nils-herrmann/clean_scirepeval huggingface dataset:
Spherical Latent Spaces for Stable Variational Autoencoders

A hallmark of variational autoencoders (VAEs) for text processing is their combination of powerful encoder-decoder models, such as LSTMs, with simple latent distributions, typically multivariate Gaussians. These models pose a difficult optimization problem: there is an especially bad local optimum where the variational posterior always equals the prior and the model does not use the latent variable at all, a kind of “collapse” which is encouraged by the KL divergence term of the objective. In this work, we experiment with another choice of latent distribution, namely the von Mises-Fisher (vMF) distribution, which places mass on the surface of the unit hypersphere. With this choice of prior and posterior, the KL divergence term now only depends on the variance of the vMF distribution, giving us the ability to treat it as a fixed hyperparameter. We show that doing so not only averts the KL collapse, but consistently gives better likelihoods than Gaussians across a range of modeling conditions, including recurrent language modeling and bag-ofwords document modeling. An analysis of the properties of our vMF representations shows that they learn richer and more nuanced structures in their latent representations than their Gaussian
BeeHive Lab project - sensorized hive for bee colonies life study

The rearing of bees is a quite difficult job since it requires experience and time. Beekeepers are used to take care of their bee colonies observing them and learning to interpret their behavior. Despite the rearing of bees represents one of the most antique human habits, nowadays bees risk the extinction principally because of the increasing pollution levels related to human activity. It is important to increase our knowledge about bees in order to develop new practices intended to improve their protection. These practices could include new technologies, in order to increase profitability of beekeepers and economical interest related to bee rearing, but also innovative rearing techniques, genetic selections, environmental politics and so on. Moreover bees, since they are very sensitive to pollution, are considered environmental indicators, and the research on bees could give important information about the conditions of soil, air and water. In this paper we propose a real hardware and software solution for apply the internet-of-things concept to bees in order to help beekeepers to improve their business and collect data for research purposes.
A Low-Power Processor With Configurable Embedded Machine-Learning Accelerators for High-Order and Adaptive Analysis of Medical-Sensor Signals

Low-power sensing technologies have emerged for acquiring physiologically indicative patient signals. However, to enable devices with high clinical value, a critical requirement is the ability to analyze the signals to extract specific medical information. Yet given the complexities of the underlying processes, signal analysis poses numerous challenges. Data-driven methods based on machine learning offer distinct solutions, but unfortunately the computations are not well supported by traditional DSP. This paper presents a custom processor that integrates a CPU with configurable accelerators for discriminative machine-learning functions. A support-vector-machine accelerator realizes various classification algorithms as well as various kernel functions and kernel formulations, enabling range of points within an accuracy-versus-energy and -memory trade space. An accelerator for embedded active learning enables prospective adaptation of the signal models by utilizing sensed data for patient-specific customization, while minimizing the effort from human experts. The prototype is implemented in 130-nm CMOS and operates from 1.2 V-0.55 V (0.7 V for SRAMs). Medical applications for EEG-based seizure detection and ECG-based cardiac-arrhythmia detection are demonstrated using clinical data, while consuming 273 μJ and 124 μJ per detection, respectively; this represents 62.4&times; and 144.7&times; energy reduction compared to an implementation based on the CPU. A patient-adaptive cardiac-arrhythmia detector is also demonstrated, reducing the analysis-effort required for model customization by 20 &times;.
FCLT - A Fully-Correlational Long-Term Tracker

We propose FCLT – a fully-correlational long-term tracker. The two main components of FCLT are a shortterm tracker which localizes the target in each frame and a detector which re-detects the target when it is lost. Both the short-term tracker and the detector are based on correlation filters. The detector exploits properties of the recent constrained filter learning and is able to re-detect the target in the whole image efficiently. A failure detection mechanism based on correlation response quality is proposed. The FCLT is tested on recent short-term and long-term benchmarks. It achieves state-of-the-art results on the short-term benchmarks and it outperforms the current best-performing tracker on the long-term benchmark by over 18%.
Vesp’R: design and evaluation of a handheld AR device

This paper focuses on the design of devices for handheld spatial interaction. In particular, it addresses the requirements and construction of a new platform for interactive AR, described from an ergonomics stance, prioritizing human factors of spatial interaction. The result is a multi-configurable platform for spatial interaction, evaluated in two AR application scenarios. The user tests validate the design with regards to grip, weight balance and control allocation, and provide new insights on the human factors involved in handheld spatial interaction.
Second generation pleated pneumatic artificial muscle and its robotic applications

