Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical Solution of Piecewise-Stationary Mt/Gt/1 Queues

We develop an algorithm for computing the (exact) cumulative distribution function of the time-dependent workload in a piecewise-stationary Mt/Gt/1 queue with a work-conserving service discipline and general service-time distributions, where service times are determined at arrival instants. The t subscripts indicate that the arrival rate and the general service-time distribution may change with time, but we allow changes only at finitely many time points. The algorithm is based on numerical transform inversion, using the classical Takacs double-transform of the transient workload in an M/G/1 queue recursively over the successive stationary intervals. In particular, we apply our recently developed Fourier-series-based inversion algorithms for two-dimensional transforms and nested one-dimensional transforms. We also do additional work to greatly speed up the computation while tightly controlling the error. As a consequence, the computation time grows only quadratically with the number of intervals. The algo.
A Novel Texture Sensor for Fabric Texture Measurement and Classification

Surface texture is one of the important cues for human beings to identify different fabrics. This paper presents a novel design of a surface texture sensor by imitating human active texture perception by touch. A thin polyvinylidene fluoride (PVDF) film is used as the sensitive element to fabricate a high-accuracy, high-speed-response fabric surface texture sensor, and a mechanism is designed to produce the relative motion at a certain speed between the texture sensor and the surface of the perceived fabric with constant contact force. Thus, the surface texture property can be measured as the output charge of the PVDF film of the sensor induced by the small height/depth variation of the moving fabric surface. A texture feature extraction method by compressing the zero value spectral lines in frequency domain is proposed. In addition, a radial basis function (RBF) neural networks based on unsupervised K-means clustering algorithm is used as classifier for texture recognition. The experiments show that the proposed texture sensor is effective in detecting the feature signals of fabric surface textures, which are suitable for the RBF networks to classify the different fabrics.
Attention-Based Syllable Level Neural Machine Translation System for Myanmar to English Language Pair

Neural machine translation is a new approach to machine translation that has shown the effective results for high-resource languages. Recently, the attention-based neural machine translation with the large scale parallel corpus plays an important role to achieve high performance for translation results. In this research, a parallel corpus for Myanmar-English language pair is prepared and attention-based neural machine translation models are introduced based on word to word level, character to word level, and syllable to word level. We do the experiments of the proposed model to translate the long sentences and to address morphological problems. To decrease the low resource problem, source side monolingual data are also used. So, this work investigates to improve Myanmar to English neural machine translation system. The experimental results show that syllable to word level neural mahine translation model obtains an improvement over the baseline systems.
Thermodynamic cycle design of a 700W@3K sub-cooled helium refrigerator test facility

In order to increase the magnetic field and temperature margin of superconducting magnets, a test facility for a 3K sub-cooled helium refrigerator was proposed to decrease the operation temperature from the saturated helium to the sub-cooled helium. The process flow of 3K sub-cooled helium refrigerator has been analysed, including the 4.5K refrigerator unit and 3K refrigerator unit. Two cold compressors arranged in series have been adapted to decompress pressure to achieved 3K sub-cooled helium. The thermodynamic cycle of the 3K sub-cooled helium refrigerator have been designed, which can provide 2.5kW@4.5K in 4.5K mode or 1.2KW@4.5K + 700W@3K in 3K mode. As the most important port, the design parameters and mechanical test at normal temperature test of cold compressors were shown.
Comparison between Antioxidant Activities of Phenolic Extracts from Different Parts of Peanut

