nils-herrmann/clean_scirepeval huggingface dataset:
A Model of the Perception of Facial Expressions of Emotion by Humans: Research Overview and Perspectives

In cognitive science and neuroscience, there have been two leading models describing how humans perceive and classify facial expressions of emotion-the continuous and the categorical model. The continuous model defines each facial expression of emotion as a feature vector in a face space. This model explains, for example, how expressions of emotion can be seen at different intensities. In contrast, the categorical model consists of C classifiers, each tuned to a specific emotion category. This model explains, among other findings, why the images in a morphing sequence between a happy and a surprise face are perceived as either happy or surprise but not something in between. While the continuous model has a more difficult time justifying this latter finding, the categorical model is not as good when it comes to explaining how expressions are recognized at different intensities or modes. Most importantly, both models have problems explaining how one can recognize combinations of emotion categories such as happily surprised versus angrily surprised versus surprise. To resolve these issues, in the past several years, we have worked on a revised model that justifies the results reported in the cognitive science and neuroscience literature. This model consists of C distinct continuous spaces. Multiple (compound) emotion categories can be recognized by linearly combining these C face spaces. The dimensions of these spaces are shown to be mostly configural. According to this model, the major task for the classification of facial expressions of emotion is precise, detailed detection of facial landmarks rather than recognition. We provide an overview of the literature justifying the model, show how the resulting model can be employed to build algorithms for the recognition of facial expression of emotion, and propose research directions in machine learning and computer vision researchers to keep pushing the state of the art in these areas. We also discuss how the model can aid in studies of human perception, social interactions and disorders.
Token-based authentication using JSON Web Token on SIKASIR RESTful Web Service

The role of SMEs in the economy of Indonesia is significant. However, the problems faced by the SMEs as less professional management often become an obstacle to the progress of the business. To solve these problems, we need an application that can help small business owners to help manage their business and can also be accessed by a variety of platforms. All these problems can be solved with the application SIKASIR. In order SIKASIR application can be accessed by multiple platforms, it takes a web service that resides on a server. Each application SIKASIR of various platforms to be connected to the service RESTful Web Service so interconnected and synchronized. However, there are some problems when making a multi-platform service, one of them is an authentication problem. In REST architecture, we need authentication methods that are stateless, one of which is a token-based authentication methods. On this occasion, this study will use JSON Web Token.  Web Token is the new industry standard based (RFC 7519).  Web Token is stateless, so it is suitable to be implemented on a RESTful Web Service application. In this research will be discussed how to implement authentication technique using token-based authentication method with a JSON Web Token, which will produce a secure authentication and make web service SIKASIR be multi-platform.
Workspace augmentation of spatial 3-DOF cable parallel robots using differential actuation

In this paper, it is proposed to use spatial differentials instead of independently actuated cables to drive cable robots. Spatial cable differentials are constituted of several cables attaching the moving platform to the base but all of these cables are pulled by the same actuator through a differential system. To this aim, cable differentials with both planar and spatial architectures are first described in this work and then, their resultant properties on the force distribution is presented. Next, a special cable differential is selected and used to design the architecture of two incompletely and fully restrained robots. Finally, by comparing the workspaces of these robots with their classically actuated counterparts, the advantage of using differentials on their wrench-closure and wrench-feasible workspaces is illustrated.
A Novel Remote-Center-of-Motion Serial Manipulator for Inner Ear Drug Delivery

To improve the usefulness of actuators dedicated to micrometric applications, a compact and accurate positioning mechanism for inner ear drug delivery is proposed in this paper. This serial novel robotic system is able to position a magnetic actuator based on permanent magnets, used as an end-effector of this robotic manipulator for steering magnetic microrobot throughout the cochlea. The serial kinematics is based on two rotations and one translation which axes intersect at the center of the cochlea that is the remote center of rotation (RCM). The conceptual design of this system which takes account of the medical and geometrical constraints is presented in this paper. Direct and inverse kinematics are developed using the classical mathematical tools of serial robotics. This, besides a finite-element analysis of the structure and motors sizing. The stiff and highly compact manipulator allows saving space while guaranteeing the safety of the patient.
Convolutional Normalizing Flows

Bayesian posterior inference is prevalent in various machine learning problems. Variational inference provides one way to approximate the posterior distribution, however its expressive power is limited and so is the accuracy of resulting approximation. Recently, there has a trend of using neural networks to approximate the variational posterior distribution due to the flexibility of neural network architecture. One way to construct flexible variational distribution is to warp a simple density into a complex by normalizing flows, where the resulting density can be analytically evaluated. However, there is a trade-off between the flexibility of normalizing flow and computation cost for efficient transformation. In this paper, we propose a simple yet effective architecture of normalizing flows, ConvFlow, based on convolution over the dimensions of random input vector. Experiments on synthetic and real world posterior inference problems demonstrate the effectiveness and efficiency of the proposed method.
Antibiotic perturbation of the murine gut microbiome enhances the adiposity, insulin resistance, and liver disease associated with high-fat diet

