Miracle-dz/UniFAR-FTUs huggingface dataset:
Differentially Private Smart Metering With Fault Tolerance and Range-Based Filtering

Smart grid enables two-way communications between operation centers and smart meters to collect power consumption and achieve demand response to improve flexibility, reliability, and efficiency of electricity system. However, power consumption data may contain users’ privacy, e.g., activities, references, and habits. Many smart metering schemes have been proposed utilizing homomorphic encryption for users’ privacy preservation. Unfortunately, some abnormality of smart meter reading, e.g., caused by electricity theft, cannot be discovered since data is encrypted. Meanwhile, operation centers could become curious in reality. To address the above issues, we propose a new privacy-preserving smart metering scheme for smart grid, which supports data aggregation, differential privacy, fault tolerance, and range-based filtering simultaneously. Specifically, we extend lifted ElGamal encryption to aggregate users’ consumption reports at the gateway to reduce communication overhead, while supporting fault tolerance of malfunctioning smart meters effectively. We also leverage zero-knowledge range proof to filter abnormal measurements caused by electricity theft or false data injection attacks without exposing individual measurements. In addition, our scheme can resist differential attacks, by which the curious operation center can violate users’ privacy through comparing two aggregations of the similar data set. Finally, we discuss the properties of the proposed scheme and evaluate its performance in terms of security and efficiency.
Performance improvement of the general-purpose CFD code FrontFlow/blue on the K computer

The general-purpose fluid simulation software FrontFlow/blue (FFB) is based on the finite element method (FEM). It was designed to accept extremely large-scale simulations and is an important application in the manufacturing field in Japan. Moreover, since this application is significant in both the manufacturing field and the development of the post-K supercomputer, it is employed as an important application for the new post-K supercomputer that is under development. The K computer is still the important infrastructure in Japan. And there are some supercomputers having the same architecture to the K computer. Therefore we continue to improve the performance of the FFB on the K computer. On significant subroutines, several improvement techniques, store order based loop modification decreasing total load and store operations, unrolled loop rerolling to employ SIMD load instruction, adjusting number of arrays in loop, using sector cache function, and so on, were employed. As a result, an improvement of 160% was obtained on a single CPU performance. This paper shows and discusses the detail of these improvements.
Talker-specific perceptual processing: influences on internal category structure

Research has shown that listeners accommodate talker differences in speech production by adjusting phonetic boundaries in light of a talker’s unique productions. However, phonetic categories are not marked solely by boundaries, they also have a graded internal structure in that not all members of a category are considered equally good members. The current work examines whether listeners adjust the internal structure of a phonetic category for individual talkers and whether such adjustments would transfer to a novel word. Listeners participated in training and test phases. During training, two groups of listeners heard a talker produce “cane. ” Voice-onset-time (VOT) was manipulated such that one group heard the talker produce /k/ with relatively short VOTs and the other group heard relatively longer VOTs. During test, listeners were presented with a VOT continuum from "gain" to "cane" or “goal” to “coal” and asked to rate each member for goodness as /k/. The results showed that the best exemplar region at test differed between the two groups in line with exposure during training, though this effect was greater for the training word compared to the novel word. These results suggest that internal category structure is dynamically adjusted in order to accommodate talker-specific phonetic variation.
Point-Based 3D Reconstruction of Thin Objects

3D reconstruction deals with the problem of finding the shape of an object from a set of images. Thin objects that have virtually no volume pose a special challenge for reconstruction with respect to shape representation and fusion of depth information. In this paper we present a dense point-based reconstruction method that can deal with this special class of objects. We seek to jointly optimize a set of depth maps by treating each pixel as a point in space. Points are pulled towards a common surface by pair wise forces in an iterative scheme. The method also handles the problem of opposed surfaces by means of penalty forces. Efficient optimization is achieved by grouping points to super pixels and a spatial hashing approach for fast neighborhood queries. We show that the approach is on a par with state-of-the-art methods for standard multi view stereo settings and gives superior results for thin objects.
Redefining smart city concept with resilience approach

