Miracle-dz/UniFAR-FTUs huggingface dataset:
Research of the LDA algorithm processing results on high-level classes of patents

—The purpose of the article is to study the similarity of extractable topics from different high-level classes of patents and the possibility of classifying these documents according to the generally-trained model. The optimal number of topics can be selected from the interpretation of the resulting topics for the coherence of words in the topic and the reflection of the general discourse. In the presented dataset only general themes are known, is not possible to suggest which sub-themes can discover. In the course of the research, the dynamics of the change in the models’ quality with the change of parameters, according to which relatively optimal parameters are chosen, is considered.
Deep Learning Neural Networks with Controlled Switching of Neural Planes

The algorithm of network topology construction and training of twodimensional fast neural networks with additional switched planes is considered. It is noted that the structure of fast neural networks has a fractal nature. The constructed topology is ideologically close to the topology of convolutional neural networks of deep learning, but it has regular topology with the number of layers established by the factor representation of the dimensions of the image and the output plane of the classes. The learning algorithm has an analytical representation, and it is stable and converges in a finite number of steps. Additional planes extend the information capacity of the tunable transformation to the maximum possible. Control of the planes in the training and processing mode is realized by numerical coordinate codes of the output plane. The architecture of a regular neural network with additional planes is presented. Variants for image ordering in the output plane are considered. Examples are given.
Network Automation for Path Selection: A New Knowledge Transfer Approach

Due to the ever-increasing complexity of modern communication networks, network operators are making tremendous efforts on achieving objectives for the network to meet the diversified requirements of many real-world applications. However, network operators are repeatedly taking a lot of time on some common tasks shared by different networks. In order to reduce repetitive human efforts on network management, advanced machine learning paradigms, such as deep reinforcement learning, has received numerous attention in the networking community. Nevertheless, it encounters great difficulty in transferring learned policies to new environments, resulting in new model training and testing for each changed environment setting. To tackle this important issue, in this paper we propose a new framework that is the first of its kind to enable an agent to have transferable knowledge for network management, specifically, for network path selection tasks. Through this framework, an agent can efficiently learn and express the transferable network knowledge for achieving task objectives. Extensive experimental results show that the learned knowledge through the proposed framework can realize some common objectives of path selection tasks across different network environments. In addition, the knowledge learned from one network task can significantly improve the learning performance of another similar but different task.
The Impact of Insurgent Activities on Government Senior Secondary Education in Borno and Yobe States

This study was on the impact of insurgent activities on government senior secondary education in Borno and Yobe States. Three purpose of the study with three corresponding research questions and hypotheses guided the study. Descriptive survey research design was adopted for this study. The population comprised of 2600 teaching staff and 276 administrators for Borno and 957 teaching staff and 123 administrators for Yobe State. The sample was randomly selected among the senior secondary schools in those areas while the sampling technique used was proportionate stratified sampling. The instrument used for data collection was “Impact of Insurgents’ Activities on Senoir Secondary Schools in Borno and Yobe States Questionnaire (IIASSSBYQ). The reliability of the instrument was . 825. The findings of the result revealed that Students were reluctant to go school for fear of insurgency (.768). Also, the coefficient of influence of insurgency activities on students enrolment reveal that forceful recruitment of student as child soldiers and suicide bombers by insurgency lower enrolment in my school (.817). Conclusions and recommendations were made among which are Nigerian Government should address the root and symptom of the problem by eradicating poverty, providing employment to the people and enhancing security efforts. Government should ensure that there is equity and transparency in the distribution of resources and maintain justice and fairness in resolving
Investigating Affecting Quality of E-Insurance on Trust and Satisfaction Customer With Lee and Chung Model (Case Study: Iran Insurance in Khoarsen Razavi)

The present study investigated the effects of quality e-insurance on trust and customer satisfaction derived from the model of Kun Chang Lee, Namho Chung (2009) in Iran insured of Khorasan Razavi. In this study, the methodologies of descriptive-correlational and crosssectional survey were employed. The population studied included all eligible individuals use of electronic insurance in Khorasan, using simple random sampling were available, 210 were selected as sample. For data analysis, the descriptive statistics to examine the demographic characteristics of society were applied and data normality was checked and the last Effect of variables and hypotheses testing, multivariate regression test T_Value and P use will. At all stages of data analysis, software SPSS/18 was applied. The results showed that system quality, information quality and design quality have a direct significant relationship to trust and customer satisfaction of Iran Electronic Insurance.
Performance Implications of Memory Affinity on Filesystem Caches in a Non-Uniform Memory Access Environment