This paper reports on the second generation of the Pleated Pneumatic Artificial Muscle (PPAM) which has been developed to extend the life span of its first prototype. This type of artificial was developed to overcome dry friction and material deformation which is present in the widely used McKibben type of artificial muscle. The essence of the PPAM is its pleated membrane structure which enables the muscle to work at low pressures and at large contractions. There is a growing interest in this kind of actuation for robotics applications due to its high power to weight ratio and the adaptable compliance, especially for legged locomotion and robot applications in direct contact with a human. This paper describes the design of the second generation PPAM, for which specifically the membrane layout has been changed. In function of this new layout the mathematical model, developed for the first prototype, has been reformulated. This paper gives an elaborate discussion on this mathematical model which represents the force generation and enclosed muscle volume. Static load tests on some real muscles, which have been carried out in order to validate the mathematical model, are then discussed. Furthermore are given two robotic applications which currently use these pneumatic artificial muscles. One is the biped Lucy and the another one is a manipulator application which works in direct contact with an operator.
Transverse flux machines with distributed windings for in-wheel applications

Transverse flux machine (TFM) useful for in-wheel motor applications is presented. This transverse flux permanent magnet motor is designed to achieve high torque-to-weight ratio and is suitable for direct-drive wheel applications. As in conventional TFM, the phases are located under each other, which will increase the axial length of the machine. The idea of this design is to reduce the axial length of TFM, by placing the windings around the stator and by shifting those from each other by electrically 120° or 90°, for three- or two-phase machine, respectively. Therefore, a remarkable reduction on the total axial length of the machine will be achieved while keeping the torque density high. This TFM is compared to another similar TFM, in which the three phases have been divided into two halves and placed opposite each other to ensure the mechanical balance and stability of the stator. The corresponding mechanical phase shifts between the phases have accordingly been taken into account. The motors are modelled in finite-element method (FEM) program, Flux3D, and designed to meet the specifications of an optimisation scheme, subject to certain constraints, such as construction dimensions, electric and magnetic loading. Based on this comparison study, many recommendations have been suggested to achieve optimum results.
The Controversy Surrounding The Man Who Would Be Queen: A Case History of the Politics of Science, Identity, and Sex in the Internet Age

In 2003, psychology professor and sex researcher J. Michael Bailey published a book entitled The Man Who Would Be Queen: The Science of Gender-Bending and Transsexualism. The book's portrayal of male-to-female (MTF) transsexualism, based on a theory developed by sexologist Ray Blanchard, outraged some transgender activists. They believed the book to be typical of much of the biomedical literature on transsexuality-oppressive in both tone and claims, insulting to their senses of self, and damaging to their public identities. Some saw the book as especially dangerous because it claimed to be based on rigorous science, was published by an imprint of the National Academy of Sciences, and argued that MTF sex changes are motivated primarily by erotic interests and not by the problem of having the gender identity common to one sex in the body of the other. Dissatisfied with the option of merely criticizing the book, a small number of transwomen (particularly Lynn Conway, Andrea James, and Deirdre McCloskey) worked to try to ruin Bailey. Using published and unpublished sources as well as original interviews, this essay traces the history of the backlash against Bailey and his book. It also provides a thorough exegesis of the book's treatment of transsexuality and includes a comprehensive investigation of the merit of the charges made against Bailey that he had behaved unethically, immorally, and illegally in the production of his book. The essay closes with an epilogue that explores what has happened since 2003 to the central ideas and major players in the controversy.
Overlapping Community Detection by Collective Friendship Group Inference

There has been considerable interest in improving the capability to identify communities within large collections of social networking data. However, many of the existing algorithms will compartment an actor (node) into a single group, ignoring the fact that in real-world situations people tend to belong concurrently to multiple groups. Our work focuses on the ability to find overlapping communities by aggregating the community perspectives of friendship groups, derived from egonets. We will demonstrate that our algorithm not only finds overlapping communities, but additionally helps identify key members, which bind communities together. Additionally, we will highlight the parallel feature of the algorithm as a means of improving runtime performance.
Unsupervised algorithms for non-intrusive load monitoring: An up-to-date overview

Research on Smart Grids has recently focused on the energy monitoring issue, with the objective to maximize the user consumption awareness in building contexts on one hand, and to provide a detailed description of customer habits to the utilities on the other. One of the hottest topic in this field is represented by Non-Intrusive Load Monitoring (NILM): it refers to those techniques aimed at decomposing the consumption aggregated data acquired at a single point of measurement into the diverse consumption profiles of appliances operating in the electrical system under study. The focus here is on unsupervised algorithms, which are the most interesting and of practical use in real case scenarios. Indeed, these methods rely on a sustainable amount of a-priori knowledge related to the applicative context of interest, thus minimizing the user intervention to operate, and are targeted to extract all information to operate directly from the measured aggregate data. This paper reports and describes the most promising unsupervised NILM methods recently proposed in the literature, by dividing them into two main categories: load classification and source separation approaches. An overview of the public available dataset used on purpose and a comparative analysis of the algorithms performance is provided, together with a discussion of challenges and future research directions.
Effective Social Graph Deanonymization Based on Graph Structure and Descriptive Information

