nils-herrmann/clean_scirepeval huggingface dataset:
Multiresolution EO/IR target tracking and identification

Simultaneous target tracking and identification through feature association, attribute matching, or blob analysis is dependent on spatio-temporal measurements. Improved track maintenance should be achievable by maintaining coarse sensor resolutions on maneuvering targets and utilizing finer sensor resolutions to resolve closely-spaced targets. There are inherent optimal resolutions for sensors and restricted altitudes that constrain operational performance that a sensor manager must optimize for both wide-area surveillance and precision tracking. The advent of better optics, coordinated sensor management, and fusion strategies provide an opportunity to enhance simultaneous tracking and identification algorithms. We investigate utilizing electro-optical (EO) and infrared (IR) sensors operating at various resolutions to optimize target tracking and identification. We use a target-dense maneuvering scenario to highlight the performance gains with the multiresolution EO/IR data association (MEIDA) algorithm in tracking crossing targets.
Short- and long-term blood pressure variability: present and future.

It has long been known that blood pressure (BP) is characterized by an array of spontaneous variations. That is, BP values vary markedly within the 24 hours because of day-night changes but also because of differences among hours, minutes, and even adjacent beats. They also show variations over more prolonged periods because of differences among days, months, and seasons, also with a trend for systolic BP to increase over the years and for diastolic BP to display an age-related biphasic change. This article summarizes current knowledge on short-term (within the 24 hours) and long-term BP variability. Mention will be made of the following: (1) factors responsible for BP variations and mechanisms through which they occur; (2) the relationship of BP variability with BP mean values in absence and during antihypertensive treatment; and (3) the prognostic significance of BP variability. Current limitations of the studies on BP variability will be also discussed to emphasize what future studies should address.
The automated will: nonconscious activation and pursuit of behavioral goals.

It is proposed that goals can be activated outside of awareness and then operate nonconsciously to guide self-regulation effectively (J. A. Bargh, 1990). Five experiments are reported in which the goal either to perform well or to cooperate was activated, without the awareness of participants, through a priming manipulation. In Experiment 1 priming of the goal to perform well caused participants to perform comparatively better on an intellectual task. In Experiment 2 priming of the goal to cooperate caused participants to replenish a commonly held resource more readily. Experiment 3 used a dissociation paradigm to rule out perceptual-construal alternative explanations. Experiments 4 and 5 demonstrated that action guided by nonconsciously activated goals manifests two classic content-free features of the pursuit of consciously held goals. Nonconsciously activated goals effectively guide action, enabling adaptation to ongoing situational demands.
An extra low-power 1Tbit/s bandwidth PLL/DLL-less eDRAM PHY using 0.3V low-swing IO for 2.5D CoWoS application

A 1Tbit/s bandwidth PHY is demonstrated through 2.5D CoWoS platform.  SOC and eDRAM have been fabricated in TSMC 40nm CMOS technology and stacked on another silicon interposer chip in 65nm technology. Total 1024 DQ bus operating in 1.1Gbit/s with Vmin=0.3V are proven in experimental results. A novel timing compensation mechanism is presented to achieve a low-power and small area eDRAM PHY that excludes PLL/DLL but retains good timing margin. Another data sampling alignment training approach is reserved to enhance timing robustness. A compact low-swing IO also achieves great power efficiency of 0.105mW/Gbps.
CDM peak current variations and impact upon CDM performance thresholds

Effect of device size on the peak CDM current is discussed. The current increases monotonically for small packages and then saturates for sizes larger than 1000 mm2. Size of the charge plate of the CDM tester contributed to this behavior. The current was not found to be constant across the package. Instead, it showed maximum value in the middle and minimum at the outer edge. An unexpected variation in CDM current is also reported for long pulse sequences.
Antenna grouping based feedback reduction for FDD-based massive MIMO systems

Recent works on massive multiple-input multiple-output (MIMO) have shown that a potential breakthrough in capacity gains can be achieved by deploying a very large number of antennas at the basestation. Although transmit-side channel state information (CSI) can be obtained by employing channel reciprocity in time division duplexing (TDD) systems, explicit feedback of CSI from the user to the basestation is required for frequency division duplexing (FDD) systems. In this paper, we propose an antenna grouping based feedback reduction technique for FDD-based massive MIMO systems. The proposed algorithm, dubbed antenna group beamforming (AGB), groups antenna elements using pre-designed patterns. The proposed method introduces the concept of using a header of overall feedback resources to select a suitable group pattern and the payload to quantize the effective channel vector. Simulation results show that the proposed method achieves significant feedback overhead reduction over conventional approach.
Learning from One Example through Shared Densities on Transforms

