Miracle-dz/UniFAR-FTUs huggingface dataset:
SOC: hunting the underground inside story of the ethereum Social-network Opinion and Comment

The cryptocurrency is attracting more and more attention because of the blockchain technology. Ethereum is gaining a significant popularity in blockchain community, mainly due to the fact that it is designed in a way that enables developers to write smart contracts and decentralized applications (Dapps). There are many kinds of cryptocurrency information on the social network. The risks and fraud problems behind it have pushed many countries including the United States, South Korea, and China to make warnings and set up corresponding regulations. However, the security of Ethereum smart contracts has not gained much attention. Through the Deep Learning approach, we propose a method of sentiment analysis for Ethereum's community comments. In this research, we first collected the users' cryptocurrency comments from the social network and then fed to our LSTM + CNN model for training. Then we made prediction through sentiment analysis. With our research result, we have demonstrated that both the precision and the recall of sentiment analysis can achieve 0.80+. More importantly, we deploy our sentiment analysis1 on RatingToken and Coin Master (mobile application of Cheetah Mobile Blockchain Security Center23). We can effectively provide detail information to resolve the risks of being fake and fraud problems.
Accurate PPCC-Based DoA Estimation Using Multiple Calibration Planes for WSN Nodes Equipped with ESPAR Antennas

In this paper, we have introduced a new direction-of-arrival (DoA) estimation method, which relies on received signal strength (RSS) values measured at the output port of electronically steerable parasitic array radiator (ESPAR) antenna and uses the power pattern cross-correlation (PPCC) estimator. In the method, we have successfully incorporated measurements of ESPAR antenna's radiation patterns performed at multiple calibration planes within the PPCC estimator in a way easily implementable in wireless sensor network (WSN) nodes equipped with ESPAR antennas. Performed anechoic chamber measurements of our ESPAR antenna prototype indicate that the proposed approach provides much lower DoA estimation errors in a wide span of incoming signal elevation angles than other methods currently available in the literature, which makes the concept applicable in practical WSN deployment scenarios.
Privacy-Preserving Outsourced Similarity Test for Access Over Encrypted Data in the Cloud

In the era of cloud computing, the cloud server always plays a significant role to carry the heavy tasks of computation. As for storage services, it provides an efficient manner for accessing data. For data privacy, encryption is usually referred to as a simple approach, but in fact cloud services cannot work with the traditional encryption. Therefore, outsourced computing over encrypted data receives attention of preserving privacy in the cloud setting. The notion, privacy-preserving outsourced similarity test (PPOS) over encrypted data, is introduced to capture the following scenario. The cloud stores encrypted data with the encrypted feature vector and then picks up the target data by testing similarity between those vectors and the search query. Recently, Zhang et al. proposed a PPOS scheme based on additive homomorphic encryption, garbled circuits, and ciphertext-policy attribute-based encryption. In this paper, we aim for presenting the formal security model and new scheme of PPOS. We use as few primitives as possible to minimize cryptographic building blocks. Our solution avoids using homomorphic encryption and constructs the PPOS scheme simply from garbled circuits and ciphertext-policy attribute-based encryption.
Phase-Only Image Based Kernel Estimation for Single Image Blind Deblurring

The image motion blurring process is generally modelled as the convolution of a blur kernel with a latent image. Therefore, the estimation of the blur kernel is essentially important for blind image deblurring. Unlike existing approaches which focus on approaching the problem by enforcing various priors on the blur kernel and the latent image, we are aiming at obtaining a high quality blur kernel directly by studying the problem in the frequency domain. We show that the auto-correlation of the absolute phase-only image 1 can provide faithful information about the motion (e.g., the motion direction and magnitude, we call it the motion pattern in this paper.) that caused the blur, leading to a new and efficient blur kernel estimation approach. The blur kernel is then refined and the sharp image is estimated by solving an optimization problem by enforcing a regularization on the blur kernel and the latent image. We further extend our approach to handle non-uniform blur, which involves spatially varying blur kernels. Our approach is evaluated extensively on synthetic and real data and shows good results compared to the state-of-the-art deblurring approaches.
The never-ending search of an ideal culture system for domestic cat oocytes and embryos.

