nils-herrmann/clean_scirepeval huggingface dataset:
Affordable Infrared-Optical Pose-Tracking for Virtual and Augmented Reality

In this paper, we describe the hardand software of a new low-cost infrared-optical pose-tracking system for room-sized virtual environments. The system consists of 4-8 shutter-synchronized 1394cameras with an optical bandpass filter and infrared illuminator. All image-processing is done in software on an attached workstation. Preliminary results indicate low latency (20-40ms), minimal jitter (RMS less than 0.05mm/0.02°), submillimeter location resolution and an absolute accuracy of ±0.5cm. Currently, ten independent 6-DOF targets can be tracked in real-time with up to 60Hz.
Understanding together: sensemaking in collaborative information seeking

An important aspect of collaborative information seeking (CIS) is making sense of the information found, i.e., collaborative sensemaking. We conducted an ethnographic study of the CIS practices of healthcare providers in a hospital emergency department to gain a conceptual understanding of when and how collaborative sensemaking occurs during CIS activities. We present occasions and characteristics of collaborative sensemaking and design implications for collaborative information retrieval tools to support sensemaking.
Network on Chip : An architecture for billion transistor era

Looking into the future, when the billion transitor ASICs will become reality, this p per presents Network on a chip (NOC) concept and its associated methodology as solu the design productivity problem. NOC is a network of computational, storage and I/O resou interconnected by a network of switches. Resources communcate with each other usi dressed data packets routed to their destination by the switch fabric. Arguments are pre to justify that in the billion transistor era, the area and performance penalty would be minim A concrete topology for the NOC, a honeycomb structure, is proposed and discussed. A odology to support NOC is presented. This methodology outlines steps from requirements to implementation. As an illustration of the concepts, a plausible mapping of an entire ba tion on hypothetical NOC is discussed.
LDPC Codes: An Introduction

LDPC codes are one of the hottest topics in coding theory today. Originally invented in the early 1960’s, they have experienced an amazing comeback in the last few years. Unlike many other classes of codes LDPC codes are already equipped with very fast (probabilistic) encoding and decoding algorithms. The question is that of the design of the codes such that these algorithms can recover the original codeword in the face of large amounts of noise. New analytic and combinatorial tools make it possible to solve the design problem. This makes LDPC codes not only attractive from a theoretical point of view, but also perfect for practical applications. In this note I will give a brief overview of the origins of LDPC codes and the methods used for their analysis and design.
Progress in Silicon Single-Photon Avalanche Diodes

Silicon single-photon avalanche diodes (SPADs) are nowadays a solid-state alternative to photomultiplier tubes (PMTs) in single-photon counting (SPC) and time-correlated single-photon counting (TCSPC) over the visible spectral range up to 1-mum wavelength. SPADs implemented in planar technology compatible with CMOS circuits offer typical advantages of microelectronic devices (small size, ruggedness, low voltage, low power, etc.). Furthermore, they have inherently higher photon detection efficiency, since they do not rely on electron emission in vacuum from a photocathode as do PMTs, but instead on the internal photoelectric effect. However, PMTs offer much wider sensitive area, which greatly simplifies the design of optical systems; they also attain remarkable performance at high counting rate, and offer picosecond timing resolution with microchannel plate models. In order to make SPAD detectors more competitive in a broader range of SPC and TCSPC applications, it is necessary to face several issues in the semiconductor device design and technology. Such issues will be discussed in the context of the two possible approaches to such a challenge: employing a standard industrial high-voltage CMOS technology or developing a dedicated CMOS-compatible technology. Advances recently attained in the development of SPAD detectors will be outlined and discussed with reference to both single-element detectors and integrated detector arrays.
Generating Coherent Summaries of Scientific Articles Using Coherence Patterns

Previous work on automatic summarization does not thoroughly consider coherence while generating the summary. We introduce a graph-based approach to summarize scientific articles. We employ coherence patterns to ensure that the generated summaries are coherent. The novelty of our model is twofold: we mine coherence patterns in a corpus of abstracts, and we propose a method to combine coherence, importance and non-redundancy to generate the summary. We optimize these factors simultaneously using Mixed Integer Programming. Our approach significantly outperforms baseline and state-of-the-art systems in terms of coherence (summary coherence assessment) and relevance (ROUGE scores).
Towards Time-Aware Knowledge Graph Completion

