nils-herrmann/clean_scirepeval huggingface dataset:
WatchWriter: Tap and Gesture Typing on a Smartwatch Miniature Keyboard with Statistical Decoding

We present WatchWriter, a finger operated keyboard that supports both touch and gesture typing with statistical decoding on a smartwatch. Just like on modern smartphones, users type one letter per tap or one word per gesture stroke on WatchWriter but in a much smaller spatial scale. WatchWriter demonstrates that human motor control adaptability, coupled with modern statistical decoding and error correction technologies developed for smartphones, can enable a surprisingly effective typing performance despite the small watch size. In a user performance experiment entirely run on a smartwatch, 36 participants reached a speed of 22-24 WPM with near zero error rate.
Network coding mechanisms for Ka-band satellite time varying channel

Network coding over time varying channels has been investigated and a new scheme is proposed. We propose a novel model for packet transmission over time variant channels that exploits the channel delay profile and the dependency between channel states via first order auto-regression for Ka-band satellite communications. We provide an approximation of the delay induced assuming finite number of time slots to transmit a given number of packets. We also propose a novel adaptive transmission scheme that compensates for the lost degrees of freedom by tracking the packet erasures over time. Our results show that network coding non-adaptive mechanism for time variant channels has around 2 times throughput and delay performance gains for small size packets over network coding mechanisms with fixed channel erasures and similar performance gains for large size packets. In addition, it is shown that network coding non-adaptive mechanism for time variant channels has similar performance to the Selective Repeat (SR) with ARQ, and better performance when packet error probability is high, while due to better utilization of channel resources SR performance is similar or moderately better at very low erasures, i.e., at high SNR. However, our adaptive transmission scheme outperforms the network coding non-adaptive mechanism and SR with more than 7 times in throughput and delay performance gains.
Replaying history on process models for conformance checking and performance analysis

Process mining techniques use event data to discover process models, to check the conformance of predefined process models, and to extend such models with information about bottlenecks, decisions, and resource usage. These techniques are driven by observed events rather than hand-made models. Event logs are used to learn and enrich process models. By replaying history on the model, it is possible to establish a precise relationship between events and model elements. This relationship can be used to check conformance and to analyze performance. For example, it is possible to diagnose deviations from the modeled behavior. The severity of each deviation can be quantified. Moreover, the relationship established during replay and the timestamps in the event log can be combined to show bottlenecks. These examples illustrate the importance of maintaining a proper alignment between event log and process model. Therefore, we elaborate on the realization of such alignments and their application to conformance checking and performance analysis.
Sequential Prediction of Social Media Popularity with Deep Temporal Context Networks

Prediction of popularity has profound impact for social media, since it offers opportunities to reveal individual preference and public attention from evolutionary social systems. Previous research, although achieves promising results, neglects one distinctive characteristic of social data, i.e., sequentiality. For example, the popularity of online content is generated over time with sequential post streams of social media. To investigate the sequential prediction of popularity, we propose a novel prediction framework called Deep Temporal Context Networks (DTCN) by incorporating both temporal context and temporal attention into account. Our DTCN contains three main components, from embedding, learning to predicting. With a joint embedding network, we obtain a unified deep representation of multi-modal user-post data in a common embedding space. Then, based on the embedded data sequence over time, temporal context learning attempts to recurrently learn two adaptive temporal contexts for sequential popularity. Finally, a novel temporal attention is designed to predict new popularity (the popularity of a new userpost pair) with temporal coherence across multiple time-scales. Experiments on our released image dataset with about 600K Flickr photos demonstrate that DTCN outperforms state-of-the-art deep prediction algorithms, with an average of 21.51% relative performance improvement in the popularity prediction (Spearman Ranking Correlation).
Burst firing in sensory systems

Neurons that fire high-frequency bursts of spikes are found in various sensory systems. Although the functional implications of burst firing might differ from system to system, bursts are often thought to represent a distinct mode of neuronal signalling. The firing of bursts in response to sensory input relies on intrinsic cellular mechanisms that work with feedback from higher centres to control the discharge properties of these cells. Recent work sheds light on the information that is conveyed by bursts about sensory stimuli, on the cellular mechanisms that underlie bursting, and on how feedback can control the firing mode of burst-capable neurons, depending on the behavioural context. These results provide strong evidence that bursts have a distinct function in sensory information transmission.
SIRIUS-LTG-UiO at SemEval-2018 Task 7: Convolutional Neural Networks with Shortest Dependency Paths for Semantic Relation Extraction and Classification in Scientific Papers