We define a process called congealingin which elements of a dataset (images) are brought into correspondence with each other jointly, producing a data-defined model. It is based upon minimizing the summed component-wise (pixelwise) entropies over a continuous set of transforms on the data. One of the biproducts of this minimization is a set of transforms, one associated with each original training sam ple. We then demonstrate a procedure for effectively bringing test data into correspondence with the data-defined model produced in the congealing process. Subsequently, we develop a probability density over the set of transforms that arose from the congealing process. We suggest that this density over transforms may be shared by many classes, and demonstrate how using this density as “prior knowledge” can be used to develop a classifier based on only a single training example for each class.
Comparative analysis of K-Means with other clustering algorithms to improve search result

The paper identifies the scope of improvement for the search result of a web site. The study includes some commonly used clustering algorithms to identify the usage of clustering approach for improving web elements analysis, in various ways. As the Search result option is extensively used at almost every web site, the main focus is to optimize search result of a web site using clustering approach. Sementic web using the concept of ontology is included, to retrieve more relevant and meaning full serach results. Some most commomly used algorithms are experimented using web data, and it is observed that K-Means clustering algorithm gives best result in term of accuracy and speed. Thus the proposed hybrid model will be using K-Means and Genetic algorithm to overcome the drawbacks of K-Means. The evaluation parameters; accuracy in terms of objects placement in correct cluster, relevancy, speed and user satisfaction are the main parameters considered for the study.
Attention Strategies for Multi-Source Sequence-to-Sequence Learning

Modeling attention in neural multi-source sequence-to-sequence learning remains a relatively unexplored area, despite its usefulness in tasks that incorporate multiple source languages or modalities. We propose two novel approaches to combine the outputs of attention mechanisms over each source sequence, flat and hierarchical. We compare the proposed methods with existing techniques and present results of systematic evaluation of those methods on the WMT16 Multimodal Translation and Automatic Post-editing tasks. We show that the proposed methods achieve competitive results on both tasks.
Temporal Interaction and Causal Influence in Community-Based Question Answering

During the last decade, community-based question answering (CQA) sites have accumulated a vast amount of questions and their crowdsourced answers over time. How to efficiently identify the quality of answers that are relevant to a given question has become an active line of research in CQA. The major challenge of CQA is the accurate selection of high-quality answers w.r.t given questions. Previous approaches tend to model the semantic matching between individual pair of one question and its corresponding answer (how fitting an answer is to a posted question). However, these works ignore the temporal interactions between answers (how previous answers influence the late posted answers). For example, a rational user likely adapts others’ opinions, revises his inclinations, and posts a more appropriate answer after understanding the given question and previously posted answers. As a result, this paper devises an architecture named Temporal Interaction and Causal Influence LSTM (TC-LSTM) to effectively leverage not only the causal influence between question-answer (how appropriate an answer is for a given question) but also the temporal interactions between answers-answer (how a high-quality answer gradually forms). In particular, long short-term memory (LSTM) is used to capture the explicit question-answer influence and the implicit answers-answer interactions. Experiments are conducted on SemEval 2015 CQA dataset for answer classification task and Baidu Zhidao Dataset for answer ranking task. The experimental results show the advantage of our model comparing with other state-of-the-art methods.
Common factors influence bee foraging in urban and wildland landscapes

Bees are important flower-visiting insects that display differential occurrences at food resources throughout urban and wildland landscapes. This study examined the visitation rates and foraging patterns of eight taxonomic groups of bees that are common to California poppies, Eschscholzia californica, in both landscape types. Bee occurrence was documented in relation to floral resource characteristics (patch area, poppy coverage, and poppy density), local landscape characteristics (distance to the wildland-urban interface, distance to riparian areas, distance to green space, and land use), and regional landscape context (urban versus wildland). Similar abundance and richness measures were recorded at both urban and wildland poppy patches, but community composition varied in each landscape. Bumble bees were more abundant at poppies in the wildland whereas species in the family Halictidae (sweat bees) were more abundant at poppies within the urban landscape. Resource patch size and density consistently correlated with increased bee presence for all bee types foraging in the wildland. Individual patterns of occurrence in the urban landscape were somewhat divergent; the foraging dynamics of larger bodied-bees (Bombus vosnesenskii and Megachile species) correlated significantly with resource patch size and density, whereas smaller-bodied bees (family Halictidae and Andrena species) were influenced by landscape characteristics such as distance to the wildland-urban interface and distance to riparian areas. In summary, the surrounding landscape had an influence on community composition, but the magnitude of the floral resource present at a site and factors relating to foraging energetics were dominant drivers of local occurrence. These results suggest that management strategies that provide dense and abundant floral resources should be successful in attracting bees, irrespective of their location within the urban matrix.
MetisIDX - From Adaptive to Predictive Data Indexing