Knowledge graph (KG) completion adds new facts to a KG by making inferences from existing facts. Most existing methods ignore the time information and only learn from time-unknown fact triples. In dynamic environments that evolve over time, it is important and challenging for knowledge graph completion models to take into account the temporal aspects of facts. In this paper, we present a novel time-aware knowledge graph completion model that is able to predict links in a KG using both the existing facts and the temporal information of the facts. To incorporate the happening time of facts, we propose a time-aware KG embedding model using temporal order information among facts. To incorporate the valid time of facts, we propose a joint time-aware inference model based on Integer Linear Programming (ILP) using temporal consistency information as constraints. We further integrate two models to make full use of global temporal information. We empirically evaluate our models on time-aware KG completion task. Experimental results show that our time-aware models achieve the state-of-the-art on temporal facts consistently.
LogNet: Energy-efficient neural networks using logarithmic computation

We present the concept of logarithmic computation for neural networks. We explore how logarithmic encoding of non-uniformly distributed weights and activations is preferred over linear encoding at resolutions of 4 bits and less. Logarithmic encoding enables networks to 1) achieve higher classification accuracies than fixed-point at low resolutions and 2) eliminate bulky digital multipliers. We demonstrate our ideas in the hardware realization, LogNet, an inference engine using only bitshift-add convolutions and weights distributed across the computing fabric. The opportunities from hardware work in synergy with those from the algorithm domain.
Taxonomies of workflow scheduling problem and techniques in the cloud

Scientific workflows, like other applications, benefit from the cloud computing, which offers access to virtually unlimited resources provisioned elastically on demand. In order to efficiently execute a workflow in the cloud, scheduling is required to address many new aspects introduced by cloud resource provisioning. In the last few years, many techniques have been proposed to tackle different cloud environments enabled by the flexible nature of the cloud, leading to the techniques of different designs. In this paper, taxonomies of cloud workflow scheduling problem and techniques are proposed based on analytical review. We identify and explain the aspects and classifications unique to workflow scheduling in the cloud environment in three categories, namely, scheduling process, task and resource. Lastly, review of several scheduling techniques are included and classified onto the proposed taxonomies. We hope that our taxonomies serve as a stepping stone for those entering this research area and for further development of scheduling technique.
The design and evaluation of multitouch marking menus

Despite the considerable quantity of research directed towards multitouch technologies, a set of standardized UI components have not been developed. Menu systems provide a particular challenge, as traditional GUI menus require a level of pointing precision inappropriate for direct finger input. Marking menus are a promising alternative, but have yet to be investigated or adapted for use within multitouch systems. In this paper, we first investigate the human capabilities for performing directional chording gestures, to assess the feasibility of multitouch marking menus. Based on the positive results collected from this study, and in particular, high angular accuracy, we discuss our new multitouch marking menu design, which can increase the number of items in a menu, and eliminate a level of depth. A second experiment showed that multitouch marking menus perform significantly faster than traditional hierarchal marking menus, reducing acquisition times in both novice and expert usage modalities.
Filling n-sided regions with NURBS patches

-sided region with G ɛ continuous NURBS patches that interpolate boundary curves and approximate given cross-boundary derivatives. The NURBS surfaces joining along inner or boundary curves have normal vectors that do not deviate more than the user-specified angular tolerance ɛ. The method is general in that there are no restrictions on the number of boundary curves, and the cross-boundary derivatives can be specified independently. To satisfy all conditions, only one degree elevation is needed.
Comparisons of 6.5kV 25A Si IGBT and 10-kV SiC MOSFET in Solid-State Transformer application

A 6.5kV 25A dual IGBT module is customized and packaged specially for high voltage low current application like solid state transformer and its characteristics and losses have been tested under the low current operation and compared with 10kV SiC MOSFET. Based on the test results, the switching losses under different frequencies in a 20kVA Solid-State Transformer (SST) has been calculated for both devices. The result shows 10kV SiC MOSFET has 7–10 times higher switching frequency capability than 6.5kV Si IGBT in the SST application.
ELECTRONIC CUSTOMER RELATIONSHIP MANAGEMENT ( eCRM ) : OPPORTUNITIES AND CHALLENGES IN A DIGITAL WORLD

Electronic customer relationship management (eCRM) is seen to arise from the consolidation of traditional CRM with the e-business applications marketplace and has created a flurry of activity among companies. eCRM is the proverbial double-edged sword, presenting both opportunities and challenges for companies considering its adoption and implementation. This paper explores the marketing opportunities eCRM creates for companies such as enhanced customer interactions and relationships as well as personalisation options, all of which are potential sources of competitive advantage. It also explores the challenges confronting companies implementing eCRM such as managing an on-line channel, data integration issues and information technology (IT) architecture challenges. Directions for future research are also suggested.
Composable Deep Reinforcement Learning for Robotic Manipulation

