Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluating Statistical Interaction between Various Therapy in Clinical Trials

We aim to evaluate the interaction between the two factors above (i.e. the status of mono or combined and the treatment) by adjusting for patients’ covariates which may affect the registration in the each trial. In this poster, outcome is odds ratio about worsening of the neuropsychiatric symptoms as against before administration, included newly symptoms. ●Trial registration is not randomized drug A
Multiple Characters Matching in Document Image Using Handwriting Recoganization

Objectives: To propose suitable system to filter the particular keyword in the document image. Methods/Statistical Analysis: Input image is pre-processed by using Gabor filtering. The preprocessed image is segmented by using segmentation free method. Then the feature extraction is done by using block based method. The Connectionist Temporal Classification (CTC) token passing calculation restores an arrangement of words from the lexicon. Then the word matching is performed by using normalized cross correlation. Findings: The performance of the proposed and existing methods are analysed with the help of word similarity, recognition accuracy, Recognition rate, computational time. Template matching method is utilized to discover and perceive the format picture which is found in the given info picture. Standardized cross connection techniques give precise outcome for the archive pictures. Application/Improvements: There are many applications such as data entry from printed paper data records, whether passport documents, invoices, bank statements, computerized receipts, business cards, mail, printouts of static-data, or any suitable documentation.
Mobile Learning Design Models for State University of Jakarta, Indonesia

In facing the industrial era 4.0, the instructional paradigm requires the involvement of technology in it. The use of technology in instructional is sometimes regarded as a medium or tool for delivering information through technology from lecturers to students without considering the learning process of students with technology that makes learning outcomes more meaningful. Meanwhile, instructional media such as mobile learning applications through Android is a type of mobile learning that can change the way students communicate and interact. The use of mobile learning applications through Android is adjusted to the characteristics of students and learning needs in the industrial era 4.0. This study resulted in an Android-based mobile learning application so that through product effectiveness, the concept of the conceptual model of mobile learning was obtained in the industrial era 4.0 for learning in college. The research method used is the development research of William W. Lee and Diana L. Owens. The results showed that the mobile learning application influenced student academic achievement so that the conceptual model of mobile learning was obtained in the industrial era 4.0 for learning in college. This study concludes that mobile learning applications encourage students in fun learning activities and place students as the main subject in learning, so learning becomes meaningful.
Transductive Learning of Neural Language Models for Syntactic and Semantic Analysis

In transductive learning, an unlabeled test set is used for model training. Although this setting deviates from the common assumption of a completely unseen test set, it is applicable in many real-world scenarios, wherein the texts to be processed are known in advance. However, despite its practical advantages, transductive learning is underexplored in natural language processing. Here we conduct an empirical study of transductive learning for neural models and demonstrate its utility in syntactic and semantic tasks. Specifically, we fine-tune language models (LMs) on an unlabeled test set to obtain test-set-specific word representations. Through extensive experiments, we demonstrate that despite its simplicity, transductive LM fine-tuning consistently improves state-of-the-art neural models in in-domain and out-of-domain settings.
Multi-Task Deep Learning for Satellite Image Pansharpening and Segmentation

In this work, we propose a novel multi-task framework, to learn satellite image pansharpening and segmentation jointly. Our framework is based on the encoder-decoder architecture, where both tasks share the same encoder but each one has its own decoder. We compare our framework against singletask models with different architectures. Results show that our framework outperforms all other approaches in both tasks.
Inexpensive Custom Built 6-Axis Force/Torque Sensor for Suturing Simulator

This paper details the design of a six degree-of-freedom low-cost force/torque sensing platform for use in a suturing simulator for surgical training. This platform is based on the Stewart Platform configuration in which the linear actuators are replaced with metal wire kept in tension by multiple elastically loaded support columns. Flexible wire legs are used in lieu of rigid connectors so as to reduce the platforms susceptibility to inter-component friction in measurements and to allow the platform to exhibit built-in stress relief for the cantilevers in directions of concern. The static structural elements of the platform are designed to be manufactured from standard 3D printing technology, decreasing the overall weight of the platform while keeping costs low and allowing for quick replacement of any damaged structural elements. Due to errors in fabricating the cantilevers, a slow transient response is observed in the platform’s voltage outputs which is carried over into the measured loads on the platform. By experimenting with different cantilever designs and the techniques used to manufacture them, new cantilever designs are proposed which correct for the slow transient response. The platform is calibrated by applying standardized weights to cause forces and torques in each axes and sampling the corresponding voltage outputs. When the platform is subjected to unknown loads after calibration, the platform measures forces and torques in each axes for short periods of time with errors attributable to the non-linear response . Tests simulating the suturing system are conducted on the platform with the results showing that the platform is able to measure forces and torques in each axes at any position on the membrane. While the current sensor can not be used in the suturing system as is, implementing the new cantilever design in the platform will make the platform an inexpensive component capable of providing accurate measurements of the loads on the membrane.
Algorithms for load balancing in electricity markets and data centers

