Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the use of Lipschitz Neural Networks for Automating the Design of Differentially Private Mechanisms

Designing differentially-private mechanisms that provide both privacy and utility is a challenging task that requires human expertise. Automating mechanism design could ease the adoption of differential privacy in new contexts. In this work, we frame the task of finding a differentially private mechanism as a machine learning problem. Specifically, we construct mechanisms using recentlypopularized Lipschitz Neural Networks and outline the foundations for optimizing such mechanisms via gradient descent. We apply this approach to learn mechanisms for several queries on simple datasets, and discuss our results.
Molecular Mechanisms of Necrotic Cell Death in Ischemic Renal Injury

Molecular mechanisms of necrotic cell death in ischemic renal injury Yuan Ying, M.D., Ph.D. University of Nebraska, 2016 Supervisor: Babu J. Padanilam, Ph.D. Acute kidney injury is a common clinical syndrome particularly in hospitalized patients. Necrotic cell death, as one type of major cell death after ischemic reperfusion injury, is partially responsible for the rapid decline in GFR. p53 is a novel apoptosis and necrosis inducer and is found to be activated after ischemic renal injury. Although previous studies suggest that p53 could be an important mediator of kidney dysfunction, no studies have examined its role in necrotic cell death and the cross talk between p53 and other necrotic cell inducers such as PARP-1 and CypD. Thus, in this study our first goal was to examine the effect of gene ablation of p53 specifically in the proximal tubules on ischemic renal injury. Our second goal was to examine the possible mechanism by which p53 may induce necrosis. Our third goal was to test if double knockout of PARP-1/CypD or PARP-1/p53 additively/synergistically protects kidney from ischemic injury. Our results suggest that knockout of p53 specifically in the proximal tubules protected the kidney form ischemic renal injury by reducing apoptotic/necrotic cell death, inflammation, and long term fibrosis. Further, we found that double knockout of PARP-1/CypD or PARP-1/p53 in the kidney had better protective effects than respective single knockout mice. In addition, kidneys from double knockout mice also have better histological findings and fewer necrotic tubules. Our data demonstrate that activation of p53 significantly increased PARP-1 expression, which could contribute to necrosis in tubular cells. Furthermore, activation of p53 significantly decreased the expression of SLC7A11, a Glu/Cys carrier in cell membrane that inhibits ferroptosis. This
TAXONOMIC AND ECOLOGICAL NOTES ON SOME DIDEMNUM SPECIES (ASCIDIACEA, DIDEMNIDAE) FROM SÃO SEBASTIÃO CHANNEL, SOUTH-EASTERN BRAZIL

Nine species collected in very shallow waters at the São Sebastião Channel (South-Eastem Brazil) were studied. Among the species collected, one is an endemic species, two have a geographical distribution that is restricted to the West Atlantic, and the remaining species are found in both Atlantic and Pacific waters. Didemnum ahu, D. granulatum, D. ligulum, D. lutarium, and D. perlucidum are recorded from Brazil for the first time.
ON-LINE QUANTITATIVE IN VITRO MONITORING OF CELLULAR GLUCOSE AND LACTATE METABOLISM: TECHNIQUE AND APPLICATIONS

Current biosensor technology is mostly used for discrete off-line analysis. We describe a novel on-line in vitro monitoring technique, with applications for monitoring cellular glucose metabolism. Using continuous perfusion and biosensor technology, we can measure glucose and lactate metabolism at a minute-to-minute time resolution for periods up to several days. The application of our perfusion-detection technique for in vitro monitoring is demonstrated in a wide variety of cells, including primary neuronal and astroglial cultures, yeast cells and human lymphocytes. The method shows that variations in oxygen delivery or exposure to a non-competitive pseudo-substrate (here 2-deoxyglucose, (2DG)) affects normal glucose metabolism. An innovative advantage of this system is that metabolism can be calculated per cell. The potential of in vitro on-line monitoring is discussed for application in studying normal and abnormal metabolism, toxic and non-toxic drug effects, and human tissue biopsies.
ENERGY UTILIZATION OF BY-PRODUCTS FROM MECHANICAL RECYCLING PROCESS OF ELECTRONIC WASTE

