Miracle-dz/UniFAR-FTUs huggingface dataset:
PMDA: Privacy-Preserving Multi-functional Data Aggregation Without TTP in Smart Grid

In the smart grid, residents' electricity usage needs to be periodically measured and reported for the purpose of better energy management. At the same time, real-time collection of residents' electricity consumption may unfavorably incur privacy leakage, which has motivated the research on privacy-preserving aggregation of electricity readings. Most previous studies either rely on a trusted third party (TTP) or suffer from expensive computation. In this paper, we first reveal the privacy flaws of a very recent scheme pursing privacy preservation without relying on the TTP. By presenting concrete attacks, we show that this scheme has failed to meet the design goals. Then, for better privacy protection, we construct a new scheme called PMDA, which utilizes Shamir's secret sharing to allow smart meters to negotiate aggregation parameters in the absence of a TTP. Using only lightweight cryptography, PMDA efficiently supports multi-functional aggregation of the electricity readings, and simultaneously preserves residents' privacy. Theoretical analysis is provided with regard to PMDA's security and efficiency. Moreover, experimental data obtained from a prototype indicates that our proposal is efficient and feasible for practical deployment.
Asymmetric Commutative Encryption Scheme Based Efficient Solution to the Millionaires' Problem

Secure multiparty computation (SMC) is an important scheme in cryptography and can be applied in various real-life problems. The first SMC problem is the millionaires' problem which involves two-party secure computation. Because the efficiency of public key encryption scheme appears less than symmetric encryption scheme, most existing solutions based on public key cryptography to this problem is inefficient. Thus, a solution based on the symmetric encryption scheme has been proposed. Although it is claimed that this approach can be efficient and practical, we discover that there exist several severe security flaws in this solution. In this paper, we analyze the vulnerability of existing solutions, and propose a new scheme based on the Decisional Diffie-Hellman hypothesis (DDH). Our solution also uses two special encodings (0-encoding and 1-encoding) generated by our modified encoding method to reduce the computation cost of modular multiplications. Extensive experiments are conducted to evaluate the efficiency of our solution, and the experimental results show that our solution can be much more efficient and be approximately 8000 times faster than the solution based on symmetric encryption scheme for a 32-bit input and short-term security. Moreover, our solution is also more efficient than the state-of-the-art solution.
Automatic Seizure Detection in a Mobile Multimedia Framework

Nowadays, the mobile healthcare industry is prospering due to the increase in computer processing power, improvement of next-generation communication technologies, and high storage capacity. Mobile multimedia sensors can acquire healthcare data, which can be processed to make decisions on the health status of users. In line with this, we propose a mobile multimedia healthcare framework in this paper, where an automatic seizure detection system is embedded as a case study. In the proposed system, electroencephalogram signals from a head-mounted set are recorded and processed using convolutional neural networks. A classification module determines whether the signals exhibit seizure. Experimental results show that the proposed system can achieve high levels of accuracy and sensitivity. The Children’s Hospital Boston–Massachusetts Institute of Technology database indicates the system accuracy and sensitivity to be 99.02% and 92.35% in a cross-patient scenario, respectively.
Broadband SIW Horn Antenna Loaded With Offset Double-Sided Parallel-Strip Lines

A novel substrate integrated waveguide horn antenna loaded with offset double-sided parallel-strip lines (DSPSL) is proposed in this letter. The severe mismatch between horn aperture and free space leads to narrow impedance bandwidth when the substrate thickness is much smaller than the wavelength (typically less than <inline-formula> <tex-math notation="LaTeX">${\lambda _0}$</tex-math></inline-formula>/6). Two groups of periodic offset DSPSL structure are placed in front of the horn aperture, which acts as an impedance transformer to realize smooth transition. Good impedance matching can be easily realized by modifying the constitutive parameters of the offset DSPSL structure. Measured results show that the impedance bandwidth can be improved up to 20.5% from 14 to 17.2 GHz with a low profile less than <inline-formula><tex-math notation="LaTeX">${\lambda _0}$</tex-math></inline-formula>/13. Stable end-fire radiation patterns are obtained across the bandwidth.
Reconstruction of Simulation-Based Physical Field by Reconstruction Neural Network Method.

