Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved distributed approximate matching

We present improved algorithms for finding approximately optimal matchings in both weighted and unweighted graphs. For unweighted graphs, we give an algorithm providing >(1-ε-approximation in O(log n) time for any constant ε > 0. This result improves on the classical 1 over 2-approximation due to Israeli and Itai. As a by-product, we also provide an improved algorithm for unweighted matchings in bipartite graphs. In the context of weighted graphs, we give another algorithm which provides (1 over 2-ε) approximation in general graphs in O(log n)time. The latter result improves on the known (1 over 4-ε-approximation in O(log n)time.
Modeling and Predicting Citation Count via Recurrent Neural Network with Long Short-Term Memory

The rapid evolution of scientific research has been creating a huge volume of publications every year. Among the many quantification measures of scientific impact, citation count stands out for its frequent use in the research community. Although peer review process is the mainly reliable way of predicting a paper's future impact, the ability to foresee lasting impact on the basis of citation records is increasingly important in the scientific impact analysis in the era of big data. This paper focuses on the long-term citation count prediction for individual publications, which has become an emerging and challenging applied research topic. Based on the four key phenomena confirmed independently in previous studies of long-term scientific impact quantification, including the intrinsic quality of publications, the aging effect and the Matthew effect and the recency effect, we unify the formulations of all these observations in this paper. Building on a foundation of the above formulations, we propose a long-term citation count prediction model for individual papers via recurrent neural network with long short-term memory units. Extensive experiments on a real-large citation data set demonstrate that the proposed model consistently outperforms existing methods, and achieves a significant performance improvement.
Spatial-Temporal Aware Intelligent Service Recommendation Method Based on Distributed Tensor factorization for Big Data Applications

With the dramatic growth of public cloud offerings and heterogeneous data information, how to discover potentially valuable information from big history behavior data and design intelligent recommendation techniques has become more and more important. Due to the dynamics of cloud environment, both user behaviors and QoS (Quality of Service) performance of cloud services are sensitive to contextual information, such as time and location. However, the consideration of time and location information brings the increase in the order of rating matrix and the data sparsity problem. In view of these challenges, we propose a spatial-temporal aware intelligent service recommendation method based on distributed tensor factorization to address the above problems. First, the time and location information are introduced into the recommendation models by distinguishing time-sensitive QoS metrics and region-sensitive QoS metrics from stable QoS metrics. To deal with the sparse rating data, time slots and regions are clustered respectively. Then, a high-order tensor factorization technique is applied to mine the latent factors among users, services, time information, and location information. Moreover, to improve the scalability of our recommendation models in big data environment, a fast distributed asynchronous SGD (Stochastic Gradient Descent) mechanism is employed to get a good balance between the convergence speed and prediction accuracy. Finally, experiments based on both real-world data set and big synthetic data set are conducted to validate the effectiveness and scalability of our proposal. The experimental results show that our proposal achieves a good balance between the recommendation accuracy and scalability.
Sugar Addiction: From Evolution to Revolution

The obesity epidemic has been widely publicized in the media worldwide. Investigators at all levels have been looking for factors that have contributed to the development of this epidemic. Two major theories have been proposed: (1) sedentary lifestyle and (2) variety and ease of inexpensive palatable foods. In the present review, we analyze how nutrients like sugar that are often used to make foods more appealing could also lead to habituation and even in some cases addiction thereby uniquely contributing to the obesity epidemic. We review the evolutionary aspects of feeding and how they have shaped the human brain to function in “survival mode” signaling to “eat as much as you can while you can. ” This leads to our present understanding of how the dopaminergic system is involved in reward and its functions in hedonistic rewards, like eating of highly palatable foods, and drug addiction. We also review how other neurotransmitters, like acetylcholine, interact in the satiation processes to counteract the dopamine system. Lastly, we analyze the important question of whether there is sufficient empirical evidence of sugar addiction, discussed within the broader context of food addiction.
Family Experiences and Perspectives on Infection Prevention in Pediatric Long-Term Care

Purpose Residents of pediatric long-term care facilities (pLTCF) are particularly vulnerable to healthcare-associated infections. The aims of this qualitative study were to (a) explore perspectives on infection prevention among families visiting children in pLTCF and (b) identify facilitators of and barriers to optimal hand hygiene. Design and Methods Semistructured, in-depth interviews with 10 family members visiting two New York City metropolitan area facilities were analyzed to identify themes. Findings “Everyone follows the rules” and “infections are inevitable” were primary themes. Participants reported “common sense” as a facilitator and “distraction” as a major barrier to prevention practices. Conclusion Current education for visitors may be inadequate to improve infection prevention behaviors. Clinical Relevance Nurse-led education strategies for infection prevention should be tested and modified for families visiting pLTCF.
Escalator to extinction

