nils-herrmann/clean_scirepeval huggingface dataset:
WaCo: A Wake-Up Radio COOJA Extension for Simulating Ultra Low Power Radios

Radio communication remains the primary battery consuming activity in wireless systems. Advances in MAC protocols have enabled significant lifetime improvements, but in systems with low data rate, idle listening, and other communication artifacts can begin to dominate costs. One proposal to combat this is the addition of a second, extremely low power radio component that is always-on. As a consequence of the extremely low power, such radios are incapable of decoding general data, and thus are often delegated the task of listening for a trigger, leading to the terminology wake-up radio, as this extremely low power radio is used to wake up a higher power radio, which is then used for data communication. While wake-up technology has been steadily evolving over the last decade in the hardware arena, few protocols have been developed to exploit it. In this work, we present WaCo, our wake-up radio COOJA extension that allows exploration of the capabilities of the wake-up radio from the desktop environment. We also use our extended simulator to concretely show the potential benefits of the wake-up radio hardware with two, standard data collection protocols. Our results simultaneously confirm that wake-up technology has tremendous potential and that our simulator extension provides an effective mechanism for such exploration.
An Energy Consumption Framework for Distributed Java-Based Software

In this paper we define and evaluate a framework for estimating the energy consumption of Java-based software systems. Our primary objective in devising the framework is to enable an engineer to make informed decisions when adapting a system's architecture, such that the energy consumption on hardware devices with a finite battery life is reduced, and the lifetime of the system's key software services increases. Our framework explicitly takes a component-based perspective, which renders it well suited for a large class of today's distributed, dynamic, and mobile applications. The framework allows the engineer to estimate the software system's energy consumption at construction time and refine it at runtime. In a large number of distributed application scenarios, the framework showed very good precision on the whole, giving results that were within 5% (and often less) of the actually measured power losses incurred by executing the software. While our empirical evidence suggests that the framework is broadly applicable as-is, our work to date has highlighted a number of future enhancements.
Public Health Autonomy: A Critical Reappraisal.

The ethical principle of autonomy is among the most fundamental in ethics, and it is particularly salient for those in public health, who must constantly balance the desire to improve health outcomes by changing behavior with respect for individual freedom. Although there are some areas in which there is a genuine tension between public health and autonomy-childhood vaccine mandates, for example-there are many more areas where not only is there no tension, but public health and autonomy come down to the same thing. These areas of overlap are often rendered invisible by a thin understanding of autonomy. Better integrating newer theoretical insights about autonomy into applied ethics can make discussions of public health ethics more rigorous, incisive, and effective. Even more importantly, bringing modern concepts of autonomy into public health ethics can showcase the many areas in which public health and autonomy have the same goals, face the same threats, and can be mutually advanced by the same kinds of solutions. This article provides a schema for relational autonomy in a public health context and gives concrete examples of how autonomy can be served through public-health interventions. It marshals insights from sociology, psychology, and philosophy to advance a theory of autonomy and coercion that recognizes three potential threats to autonomy: threats to choice sets, threats to knowledge, and threats to preferences.
Obsessive-compulsive disorder and gut microbiota dysregulation.

Obsessive-compulsive disorder (OCD) is a debilitating disorder for which the cause is not known and treatment options are modestly beneficial. A hypothesis is presented wherein the root cause of OCD is proposed to be a dysfunction of the gut microbiome constituency resulting in a susceptibility to obsessional thinking. Both stress and antibiotics are proposed as mechanisms by which gut microbiota are altered preceding the onset of OCD symptomology. In this light, pediatric autoimmune neuropsychiatric disorders associated with streptococcal infections (PANDAS) leading to episodic OCD is explained not by group A beta-hemolytic streptococcal infections, but rather by prophylactic antibiotics that are administered as treatment. Further, stressful life events known to trigger OCD, such as pregnancy, are recast to show the possibility of altering gut microbiota prior to onset of OCD symptoms. Suggested treatment for OCD would be the directed, specie-specific (re)introduction of beneficial bacteria modifying the gut microbiome, thereby ameliorating OCD symptoms. Special considerations should be contemplated when considering efficacy of treatment, particularly the unhealthy coping strategies often observed in patients with chronic OCD that may need addressing in conjunction with microbiome remediation.
Continuous speech recognition using hidden Markov models

The use of hidden Markov models (HMMs) in continuous speech recognition is reviewed. Markov models are presented as a generalization of their predecessor technology, dynamic programming. A unified view is offered in which both linguistic decoding and acoustic matching are integrated into a single, optimal network search framework. Advances in recognition architectures are discussed. The fundamentals of Viterbi beam search, the dominant search algorithm used today in speed recognition, are presented. Approaches to estimating the probabilities associated with an HMM model are examined. The HMM-supervised training paradigm is examined. Several examples of successful HMM-based speech recognition systems are reviewed.<<ETX>>
A robust and scalable clustering algorithm for mixed type attributes in large database environment

