nils-herrmann/clean_scirepeval huggingface dataset:
Are improvements in shame and self-compassion early in eating disorders treatment associated with better patient outcomes?

Compassion-focused therapy (CFT; Gilbert, 2005, 2009) is a transdiagnostic treatment approach focused on building self-compassion and reducing shame. It is based on the theory that feelings of shame contribute to the maintenance of psychopathology, whereas self-compassion contributes to the alleviation of shame and psychopathology. We sought to test this theory in a transdiagnostic sample of eating disorder patients by examining whether larger improvements in shame and self-compassion early in treatment would facilitate faster eating disorder symptom remission over 12 weeks. Participants were 97 patients with an eating disorder admitted to specialized day hospital or inpatient treatment. They completed the Eating Disorder Examination-Questionnaire, Experiences of Shame Scale, and Self-Compassion Scale at intake, and again after weeks 3, 6, 9, and 12. Multilevel modeling revealed that patients who experienced greater decreases in their level of shame in the first 4 weeks of treatment had faster decreases in their eating disorder symptoms over 12 weeks of treatment. In addition, patients who had greater increases in their level of self-compassion early in treatment had faster decreases in their feelings of shame over 12 weeks, even when controlling for their early change in eating disorder symptoms. These results suggest that CFT theory may help to explain the maintenance of eating disorders. Clinically, findings suggest that intervening with shame early in treatment, perhaps by building patients' self-compassion, may promote better eating disorders treatment response.
A Framework for Detecting MAC and IP Spoofing Attacks with Network Characteristics

This paper presents a spoofing attack detection framework based on physical network characteristics (e.g., received signal strength indicator round trip time and link quality indicator) that cannot easily be mimicked by artificial means. Unlike most previous studies that are sensitive to changes in network conditions, we propose a spoofing attack detection method, which is highly robust to the changes of network conditions over time. The proposed framework can monitor devices' physical network characteristics in real time and check if any significant changes have been made in the monitored measurements to effectively detect device spoofing attacks. To demonstrate the feasibility of the proposed framework, we analyzed how the RSSI values of packets were changed with varying physical distances in a real ZigBee (IEEE 802.15.4) network environment.
Trained Ternary Quantization

Deep neural networks are widely used in machine learning applications. However, the deployment of large neural networks models can be difficult to deploy on mobile devices with limited power budgets. To solve this problem, we propose Trained Ternary Quantization (TTQ), a method that can reduce the precision of weights in neural networks to ternary values. This method has very little accuracy degradation and can even improve the accuracy of some models (32, 44, 56-layer ResNet) on CIFAR-10 and AlexNet on ImageNet. And our AlexNet model is trained from scratch, which means it’s as easy as to train normal full precision model. We highlight our trained quantization method that can learn both ternary values and ternary assignment. During inference, only ternary values (2-bit weights) and scaling factors are needed, therefore our models are nearly 16× smaller than fullprecision models. Our ternary models can also be viewed as sparse binary weight networks, which can potentially be accelerated with custom circuit. Experiments on CIFAR-10 show that the ternary models obtained by trained quantization method outperform full-precision models of ResNet-32,44,56 by 0.04%, 0.16%, 0.36%, respectively. On ImageNet, our model outperforms full-precision AlexNet model by 0.3% of Top-1 accuracy and outperforms previous ternary models by 3%.
Task assignment in spatial crowdsourcing: challenges and approaches

Spatial crowdsourcing (a.k.a mobile crowdsourcing) is a new paradigm of data collection, which has been emerged in the last few years to enable workers to perform tasks in the physical world. The objective of spatial crowdsourcing is to outsource a set of location-specific tasks to a set of workers, in which the workers are required to physically be at the task locations to complete them, i.e., taking pictures or collecting air quality information at specified locations of interest. In this paper, we discuss the unique challenges of spatial crowdsourcing: task assignment, incentive mechanism, worker's location privacy and the absence of real-world datasets. Thereafter, we present our current approaches to those issues.
Moving tag detection via physical layer analysis for large-scale RFID systems