Peanut hull and skin are waste products of the food industry. Adding value to these products was the aim of this work. This goal was achieved by studying the phenolic content of the skin, hull and defatted flour of both roasted and unroasted peanuts. The antioxidant activity of the phenolic extracts was determined. The roasted peanut skin extract was then chosen and tested for its power of inhibition of flaxseed oil oxidation. The anticarcinogenic activity of the roasted peanut skin extract on different cell line carcinomas was examined. Both the extractable polyphenols (EPP) and the non extractable polyphenols (NEPP) were determined in the examined parts of the peanuts. Results revealed that NEPP was always higher than EPP and that highest phenolic content was found to be present in the skin. Roasted hull, unroasted hull, roasted skin, unroasted skin, roasted defatted flour, and unroasted defatted flour contained EPP 4.33, 3.38, 41.5, 56.2, 7.33 and 7.23 mg/g, respectively; and contained NEPP 7.4, 5.3, 102, 113.38, 10.3, and 13.4 mg/g, respectively; they also contain total polyphenol extract (TPE) 11.69, 8.73, 144.37, 169.19, 17.48, and 21.31 mg/g, respectively. Free radical scavenging activity (FRSA%) at 100μl conc. reached 87.0 % for roasted and unroasted peanut skin , ca. 79% for roasted and unroasted peanut hull, and least between 45.5160. 45% for unroasted and roasted defatted peanut flour, respectively. FRSA of BHT (0.1%) was 77.81%. Antioxidant activity (AOA) as measured by β-carotene/ linoleate method revealed AOA for roasted skin > unroasted skin > roasted hull > BHT > unroasted hull > unroasted defatted flour > roasted defatted flour, with values 89.13 > 86.65 > 80.33 > 76.33 > 75.27 > 39.34 > 30.37%, respectively. Roasted peanut skin extract (PSE) was chosen for further investigation. Percent reduction of oxidation in flaxseed oil when compared to control oil reached 27.10% and 22% for oil + PSE, and oil + BHT, respectively, as measured by p-anisidine value; and 41.99% and 38.88%, respectively, as measured by peroxide value. PSE exhibited potential as an anticarcinogenic agent, but needs further investigations. [Fakhriya S. Taha, Suzanne M. Wagdy and Fatma A. Singer. Comparison between Antioxidant Activities of Phenolic Extracts from Different Parts of Peanut. Life Sci J 2012;9(2):207-215]. (ISSN: 1097-8135). http://www.lifesciencesite. com. 35
The pricing of the Grade of service guarantees in the service overlay networks

We studied a class of Service Overlay Network (SON) capacity allocation problem. By analyzing the problem with two different nonlinear optimization formulations, we show that the prices of offering service guarantees are closely related to a set of Lagrange multipliers. Moreover, if the Grade of service (GoS) constraints are not hard requirements, the network design resulting from the set of prices is on the Pareto frontier of a multi-criteria optimization problem. A scheme was developed to derive the prices for various classes of customers by referring to the Lagrange multipliers. The major contribution of the article is the use of the Lagrange multipliers to provide a set of Pareto efficient prices in providing GoS guarantees.
How Teachers Are Engaging Reform in Chicago: Differences among Schools.

Findings of a study that examined the level of teacher engagement in school reform in Chicago Public Schools and differences in the level of engagement across schools are presented in this paper. The School Reform Act of 1988 transferred much of the policymaking and administrative authority from the central school board to Local School Councils (LSCs). Teachers had little input on shaping the reform and on governance structure. A survey mailed to teachers in 473 elementary schools yielded responses from 12,000 teachers in 401 schools, with an average response rate of 78 percent for each school. Findings indicate a moderate level of teacher engagement in reform and generally positive attitudes toward changes in their schools and the potential for improvement. The level of teacher engagement varied considerably within schools. Teachers were more favorable toward reform in schools characterized by shared decision-making, strong leadership, teacher collegiality, and community support. A recommendation is made to provide a supportive teachers' work environment, especially in the areas of social and human relations, to facilitate their commitment to the goals of reform. Two figures and two tables are included. The appendix provides a glossary of variable terms used in the statistical analysis. (LMI) ***************:-%AAA*************************************************** JeprubucLiona auppileo oy Luic are Lne best that can be macte ,: * from the original document. * *********************************************************************** U.S. DEPARTMENT OF EDUCATION ORice of Educational Research and improvement EDUCATIONAL RESOURCES INFORMATION CENTER (ERIC) Co/us document has been reproduced as received from the person or organization originating . 1 C F.A. nOr changes Sane been mace to n ,Ove reproduction Quality POIntS Ot view or 017.10n3 Slated mihisdOCu ment do not necessarily represent official OERI position or policy How Teachers Are Engaging Reform in Chicago: Differences Among Schools
Research on Smoke Detection based on DenseNet