Clustering is a widely used technique in data mining applications to discover patterns in the underlying data. Most traditional clustering algorithms are limited to handling datasets that contain either continuous or categorical attributes. However, datasets with mixed types of attributes are common in real life data mining problems. In this paper, we propose a distance measure that enables clustering data with both continuous and categorical attributes. This distance measure is derived from a probabilistic model that the distance between two clusters is equivalent to the decrease in log-likelihood function as a result of merging. Calculation of this measure is memory efficient as it depends only on the merging cluster pair and not on all the other clusters. Zhang et al  proposed a clustering method named BIRCH that is especially suitable for very large datasets. We develop a clustering algorithm using our distance measure based on the framework of BIRCH. Similar to BIRCH, our algorithm first performs a pre-clustering step by scanning the entire dataset and storing the dense regions of data records in terms of summary statistics. A hierarchical clustering algorithm is then applied to cluster the dense regions. Apart from the ability of handling mixed type of attributes, our algorithm differs from BIRCH in that we add a procedure that enables the algorithm to automatically determine the appropriate number of clusters and a new strategy of assigning cluster membership to noisy data. For data with mixed type of attributes, our experimental results confirm that the algorithm not only generates better quality clusters than the traditional k-means algorithms, but also exhibits good scalability properties and is able to identify the underlying number of clusters in the data correctly. The algorithm is implemented in the commercial data mining tool Clementine 6.0 which supports the PMML standard of data mining model deployment.
Serious games as new educational tools: how effective are they? A meta-analysis of recent studies

Computer-assisted learning is known to be an effective tool for improving learning in both adults and children. Recent years have seen the emergence of the so-called ‘serious games (SGs)’ that are flooding the educational games market. In this paper, the term ‘serious games’ is used to refer to video games (VGs) intended to serve a useful purpose. The objective was to review the results of experimental studies designed to examine the effectiveness of VGs and SGs on players’ learning and engagement. After pointing out the varied nature of the obtained results and the impossibility of reaching any reliable conclusion concerning the effectiveness of VGs and SGs in learning, we stress the limitations of the existing literature and make a number of suggestions for future studies.
Design of a new Dual Rotor Radial Flux BLDC motor with Halbach array magnets for an electric vehicle

This paper presents a novel topology of dual rotor radial flux-BLDC motor with Halbach array magnets as an efficient solution for drives. Energy efficient motors are the need of day for electric vehicles, industrial and consumer products. Especially this concept is arrived to cater for the requirements of electrical vehicle. Due to advent of high permeability, high saturation magnetic materials, high energy product permanent magnets, BLDC motors are suitable candidate for electric vehicle propulsion. Traditionally, outer rotor BLDC motor is a wide choice which is being used in commercially available electric vehicles. New topologies of BLDC motors are being proposed by research activities which aim at improvement of torque density of motors. Dual rotor brushless DC machine (DR-BLDC) is one of the consequences of such research and claimed to have very high torque density and power density. In this paper, a new topology of Dual Rotor Halbach (DR-HB) BLDC motor is proposed which is aimed to increase the torque density of motor. The complete design of DR-HB BLDC motor is carried out and compared with DR-BLDC of the same specifications and volume. The obtained performance characteristics of the proposed design are very much encouraging in terms of torque density.
Simultaneous estimation of food categories and calories with multi-task CNN

In this paper, we propose simultaneous estimation of food categories and calories for food photos. Since there exists strong correlation between food categories and calories in general, we expect that simultaneous training of both brings performance boosting compared to independent single training. To this end, we use a multitask CNN. In the experiments, we collected calorie-annotated recipe data from the online cooking recipe sites, and trained multi-task and single-task CNNs. As results, the multi-task CNN achieved the better performance on both food category estimation and food calorie estimation than single-task CNNs.
Rollable Multisegment Dielectric Elastomer Minimum Energy Structures for a Deployable Microsatellite Gripper

Debris in space presents an ever-increasing problem for spacecraft in Earth orbit. As a step in the mitigation of this issue, the CleanSpace One (CSO) microsatellite has been proposed. Its mission is to perform active debris removal of a decommissioned nanosatellite (the CubeSat SwissCube). An important aspect of this project is the development of the gripper system that will entrap the capture target. We present the development of rollable dielectric elastomer minimum energy structures (DEMES) as the main component of CSO's deployable gripper. DEMES consist of a prestretched dielectric elastomer actuator membrane bonded to a flexible frame. The actuator finds equilibrium in bending when the prestretch is released and the bending angle can be changed by the application of a voltage bias. The inherent flexibility and lightweight nature of the DEMES enables the gripper to be stored in a rolled-up state prior to deployment. We fabricated proof-of-concept actuators of three different geometries using a robust and repeatable fabrication methodology. The resulting actuators were mechanically resilient to external deformation, and display conformability to objects of varying shapes and sizes. Actuator mass is less than 0.65 g and all the actuators presented survived the rolling-up and subsequent deployment process. Our devices demonstrate a maximum change of bending angle of more than 60° and a maximum gripping (reaction) force of 2.2 mN for a single actuator.
From checking on to checking in: designing for low socio-economic status older adults

