Reviewerly/exHarmony huggingface dataset:
Jacobian Policy Optimizations.

Recently, natural policy gradient algorithms gained widespread recognition due to their strong performance in reinforcement learning tasks. However, their major drawback is the need to secure the policy being in a ``trust region'' and meanwhile allowing for sufficient exploration. The main objective of this study was to present an approach which models dynamical isometry of agents policies by estimating conditioning of its Jacobian at individual points in the environment space. We present a Jacobian Policy Optimization algorithm for policy optimization, which dynamically adapts the trust interval with respect to policy conditioning. The suggested approach was tested across a range of Atari environments. This paper offers some important insights into an improvement of policy optimization in reinforcement learning tasks.
A Novel Parallel Convolutional Network Architecture for Depth-Dependent Object Recognition

This article presents the evaluation of a novel parallel convolutional neural network, oriented to recognize objects at different distances, in order to find a solution to the problem of variability in the value of confidence with which an object is recognized, by varying the distance of capture of the image with respect to the object. In order to test its performance, two additional convolutional neural network architectures are implemented, a conventional one with multiple branches of identification in parallel and a Directed Acyclic Graph convolutional network with the same parameters as the proposed one, which differ in the training database used and the structure of the network's output. This problem is identified when trying to develop assistive robotic systems that should recognize a particular object in a group of objects in order to be taken by an end effector capable of changing trajectories, avoiding possible collisions in human-machine work environments. Here, four different tools must be recognized at four distances (20, 40, 60 and 80 cm), where the conventional CNN obtain the lowest accuracy (80.6%), while, in comparison between the DAG and the parallel CNN, although their performances have been close, the proposed architecture has obtained better results, with 93% average accuracy. It is concluded that this network is able to function in environments with dynamic reference positions of the objects, allowing being implemented in mobile agents that require relocating an object and establishing a new path given an obstacle to evade.
Sparse Ensemble Learning with Truncated Convolutional Autoencoders for Cleaning Stained Documents

This paper mainly focus on how to extract clean text from the stained document. It may happen sometimes that due to stains it becomes very difficult to understand the documents and from the previous work it has been seen that one particular modelling technique either through Image processing or Machine learning which alone can’t perform for all the cases in general. As we all know ensemble techniques combine many of the modelling techniques and result in much reduced error that would not be possible by just having single model. But the features used for different models should be sparse or non-overlapping enough to guarantee the independence of each of the modelling techniques. XGBoost is one such ensemble technique in comparison to gradient boosting machines which are very slow due to this it’s not possible to combine more than three models with reasonable execution time. This work mainly focus on combining the truncated convolutional Autoencoders with sparsity take into account to that of machine learning and Image processing models using XGBoost such that the whole model results in much reduced error as compared to single modelling techniques. Experimentation’s are carried out on the public dataset NoisyOffice published on UCI machine learning repository, this dataset contains training, validation and test dataset with variety of noisy greyscale images some with ink spots, coffee spots and creased documents. Evaluation metric is taken to be RMSE(Reduced Mean Squared Error) to show the performance improvement on the variety of images which are corrupted badly
دراسة أداء أنظمة وصف الصور المعتمدة على نماذج مختلفة للتعلم العميق