In a number of RFID-based applications such as logistics monitoring, the RFID systems are deployed to monitor a large number of RFID tags. They are usually required to track the movement of all tags in a real-time approach, since the tagged-goods are moved in and out in a rather frequent approach. However, a typical cycle of tag inventory in COTS RFID system usually takes tens of seconds to interrogate hundreds of RFID tags. This hinders the system to track the movement of all tags in time. One critical issue in such type of tag monitoring is to efficiently distinguish the motion status of all tags, i.e., stationary or moving. According to the motion status of different tags, the state-of-art localization schemes can further track those moving tags, instead of tracking all tags. In this paper, we propose a real-time approach to detect the moving tags in the monitoring area, which is a fundamental premise to support tracking the movement of all tags. We achieve the time efficiency by decoding collisions from the physical layer. Instead of using the EPC ID, which cannot be decoded in collision slots, we are able to extract two kinds of physical-layer features of RFID tags, i.e., the phase profile and the backscatter link frequency, to distinguish among different tags in different positions. By resolving the two physical-layer features from the tag collisions, we are able to derive the motion status of multiple tags simultaneously, and greatly improve the time-efficiency. Experiment result shows that our solution can accurately detect the moving tags while reducing 80% of inventory time compared with the state-of-art solutions.
Influence Maximization in Dynamic Social Networks

Social influence and influence diffusion has been widely studied in online social networks. However, most existing works on influence diffusion focus on static networks. In this paper, we study the problem of maximizing influence diffusion in a dynamic social network. Specifically, the network changes over time and the changes can be only observed by periodically probing some nodes for the update of their connections. Our goal then is to probe a subset of nodes in a social network so that the actual influence diffusion process in the network can be best uncovered with the probing nodes. We propose a novel algorithm to approximate the optimal solution. The algorithm, through probing a small portion of the network, minimizes the possible error between the observed network and the real network. We evaluate the proposed algorithm on both synthetic and real large networks. Experimental results show that our proposed algorithm achieves a better performance than several alternative algorithms.
Developing pervasive multi-agent systems with nature-inspired coordination

Pervasive computing systems can bemodelled effectively as populations of interacting autonomous components. The key challenge to realizing suchmodels is in getting separatelyspecified and -developed sub-systems to discover and interoperate with each other in an open and extensible way, supported by appropriate middleware services. In this paper, we argue that nature-inspired coordination models offer a promising way of addressing this challenge.We first frame the various dimensions alongwhich nature-inspired coordination models can be defined, and survey themost relevant proposals in the area.We describe the nature-inspired coordination model developed within the SAPERE project as a synthesis of existing approaches, and show how it can effectively support the multifold requirements of modern and emerging pervasive services. We conclude by identifying what we think are the open research challenges in this area, and identify some research directions that we believe are promising.
A Scalable Multi-User Uplink for Wi-Fi

Mobile devices have fewer antennas than APs due to size and energy constraints. This antenna asymmetry restricts uplink capacity to the client antenna array size rather than the AP’s. To overcome antenna asymmetry, multiple clients can be grouped into a simultaneous multiuser transmission to achieve a full rank transmission that matches the number of antennas at the AP. In this paper, we design, implement, and experimentally evaluate MUSE, the first distributed and scalable system to achieve full-rank uplink multi-user capacity without control signaling for channel estimation, channel reporting, or user selection. Our experiments demonstrate full-rank multiplexing gains in the evaluated scenarios that show linear gains as the number of users increase while maintaining constant overhead.
Hybrid digital and analog beamforming design for large-scale MIMO systems

Large-scale multiple-input multiple-output (MIMO) systems enable high spectral efficiency by employing large antenna arrays at both the transmitter and the receiver of a wireless communication link. In traditional MIMO systems, full digital beamforming is done at the baseband; one distinct radio-frequency (RF) chain is required for each antenna, which for large-scale MIMO systems can be prohibitive from either cost or power consumption point of view. This paper considers a two-stage hybrid beamforming structure to reduce the number of RF chains for large-scale MIMO systems. The overall beamforming matrix consists of analog RF beamforming implemented using phase shifters and baseband digital beamforming of much smaller dimension. This paper considers precoder and receiver design for maximizing the spectral efficiency when the hybrid structure is used at both the transmitter and the receiver. On the theoretical front, bounds on the minimum number of transmit and receive RF chains that are required to realize the theoretical capacity of the large-scale MIMO system are presented. It is shown that the hybrid structure can achieve the same performance as the fully-digital beamforming scheme if the number of RF chains at each end is greater than or equal to twice the number of data streams. On the practical design front, this paper proposes a heuristic hybrid beamforming design strategy for the critical case where the number of RF chains is equal to the number of data streams, and shows that the performance of the proposed hybrid beamforming design can achieve spectral efficiency close to that of the fully-digital solution.
An Approach to Data Analysis in 5G Networks

