and the nasal framework in three-layer defects. The details to be considered include planning the flap, subunit reconstruction and outline of margins, dealing with hair-bearing forehead skin, sequence of stages, intermediate debulking, details of pedicle dissection, brow reconstruction, forehead closure, forehead expansion, and complication management.
How to Build a Highly Available System Using Consensus

Lamport showed that a replicated deterministic state machine is a general way to implement a highly available system, given a consensus algorithm that the replicas can use to agree on each input. His Paxos algorithm is the most fault-tolerant way to get consensus without real-time guarantees. Because general consensus is expensive, practical systems reserve it for emergencies and use leases (locks that time out) for most of the computing. This paper explains the general scheme for efficient highly available computing, gives a general method for understanding concurrent and fault-tolerant programs, and derives the Paxos algorithm as an example of the method.
Compact magnetic wheeled robot with high mobility for inspecting complex shaped pipe structures

This paper describes a compact robot with two magnetic wheels in a bicycle arrangement, which is intended for inspecting the inner casing of pipes with complex shaped structures. The locomotion concept is based on an adapted magnetic wheel unit integrating two lateral lever arms. These arms allow for slightly lifting off the wheel in order to locally decrease the magnetic force, as well as laterally stabilizing the wheel unit. The robot has the main advantage to be compact and mechanically simple. It features 5 active degrees of freedom: 2 driven wheels each equipped with an active lifter-stabilizer and 1 steering unit. This paper also presents the design and implementation of a prototype robot and its high mobility is shown. It is able to pass 90deg convex and concave obstacles with any inclination regarding the gravity. Finally, it only requires limited space to maneuver, since turning on spot around the rear wheel is possible.
An intention driven hand functions task training robotic system

A novel design of a hand functions task training robotic system was developed for the stroke rehabilitation. It detects the intention of hand opening or hand closing from the stroke person using the electromyography (EMG) signals measured from the hemiplegic side. This training system consists of an embedded controller and a robotic hand module. Each hand robot has 5 individual finger assemblies capable to drive 2 degrees of freedom (DOFs) of each finger at the same time. Powered by the linear actuator, the finger assembly achieves 55 degree range of motion (ROM) at the metacarpophalangeal (MCP) joint and 65 degree range of motion (ROM) at the proximal interphalangeal (PIP) joint. Each finger assembly can also be adjusted to fit for different finger length. With this task training system, stroke subject can open and close their impaired hand using their own intention to carry out some of the daily living tasks.
A Behavioral Investigation of Dimensionality Reduction

A cornucopia of dimensionality reduction techniques have emerged over the past decade, leaving data analysts with a wide variety of choices for reducing their data. Means of evaluating and comparing low-dimensional embeddings useful for visualization, however, are very limited. When proposing a new technique it is common to simply show rival embeddings side-by-side and let human judgment determine which embedding is superior. This study investigates whether such human embedding evaluations are reliable, i.e., whether humans tend to agree on the quality of an embedding. We also investigate what types of embedding structures humans appreciate a priori. Our results reveal that, although experts are reasonably consistent in their evaluation of embeddings, novices generally disagree on the quality of an embedding. We discuss the impact of this result on the way dimensionality reduction researchers should present their results, and on applicability of dimensionality reduction outside of machine learning.
EFFICIENT HOUGH TRANSFORM FOR AUTOMATIC DETECTION OF CYLINDERS IN POINT CLOUDS

We present an efficient Hough transform for automatic detection of cylinders in point clouds. As cylinders are one of the most frequently used primitives for industrial design, automatic and robust methods for their detection and fitting are essential for reverse engineering from point clouds. The current methods employ automatic segmentation followed by geometric fitting, which requires a lot of manual interaction during modelling. Although Hough transform can be used for automatic detection of cylinders, the required 5D Hough space has a prohibitively high time and space complexity for most practical applications. We address this problem in this paper and present a sequential Hough transform for automatic detection of cylinders in point clouds. Our algorithm consists of two sequential steps of low dimensional Hough transforms. The first step, called Orientation Estimation, uses the Gaussian sphere of the input data and performs a 2D Hough Transform for finding strong hypotheses for the direction of cylinder axis. The second step of Position and Radius Estimation, consists of a 3D Hough transform for estimating cylinder position and radius. This sequential breakdown reduces the space and time complexity while retaining the advantages of robustness against outliers and multiple instances. The results of applying this algorithm to real data sets from two industrial sites are presented that demonstrate the effectiveness of this procedure for automatic cylinder detection.
Origins of Human Communication

