nils-herrmann/clean_scirepeval huggingface dataset:
Quantized Densely Connected U-Nets for Efficient Landmark Localization

In this paper, we propose quantized densely connected UNets for efficient visual landmark localization. The idea is that features of the same semantic meanings are globally reused across the stacked U-Nets. This dense connectivity largely improves the information flow, yielding improved localization accuracy. However, a vanilla dense design would suffer from critical efficiency issue in both training and testing. To solve this problem, we first propose order-K dense connectivity to trim off long-distance shortcuts; then, we use a memory-efficient implementation to significantly boost the training efficiency and investigate an iterative refinement that may slice the model size in half. Finally, to reduce the memory consumption and high precision operations both in training and testing, we further quantize weights, inputs, and gradients of our localization network to low bit-width numbers. We validate our approach in two tasks: human pose estimation and face alignment. The results show that our approach achieves state-of-the-art localization accuracy, but using ∼70% fewer parameters, ∼98% less model size and saving ∼75% training memory compared with other benchmark localizers. The code is available at https://github.com/zhiqiangdon/CU-Net.
Emerging technologies and research challenges for 5G wireless networks

As the take-up of Long Term Evolution (LTE)/4G cellular accelerates, there is increasing interest in technologies that will define the next generation (5G) telecommunication standard. This article identifies several emerging technologies which will change and define the future generations of telecommunication standards. Some of these technologies are already making their way into standards such as 3GPP LTE, while others are still in development. Additionally, we will look at some of the research problems that these new technologies pose.
Inference is Everything: Recasting Semantic Resources into a Unified Evaluation Framework

We propose to unify a variety of existing semantic classification tasks, such as semantic role labeling, anaphora resolution, and paraphrase detection, under the heading of Recognizing Textual Entailment (RTE). We present a general strategy to automatically generate one or more sentential hypotheses based on an input sentence and pre-existing manual semantic annotations. The resulting suite of datasets enables us to probe a statistical RTE model’s performance on different aspects of semantics. We demonstrate the value of this approach by investigating the behavior of a popular neural network RTE model.
Avoiding moving obstacles during visual navigation

Moving obstacle avoidance is a fundamental requirement for any robot operating in real environments, where pedestrians, bicycles and cars are present. In this work, we design and validate a new approach that takes explicitly into account obstacle velocities, to achieve safe visual navigation in outdoor scenarios. A wheeled vehicle, equipped with an actuated pinhole camera and with a lidar, must follow a path represented by key images, without colliding with the obstacles. To estimate the obstacle velocities, we design a Kalman-based observer. Then, we adapt the tentacles designed in , to take into account the predicted obstacle positions. Finally, we validate our approach in a series of simulated and real experiments, showing that when the obstacle velocities are considered, the robot behaviour is safer, smoother, and faster than when it is not.
A comparison between neural-network forecasting techniques-case study: river flow forecasting

Estimating the flows of rivers can have significant economic impact, as this can help in agricultural water management and in protection from water shortages and possible flood damage. The first goal of this paper is to apply neural networks to the problem of forecasting the flow of the River Nile in Egypt. The second goal of the paper is to utilize the time series as a benchmark to compare between several neural-network forecasting methods.We compare between four different methods to preprocess the inputs and outputs, including a novel method proposed here based on the discrete Fourier series. We also compare between three different methods for the multistep ahead forecast problem: the direct method, the recursive method, and the recursive method trained using a backpropagation through time scheme. We also include a theoretical comparison between these three methods. The final comparison is between different methods to perform longer horizon forecast, and that includes ways to partition the problem into the several subproblems of forecasting K steps ahead.
A review of the literature on citation impact indicators

Citation impact indicators nowadays play an important role in research evaluation, and consequently these indicators have received a lot of attention in the bibliometric and scientometric literature. This paper provides an in-depth review of the literature on citation impact indicators. First, an overview is given of the literature on bibliographic databases that can be used to calculate citation impact indicators (Web of Science, Scopus, and Google Scholar). Next, selected topics in the literature on citation impact indicators are reviewed in detail. The first topic is the selection of publications and citations to be included in the calculation of citation impact indicators. The second topic is the normalization of citation impact indicators, in particular normalization for field differences. Counting methods for dealing with co-authored publications are the third topic, and citation impact indicators for journals are the last topic. The paper concludes by offering some recommendations for future research.
A One-Hour Curriculum to Engage Middle School Students in Robotics and Computer Science Using Cubelets