Aiming at the difficulty of video smoke detection in complex scene, this paper proposes a method based on DenseNet to identify smoke. By extracting the color features of smoke and the property of upward movement of smoke, the method trains data through dense convolutional neural networks to learn the features between pictures. Experimental results show that the dense convolution neural network model can be effectively applied to real-time detection of smoke events in complex video scenarios.
HoPP: publish-subscribe for the constrained IoT

In this demo, we showcase our NDN based Publish-Subscribe scheme HoPP in a multi-hop low-power and lossy IoT deployment using constrained devices that operate RIOT. These devices publish temperature sensor readings and subscribe to fan control commands. We manually induce network disruptions to illustrate a seamless publisher and subscriber mobility with HoPP. A webbased dashboard highlights the network resilience and visualizes topology maintenance as well as traffic flows.
Vocabulary and Reading Growth in Children with Intellectual Disabilities: The Influences of Risks, Adaptive Behavior, and a Reading Intervention.

Risk factors tend to be negatively associated with developmental outcomes such as academic achievement and language skills. Promotive factors, on the other hand, may foster resilience in at-risk children. Some children, such as children with intellectual disabilities, experience relatively more risks than other children do. The purpose of this study was to examine the effects of risks, adaptive behavior, and an intervention on the language and reading growth of children with intellectual abilities over the course of a yearlong reading intervention in which they were participants. The results suggested that, on average, risks were negatively associated and adaptive behaviors were positively associated with initial language and reading scores. Additionally, participants evidenced significant progress on their language and reading scores over the course of the intervention, but neither adaptive behavior nor risk was related to this growth, which may suggest that students from differing backgrounds and with differing levels of adaptive skill can profit from high-quality reading instruction. INDEX WORDS: Disability, Risk, Achievement, Adaptive Behavior, Intervention VOCABULARY AND READING GROWTH IN CHILDREN WITH INTELLECTUAL DISABILITIES: THE INFLUENCES OF RISKS, ADAPTIVE BEHAVIOR, AND A READING INTERVENTION
An optimal algorithm for querying tree structures and its applications in bioinformatics

Trees and graphs are widely used to model biological databases. Providing efficient algorithms to support tree-based or graph-based querying is therefore an important issue. In this paper, we propose an optimal algorithm which can answer the following question: "Where do the root-to-leaf paths of a rooted labeled tree <i>Q</i> occur in another rooted labeled tree <i>T</i>? " in time <i>O</i>(<i>m</i> + <i>Occ</i>), where <i>m</i> is the size of <i>Q</i> and <i>Occ</i> is the output size. We also show the problem of querying a general graph is NP-complete and not approximable within <i>n<sup>k</sup></i> for any <i>k</i> < 1, where <i>n</i> is the number of nodes in the queried graph, unless P = NP.
Deep Learning-based Model to Fight Against Ad Click Fraud