Exploratory data analysis characterized by analytic query workloads over large databases is now commonplace on both academia and industry. In these scenarios, data production velocity and unknown and drifting access patterns make the choice of access methods a challenging task. In this context, adaptive indexing techniques propose the use of partial indexes that are incrementally built in response to the actual query sequence and as a byproduct of query processing to optimize the access only to the key ranges of interest. This work presents a further development to this principle by leveraging the recent query history to predict the next key ranges and index them in advance, so the queries arriving in the near future find data in its final representation and higher placed in the storage hierarchy, since data must be loaded into main memory in order to be indexed. Adaptive merging is used as base architecture for the data structures and merge operations are executed in parallel with query execution instead of being the same operation. An extreme learning machine is used to perform key range forecasting and undergo continuous training by the indexing thread. The experiments show up to 38% lower query response times over a 1000 queries than adaptive merging, therefore lower overall response times and the decoupling of indexing operations during scan executions.
OSCAR: Object security architecture for the Internet of Things

Billions of smart, but constrained objects wirelessly connected to the global network require novel paradigms in network design. New protocol standards, tailored to constrained devices, have been designed taking into account requirements such as asynchronous application traffic, need for caching, and group communication. The existing connection-oriented security architecture is not able to keep up-first, in terms of the supported features, but also in terms of the scale and resulting latency on small constrained devices. In this paper, we propose an architecture that leverages the security concepts both from content-centric and traditional connection-oriented approaches. We rely on secure channels established by means of (D)TLS for key exchange, but we get rid of the notion of the “state” among communicating entities. We provide a mechanism to protect from replay attacks by coupling our scheme with the CoAP application protocol. Our object-based security architecture (OSCAR) intrinsically supports caching and multicast, and does not affect the radio duty-cycling operation of constrained objects. We evaluate OSCAR in two cases: 802.15.4 Low Power and Lossy Networks (LLN) and Machine-to-Machine (M2M) communication for two different hardware platforms and MAC layers on a real testbed and using the Cooja emulator. We show significant energy savings at constrained servers and reasonable delays. We also discuss the applicability of OSCAR to Smart City deployments.
Thank You, Come Again: Examining the Role of Quality and Trust on eCommerce Repurchase Intentions

As the pertinence of online consumer shopping continues to grow, more and more e-retailers are erecting websites. In this increasingly competitive environment, building customer loyalty and retaining customers is integral to achieving sustained profitability. While one stream of literature has suggested that e-retailers should concentrate on improving quality, another stream has recommended that the focus should be on building trust with customers. This paper represents an early, working attempt to synthesize these parallel streams, investigating how the interplay between three forms of quality (information, system, and service) and trust help to retain customers. Integrating information systems and marketing research, the results of this paper suggest that trust mediates the relationship between each type of quality and both satisfaction and repurchase intentions. Furthermore, of the three types of quality that are examined, service quality engenders the greatest impact on trust, followed by information quality then system quality. The paper concludes with a discussion of this preliminary model as well as directions for the future development of this project.
Modeling WiFi Active Power/Energy Consumption in Smartphones

We conduct the first detailed measurement study of the properties of a class of WiFi active power/energy consumption models based on parameters readily available to smartphone app developers. We first consider a number of parameters used by previous models and show their limitations. We then focus on a recent approach modeling the active power consumption as a function of the application layer throughput. Using a large dataset and an 802.11n-equipped smartphone, we build four versions of a previously proposed linear power-throughput model, which allow us to explore the fundamental trade off between accuracy and simplicity. We study the properties of the model in relation to other parameters such as the packet size and/or the transport layer protocol, and we evaluate its accuracy under a variety of scenarios which have not been considered in previous studies. Our study shows that the model works well in a number of scenarios but its accuracy drops with high throughput values or when tested on different hardware. We further show that a non-linear model can greatly improve the accuracy in these two cases.
Sketch Recognition by Ensemble Matching of Structured Features