Robotics has become a standard tool in outreaching to grades K-12 and attracting students to the STEM disciplines. Performing these activities in the class room usually requires substantial time commitment by the teacher and integration into the curriculum requires major effort, which makes spontaneous and short-term engagements difficult. This paper studies using “Cubelets”, a modular robotic construction kit, which requires virtually no setup time and allows substantial engagement and change of perception of STEM in as little as a 1-hour session. This paper describes the constructivist curriculum and provides qualitative and quantitative results on perception changes with respect to STEM and computer science in particular as a field of study.
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224<inline-formula><tex-math>$\times$ </tex-math><alternatives><inline-graphic xlink:type="simple" xlink:href="he-ieq1-2389824.gif"/></alternatives></inline-formula>224) input image. This requirement is “artificial” and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with another pooling strategy, “spatial pyramid pooling”, to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramid pooling is also robust to object deformations. With these advantages, SPP-net should in general improve all CNN-based image classification methods. On the ImageNet 2012 dataset, we demonstrate that SPP-net boosts the accuracy of a variety of CNN architectures despite their different designs. On the Pascal VOC 2007 and Caltech101 datasets, SPP-net achieves state-of-the-art classification results using a single full-image representation and no fine-tuning. The power of SPP-net is also significant in object detection. Using SPP-net, we compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features. In processing test images, our method is 24-102 <inline-formula><tex-math>$\times$</tex-math><alternatives><inline-graphic xlink:type="simple" xlink:href="he-ieq2-2389824.gif"/> </alternatives></inline-formula> faster than the R-CNN method, while achieving better or comparable accuracy on Pascal VOC 2007. In ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2014, our methods rank #2 in object detection and #3 in image classification among all 38 teams. This manuscript also introduces the improvement made for this competition.
A Wideband Dual-Polarized Antenna With Two Independently Controllable Resonant Modes and Its Array for Base-Station Applications

A wideband dual-polarized antenna with two independently controllable resonant modes for 2G/3G/4G bands is proposed. Its radiator mainly consists of two crossed stepped-width loop dipoles loaded with two crossed straight dipoles. Two approaches are adopted to design the proposed antenna with large freedom. First, by replacing the traditional uniform-width loop dipole with a stepped-width one, the first resonant mode can be adjusted independently by controlling the width ratio of the loop. Second, a pair of crossed straight dipoles are embedded into the gap of the crossed loop dipoles, and the second resonant mode can be controlled independently by altering the length or position of the straight dipoles. Measurements show that the antenna achieves an impedance bandwidth of 54.5% with VSWR< 1.5 (1.68–2.94 GHz) and isolation of greater than 28.5 dB within the bandwidth. Also, an antenna array with ten elements is developed with electrical downtilt from 0° to 8° for practical base-station applications.
Toward Engineering a Secure Android Ecosystem: A Survey of Existing Techniques

The openness and extensibility of Android have made it a popular platform for mobile devices and a strong candidate to drive the Internet-of-Things. Unfortunately, these properties also leave Android vulnerable, attracting attacks for profit or fun. To mitigate these threats, numerous issue-specific solutions have been proposed. With the increasing number and complexity of security problems and solutions, we believe this is the right moment to step back and systematically re-evaluate the Android security architecture and security practices in the ecosystem. We organize the most recent security research on the Android platform into two categories: the software stack and the ecosystem. For each category, we provide a comprehensive narrative of the problem space, highlight the limitations of the proposed solutions, and identify open problems for future research. Based on our collection of knowledge, we envision a blueprint for engineering a secure, next-generation Android ecosystem.
Connecting users across social media sites: a behavioral-modeling approach

People use various social media for different purposes. The information on an individual site is often incomplete. When sources of complementary information are integrated, a better profile of a user can be built to improve online services such as verifying online information. To integrate these sources of information, it is necessary to identify individuals across social media sites. This paper aims to address the cross-media user identification problem. We introduce a methodology (MOBIUS) for finding a mapping among identities of individuals across social media sites. It consists of three key components: the first component identifies users' unique behavioral patterns that lead to information redundancies across sites; the second component constructs features that exploit information redundancies due to these behavioral patterns; and the third component employs machine learning for effective user identification. We formally define the cross-media user identification problem and show that MOBIUS is effective in identifying users across social media sites. This study paves the way for analysis and mining across social media sites, and facilitates the creation of novel online services across sites.
Multimodal fusion using dynamic hybrid models