The article deals with energy utilization of by-products from the mechanical recycling of electrical and electronic equipment. This is essentially crushed mixed plastic and dust from dust removal and filtration process. Final products of this technology are primarily metal parts, other by-products are landfilled. Individual samples are analysed for fuel properties and are determined element analyses and defined stoichiometric models of combustion. The values of elemental composition show a high proportion of non-combustible particles that reach up to 77%. Mixed plastic is only material which can be used without major modifications (under 0.7 mm) and has 34% of ash with calorific value of 22.39 MJ.kg -1 . The values of stoichiometric calculations indicate different parameters of supplied combustion air amount, amount of flue gas and combustion temperatures. These differences are based primarily on total amount of carbon and hydrogen in the elemental composition of the original samples and the differences are also recorded in the graphic dependency of CO2 on oxygen content in exhaust gas.
AN IN-VITRO STUDY OF EFFECTS OF CENTELLA ASIATICA ON PROLIFERATION RATE OF HUMAN PERIODONTAL LIGAMENT FIBROBLASTS

Centella asiatica is a member of a tropical plant family which is considered to be a good source of phenolic compounds. The present study was designed to assess the effect of different concentrations of aqueous extract of Centella asiatica on the proliferation of human periodontal ligament fibroblast in-vitro. Aqueous extract of Centella asiatica was prepared and concentrations of 2-40 μg/ml were applied to the human periodontal ligament fibroblasts culture. The viability of human periodontal ligament fibroblast was investigated by MTT cell proliferation assay. The data were subjected to statistical analysis using ANOVA and Tukey's post hoc test. The concentration of aqueous extract of Centella asiatica that enhances highest proliferation of human periodontal ligament fibroblast cell was 20 μg/ml. the regeneration and protective effects of Centella asiatica can be utilize to treat the periodontal disease.
The Potential of Gastronomy Tourism in Nong Khai Special Economic Zone

The research regarding the potential of gastronomy tourism in Nong Khai Special Economic Zone have the purpose of finding out the feasibility and values of gastronomy tourism in Nong Khai as a tourist and gastronomy resource. The researcher collected the information from the survey, textbooks, articles and other researches previously done on the gastronomy tourism. The results found that Nong Khai have the potential to develop the new, unique and different form of tourism. The tourists have the opportunities to test, including preparing and cooking by themselves. This would allow the tourists to learn firsthand relationships between communities in the province.
LMP1 Up-regulates Calreticulin to Induce Epithelial-mesenchymal Transition via TGF-β/Smad3/NRP1 Pathway in Nasopharyngeal Carcinoma Cells

Background: Latent membrane protein 1 (LMP1) is known as an oncogenic protein encoded by the EBV genome. The purpose of this study was to investigate the mechanism of LMP1-induced cell epithelial-mesenchymal transition (EMT). Methods: The NP69 cell line of nasopharyngeal epithelial cells with high expression of LMP1 was established to observe the effect of high expression of LMP1 on cell growth, proliferation, cycle, apoptosis, migration and invasion. We used proteomics to screen and identify differentially expressed proteins related to LMP1-mediated epithelial cell transformation. Then, we analyzed the expression and significance of differentially expressed calreticulin (CRT) in nasopharyngeal carcinoma (NPC), and observed the effect of CRT expression on EMT in CNE2 cells of NPC. Finally, the expression of neuropilin-1 (NRP1), which is a protein downstream of the EMT-related signaling pathway TGF-β (transforming growth factor β), was detected. Results: LMP1 promoted NP69 cells proliferation, inhibited apoptosis and induced EMT. We identified 22 differentially expressed proteins associated with LMP1-induced EMT. Among them, CRT expression level was significantly increased in NPC compared with adjacent tissues, and was interrelated with TNM staging and lymph node metastasis of NPC. After knockdown of CRT expression, the phenomenon of cell EMT was reduced and the ability of cell migration and invasion was weakened. CRT regulated NRP1 expression by affecting SMAD3 phosphorylation. Conclusion: LMP1 induced cell EMT via TGF-β/Smad3/NRP1 pathway, which promoted migration and invasion of NPC cells.
Let’s Authenticate: Automated Cryptographic Authentication for the Web with Simple Account Recovery