A variety of modeling techniques have been developed in the past decade to reduce the computational expense and improve the accuracy of modeling. In this study, a new framework of modeling is suggested. Compared with other popular methods, a distinctive characteristic is "from image based model to analysis based model (e.g. stress, strain, and deformation)". In such a framework, a reconstruction neural network (ReConNN) model designed for simulation-based physical field's reconstruction is proposed. The ReConNN contains two submodels that are convolutional neural network (CNN) and generative adversarial net-work (GAN). The CNN is employed to construct the mapping between contour images of physical field and objective function. Subsequently, the GAN is utilized to generate more images which are similar to the existing contour images. Finally, Lagrange polynomial is applied to complete the reconstruction. However, the existing CNN models are commonly applied to the classification tasks, which seem to be difficult to handle with regression tasks of images. Meanwhile, the existing GAN architectures are insufficient to generate high-accuracy "pseudo contour images". Therefore, a ReConNN model based on a Convolution in Convolution (CIC) and a Convolutional AutoEncoder based on Wasserstein Generative Adversarial Network (WGAN-CAE) is suggested. To evaluate the performance of the proposed model representatively, a classical topology optimization procedure is considered. Then the ReConNN is utilized to the reconstruction of heat transfer process of a pin fin heat sink. It demonstrates that the proposed ReConNN model is proved to be a potential capability to reconstruct physical field for multidisciplinary, such as structural optimization.
Decoupling of Multi-Element MIMO Antennas

An efficient decoupling technique for multi-element MIMO antennas is proposed in this paper. A general model of using parasitic decoupling structure (PDS) to satisfy the criterions derived from antenna impedances for perfect isolation is presented. A prototype of three monopole antennas is realized on a Rogers PCB for demonstration purpose. The simulation results show that good impedance matching and high port isolation are achieved simultaneously.
Differentially Private Matrix Completion via Distributed Matrix Factorization

Recovering a decentralized low-rank matrix from an incomplete set of its entries is one of great research interests. Privacy makes our issue difficult. In this paper, we propose a novel scheme that allows analysts to perform great aggregate analysis while guaranteeing meaningful protection of each individuals privacy. Differential privacy aims to ensure means to maximize the accuracy of queries from statistical databases while minimizing the probabilities of identifying its records. With adding Gaussian noise, we are able to achieve this goal. First, we present an algorithm for private matrix completion. Secondly, we provide theoretical results for required Gaussian noise. Finally, we compare the performance of the proposed algorithm with the state-of-the-art, while both achieves the same level of differential privacy.
Empirical Study on Classification of Heart Disease Dataset-its Prediction and Mining

Despite the significance of data mining techniques to Heart Disease Data (HDD), there be a short of comprehensive literature review and a classification for it. This is the particular academic literature review of the application of data mining techniques to HDD (Heart Disease Data). It provides an academic database of literature amid the periods of 20062016 and proposes a classification scheme to classify the Mining techniques. The analysis and classification process was autonomously verified. Findings of this paper indicate that the research area of Heart Disease received most research attention. Classification and Association models are the two most commonly used models for data mining in Heart Disease Data. Our study provides a path to provide future research ideas and make easy creation about the application of data mining techniques in HDD.
Walking and hypertension: greater reductions in subjects with higher baseline systolic blood pressure following six months of guided walking

Background The aim of the study was to assess the effects of walking on the blood pressure in sedentary adults with differing degrees of systolic blood pressure (SBP). Methods A total of 529 subjects with SBP above 120 mmHg were enrolled. Blood pressure, body weight, body mass index, waist circumference and walking speed were determined at enrolment and after six months. Walking sessions were supervised by exercise physiologists. Results The weekly walking time of the subjects completing the project was uniform and reached 300 minutes by the second month. 56% of participants completed the 6 months intervention (182 women 59.6 ± 9.0 years, and 114 men, 65.4 ± 8.6 years) 27 had a baseline SBP >160 mm Hg, 35 between 150–159, 70 between 140–149, 89 between 130–139 and 75 between 120–129 mmHg. Following six months of supervised walking, SBP was significantly reduced in all subgroups (p < 0.001), with the greatest reduction (−21.3 mmHg) occurring in subjects with baseline SBP >160 and the smallest reduction (−2.6 mmHg) occurring in subjects with baseline SBP of 120–129 mmHg. Diastolic blood pressure, body weight, body mass index and waist circumference were also significantly reduced following the walking intervention (p < 0.001). These reductions were nearly identical within the various groups. Discussion In a large group of sedentary adults with varying degrees of SBP, 6 months of supervised walking elicited a marked reduction in systolic blood pressure with the largest reductions in pressure occurring in individuals with higher baseline SBP.
UCAM: Usage, Communication and Access Monitoring Based Detection System for IoT Botnets

