nils-herrmann/clean_scirepeval huggingface dataset:
A Smart Fridge with an Ability to Enhance Health and Enable Better Nutrition

Intelligent appliances with multimedia capability have been emerging into our daily life, thanks to the fast advance of computing technology and the wide use of the Interne. Smart home is one of the most prominent areas of intelligent appliances. Kitchen is one of the places where such intelligent appliances have been used. Since modern life style is driving people spending less time on cooking healthy food at home, an enjoyable and healthy life style can be assisted with an intelligent kitchenware such as a smart fridge. In this paper we introduce a novel idea of the smart fridge which can enable better nutrition and enhance health. It is designed for managing items stored in it and advising its users with cooking methods depending on what kind of food is stored. More importantly, it can perform other actions such as dietary control, nutrition monitoring, eating habit analysis, etc. The characteristics, functions and design details of the smart fridge are presented in details. We are confident that such a smart fridge will be an important component in future smart home.
Supervised and Unsupervised Intrusion Detection Based on CAN Message Frequencies for In-vehicle Network

Modern vehicles are equipped with Electronic Control Units (ECUs) and external communication devices. The Controller Area Network (CAN), a widely used communication protocol for ECUs, does not have a security mechanism to detect improper packets; if attackers exploit the vulnerability of an ECU and manage to inject a malicious message, they are able to control other ECUs to cause improper operation of the vehicle. With the increasing popularity of connected cars, it has become an urgent matter to protect in-vehicle networks against security threats. In this paper, we study the applicability of statistical anomaly detection methods for identifying malicious CAN messages in invehicle networks. We focus on intrusion attacks of malicious messages. Because the occurrence of an intrusion attack certainly influences the message traffic, we focus on the number of messages observed in a fixed time window to detect intrusion attacks. We formalize features to represent a message sequence that incorporates the number of messages associated with each receiver ID. We collected CAN message data from an actual vehicle and conducted a quantitative analysis of the methods and the features in practical situations. The results of our experiments demonstrated our proposed methods provide fast and accurate detection in various cases.
Multimodal emotion recognition in speech-based interaction using facial expression, body gesture and acoustic analysis

In this paper a study on multimodal automatic emotion recognition during a speech-based interaction is presented. A database was constructed consisting of people pronouncing a sentence in a scenario where they interacted with an agent using speech. Ten people pronounced a sentence corresponding to a command while making 8 different emotional expressions. Gender was equally represented, with speakers of several different native languages including French, German, Greek and Italian. Facial expression, gesture and acoustic analysis of speech were used to extract features relevant to emotion. For the automatic classification of unimodal data, bimodal data and multimodal data, a system based on a Bayesian classifier was used. After performing an automatic classification of each modality, the different modalities were combined using a multimodal approach. Fusion of the modalities at the feature level (before running the classifier) and at the results level (combining results from classifier from each modality) were compared. Fusing the multimodal data resulted in a large increase in the recognition rates in comparison to the unimodal systems: the multimodal approach increased the recognition rate by more than 10% when compared to the most successful unimodal system. Bimodal emotion recognition based on all combinations of the modalities (i.e., ‘face-gesture’, ‘face-speech’ and ‘gesture-speech’) was also investigated. The results show that the best pairing is ‘gesture-speech’. Using all three modalities resulted in a 3.3% classification improvement over the best bimodal results.
Punctuation Prediction with Transition-based Parsing

Punctuations are not available in automatic speech recognition outputs, which could create barriers to many subsequent text processing tasks. This paper proposes a novel method to predict punctuation symbols for the stream of words in transcribed speech texts. Our method jointly performs parsing and punctuation prediction by integrating a rich set of syntactic features when processing words from left to right. It can exploit a global view to capture long-range dependencies for punctuation prediction with linear complexity. The experimental results on the test data sets of IWSLT and TDT4 show that our method can achieve high-level performance in punctuation prediction over the stream of words in transcribed speech text.
Efficiency-Oriented Design of ZVS Half-Bridge Series Resonant Inverter With Variable Frequency Duty Cycle Control