Click fraud is a fast-growing cyber-criminal activity with the aim of deceptively clicking on the advertisements to make the profit to the publisher or cause loss to the advertiser. Due to the popularity of smartphones since the last decade, most of the modern-day advertisement businesses have been shifting their focus toward mobile platforms. Nowadays, in-app advertisement on mobile platforms is the most targeted victim of click fraud. Malicious entities launch attacks by clicking ads to artificially increase the click rates of specific ads without the intention of using them for legitimate purposes. The fraud clicks are supposed to be caught by the ad providers as part of their service to the advertisers; however, there is a lack of research in the current literature for addressing and evaluating different techniques of click fraud detection and prevention. Another challenge toward click fraud detection is that the attack model can itself be an active learning system (smart attacker) with the aim of actively misleading the training process of fraud detection model via polluting the training data. In this paper, we propose a deep-learning based model to address the challenges as mentioned above. The model is a hybrid of artificial neural network (ANN), autoencoder and semi-supervised generative adversarial network (GAN). Our proposed approach triumphs excellent accuracy than other models.
Multiplet selection: a practical study of multi-faces recognition for student attendance system

This paper presents a new approach, Multiplet Selection, for multi-faces recognition and its application in student attendance system. Instead of using a linear classifier such as SVM to classify face feature vectors, we adopt a "multiplet selection" approach such that Euclidean distances score between each identity's Anchor face [4] and a random input face are computed. Together with a pre-determined threshold parameter, this score is used for input face-identity pair association. We also develop a student attendance system based on the proposed multi-face recognition algorithm. And testing results video are available at the following URL: https://youtu.be/OZOgcw7B1YI.
NUCLEOSIDES, NUCLEOTIDES & NUCLEIC ACIDS

In human neuroblastoma cell lines (LAN5, SHEP and IMR32), mycophenolic acid (MPA) at concentrations (10 7 –10 6 M) readily attainable during immunosuppressive therapy with mycophenolate mofetil (Cellcept), induces guanine nucleotide depletion leading to cell cycle arrest and apoptosis through a p53 mediated pathway (up-regulation of p53, p21 and bax and down-regulation of bcl-2 and survivin). MPA-induced apoptosis is also associated to a marked decrease of p27 protein. In the same cell lines MPA, at lower concentrations (50 nM), corresponding to the plasma levels of the active free drug during Cellcept therapy, induces differentiation toward the neuronal phenotype by causing a partial chronic guanine nucleotide depletion. MPA-induced differentiation is not associated to p27 accumulation as occurs using retinoic acid. At a fixed concentration of MPA a higher percentage of apoptotic or differentiated cells is obtained when non dialysed serum substitutes for the dialysed one, due to the higher hypoxanthine concentration in the former (about 10 mM) leading to competition on HPRT-mediated salvage of guanine. At hypoxanthine or oxypurinol concentrations higher than 1 mM (up to 100 mM) no further enhancement of MPA effects was obtained, in agreement with the recently described safety of the allopurinol-mycophenolate mofetil combination in the treatment of hyperuricemia of kidney transplant recipients. The apoptotic effects of MPA *Correspondence: E. Messina, Department of Experimental Medicine and Pathology, ‘‘La Sapienza’’ University, Rome, Italy.
Few-shot learning for ear recognition

Ear recognition is a popular field of research within the biometric community. It plays an important part in automatic recognition systems. The ability to capture image of the ear from a distance and perform identity recognition makes ear recognition technology an attractive choice for security application as well as other related applications. However, datasets of ear images are still limited in size, while in other biometric modal communities, like face recognition, they possess large datasets and the most of them are collected in uncontrolled condition. As a result, deep learning technology still cannot yield satisfactory result in ear recognition area. In this paper, we tackle ear recognition problem by using few-shot learning based methods. We explore different methods towards model training with limited amounts of training data and show that by using them, with the help of data augmentation, the model can be flexible and can quickly adapt to new identity to perform fast recognition. The result of our work is the first few-shot learning based work to ear recognition. With our work we are able to significantly improve the accuracy of 23% on a regular dataset, and even 21% on a challenging dataset that is collected from the web, which is comparable with state-of-the-art in ear recognition area.
End-to-end encrypted scalable abstract data types over ICN