Botnets are being used for launching Distributed Denial of Service (DDoS) attacks causing massive financial loses to the enterprises. With the rise of low-cost, less protected Internet of Things (IoT) devices the likelihood of harm from botnets have also increased. Open sourcing the source code of Mirai IoT malware has provided the foundation step for the development and subsequent launch of variants of Mirai IoT botnets. Detecting such kind of threats is essential for the smooth operation of the Internet. Such security measures are basic requirements for the establishment of user trust on the Internet of Things devices. This paper presents UCAM: Usage, Communication and Access Monitoring Based Botnets Detection System for IoT. The proposed solution has three main components i.e. descriptor, monitor and comparator. Descriptor defines Device Usage, Communication and Access policies. Monitor observes the current state of device Usage, Communication and Access. Anomalies are Detected by the Comparator. Results show that the proposed detection system successfully detects Mirai IoT malware.
Mergers and acqusitions

This course introduces students to the various legal problems surrounding corporate acquisitions, both from the perspective of the acquiring company and the company acquired. The course will also call on students to consider the policy problems raised by various aspects of mergers and acquisitions. In doing so, it will introduce students to basic corporate finance concepts necessary for an understanding of those problems. The course will not, however, presume any previous knowledge of either economics or finance theory.
An Efficient Implementation of Mobile Raspberry Pi Hadoop Clusters for Robust and Augmented Computing Performance

Rapid advances in science and technology with exponential development of smart mobile devices, workstations, supercomputers, smart gadgets and network servers has been witnessed over the past few years. The sudden increase in the Internet population and manifold growth in internet speeds has occasioned the generation of an enormous amount of data, now termed ‘big data’. Given this scenario, storage of data on local servers or a personal computer is an issue, which can be resolved by utilizing cloud computing. At present, there are several cloud computing service providers available to resolve the big data issues. This paper establishes a framework that builds Hadoop clusters on the new single-board computer (SBC) Mobile Raspberry Pi. Moreover, these clusters offer facilities for storage as well as computing. Besides the fact that the regular data centers require large amounts of energy for operation, they also need cooling equipment and occupy prime real estate. However, this energy consumption scenario and the physical space constraints can be solved by employing a Mobile Raspberry Pi with Hadoop clusters that provides a cost-effective, low-power, high-speed solution along with micro-data center support for big data. Hadoop provides the required modules for the distributed processing of big data by deploying map-reduce programming approaches. In this work, the performance of SBC clusters and a single computer were compared. It can be observed from the experimental data that the SBC clusters exemplify superior performance to a single computer, by around 20%. Furthermore, the cluster processing speed for large volumes of data can be enhanced by escalating the number of SBC nodes. Data storage is accomplished by using a Hadoop Distributed File System (HDFS), which offers more flexibility and greater scalability than a single computer system.
Comparative Analysis of KNN, SVM, DT for EOG based Human Computer Interface