The FIDO2 Alliance is proposing standards for cryptographic authentication that are intended to replace passwords. We show that their standards could lead to difﬁcult registration, cumbersome account recovery, and potential privacy leaks and tracking. We propose an alternative architecture based on certiﬁcates instead of bare keys that provides automatic registration and login and simpliﬁed account recovery. We use a framework to compare our approach to related work, illustrating usability, security, and privacy trade-offs.
3D SCANNING WITH PROXIMITY PLANAR SCANNER

This thesis describes construction of scanning system providing three-dimensional models. Use of laser scanner mounted on robotic manipulator provides very flexible device capable building models of both tiny detailed structures and large object. It could be used in many various applications, especially in health care, where brings lot of advantages comparing to present scanning systems. Mechanical constitution, interfacing approaches, operating principles and calibration problems are described. This thesis also deals with visualizing of measured point clouds and its processing to form of shaded surface. Result of this work is ready-to-use device with software.
RISK FACTORS OF SEVEN GROUPS OF HEALTH DISORDERS IN IRANIAN HOLSTEIN COWS

Objective: The cows that don’t have access to sufficient energy in their ration can get sickness. Milk production has a high priority in metabolism of dairy cows so lactating cows are more prone to get sick This can ruin all the benefits of higher milk yield because of the higher prevalence of the disorders. Methods: Data for this research had gathered from a herd with 1600 Holstein cattle in East Azarbayjan province of Iran. Data were from 5052 lactation period of 1796 Holstein-Frisian cows which had parturition among 2005-2009. Seven groups of disorders have been analyzed. They are as follow: Cecal dilatation and rotation, diarrhea, abomasal distention and displacement to the right or left, locomotion problems and metritis. Each model consists of milk yield, parity, season and the year of calving. To determine the correlation between 305-day milk yield and occurring of disorders data have been analyzed with logistic regression model. Results: In this research current lactation was used as the lactation with occurrence of particular disorder. According to performed analysis on all cattle (heifers and the cows) higher milk yield was not a risk factor for the disorders occurrence except for diarrhea. Our outcomes showed a negative correlation between milk production with abomasal distention and left abomasal displacement. Analysis of correlation between prevalence of the disorders and 305- day yield showed a negative correlation with abomasal dilatation and displacement to the left and a positive correlation with diarrhea. Parity had a negative correlation with cecal rotation and dilatation, and a positive correlation with diarrhea. The year of calving had a significant correlation with abomasal dilatation and displacement of that to the right, cecal dilatation and rotation, diarrhea and locomotion problems. Calving season had only a significant relationship with cecal dilation and rotation. This research showed a complete correlation between some diseases and disorders in dairy cows and parameters that refer to milk production.
Face Presentation Attack Detection Based on a Statistical Model of Image Noise

The vulnerability of most existing face recognition and authentication systems against face presentation attacks (a.k.a. face spoofing attacks) has been mentioned and studied in many works. This paper introduces a novel parametric approach for face PAD using a statistical model of image noise. In fact, facial images from a presentation attack contain specific textural information caused by the presentation process which makes them different from bona-fide images. The subtle difference between bona-fide and presentation attack images can be interpreted by the difference regarding noise statistics within the skin zone of the face. Our solution is casted in the hypothesis testing framework. A new database for face PAD containing face bona-fide images and images of high-quality presentation attacks has been also introduced. The performance of the proposed approach was proven in the mentioned database. Experimental results show that, in a controlled situation, our solution performs better than the other approaches in the literature.
MapReduce-based Capsule Networks