تم في البحث الحالي إنجاز عملية وصف الصورة Image Description عبر وضع تسميات توضيحية لمكونات الصورة Image Captioning بثلاث نماذج مختلفة والمقارنة بينها. من أجل بناء نماذج الوصف، تم استخدام مكتبة Keras وهي مكتبة تستخدم كإطار عمل لتعلم الآلة Machine Learning Framework والتي تضم أهم المكاتب اللازمة لإنشاء وتدريب شبكات التعلم العميق. تم استخدام ثلاثة نماذج مطبقة على مكتبة Keras وذلك لاستخدامها في استخلاص سمات الصور وهي نموذج شبكة ResNet50 ونموذج شبكة VGG16 بالإضافة إلى نموذج شبكة VGG19. تتميز هذه النماذج باعتمادها على بنية الشبكة العصبونية الالتفافية Convolutional Neural Network (CNN) وأكثر دقة في استخلاص سمات الصورة من النماذج السابقة. أما لعملية التدريب وربط الوصف مع السمات فقد تم استخدام شبكة عصبونية تكرارية Recurrent Neural Network (RNN). تم استخدام مجموعة بيانات MSCOCO العالمية حيث تم أخذ مجموعة جزئية منها مؤلفة من 10000 صورة، حيث تم أخذ 9000 صورة منها لمجموعة التدريب Training و1000 صورة لمجموعة التحقق Validation. أما لعملية الاختبار فقد تم استخدام صور من الحياة الطبيعية من خارج مجموعتي التدريب والتحقق. تمت مقارنة النماذج الثلاثة باستخدام معايير تقييم مختلفة وهي Top-1 وTop-5والعمق والدقة والتي تحدد مدى قرب الوصف الناتج من الوصف الفعلي للصورة. من النتائج تبين أن النموذج ResNet50 يتفوق على النموذجين VGG16 وVGG19 من ناحية دقة الوصف ومدى التشابه مع الوصف الصحيح للصور المدروسة. كما تم ملاحظة أن النماذج الثلاثة تعطي وصفا أدق وأكثر تشابها للصورة عند حساب القيمة المتوسطة لأفضل ثالث توصيف من خرج النظام. In the current research, an image description process is done by generating captions of the image’s components by three different models and comparing them. In order to build the description models, the Keras library was used as Machine Learning Framework that includes the most important libraries needed to establish and train deep learning networks. Three models were applied to the Keras library to extract features from images, the ResNet50, the VGG16 and the VGG19 network models. These models were characterized by their reliance on the Convolutional Neural Network (CNN) and more accurately in extracting image attributes from previous models. For the training process and matching the description with the features, a Recurrent Neural Network (RNN) was used. The MSCOCO dataset was used with a subset of 10,000 images taken, 9,000 of which were taken for Training and 1000 for Validation. For the testing process, images of normal life were taken from outside the training and verification groups. The three models were compared using several measurements which are Top-1, Top-5, depth and accuracy. These metrics define how closely the resulting description of the actual description of the image. The results show that the ResNet50 model outperforms both models VGG16 and VGG19 in terms of the accuracy and the convergence with the correct description of the studied images. Furthermore, the three models give a better accurate and convergence when they use the average value of the best third description of the system output.
The analysis of cloud computing service level agreement (SLA) to support cloud service consumers with the SLA creation process

Cloud computing has been developing rapidly in the past few years. Stakeholders and several users started to migrate their data to the cloud to benefit from its usage and storage. The first step cloud service provider (CSP) and cloud service consumers (CSC) deal with is the service level agreement (SLA). It is an agreement issued between the cloud service provider and the cloud service customer that contains the consumer’s service requirements and the service level provided by cloud service providers. The process to create an SLA agreement is very essential for both cloud service providers and consumers. However, it could be vague and not well defined. To enhance the usage of any cloud services, the content and the creation of the SLA agreement should be handled efficiently and carefully.In this thesis, first we conducted a literature review to check the level of occurrence of SLA and SLO parameters in the academia and generate a list of the most and least studied SLA and SLO parameters. In the second chapter, we confronted this list to the three most popular cloud service providers in the modern market (Amazon, Microsoft and Google) to investigate their SLA agreement coverage.Finally, in the last chapter, we proposed an SLA toolkit that could assist cloud service providers and consumers in the process of SLA creation. This SLA toolkit would help the consumers through the negation phase and facilitate the selection process to choose the most applicable cloud service providers’ offer.
Creating an Immersive Fold-Out : Look Ahead

A comprehensive autoenthnographic case study, detailing the practice, engineeringscience and research used to create a stereo to 5.1-fold-out of the Canadianpiano and bass jazz duo recording, Look ...
Enabling Custom Security Controls as Plugins in Service Oriented Environments

Service oriented environments such as cloud computing infrastructures aim at facilitating the requirements of users and enterprises by providing services following an on-demand orientation. While the advantages of such environments are clear and lead to wide adoption, the key concern of the non-adopters refers to privacy and security. Even though providers put in place several measures to minimize security and privacy vulnerabilities, the users are still in many cases reluctant to move their data and applications to clouds. In this paper an approach is presented that proposes the use of security controls as plugins that can be ingested in service-oriented environments. The latter allows users to tailor the corresponding security and privacy levels by utilizing security measures that have been selected and implemented by themselves, thus alleviating their security and privacy concerns. The challenges and an architecture with the corresponding key building blocks that address these challenges are presented. Furthermore, results in the context of trustworthy requirements, i.e. dependability, are presented to evaluate the proposed approach.
PS1235 RED BLOOD CELL GENERATION FROM HUMAN INDUCED PLURIPOTENT STEM CELLS