5G networks expect to provide significant advances in network management compared to traditional mobile infrastructures by leveraging intelligence capabilities such as data analysis, prediction, pattern recognition and artificial intelligence. The key idea behind these actions is to facilitate the decision-making process in order to solve or mitigate common network problems in a dynamic and proactive way. In this context, this paper presents the design of Self-Organized Network Management in Virtualized and Software Defined Networks (SELFNET) Analyzer Module, which main objective is to identify suspicious or unexpected situations based on metrics provided by different network components and sensors. The SELFNET Analyzer Module provides a modular architecture driven by use cases where analytic functions can be easily extended. This paper also proposes the data specification to define the data inputs to be taking into account in diagnosis process. This data specification has been implemented with different use cases within SELFNET Project, proving its effectiveness.
A Passive RFID Information Grid for Location and Proximity Sensing for the Blind User

We describe a navigation and location determination system for the blind using an RFID tag grid. Each RFID tag is programmed upon installation with spatial coordinates and information describing the surroundings. This allows for a self-describing, localized information system with no dependency on a centralized database or wireless infrastructure for communications. The system could be integrated into building code requirements as part of ADA (Americans with Disabilities Act) at a cost of less than $1 per square foot. With an established RFID grid infrastructure blind children and adults will gain the independence and freedom to explore and participate in activities without external assistance. An established RFID grid infrastructure will also enable advances in robotics which will benefit from knowing precise location. In this paper, we present an RFID based information grid system with a reader integrated into the user’s shoe, which is connected to the user PDA or cell phone via a Bluetooth. An emphasis is placed on the architecture and design to allow for a truly integrated pervasive environment.
Label propagation in video sequences

This paper proposes a probabilistic graphical model for the problem of propagating labels in video sequences, also termed the label propagation problem. Given a limited amount of hand labelled pixels, typically the start and end frames of a chunk of video, an EM based algorithm propagates labels through the rest of the frames of the video sequence. As a result, the user obtains pixelwise labelled video sequences along with the class probabilities at each pixel. Our novel algorithm provides an essential tool to reduce tedious hand labelling of video sequences, thus producing copious amounts of useable ground truth data. A novel application of this algorithm is in semi-supervised learning of discriminative classifiers for video segmentation and scene parsing. The label propagation scheme can be based on pixel-wise correspondences obtained from motion estimation, image patch based similarities as seen in epitomic models or even the more recent, semantically consistent hierarchical regions. We compare the abilities of each of these variants, both via quantitative and qualitative studies against ground truth data. We then report studies on a state of the art Random forest classifier based video segmentation scheme, trained using fully ground truth data and with data obtained from label propagation. The results of this study strongly support and encourage the use of the proposed label propagation algorithm.
Heimdall: Mitigating the Internet of Insecure Things

The Internet of Things (IoT) is built of many small smart objects continuously connected to the Internet. This makes these devices an easy target for attacks exploiting vulnerabilities at the network, application, and mobile level. With that it comes as no surprise that distributed denial of service attacks leveraging these vulnerable devices have become a new standard for effective botnets. In this paper, we propose Heimdall, a whitelist-based intrusion detection technique tailored to IoT devices. Heimdall operates on routers acting as gateways for IoT as a homogeneous defense for all devices behind the router. Our experimental results show that our defense mechanism is effective and has minimal overhead.
White Matter MS-Lesion Segmentation Using a Geometric Brain Model

Brain magnetic resonance imaging (MRI) in patients with Multiple Sclerosis (MS) shows regions of signal abnormalities, named plaques or lesions. The spatial lesion distribution plays a major role for MS diagnosis. In this paper we present a 3D MS-lesion segmentation method based on an adaptive geometric brain model. We model the topological properties of the lesions and brain tissues in order to constrain the lesion segmentation to the white matter. As a result, the method is independent of an MRI atlas. We tested our method on the MICCAI MS grand challenge proposed in 2008 and achieved competitive results. In addition, we used an in-house dataset of 15 MS patients, for which we achieved best results in most distances in comparison to atlas based methods. Besides classical segmentation distances, we motivate and formulate a new distance to evaluate the quality of the lesion segmentation, while being robust with respect to minor inconsistencies at the boundary level of the ground truth annotation.
The formation and function of plant cuticles.