Currently, artificial intelligence technology is attracting much attention, and image processing field is also making remarkable progress in recognition rate through CNN models. Furthermore, Capsule Network which is flexible in changing pose of image is being studied in various fields by improving disadvantage of Pooling Layer of CNN model. We propose a method to accelerate the learning of CapsNet model, which is much slower than the existing neural network model. TensorFlow, Google's deep-running library, and the Apache Foundation's Hadoop framework are run through Python. We can confirm that the learning time is decreased in inverse proportion to the increase of the number of nodes in learning CapsNet according to the proposed method of distributed processing.
Secure Keying Scheme for Network Slicing in 5G Architecture

Network slicing is one of the key enabling technologies of evolving fifth generation (5G) mobile communication that fulfills multitudes of service demands of 5G networks. Although the concept of network slicing, its deployment scenarios and some security aspects like slice isolation are discussed in detail, key management for network slicing based applications is still not a well-investigated research area. In this paper, we propose a secure keying scheme that is suitable for network slicing architecture when the slices are accessed by the third party applications. Since the secure keying scheme is designed using a multi-party computation mechanism, it ensures the consent of monitored use cases or devices which the data is acquired. We discuss the performance, scalability and security properties of the keying scheme to demonstrate its appropriateness under evolving 5G paradigm.
Distributed Event-Triggered Consensus for Nonlinear Networked Robotic Systems over an Uncertain Network

Since each robot communicates with each other though a wireless network with limited width and uncertainties, it is significant and necessary to propose a robust consensus control strategy which can also save bandwidth resources. In this paper, we concern on the event-triggered consensus control problem of networked robotic systems modeled by Euler-Lagrange systems over an uncertain network. The contributions of this paper are twofold: First, the uncertain directed network is considered which modeled by an uncertain part in edge weight. Several communication constraints can be covered by this configuration. Second, an event-triggered consensus protocol is proposed for the nonlinear robotic systems, which can greatly save bandwidth resources. Under the proposed control approach, Zeno behavior is proved to be avoided. Finally, some simulation results are presented to verify the effectiveness of the proposed control strategy.
Manipulating Surface Waves and Nanoscale Forces/Torques with Nonreciprocal Platforms

In this work, we investigate the potential of nonreciprocal platforms to control and enhance surface waves and nanoscale forces/torques in unconventional ways. We consider a gyrotropic magnetized plasma as a model system that can be designed to exhibit either weak or strong forms of nonreciprocity, as well as elliptical or hyperbolic modal dispersion. We show that this continuum material platform supports ultra-narrow unidirectional surface-plasmon-polariton beams, tunable via the emitter operational frequency or the cyclotron frequency of the biased plasma. In addition, we also investigate the effect of launching such unidirectional surface modes on the emitter itself. We show that, due to the asymmetric release of angular momentum, a non-trivial optical torque is exerted a generic dipolar emitter. We obtain a closed-form expression for this optical torque in terms of the system Green function valid for any three-dimensional, inhomogeneous, dissipative, dispersive, nonreciprocal, and bianisotropic structure. Advanced material platforms of this type may provide new degrees of freedom to control the orientation and rotation of nano-objects and optically-driven rotary nanomachines.
Design and Development of a Finger Exoskeleton for Motor Rehabilitation using Electromyography Signals

