nils-herrmann/clean_scirepeval huggingface dataset:
Boosting The Benefits of Hybrid SDN

The upgrade of a legacy network to a full software-defined networking (SDN) deployment is usually an incremental process, during which SDN switches and legacy switches coexist in the hybrid network. However, with inappropriate deployment of SDN switches and design of hybrid control, the advantages of SDN control could not exert, and it even results in performance degradation or inconsistency (e.g., loops, black-holes). Therefore, the hybrid SDN requires considerable coordination of the centralized control and distributed routing. In this paper, we propose a solution to handle the heterogeneity caused by distinct forwarding characteristics of SDN and legacy switches, therefore boosting the benefits of hybrid SDN. We plan SDN placement to enhance the SDN controllability over the hybrid network, and conduct traffic engineering considering both the forwarding characteristics of SDN and legacy switches. The experiments with various topologies show that the SDN placement planning and hybrid forwarding yield better network performance especially in the early 70% SDN deployment.
New acquisition method based on firmware update protocols for Android smartphones

Android remains the dominant OS in the smartphone market even though the iOS share of the market increased during the iPhone 6 release period. As various types of Android smartphones are being launched in the market, forensic studies are being conducted to test data acquisition and analysis. However, since the application of new Android security technologies, it has become more difficult to acquire data using existing forensic methods. In order to address this problem, we propose a new acquisition method based on analyzing the firmware update protocols of Android smartphones. A physical acquisition of Android smartphones can be achieved using the flash memory read command by reverse engineering the firmware update protocol in the bootloader. Our experimental results demonstrate that the proposed method is superior to existing forensic methods in terms of the integrity guarantee, acquisition speed, and physical dump with screen-locked smartphones (USB debugging disabled).
Detecting Moving Shadows: Algorithms and Evaluation

Moving shadows need careful consideration in the development of robust dynamic scene analysis systems. Moving shadow detection is critical for accurate object detection in video streams since shadow points are often misclassified as object points, causing errors in segmentation and tracking. Many algorithms have been proposed in the literature that deal with shadows. However, a comparative evaluation of the existing approaches is still lacking. In this paper, we present a comprehensive survey of moving shadow detection approaches. We organize contributions reported in the literature in four classes two of them are statistical and two are deterministic. We also present a comparative empirical evaluation of representative algorithms selected from these four classes. Novel quantitative (detection and discrimination rate) and qualitative metrics (scene and object independence, flexibility to shadow situations, and robustness to noise) are proposed to evaluate these classes of algorithms on a benchmark suite of indoor and outdoor video sequences. These video sequences and associated “groundtruth” data are made available at http://cvrr.ucsd.edu/aton/shadow to allow for others in the community to experiment with new algorithms and metrics.
H2O: a hands-free adaptive store

Modern state-of-the-art database systems are designed around a single data storage layout. This is a fixed decision that drives the whole architectural design of a database system, i.e., row-stores, column-stores. However, none of those choices is a universally good solution; different workloads require different storage layouts and data access methods in order to achieve good performance.
 In this paper, we present the H2O system which introduces two novel concepts. First, it is flexible to support multiple storage layouts and data access patterns in a single engine. Second, and most importantly, it decides on-the-fly, i.e., during query processing, which design is best for classes of queries and the respective data parts. At any given point in time, parts of the data might be materialized in various patterns purely depending on the query workload; as the workload changes and with every single query, the storage and access patterns continuously adapt. In this way, H2O makes no a priori and fixed decisions on how data should be stored, allowing each single query to enjoy a storage and access pattern which is tailored to its specific properties.
 We present a detailed analysis of H2O using both synthetic benchmarks and realistic scientific workloads. We demonstrate that while existing systems cannot achieve maximum performance across all workloads, H2O can always match the best case performance without requiring any tuning or workload knowledge.
Realtime multi-aircraft tracking in aerial scene with deep orientation network

Tracking the aircrafts from an aerial view is very challenging due to large appearance, perspective angle, and orientation variations. The deep-patch orientation network (DON) method was proposed for the multi-ground target tracking system, which is general and can learn the target’s orientation based on the structure information in the training samples. Such approach leverages the performance of tracking-by-detection framework into two aspects: one is to improve the detectability of the targets by using the patch-based model for the target localization in the detection component and the other is to enhance motion characteristics of the individual tracks by incorporating the orientation information as an association metric in the tracking component. Based on the DON structure, you only look once (YOLO) and faster region convolutional neural network (FrRCNN) detection frameworks with simple online and realtime tracking (SORT) tracker are utilized as a case study. The Comparative experiments demonstrate that the overall detection accuracy is improved at the same processing speed of both detection frameworks. Furthermore, the number of Identity switches (IDsw) has reduced about 67% without affecting the computational complexity of the tracking component. Consequently, the presented method is efficient for realtime ground target-tracking scenarios.
A Clustering Based Adaptive Sequence-to-Sequence Model for Dialogue Systems