The efficiency of zero voltage switching half-bridge series resonant inverter can be decreased under certain load conditions due to the high switching frequencies required. The proposed variable frequency duty cycle (VFDC) control is intended to improve the efficiency in the medium and low output power levels because of the decreased switching frequencies. The study performed in this letter includes, in a first step, a theoretical analysis of power balance as a function of control parameters. In addition, restrictions due to snubber capacitors and deadtime, and variability of the loads have been considered. Afterward, an efficiency analysis has been carried out to determine the optimum operation point. Switching and conduction losses have been calculated to examine the overall efficiency improvement. VFDC strategy efficiency improvement is achieved by means of a switching-frequency reduction, mainly at low-medium power range, and with low-quality factor loads. Domestic induction heating application is suitable for the use of VFDC strategy due to its special load characteristics. For this reason, the simulation results have been validated using an induction heating inverter with a specially designed load.
Detection of Influential Observation in Linear Regression

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Low-power manycore accelerator for personalized biomedical applications

Wearable personal health monitoring systems can offer a cost effective solution for human healthcare. These systems must provide both highly accurate, secured and quick processing and delivery of vast amount of data. In addition, wearable biomedical devices are used in inpatient, outpatient, and at home e-Patient care that must constantly monitor the patient's biomedical and physiological signals 24/7. These biomedical applications require sampling and processing multiple streams of physiological signals with strict power and area footprint. The processing typically consists of feature extraction, data fusion, and classification stages that require a large number of digital signal processing and machine learning kernels. In response to these requirements, in this paper, a low-power, domain-specific many-core accelerator named Power Efficient Nano Clusters (PENC) is proposed to map and execute the kernels of these applications. Experimental results show that the manycore is able to reduce energy consumption by up to 80% and 14% for DSP and machine learning kernels, respectively, when optimally parallelized. The performance of the proposed PENC manycore when acting as a coprocessor to an Intel Atom processor is compared with existing commercial off-the-shelf embedded processing platforms including Intel Atom, Xilinx Artix-7 FPGA, and NVIDIA TK1 ARM-A15 with GPU SoC. The results show that the PENC manycore architecture reduces the energy by as much as 10X while outperforming all off-the-shelf embedded processing platforms across all studied machine learning classifiers.
A brain-computer interface for high-level remote control of an autonomous, reinforcement-learning-based robotic system for reaching and grasping

We present an Internet-based brain-computer interface (BCI) for controlling an intelligent robotic device with autonomous reinforcement-learning. BCI control was achieved through dry-electrode electroencephalography (EEG) obtained during imaginary movements. Rather than using low-level direct motor control, we employed a high-level control scheme of the robot, acquired via reinforcement learning, to keep the users cognitive load low while allowing control a reaching-grasping task with multiple degrees of freedom. High-level commands were obtained by classification of EEG responses using an artificial neural network approach utilizing time-frequency features and conveyed through an intuitive user interface. The novel ombination of a rapidly operational dry electrode setup, autonomous control and Internet connectivity made it possible to conveniently interface subjects in an EEG laboratory with remote robotic devices in a closed-loop setup with online visual feedback of the robots actions to the subject. The same approach is also suitable to provide home-bound patients with the possibility to control state-of-the-art robotic devices currently confined to a research environment. Thereby, our BCI approach could help severely paralyzed patients by facilitating patient-centered research of new means of communication, mobility and independence.
Modeling Documents with Deep Boltzmann Machines

We introduce a type of Deep Boltzmann Machine (DBM) that is suitable for extracting distributed semantic representations from a large unstructured collection of documents. We overcome the apparent difficulty of training a DBM with judicious parameter tying. This enables an efficient pretraining algorithm and a state initialization scheme for fast inference. The model can be trained just as efficiently as a standard Restricted Boltzmann Machine. Our experiments show that the model assigns better log probability to unseen data than the Replicated Softmax model. Features extracted from our model outperform LDA, Replicated Softmax, and DocNADE models on document retrieval and document classification tasks.
On Power Quality of Variable-Speed Constant-Frequency Aircraft Electric Power Systems