Robotic devices can accelerate the rehabilitation process of an impaired patient by increasing the frequency of treatment and eliminating the need for a physiotherapist. This paper presents the development of a novel human finger exoskeleton for the motor rehabilitation of post-stroke patients by providing assistance-as-required support to patients in active flexion of the finger. The proposed design comprises of a two degrees of freedom, seven-link concatenated mechanism with two links attached to proximal and distal phalanges and actuated by two electric linear actuators for compact size. We synthesized the mechanism on an interactive geometry software to make the mechanism follow natural joint trajectories. Kinematic analysis of the exoskeleton showed very low deviation from ideal trajectory and finite element analyses showed good structural rigidity under maximum load conditions. The paper further describes the electro-mechanical system and software architecture of the prototype. The prototype makes use of electromyography signal to detect human intention and has the ability to provide assistance-as-required rehabilitation through a developed intention based control algorithm. The algorithm makes use of signal processing and statistical techniques to recognise patterns from electromyography data. A classifier was modelled with human intention detection accuracy of nearly 90% The results of the testing on real human finger demonstrate the potential for clinical applications in the robot-assisted rehabilitation of the human hand for grasping tasks.
A characterization system for LEO satellites batteries

A model-based characterization technique for lithium-ion batteries adopted in small satellites is proposed. The architecture of a hardware/software platform designed for the experimental analysis is described. Data obtained with suitable testing campaigns are exploited for the identification of the parameters of the battery model through the minimization of a cost function. In particular, the battery model represents either charging and discharging phases, with specific attention to the scenarios typical of electrical power systems in small satellites. Experimental and numerical results show the effectiveness of the proposed solution in terms of accuracy of the identified model.
Automatic Identification of Fake News Using Deep Learning

The rapid development of computing trends, wireless communications, and the smart devices industry has contributed to the widespread of the internet. People can access internet services and applications from anywhere in the world at any time. There is no doubt that these technological advances have made our lives easier and saved our time and efforts. On the other side, we should admit that there is a misuse of internet and its applications including online platforms. As an example, online platforms have been involved in spreading fake news all over the world to serve certain purposes (political, economic, or social media). Detecting fake news is considered one of the hard challenges in term of the existing content-based analysis of traditional methods. Recently, the performance of neural network models have outperformed traditional machine learning methods due to the outstanding ability of feature extraction. Still, there is a lack of research work on detecting fake news in news and time critical events. Therefore, in this paper, we have investigated the automatic identification of fake news over online communication platforms. Moreover, We propose an automatic identification of fake news using modern machine learning techniques. The proposed model is a bidirectional LSTM concatenated model that is applied on the FNC-1 dataset with 85.3 % accuracy performance.
Sentiment Analysis of Finnish Customer Reviews

This paper presents a hybrid algorithm that takes advantage of polarity lexicons along with state-of-the-art word embeddings with weighted average of reviews and headlines for predicting polarity of reviews. The algorithm gives a significantly better coverage compared to polarity lexicons and a better classification accuracy in comparision to the word embedding models. We implemented Finnish polarity lexicons with different construction techniques, and evaluated the quality of the lexicons with translated polarity lexicons. The polarity lexicon construction and the polarity classification involves a new dataset, which was extracted from online user reviews in Finnish. In general, using headlines instead of reviews as sentiment data source provides better results at the expense of coverage.
Semantic and Topological Graphs for Patent Retrieval

Information Retrieval often relies on the indexed terms that occur in the documents, but rarely is the available associated metadata processed to aid in the retrieval process. In addition to this, the associated metadata is often interlinked data that can be the basis for graphs, such as citation or coauthorship graphs. One of these types of graphs can be build from the classification systems. Classification systems aim to organize the knowledge in the documents and make retrieval easier. This work utilizes two types of graphs that were developed out of the classification systems in previous work: Topological and semantic graphs. The topological graph is based on the classification system assigned by experts and the co-classification that can be found with those. The semantic graph is based on the textual content of the documents and the relations that can be found when vectors are assigned to the classes based on this content. The structure and weights of those graphs can then be utilized to supplement the retrieval process and re-rank the result lists. In this work we show how those graphs are a valuable addition to improve performance in patent retrieval tasks and how different versions of this retrieval process compare to each other.