Background: Multiparameter flow cytometry (MFC) is an essential tool for the diagnosis and follow-up of hematological malignancies. Numerous panels have been devised that best allow to achieve these goals. However, analysis of MFC graphs still heavily relies on the expertise of biologists and on specifically well adapted cascade-gating strategies. Because of the development of mass cytometry, which generates data in a multidimensional environment of up to 50 dimensions, new strategies have been developed, that could be applied to classical MFC. Aims: In this study, the capacities of a rapid new tool for multidimensional analysis, FlowSOM (self-organizing maps) have been combined to a classical MFC analysis tool, Kaluza®, for the identification of normal and diseased human bone marrow (BM) cell subpopulations. Methods: Normal BM samples collected during bone surgery were analyzed in MFC with four antibody panels adapted to the exploration of acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) respectively. The listmode files obtained were merged and submitted to FlowSOM analysis, using R packages, which yielded trees of 100 nodes defining normal BM cell subsets. FlowSOM parameters were then transferred to Kaluza® for a bidimensional representation of the trees within which selected navigation could be performed with Kaluza® tools. In a second part of the study, the same stainings were performed on myelodysplastic or leukemic (AML or ALL) samples (diagnosis or follow-up), which were submitted to FlowSOM together with the reference merged BM. Results: Node-by-node selection allowed for a precise definition of each of the unsupervised subsets generated by the software. This disclosed a selective segregation of delicate subsets such as non-classical monocytes, hematogones or stem cells. Direct simultaneous comparison of diseased samples and normal BM allowed for a precise definition of abnormal cells (blasts) at diagnosis, and their immediate recognition as minimal residual disease in follow-up samples. Summary/Conclusion: Flow SOM appears as a new and efficient tool for a rapid identification of BM cell subsets, highlighting differences from normal BM in dysplastic of leukemic samples. The node-by-node approach facilitates the definition of abnormal cells and their quantification, supervision intervening only after the generation of subsets delineated by artificial intelligence.
ANALISIS NILAI PENDIDIKAN DALAM NOVEL “HABIBIE & AINUN” KARYA B.J HABIBIE

The making of this article aims to examine and know the values that educate after reading the novel & Ainun by B.J Habibie. With the formulation of the problem in it: what educational values are there in the novels & Ainun? The message of the value of education as an indicator of this research are moral values, religious values, nationalism values, patriotism values, and romantic values in the family. The method used is a qualitative descriptive method with the technique of reading and writing important things, meaning that the researcher looks for data by reading the novel to find out the educational value contained in it. This novel by B.J Habibie is analyzed through signs in a sentence, paragraph, and quote dialog. The researcher uses the method that is easier to understand according to the conditions of the novel based on theory. The results of the study show that the novel & Ainun has educational values: moral, religious, tolerance, a spirit of nationalism, patriotism and true love.
NetBIOS and WINS

NetBIOS was the protocol originally used to communicate in Windows environments. Developed during the 1980s, this protocol is easy to implement and provides three different services: name service, datagram distribution service, and session service. The name server role is usually assumed by a Windows Internet Name Service (WINS) server. This WINS server is the Microsoft implementation of the NetBIOS name server service. In this chapter we’ll cover the following concepts:
Demo Abstract: How Many Climb the Matterhorn?

In this demo abstract we present a custom-built low-power geophone sensor node which features on-device mountaineer classification using a convolutional neural network. The execution of such a processing-heavy algorithm on an embedded platform is enabled by optimizing the memory requirement of the neural network through advanced quantization and pipelining techniques. As a result, real-time classification with low energy consumption can be achieved.
Active Learning by Greedy Split and Label Exploration

Annotating large unlabeled datasets can be a major bottleneck for machine learning applications. We introduce a scheme for inferring labels of unlabeled data at a fraction of the cost of labeling the entire dataset. We refer to the scheme as greedy split and label exploration (GSAL). GSAL greedily queries an oracle (or human labeler) and partitions a dataset to find data subsets that have mostly the same label. GSAL can then infer labels by majority vote of the known labels in each subset. GSAL makes the decision to split or label from a subset by maximizing a lower bound on the expected number of correctly labeled examples. GSAL improves upon existing hierarchical labeling schemes by using supervised models to partition the data, therefore avoiding reliance on unsupervised clustering methods that may not accurately group data by label. We design GSAL with strategies to avoid bias that could be introduced through this adaptive partitioning. We evaluate GSAL on labeling of three datasets and find that it outperforms existing strategies for adaptive labeling.
The Prediction and Analysis of Opioid Crisis

Using a linear fitting method to identify locations where specific opioid uses might has started in five states. Next, using the logistic model to find out the growing rules of reported opioids incidents, and getting the threshold is 268 380. Finally, this paper uses a grey model to predict the future.
Pengembangan Kompetensi Membaca Cepat Melalui Teknik Gaya ”SAVI” Dalam Pembelajaran Bahasa Inggris Di Kelas X SMA Negeri 3 Pandeglang