Non-Uniform Memory Access imposes unique challenges on every component of an operating system and the applications that run on it. One such component is the filesystem which, while not directly impacted by NUMA in most cases, typically has some form of cache whose performance is constrained by the latency and bandwidth of the memory that it is stored in. One such filesystem is ZFS, which contains its own custom caching system, known as the Adaptive Replacement Cache. This work looks at the impact of NUMA on this cache via sequential read operations, shows how current solutions intended to reduce this impact do not adequately account for these caches, and develops a prototype that reduces the impact of memory affinity by relocating applications to be closer to the caches that they use. This prototype is then tested and shown, in some situations, to restore the performance that would otherwise be lost.
Neural network biometric cryptography system *

In this paper, an approach to the construction of a neural network system of biometric authentication is proposed, which allows organizing the distributed stor-age of the base of biometric images and using a secret cryptographic key generated on the basis of the input biometric image as an output of the neural network. The object of the research is the biometric authentication system, and the subject of the research is the algorithms for converting parameters into a cryptographic key based on neural network technologies. The structure of a biometric authentication system has been developed, which identifies biometric features of a face image. The main difference between the developed system and existing solutions is the method of constructing a vector of primary biometric features based on neural network models and methods of machine learning and data mining, which allows assigning a unique private cryptographic key to each authentication subject. The mechanism of a distributed neural network representation of private key components significantly reduces the likelihood of compromising the vector of biomedical features. The use of the developed system and algorithms will make it possible to create highly reliable biometric security systems that ensure the ability of users to work with confidential information in open and weakly protected information systems.
Multi-Sector Oriented Object Detector for Accurate Localization in Optical Remote Sensing Images

Oriented object detection in optical remote sensing images (ORSIs) is a challenging task since the targets in ORSIs are displayed in an arbitrarily oriented manner and on small scales, and are densely packed. Current state-of-the-art oriented object detection models used in ORSIs primarily evolved from anchor-based and direct regression-based detection paradigms. Nevertheless, they still encounter a design difficulty from handcrafted anchor definitions and learning complexities in direct localization regression. To tackle these issues, in this paper, we proposed a novel multi-sector oriented object detection framework called MSO2-Det, which quantizes the scales and orientation prediction of targets in ORSIs via an anchor-free classification-to-regression approach. Specifically, we first represented the arbitrarily oriented bounding box as four scale offsets and angles in four quadrant sectors of the corresponding Cartesian coordinate system. Then, we divided the scales and angle space into multiple discrete sectors and obtained more accurate localization information by a coarse-granularity classification to fine-grained regression strategy. In addition, to decrease the angular-sector classification loss and accelerate the network’s convergence, we designed a smooth angular-sector label (SASL) that smoothly distributes label values with a definite tolerance radius. Finally, we proposed a localization-aided detection score (LADS) to better represent the confidence of a detected box by combining the category-classification score and the sector-selection score. The proposed MSO2-Det achieves state-of-the-art results on three widely used benchmarks, including the DOTA, HRSC2016, and UCAS-AOD data sets.
Type 2 Antigens Differentiation in Response to T-Independent TACI Is Required for Efficient Plasma Cell

The control of systemic infection by encapsulated microorganisms requires T-independent type II (TI-2) Ab responses to bacterial polysaccharides. To understand how such responses evolve, we explored the function of transmembrane activator calcium modulator and cyclophilin ligand interactor (TACI), a member of the TNFR family, required for TI-2 Ab production. Quasimonoclonal (QM) mice produce robust TI-2 responses to 4-hydroxy-3-nitrophenylacetate (NP)-Ficoll, owing to the high precursor frequency of NP-speciﬁc B cells in the marginal zone of the spleen. QM mice that lack TACI produce decreased numbers of IgM (2-fold) and IgG (1.6-fold) NP-speciﬁc ASCs, compared with TACI-positive QM mice in response to immunization with NP-Ficoll. Our studies indicate that TACI acts at a remote time from activation because TACI is not necessary for activation and proliferation of B cells both in vitro and in vivo. Instead, TACI-deﬁcient QM B cells remained in the cell cycle longer than TACI-proﬁcient QM cells and had impaired plasma cell differentiation in response to NP-Ficoll. We conclude that TACI has dual B cell-autonomous functions, inhibiting prolonged B cell proliferation and stimulating plasma cell differentiation, thus resolving the longstanding paradox that TACI may have both B cell-inhibitory and -stimulatory functions. By promoting plasma cell differentiation earlier during clonal expansion, TACI may decrease the chances of autoantibody production by somatic hypermutation of Ig genes in response to T-independent Ags. The Journal of Immunology, 2007, 179: 2282–2288.
LUČKA BIBIČ FORMULATION OF ALBENDAZOLE MICROCAPSULES FOR THE ORAL ADMINISTRATION IN A SEMI-SOLID CARRIER