This article presents the SIRIUS-LTG-UiO system for the SemEval 2018 Task 7 on Semantic Relation Extraction and Classification in Scientific Papers. First we extract the shortest dependency path (sdp) between two entities, then we introduce a convolutional neural network (CNN) which takes the shortest dependency path embeddings as input and performs relation classification with differing objectives for each subtask of the shared task. This approach achieved overall F1 scores of 76.7 and 83.2 for relation classification on clean and noisy data, respectively. Furthermore, for combined relation extraction and classification on clean data, it obtained F1 scores of 37.4 and 33.6 for each phase. Our system ranks 3rd in all three sub-tasks of the shared task.
Recurrent Neural Networks for Drum Transcription

Music transcription is a core task in the field of music information retrieval. Transcribing the drum tracks of music pieces is a well-defined sub-task. The symbolic representation of a drum track contains much useful information about the piece, like meter, tempo, as well as various style and genre cues. This work introduces a novel approach for drum transcription using recurrent neural networks. We claim that recurrent neural networks can be trained to identify the onsets of percussive instruments based on general properties of their sound. Different architectures of recurrent neural networks are compared and evaluated using a well-known dataset. The outcomes are compared to results of a state-of-the-art approach on the same dataset. Furthermore, the ability of the networks to generalize is demonstrated using a second, independent dataset. The experiments yield promising results: while F-measures higher than state-of-the-art results are achieved, the networks are capable of generalizing reasonably well.
Measurement of Commercial Peer-To-Peer Live Video Streaming

Peer-to-peer technologies have proved to be effective for various bandwidth intensive, large scale applications such as file-transfer. For many years, there has been tremendous interest in academic environments for live video streaming as another application of P2P. Recently, a number of new commercial scale video streaming systems have cropped up. These systems differ from others in the type of content that they provide and attract a large number of users from across the globe. These are proprietary systems and very little is known about their architecture and behavior. This study is one of the first of its kind to analyze the performance and characteristics of P2P live streaming applications. In particular, we analyze PPLive and SOPCast, two of the most popular systems in this class. In this paper, we (1) present a framework in which to analyze these P2P applications from a single observable point, (2) analyze control traffic to present a probable operation model and (3) present analysis of resource usage, locality and stability of data distribution. We conclude that P2P live streaming has an even greater impact on network bandwidth utilization and control than P2P file transfer applications.
Improved Adversarial Systems for 3D Object Generation and Reconstruction

This paper describes a new approach for training generative adversarial networks (GAN) to understand the detailed 3D shape of objects. While GANs have been used in this domain previously, they are notoriously hard to train, especially for the complex joint data distribution over 3D objects of many categories and orientations. Our method extends previous work by employing the Wasserstein distance normalized with gradient penalization as a training objective. This enables improved generation from the joint object shape distribution. Our system can also reconstruct 3D shape from 2D images and perform shape completion from occluded 2.5D range scans. We achieve notable quantitative improvements in comparison to existing baselines.
Sensing as a Service: Challenges, Solutions and Future Directions

Sensors on (or attached to) mobile phones can enable attractive sensing applications in different domains, such as environmental monitoring, social networking, healthcare, transportation, etc. We introduce a new concept, sensing as a service (S2aaS), i.e., providing sensing services using mobile phones via a cloud computing system. An S2aaS cloud needs to meet the following requirements: 1) it must be able to support various mobile phone sensing applications on different smartphone platforms; 2) it must be energy-efficient; and 3) it must have effective incentive mechanisms that can be used to attract mobile users to participate in sensing activities. In this vision paper, we identify unique challenges of designing and implementing an S2aaS cloud, review existing systems and methods, present viable solutions, and point out future research directions.
Learning Paraphrase Identification with Structural Alignment

Semantic similarity of text plays an important role in many NLP tasks. It requires using both local information like lexical semantics and structural information like syntactic structures. Recent progress in word representation provides good resources for lexical semantics, and advances in natural language analysis tools make it possible to efficiently generate syntactic and semantic annotations. However, how to combine them to capture the semantics of text is still an open question. Here, we propose a new alignment-based approach to learn semantic similarity. It uses a hybrid representation, attributed relational graphs, to encode lexical, syntactic and semantic information. Alignment of two such graphs combines local and structural information to support similarity estimation. To improve alignment, we introduced structural constraints inspired by a cognitive theory of similarity and analogy. Usually only similarity labels are given in training data and the true alignments are unknown, so we address the learning problem using two approaches: alignment as feature extraction and alignment as latent variable. Our approach is evaluated on the paraphrase identification task and achieved results competitive with the state-of-theart.
STUDY ON DESIGN AND TRANSITIONAL FLIGHT OF TAIL-SITTING VTOL UAV