In Information Centric Networking (ICN), data items are made accessible through their name rather than their storage location. We extend this storage abstraction and complement it via a name lookup service for retrieving the latest version of a name binding in order to implement abstract data types (ADT), specifically "append-only logs" and "mutable key-value stores". Moreover, we make these data types scalable by choosing implementation techniques known as CRDTs (conflict-free replicated data types) and we use end-to-end encryption for protecting the content and structure against untrusted storage providers and forwarding elements. In this paper we describe our architecture, the interface to the ADTs and report on a prototype implementation for the cloud that is inspired by a real Fintech use case.
A Probabilistic Fusion Framework

There are numerous methods for fusing document lists retrieved from the same corpus in response to a query. Many of these methods are based on seemingly unrelated techniques and heuristics. Herein we present a probabilistic framework for the fusion task. The framework provides a formal basis for deriving and explaining many fusion approaches and the connections between them. Instantiating the framework using various estimates yields novel fusion methods, some of which significantly outperform state-of-the-art approaches.
Large Scale Cohesive Subgraphs Discovery for Social Network Visual Analysis

Graphs are widely used in large scale social network analysis nowadays. Not only analysts need to focus on cohesive subgraphs to study patterns among social actors, but also normal users are interested in discovering what happening in their neighborhood. However, effectively storing large scale social network and efficiently identifying cohesive subgraphs is challenging. In this work we introduce a novel subgraph concept to capture the cohesion in social interactions, and propose an I/O efficient approach to discover cohesive subgraphs.Besides, we propose an analytic system which allows users to perform intuitive, visual browsing on large scale social networks. Our system stores the network as a social graph in the graph database, retrieves a local cohesive subgraph based on the input keywords, and then hierarchically visualizes the subgraph out on orbital layout, in which more important social actors are located in the center. By summarizing textual interactions between social actors as tag cloud, we provide a way to quickly locate active social communities and their interactions in a unified view.
Betweenness centrality and cache privacy in information-centric networks

In-network caching is a feature shared by all proposed Information Centric Networking (ICN) architectures as it is critical to achieving a more efficient retrieval of content. However, the default "cache everything everywhere" universal caching scheme has caused the emergence of several privacy threats. Timing attacks are one such privacy breach where attackers can probe caches and use timing analysis of data retrievals to identify if content was retrieved from the data source or from the cache, the latter case inferring that this content was requested recently. We have previously proposed a betweenness centrality based caching strategy to mitigate such attacks by increasing user anonymity. We demonstrated its efficacy in a transit-stub topology. In this paper, we further investigate the effect of betweenness centrality based caching on cache privacy and user anonymity in more general synthetic and real world Internet topologies. It was also shown that an attacker with access to multiple compromised routers can locate and track a mobile user by carrying out multiple timing analysis attacks from various parts of the network. We extend our privacy evaluation to a scenario with mobile users and show that a betweenness centrality based caching policy provides a mobile user with path privacy by increasing an attacker's difficulty in locating a moving user or identifying his/her route.
Using ICN slicing framework to build an IoT edge network

We demonstrate 5G network slicing as a unique deployment opportunity for information centric networking (ICN), by using a generic service orchestration framework that operates on commodity compute, storage, and bandwidth resource pools to realize ICN service slices. In this demo, we specifically propose a service slice for the IoT Edge network. ICN has often been considered pertinent for IoT use due to its benefits like simpler stacks on resource constrained devices, in-network caching, and in-built data provenance. We use a lightweight ICN stack on IoT devices connected with sensors and actuators to build a network, where clients can set realistic policies using their legacy hand-held devices. We employ name based authentication protocols between the service end-points and IoT devices to allow secure onboarding. The IoT slice co-exists with other service slices that cater to different classes of applications (e.g., bandwidth intensive applications, such as video conferencing) allowing resource management flexibility. Our design creates orchestrated service Edge functions to which the clients connect, and these can in turn utilize in-network stateless functions to perform tasks, such as decision making and analytics using the available compute resources efficiently.