Sketch recognition aims to automatically classify human hand sketches of objects into known categories. This has become increasingly a desirable capability due to recent advances in human computer interaction on portable devices. The problem is nontrivial because of the sparse and abstract nature of hand drawings as compared to photographic images of objects, compounded by a highly variable degree of details in human sketches. To this end, we present a method for the representation and matching of sketches by exploiting not only local features but also global structures of sketches, through a star graph based ensemble matching strategy. Different local feature representations were evaluated using the star graph model to demonstrate the effectiveness of the ensemble matching of structured features. We further show that by encapsulating holistic structure matching and learned bag-of-features models into a single framework, notable recognition performance improvement over the state-of-the-art can be observed. Extensive comparative experiments were carried out using the currently largest sketch dataset released by Eitz et al. , with over 20,000 sketches of 250 object categories generated by AMT (Amazon Mechanical Turk) crowd-sourcing.
Online Anomaly Detection in Wireless Body Area Networks for Reliable Healthcare Monitoring

In this paper, we propose a lightweight approach for online detection of faulty measurements by analyzing the data collected from medical wireless body area networks. The proposed framework performs sequential data analysis using a smart phone as a base station, and takes into account the constrained resources of the smart phone, such as processing power and storage capacity. The main objective is to raise alarms only when patients enter in an emergency situation, and to discard false alarms triggered by faulty measurements or ill-behaved sensors. The proposed approach is based on the Haar wavelet decomposition, nonseasonal Holt-Winters forecasting, and the Hampel filter for spatial analysis, and on for temporal analysis. Our objective is to reduce false alarms resulting from unreliable measurements and to reduce unnecessary healthcare intervention. We apply our proposed approach on real physiological dataset. Our experimental results prove the effectiveness of our approach in achieving good detection accuracy with a low false alarm rate. The simplicity and the processing speed of our proposed framework make it useful and efficient for real time diagnosis.
Rescheduling Manufacturing Systems: A Framework of Strategies, Policies, and Methods

Many manufacturing facilities generate and update production schedules, which are plans that state when certain controllable activities (e.g., processing of jobs by resources) should take place. Production schedules help managers and supervisors coordinate activities to increase productivity and reduce operating costs. Because a manufacturing system is dynamic and unexpected events occur, rescheduling is necessary to update a production schedule when the state of the manufacturing system makes it infeasible. Rescheduling updates an existing production schedule in response to disruptions or other changes. Though many studies discuss rescheduling, there are no standard definitions or classification of the strategies, policies, and methods presented in the rescheduling literature. This paper presents definitions appropriate for most applications of rescheduling manufacturing systems and describes a framework for understanding rescheduling strategies, policies, and methods. This framework is based on a wide variety of experimental and practical approaches that have been described in the rescheduling literature. The paper also discusses studies that show how rescheduling affects the performance of a manufacturing system, and it concludes with a discussion of how understanding rescheduling can bring closer some aspects of scheduling theory and practice.
Blind separation of speech mixtures via time-frequency masking

Binary time-frequency masks are powerful tools for the separation of sources from a single mixture. Perfect demixing via binary time-frequency masks is possible provided the time-frequency representations of the sources do not overlap: a condition we call W-disjoint orthogonality. We introduce here the concept of approximate W-disjoint orthogonality and present experimental results demonstrating the level of approximate W-disjoint orthogonality of speech in mixtures of various orders. The results demonstrate that there exist ideal binary time-frequency masks that can separate several speech signals from one mixture. While determining these masks blindly from just one mixture is an open problem, we show that we can approximate the ideal masks in the case where two anechoic mixtures are provided. Motivated by the maximum likelihood mixing parameter estimators, we define a power weighted two-dimensional (2-D) histogram constructed from the ratio of the time-frequency representations of the mixtures that is shown to have one peak for each source with peak location corresponding to the relative attenuation and delay mixing parameters. The histogram is used to create time-frequency masks that partition one of the mixtures into the original sources. Experimental results on speech mixtures verify the technique. Example demixing results can be found online at http://alum.mit.edu/www/rickard/bss.html.