nils-herrmann/clean_scirepeval huggingface dataset:
Local Privacy and Statistical Minimax Rates

Working under local differential privacy-a model of privacy in which data remains private even from the statistician or learner-we study the tradeoff between privacy guarantees and the utility of the resulting statistical estimators. We prove bounds on information-theoretic quantities, including mutual information and Kullback-Leibler divergence, that influence estimation rates as a function of the amount of privacy preserved. When combined with minimax techniques such as Le Cam's and Fano's methods, these inequalities allow for a precise characterization of statistical rates under local privacy constraints. In this paper, we provide a treatment of two canonical problem families: mean estimation in location family models and convex risk minimization. For these families, we provide lower and upper bounds for estimation of population quantities that match up to constant factors, giving privacy-preserving mechanisms and computationally efficient estimators that achieve the bounds.
Saliency-guided region proposal network for CNN based object detection

Robust sensing of the environment is fundamental for driver assistance systems performing safe maneuvers. While approaches to object detection have experienced tremendous improvements since the introduction and combination of region proposal and convolutional neural networks in one framework, the detection of distant objects occupying just a few pixels in images can be challenging though. The convolutional and pooling layers reduce the image information to feature maps; yet, relevant information may be lost through pooling and convolution for small objects. In order to address this challenge, a new approach to proposing regions is presented that extends the architecture of a region proposal network by incorporating priors to guide the proposals towards regions containing potential target objects. Moreover, inspired by the concept of saliency, a saliency-based prior is chosen to guide the RPN towards important regions in order to make efficient use of differences between objects and background in an unsupervised fashion. This allows the network not only to consider local information provided by the convolutional layers, but also to take into account global information provided by the saliency priors. Experimental results based on a distant vehicle dataset and different configurations including three priors show that the incorporation of saliency-inspired priors into a region proposal network can improve its performance significantly.
Building book inventories using smartphones

Manual generation of a book inventory is time-consuming and tedious, while deployment of barcode and radio-frequency identification (RFID) management systems is costly and affordable only to large institutions. In this paper, we design and implement a mobile book recognition system for conveniently generating an inventory of books by snapping photos of a bookshelf with a smartphone. Since smartphones are becoming ubiquitous and affordable, our inventory management solution is cost-effective and very easy to deploy. Automatic and robust book recognition is achieved in our system using a combination of spine segmentation and bag-of-features image matching. At the same time, the location of each book is inferred from the smartphone's sensor readings, including accelerometer traces, digital compass measurements, and WiFi signatures. This location information is combined with the image recognition results to construct a location-aware book inventory. We demonstrate the effectiveness of our book spine recognition and location estimation techniques in recognition experiments and in an actual mobile book recognition system.
A linear CMOS SOI SP14T antenna switch for cellular applications

A high-power low-loss antenna switch is designed and fabricated in a 180nm SOI CMOS process. The RF switch is intended for mobile front ends which cover Quad band GSM and multiple WCDMA and LTE bands. The prototype SP14T has demonstrated a 0.1dB compression point of 28dBm at all cellular bands. The low loss and high linearity are made possible by the distributed RF architecture along with the on-chip multiple domain voltage generation which includes a negative voltage generator. The low loss, high dynamic range and superior isolation of this switch makes it an ideal candidate for next generation smart phones.
Identifying Sections in Scientific Abstracts using Conditional Random Fields

The prior knowledge about the rhetorical structure of scientific abstracts is useful for various text-mining tasks such as information extraction, information retrieval, and automatic summarization. This paper presents a novel approach to categorize sentences in scientific abstracts into four sections, objective, methods, results, and conclusions.  Formalizing the categorization task as a sequential labeling problem, we employ Conditional Random Fields (CRFs) to annotate section labels into abstract sentences. The training corpus is acquired automatically from Medline abstracts.  The proposed method outperformed the previous approaches, achieving 95.5% per-sentence accuracy and 68.8% per-abstract accuracy.  The experimental results showed that CRFs could model the rhetorical structure of abstracts more suitably.
A Compact UWB MIMO Antenna With Reflector to Enhance Isolation

A compact ultrawideband (UWB) multiple-input multiple-output (MIMO) antenna, with high isolation, is proposed for portable UWB MIMO systems. Two coplanar stripline-fed staircase-shaped radiating elements are connected back-to-back. The prototype is designed on a substrate of dielectric constant 4.4 with an overall dimension of 25 mm × 30 mm × 1.6 mm. This antenna configuration with an isolating metal strip placed in between the two radiating elements ensures high isolation in the entire UWB band. The proposed antenna exhibits a good 2:1 VSWR impedance bandwidth covering the entire UWB band (3.1-10.6 GHz) with a high isolation better than 20 dB, peak gain of 5.2 dBi, peak efficiency of 90%, and guaranteed value of envelope correlation coefficient (ECC) ≤0.1641.
Echoes from the past: how technology mediated reflection improves well-being