In the domestic cat, in vitro fertilization started 40 years ago, but an ideal culture system has yet to be achieved. The physiological microenvironments, which interact with oocytes and embryos promoting their competence, have been investigated. However, recreating in vitro follicle- and oviduct-like conditions is challenging and a matter of both chemistry and physics. This review presents an excursus of the experimental investigations focused on the improvement of feline oocytes and embryos culture through the modulation of chemical and physical factors. Medium supplementation with components of follicular and oviductal fluids, or the use of different co-cultures, supports or substrata have been considered. Innovative and sophisticated systems as "organ-on-a-chip" might lead to the creation of artificial follicles and oviducts and they may represent the ideal combination of chemical and physical factors. Will the search ever end?
Multi‐tissue integration of transcriptomic and specialized metabolite profiling provides tools for assessing the common bean (Phaseolus vulgaris) metabolome

Summary Common bean (Phaseolus vulgaris L.) is an important legume species with a rich natural diversity of landraces that originated from the wild forms following multiple independent domestication events. After the publication of its genome, several resources for this relevant crop have been made available. A comprehensive characterization of specialized metabolism in P. vulgaris, however, is still lacking. In this study, we used a metabolomics approach based on liquid chromatography‐mass spectrometry to dissect the chemical composition at a tissue‐specific level in several accessions of common bean belonging to different gene pools. Using a combination of literature search, mass spectral interpretation, 13C‐labeling, and correlation analyses, we were able to assign chemical classes and/or putative structures for approximately 39% of all measured metabolites. Additionally, we integrated this information with transcriptomics data and phylogenetic inference from multiple legume species to reconstruct the possible metabolic pathways and identify sets of candidate genes involved in the biosynthesis of specialized metabolites. A particular focus was given to flavonoids, triterpenoid saponins and hydroxycinnamates, as they represent metabolites involved in important ecological interactions and they are also associated with several health‐promoting benefits when integrated into the human diet. The data are presented here in the form of an accessible resource that we hope will set grounds for further studies on specialized metabolism in legumes.
Isolation, Characterization and Performance of Autochthonous Spray Dried Lactic Acid Bacteria in Maize Micro and Bucket-Silos

The aim of this study was to isolate, identify and characterize lactic acid bacteria (LAB) from spontaneously fermented maize silage, and evaluate their performance as spray-dried (SD) cultures to enhance the fermentation and the aerobic stability of maize micro-silos. Eleven strains of LAB were characterized for growth kinetics, the capability to grow in vegetable-based medium (VBM), production of organic acids and the ability to tolerate heat–stress. Three strains (Lactobacillus plantarum Ls71, Pediococcus acidilactici Ls72, and Lactobacillus buchneri Ls141) were selected and further characterized for the ability to grow as single strain or in co-culture in MRS and VMB medium, to survive at freeze and spray-drying process, for their performance as SD bacteria in micro-silos and for the aerobic stability in bucket silos. L. buchneri Ls141 showed the highest growth capability in VBM and produced the highest amount of acetic acid, while L. plantarum Ls71 produced the highest amounts of lactic acid. P. acidilactici Ls72 was the most heat-resistant strain, with a reduction of 0.2 log10 CFU/mL (15 min at 55°C). The three strains satisfactorily tolerated both spray and freeze-drying. After 4 days of fermentation, all the samples reached a pH value of about 3.7–3.8. A significantly lower cell load of filamentous fungi and yeasts (< 3 log10 CFU/g) and a higher concentration of total LAB (> 8.7 log10 CFU/g) was observed after 30 days of fermentation. A greater amount of acetic acid, crude protein, ash and ammonia nitrogen/total nitrogen was detected in inoculated silages. A significant reduction of filamentous fungi and yeasts was also observed in inoculated bucket silos after 50 d of fermentation. The aerobic stability was significantly improved in inoculated silage since the temperature remained stable after 16 days (384 h). On the contrary, an increase of 5°C was observed in control samples after 1 day. The selected strains have the potential to be produced as SD silage inoculant as they were able to accelerate the fermentation process, to control filamentous fungi and yeasts, to improve some nutritional and chemical parameters of silage and to improve aerobic stability.
A target image–oriented dictionary learning–based method for fully automated latent fingerprint forensic