Dialogue systems which can communicate with people in natural language is popularly used in entertainments and language learning tools. As the development of deep neural networks, Sequence-to-Sequence models become the main stream models of conversation generation tasks which are the key part of dialogue systems, because Sequence-to-Sequence models is good at dealing with the tasks like machine translation and conversation generation whose input's length and output's length is unknown previously. However, recent works find that Sequence-to-Sequence models tend to respond in dull sentences. We propose a clustering based adaptive Sequence-to-Sequence model to improve the performance of dialogue systems. Different with previous models who treat all the dialogue data as input of a single model, we cluster the dialogue data and use several Sequence-to-Sequence models to train different cluster of data to catch different characteristic in different cluster. Our experiments show that our models can improve the performance of dialogue systems.
Systematic review of the efficacy of meditation techniques as treatments for medical illness.

Meditative techniques are sought frequently by patients coping with medical and psychological problems. Because of their increasingly widespread appeal and use, and the potential for use as medical therapies, a concise and thorough review of the current state of scientific knowledge of these practices as medical interventions was conducted.



To systematically review the evidence supporting efficacy and safety of meditative practices in treating illnesses, and examine areas warranting further study. Studies on normal healthy populations are not included.



Searches were performed using PubMed, PsycInfo, and the Cochrane Database. Keywords were Meditation, Meditative Prayer, Yoga, Relaxation Response. Qualifying studies were reviewed and independently rated based on quality by two reviewers. Mid-to-high-quality studies (those scoring above 0.65 or 65% on a validated research quality scale) were included.



From a total of 82 identified studies, 20 randomized controlled trials met our criteria. The studies included 958 subjects total (397 experimentally treated, 561 controls). No serious adverse events were reported in any of the included or excluded clinical trials. Serious adverse events are reported in the medical literature, though rare. The strongest evidence for efficacy was found for epilepsy, symptoms of the premenstrual syndrome and menopausal symptoms. Benefit was also demonstrated for mood and anxiety disorders, autoimmune illness, and emotional disturbance in neoplastic disease.



The results support the safety and potential efficacy of meditative practices for treating certain illnesses, particularly in nonpsychotic mood and anxiety disorders. Clear and reproducible evidence supporting efficacy from large, methodologically sound studies is lacking.
Limits of predictability in human mobility.

A range of applications, from predicting the spread of human and electronic viruses to city planning and resource management in mobile communications, depend on our ability to foresee the whereabouts and mobility of individuals, raising a fundamental question: To what degree is human behavior predictable? Here we explore the limits of predictability in human dynamics by studying the mobility patterns of anonymized mobile phone users. By measuring the entropy of each individual's trajectory, we find a 93% potential predictability in user mobility across the whole user base. Despite the significant differences in the travel patterns, we find a remarkable lack of variability in predictability, which is largely independent of the distance users cover on a regular basis.
ICWSM - A Great Catchy Name: Semi-Supervised Recognition of Sarcastic Sentences in Online Product Reviews

Sarcasm is a sophisticated form of speech act widely used in online communities. Automatic recognition of sarcasm is, however, a novel task. Sarcasm recognition could contribute to the performance of review summarization and ranking systems. This paper presents SASI, a novel Semi-supervised Algorithm for Sarcasm Identification that recognizes sarcastic sentences in product reviews. SASI has two stages: semisupervised pattern acquisition, and sarcasm classification. We experimented on a data set of about 66000 Amazon reviews for various books and products. Using a gold standard in which each sentence was tagged by 3 annotators, we obtained precision of 77% and recall of 83.1% for identifying sarcastic sentences. We found some strong features that characterize sarcastic utterances. However, a combination of more subtle pattern-based features proved more promising in identifying the various facets of sarcasm. We also speculate on the motivation for using sarcasm in online communities and social networks.
Spatial-semantics: How users derive shape from information space