The smart city concept originally aimed at dealing with various urban problems, in particular, those related to the urban environment and infrastructure, such as modeling transport flow in a city. As it developed, the concept is now widely used to accelerate the process of urban management by using IT technology and by the availability of big data. However, the smart city discourses are still debated. There is a number of critical literature on the discourses; some are more concerned with the use and development of information communication technology (ICT). ICT and modern technology are considered the key aspect of the smart city concept. Meanwhile, others emphasize the importance of the people who operate the technology. Very few, if any, literature emphasizes the importance of resilience in the smart city discourse. The city as a complex system should have the ability to be resilient, especially when technology fails either due to technical/man-made or natural disasters. This paper aims to redefine the smart city concept in urban planning through a literature study in the context of planning using a resilience approach. This paper describes and defines what the smart city concept is, what it means, as well as explains the relation and linkage of the importance of using resilience approach in defining the smart city. Factors of resilience will lead to a soft infrastructure approach, such as enhancement in many aspects, e.g. community capacity, social and human capital, knowledge inclusion, participation, social innovation, and social equity. Discussion and analysis are conducted through a deep literature study using systematic literature review methodology.
iMake: computer-aided eye makeup

Many women enjoy applying makeup. Eye makeup is especially important for face makeup, because eyeshadow color and eye line shape can dramatically change a person's impression given to others. In addition to standard eye makeup, there is "artistic eye makeup," which tends to have a greater variety of designs and is more ostentatious than standard eye makeup. Artistic eye makeup often has a motif of characters or symbols, such as a butterfly or a musical note. Needless to say, it is often difficult for non-artistic people to apply this type of eye makeup. Artistic eye makeup requires a special technique; therefore, we propose and implement a computer-aided eye makeup design system called "iMake. " This system generates eye makeup designs from the colors and shapes of a favorite characters selected by a user. Once the user has selected the desired eye makeup pattern, an ink-jet color printer prints it on a transfer sheet that the user can apply to his/her eyelids. The user can design any type of eye makeup with a simple operation, and then apply the transfer sheet makeup without any special techniques.
Molecular Motions in Functional Self-Assembled Nanostructures

The construction of “smart” materials able to perform specific functions at the molecular scale through the application of various stimuli is highly attractive but still challenging. The most recent applications indicate that the outstanding flexibility of self-assembled architectures can be employed as a powerful tool for the development of innovative molecular devices, functional surfaces and smart nanomaterials. Structural flexibility of these materials is known to be conferred by weak intermolecular forces involved in self-assembly strategies. However, some fundamental mechanisms responsible for conformational lability remain unexplored. Furthermore, the role played by stronger bonds, such as coordination, ionic and covalent bonding, is sometimes neglected while they can be employed readily to produce mechanically robust but also chemically reversible structures. In this review, recent applications of structural flexibility and molecular motions in self-assembled nanostructures are discussed. Special focus is given to advanced materials exhibiting significant performance changes after an external stimulus is applied, such as light exposure, pH variation, heat treatment or electromagnetic field. The crucial role played by strong intra- and weak intermolecular interactions on structural lability and responsiveness is highlighted.
Splenectomy Associated Changes in IgM Memory B Cells in an Adult Spleen Registry Cohort

Asplenic patients have a lifelong risk of overwhelming post-splenectomy infection and have been reported to have low numbers of peripheral blood IgM memory B cells. The clinical value of quantitation of memory B cells as an indicator of splenic abnormality or risk of infection has been unclear. To assess changes in B cell sub-populations after splenectomy we studied patients recruited to a spleen registry (n = 591). A subset of 209 adult asplenic or hyposplenic subjects, and normal controls (n = 140) were tested for IgM memory B cells. We also determined a) changes in IgM memory B cells with time after splenectomy using the cross-sectional data from patients on the registry and b) the kinetics of changes in haematological markers associated with splenectomy(n = 45). Total B cells in splenectomy patients did not differ from controls, but memory B cells, IgM memory B cells and switched B cells were significantly (p<0.001) reduced. The reduction was similar for different indications for splenectomy. Changes of asplenia in routine blood films including presence of Howell-Jolly bodies (HJB), occurred early (median 25 days) and splenectomy associated thrombocytosis and lymphocytosis peaked by 50 days. There was a more gradual decrease in IgM memory B cells reaching a stable level within 6 months after splenectomy. IgM memory B cells as proportion of B cells was the best discriminator between splenectomized patients and normal controls and at the optimal cut-off of 4.53, showed a true positive rate of 95% and false positive rate of 20%. In a survey of 152 registry patients stratified by IgM memory B cells around this cut-off there was no association with minor infections and no registry patients experienced OPSI during the study. Despite significant changes after splenectomy, conventional measures of IgM memory cells have limited clinical utility in this population.
Risk assessment and predicting outcomes in patients with depressive symptoms: a review of potential role of peripheral blood based biomarkers