Thirty years ago, biologists began to predict how climate change would alter species’ ranges, extinctions, and their roles in ecosystems (1). Since then, evidence has begun to confirm these nascent predictions. The current global temperature rise of ∼1 °C has shifted species up mountains and latitudes (2), altered the timing of key life events such as flowering and migration (3), and eradicated populations and species (4). As the climate continues to change, these impacts on biodiversity are expected to accelerate (5). In PNAS, Freeman et al. (6) add to the growing drumbeat of climate change’s pervasive impacts on nature by demonstrating the mountaintop extirpations of five tropical bird populations. In 1985, tropical biologists charted an elevational transect in the mountains of Cerro de Pantiacolla, which rise from the Amazonian lowlands in Peru. Along this transect, they recorded each bird they encountered. Three decades later, biologists returned to resurvey the original transect. Such historical resurveys offer a particularly effective way to gauge biotic changes in response to long-term environmental responses, assuming they apply similar methods and effort. Oftentimes, these comparisons are difficult to ascribe to climate change because other factors, such as land use, also change during this time. In this study, the study area remained undisturbed, making it an exceptional long-term catalog of nature’s history. What did change was temperature, which rose 0.4 °C between surveys. After comparing observations between 1985 and 2017, the biologists discovered that the upper elevational ranges of birds had shifted upslope an average of 68 m, which was consistent with the temperature rise. Mountaintop species suffered the most. High-elevation species lost on average 110 m of elevational range and substantial habitat … [↵][1]1Email: mark.urban{at}uconn.edu. [1]: #xref-corresp-1-1
Virtual mortality and near-death experience after a prolonged exposure in a shared virtual reality may lead to positive life-attitude changes

Mortality is an obvious if uncomfortable part of the human condition, yet it is impossible to study its impact on anyone who experiences it. Reports of phenomena associated with death such as out-of-the-body (OBE) and near death experiences (NDE) can only be studied post-hoc, since it is impossible to design a scientific study where an experimental group experiences death (and returns) and a control group does not. Yet NDEs seem to have a profound influence on the subsequent lives of people and are therefore worthy of study. Terror Management Theory, which argues that death anxiety contributes to in-group solidarity and hostility to out-groups, relies on studies that manipulate opinions and cannot be based on experiential evidence. Here we introduce a potential methodology that uses immersive virtual reality (VR) for the study of mortality and NDEs. Participants are embodied in alternate bodies in a beautiful island along with two companions. They explore the island and carry out tasks together. The mechanism of embodiment produces strong illusions of ownership over their life-sized virtual bodies. Over time each participant witnesses the death of the two companions and then her own death—which includes the reported features of an NDE (OBE, life review, the tunnel leading to white light) followed by a period of observation of the continuing activities in the virtual world on an external screen. Fifteen female participants experienced 6 sessions in the island, each starting as a child and gradually maturing, and eventually ageing and dying. Sixteen control subjects formed a waiting group. We introduce this as a methodology for the study of these issues, and present promising results, suggesting that those who experienced the island report life attitude changes, becoming more concerned with others and more interested in global rather than material issues compared to the control group. The results are based on a small sample size, and should be considered as indicative of the possibilities of this new methodology as a way forward for future studies in this field.
The application of post-translational modification oriented serum proteomics to assess experimental diabetes with complications

Proteome analysis of serum from type 2 diabetics with complications may lead to the discovery of diagnostic or prognostic biomarkers. To circumvent the principal barrier of serum proteomics, our investigation aimed to evaluate whether a study of post-translational modification enriched serum proteins could be valuable for the discovery of biomarkers or metabolic pathways related to type 2 diabetes pathogenesis. Type 2 diabetes was induced from high-fat diet fed Sprague Dawley rats with streptozotocin injection. Once diabetic status was confirmed, serum samples from either fasted healthy or diabetic rats were pooled and profiled by two-dimensional difference gel electrophoresis or comparative 2D electrophoresis after protein enrichments using immobilized metal ion, concanavalin A, and lentil affinity chromatography, respectively. Differential expressed proteins were identified and the associated networks were established by an Ingenuity Pathway Analysis. As a result, induced rats became severe diabetic and accompanied by hyperlipidemia, fatty liver, and glomerular hypertrophy. There were 3 total, 14 phosphorylated and 23 glycosylated protein targets differentially expressed. Proteins could be linked to HNF4A, HNF1A, and NFκB transcriptional factors and antigen presentation, humoral immune response, and inflammatory response pathways. Predicted organ toxicity in kidney, heart, and liver matched with our histopathological results. In conclusion, post-translational modification based serum protein enrichment could be a valuable approach to enhance the resolution of serum proteomics without depleting potentially valuable abundant proteins. Our results also indicated the potential association of the hepatic secretome and hepatocyte nuclear factors in the pathogenesis of type 2 diabetes and its complications.
Machine Learning Techniques for Security of Internet of Things (IoT) and Fog Computing Systems