Obesity, type 2 diabetes, and non-alcoholic fatty liver disease (NAFLD) are serious health concerns, especially in Western populations. Antibiotic exposure and high-fat diet (HFD) are important and modifiable factors that may contribute to these diseases. To investigate the relationship of antibiotic exposure with microbiome perturbations in a murine model of growth promotion, C57BL/6 mice received lifelong sub-therapeutic antibiotic treatment (STAT), or not (control), and were fed HFD starting at 13 weeks. To characterize microbiota changes caused by STAT, the V4 region of the 16S rRNA gene was examined from collected fecal samples and analyzed. In this model, which included HFD, STAT mice developed increased weight and fat mass compared to controls. Although results in males and females were not identical, insulin resistance and NAFLD were more severe in the STAT mice. Fecal microbiota from STAT mice were distinct from controls. Compared with controls, STAT exposure led to early conserved diet-independent microbiota changes indicative of an immature microbial community. Key taxa were identified as STAT-specific and several were found to be predictive of disease. Inferred network models showed topological shifts concurrent with growth promotion and suggest the presence of keystone species. These studies form the basis for new models of type 2 diabetes and NAFLD that involve microbiome perturbation.
How to Make Zuse's Z3 a Universal Computer

The computing machine Z3, buHt by Konrad Zuse from 1938 to 1941, could only execute fixed sequences of floating-point arithmetical operations (addition, subtraction, multiplication, division and square root) coded in a punched tape. We show in this paper that a single program loop containing this type of instructions can simulate any Turing machine whose tape is of bounded size. This is achieved by simulating conditional branching and indirect addressing by purely arithmetical means. Zuse's Z3 is therefore, at least in principle, as universal as today's computers which have a bounded memory size. This result is achieved at the cost of blowing up the size of the program stored on punched tape. Universal Machines and Single Loops Nobody has ever built a universal computer. The reason is that a universal computer consists, in theory, of a fixed processor and a memory of unbounded size. This is the case of Turing machines with their unbounded tapes. In the theory of general recursive functions there is also a small set of rules and some predefined functions, but there is no upper bound on the size of intermediate reduction terms. Modern computers are only potentially universal: They can perform any computation that a Turing machine with a bounded tape can perform. If more storage is required, more can be added without having to modify the processor (provided that the extra memory is still addressable).
Candidate Selection for Large Scale Personalized Search and Recommender Systems

Modern day social media search and recommender systems require complex query formulation that incorporates both user context and their explicit search queries. Users expect these systems to be fast and provide relevant results to their query and context. With millions of documents to choose from, these systems utilize a multi-pass scoring function to narrow the results and provide the most relevant ones to users. Candidate selection is required to sift through all the documents in the index and select a relevant few to be ranked by subsequent scoring functions. It becomes crucial to narrow down the document set while maintaining relevant ones in resulting set. In this tutorial we survey various candidate selection techniques and deep dive into case studies on a large scale social media platform. In the later half we provide hands-on tutorial where we explore building these candidate selection models on a real world dataset and see how to balance the tradeoff between relevance and latency.
An RF scanning receiver based on photonics for electronic warfare applications

We present the design and simulative results of an innovative RF receiver based on photonic techniques that quickly scans the RF band of interest in warfare applications, avoiding the spectrum channelization and thus reducing the receiver SWaP. The scheme is based on a stable mode-locked laser and a specifically designed tunable optical filter, and it simultaneously samples, filters, and down-converts the detected spectrum. The analysis confirms the scheme capability for detecting signals beyond 18GHz, with instantaneous bandwidth >1GHz and dynamic range >40dB. The scheme implementation with integrated photonics technologies will ensure fast scanning (filter repositioning in <;100ns), high environmental stability, and reduced SWaP, meeting the increasingly stringent requirements of electronic warfare applications.
Visuomotor Coupling Shapes the Functional Development of Mouse Visual Cortex