In this paper, a comprehensive model of the variable-speed constant-frequency aircraft electric power system is developed to study the performance characteristics of the system and, in particular, the system power quality over a frequency range of operation of 400 Hz to 800 Hz. A fully controlled active power filter is designed to regulate the load terminal voltage, eliminate harmonics, correct supply power factor, and minimize the effect of unbalanced loads. The control algorithm for the active power filter (APF) is based on the perfect harmonic cancellation method which provides a three-phase reference supply current in phase with its positive-sequence fundamental voltage. The proposed APF is integrated into the model of a 90-kVA advanced aircraft electric power system under VSCF operation. The performance characteristics of the system are studied with the frequency of the generator's output voltage varied from 400 Hz to 800 Hz under different loading conditions. Several case studies are presented including dc loads as well as passive and dynamic ac loads. The power quality characteristics of the studied aircraft electric power system with the proposed active filter are shown to be in compliance with the most recent military aircraft electrical standards MIL-STD-704F as well as with the IEEE Std. 519.
A New SiC Trench MOSFET Structure With Protruded p-Base for Low Oxide Field and Enhanced Switching Performance

The high OFF-state oxide field in the SiC trench MOSFET is a threat for its long term reliability, and thus hinders the wide acceptance of the SiC trench MOSFETs. In this paper, an SiC trench MOSFET with protruded p-bases (PB-MOS) is proposed, which features protruded p-bases to shield the gate oxide at the trench bottom against the high OFF-state drain voltage. Numerical device simulations based on Sentaurus TCAD verify the benefits of the structure. The OFF-state oxide field (<inline-formula> <tex-math notation="LaTeX">${ {E}}_{\text {ox-m}}$ </tex-math></inline-formula>) in the PB-MOS is 1.7 MV/cm, which is dramatically lower compared to the high <inline-formula> <tex-math notation="LaTeX">${ {E}}_{\text {ox-m}}$ </tex-math></inline-formula> of 8.6 MV/cm in the conventional trench MOSFET (C-MOS). The above benefit is achieved without sacrificing device performances. The reverse transfer capacitance (<inline-formula> <tex-math notation="LaTeX">${ {C}}_{\text{rss}}$ </tex-math></inline-formula>) of the PB-MOS is around ten times lower than that in the C-MOS. Both the gate charge (<inline-formula> <tex-math notation="LaTeX">${ {Q}}_{\text{G}}$ </tex-math></inline-formula>) and the gate-to-drain charge (<inline-formula> <tex-math notation="LaTeX">${ {Q}}_{\text{GD}}$ </tex-math></inline-formula>) of the PB-MOS are significantly improved compared to the C-MOS. A low specific ON-resistance (<inline-formula> <tex-math notation="LaTeX">${ {R}}_{\text{ON}}$ </tex-math></inline-formula>) is maintained in the PB-MOS by using additional JFET doping to compensate the JFET effect. As a result, the PB-MOS presents much better figures of merit <inline-formula> <tex-math notation="LaTeX">${ {Q}}_{\text{G}} \cdot { {R}}_{\text{ON}}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">${ {Q}}_{\text{GD}} \cdot { {R}}_{\text{ON}}$ </tex-math></inline-formula> than those of the C-MOS. The PB-MOS achieves a much faster switching speed than the C-MOS, and consequently exhibits an appreciable reduction in the switching energy loss.
Virtual reality-enhanced stroke rehabilitation

A personal computer (PC)-based desktop virtual reality (VR) system was developed for rehabilitating hand function in stroke patients. The system uses two input devices, a CyberGlove and a Rutgers Master II-ND (RMII) force feedback glove, allowing user interaction with a virtual environment. This consists of four rehabilitation routines, each designed to exercise one specific parameter of hand movement: range, speed, fractionation or strength. The use of performance-based target levels is designed to increase patient motivation and individualize exercise difficulty to a patient's current state. Pilot clinical trials have been performed using the above system combined with noncomputer tasks, such as pegboard insertion or tracing of 2D patterns. Three chronic stroke patients used this rehabilitation protocol daily for two weeks. Objective measurements showed that each patient showed improvement on most of the hand parameters over the course of the training. Subjective evaluation by the patients was also positive. This technical report focuses on this newly developed technology for VR rehabilitation.
Entity Linking: An Issue to Extract Corresponding Entity With Knowledge Base

Entity linking is a task to extract query mentions in documents, and then link them to their corresponding entities in a knowledge base. It can improve the performances of knowledge network construction, knowledge fusion, information retrieval, natural language processing, and knowledge base population. In this paper, we introduce the difficulties and applications of entity linking and focus on the main methods to address this issue. At last, we list the knowledge bases, data sets, and the evaluation criterion and some challenges of entity linking.
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume

We present a compact but effective CNN model for optical flow, called PWC-Net. PWC-Net has been designed according to simple and well-established principles: pyramidal processing, warping, and the use of a cost volume. Cast in a learnable feature pyramid, PWC-Net uses the current optical flow estimate to warp the CNN features of the second image. It then uses the warped features and features of the first image to construct a cost volume, which is processed by a CNN to estimate the optical flow. PWC-Net is 17 times smaller in size and easier to train than the recent FlowNet2 model. Moreover, it outperforms all published optical flow methods on the MPI Sintel final pass and KITTI 2015 benchmarks, running at about 35 fps on Sintel resolution (1024 Ã— 436) images. Our models are available on our project website.
PccP: A model for Preserving cloud computing Privacy

The widespread focus on the Cloud Computing has necessitated the corresponding mechanisms to ensure privacy and security. Various attempts have been made in the past to safeguard the privacy of the individual or agency trying to utilize the services being provided by the cloud. The most challenging task is to provide services to the users while also preserving the privacy of the user's information. In this paper a model that incorporates a three-level architecture, Preserving cloud computing Privacy (PccP) model is proposed which aims to preserve privacy of information pertaining to cloud users. The Consumer Layer deals with all the aspects which relate to enabling the user of the cloud to access the cloud services being provided by the cloud service provider. The Network Interface Layer creates an appropriate mapping between the original IP addresses of the users with a modified IP address, and thereby ensuring the privacy of the IP address of the users. The Privacy Preserved Layer utilizes the functionality of the Unique User Cloud Identity Generator for which an algorithm is proposed in this paper to generate an unique User Service Dependent Identity(USID) with privacy check by establishing mapping among the existing user identity(ID), if any to ID's available in a pool of User ID's to enhance the privacy of sensitive user information. A Privacy check method based on information privacy is being proposed which contributes significantly in maintaining user control over the generated user identities (USID's).
Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control

Deep reinforcement learning (RL) algorithms can learn complex robotic skills from raw sensory inputs, but have yet to achieve the kind of broad generalization and applicability demonstrated by deep learning methods in supervised domains. We present a deep RL method that is practical for real-world robotics tasks, such as robotic manipulation, and generalizes effectively to never-before-seen tasks and objects. In these settings, ground truth reward signals are typically unavailable, and we therefore propose a self-supervised model-based approach, where a predictive model learns to directly predict the future from raw sensory readings, such as camera images. At test time, we explore three distinct goal specification methods: designated pixels, where a user specifies desired object manipulation tasks by selecting particular pixels in an image and corresponding goal positions, goal images, where the desired goal state is specified with an image, and image classifiers, which define spaces of goal states. Our deep predictive models are trained using data collected autonomously and continuously by a robot interacting with hundreds of objects, without human supervision. We demonstrate that visual MPC can generalize to never-before-seen objects—both rigid and deformable—and solve a range of user-defined object manipulation tasks using the same model.
Multi-Modal Fashion Product Retrieval

Finding a product in the fashion world can be a daunting task. Everyday, e-commerce sites are updating with thousands of images and their associated metadata (textual information), deepening the problem. In this paper, we leverage both the images and textual metadata and propose a joint multi-modal embedding that maps both the text and images into a common latent space. Distances in the latent space correspond to similarity between products, allowing us to effectively perform retrieval in this latent space. We compare against existing approaches and show significant improvements in retrieval tasks on a largescale e-commerce dataset.
A Protocol for Preventing Insider Attacks in Untrusted Infrastructure-as-a-Service Clouds

Recent technical advances in utility computing have allowed small and medium sized businesses to move their applications to the cloud, to benefit from features such as auto-scaling and pay-as-you-go facilities. Before clouds are widely adopted, there is a need to address privacy concerns of customer data outsourced to these platforms. In this paper, we present a practical approach for protecting the confidentiality and integrity of client data and computation from insider attacks such as cloud clients as well as from the Infrastructure-as-a-Service (IaaS) based cloud system administrator himself. We demonstrate a scenario of how the origin integrity and authenticity of health-care multimedia content processed on the cloud can be verified using digital watermarking in an isolated environment without revealing the watermark details to the cloud administrator. Finally to verify that our protocol does not compromise confidentiality and integrity of the client data and computation or degrade performance, we have tested a prototype system using two different approaches. Formal verification using ProVerif tool shows that cryptographic operations and protocol communication cannot be compromised using a realistic attacker model. Performance analysis of our implementation demonstrates that it adds negligible overhead.