Model-free deep reinforcement learning has been shown to exhibit good performance in domains ranging from video games to simulated robotic manipulation and locomotion. However, model-free methods are known to perform poorly when the interaction time with the environment is limited, as is the case for most real-world robotic tasks. In this paper, we study how maximum entropy policies trained using soft Q-learning can be applied to real-world robotic manipulation. The application of this method to real-world manipulation is facilitated by two important features of soft Q-learning. First, soft Q-learning can learn multimodal exploration strategies by learning policies represented by expressive energy-based models. Second, we show that policies learned with soft Q-learning can be composed to create new policies, and that the optimality of the resulting policy can be bounded in terms of the divergence between the composed policies. This compositionality provides an especially valuable tool for real-world manipulation, where constructing new policies by composing existing skills can provide a large gain in efficiency over training from scratch. Our experimental evaluation demonstrates that soft Q-learning is substantially more sample efficient than prior model-free deep reinforcement learning methods, and that compositionality can be performed for both simulated and real-world tasks.
Multi-Task Vehicle Detection With Region-of-Interest Voting

Vehicle detection is a challenging problem in autonomous driving systems, due to its large structural and appearance variations. In this paper, we propose a novel vehicle detection scheme based on multi-task deep convolutional neural networks (CNNs) and region-of-interest (RoI) voting. In the design of CNN architecture, we enrich the supervised information with subcategory, region overlap, bounding-box regression, and category of each training RoI as a multi-task learning framework. This design allows the CNN model to share visual knowledge among different vehicle attributes simultaneously, and thus, detection robustness can be effectively improved. In addition, most existing methods consider each RoI independently, ignoring the clues from its neighboring RoIs. In our approach, we utilize the CNN model to predict the offset direction of each RoI boundary toward the corresponding ground truth. Then, each RoI can vote those suitable adjacent bounding boxes, which are consistent with this additional information. The voting results are combined with the score of each RoI itself to find a more accurate location from a large number of candidates. Experimental results on the real-world computer vision benchmarks KITTI and the PASCAL2007 vehicle data set show that our approach achieves superior performance in vehicle detection compared with other existing published works.
Wearable Sensors for Reliable Fall Detection

Unintentional falls are a common cause of severe injury in the elderly population. By introducing small, non-invasive sensor motes in conjunction with a wireless network, the Ivy Project aims to provide a path towards more independent living for the elderly. Using a small device worn on the waist and a network of fixed motes in the home environment, we can detect the occurrence of a fall and the location of the victim. Low-cost and low-power MEMS accelerometers are used to detect the fall while RF signal strength is used to locate the person
A Novel Design Method for Resolver-to-Digital Conversion

A simple and novel design method for resolver-to-digital conversion is presented. The proposed method takes advantage of the auxiliary sinusoidal signals generated by addition and subtraction operations of the demodulated signals of the resolver's outputs. A pseudolinear signal is obtained through appropriate mathematical manipulation between the auxiliary sinusoidal signals and the demodulated ones. A calibration strategy is then provided to achieve the highest possible precision. It is shown that the error between the calibrated angular and the true angular position is less than 0.00235° over 0° ~ 360°. The proposed method enables the determination of the angular position without utilizing a lookup table or additional reference signals. Furthermore, the presented strategy is implemented using a field-programmable gate array. Finally, our method is tested on a tracking radar antenna servo system where resolvers are installed on it. Several experiments under different conditions are performed to further verify the effectiveness of the proposed method.
Effiicient BackProp

The convergence of back-propagation learning is analyzed so as to explain common phenomenon observed by practitioners. Many undesirable behaviors of backprop can be avoided with tricks that are rarely exposed in serious technical publications. This paper gives some of those tricks, and offers explanations of why they work. Many authors have suggested that second-order optimization methods are advantageous for neural net training. It is shown that most “classical” second-order methods are impractical for large neural networks. A few methods are proposed that do not have these limitations.
Balanced Neighborhoods for Fairness-Aware Collaborative Recommendation

Recent work on fairness in machine learning has begun to be extended to recommender systems. While there is a tension between the goals of fairness and of personalization, there are contexts in which a global evaluations of outcomes is possible and where equity across such outcomes is a desirable goal. In this paper, we introduce the concept of a balanced neighborhood as a mechanism to preserve personalization in recommendation while enhancing the fairness of recommendation outcomes. We show that a modified version of the SLIM algorithm can be used to improve the balance of user neighborhoods, with the result of achieving greater outcome fairness in a real-world dataset with minimal loss in ranking performance.
Measuring the diffusion of an innovation: A citation analysis