of the Dissertation Algorithms For Load Balancing In Electricity Markets And Data Centers by Bochao Shen Doctor of Philosophy in Computer Science Northeastern University, April 2018 Dr. Ravi Sundaram, Adviser Electricity and computers are two corner stones of this information era. Energy and computation are critical resources in perennial short supply because our consumption continues to grow day by day. Increasing supply in a substantial way requires fundamental advances in energy generation and computing technology, but such advances are few and far between. Load balancing is an important technique for mitigating the issue of scarce resources. We broadly interpret load balancing to include both the optimization of resource distribution as well as the management of end-user demand. In this thesis, we study algorithms for load balancing in electricity markets and data centers. First, we study the temporal load balancing problem in electricity markets where peak demand and supply-demand imbalance are major problems. It is often suggested that exposing consumers to real-time pricing will incentivize them to change their usage and mitigate the problem. However, we show that risk-averse electricity consumers react to price fluctuations by scaling back on their total demand, leading to the unintended consequence of an overall decrease in production/consumption and reduced economic efficiency. Compared with the relatively fixed production mode of electricity power (the supply), the consumption pattern of end users (the demand) is more variable and potentially changeable. This makes temporally shifting consumers’ electricity load possible. We propose SmartShift, a new scheme that allows households to move their demands from peak hours in exchange for greater electricity consumption in non-peak hours. We show that our scheme not only enables increased consumption and consumer welfare but also allows the distribution company to increase profits. We next consider the fault-tolerant spatial load balancing problem in data centers where computational loads get balanced by being assigned on different locations (machines computational resources). k-HA (high-availability), a fault tolerance property of virtual machine (VM) placement
Handbook of clinical gender medicine

Foreword: Wainer, J. Wainer, Z. Preface: Schenck-Gustafsson, K. Introduction Gender Matters: Wainer, J. Wainer, Z. Biological Sex and the Genome: What Makes Us Ourselves? Legato, M.J. Social and Biological Determinants in Health and Disease Section Editors: DeCola, P.R. Schober, J.M. Central Nervous System and Clinical Applications Section Editor: Pfaff, D.W. Neurology Section Editor: Olsson, T. Pain Section Editor: Murphy, A.Z. Circulation Section Editor: Schenck-Gustafsson, K. Cancer Section Editor: Gustafsson, J.-A. Metabolic Disease Section Editor: Werner, S. Autoimmune, Inflammatory, and Musculoskeletal Disease Section Editor: Pisetsky, D.S. Infectious Diseases Section Editor: Britton, S. Urology, Sexual Dysfunction, and Nephrology Section Editor: Arver, S. Pharmaceutical Drugs Section Editor: Parekh, A. Geriatrics Section Editor: Herlitz, A.
Belief Propagation Bit-Strengthening Decoder for Polar Codes

Although the performance of belief propagation (BP) decoder is slightly better than that of successive cancellation (SC) decoder, it has remarkable performance gap compared with improved SC decoders. In this letter, we propose BP bit-strengthening (BPBS) decoder and concept of relative code rate for polar codes. When conventional BP decoding fails, the stable information bits are found by submatrix check and relative code rate, then a priori message of stable information bits is strengthened by hard decision. The simulation results show that BPBS decoder can achieve significant performance gains compared with conventional BP decoder. Furthermore, as signal-to-noise ratio (SNR) increases, the performance of BPBS is close to that of successive cancellation list (SCL) decoder in the medium and high SNR regions.
Security of a New Hybrid Ciphering System