In this study, EOG signal based human computer interface (HCI) has been implemented. This is a communication system that permits interaction with computer using eye movement. The necessary steps of implementation of HCI are EOG signal acquisition and analysis, feature extraction and classification. EOG signal has been acquired by placing electrodes at left and right corner of eye from 12 subjects. Dual Tree Complex Wavelet Transform (DTCWT) has been employed to denoise the EOG signal and 16 features are extracted from the time domain. Three classifiers (Decision Tree (DT), k-Nearest Neighbor (KNN) algorithm, and Support Vector Machines (SVM)) have been used to identify the horizontal eye movement i.e. left and right. Analysis and comparison of their performance is made on the basis of confusion matrix, receiver operating characteristics (ROC) and performance indices i.e. sensitivity, specificity, precision, accuracy and F1 score to evaluate the most efficient classifier for their classification task. According to classification results, out of three classifiers, KNN is the best classifier for horizontal EOG signal and has shown almost 100 percent accuracy.
Ginseng-Related Drug-Induced Liver Injury

Ginseng is commonly used as a medicinal herb for memory and concentration and general well-being. Drug-induced liver injury (DILI) is one of the most challenging disorders and trending events in the United States which are related to body building and weight loss supplements. Currently, herbal and dietary supplementation is the second most common cause of DILI. Here, we report on a 45-year-old healthy Chinese woman who presented with dull intermittent left upper quadrant abdomen pain for a month. Upon thorough history taking, she had been taking ginseng tea and supplementation for her menopausal symptoms for almost 3 months. Physical examination was unremarkable except mild tenderness in left upper quadrant of the abdomen. Liver function test showed aspartate transaminase (AST) 717 U/L, alanine transaminase (ALT) 343 U/L, total bilirubin 5 mg/dL, direct bilirubin 3.3 mg/dL, alkaline phosphatase 182 U/L, with international normalized ratio (INR) 1.2. Prior liver enzymes (6 months earlier) showed AST 21 U/L, ALT 18 U/L, total bilirubin 0.8 mg/dL, direct bilirubin 0.3 mg/dL, alkaline phosphatase 34 U/L, with INR 0.7. Viral serology for acute hepatitis B, C, E, cytomegalovirus, Epstein-Barr virus, and varicella zoster virus was negative. She was immune to hepatitis A. Her antinuclear antibody was positive. Her anti-Smith antibody, anti-smooth muscle antibody, HFE gene mutation, ceruloplasmin, alpha-1 antitrypsin serologies were within normal references. An abdomen sonogram showed fatty infiltration. Liver biopsy showed moderate to severe portal inflammation and marked lobular disarray. Portal and lobular inflammatory infiltrates consisted of a mixture of histiocytes, lymphocytes, plasma cells, eosinophils, and neutrophils with centrilobular necrosis and focal bridging necrosis, and necro-inflammation. After 6 weeks of follow-up, the patient improved physically, and the abdomen pain resolved. Ginseng has been widely used in the Chinese community as medicinal herb for a variety of conditions for decades. However, proper research has never been done regarding its pharmacokinetics, efficacy, and safety issues. In our case report, the idiosyncratic DILI resulted from ingestion of ginseng as herbal supplementation for premenopausal symptoms. Physicians should be aware of and suspect DILI in any patient with acute liver injury, and patients should be reminded that all medications and supplements have a potential to cause DILI.
Prevalence and diversity of blaTEM, blaSHV and blaCTX-M variants among multidrug resistant Klebsiella spp. from an urban riverine environment in India

ABSTRACT In the present study, we have investigated prevalence and diversity of ESBL genes among Klebsiella isolates obtained from highly polluted stretch of river Yamuna, India. Phenotypic screenings of 116 Klebsiella isolates revealed ~30% were positive for ESBL production. Antibiotic profiling showed multidrug resistance phenotype among 90% isolates. Prevalence of blaTEM, blaSHV and blaCTX-M genes were found to be 57, 54 and 48% respectively. Furthermore, we identified eight variants of blaSHV (SHV-1, SHV-11, SHV-27, SHV-28, SHV-38, SHV-61, SHV-144, SHV-148), three each of blaTEM (TEM-1, TEM-116, TEM-206) and blaCTX-M (CTX-M-15, CTX-M-55, CTX-M-188) among Klebsiella spp. Co-occurrence of blaTEM, blaSHV and blaCTX-M (any two or all three) was observed among 45% Klebsiella isolates. Occurrence of blaCTX-M-188 and blaTEM-206 in environmental isolates of K. pneumoniae has not been reported earlier. Identification of blaTEM-206, blaSHV-27 and blaSHV-144 from Klebsiella spp. and blaTEM-116 from K. quasipneumoniae and K. variicola is the first report from India.
Passive OAM-Based Radar Imaging With Single-In-Multiple-Out Mode