User problems with large information spaces multiply in complexity when we enter the digital domain. Virtual information environments can offer 3-D representations, reconfigurations and access to large databases that can overwhelm many users’ abilities to filter and represent. As a result, users frequently experience disorientation in navigating large digital spaces to locate and use information. To date, the research response has been predominantly based on the analysis of visual navigational aids that might support users' bottom-up processing of the spatial display. In the present paper an emerging alternative is considered that places greater emphasis on the top-down application of semantic knowledge by the user gleaned from their experiences within the socio-cognitive context of information production and consumption. A distinction between spatial and semantic cues is introduced and existing empirical data are reviewed that highlight the differential reliance on spatial or semantic information as domain expertise of the user increases. The conclusion is reached that interfaces for shaping information should be built on an increasing analysis of users' semantic processing.
An automated framework for software test oracle

One of the important issues of software testing is to provide an automated test oracle. Test oracles are reliable sources of how the software under test must operate. In particular, they are used to evaluate the actual results that produced by the software. However, in order to generate an automated test oracle, oracle challenges need to be addressed. These challenges are output-domain generation, input domain to output domain mapping, and a comparator to decide on the accuracy of the actual outputs.  This paper proposes an automated test oracle framework to address all of these challenges.  I/O Relationship Analysis is used to generate the output domain automatically and MultiNetworks Oracles based on artificial neural networks are introduced to handle the second challenge. The last challenge is addressed using an automated comparator that adjusts the oracle precision by defining the comparison tolerance. The proposed approach was evaluated using an industry strength case study, which was injected with some faults. The quality of the proposed oracle was measured by assessing its accuracy, precision, misclassification error and practicality. Mutation testing was considered to provide the evaluation framework by implementing two different versions of the case study: a Golden Version and a Mutated Version. Furthermore, a comparative study between the existing automated oracles and the proposed one is provided based on which challenges they can automate.  Results indicate that the proposed approach automated the oracle generation process 97% in this experiment. Accuracy of the proposed oracle was up to 98.26%, and the oracle detected up to 97.7% of the injected faults.  Consequently, the results of the study highlight the practicality of the proposed oracle in addition to the automation it offers.
Enhancing representation learning with tensor decompositions for knowledge graphs and high dimensional sequence modeling

The capability of processing and digesting raw data is one of the key features of a humanlike artificial intelligence system. For instance, real-time machine translation should be able to process and understand spoken natural language, and autonomous driving relies on the comprehension of visual inputs. Representation learning is a class of machine learning techniques that autonomously learn to derive latent features from raw data. These new features are expected to represent the data instances in a vector space that facilitates the machine learning task. This thesis studies two specific data situations that require efficient representation learning: knowledge graph data and high dimensional sequences. In the first part of this thesis, we first review multiple relational learning models based on tensor decomposition for knowledge graphs. We point out that relational learning is in fact a means of learning representations through one-hot mapping of entities. Furthermore, we generalize this mapping function to consume a feature vector that encodes all known facts about each entity. It enables the relational model to derive the latent representation instantly for a new entity, without having to re-train the tensor decomposition. In the second part, we focus on learning representations from high dimensional sequential data. Sequential data often pose the challenge that they are of variable lengths. Electronic health records, for instance, could consist of clinical event data that have been collected at subsequent time steps. But each patient may have a medical history of variable length. We apply recurrent neural networks to produce fixed-size latent representations from the raw feature sequences of various lengths. By exposing a prediction model to these learned representations instead of the raw features, we can predict the therapy prescriptions more accurately as a means of clinical decision support. We further propose Tensor-Train recurrent neural networks. We give a detailed introduction to the technique of tensorizing and decomposing large weight matrices into a few smaller tensors. We demonstrate the specific algorithms to perform the forward-pass and the back-propagation in this setting. Then we apply this approach to the input-to-hidden weight matrix in recurrent neural networks. This novel architecture can process extremely high dimensional sequential features such as video data. The model also provides a promising solution to processing sequential features with high sparsity. This is, for instance, the case with electronic health records, since they are often of categorical nature and have to be binary-coded. We incorporate a statistical survival model with this representation learning model, which shows superior prediction quality.
Design and Analysis of a Full-Bridge LLC-Based PEV Charger Optimized for Wide Battery Voltage Range

In this paper, a two-stage onboard battery charger is analyzed for plug-in electric vehicles (PEVs). An interleaved boost topology is employed in the first stage for power factor correction (PFC) and to reduce total harmonic distortion (THD). In the second stage, a full-bridge LLC-based multiresonant converter is adopted for galvanic isolation and dc/dc conversion. Design considerations are discussed, focusing on reducing the charger volume and optimizing the conversion efficiency over the wide battery-pack voltage range. A detailed design procedure is provided for a 1-kW prototype, charging the battery with an output voltage range of 320-420 V from 110-V 60-Hz single-phase grid. Experimental results show that the first-stage PFC converter achieves THD of less than 4% and a power factor higher than 0.99, and the second-stage LLC converter operates with 95.4% peak efficiency and good overall efficiency over wide output voltage ranges.
Advances in direct transesterification of algal oils from wet biomass.