The study of online social networks has attracted increasing interest. However, concerns are raised for the privacy risks of user data since they have been frequently shared among researchers, advertisers, and application developers. To solve this problem, a number of anonymization algorithms have been recently developed for protecting the privacy of social graphs. In this article, we proposed a graph node similarity measurement in consideration with both graph structure and descriptive information, and a deanonymization algorithm based on the measurement. Using the proposed algorithm, we evaluated the privacy risks of several typical anonymization algorithms on social graphs with thousands of nodes from Microsoft Academic Search, LiveJournal, and the Enron email dataset, and a social graph with millions of nodes from Tencent Weibo. Our results showed that the proposed algorithm was efficient and effective to deanonymize social graphs without any initial seed mappings. Based on the experiments, we also pointed out suggestions on how to better maintain the data utility while preserving privacy.
3 D Object Detection with a Deformable 3 D Cuboid Model

This paper addresses the problem of category-level 3D object detection. Given a monocular image, our aim is to localize the objects in 3D by enclosing them with tight oriented 3D bounding boxes. We propose a novel approach that extends the deformable part-based model  to reason in 3D. Our model represents an object class as a deformable 3D cuboid composed of faces and parts, which are both allowed to deform with respect to their anchors on the 3D box. We model the appearance of each face in fronto-parallel coordinates, thus effectively factoring out the appearance variation induced by viewpoint. We train the cuboid model jointly and discriminatively. In inference we slide and rotate the box in 3D to score the object hypotheses. We evaluate our approach in indoor and outdoor scenarios, and show that our approach outperforms the state-of-the-art in both 2D  and 3D object detection .
IEEE 802.11ax: A study on techniques to mitigate the frequency offset in the uplink multi-user MIMO

The sixth generation of IEEE 802.11 wireless local area networks is under developing in the Task Group 802.11ax. One main physical layer (PHY) novel feature in the IEEE 802.11ax amendment is the specification of orthogonal frequency division multiplexing (OFDM) uplink multi-user multiple-input multiple-output (UL MU-MIMO) techniques. A challenge issue to implement UL MU-MIMO in OFDM PHY is the mitigation of the relative carrier frequency offset (CFO), which can cause intercarrier interference and rotation of the constellation of received symbols, and, consequently, degrading the system performance dramatically if it is not properly mitigated. In this paper, we show that a frequency domain CFO estimation and correction scheme implemented at both transmitter (Tx) and receiver (Rx) coupled with pre-compensation approach at the Tx can decrease the negative effects of the relative CFO.
Application of Linear-Frequency-Modulated Continuous-Wave (LFMCW) Radars for Tracking of Vital Signs

This paper focuses on the exploitation of linear-frequency-modulated continuous-wave (LFMCW) radars for noncontact range tracking of vital signs, e.g., respiration. Such short-range system combines hardware simplicity and tracking precision, thus outperforming other remote-sensing approaches in the addressed biomedical scenario. A rigorous mathematical analysis of the operating principle of the LFMCW radar in the context of vital-sign monitoring, which includes the explanation of key aspects for the maintenance of coherence, is detailed. A precise phase-based range-tracking algorithm is also presented. Exhaustive simulations are carried out to confirm the suitability and robustness against clutter, noise, and multiple scatterers of the proposed radar architecture, which is subsequently implemented at the prototype level. Moreover, live data from real experiments associated to a metal plate and breathing subjects are obtained and studied.
Applications of Structural Balance in Signed Social Networks

We present measures, models and link prediction algorithms based on the structural balance in signed social networks. Certain social networks contain, in addition to the usual friend links, enemy links. These networks are called signed social networks. A classical and major concept for signed social networks is that of structural balance, i.e., the tendency of triangles to be balanced towards including an even number of negative edges, such as friend-friend-friend and friend-enemy-enemy triangles. In this article, we introduce several new signed network analysis methods that exploit structural balance for measuring partial balance, for finding communities of people based on balance, for drawing signed social networks, and for solving the problem of link prediction. Notably, the introduced methods are based on the signed graph Laplacian and on the concept of signed resistance distances. We evaluate our methods on a collection of four signed social network datasets.
When Can the Maximin Share Guarantee Be Guaranteed?