As people document more of their lives online, some recent systems are encouraging people to later revisit those recordings, a practice we're calling technology-mediated reflection (TMR). Since we know that unmediated reflection benefits psychological well-being, we explored whether and how TMR affects well-being. We built Echo, a smartphone application for recording everyday experiences and reflecting on them later. We conducted three system deployments with 44 users who generated over 12,000 recordings and reflections. We found that TMR improves well-being as assessed by four psychological metrics. By analyzing the content of these entries we discovered two mechanisms that explain this improvement. We also report benefits of very long-term TMR.
Sparse Subspace Clustering with Missing Entries

We consider the problem of clustering incomplete data drawn from a union of subspaces. Classical subspace clustering methods are not applicable to this problem because the data are incomplete, while classical low-rank matrix completion methods may not be applicable because data in multiple subspaces may not be low rank. This paper proposes and evaluates two new approaches for subspace clustering and completion. The first one generalizes the sparse subspace clustering algorithm so that it can obtain a sparse representation of the data using only the observed entries. The second one estimates a suitable kernel matrix by assuming a random model for the missing entries and obtains the sparse representation from this kernel. Experiments on synthetic and real data show the advantages and disadvantages of the proposed methods, which all outperform the natural approach (low-rank matrix completion followed by sparse subspace clustering) when the data matrix is high-rank or the percentage of missing entries is large.
A SMART HOME SYSTEM BASED ON SENSOR TECHNOLOGY

This paper presents a new approach to utilize technology in a practical and meaningful manner within a smart home system that can be widely deployed into residential settings. In the modern world, people are rapidly turning to technology as a fast and cost-effective way of improving quality of daily living. This primary goal is to address the needs of the end user by employing networked low-power sensors sensitive to the environment, so it can be altered to their liking. The proposed system consists of following steps: direct environment sensing, collecting and analyzing data and then allowing user to customize the settings and initiate specific commands. This research will present the design and implementation of a practical and simple smart home system, which can be further extended. The system is based on: group of sensors, Raspberry Pi device as a server system and Bluetooth as a communication protocol. These devices can be easily controlled via user-friendly interface for Android phones. The main advantage of the proposed system is that it is a sensible, secure and easily configurable system that provides end users with a neat home automation solution.
Covariance Matrix Adaptation for Multi-objective Optimization

The covariancematrix adaptation evolution strategy (CMA-ES) is one of themost powerful evolutionary algorithms for real-valued single-objective optimization. In this paper, we develop a variant of the CMA-ES for multi-objective optimization (MOO). We first introduce a single-objective, elitist CMA-ES using plus-selection and step size control based on a success rule. This algorithm is compared to the standard CMA-ES. The elitist CMA-ES turns out to be slightly faster on unimodal functions, but is more prone to getting stuck in sub-optimal local minima. In the new multi-objective CMAES (MO-CMA-ES) a population of individuals that adapt their search strategy as in the elitist CMA-ES is maintained. These are subject to multi-objective selection. The selection is based on non-dominated sorting using either the crowding-distance or the contributing hypervolume as second sorting criterion. Both the elitist single-objective CMA-ES and the MO-CMA-ES inherit important invariance properties, in particular invariance against rotation of the search space, from the original CMA-ES. The benefits of the new MO-CMA-ES in comparison to the well-known NSGA-II and to NSDE, a multi-objective differential evolution algorithm, are experimentally shown.
Mindfulness Training as a Clinical Intervention : A Conceptual and Empirical Review