⇒ NOT written, but spoken language. [Intuitions come from written.] ⇒ NOT meaning as thing, but use of linguistic forms for communicative functions o Direct att. in shared conceptual space like gestures (but w/conventions) ⇒ NOT grammatical rules, but patterns of use => schemas o Constructions themselves as complex symbols "She sneezed him the ball" o NOT 'a grammar' but a structured inventory of constructions: continuum of regularity => idiomaticity grammaticality = normativity • Many complexities = "unification" of constructions w/ incompatibilities o NOT innate UG, but "teeming modularity" (1) symbols, pred-arg structure,
New AC–DC Power Factor Correction Architecture Suitable for High-Frequency Operation

This paper presents a novel ac-dc power factor correction (PFC) power conversion architecture for a single-phase grid interface. The proposed architecture has significant advantages for achieving high efficiency, good power factor, and converter miniaturization, especially in low-to-medium power applications. The architecture enables twice-line-frequency energy to be buffered at high voltage with a large voltage swing, enabling reduction in the energy buffer capacitor size and the elimination of electrolytic capacitors. While this architecture can be beneficial with a variety of converter topologies, it is especially suited for the system miniaturization by enabling designs that operate at high frequency (HF, 3-30 MHz). Moreover, we introduce circuit implementations that provide efficient operation in this range. The proposed approach is demonstrated for an LED driver converter operating at a (variable) HF switching frequency (3-10 MHz) from 120 Vac, and supplying a 35 Vdc output at up to 30 W. The prototype converter achieves high efficiency (92%) and power factor (0.89), and maintains a good performance over a wide load range. Owing to the architecture and HF operation, the prototype achieves a high “box” power density of 50 W/in3 (“displacement” power density of 130 W/in3), with miniaturized inductors, ceramic energy buffer capacitors, and a small-volume EMI filter.
It's in your eyes: towards context-awareness and mobile HCI using wearable EOG goggles

In this work we describe the design, implementation and evaluation of a novel eye tracker for context-awareness and mobile HCI applications. In contrast to common systems using video cameras, this compact device relies on Electrooculography (EOG). It consists of goggles with dry electrodes integrated into the frame and a small pocket-worn component with a DSP for real-time EOG signal processing. The device is intended for wearable and standalone use: It can store data locally for long-term recordings or stream processed EOG signals to a remote device over Bluetooth. We describe how eye gestures can be efficiently recognised from EOG signals for HCI purposes. In an experiment conducted with 11 subjects playing a computer game we show that 8 eye gestures of varying complexity can be continuously recognised with equal performance to a state-of-the-art video-based system. Physical activity leads to artefacts in the EOG signal. We describe how these artefacts can be removed using an adaptive filtering scheme and characterise this approach on a 5-subject dataset. In addition to explicit eye movements for HCI, we discuss how the analysis of unconscious eye movements may eventually allow to deduce information on user activity and context not available with current sensing modalities.
Analysis of Trust in Internet and Mobile Commerce Adoption

Understanding user acceptance of the Internet, especially the intentions to use Internet commerce and mobile commerce, is important in explaining the fact that these commerce have been growing at an exponential rate in recent years. This paper studies factors of new technology to better understand and manage the electronic commerce activities. The theoretical model proposed in this paper is intended to clarify the factors as they are related to the technology acceptance model. More specifically, the relationship among trust and other factors are hypothesized. Using the technology acceptance model, this research reveals the importance of the hedonic factor. The result of this research implies that the ways of stimulating and facilitating customers' participation in mobile commerce should be differentiated from those in Internet commerce
Memory-enhanced Decoder for Neural Machine Translation