Depression is one of the major global health challenges and a leading contributor of health related disability and costs. Depression is a heterogeneous disorder and current methods for assessing its severity in clinical practice rely on symptom count, however this approach is unreliable and inconsistent. The clinical evaluation of depressive symptoms is particularly challenging in primary care, where the majority of patients with depression are managed, due to the presence of co-morbidities. Current methods for risk assessment of depression do not accurately predict treatment response or clinical outcomes. Several biological pathways have been implicated in the pathophysiology of depression; however, accurate and predictive biomarkers remain elusive. We conducted a systematic review of the published evidence supporting the use of peripheral biomarkers to predict outcomes in depression, using Medline and Embase. Peripheral biomarkers in depression were found to be statistically significant predictors of mental health outcomes such as treatment response, poor outcome and symptom remission; and physical health outcomes such as increased incidence of cardiovascular events and deaths, and all-cause mortality. However, the available evidence has multiple methodological limitations which must be overcome to make any real clinical progress. Despite extensive research on the relationship of depression with peripheral biomarkers, its translational application in practice remains uncertain. In future, peripheral biomarkers identified with novel techniques and combining multiple biomarkers may have a potential role in depression risk assessment but further research is needed in this area.
The NICT ASR system for IWSLT2011

This paper describes our automatic speech recognition (ASR) system for the IWSLT 2012 evaluation campaign. The target data of the campaign is selected from the TED talks, a collection of public speeches on a variety of topics spoken in English. Our ASR system is based on weighted finite-state transducers and exploits an combination of acoustic models for spontaneous speech, language models based on ngram and factored recurrent neural network trained with effectively selected corpora, and unsupervised topic adaptation framework utilizing ASR results. Accordingly, the system achieved 10.6% and 12.0% word error rate for the tst2011 and tst2012 evaluation set, respectively.
MobiCom 2010 Poster : Caché : Caching Location-Enhanced Content to Improve User Privacy

There is a great deal of location-enhanced content and services available on the Internet and through mobile phone applications. Maps, bus schedules, traffic conditions, restaurant reviews, and social events are just a few examples. A key challenge to widespread adoption of location-based services, however, is privacy [4]. Many techniques have been proposed for improving privacy when downloading nearby content, but these rely primarily on suppressing location information, which often leads to a loss in accuracy or the risk of re-identification. To address this problem, we present Caché, a generalizable approach for a class of location-based services that enables users to enjoy the benefits of those services while minimizing the associated privacy concerns. Caché takes a well-explored idea from networked systems, namely caching, and applies it in the context of privacy. More specifically, Caché has two core ideas: (1) location-enhanced content can be periodically pre-fetched in large geographic blocks onto a device, before actually needed, for regions that a person will likely be in, and (2) the content can be accessed locally, when needed, without relying on any networked services outside of the device. Thus, rather than sharing current location on each request for information, the user only needs to share general geographic region hours, days, or even weeks before the desired content is needed. Assuming that the users’ current location is determined locally on a device (e.g. GPS, SkyHook’s autonomous mode), they can still make use of location-enhanced content, without disclosing individual requests.
A Survey on Energy Harvesting and Integrated Data Sharing in Wireless Body Area Networks

Wireless body area networks (WBANs) are important branches of wireless sensor networks (WSNs). They promise unobtrusive ambulatory health monitoring for real-time updates of patients' medical records that have aroused extensive attention in various fields. In recent years, one can find a lot of researches related to WBANs that have appeared in these literatures. But there are still many key issues that need to be further investigated. This paper briefly introduces the architecture and features of WBANs. In this attempt, we focus mostly on energy acquisition, data integration and data sharing, and collaboration of WBANs, from the viewpoint of energy harvesting development, the social network and smartphone application in WBANs, and the integration of WBANs and cloud system networks computing to analyze related issues of WBANs. Finally, we put forward concluding remarks with several future research directions.
Component-based networking for simulations in medical education

For the purpose of medical education, our research team is creating LINDSAY, a 3-dimensional, interactive computer model of male and female anatomy and physiology. As part of the LINDSAY—Virtual Human project, we have developed a component-based computational framework that allows the utilization of various formal representations, computation engines and visualization technologies within a single simulation context. For our agent-based simulations, the graphics, physics and behaviours of our interacting entities are implemented through a set of component engines. We have developed a light-weight client/server component, which spreads its siblings in the system's component hierarchy over a wireless or wired network infrastructure. In this paper we demonstrate how our client/server component paves new ways for organizing, generating, computing and presenting educational contents.
Effect of Different Micronutrients on Turmeric Variety Suranjana in Terai Region of West Bengal, India