The emergence of sensory-guided behavior depends on sensorimotor coupling during development. How sensorimotor experience shapes neural processing is unclear. Here, we show that the coupling between motor output and visual feedback is necessary for the functional development of visual processing in layer 2/3 (L2/3) of primary visual cortex (V1) of the mouse. Using a virtual reality system, we reared mice in conditions of normal or random visuomotor coupling. We recorded the activity of identified excitatory and inhibitory L2/3 neurons in response to transient visuomotor mismatches in both groups of mice. Mismatch responses in excitatory neurons were strongly experience dependent and driven by a transient release from inhibition mediated by somatostatin-positive interneurons. These data are consistent with a model in which L2/3 of V1 computes a difference between an inhibitory visual input and an excitatory locomotion-related input, where the balance between these two inputs is finely tuned by visuomotor experience.
Fast Data Anonymization with Low Information Loss

Recent research studied the problem of publishing microdata without revealing sensitive information, leading to the privacy preserving paradigms of k-anonymity and `-diversity. k-anonymity protects against the identification of an individual’s record. `-diversity, in addition, safeguards against the association of an individual with specific sensitive information. However, existing approaches suffer from at least one of the following drawbacks: (i) The information loss metrics are counter-intuitive and fail to capture data inaccuracies inflicted for the sake of privacy. (ii) `-diversity is solved by techniques developed for the simpler k-anonymity problem, which introduces unnecessary inaccuracies. (iii) The anonymization process is inefficient in terms of computation and I/O cost. In this paper we propose a framework for efficient privacy preservation that addresses these deficiencies. First, we focus on one-dimensional (i.e., single attribute) quasiidentifiers, and study the properties of optimal solutions for k-anonymity and `-diversity, based on meaningful information loss metrics. Guided by these properties, we develop efficient heuristics to solve the one-dimensional problems in linear time. Finally, we generalize our solutions to multi-dimensional quasi-identifiers using space-mapping techniques. Extensive experimental evaluation shows that our techniques clearly outperform the state-of-the-art, in terms of execution time and information loss.
CWS: a model-driven scheduling policy for correlated workloads

We define CWS, a non-preemptive scheduling policy for workloads with correlated job sizes. CWS tackles the scheduling problem by inferring the expected sizes of upcoming jobs based on the structure of correlations and on the outcome of past scheduling decisions. Size prediction is achieved using a class of Hidden Markov Models (HMM) with continuous observation densities that describe job sizes. We show how the forward-backward algorithm of HMMs applies effectively in scheduling applications and how it can be used to derive closed-form expressions for size prediction. This is particularly simple to implement in the case of observation densities that are phase-type (PH-type) distributed, where existing fitting methods for Markovian point processes may also simplify the parameterization of the HMM workload model.
 Based on the job size predictions, CWS emulates size-based policies which favor short jobs, with accuracy depending mainly on the HMM used to parametrize the scheduling algorithm. Extensive simulation and analysis illustrate that CWS is competitive with policies that assume exact information about the workload.
A Model Driven Approach Accelerating Ontology-based IoT Applications Development

The Internet of Things promises several exciting opportunities and added value services in several industrial contexts. Such opportunities are enabled by the interconnectivity and cooperation between various things. However, these promises are still facing the interoperability challenge. Semantic technology and linked data are well positioned to tackle the heterogeneity problem. Several efforts contributed to the development of ontology editors and tools for storing and querying linked data. However, despite the potential and the promises, semantic technology remains in the hands of the few, a minority of experts. In this paper, we propose a model driven methodology and a software module (OLGA) that completes existing ontology development libraries and frameworks in order to accelerate the adoption of ontology-based IoT application development. We validated our approach using the ETSI SAREF ontology.
Distributed Liveness: Understanding How New Technologies Transform Performance Experiences

We identify emerging phenomena of distributed liveness, involving new relationships among performers, audiences, and technology. Liveness is a recent, technology-based construct, which refers to experiencing an event in real-time with the possibility for shared social realities. Distributed liveness entails multiple forms of physical, spatial, and social co-presence between performers and audiences across physical and virtual spaces. We interviewed expert performers about how they experience liveness in physically co-present and distributed settings. Findings show that distributed performances and technology need to support flexible social co-presence and new methods for sensing subtle audience responses and conveying engagement abstractly.
An Incentive-Compatible Routing Protocol for Two-Hop Delay-Tolerant Networks