We propose a novel hybrid model that exploits the strength of discriminative classifiers along with the representational power of generative models. Our focus is on detecting multimodal events in time varying sequences. Discriminative classifiers have been shown to achieve higher performances than the corresponding generative likelihood-based classifiers. On the other hand, generative models learn a rich informative space which allows for data generation and joint feature representation that discriminative models lack. We employ a deep temporal generative model for unsupervised learning of a shared representation across multiple modalities with time varying data. The temporal generative model takes into account short term temporal phenomena and allows for filling in missing data by generating data within or across modalities. The hybrid model involves augmenting the temporal generative model with a temporal discriminative model for event detection, and classification, which enables modeling long range temporal dynamics. We evaluate our approach on audio-visual datasets (AVEC, AVLetters, and CUAVE) and demonstrate its superiority compared to the state-of-the-art.
Molecular pharmacology, regulation and function of mammalian melatonin receptors.

Melatonin (5-methoxy-N-acetyltryptamine), dubbed the hormone of darkness, is released following a circadian rhythm with high levels at night. It provides circadian and seasonal timing cues through activation of G protein-coupled receptors (GPCRs) in target tissues (1). The discovery of selective melatonin receptor ligands and the creation of mice with targeted disruption of melatonin receptor genes are valuable tools to investigate the localization and functional roles of the receptors in native systems. Here we describe the pharmacological characteristics of melatonin receptor ligands and their various efficacies (agonist, antagonist, or inverse agonist), which can vary depending on tissue and cellular milieu. We also review melatonin-mediated responses through activation of melatonin receptors (MT1, MT2, and MT3) highlighting their involvement in modulation of CNS, hypothalamic-hypophyseal-gonadal axis, cardiovascular, and immune functions. For example, activation of the MT1 melatonin receptor inhibits neuronal firing rate in the suprachiasmatic nucleus (SCN) and prolactin secretion from the pars tuberalis and induces vasoconstriction. Activation of the MT2 melatonin receptor phase shifts circadian rhythms generated within the SCN, inhibits dopamine release in the retina, induces vasodilation, enhances splenocyte proliferation and inhibits leukocyte rolling in the microvasculature. Activation of the MT3 melatonin receptor reduces intraocular pressure and inhibits leukotriene B4-induced leukocyte adhesion. We conclude that an accurate characterization of melatonin receptors mediating specific functions in native tissues can only be made using receptor specific ligands, with the understanding that receptor ligands may change efficacy in both native tissues and heterologous expression systems.
PERFORMANCE MEASUREMENT AND ORGANIZATIONAL EFFECTIVENESS : BRIDGING THE GAP

The aim of this paper is to bridge the gap between the organizational effectiveness (OE) models developed in the field of organizational theory and the performance measurement models presented within the management accounting literature. The specific evolution of these two complementary streams of research stemming from two different fields of research are reconciled and integrated by analyzing their convergences and divergences. As a response to theoretical and practical pressures, the evolution of OE models reflects a construct perspective, while the evolution of performance measurement models mirrors a process perspective. Performance measurement models have moved from a cybernetic view whereby performance measurement was based mainly on financial measures and considered as a component of the planning and control cycle to a holistic view based on multiple nonfinancial measures where performance measurement acts as an independent process included in a broader set of activities. This paper contributes to the performance measurement literature by establishing the origins of the performance measurement models and by shedding light on unexplored fertile areas of future
Latent Dirichlet Allocation

Latent Dirichlet allocation(LDA) is a generative topic model to find latent topics in a text corpus. It can be trained via collapsed Gibbs sampling. In this project, we train LDA models on two datasets, Classic400 and BBCSport dataset. We discuss possible ways to evaluate goodness-of-fit and to detect overfitting problem of LDA model, and we use these criteria to choose proper hyperparameters, observe convergence, and evaluate the models, the criteria we use include perplexity, VI-distance, visualization of clustering results, and highest-probability words.
inScent: a wearable olfactory display as an amplification for mobile notifications

We introduce inScent, a wearable olfactory display that can be worn in mobile everyday situations and allows the user to receive personal scented notifications, i.e. scentifications. Olfaction, i.e. the sense of smell, is used by humans as a sensorial information channel as an element for experiencing the environment. Olfactory sensations are closely linked to emotions and memories, but also notify about personal dangers such as fire or foulness. We want to utilize the properties of smell as a notification channel by amplifying received mobile notifications with artificially emitted scents. We built a wearable olfactory display that can be worn as a pendant around the neck and contains up to eight different scent aromas that can be inserted and quickly exchanged via small scent cartridges. Upon emission, scent aroma is vaporized and blown towards the user. A hardware - and software framework is presented that allows developers to add scents to their mobile applications. In a qualitative user study, participants wore the inScent wearable in public. We used subsequent semi-structured interviews and grounded theory to build a common understanding of the experience and derived lessons learned for the use of scentifications in mobile situations.
On unlimited sampling