The protection of privacy is a very sensitive subject and comes into force in all areas. They represent the first priority in the development of new technologies. in fact, opt for a new Big data or IOT technology is a very difficult decision for organizations and calls into question the confidentiality, integrity, authenticity and non-repudiation of their data. Convincing these organizations to adhere to technological intelligence is tantamount to providing them with powerful tools and mechanisms of security that are resistant to new types of vulnerability. However, the problem today is that most security tools are based on old cryptographic primitives. Certainly; they have proved their resistance until today but the need to have others becomes crucial in order to meet the new technological requirements. In this paper, we propose a new hybrid encryption alternative based on two encryption systems, the first one is an evolutionary encryption system and the second one is based on an asymmetric encryption system. To present this work we begin with a description of our evolutionary cipher system. Then, we present the principle of proposed hybridization and its contribution compared to other existing systems. Finally we perform a detailed study on the safety of this system and its long-term resistance.
RIT2FIS: A Recurrent Interval Type 2 Fuzzy Inference System and its Rule Base Estimation

Two of the major challenges associated with time series modelling are handling uncertainty present in the data and tracing its dynamical behaviour. A Recurrent Interval Type 2 Fuzzy Inference System or RIT2FIS is presented in this paper. RIT2FIS adopts an interval type 2 fuzzy inference mechanism for superior handling of uncertainty. The memory neurons employed in its hidden and output layer, retain the temporal information, making RIT2FIS highly proficient in tracing system dynamics at a granular level. RIT2FIS also benefits from incorporating a k-means algorithm inspired approach to cluster the data in an unsupervised manner. An ’Elbow Method’ is utilized next to determine the optimal clustering which is then employed as the optimal fuzzy rule base for RIT2FIS, eliminating the necessity of expert knowledge for fuzzy initiation. The antecedent and consequent parameters of RIT2FIS are updated using a gradient descent based back-propagation through time algorithm where the learning is made self-regulatory to avoid over-fitting and ensure generalization. Performance of RIT2FIS is evaluated against popular neuro-fuzzy methods on different benchmark and real-world time series problems which distinctly indicates an improved accuracy and a parsimonious rule base.
Necrotizing enteritis in a calf infected with adenovirus.

Abstract A three week old Holstein calf that died after a period of anorexia and diarrhea had a severe necrotizing ileitis. The lesions which were most severe above Peyer's patches contained numerous filamentous bacteria. Prominent intranuclear inclusion bodies in vascular endothelial cells in both the affected intestine and the mesenteric lymph nodes were accompanied by vasculitis and thrombosis. Adenovirus-like particles were demonstrated ultrastructurally in the inclusions. This case demonstrates a preferential association of the intestinal necrosis with the gut associated lymphoid tissue. Also, the significance of viral infection of vascular endothelium versus intestinal epithelium in determining the type and severity of injury, is deduced from this and other previously reported cases.
Issues and Proposed Solution of E-commerce with Cloud Computing

: Cloud computing is very popular across the world. It helps in solving challenges faced by small and large scale enterprises in term of cost-effectiveness, security, availability and Information Technology resources . E-commerce in Small and medium enterprises is need to serve the customers better services to satisfy them and provide them customized services. From last two decades these Small and medium enterprises faced many issues and challenges. The different cloud services as discussed to provide better platform to e-commerce applications. The technical issues related to the e-commerce are addressed in this paper. Further this paper proposed the solution of same problems with services of cloud computing. The issues and challenges that E-commerce enterprises are facing are elaborated, and how cloud computing can be useful to enhance business with cloud computing.
A Novel Similarity Search Approach for Streaming Time Series

Time series similarity search has been widely used in many applications, such as financial data analysis, meteorological data forecasting, and multimedia data retrieval. The original task of this research was to find time series in a database similar to the query sequence, where both the results and the query sequence are static. However, along with the arrival of Industry 4.0 era, a large number of detecting instruments in various fields have produced a large amount of time series stream data. The new challenges for Similarity-based time series retrieval can be attributed as one-pass search and real time response. In this paper, we propose a novel multi-resolution search scheme to find a set of sequences similar to the query sequence in streaming time series efficiently and exactly. The scheme performs multi-resolution filtering on the raw data, and most dissimilar sequences can be filtered out at a lower level to avoid unnecessary computation and to improve the search efficiency. Extensive experiments on different kinds of typical time series datasets have been conducted to demonstrate the superiorities of our method.
A 3-D Display Pipeline from Coded-Aperture Camera to Tensor Light-Field Display Through CNN

We propose an efficient pipeline from input to output for a tensor light-field display. Conventionally, a dense light field (i.e., tens of images taken with narrow viewpoint intervals) is required as an input in such displays. However, obtaining dense light fields is a challenging task for real scenes. To make the acquisition process more efficient, we adopted a coded-aperture camera as an input device, which is suitable for acquiring dense light fields in a compressive manner. Moreover, we modeled the entire process from acquisition to display using a convolutional neural network. As a result of training the network on a massive light field data, we can reproduce the whole light field on the display from only a few images taken with the camera. Both simulative and real experiments were conducted to show the effectiveness of our method.
Design of the Baseband Physical Layer of NarrowBand IoT LTE Uplink Digital Transmitter