In recent years, the orbital angular momentum (OAM) has been widely applied in the target detection and imaging to improve the performance of conventional radar systems. A passive OAM-based radar imaging method is studied in this letter. Compared with active OAM-based imaging approaches, the proposed method can reduce the complexity of the transmitting system and achieve the azimuthal profile even with small elevation angle. The imaging model is deduced with the single-in-multiple-out mode, and the imaging algorithm based on back projection is proposed to reconstruct the targets. Theoretical and simulation results demonstrate that the target profile can be reconstructed correctly with a single snapshot, and the proposed method is effective even when the elevation angle of radar observation is small. Moreover, this imaging technique is robust against noise influence.
Demethylation and epigenetic modification with 5-azacytidine reduces IDH1 mutant glioma growth in combination with temozolomide

BACKGROUND Isocitrate deyhydrogenase (IDH) mutant glioma comprises the majority of grades II-III gliomas and nearly all secondary glioblastomas. These progressive gliomas arise from mutations in IDH1 or IDH2 that pathologically produce D-2-hydroxyglutarate (2HG), which interferes with cell reactions using alpha ketoglutarate, leading to a hypermethylated genome and epigenetic dysregulation of gene expression initiating tumorigenesis. METHODS Human IDH1 wild type (wt) and IDH1 R132H cell lines and patient-derived xenografts (PDXs) were used to evaluate the FDA-approved DNA demethylating agent 5-azacytidine (5-aza). Cell growth, protein and gene expression, chromatin immunoprecipitation, and nucleosome position assays were performed in 5-aza treated cells. To evaluate antitumor activity in vivo, 5-aza was administered alone and in combination with temozolomide (TMZ) in a PDX glioma model harboring IDH1 R132H mutation. RESULTS 5-Aza treatment has been found to reduce cell growth and increase expression of glial fibrillary acid protein (GFAP). Chromatin immunoprecipitation and nucleosome position assay showed that the mechanism of increased GFAP expression induction is associated with histone modification and nucleosome repositioning of the GFAP promoter, respectively. In vivo, 5-aza treatment extended survival in IDH1 R132H mutant but not in an IDH1 wt glioma model. Additionally, 5-aza enhances the therapeutic effect of the DNA damaging agent TMZ in both subcutaneous and orthotopic PDX models of IDH1 R132H mutant glioma. CONCLUSION 5-Aza provided a survival benefit as a single agent but worked best in combination with TMZ in 2 different IDH1 R132H mutant glioma models.
Noise Explorer: Fully Automated Modeling and Verification for Arbitrary Noise Protocols

The Noise Protocol Framework, introduced recently, allows for the design and construction of secure channel protocols by describing them through a simple, restricted language from which complex key derivation and local state transitions are automatically inferred. Noise "Handshake Patterns" can support mutual authentication, forward secrecy, zero round-trip encryption, identity hiding and other advanced features. Since the framework's release, Noise-based protocols have been adopted by WhatsApp, WireGuard and other high-profile applications. We present Noise Explorer, an online engine for designing, reasoning about, formally verifying and implementing arbitrary Noise Handshake Patterns. Based on our formal treatment of the Noise Protocol Framework, Noise Explorer can validate any Noise Handshake Pattern and then translate it into a model ready for automated verification and also into a production-ready software implementation written in Go or in Rust. We use Noise Explorer to analyze more than 57 handshake patterns. We confirm the stated security goals for 12 fundamental patterns and provide precise properties for the rest. We also analyze unsafe handshake patterns and document weaknesses that occur when validity rules are not followed. All of this work is consolidated into a usable online tool that presents a compendium of results and can parse formal verification results to generate detailed-but-pedagogical reports regarding the exact security goals of each message of a Noise Handshake Pattern with respect to each party, under an active attacker and including malicious principals. Noise Explorer evolves alongside the standard Noise Protocol Framework, having already contributed new security goal verification results and stronger definitions for pattern validation and security parameters.