In this paper we describe the design evolution of a novel technology that collects and displays presence information to be used in the homes of older adults. The first two iterations, the Ambient Plant and Presence Clock, were designed for higher socio-economic status (SES) older adults, whereas the Check-In Tree was designed for low SES older adults. We describe how feedback from older adult participants drove our design decisions, and give an in-depth account of how the Check-In Tree evolved from concept to a final design ready for in situ deployment.
Spatially Coupled Codes Optimized for Magnetic Recording Applications

Spatially coupled (SC) codes are a class of sparse graph-based codes known to have capacity-approaching performance. SC codes are constructed based on an underlying low-density parity-check (LDPC) code, by first partitioning the underlying block code and then putting replicas of the components together. Significant recent research efforts have been devoted to the asymptotic, ensemble-averaged study of SC codes, as these coupled variants of the existing LDPC codes offer excellent properties. While the asymptotic analysis is important, due to simplifying assumptions and averaging effects, results from the asymptotic domain are not readily translatable to the practical, finite-length setting. Despite this chasm, the finite-length analysis of SC codes is still largely unexplored. In this paper, we tackle the problem of optimized design of SC codes in the context of magnetic-recording (MR) applications. In particular, we identify combinatorial structures in the graphical representation of the code that are detrimental in the MR setting. An intriguing observation is that for the same SC code, the problematic objects for the MR channels are combinatorially different from the additive white Gaussian noise (AWGN) setting, thus necessitating a careful code design approach for the MR applications. We first demonstrate that the choice of the so-called cutting vector, which guides the code partitioning in the SC code design, directly affects the cardinality of these problematic objects. In particular, we show that the number of problematic objects is the highest—and consequently that the performance is the worst—in the case of the degenerate cutting vector, which precisely corresponds to uncoupled LDPC block codes. We, therefore, show that coupling always improves the performance and that the degree of improvement is dependent on the choice of the cutting vector. We then extend our analysis to different column weights and present SC codes that outperform block codes with unoptimized edge weights by more than 3.5 orders of magnitude and that also outperform both optimized block codes and unoptimized SC codes by more than two orders of magnitude. Through presented examples, we demonstrate high performance of the proposed code design methodology.
Abstract Cryptography

Cryptography Ueli Maurer ETH Zurich FOSAD 2009, Bertinoro, Aug./Sept. 2009.  Ueli Maurer ETH Zurich FOSAD 2009, Bertinoro, Aug./Sept. 2009. “I can only understand simple things.” JAMES MASSEY Abstraction Abstraction: eliminate irrelevant details from consideration Examples: group, field, vector space, relation, graph, .... Goals of abstraction: • simpler definitions • generality of results • simpler proofs • elegance • didactic suitabilityion Abstraction: eliminate irrelevant details from consideration Examples: group, field, vector space, relation, graph, .... Goals of abstraction: • simpler definitions • generality of results • simpler proofs • elegance • didactic suitability Abstraction Abstraction: eliminate irrelevant details from consideration Examples: group, field, vector space, relation, graph, .... Goals of abstraction: • simpler definitions • generality of results • simpler proofs • elegance • didactic suitability • understandingion Abstraction: eliminate irrelevant details from consideration Examples: group, field, vector space, relation, graph, .... Goals of abstraction: • simpler definitions • generality of results • simpler proofs • elegance • didactic suitability • understanding Abstraction Abstraction: eliminate irrelevant details from consideration Examples: group, field, vector space, relation, graph, .... Goals of abstraction: • simpler definitions • generality of results • simpler proofs • elegance • didactic suitability • understanding Goals of this talk: • Introduce layers of abstraction in cryptography. • Ex mples of abstract definitions and proofs. • Announce new security framework “abstract cryptography” (with Renato Renner).ion Abstraction: eliminate irrelevant details from consideration Examples: group, field, vector space, relation, graph, .... Goals of abstraction: • simpler definitions • generality of results • simpler proofs • elegance • didactic suitability • understanding Goals of this talk: • Introduce layers of abstraction in cryptography. • Ex mples of abstract definitions and proofs. • Announce new security framework “abstract cryptography” (with Renato Renner).  One-time pad 1 C , C , ... 1 ciphertext 1 2 key 2 1 key 2 1 2 2 addition modulo 2 M , M , ... M , M , ... plaintext plaintext K , K , ... K , K , ... Motivating example: One-time pad 1 C , C , ... 1 ciphertext 1 2 key 2 1 key 2 1 2 2 addition modulo 2 M , M , ... M , M , ... plaintext plaintext K , K , ... K , K , ... Perfect secrecy (Shannon): C and M statist. independent. One-time pad in terms of systems .
RSSI informed phase method for distance calculations