A new design for a tail-sitting vertical takeoff and landing (VTOL) unmanned aerial vehicle (UAV) was proposed. A nonlinear mathematical model of the vehicle dynamics was constructed by combining simple estimation methods. The flight characteristics were revealed through a trim analysis and an optimized transitional flight path analysis by using the mathematical model. The trim analysis revealed the existence of a minimum path angle to avoid stall in lowspeed flights. Although the value of the angle was positive without flaps or slats, these high lift devices (HLDs) improved this value. In particular, the slats provided a substantial improvement in the value of the angle and enabled a descent rate of 2.0 m/sec. In the optimized transitional flight path analysis, a level outbound transition without HLDs was achieved although a trimmed level flight at low speeds as was shown in the trim analysis was not possible; this was because the outbound transition was an accelerative flight. On the contrary, without HLDs, the vehicle could not avoid climbing during inbound transitions to avoid stall. The slats provided a satisfactory improvement during the transition and made a level inbound transition possible. These results showed the necessity of leading-edge slats for the proposed tail-sitting VTOL UAV.
Semiconductor losses in voltage source and current source IGBT converters based on analytical derivation

A crucial criterion for the dimensioning of three phase PWM converters is the cooling of the power semiconductors and thus determination of power dissipation in the semiconductors at certain operating points and its maximum. Methods for the calculation and simulation of semiconductor losses in the most common voltage source and current source three phase PWM converters are well known. Here a complete analytical calculation of the power semiconductor losses for both converter types is presented, most parts are already known, some parts are developed here, as far as the authors know. Conduction losses as well as switching losses are included in the calculation using a simplified model, based on power semiconductor data sheet information. This approach should benefit the prediction and further investigations of the performance of power semiconductor losses for both kinds of converters. Results of the calculation are shown. Dependencies of the semiconductor power losses on the type of converter, the operating point and the pulse width modulation are pointed out, showing the general behaviour of power losses for both converter types.
A Comparison of Classification Methods for Predicting Deception in Computer-Mediated Communication

The increased chance of deception in computer-mediated communication and the potential risk of taking action based on deceptive information calls for automatic detection of deception. To achieve the ultimate goal of automatic prediction of deception, we selected four common classification methods and empirically compared their performance in predicting deception. The deception and truth data were collected during two experimental studies. The results suggest that all of the four methods were promising for predicting deception with cues to deception. Among them, neural networks exhibited consistent performance and were robust across test settings. The comparisons also highlighted the importance of selecting important input variables and removing noise in an attempt to enhance the performance of classification methods. The selected cues offer both methodological and theoretical contributions to the body of deception and information systems research.
A Review of Relation Extraction

Many applications in information extraction, natural language understanding, information retrieval require an understanding of the semantic relations between entities. We present a comprehensive review of various aspects of the entity relation extraction task. Some of the most important supervised and semi-supervised classification approaches to the relation extraction task are covered in sufficient detail along with critical analyses. We also discuss extensions to higher-order relations. Evaluation methodologies for both supervised and semi-supervised methods are described along with pointers to the commonly used performance evaluation datasets. Finally, we also give short descriptions of two important applications of relation extraction, namely question answering and biotext mining.
A Full Proof of the BGW Protocol for Perfectly Secure Multiparty Computation

In the setting of secure multiparty computation, a set of n parties with private inputs wish to jointly compute some functionality of their inputs. One of the most fundamental results of secure computation was presented by Ben-Or, Goldwasser, and Wigderson (BGW) in 1988. They demonstrated that any n-party functionality can be computed with perfect security, in the private channels model. When the adversary is semi-honest, this holds as long as $$t<n/2$$ t < n / 2 parties are corrupted, and when the adversary is malicious, this holds as long as $$t<n/3$$ t < n / 3 parties are corrupted. Unfortunately, a full proof of these results was never published. In this paper, we remedy this situation and provide a full proof of security of the BGW protocol. This includes a full description of the protocol for the malicious setting, including the construction of a new subprotocol for the perfect multiplication protocol that seems necessary for the case of $$n/4\le t<n/3$$ n / 4 ≤ t < n / 3 .
Using Neural Network Model Predictive Control for Controlling Shape Memory Alloy-Based Manipulator