Albendazole (ALB) is an anthelmintic drug, classified as BCS type II compound, mainly used in the pediatric population in the third world countries. As the main setback of ALB is its low aqueous solubility (1 μg/ml), the enhancement of ALB release rate is essential. Another main disadvantage of oral drug delivery for pediatrics is the children’s inability to swallow. That is why a semi-solid formulation might be a better oral dosage form. Because the bitter taste of ALB might deter children from consuming the medicine, a microencapsulation technique would be a valuable achievement in order to avoid ALB unpleasant taste. Furthermore, it is predicted that semi-solid formulation, consisting of “UNICEF gel”, an HPMC based gel, with the incorporated taste-concealedmicroparticles, should overcome this major problems. In the first part of this thesis, a quantitative particle-size analysis, determination of flow properties and LC/MS identification of ALB were carried out. Following that step, the granulation process, with the various compositions of the numerous excipients, was conducted. After the granulation, the appropriate polymer, dextrin, was chosen, according to the dissolution studies. In order to quantitatively determine the ALB content and release from granules and microcapsules (MICs), a rapid RP-HPLC method was developed, optimized and validated. In the second stage, a fluid bed technique with a Wurster partition was selected for the coating with the Methyl Cellulose (MC) as a potential taste-concealing polymer. Once the ALB microcapsules were MC-coated, they were incorporated into UNICEF-gel and further dissolution studies were conducted at the neutral pH and pH 1.2. Finally, spectroscopy was used in order to evaluate the ALB release in the oral cavity and stomach from the MICs and the final semi-solid formulation. The results obtained from our studies, suggest that it is possible to achieve a micro sized product of poorly soluble drug ALB with the defined and narrow particle size distribution. Furthermore, dextrin not only gives a good coverage of the core material, resulting in the smooth and uniform surface and provides high entrapment efficiency, but also greatly improves the ALB release. The ALB dissolution of the pure drug was 7,80% ± 0,39% in 30 min, whereas 93,15% ± 0,34% and 67,36% ± 1,19% of the ALB present in uncoated (dextrin granules) and coated MICs, respectively, dissolved within the same time range. Thus, a remarkably surprising ALB release from the MICs was achieved. It has also been found that MICs incorporated into UNICEF gel might sufficiently conceal the bitterness of ALB for the first 2 min.
Software Design Patterns for AI-Systems

: Well-established design patterns offer the possibility of standardized construction of software systems and can be used in various ways. The systematic use of design patterns in the field of Artificial Intelligence (AI) Systems however, has received little attention so far, despite AI being a popular research area in recent years. AI systems can be used for a wide variety of applications and play an increasingly important role in business and everyday life. AI systems are becoming more complex however, the actual machine learning (ML) task comprises only a small part of the total source code of a system. In order to maintain a clear and structured architecture for such systems and to allow easy maintenance, standardized elements should be reused in the design. This paper describes possible applications of well-known design patterns in AI systems to improve traceability of the system design.
Static Detection of Unsafe DMA Accesses in Device Drivers

Direct Memory Access (DMA) is a popular mechanism for improving hardware I/O performance, and it has been widely used by many existing device drivers. However, DMA accesses can be unsafe, from two aspects. First, without proper synchronization of DMA buffers with hardware registers and CPU cache, the buffer data stored in CPU cache and hardware registers can be inconsistent, which can cause unexpected hardware behaviors. Second, a malfunctioning or untrusted hardware device can write bad data into system memory, which can trigger security bugs (such as buffer overﬂow and invalid-pointer access), if the driver uses the data without correct validation. To detect unsafe DMA accesses, some key challenges need to be solved. For example, because each DMA access is implemented as a regular variable access in the driver code, identifying DMA accesses is difﬁcult. In this paper, we propose a static-analysis approach named SADA, to automatically and accurately detect unsafe DMA accesses in device drivers. SADA consists of three basic steps. First, SADA uses a ﬁeld-based alias analysis to identify DMA accesses, according to the information of DMA-buffer creation. Second, SADA uses a ﬂow-sensitive and pattern-based analysis to check the safety of each DMA access, to detect possible unsafe DMA accesses. Finally, SADA uses an SMT solver to validate the code-path condition of each possible unsafe DMA access, to drop false positives. We have evaluated SADA on the driver code of Linux 5.6, and found 284 real unsafe DMA accesses. Among them, we highlight that 121 can trigger buffer-overﬂow bugs and 36 can trigger invalid-pointer accesses causing arbitrary read or write. We have reported these unsafe DMA accesses to Linux driver developers, and 105 of them have been conﬁrmed.
Single-Cell RNA-Seq Reveals Transcriptomic Heterogeneity and Post-Traumatic Osteoarthritis-Associated Early Molecular Changes in Mouse Articular Chondrocytes