The plant cuticle is an extracellular hydrophobic layer that covers the aerial epidermis of all land plants, providing protection against desiccation and external environmental stresses. The past decade has seen considerable progress in assembling models for the biosynthesis of its two major components, the polymer cutin and cuticular waxes. Most recently, two breakthroughs in the long-sought molecular bases of alkane formation and polyester synthesis have allowed construction of nearly complete biosynthetic pathways for both waxes and cutin. Concurrently, a complex regulatory network controlling the synthesis of the cuticle is emerging. It has also become clear that the physiological role of the cuticle extends well beyond its primary function as a transpiration barrier, playing important roles in processes ranging from development to interaction with microbes. Here, we review recent progress in the biochemistry and molecular biology of cuticle synthesis and function and highlight some of the major questions that will drive future research in this field.
Recognition with Local Features: the Kernel Recipe

Recent developments in computer vision have shown that local features can provide efficient representations suitable for robust object recognition. Support Vector Machines have been established as powerful learning algorithms with good generalization capabilities. In this paper, we combine these two approaches and propose a general kernel method for recognition with local features. We show that the proposed kernel satisfies the Mercer condition and that it is suitable for many established local feature frameworks. Large-scale recognition results are presented on three different databases, which demonstrate that SVMs with the proposed kernel perform better than standard matching techniques on local features. In addition, experiments on noisy and occluded images show that local feature representations significantly outperform global approaches.
Modern Recommender Systems: from Computing Matrices to Thinking with Neurons

Starting with the Netflix Prize, which fueled much recent progress in the field of collaborative filtering, recent years have witnessed rapid development of new recommendation algorithms and increasingly more complex systems, which greatly differ from their early content-based and collaborative filtering systems. Modern recommender systems leverage several novel algorithmic approaches: from matrix factorization methods and multi-armed bandits to deep neural networks. In this tutorial, we will cover recent algorithmic advances in recommender systems, highlight their capabilities, and their impact. We will give many examples of industrial-scale recommender systems that define the future of the recommender systems area. We will discuss related evaluation issues, and outline future research directions. The ultimate goal of the tutorial is to encourage the application of novel recommendation approaches to solve problems that go beyond user consumption and to further promote research in the intersection of recommender systems and databases.
Scaling regression testing to large software systems

When software is modified, during development and maintenance, it is <i>regression tested</i> to provide confidence that the changes did not introduce unexpected errors and that new features behave as expected. One important problem in regression testing is how to select a subset of test cases, from the test suite used for the original version of the software, when testing a modified version of the software. Regression-test-selection techniques address this problem. Safe regression-test-selection techniques select every test case in the test suite that may behave differently in the original and modified versions of the software. Among existing safe regression testing techniques, efficient techniques are often too imprecise and achieve little savings in testing effort, whereas precise techniques are too expensive when used on large systems. This paper presents a new regression-test-selection technique for Java programs that is safe, precise, and yet scales to large systems. It also presents a tool that implements the technique and studies performed on a set of subjects ranging from 70 to over 500 KLOC. The studies show that our technique can efficiently reduce the regression testing effort and, thus, achieve considerable savings.
Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition

Here, we develop an audiovisual deep residual network for multimodal apparent personality trait recognition. The network is trained end-to-end for predicting the Big Five personality traits of people from their videos. That is, the network does not require any feature engineering or visual analysis such as face detection, face landmark alignment or facial expression recognition. Recently, the network won the third place in the ChaLearn First Impressions Challenge with a test accuracy of 0.9109.
Synthetic 2D LIDAR for precise vehicle localization in 3D urban environment

This paper presents a precise localization algorithm for vehicles in 3D urban environment with only one 2D LIDAR and odometry information. A novel idea of synthetic 2D LIDAR is proposed to solve the localization problem on a virtual 2D plane. A Monte Carlo Localization scheme is adopted for vehicle position estimation, based on synthetic LIDAR measurements and odometry information. The accuracy and robustness of the proposed algorithm are demonstrated by performing real time localization in a 1.5 km driving test around the NUS campus area.
STAR-MPI: self tuned adaptive routines for MPI collective operations

Message Passing Interface (MPI) collective communication routines are widely used in parallel applications. In order for a collective communication routine to achieve high performance for different applications on different platforms, it must be adaptable to both the system architecture and the application workload. Current MPI implementations do not support such software adaptability and are not able to achieve high performance on many platforms. In this paper, we present STAR-MPI (Self Tuned Adaptive Routines for MPI collective operations), a set of MPI collective communication routines that are capable of adapting to system architecture and application workload. For each operation, STAR-MPI maintains a set of communication algorithms that can potentially be efficient at different situations. As an application executes, a STAR-MPI routine applies the Automatic Empirical Optimization of Software (AEOS) technique at run time to dynamically select the best performing algorithm for the application on the platform. We describe the techniques used in STAR-MPI, analyze STAR-MPI overheads, and evaluate the performance of STAR-MPI with applications and benchmarks. The results of our study indicate that STAR-MPI is robust and efficient. It is able to and efficient algorithms with reasonable overheads, and it out-performs traditional MPI implementations to a large degree in many cases.