Several fully automated latent print forensic techniques have been reported. In this paper, we propose a fully automated latent print segmentation module for the partition of the fingerprint region in a query latent image, which can help find a corresponding match of the suspect reliably. Being different from the existing methods that build the prelearned dictionary from the high‐quality fingerprint image patches, the proposed dictionary learning procedure is conducted on the target images. The advantages of the proposed method are the following: (i) it does not require a large number of high‐quality “ridge‐valley” atoms and (ii) not only the structure similarity but also the pattern scale has been kept consistent between the target image patches and learned dictionary atoms. Because no commercial latent fingerprint matcher is publicly available and the latent matcher reported in the literature is not accessible to the public either, a latent fingerprint matching platform is implemented to evaluate the obtained segmentation results and automated latent fingerprint matching performance. On the basis of this platform, experimental comparisons are conducted to assess closeness to the system performance upper bound when different segmentation modules are deployed. Moreover, matcher‐independent criteria such as genuine minutiae preservation rate and the segmented region of interest's accuracy are used. All the experimental results demonstrate that the proposed segmentation approach outperforms state‐of‐the‐art techniques in terms of finding the correct fingerprint of the suspect subject.
Anxiety of mothers of newborns with congenital malformations in the pre- and postnatal periods

ABSTRACT Objective: to analyze the anxiety level of the mothers of newborns with congenital malformations who were diagnosed in prenatal and postnatal care. Methods: a cross-sectional study with 115 mothers of 117 newborns with congenital malformation admitted to three neonatal units. A questionnaire containing maternal and neonatal variables was used, as well as the Trait-State Anxiety Inventory. Data were analyzed by Student’s t-test and Kolmogorov-Sminorv test. The anxiety level was categorized as low (percentile <25), moderate (25-75) and high (> 75), with a significance level of 5%. Results: most mothers had moderate levels of anxiety. Regarding the diagnosis of the malformation, 57% received the news in the prenatal and 43% in the postnatal period. The anxiety level of those who received the prenatal diagnosis was lower than those who received in the postnatal period, evaluated by the Trait Anxiety Inventory (p = 0.026). Conclusion: mothers of newborns with malformations presented moderate anxiety, and this was higher when the diagnosis was given in the postnatal period. The use of the Trait-State Anxiety Inventory can provide guidance to other studies and to clinical practice.
Polarimetric Radar Vegetation Index for Biomass Estimation in Desert Fringe Ecosystems

Biomass estimation of eastern Mediterranean shrublands was investigated using PALSAR full- and dual-polarization L-band and Sentinel-1 dual-polarization C-band data. First, we conducted an empirical assessment of single and multiple regressions between polarized backscattering coefficients and shrubland biomass distribution along the climatic gradient between semiarid and arid regions. We then found that the PALSAR L-band HV-polarized backscattering coefficient has higher biomass information content than Sentinel-1 C-band data. Based on a theoretical volume scattering model and a semiempirical model, we propose a new polarimetric radar vegetation index (PRVI) that utilizes the degree of polarization and the cross-polarized backscattering coefficient. The relationship between the new index and the biomass was assessed with reference to normalized difference vegetation index-based biomass estimates calculated using Landsat imagery. The PRVI was found to have higher correlation with biomass compared with other radar polarization parameters, in general, and an existing radar vegetation index (RVI), in particular. Assessment of PRVI-based biomass predictions compared with allometric data extracted from air photographs, Lidar, and field data for 67 sites across the desert fringe zone indicated moderate performance with an RMSE of 0.329 kg/m2, while an RVI-based biomass estimation had an RMSE of 0.439 kg/m2.
Deterministic and Probabilistic Diagnostic Challenge Generation for Arbiter Physical Unclonable Function