We propose to enhance the RNN decoder in a neural machine translator (NMT) with external memory, as a natural but powerful extension to the state in the decoding RNN. This memory-enhanced RNN decoder is called MEMDEC. At each time during decoding, MEMDEC will read from this memory and write to this memory once, both with content-based addressing. Unlike the unbounded memory in previous work(Bahdanau et al., 2014) to store the representation of source sentence, the memory in MEMDEC is a matrix with predetermined size designed to better capture the information important for the decoding process at each time step. Our empirical study on Chinese-English translation shows that it can improve by 4.8 BLEU upon Groundhog and 5.3 BLEU upon on Moses, yielding the best performance achieved with the same training set.
Herbert Simon ’ s Decision-Making Approach : Investigation of Cognitive Processes in Experts

Herbert Simon’s research endeavor aimed to understand the processes that participate in human decision making. However, despite his effort to investigate this question, his work did not have the impact in the “decision making” community that it had in other fields. His rejection of the assumption of perfect rationality, made in mainstream economics, led him to develop the concept of bounded rationality. Simon’s approach also emphasized the limitations of the cognitive system, the change of processes due to expertise, and the direct empirical study of cognitive processes involved in decision making. In this article, we argue that his subsequent research program in problem solving and expertise offered critical tools for studying decision-making processes that took into account his original notion of bounded rationality. Unfortunately, these tools were ignored by the main research paradigms in decision making, such as Tversky and Kahneman’s biased rationality approach (also known as the heuristics and biases approach) and the ecological approach advanced by Gigerenzer and others. We make a proposal of how to integrate Simon’s approach with the main current approaches to decision making. We argue that this would lead to better models of decision making that are more generalizable, have higher ecological validity, include specification of cognitive processes, and provide a better understanding of the interaction between the characteristics of the cognitive system and the contingencies of the environment.
Linking long-term dietary patterns with gut microbial enterotypes.

Diet strongly affects human health, partly by modulating gut microbiome composition. We used diet inventories and 16S rDNA sequencing to characterize fecal samples from 98 individuals. Fecal communities clustered into enterotypes distinguished primarily by levels of Bacteroides and Prevotella. Enterotypes were strongly associated with long-term diets, particularly protein and animal fat (Bacteroides) versus carbohydrates (Prevotella). A controlled-feeding study of 10 subjects showed that microbiome composition changed detectably within 24 hours of initiating a high-fat/low-fiber or low-fat/high-fiber diet, but that enterotype identity remained stable during the 10-day study. Thus, alternative enterotype states are associated with long-term diet.
Automatic Generation of Social Tags for Music Recommendation

Social tags are user-generated keywords associated with some resource on the Web. In the case of music, social tags have become an important component of “Web2.0” recommender systems, allowing users to generate playlists based on use-dependent terms such as chill or jogging that have been applied to particular songs. In this paper, we propose a method for predicting these social tags directly from MP3 files. Using a set of boosted classifiers, we map audio features onto social tags collected from the Web. The resulting automatic tags (or autotags) furnish information about music that is otherwise untagged or poorly tagged, allowing for insertion of previously unheard music into a social recommender. This avoids the ”cold-start problem” common in such systems. Autotags can also be used to smooth the tag space from which similarities and recommendations are made by providing a set of comparable baseline tags for all tracks in a recommender system.
BUFFALO: bloom filter forwarding architecture for large organizations

In enterprise and data center networks, the scalability of the data plane becomes increasingly challenging as forwarding tables and link speeds grow. Simply building switches with larger amounts of faster memory is not appealing, since high-speed memory is both expensive and power hungry. Implementing hash tables in SRAM is not appealing either because it requires significant overprovisioning to ensure that all forwarding table entries fit. Instead, we propose the BUFFALO architecture, which uses a small SRAM to store one Bloom filter of the addresses associated with each outgoing link. We provide a practical switch design leveraging flat addresses and shortest-path routing. BUFFALO gracefully handles false positives without reducing the packet-forwarding rate, while guaranteeing that packets reach their destinations with bounded stretch with high probability. We tune the sizes of Bloom filters to minimize false positives for a given memory size. We also handle routing changes and dynamically adjust Bloom filter sizes using counting Bloom filters in slow memory. Our extensive analysis, simulation, and prototype implementation in kernel-level Click show that BUFFALO significantly reduces memory cost, increases the scalability of the data plane, and improves packet-forwarding performance.
A Survey of Communication Sub-systems for Intersatellite Linked Systems and CubeSat Missions