Innovations transform our research traditions and become the driving force to advance individual, group, and social creativity. Meanwhile, interdisciplinary research is increasingly being promoted as a route to advance the complex challenges we face as a society. In this paper, we use Latent Dirichlet Allocation (LDA) citation as a proxy context for the diffusion of an innovation. With an analysis of topic evolution, we divide the diffusion process into five stages: testing and evaluation, implementation, improvement, extending, and fading. Through a correlation analysis of topic and subject, we show the application of LDA in different subjects. We also reveal the cross-boundary diffusion between different subjects based on the analysis of the interdisciplinary studies. Results show that as LDA is transferred into different areas, the adoption of each subject is relatively adjacent to those with similar research interests. Our findings further support researchers’ understanding of the impact formation of innovation.
A Dual-Network Progressive Approach to Weakly Supervised Object Detection

A major challenge that arises in Weakly Supervised Object Detection (WSOD) is that only image-level labels are available, whereas WSOD trains instance-level object detectors. A typical approach to WSOD is to 1) generate a series of region proposals for each image and assign the image-level label to all the proposals in that image; 2) train a classifier using all the proposals; and 3) use the classifier to select proposals with high confidence scores as the positive instances for another round of training. In this way, the image-level labels are iteratively transferred to instance-level labels.
 We aim to resolve the following two fundamental problems within this paradigm. First, existing proposal generation algorithms are not yet robust, thus the object proposals are often inaccurate. Second, the selected positive instances are sometimes noisy and unreliable, which hinders the training at subsequent iterations. We adopt two separate neural networks, one to focus on each problem, to better utilize the specific characteristic of region proposal refinement and positive instance selection. Further, to leverage the mutual benefits of the two tasks, the two neural networks are jointly trained and reinforced iteratively in a progressive manner, starting with easy and reliable instances and then gradually incorporating difficult ones at a later stage when the selection classifier is more robust. Extensive experiments on the PASCAL VOC dataset show that our method achieves state-of-the-art performance.
Reflections on 5 Years of Personal Informatics: Rising Concerns and Emerging Directions

The real world use and design of personal informatics has been increasingly explored in HCI research in the last five years. However, personal informatics research is still a young multidisciplinary area of concern facing unrecognised methodological differences and offering unarticulated design challenges. In this review, we analyse how personal informatics has been approached so far using the Grounded Theory Literature Review method. We identify a (1) psychologically, (2) phenomenologically, and (3) humanistically informed stream and provide guidance on the design of future personal informatics systems by mapping out rising concerns and emerging research directions.
Can mobile augmented reality systems assist in portion estimation? A user study

Accurate assessment of nutrition information is an important part in the prevention and treatment of a multitude of diseases, but remains a challenging task. We present a novel mobile augmented reality application, which assists users in the nutrition assessment of their meals. Using the realtime camera image as a guide, the user overlays a 3D form of the food. Additionally the user selects the food type. The corresponding nutrition information is automatically computed. Thus accurate volume estimation is required for accurate nutrition information assessment. This work presents an evaluation of our mobile augmented reality approaches for portion estimation and offers a comparison to conventional portion estimation approaches. The comparison is performed on the basis of a user study (n=28). The quality of nutrition assessment is measured based on the error in energy units. In the results of the evaluation one of our mobile augmented reality approaches significantly outperforms all other methods. Additionally we present results on the efficiency and effectiveness of the approaches.
End-to-End Memory Networks with Knowledge Carryover for Multi-Turn Spoken Language Understanding

Spoken language understanding (SLU) is a core component of a spoken dialogue system. In the traditional architecture of dialogue systems, the SLU component treats each utterance independent of each other, and then the following components aggregate the multi-turn information in the separate phases. However, there are two challenges: 1) errors from previous turns may be propagated and then degrade the performance of the current turn; 2) knowledge mentioned in the long history may not be carried into the current turn. This paper addresses the above issues by proposing an architecture using end-to-end memory networks to model knowledge carryover in multi-turn conversations, where utterances encoded with intents and slots can be stored as embeddings in the memory and the decoding phase applies an attention model to leverage previously stored semantics for intent prediction and slot tagging simultaneously. The experiments on Microsoft Cortana conversational data show that the proposed memory network architecture can effectively extract salient semantics for modeling knowledge carryover in the multi-turn conversations and outperform the results using the state-of-the-art recurrent neural network framework (RNN) designed for single-turn SLU.
Deep Spatio-Temporal Features for Multimodal Emotion Recognition

Automatic emotion recognition has attracted great interest and numerous solutions have been proposed, most of which focus either individually on facial expression or acoustic information. While more recent research has considered multimodal approaches, individual modalities are often combined only by simple fusion at the feature and/or decision-level. In this paper, we introduce a novel approach using 3-dimensional convolutional neural networks (C3Ds) to model the spatio-temporal information, cascaded with multimodal deep-belief networks (DBNs) that can represent the audio and video streams. Experiments conducted on the eNTERFACE multimodal emotion database demonstrate that this approach leads to improved multimodal emotion recognition performance and significantly outperforms recent state-of-the-art proposals.