In an attempt to find an accurate, environmentally robust, and fast process for RFID distance estimations, a method was developed called RSSI Informed Phase. This method uses both phase and RSSI measurements to estimate the distance, and potentially location, of a moving RFID tag. RSSI is initially used to find an approximate reader to tag separation distance. This distance value is applied to find an approximate slope of the phase angle vs. frequency curve. The estimated slope can inform the phase distance calculation, meaning fewer reads are required to find the actual phase angle vs. frequency slope. The reduction in the number of necessary reads accelerates the localization process and makes this method more robust for dynamic environments.
Validating Formative Partial Least Squares (PLS) Models: Methodological Review and Empirical Illustration

The issue of formative constructs, as opposed to th e more frequently used reflective ones, has recently gained momentum among IS and Management re searchers. Most researchers maintain that formative constructs have been understudied, a n that there is paucity in methodological literature to guide researchers on how such constru cts should be developed and estimated. A survey of IS research has revealed that about 29% o f constructs were misspecified as reflective rather than formative constructs. Furthermore, guid elines about how models containing formative constructs should be indentified and estimated are fragmented and inconsistent. Thus, this paper aims to present a methodological review of formativ e model identification and evaluation. We bring a brief theoretical overview of formative con structs, and put together a guideline for estimating formative measurement and structural mod els. We then present a simplified model composed of three formative constructs and illustra te how it is assessed and estimated using
Image Segmentation by Cascaded Region Agglomeration

We propose a hierarchical segmentation algorithm that starts with a very fine over segmentation and gradually merges regions using a cascade of boundary classifiers. This approach allows the weights of region and boundary features to adapt to the segmentation scale at which they are applied. The stages of the cascade are trained sequentially, with asymetric loss to maximize boundary recall. On six segmentation data sets, our algorithm achieves best performance under most region-quality measures, and does it with fewer segments than the prior work. Our algorithm is also highly competitive in a dense over segmentation (super pixel) regime under boundary-based measures.
An empirical study of tokenization strategies for biomedical information retrieval

Due to the great variation of biological names in biomedical text, appropriate tokenization is an important preprocessing step for biomedical information retrieval. Despite its importance, there has been little study on the evaluation of various tokenization strategies for biomedical text. In this work, we conducted a careful, systematic evaluation of a set of tokenization heuristics on all the available TREC biomedical text collections for ad hoc document retrieval, using two representative retrieval methods and a pseudo-relevance feedback method. We also studied the effect of stemming and stop word removal on the retrieval performance. As expected, our experiment results show that tokenization can significantly affect the retrieval accuracy; appropriate tokenization can improve the performance by up to 96%, measured by mean average precision (MAP). In particular, it is shown that different query types require different tokenization heuristics, stemming is effective only for certain queries, and stop word removal in general does not improve the retrieval performance on biomedical text.
Emotion recognition using Speech Processing Using k-nearest neighbor algorithm

Speech Emotion Recognition (SER) is a current research topic in the field of Human Computer Interaction (HCI) with wide range of applications. The purpose of speech emotion recognition system is to automatically classify speaker's utterances into four emotional states such as anger, sadness, neutral, and happiness. The speech samples are from Berlin emotional database and the features extracted from these utterances are energy, pitch, ZCC, entropy, Mel Frequency cepstrum coefficients (MFCC). The K Nearest Neighbor (KNN) is used as a classifier to classify different emotional states. The system gives 86.02% classification accuracy for using energy, entropy, MFCC, ZCC, pitch Features. KeywordsSpeech Emotion; Automatic Emotion Recognition; KNN; Energy; Pitch; MFCC; ZCC.
MagPIE: A dataset for indoor positioning with magnetic anomalies

In this paper, we present a publicly available dataset for the evaluation of indoor positioning algorithms that use magnetic anomalies. Our dataset contains IMU and magnetometer measurements along with ground truth position measurements that have centimeter-level accuracy. To produce this dataset, we collected over 13 hours of data (51 kilometers of total distance traveled) from three different buildings, with sensors both handheld and mounted on a wheeled robot, in environments with and without changes in the placement of objects that affect magnetometer measurements ("live loads”). We conclude the paper with a discussion of why these characteristics of our dataset are important when evaluating positioning algorithms.