Intersatellite links or crosslinks provide direct connectivity between two or more satellites, thus eliminating the need for intermediate ground stations when sending data. Intersatellite links have been considered for satellite constellation missions involving earth observation and communications. Historically, a large satellite system has needed an extremely high financial budget. However, the advent of the successful CubeSat platform allows for small satellites of less than one kilogram. This low-mass pico-satellite class platform could provide financially feasible support for large platform satellite constellations. This article surveys past and planned large intersatellite linking systems. Then, the article chronicles CubeSat communication subsystems used historically and in the near future. Finally, we examine the history of inter-networking protocols in space and open research issues with the goal of moving towards the next generation intersatellite-linking constellation supported by CubeSat platform satellites.
Recurrent Neural Networks for Emotion Recognition in Video

Deep learning based approaches to facial analysis and video analysis have recently demonstrated high performance on a variety of key tasks such as face recognition, emotion recognition and activity recognition. In the case of video, information often must be aggregated across a variable length sequence of frames to produce a classification result. Prior work using convolutional neural networks (CNNs) for emotion recognition in video has relied on temporal averaging and pooling operations reminiscent of widely used approaches for the spatial aggregation of information. Recurrent neural networks (RNNs) have seen an explosion of recent interest as they yield state-of-the-art performance on a variety of sequence analysis tasks. RNNs provide an attractive framework for propagating information over a sequence using a continuous valued hidden layer representation. In this work we present a complete system for the 2015 Emotion Recognition in the Wild (EmotiW) Challenge. We focus our presentation and experimental analysis on a hybrid CNN-RNN architecture for facial expression analysis that can outperform a previously applied CNN approach using temporal averaging for aggregation.
Handover performance in LTE-A HetNets through inter-site distance differentiation

Mobility Management in future cellular networks is becoming more challenging due to transition from macro only to multi-tier deployments. In this framework, the massive use of small cells rendered traditional Handover algorithms inappropriate to deal effectively with frequent Handovers, especially for fast users, in dense urban scenarios. Studies in this area focus mainly on the adjustment of the Hysteresis Margin and on the Time-to-Trigger (TTT) selection in line with the Self-Organized Networks (SON), concept. In that sense, the ability of each node to adapt its parameters to the actual scenario is considered vital for the smooth operation of the network. This work contributes to the latter by analyzing the dependence of the Handover performance on the inter-site distance between the macro cell and the small cell. Specifically, the most common KPIs (i.e. Handover, Ping Pong and Radio Link Failure probabilities) are analyzed for different inter-site distances and TTT values to provide solid basis for the TTT selection.
Analysis of open source drivers for IEEE 802.11 WLANs

The purpose of this study is to analyze the open source IEEE 802.11 wireless local area network (WLAN) stack implementation for further enhancement and implementations. We discuss the IEEE 802.11 WLAN implementation in the latest Linux kernel. This includes a functional breakdown of the driver and the overall flow of information via functions. We also survey the specific implementation methods used in the WLAN Linux stack. We compare the legacy driver implementation with the newer Linux kernel implementation. For reference, the Atheros network device driver is taken as an example to discuss the WLAN structure, stack and driver implementation.
Feature Transfer Learning for Deep Face Recognition with Long-Tail Data

Real-world face recognition datasets exhibit long-tail characteristics, which results in biased classifiers in conventionally-trained deep neural networks, or insufficient data when long-tail classes are ignored. In this paper, we propose to handle long-tail classes in the training of a face recognition engine by augmenting their feature space under a center-based feature transfer framework. A Gaussian prior is assumed across all the head (regular) classes and the variance from regular classes are transferred to the long-tail class representation. This encourages the long-tail distribution to be closer to the regular distribution, while enriching and balancing the limited training data. Further, an alternating training regimen is proposed to simultaneously achieve less biased decision boundaries and a more discriminative feature representation. We conduct empirical studies that mimic long-tail datasets by limiting the number of samples and the proportion of long-tail classes on the MS-Celeb-1M dataset. We compare our method with baselines not designed to handle long-tail classes and also with state-of-the-art methods on face recognition benchmarks. State-of-the-art results on LFW, IJB-A and MS-Celeb-1M datasets demonstrate the effectiveness of our feature transfer approach and training strategy. Finally, our feature transfer allows smooth visual interpolation, which demonstrates disentanglement to preserve identity of a class while augmenting its feature space with non-identity variations.