Turmeric (Curcuma longa L.) is an important rhizomatous spice crop of India and World too. India is a leading producer, consumer and exporter of turmeric in the world. India is the major producer and exporter of turmeric and earned a foreign exchange of 2000 million $ (Anonymous, 2012). Apart from its spice and medicinal value it is also used in the dye, food and cosmetic industries. It is also used in the auspicious religious occasions. Turmeric inhibits the development of cataracts, breast cancer, colon cancer, and lymphoma (Devi et al., 2011). In India it is cultivated with an area of 1.95 lakh hectare (ha) with a production of 9.99 lakh tonnes. In West Bengal, it is cultivated with an area of 15.8 thousand ha and production of 42 thousand tonnes. Productivity of turmeric in West Bengal is quite low (2.66 tones/ha) compared to national average (5.11 tones/ha). It is a soil exhausting crop and application of N, P and K are recommended for its cultivation. Normally micronutrients do not find a place in the nutrient recommendations. However, high rhizome yields in turmeric with increasing concentrations of foliar sprays of International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 6 Number 5 (2017) pp. 1471-1482 Journal homepage: http://www.ijcmas. com
Requirements for measuring the success of persuasive technology applications

The area of ambient persuasive technology looks very promising in multiple ways. Yet, it also faces with challenges. Thus far the major weakness in research into ambient persuasive technology has been that the behavior change support systems have been described in such a coarse manner that it has been very difficult to demonstrate what actually caused the change, or to generalize the findings. This paper will discuss prerequisites for successful measuring of ambient persuasive technology. The O/C Matrix and the PSD Model are suggested as vehicles to better frame the research. These methodological research aids can be used in a variety of research and development settings ranging from studying full-fledged commercial systems to evaluating software specifications for systems under development.
Design and deployment considerations for high performance MIMO testbeds

MIMO (Multiple Input Multiple Output) enabled systems are characterized by higher reliability and transmission rates, as compared to conventional SISO (Single Input Single Output) systems. However, unless administered properly, the MIMO technology may not facilitate very high throughputs on point-to-point wireless links. Therefore, it becomes imperative for the network architect to design such networks in ways that fully exploit the inherent properties of MIMO. In this paper, we first conduct an extensive experimental study, using a powerful hardware platform, in order to understand the behavior of MIMO links in different topological scenarios. Our experiments involve scenarios with MIMO links in isolation, as well as in competition with other MIMO and SISO links. Second, we perform measurements with different commercial platforms towards assessing the ability of each platform to efficiently support the MIMO technology. Based on our experimental observations we deduce that the CPU processing speed of the underlying hardware platform is an important factor that can hide the performance benefits of a MIMO enabled tranceiver. We comment on the applicability of the different hardware choices that we test; furthermore, we suggest the most appropriate choice for building a MIMO testbed, taking into account the cost, the extend-ability and the re-usability of the selected platform. Finally, having adopted this choice in our testbed design, we provide a description of our testbed architecture.
Who is repinning?: predicting a brand's user interactions using social media retrieval

Despite the fact that firms spend heavily in marketing their brands across social media platforms, very little is understood about what media content, in a predictive manner, can generate high interaction rates among their prospects and customers. However, such understanding can significantly help brand marketers generate desired engagements with their target audience in marketing campaigns. In this paper, we study the problem of predicting a brand's user interactions on social media using the example of Pinterest, an emerging platform that has provided a large volume of brand as well as user data in the form of images. Specifically, we treat the prediction of a brand's user interactions, captured through "repinnings" on Pinterest, as the retrieval of relevant user-pinned images given a brand image. The prototype system that we build incorporates this basic principle, and is tested on a large-scale Pinterest dataset of more than one million images. We demonstrate that our system achieves significant lifts in recalling ground truth repinners of brand images for a variety of brands across several major industry categories.
Factored Translation with Unsupervised Word Clusters

Unsupervised word clustering algorithms --- which form word clusters based on a measure of distributional similarity --- have proven to be useful in providing beneficial features for various natural language processing tasks involving supervised learning. This work explores the utility of such word clusters as factors in statistical machine translation. Although some of the language pairs in this work clearly benefit from the factor augmentation, there is no consistent improvement in translation accuracy across the board. For all language pairs, the word clusters clearly improve translation for some proportion of the sentences in the test set, but has a weak or even detrimental effect on the rest. It is shown that if one could determine whether or not to use a factor when translating a given sentence, rather substantial improvements in precision could be achieved for all of the language pairs evaluated. While such an "oracle" method is not identified, evaluations indicate that unsupervised word cluster are most beneficial in sentences without unknown words.