Delay-tolerant networks (DTNs) rely on the mobility of nodes and their contacts to make up with the lack of continuous connectivity and, thus, enable message delivery from source to destination in a “store-carry-forward” fashion. Since message delivery consumes resource such as storage and power, some nodes may choose not to forward or carry others' messages while relying on others to deliver their locally generated messages. These kinds of selfish behaviors may hinder effective communications over DTNs. In this paper, we present an efficient incentive-compatible (IC) routing protocol (ICRP) with multiple copies for two-hop DTNs based on the algorithmic game theory. It takes both the encounter probability and transmission cost into consideration to deal with the misbehaviors of selfish nodes. Moreover, we employ the optimal sequential stopping rule and Vickrey-Clarke-Groves (VCG) auction as a strategy to select optimal relay nodes to ensure that nodes that honestly report their encounter probability and transmission cost can maximize their rewards. We attempt to find the optimal stopping time threshold adaptively based on realistic probability model and propose an algorithm to calculate the threshold. Based on this threshold, we propose a new method to select relay nodes for multicopy transmissions. To ensure that the selected relay nodes can receive their rewards securely, we develop a signature scheme based on a bilinear map to prevent the malicious nodes from tampering. Through simulations, we demonstrate that ICRP can effectively stimulate nodes to forward/carry messages and achieve higher packet delivery ratio with lower transmission cost.
The combination of context information to enhance simple question answering

With the rapid development of knowledge base, question answering based on knowledge base has been a hot research issue. In this paper, we focus on answering singlerelation factoid questions based on knowledge base. We build a question answering system and study the effect of context information on fact selection, such as entity’s notable type, outdegree. Experimental results show that context information can improve the result of simple question answering.
Architecture and design flow of Tele-Health Monitoring system using STM32 platform

In recent years, with the technological advancements in healthcare electronics, we see a number of digital data acquisition devices that can monitor certain body parameters of a patient and alert the concerned person in case of an emergency. However, if one desires to monitor multiple body parameters, they are forced to use multiple data acquisition devices. This determines the need for a platform that will allow users to configure their own digital data acquisition device and monitor their health parameters in real-time. This paper discusses the architecture and design flow of a Tele-Health Monitoring (THM) platform using effective usage of the computation power and various inbuilt peripherals of STM32 microcontroller that will allow users to have a reliable and interactive health monitoring system. The proposed platform provides a channel to interface various health sensors and data acquisition devices such that the individual himself or an authorized health provider can monitor and analyze the physical activity of an individual on regular basis. This platform finds great use in cases where patients are under transportation, homecare or frequent health checkup.
Papular acantholytic dyskeratosis of the vulva.

We describe a 63-year-old woman with an asymptomatic papular eruption on the vulva. Clinically, the lesions showed multiple pin-head-sized whitish papules on the labia major. Histologically, the biopsy specimen showed acantholysis throughout the epidermis with the presence of dyskeratotic cells resembling corps ronds and grains, hyperkeratosis and parakeratosis. These clinical and histological findings were consistent with the diagnosis of papular acantholytic dyskeratosis of the vulva which is a rare disorder, first described in 1984.
Learning Attention from Multi-Modal Imaging and Text : Application for Lesion Localisation in DWI

Strokes are one of the leading causes of death and disability in the UK. There are two main types of stroke: ischemic and hemorrhagic, with the majority of stroke patients suffering from the former. During an ischemic stroke, parts of the brain lose blood supply, and if not treated immediately, can lead to irreversible tissue damage and even death. Ischemic lesions can be detected by diffusion weighted magnetic resonance imaging (DWI), but localising and quantifying these lesions can be a time consuming task for clinicians. Work has already been done in training neural networks to segment these lesions, but these frameworks require a large amount of manually segmented 3D images, which are very time consuming to create. We instead propose to use past examinations of stroke patients which consist of DWIs, corresponding radiological reports and diagnoses in order to develop a learning framework capable of localising lesions. This is motivated by the fact that the reports summarise the presence, type and location of the ischemic lesion for each patient, and thereby provide more context than a single diagnostic label. Acute lesions prediction is aided by an attention mechanism which implicitly learns which regions within the DWI are most relevant to the classification.
Real-Time Color Coded Object Detection Using a Modular Computer Vision Library

In this paper we present a novel computer vision library called UAVision that provides support for different digital cameras technologies, from image acquisition to camera calibration, and all the necessary software for implementing an artificial vision system for the detection of color-coded objects. The algorithms behind the object detection focus on maintaining a low processing time, thus the library is suited for real-world real-time applications. The library also contains a TCP Communications Module, with broad interest in robotic applications where the robots are performing remotely from a basestation or from an user and there is the need to access the images acquired by the robot, both for processing or debug purposes. Practical results from the implementation of the same software pipeline using different cameras as part of different types of vision systems are presented. The vision system software pipeline that we present is designed to cope with application dependent time constraints. The experimental results show that using the UAVision library it is possible to use digital cameras at frame rates up to 50 frames per second when working with images of size up to 1 megapixel. Moreover, we present experimental results to show the effect of the frame rate in the delay between the perception of the world and the action of an autonomous robot, as well as the use of raw data from the camera sensor and the implications of this in terms of the referred delay.