The fairness notion of maximin share (MMS) guarantee underlies a deployed algorithm for allocating indivisible goods under additive valuations. Our goal is to understand when we can expect to be able to give each player his MMS guarantee. Previous work has shown that such an MMS allocation may not exist, but the counterexample requires a number of goods that is exponential in the number of players; we give a new construction that uses only a linear number of goods. On the positive side, we formalize the intuition that these counterexamples are very delicate by designing an algorithm that provably finds an MMS allocation with high probability when valuations are drawn at random.
A framework for adaptive training and games in virtual reality rehabilitation environments

The training of severely disabled individuals on the use of electric power wheelchairs creates many challenges, particularly in the case of children. The adjustment of equipment and training on a per-patient basis in an environment with limited specialists and resources often leads to a reduced amount of training time per patient. Virtual reality rehabilitation has recently been proven an effective way to supplement patient rehabilitation, although some important challenges remain including high setup/equipment costs and time-consuming continual adjustments to the simulation as patients improve. We propose a design for a flexible, low-cost rehabilitation system that uses virtual reality training and games to engage patients in effective instruction on the use of powered wheelchairs. We also propose a novel framework based on Bayesian networks for self-adjusting adaptive training in virtual rehabilitation environments. Preliminary results from a user evaluation and feedback from our rehabilitation specialist collaborators support the effectiveness of our approach.
Deep Reinforcement Learning for Simulated Autonomous Vehicle Control

We investigate the use of Deep Q-Learning to control a simulated car via reinforcement learning. We start by implementing the approach of  ourselves, and then experimenting with various possible alterations to improve performance on our selected task. In particular, we experiment with various reward functions to induce specific driving behavior, double Q-learning, gradient update rules, and other hyperparameters. We find we are successfully able to train an agent to control the simulated car in JavaScript Racer  in some respects. Our agent successfully learned the turning operation, progressively gaining the ability to navigate larger sections of the simulated raceway without crashing. In obstacle avoidance, however, our agent faced challenges which we suspect are due to insufficient training time.
Dimming-Feedback Control Method for TRIAC Dimmable LED Drivers

This paper presents a small-signal ac modeling of a feedback system for a flyback light-emitting diode (LED) driver. From this analysis, a dimming-feedback control method, which has a constant loop gain at dc, is proposed for dimmable LED drivers. Since the proposed method controls the steady-state error of the feedback system, an additional phase angle detection circuit and a LED current adjusting circuit are not required to control the output LED current according to the phase angle of the input voltage in a TRIAC-dimmable LED driver. Therefore, the proposed control method provides a simple structure, small size, and low bill of materials. A prototype of a flyback LED driver with the proposed dimming-feedback control method is implemented and experimented on to verify the validity of the analysis and the proposed control method. The prototype shows that the proposed control method varies the output LED current according to the phase angle of the TRIAC dimmer with no additional circuits. Thus, the proposed control method has high compatibility with TRIAC dimmers and can be applied to any dimmable LED driver with a nondimmable control integrated circuit.
Indoor PDR performance enhancement using minimal map information and particle filters

For professional users such as firefighters and other first responders, GNSS positioning technology (GPS, assisted GPS) can satisfy outdoor positioning requirements in many instances. However, there is still a need for high-performance deep indoor positioning for use by these same professional users. This need has already been clearly expressed by various communities of end users in the context of WearIT@Work, an R&D project funded by the European Community's Sixth Framework Program. It is known that map matching can help for indoor pedestrian navigation. In most previous research, it was assumed that detailed building plans are available. However, in many emergency / rescue scenarios, only very limited building plan information may be at hand. For example a building outline might be obtained from aerial photographs or cataster databases. Alternatively, an escape plan posted at the entrances to many building would yield only approximate exit door and stairwell locations as well as hallway and room orientation. What is not known is how much map information is really required for a USAR mission and how much each level of map detail might help to improve positioning accuracy. Obviously, the geometry of the building and the course through will be factors consider. The purpose of this paper is to show how a previously published Backtracking Particle Filter (BPF) can be combined with different levels of building plan detail to improve PDR performance. A new in/out scenario that might be typical of a reconnaissance mission during a fire in a two-story office building was evaluated. Using only external wall information, the new scenario yields positioning performance (2.56 m mean 2D error) that is greatly superior to the PDR-only, no map base case (7.74 m mean 2D error). This result has a substantial practical significance since this level of building plan detail could be quickly and easily generated in many emergency instances. The technique could be used to mitigate heading errors that result from exposing the IMU to extreme operating conditions. It is hoped that this mitigating effect will also occur for more irregular paths and in larger traversed spaces such as parking garages and warehouses.