This research departs from the phenomenon that appears during initial reflection. The results of the competency tests conducted on class X.1 students of State High School 3 of Pandeglang - Banten, after participating in speed reading learning, showed that of the 22 students only 53.6% who were able to fulfill the demands of the basic competency indicators that were targeted for learning. Starting from these problems, it needs to be followed up by applying SAVI style techniques in speed reading learning. The research method used is descriptive method and research design using a classroom action research model designed in three cycles. The results of this study show that the development of speed reading competencies using SAVI style techniques could improve student’s learning activities and outcomes. Changes in the increase in student activity during the learning process from cycle 1 to cycle 2 averaged 1.86, while the average from cycle 2 to cycle is 3 1.64. Student learning outcomes from the average value of cycle 1 to cycle 2 is 7.41, while the average of cycle 2 to cycle 3 is 3.42.
The Generation of Mass and Its Directivity

The mass of matter includes the mass of elementary particles and the interaction mass between the particles. So far there has been a great deal of research on the origin of the mass of the elementary particles, but there are few studies on the origin of the interaction mass. In this paper, we show that the electric field of a uniformly accelerating point charge in vacuum is curved toward the opposite direction of acceleration. When a positive charge system is accelerated, the interaction forces between its charges cannot cancel each other out; therefore the charge system will produce a self-action force opposite to the acceleration, thus generating interaction mass. The interaction mass is not constant, but varies with the direction of the acceleration. Finally, we conclude that the mass of object is related to its self-action and has directivity.
Research Guides: LING 319: Historical Linguistics: Books

This is a companion guide to the Library research session for Dr. Valentina Colasanti’s LING 319 (Historical Linguistics) class, Winter Term 2, 2019.
Logical and Semantic Frameworks with Applications

Information theoretically secure multi-party computation implies severe communication overhead among the computing participants, as there is a need to reduce the polynomial degree after each multiplication. In particular, when the input is (practically) unbounded, the number of multiplications and therefore the communication bandwidth among the participants may be practically unbounded. In some scenarios the communication among the participants should better be avoided altogether, avoiding linkage among the secret share holders. For example, when processes in clouds operate over streaming secret shares without communicating with each other, they can actually hide their linkage and activity in the crowd. An adversary that is able to compromise processes in the cloud may need to capture and analyze a very large number of possible shares.Consider a dealer that wants to repeatedly compute functions on a long file with the assistance of m servers. The dealer does not wish to leak either the input file or the result of the computation to any of the servers. We investigate this setting given two constraints. The dealer is allowed to share each symbol of the input file among the servers and is allowed to halt the computation at any point. However, the dealer is otherwise stateless. Furthermore, each server is not allowed any communication beyond the shares of the inputs that it receives and the information it provides to the dealer during reconstruction.We present a protocol in this setting for generalized string matching, including wildcards. We also present solutions for identifying other regular languages, as well as particular context free and context sensitive languages. The results can be described by a newly defined accumulating automata (AA) and cascaded equations automata (CEA) which may be of an independent interest. As an application of accumulating automata and cascaded equations automata, secure and private repeated computations on a secret shared file among communicationless clouds are presented.
Training of Students to Tolerant Relations in the Process of Higher Education

In higher education system in preparation of specialist special role is played by development of base abilities to understand other person, to acceptance of universal values, readiness for dialogue, and interaction of different cultures. Tolerance is as ability of person, community, state to respect opinion of another gives the chance without enmity to meet distinct from own opinions and positions.
Red MATE: una experiencia de intervención en discapacidad visual

In this article, we aim at sharing the experience of Red MATE (Red de Materiales y Apoyos Tiflo Educativos) of the School of Communication Sciences of the Universidad Nacional de Cordoba, a project that has been underway for ten years. At the beginning, we started by addressing a specific problem, namely that of the difficulty students face to access their study  materials.  We were later able to broaden our objectives, aiming on the one hand at encouraging greater reflection in relation  to the rights of people with disabilities within the university and society in general; and  on the other hand, at fostering the involvement and social commitment of people with disabilities and of the whole community. From the outset of the project, the main objective was that the recipients play the most important role, and this has posed a great challenge throughout these ten years.
Fit for the future : Physical fitness of children and adolescents with intellectual disabilities

markdownabstractChildren and adolescents with an intellectual disability (ID) are more vulnerable for health problems than typically developing peers. Good physical fitness is therefore important. This prevents health risk now and in the future. This thesis describes the study on physical fitness of children and adolescents with ID. It starts with a systematic literature review and pilot study on appropriate fitness tests for children and adolescents with moderate to severe ID. In the next step, the physical fitness and physical activity was measured in 128 participants. Using retrospective data, the maximal heart rates of children and adolescents with ID were studied as well. The results indicate that the physical fitness of children and adolescents with moderate to severe ID is very low. Secondly, more than half of the participants were not active enough according to the physical activity guidelines. Thirdly, children and adolescents with ID are shown to have lower maximal heart rates than typically developing peers. This thesis ends with recommendations to increase the physical fitness of children and adolescents with ID in clinical practice.