With the new age of technology and the release of the Internet of Things (IoT) revolution, there is a need to connect a wide range of devices with varying throughput and performance requirements. In this paper, a digital transmitter of NarrowBand Internet of Things (NB-IoT) is proposed targeting very low power and delay-insensitive IoT applications with low throughput requirements. NB-IoT is a new cellular technology introduced by 3GPP in release 13 to provide wide-area coverage for the IoT. The low-cost receivers for such devices should have very low complexity, consume low power and hence run for several years. In this paper, the implementation of the data path chain of digital uplink transmitter is presented. The standard specifications are studied carefully to determine the required design parameters for each block. And the design is synthesized in UMC 130-nm technology.
Interdisciplinary Stroke Rehabilitation Delivered by a Humanoid Robot: Simultaneous vs. Alternating Therapy Schedules

A great number of stroke survivors experience disabilities in multiple domains (e.g., aphasia, hemiparesis). However, no previous research has investigated how treatment in one domain (e.g., speech therapy) affects the progress in other areas (e.g., physical recovery). The current study is comparing two therapy schedules: simultaneous where a patient receives speech AND physical therapy services for four weeks; and alternating where a patient receives speech therapy for four weeks and then receives physical therapy for four weeks. This interdisciplinary intervention is delivered by a humanoid robot that can potentially enhance the intensity and accessibility of stroke rehabilitation.
Learning the Set Graphs: Image-Set Classification Using Sparse Graph Convolutional Networks

Image-set classification has recently made great progress in computer vision. Compared with traditional image classification tasks, set-based classification exhibits great challenges due to huge intra-class variability and high inter-class ambiguity. In this paper, we propose to model the image set as a graph and formulate image set classification as the graph matching task. Without relying on the strong structure assumption, we build the first end-to-end graph convolutional network, the Deep SetNet, to learn the graph structure of an image set. Specifically, the SetNet consists of one convolutional network (CNN) to sufficiently extract the discriminative vertex, one graph convolutional Network (GCN) to faithfully learn the substructure in set graphs and graph pooling layers to aggregate the vertex features from the GCN. Moreover, we propose imposing the ℓ1,2-norm based sparsity constraint to select vertex features, which largely improves the model generalization capability. Extensive experiments demonstrate that our method consistently outperforms state-of-the-art methods, showing its great effectiveness in set-based image classification.
Weak Supervision for Learning Discourse Structure

This paper provides a detailed comparison of a data programming approach with (i) off-the-shelf, state-of-the-art deep learning architectures that optimize their representations (BERT) and (ii) handcrafted-feature approaches previously used in the discourse analysis literature. We compare these approaches on the task of learning discourse structure for multi-party dialogue. The data programming paradigm offered by the Snorkel framework allows a user to label training data using expert-composed heuristics, which are then transformed via the “generative step” into probability distributions of the class labels given the data. We show that on our task the generative model outperforms both deep learning architectures as well as more traditional ML approaches when learning discourse structure—it even outperforms the combination of deep learning methods and hand-crafted features. We also implement several strategies for “decoding” our generative model output in order to improve our results. We conclude that weak supervision methods hold great promise as a means for creating and improving data sets for discourse structure.
RNNs develop history biases in an expectation-guided two-alternative forced choice task

Understanding how expectations bias perceptual decisions constitutes an unavoidable step towards deciphering how we make decisions. Here, we trained Recurrent Neural Networks (RNNs) in a novel two-alternative forcedchoice (2AFC) task where both the current sensory evidence and the recent trial history provide information about the identity of the correct choice. We found that RNNs learned both to integrate the stimuli and to capitalize on the serial correlations of the trial sequence by developing history biases. Interestingly, during early stages of training, all networks reset their biases after an error response, which is consistent with data from rats performing the same task. At later stages of the training, approximately half of the networks moved from this initial, sub-optimal, strategy and developed after-error biases. A more detailed characterization of these different behaviors revealed that the percentage of networks showing after-error reset could be increased by limiting the resources of the networks, such as reducing their size, the information they receive or the training time. Together, these results suggest that rats develop a sub-optimal but easier to reach strategy to solve the task due to some limiting factor such as lack of computational capacity or time constraints.