Physical unclonable functions (PUFs) have broad application prospects in the field of hardware security. Like faults in general-purpose circuits, faults may also occur in PUFs. Fault diagnosis plays an important role in the yield learning process. Traditional fault diagnosis methods are based on comparing the fault-free responses of a design and the failing responses of chips. However, different manufactured, fault-free PUFs with the same design have different challenge-response pairs, so PUFs do not have deterministic, fault-free responses. Hence, traditional fault diagnosis methods are unsuitable for PUFs. To effectively diagnose PUFs, this paper proposes a diagnostic challenge generation method for the typical PUF: arbiter PUF. The diagnostic challenges that can deterministically or probabilistically distinguish the suspect faults of arbiter PUFs are generated. Simulation experiments on diagnosing failing arbiter PUF instances show that all the actual fault locations are accurately included in the candidate sets, and the average number of candidate locations (i.e., diagnostic resolution) is 1.585. FPGA experiments on diagnosing real PUFs show that the diagnostic accuracy is also 1, and the average diagnostic resolution is 1.602.
MYCN is amplified during S phase, and c-myb is involved in controlling MYCN expression and amplification in MYCN-amplified neuroblastoma cell lines

Neuroblastoma derived from primitive sympathetic neural precursors is a common type of solid tumor in infants. MYCN proto-oncogene bHLH transcription factor (MYCN) amplification and 1p36 deletion are important factors associated with the poor prognosis of neuroblastoma. Expression levels of MYCN and c-MYB proto-oncogene transcription factor (c-myb) decline during the differentiation of neuroblastoma cells; E2F transcription factor 1 (E2F1) activates the MYCN promoter. However, the underlying mechanism of MYCN overexpression and amplification requires further investigation. In the present study, potential c-Myb target genes, and the effect of c-myb RNA interference (RNAi) on MYCN expression and amplification were investigated in MYCN-amplified neuroblastoma cell lines. The mRNA expression levels and MYCN gene copy number in five neuroblastoma cell lines were determined by quantitative polymerase chain reaction. In addition, variations in potential target gene expression and MYCN gene copy number between pre- and post-c-myb RNAi treatment groups in MYCN-amplified Kelly, IMR32, SIMA and MHH-NB-11 cell lines, normalized to those of non-MYCN-amplified SH-SY5Y, were examined. To determine the associations between gene expression levels and chromosomal aberrations, MYCN amplification and 1p36 alterations in interphases/metaphases were analyzed using fluorescence in situ hybridization. Statistical analyses revealed correlations between 1p36 alterations and the expression of c-myb, MYB proto-oncogene like 2 (B-myb) and cyclin dependent kinase inhibitor 1A (p21). Additionally, the results of the present study also demonstrated that c-myb may be associated with E2F1 and L3MBTL1 histone methyl-lysine binding protein (L3MBTL1) expression, and that E2F1 may contribute to MYCN, B-myb, p21 and chromatin licensing and DNA replication factor 1 (hCdt1) expression, but to the repression of geminin (GMNN). On c-myb RNAi treatment, L3MBTL1 expression was silenced, while GMNN was upregulated, indicating G2/M arrest. In addition, MYCN gene copy number increased following treatment with c-myb RNAi. Notably, the present study also reported a 43.545% sequence identity between upstream of MYCN and Drosophila melanogaster amplification control element 3, suggesting that expression and/or amplification mechanisms of developmentally-regulated genes may be evolutionarily conserved. In conclusion, c-myb may be associated with regulating MYCN expression and amplification. c-myb, B-myb and p21 may also serve a role against chromosome 1p aberrations. Together, it was concluded that MYCN gene is amplified during S phase, potentially via a replication-based mechanism.
Concurrent Programming in Java. Second Edition: Design Principles and Patterns