The Internet of Things (IoT) is a network of interconnected computing devices having the ability to transfer data over a network, without needing human interaction. Software and communication technologies have seen a tremendous amount of growth in the past couple of decades, and this has resulted in a significant increase in IoT devices. Today, in 2017, IoT devices are ubiquitous and have pervaded almost every sphere of our lives ushering an era of 'smart things. ' The explosive growth has resulted in much security and privacy concerns. The other concern is the scalability issue with the primary model of analyzing IoT data in the cloud. A cloud-only model leads to network congestion, data bottleneck and slower reaction times to security issues. By selectively moving computation and storage closer to the network edge, fog computing provides an effective solution for these issues. This paper presents a holistic picture of IoT, its growth, fog computing, and Machine Learning (ML) techniques for securing IoT devices and fog computing systems. It surveys ML techniques for detecting abnormalities and attacks, illustrates solutions to the data growth in IoT, and dives into the security issues concerning fog computing. It also discusses future research directions in this important topic.
Designing the Vertigo Experience: Vertigo as a Design Resource for Digital Bodily Play

Vertigo can be described as an attempt to momentarily destroy the stability of perception and inflict a kind of voluptuous panic upon an otherwise lucid mind. Vertigo has, however, not been generally considered as a design resource and we believe it to be under-explored in the area of digital bodily play. To investigate how vertigo could be considered as a design resource in this context, we conducted a review of relevant literature and held a design workshop with nine students to explore the potential of vertigo as a design resource for digital bodily play. From our exploration we identify five key design themes that designers might consider when designing a Vertigo Experience. Through this work we hope to encourage designers of bodily play experiences to consider vertigo as a design resource in their games.
'Welcome' Changes?

Open online communities rely on social norms for behavior regulation, group cohesion, and sustainability. Research on the role of social norms online has mainly focused on one source of influence at a time, making it difficult to separate different normative influences and understand their interactions. In this study, we use the Focus Theory to examine interactions between several sources of normative influence in a Wikipedia sub-community: local descriptive norms, local injunctive norms, and norms imported from similar sub-communities. We find that exposure to injunctive norms has a stronger effect than descriptive norms, that the likelihood of performing a behavior is higher when both injunctive and descriptive norms are congruent, and that conflicting social norms may negatively impact pro-normative behavior. We contextualize these findings through member interviews, and discuss their implications for both future research on normative influence in online groups and the design of systems that support open collaboration.
Optimal parallel string algorithms: sorting, merging and computing the minimum

We study fundamental comparison problems on strings of characters, equipped with the usual lexicographical order~g. For each problem studied, we give a parallel algorithm that is optimal with respect to at least one criterion for which no optimal algorithm was previously known. Specifically, our main results are: ● Two sorted sequences of strings, cent aining altogether n characters, can be merged in O(log n) time using O(n) operations on an EREW PRAM. This is optimal as regards both the runnin g time and the number of operations. ● A sequence of strings, cent -g altogether n characters represent ed by integers of size polynomial in n, can be sorted in O(log n/log log n) time using O(n log log n) operations on a CRCW PRAM. The rurmin g time is opt imal for any polynomial number of processors. ● The minimum string in a sequence of strings containing altogether n characters can be found using (expected) O(n) operations in (expected) time ranging from O(1) on a randomized CRC W PRAM to O(log n log log n) on a deterministic EREW PRAM.
The Comparative Effect on Satiety and Subsequent Energy Intake of Ingesting Sucrose or Isomaltulose Sweetened Trifle: A Randomized Crossover Trial

The effect that blood glucose concentration has on feelings of satiety is unclear. Our aims were to assess satiety and subsequent energy intake following the ingestion of trifle sweetened with sucrose or isomaltulose whilst measuring plasma glucose concentration to confirm glycemic differences between trifles. Seventy-seven healthy adults participated in a double-blind crossover trial where trifle sweetened with sucrose or isomaltulose was consumed on separate days with a two-week washout. Blood was sampled at the baseline, 1 and 2 h postprandially, and satiety assessed using visual analogue scales (VAS). Weighed diet records were taken on test days. A statistically significant difference in blood glucose concentration between trifles was found at 60 min following consumption, with the isomaltulose trifle having a 0.69 mmol/L (95% confidence interval: −1.07, −0.31) lower concentration when compared with the sucrose trifle. Mean satiety response by area-under-the-curve (AUC) was not significantly different between trifles. Mean (SD) appetite scores for the sucrose and isomaltulose trifles were 4493 (2393) and 4527 (2590) mm·min, respectively, with a between trifle difference of −9 (95% CI: −589, 572) mm·min. Mean (SD) energy intake for the remainder of the day following trifle consumption was 3894 kJ (1950 kJ) and 3530 kJ (1926 kJ) after the sucrose and isomaltulose trifles, respectively, and was not significantly different (p = 0.133). The differing glycemic response to trifle was not related to satiety or to subsequent energy intake.
Development of an Approach for Monitoring Sugarcane Harvested and Non-Harvested Conditions Using Time Series Sentinel-1 Data