Mindfulness is a way of paying attention that originated in Eastern meditation practices. It has been described as “bringing one’s complete attention to the present experience on a moment-to-moment basis” (Marlatt & Kristeller, 1999, p. 68) and as “paying attention in a particular way: on purpose, in the present moment, and nonjudgmentally” (Kabat-Zinn, 1994, p. 4). The ability to direct one’s attention in this way can be developed through the practice of meditation, which is defined as the intentional self-regulation of attention from moment to moment (Goleman & Schwartz, 1976;Kabat-Zinn, 1982). The current mindfulness literature describes numerous meditation exercises designed to develop mindfulness skills (Hanh, 1976; Kabat-Zinn, 1990, 1994; Linehan, 1993b). Many encourage individuals to attend to the internal experiences occurring in each moment, such as bodily sensations, thoughts, and emotions. Others encourage attention to aspects of the environment, such as sights and sounds (Kabat-Zinn, 1994; Linehan, 1993b). All suggest that mindfulness should be practiced with an attitude of nonjudgmental acceptance. That is, phenomena that enter the individual’s awareness during mindfulness practice, such as perceptions, cognitions, emotions, or sensations, are observed carefully but are not evaluated as good or bad, true or false, healthy or sick, or important or trivial (Marlatt & Kristeller, 1999). Thus, mindfulness is the nonjudgmental observation of the ongoing stream of internal and external stimuli as they arise. Until recently, mindfulness has been a relatively unfamiliar concept in much of our culture (Kabat-Zinn, 1982), perhaps because of its origins in Buddhism. Kabat-Zinn (2000) suggests that mindfulness practice may be beneficial to many people in Western society who might be unwilling to adopt Buddhist traditions or vocabulary. Thus, Western researchers and clinicians who have introduced mindfulness practice into mental health treatment programs usually teach these skills independently of the religious and cultural traditions of their origins (Kabat-Zinn, 1982;Linehan, 1993b). In the current empirical literature, clinical interventions based on training in mindfulness skills are described with increasing frequency, and their popularity appears to be growing rapidly. According to Salmon, Santorelli, and Kabat-Zinn (1998), over 240 hospitals and clinics in the United States and abroad were offering stress reduction programs based on mindfulness training as of Mindfulness Training as a Clinical Intervention: A Conceptual and Empirical Review
Prediction of Helpful Reviews Using Emotions Extraction

Reviews keep playing an increasingly important role in the decision process of buying products and booking hotels. However, the large amount of available information can be confusing to users. A more succinct interface, gathering only the most helpful reviews, can reduce information processing time and save effort. To create such an interface in real time, we need reliable prediction algorithms to classify and predict new reviews which have not been voted but are potentially helpful. So far such helpfulness prediction algorithms have benefited from structural aspects, such as the length and readability score. Since emotional words are at the heart of our written communication and are powerful to trigger listeners’ attention, we believe that emotional words can serve as important parameters for predicting helpfulness of review text. Using GALC, a general lexicon of emotional words associated with a model representing 20 different categories, we extracted the emotionality from the review text and applied supervised classification method to derive the emotion-based helpful review prediction. As the second contribution, we propose an evaluation framework comparing three different real-world datasets extracted from the most well-known product review websites. This framework shows that emotion-based methods are outperforming the structure-based approach, by up to 9%.
Multiclass Common Spatial Patterns and Information Theoretic Feature Extraction

We address two shortcomings of the common spatial patterns (CSP) algorithm for spatial filtering in the context of brain-computer interfaces (BCIs) based on electroencephalography/magnetoencephalography (EEG/MEG): First, the question of optimality of CSP in terms of the minimal achievable classification error remains unsolved. Second, CSP has been initially proposed for two-class paradigms. Extensions to multiclass paradigms have been suggested, but are based on heuristics. We address these shortcomings in the framework of information theoretic feature extraction (ITFE). We show that for two-class paradigms, CSP maximizes an approximation of mutual information of extracted EEG/MEG components and class labels. This establishes a link between CSP and the minimal classification error. For multiclass paradigms, we point out that CSP by joint approximate diagonalization (JAD) is equivalent to independent component analysis (ICA), and provide a method to choose those independent components (ICs) that approximately maximize mutual information of ICs and class labels. This eliminates the need for heuristics in multiclass CSP, and allows incorporating prior class probabilities. The proposed method is applied to the dataset IIIa of the third BCI competition, and is shown to increase the mean classification accuracy by 23.4% in comparison to multiclass CSP.
Summarizing Complex Events: a Cross-Modal Solution of Storylines Extraction and Reconstruction

The rapid development of Web2.0 leads to significant information redundancy. Especially for a complex news event, it is difficult to understand its general idea within a single coherent picture. A complex event often contains branches, intertwining narratives and side news which are all called storylines. In this paper, we propose a novel solution to tackle the challenging problem of storylines extraction and reconstruction. Specifically, we first investigate two requisite properties of an ideal storyline. Then a unified algorithm is devised to extract all effective storylines by optimizing these properties at the same time. Finally, we reconstruct all extracted lines and generate the high-quality story map. Experiments on real-world datasets show that our method is quite efficient and highly competitive, which can bring about quicker, clearer and deeper comprehension to readers.
Association between online social networking and depression in high school students: behavioral physiology viewpoint.