This paper presents a new setup and investigates neural model predictive and variable structure controllers designed to control the single-degree-of-freedom rotary manipulator actuated by shape memory alloy (SMA). SMAs are a special group of metallic materials and have been widely used in the robotic field because of their particular mechanical and electrical characteristics. SMA-actuated manipulators exhibit severe hysteresis, so the controllers should confront this problem and make the manipulator track the desired angle. In this paper, first, a mathematical model of the SMA-actuated robot manipulator is proposed and simulated. The controllers are then designed. The results set out the high performance of the proposed controllers. Finally, stability analysis for the closed-loop system is derived based on the dissipativity theory.
A Hidden Markov Model-based continuous gesture recognition system for hand motion trajectory

In this paper, we propose an automatic system that recognizes both isolated and continuous gestures for Arabic numbers (0-9) in real-time based on hidden Markov model (HMM). To handle isolated gestures, HMM using ergodic, left-right (LR) and left-right banded (LRB) topologies with different number of states ranging from 3 to 10 is applied. Orientation dynamic features are obtained from spatio-temporal trajectories and then quantized to generate its codewords. The continuous gestures are recognized by our novel idea of zero-codeword detection with static velocity motion. Therefore, the LRB topology in conjunction with forward algorithm presents the best performance and achieves average rate recognition 98.94% and 95.7% for isolated and continuous gestures, respectively.
Admittance control for physical human-quadrocopter interaction

This paper analyzes the application of admittance control to quadrocopters, focusing on physical human-vehicle interaction. Admittance control allows users to define the apparent inertia, damping, and stiffness of a robot, providing an intuitive way to physically interact with it. In this work, external forces acting on the quadrocopter are estimated from position and attitude information and then input to the admittance controller, which modifies the vehicle reference trajectory accordingly. The reference trajectory is tracked by an underlying position and attitude controller. The characteristics of the overall control scheme are investigated for the near-hover case. Experimental results complement the paper, demonstrating the suitability of the method for physical human-quadrocopter interaction.
Security analysis of a femtocell device

Mobile network operators are adapting femtocells in order to simplify their network architecture for increased coverage, performance, and greater revenue opportunities. While emerging as a new low-cost technology which assures best connectivity, it has also introduced a range of new potential security risks for the mobile network operators. In this paper, we analyze these security issues and demonstrate the weaknesses of femtocell security. We demonstrate several security flaws that allowing attackers to gain root access and to install malicious applications on the femtocell. Furthermore, we experimentally evaluate and show a wide range of possible threats to femtocell; including compromise of femtocell credentials; physical, configuration, and protocol attacks; user data and identity privacy attacks. The vulnerabilities we found suggest that commercial-available femtocells fail to fulfill 3GPP security requirements and could expose operator network elements to the attacker. Our findings and successful attacks exhibit the need for further research to bridge the gap between theoretical and practical security of femtocell devices.
Iterative Machine Teaching

In this paper, we consider the problem of machine teaching, the inverse problem of machine learning. Different from traditional machine teaching which views the learners as batch algorithms, we study a new paradigm where the learner uses an iterative algorithm and a teacher can feed examples sequentially and intelligently based on the current performance of the learner. We show that the teaching complexity in the iterative case is very different from that in the batch case. Instead of constructing a minimal training set for learners, our iterative machine teaching focuses on achieving fast convergence in the learner model. Depending on the level of information the teacher has from the learner model, we design teaching algorithms which can provably reduce the number of teaching examples and achieve faster convergence than learning without teachers. We also validate our theoretical findings with extensive experiments on different data distribution and real image datasets.
Dynamic 3-D Scene Analysis Through Synthesis Feedback Control

The analysis of 3D scenes consisting of nonrigid moving objects from 2D image sequences is discussed. A parametric description of dynamic objects is extracted and the time variant scene parameters are estimated throughout the sequence by employing an analysis by synthesis approach. From the parametric scene description images are synthesized and compared with the original input images of the camera. Frame differences between both images are evaluated to estimate a scene parameter update. The analysis system is applied to video phone scenes as a data compression algorithm where the scene parameters are transmitted and the output sequence is synthesized at the receiver. Index Terms – 3D scene analysis, dynamic scene analysis, image sequence analysis, 3D motion estimation, 3D object tracking, 3D shape reconstruction, 3D shape adaptation, analysis by synthesis, object–oriented video phone coding.