With the recent launch of Sentinel-1 constellations and frequent availability of C-band synthetic aperture radar (SAR) data at no cost, there is an opportunity to monitor crops on regular basis, which is still not explored. Therefore, in this paper an approach has been proposed to monitor sugarcane harvest status using time series Sentinel-1 data. The proposed approach uses knowledge based classification and temporal profile for obtaining harvest status of crops. The approach is able to identify harvested and non-harvested sugarcane areas from other crops with an overall accuracy of 82.17%.
Adaptive Narrowband Antijam Method for Satellite Navigation

In the satellite navigation systems, the existing methods for narrow-band antijam have low jamming-to-signal ratio (JSR), large number of iterations, and poor robustness. Therefore, in this paper, a novel method with the adaptive multi-threshold detector is proposed for the narrowband jamming suppression. Both the window function and multi-threshold detector are chosen adaptively according to the strategy of jamming suppression. The proposed method can reduce the number of iterations and also improve the signal-to-jamming-and-noise ratio (SJNR) of the received signal significantly. Simulation results show that the satellite navigation system can work well with the power of narrowband jamming being 100 dB, 75 dB and 50 dB. Additionally, with JSR being 100 dB and the jamming bandwidth being 6 MHz, the jamming can be also suppressed.
Antenna Pattern Effects in a Low Sidelobe Circularly Polarized Planar Array Due to Element Errors

Random amplitude and phase errors at the elements of a phased array antenna cause elevated sidelobe levels. Formulas for predicting the sidelobe levels based on the error statistics are well known. These same random errors also cause an error in the axial ratio of a circular polarized array. This paper presents some new expressions for the change in axial ratio as a function of the random errors.
Enforcing Privacy and Security in Public Cloud Storage

Cloud storage allows users to remotely store their data, giving access anywhere and to anyone with an Internet connection. The accessibility, lack of local data maintenance and absence of local storage hardware are the main advantages of this type of storage. The adoption of this type of storage is being driven by its accessibility. However, one of the main barriers to its widespread adoption is the sovereignty issues originated by lack of trust in storing private and sensitive information in such a medium. Recent attacks to cloud-based storage show that current solutions do not provide adequate levels of security and subsequently fail to protect users' privacy. Usually, users rely solely on the security supplied by the storage providers, which in the presence of a security breach will ultimate lead to data leakage. In this paper, we propose and implement a broker (ARGUS) that acts as a proxy to the existing public cloud infrastructures by performing all the necessary authentication, cryptography and erasure coding. ARGUS uses erasure code as a way to provide efficient redundancy (opposite to standard replication) while adding an extra layer to data protection in which data is broken into fragments, expanded and encoded with redundant data pieces that are stored across a set of different storage providers (public or private). The key characteristics of ARGUS are confidentiality, integrity and availability of data stored in public cloud systems.
GNSS Time Synchronization in Vehicular Ad-Hoc Networks: Benefits and Feasibility

Time synchronization is critical for the operation of distributed systems in networked environments. It is also demanded in vehicular ad-hoc networks (VANETs), which, as a special type of wireless networks, are becoming increasingly important for emerging cooperative intelligent transport systems. Global navigation satellite system (GNSS) is a proven technology to provide precise timing information in many distributed systems. It is well recognized to be the primary means for vehicle positioning and velocity determination in VANETs. However, GNSS-based time synchronization is not well understood for its role in the coordination of various tasks in VANETs. To address this issue, this paper examines the requirements, potential benefits, and feasibility of GNSS time synchronization in VANETs. The availability of GNSS time synchronization is characterized by almost 100% in our experiments in high-rise urban streets, where the availability of GNSS positioning solutions is only 80%. Experiments are also conducted to test the accuracy of time synchronization with 1-PPS signals output from consumer-grade GNSS receivers. They have shown 30-ns synchronization accuracy between two receivers of different models. All these experimental results demonstrate the feasibility of GNSS time synchronization for stringent VANET applications.