Articular cartilage is a connective tissue lining the surfaces of synovial joints. When the cartilage severely wears down, it leads to osteoarthritis (OA), a debilitating disease that affects millions of people globally. The articular cartilage is composed of a dense extracellular matrix (ECM) with a sparse distribution of chondrocytes with varying morphology and potentially different functions. Elucidating the molecular and functional profiles of various chondrocyte subtypes and understanding the interplay between these chondrocyte subtypes and other cell types in the joint will greatly expand our understanding of joint biology and OA pathology. Although recent advances in high-throughput OMICS technologies have enabled molecular-level characterization of tissues and organs at an unprecedented resolution, thorough molecular profiling of articular chondrocytes has not yet been undertaken, which may be in part due to the technical difficulties in isolating chondrocytes from dense cartilage ECM. In this study, we profiled articular cartilage from healthy and injured mouse knee joints at a single-cell resolution and identified nine chondrocyte subtypes with distinct molecular profiles and injury-induced early molecular changes in these chondrocytes. We also compared mouse chondrocyte subpopulations to human chondrocytes and evaluated the extent of molecular similarity between mice and humans. This work expands our view of chondrocyte heterogeneity and rapid molecular changes in chondrocyte populations in response to joint trauma and highlights potential mechanisms that trigger cartilage degeneration.
Geography of Growth and Development-Oxford Research Encyclopedia of Economics and Finance

The geography of economic activity refers to the distribution of population, production, and consumption of goods and services in geographic space. The geography of growth and development refers to the local growth and decline of economic activity and the over­ all distribution of these local changes within and across countries. The pattern of growth in space can vary substantially across regions, countries, and industries. Ultimately, these patterns can help explain the role that spatial frictions (like transport and migration costs) can play in the overall development of the world economy.
Semibricks over split-by-nilpotent extensions

In this paper, we prove that there is a bijection between the τ -tilting modules and the sincere left finite semibricks. We also construct (sincere) semibricks over split-by-nilpotent extensions. More precisely, let Γ be a split-by-nilpotent extension of a finite-dimensional algebra Λ by a nilpotent bimodule ΛEΛ, and S ⊆ mod Λ. We prove that S ⊗Λ Γ is a (sincere) semibrick in mod Γ if and only if S is a semibrick in mod Λ and HomΛ(S,S ⊗Λ E) = 0 (and S ∪ S ⊗Λ E is sincere). As an application, we can construct τ -tilting modules and support τ -tilting modules over τ -tilting finite cluster-tilted algebras.
Intelligence Transportation Safety Information System

These days the large-scale car accidents have often been occurred by overspeeding in disregard of sharp curve, foggy and freezing regions. This paper has proposed the algorithm to calculate the safety speed in real time that can protect the car accidents under these weather and road conditions using Fuzzy reasoning theory. Under raining and snowing, drivers have to slow down the traffic safety speed by 1/3 of the traffic safety speed indicated on the existing speed sign plate based on their decision. So it is difficult to calculate and then observe the safety speed. This paper has performed the simulation that provides the deivers with the optimal safety speed considering the road and weather conditions in real time to improve these problems. We have proved this method can improve more 25% than the existing one
Non-linear Modifications of Black-Scholes Pricing Model with Diminishing Marginal Transaction Cost

In the field of quantitative financial analysis, the Black-Scholes Model has exerted significant influence on the booming of options trading strategies. Publishing in their Nobel Prize Work in 1973, the model was generated by Black and Scholes. Using Ito’s Lemma and portfolio management methodology, they employed partial differential equation to provide a theoretical estimate of the price of European-style options. This paper is interested in deriving non-linear modifications of the Black-Scholes model with diminishing marginal transaction cost.
A STOCHASTIC VARIANCE REDUCTION METHOD FOR PCA BY AN EXACT PENALTY APPROACH

For principal component analysis (PCA) to eﬃciently analyze large scale matrices, it is crucial to ﬁnd a few singular vectors in cheaper computational cost and under lower memory requirement. To compute those in a fast and robust way, we propose a new stochastic method. Especially, we adopt the stochastic variance reduced gradient (SVRG) method [11] to avoid asymptotically slow convergence in stochastic gradient descent methods. For that purpose, we reformulate the PCA problem as a unconstrained optimization problem using a quadratic penalty. In general, increasing the penalty parameter to inﬁnity is needed for the equivalence of the two problems. However, in this case, exact pe-nalization is guaranteed by applying the analysis in [24]. We establish the convergence rate of the proposed method to a stationary point and numerical experiments illustrate the validity and eﬃciency of the proposed method.
Age Determination of the Social Media Post's Author Using Deep Neural Networks and Facial Processing Models

This article describes an original approach based on deep neural network models to the problem of determining the age of the text author. A detailed description of the author's method and the results of its application is demonstrated. The analysis of tools for filtration of unreliable data in photo age determination, methods of text author's age determination are also present in the paper. This method can be considered a deserving competitor for other approaches due to the obtained result - 82% accuracy.