Shannon's sampling theorem provides a link between the continuous and the discrete realms stating that bandlimited signals are uniquely determined by its values on a discrete set. This theorem is realized in practice using so called analog-to-digital converters (ADCs). Unlike Shannon's sampling theorem, the ADCs are limited in dynamic range. Whenever a signal exceeds some preset threshold, the ADC saturates, resulting in aliasing due to clipping. The goal of this paper is to analyze an alternative approach that does not suffer from these problems. Our work is based on recent developments in ADC design, which allow for ADCs that reset rather than to saturate, thus producing modulo samples. An open problem that remains is: Given such modulo samples of a bandlimited function as well as the dynamic range of the ADC, how can the original signal be recovered and what are the sufficient conditions that guarantee perfect recovery? In this paper, we prove such sufficiency conditions and complement them with a stable recovery algorithm. Our results are not limited to certain amplitude ranges, in fact even the same circuit architecture allows for the recovery of arbitrary large amplitudes as long as some estimate of the signal norm is available when recovering. Numerical experiments that corroborate our theory indeed show that it is possible to perfectly recover function that takes values that are orders of magnitude higher than the ADC's threshold.
Online video game therapy for mental health concerns: a review.

There has been research on the use of offline video games for therapeutic purposes but online video game therapy is still fairly under-researched. Online therapeutic interventions have only recently included a gaming component. Hence, this review represents a timely first step toward taking advantage of these recent technological and cultural innovations, particularly for the treatment of special-needs groups such as the young, the elderly and people with various conditions such as ADHD, anxiety and autism spectrum disorders.



A review integrating research findings on two technological advances was conducted: the home computer boom of the 1980s, which triggered a flood of research on therapeutic video games for the treatment of various mental health conditions; and the rise of the internet in the 1990s, which caused computers to be seen as conduits for therapeutic interaction rather than replacements for the therapist.



We discuss how video games and the internet can now be combined in therapeutic interventions, as attested by a consideration of pioneering studies.



Future research into online video game therapy for mental health concerns might focus on two broad types of game: simple society games, which are accessible and enjoyable to players of all ages, and online worlds, which offer a unique opportunity for narrative content and immersive remote interaction with therapists and fellow patients. Both genres might be used for assessment and training purposes, and provide an unlimited platform for social interaction. The mental health community can benefit from more collaborative efforts between therapists and engineers, making such innovations a reality.
Multilevel thresholding selection based on chaotic multi-verse optimization for image segmentation

Multilevel thresholding is the most important method for image processing. Conventional multilevel thresholding methods have proven to be efficient in bi-level thresholding; however, when extended to multilevel thresholding, they prove to be computationally more costly, as they comprehensively search the optimal thresholds for the objective function. This paper presents a chaotic multi-verse optimizer (CMVO) algorithm using Kapur's objective function in order to determine the optimal multilevel thresholds for image segmentation. The proposed CMVO algorithm was applied to various standard test images, and evaluated by peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). The CMVO algorithm efficiently and accurately searched multilevel thresholds and reduced the required computational times.
Comparative analysis of solid waste management in 20 cities.

This paper uses the 'lens' of integrated and sustainable waste management (ISWM) to analyse the new data set compiled on 20 cities in six continents for the UN-Habitat flagship publication Solid Waste Management in the World's Cities. The comparative analysis looks first at waste generation rates and waste composition data. A process flow diagram is prepared for each city, as a powerful tool for representing the solid waste system as a whole in a comprehensive but concise way. Benchmark indicators are presented and compared for the three key physical components/drivers: public health and collection; environment and disposal; and resource recovery--and for three governance strategies required to deliver a well-functioning ISWM system: inclusivity; financial sustainability; and sound institutions and pro-active policies. Key insights include the variety and diversity of successful models - there is no 'one size fits all'; the necessity of good, reliable data; the importance of focusing on governance as well as technology; and the need to build on the existing strengths of the city. An example of the latter is the critical role of the informal sector in the cities in many developing countries: it not only delivers recycling rates that are comparable with modern Western systems, but also saves the city authorities millions of dollars in avoided waste collection and disposal costs. This provides the opportunity for win-win solutions, so long as the related wider challenges can be addressed.