From the Publisher: The book targets intermediate to advanced programmers interested in mastering the complexities of concurrent programming. Taking a design pattern approach, the book offers standard design techniques for creating and implementing components that solve common concurrent programming challenges. The numerous code examples throughout help clarify the subtleties of the concurrent programming concepts discussed.
Sex Differences in Cardiovascular Risk Factors for Dementia

Dementia, characterized by a progressive cognitive decline and a cumulative inability to behave independently, is highly associated with other diseases. Various cardiovascular disorders, such as coronary artery disease and atrial fibrillation, are well-known risk factors for dementia. Currently, increasing evidence suggests that sex factors may play an important role in the pathogenesis of diseases, including cardiovascular disease and dementia. Recent studies show that nearly two-thirds of patients diagnosed with Alzheimer’s disease are women; however, the incidence difference between men and women remains vague. Therefore, studies are needed to investigate sex-specific differences, which can help understand the pathophysiology of dementia and identify potential therapeutic targets for both sexes. In the present review, we summarize sex differences in the prevalence and incidence of dementia by subtypes. This review also describes sex differences in the risk factors of dementia and examines the impact of risk factors on the incidence of dementia in both sexes.
Fuzzy coded space frequency quantization

In this paper we propose a new image coding technique, which consists of the space frequency quantization (SFQ), and a fuzzy context based modeling. The space frequency quantization (SFQ) is an effective coding technique, which uses a zerotree pruning of wavelet coefficients. After the space frequency quantization is applied to the wavelet coefficients the non-pruned coefficients are quantized using context modeling. The context based quantization technique uses the partitioning proposed by the trellis coded quantization and fuzzy logic to predict the next TCQ state from the context. Quantized indices are entropy coded using an arithmetic coder. The probability estimation is based on the observation of the past coded bits. The results of the proposed compression scheme showed that the proposed coder outperforms the state-of-the-art coders in the RD sense for the SAR data compression.
A very fast VLSI rangefinder

The authors present a very fast lightstripe rangefinder based on an IC array of photoreceptor and analog signal processor cells which acquires 1000 frames of range image per second-two orders of magnitude faster than currently available rangefinding methods. Unlike a conventional lightstripe range-finder, which obtains a frame of range image by the step-and-repeat process of projecting a stripe and grabbing and analyzing a camera image, the VLSI sensor array of this rangefinder gathers range data in parallel as a scene is swept continuously by a moving stripe. Each cell continuously monitors the output of its photoreceptor, and detects and remembers the time at which it observed the peak incident light intensity during the sweep of the stripe. Prototype rangefinding systems have been built using a 28*32 array of these sensing elements. <<ETX>>
Data Offloading Techniques Through Vehicular Ad Hoc Networks: A Survey

Recently, for satisfying users’ various mobile Internet service requests for data exchange anytime and anywhere even in their moving vehicles, the generated mobile data traffic has been rapidly increasing and has become a serious burden on current cellular networks. To partially address such a serve challenge, vehicular ad hoc networks (VANETs) have emerged as an effective approach for enhancing vehicular services and applications by equipping vehicles with wireless and processing capabilities. In this paper, we survey the recent advances in the data offloading techniques through VANETs. Particularly, based on the communication patterns among vehicles and infrastructures, we classify these techniques into three categories, i.e., data offloading through vehicle-to-vehicle communications, vehicle-to-infrastructure communications, and vehicle-to-everything communications. Besides, we present a detailed taxonomy of the related techniques by discussing the pros and cons for various offloading techniques for different problems in VANETs. Finally, some opening research issues and challenges are outlined to provide guidelines for future research work.
Cryptographic analysis of the Modified Matrix Modular Cryptosystem

We show that the Modified Matrix Modular Cryptosystem proposed by S.K. Rososhek is not secure against the attack based on the linear decomposition method. The security of the encryption scheme in the Rososhek's system is based on the mix of the conjugacy search problem and random "salt". We do not solve the conjugacy search problem and we are not looking for the exact meaning of the "salt". The transported secret message in the system is recovered without computation the secret parameters, that have been used for its encryption.