Frequent use of Facebook and other social networks is thought to be associated with certain behavioral changes, and some authors have expressed concerns about its possible detrimental effect on mental health. In this work, we investigated the relationship between social networking and depression indicators in adolescent population.



Total of 160 high school students were interviewed using an anonymous, structured questionnaire and Back Depression Inventory - second edition (BDI-II-II). Apart from BDI-II-II, students were asked to provide the data for height and weight, gender, average daily time spent on social networking sites, average time spent watching TV, and sleep duration in a 24-hour period.



Average BDI-II-II score was 8.19 (SD=5.86). Average daily time spent on social networking was 1.86 h (SD=2.08 h), and average time spent watching TV was 2.44 h (SD=1.74 h). Average body mass index of participants was 21.84 (SD=3.55) and average sleep duration was 7.37 (SD=1.82). BDI-II-II score indicated minimal depression in 104 students, mild depression in 46 students, and moderate depression in 10 students. Statistically significant positive correlation (p<0.05,  was found between BDI-II-II score and the time spent on social networking.



Our results indicate that online social networking is related to depression. Additional research is required to determine the possible causal nature of this relationship.
Is Dysfunctional Use of the Mobile Phone a Behavioural Addiction? Confronting Symptom-Based Versus Process-Based Approaches.

Dysfunctional use of the mobile phone has often been conceptualized as a 'behavioural addiction' that shares most features with drug addictions. In the current article, we challenge the clinical utility of the addiction model as applied to mobile phone overuse. We describe the case of a woman who overuses her mobile phone from two distinct approaches: (1) a symptom-based categorical approach inspired from the addiction model of dysfunctional mobile phone use and (2) a process-based approach resulting from an idiosyncratic clinical case conceptualization. In the case depicted here, the addiction model was shown to lead to standardized and non-relevant treatment, whereas the clinical case conceptualization allowed identification of specific psychological processes that can be targeted with specific, empirically based psychological interventions. This finding highlights that conceptualizing excessive behaviours (e.g., gambling and sex) within the addiction model can be a simplification of an individual's psychological functioning, offering only limited clinical relevance.



The addiction model, applied to excessive behaviours (e.g., gambling, sex and Internet-related activities) may lead to non-relevant standardized treatments. Clinical case conceptualization allowed identification of specific psychological processes that can be targeted with specific empirically based psychological interventions. The biomedical model might lead to the simplification of an individual's psychological functioning with limited clinical relevance.
Constraint-Based Question Answering with Knowledge Graph

WebQuestions and SimpleQuestions are two benchmark data-sets commonly used in recent knowledge-based question answering (KBQA) work. Most questions in them are ‘simple’ questions which can be answered based on a single relation in the knowledge base. Such data-sets lack the capability of evaluating KBQA systems on complicated questions. Motivated by this issue, we release a new data-set, namely ComplexQuestions1, aiming to measure the quality of KBQA systems on ‘multi-constraint’ questions which require multiple knowledge base relations to get the answer. Beside, we propose a novel systematic KBQA approach to solve multi-constraint questions. Compared to state-of-the-art methods, our approach not only obtains comparable results on the two existing benchmark data-sets, but also achieves significant improvements on the ComplexQuestions.
Design of omnimobile robot wheels

Typical mobile robot structures (e.g. wheelchair or carlike, ...) do not have the required mobility for common applications such as displacement in a corridor, hospital, office, ... New structures based on the "universal wheel" (i.e. a wheel equipped with free rotating rollers) have been developed to increase mobility. However these structures have important drawbacks such as vertical vibration and limited load capacity. This paper presents a comparison of several types of universal wheel performances based on the three following criteria: load capacity, surmountable bumps and vertical vibration. It is hoped that this comparison will help the designer in the selection of the best suitable solution for his application.
Distributed Prefetch-buffer/Cache Design for High-Performance Memory Systems

Microprocessor execution speeds are improving at a rate of 50%-80% per year while DRAM access times are improving at a much lower rate of 5%-10% per year. Computer systems are rapidly approaching the point at which overall system performance is determined not by the speed of the CPU but by the memory system speed. We present a high performance memory system architecture that overcomes the growing speed disparity between high performance microprocessors and current generation DRAMs. A novel prediction and prefetching technique is combined with a distributed cache architecture to build a high performance memory system. We use a table based prediction scheme with a prediction cache to prefetch data from the on-chip DRAM array to an on-chip SRAM prefetch bu er. By prefetching data we are able to hide the large latency associated with DRAM access and cycle times. Our experiments show that with a small (32 KB) prediction cache we can get an e ective main memory access time that is close to the access time of larger secondary caches.