An interest in biodiesel as an alternative fuel for diesel engines has been increasing because of the issue of petroleum depletion and environmental concerns related to massive carbon dioxide emissions. Researchers are strongly driven to pursue the next generation of vegetable oil-based biodiesel. Oleaginous microalgae are considered to be a promising alternative oil source. To commercialize microalgal biodiesel, cost reductions in oil extraction and downstream biodiesel conversion are stressed. Herein, starting from an investigation of oil extraction from wet microalgae, a review is conducted of transesterification using enzymes, homogeneous and heterogeneous catalysts, and yield enhancement by ultrasound, microwave, and supercritical process. In particular, there is a focus on direct transesterification as a simple and energy efficient process that omits a separate oil extraction step and utilizes wet microalgal biomass; however, it is still necessary to consider issues such as the purification of microalgal oils and upgrading of biodiesel properties.
Sexual Orientation and Gender Identity Data Collection in Clinical Settings and in Electronic Health Records: A Key to Ending LGBT Health Disparities.

The Institute of Medicine's (IOM's) 2011 report on the health of LGBT people pointed out that there are limited health data on these populations and that we need more research. It also described what we do know about LGBT health disparities, including lower rates of cervical cancer screening among lesbians, and mental health issues related to minority stress. Patient disclosure of LGBT identity enables provider-patient conversations about risk factors and can help us reduce and better understand disparities. It is essential to the success of Healthy People 2020's goal of eliminating LGBT health disparities. This is why the IOM's report recommended data collection in clinical settings and on electronic health records (EHRs). The Center for Medicare and Medicaid Services and the Office of the National Coordinator of Health Information Technology rejected including sexual orientation and gender identity (SOGI) questions in meaningful use guidelines for EHRs in 2012 but are considering this issue again in 2013. There is overwhelming community support for the routine collection of SOGI data in clinical settings, as evidenced by comments jointly submitted by 145 leading LGBT and HIV/AIDS organizations in January 2013. Gathering SOGI data in EHRs is supported by the 2011 IOM's report on LGBT health, Healthy People 2020, the Affordable Care Act, and the Joint Commission. Data collection has long been central to the quality assurance process. Preventive health care from providers knowledgeable of their patients' SOGI can lead to improved access, quality of care, and outcomes. Medical and nursing schools should expand their attention to LGBT health issues so that all clinicians can appropriately care for LGBT patients.
Synthesizing data for text recognition with style transfer

Most of the existing datasets for scene text recognition merely consist of a few thousand training samples with a very limited vocabulary, which cannot meet the requirement of the state-of-the-art deep learning based text recognition methods. Meanwhile, although the synthetic datasets (e.g., SynthText90k) usually contain millions of samples, they cannot fit the data distribution of the small target datasets in natural scenes completely. To address these problems, we propose a word data generating method called SynthText-Transfer, which is capable of emulating the distribution of the target dataset. Transfer uses a style transfer method to generate samples with arbitray text content, which preserve the texture of the reference sample in the target dataset. The generated images are not only visibly similar with real images, but also capable of improving the accuracy of the state-of-the-art text recognition methods, especially for the English and Chinese dataset with a large alphabet (in which many characters only appear in few samples, making it hard to learn for sequence models). Moreover, the proposed method is fast and flexible, with a competitive speed among common style transfer methods.
Active Magnetic Anomaly Detection Using Multiple Micro Aerial Vehicles

Magnetic anomaly detection (MAD) is an important problem in applications ranging from geological surveillance to military reconnaissance. MAD sensors detect local disturbances in the magnetic field, which can be used to detect the existence of and to estimate the position of buried, hidden, or submerged objects, such as ore deposits or mines. These sensors may experience false positive and false negative detections and, without prior knowledge of the targets, can only determine proximity to a target. The uncertainty in the sensors, coupled with a lack of knowledge of even the existence of targets, makes the estimation and control problems challenging. We utilize a hierarchical decomposition of the environment, coupled with an estimation algorithm based on random finite sets, to determine the number of and the locations of targets in the environment. The small team of robots follow the gradient of mutual information between the estimated set of targets and the future measurements, locally maximizing the rate of information gain. We present experimental results of a team of quadrotor micro aerial vehicles discovering and localizing an unknown number of permanent magnets.