Reviewerly/exHarmony huggingface dataset:
Comparison of non-parametric tests of ordered alternatives for repeated measures in randomized blocks

Page, Jonckheere, Hollander tests are the most common non-parametric tests for ordered alternative pattern in randomized blocks where contain independent observations. However, when randomized blocks include correlated observations, properties of null distribution for these tests under independence cannot be applied to the situations where randomized blocks contain dependent observations. Circular bootstrap versions of these tests are suggested to test for ordered alternative hypothesis in randomized blocks which include correlated measurements when the underlying distributions are not known. In this study the effect of correlation coefficient, number of treatment/time point and block sizes on type 1 error and power values of some existing non-parametric test statistics based on the circular bootstrap method are taken into consideration with a stationary auto regressive process. The significance levels and power values of these tests based on circular bootstrap method are simulated by using the statistical software package R, 3.4.2. Based on findings in the simulation study, circular bootstrap version of original Page Test provides better power values among other trend statistics. Finally, these circular bootstrap-based non-parametric tests are applied to a real life dataset.
Recognizing Presence of Hematological Disease using Deep Learning

Accurate classification and counting of blood components is crucial in detection of illnesses of an individual. The widely used methods to count blood components are manual counting and hematology analyzer. With advancement in the field of image processing and machine learning, new and better methods are available for counting and classifying blood components. Deep leaning is training the computer with labelled data for classification tasks. Such techniques have shown high performance and accuracy. Most Deep learning models uses neural network architecture. One of the most popular type of deep learning model is Convolutional Neural Network. CNN convolves learned features with input data, and uses 2D convolutional layers, making this architecture well suited to processing 2D data, such as images. CNN's extract the features from the image automatically using numerous hidden layers. Most Deep learning models use transfer learning that is fine-tuning a pre-trained model. RCNN stands for Region based CNN. Unlike CNN which is used for image classification, RCNN is used for object detection. Thus in this paper, we have proposed a method to classify various components of blood : RBCs, WBCs (Monocyte, Lymphocytes, Eosinophils, Neutrophils and Basophils) and find their count from a microscopic blood image using Faster R-CNN model. Thus generating a CBC (Complete Blood Count) report which can be used by medical professionals to diagnose, suggest tests and treatments to their patients.
Finding out Noisy Patterns for Relation Extraction of Bangla Sentences

Relation extraction is one of the most important parts of natural language processing. It is the process of extracting relationships from a text. Extracted relationships actually occur between two or more entities ofa certain type and these relations may have different patterns. The goal of the paper is to find out the noisypatterns for relation extraction of Bangla sentences. For the research work, seed tuples were needed containing two entities and the relation between them. We can get seed tuples from Freebase. Freebase is alarge collaborative knowledge base and database of general, structured information for public use. But for Bangla language, there is no available Free base. So we made Bangla Freebase which was the real challenge and it can be used for any other NLP based works. Then we tried to find out the noisy patterns for relation extraction by measuring conflict score.
Reshaping IOT Through Blockchain

From the beginning of crypto currency in 2008, blockchain technology rise as progressive technology. Block chain technology was began as a core technology of bitcoin, but it is growing to numerous fields such as, security of Internet of Things (IoT), transport sector, robotics, smart energy, banking sector, industries, insurance, medical etc. Currently more businesses are moving through online system. The digitization of data can be carried through the centralization and decentralization process with higher protection. All the data can be stored through blocks with transaction history as block chain. Unfortunately, In a block chain the transaction can be simplified with peer-peer network that excludes the involvement of third party. Today as a world is going towards automation and smart objects internet of things(IoT) has gained popularity due to its usage in smart homes, smart farming, and smart city projects around world. Unfortunately, internet of things devices possess, low compatibility, high complexity, limited computing power, low security, low storage capability and network capacity therefore internet of things devices are more prone to attacks than other components such as phones, ipods, tablets, or PCs. This paper focus on identifying significant security and privacy issues for IoT, proposing anew IoT model through block chain technology and implementing the proposed model for solving IoT issues through block chain technology. Block chain technology can be a key empowering influence to take care of numerous IoT privacy and security issues. Finally describe the future work directions.
Visual object tracking via iterative ant particle filtering

Visual object tracking remains a challenging task in computer vision although important progress has been made in the past decades. Particle filter (PF) is now a standard framework for solving non-linear/non-Gaussian problems, especially in visual object tracking. This study proposes an ant colony optimisation (ACO)-based iterative PF for object tracking. In the proposed method, the basic idea of ACO is used to simulate the behaviour of a particle moving toward the posterior distribution. Such idea is incorporated into the particle filtering framework in order to overcome the well-known particle impoverishment problem. An iterative unscented Kalman filter is used to design a proposal distribution for particle generation in order to generate better predicted sample states. For the likelihood model, the authors adopt the locality sensitive histogram to model the appearance of the target object, which can better handle the illumination variation during tracking. The experimental results demonstrate that the proposed tracker shows better performance than the other tracking methods.
The Enabling Power of Mobile Devices

Mobile devices have been helping to empower underserved communities around the world. From bringing information on health and farming to substituting expensive devices, cellular phones and apps have proved to be powerful tools in poverty alleviation and disaster relief. The Mobile Lab at Santa Clara University focuses on developing mobile solutions for social benefit. We partner with organizations that operate in the field to develop appropriate solutions to solve specific problems. In this short paper, we illustrate the enabling power of mobile technology by describing some projects designed to empower people and their communities in a variety of ways.
Hybrid Autoregressive Transducer (hat)

This paper proposes and evaluates the hybrid autoregressive transducer (HAT) model, a time-synchronous encoderdecoder model that preserves the modularity of conventional automatic speech recognition systems. The HAT model provides a way to measure the quality of the internal language model that can be used to decide whether inference with an external language model is beneficial or not. This article also presents a finite context version of the HAT model that addresses the exposure bias problem and significantly simplifies the overall training and inference. We evaluate our proposed model on a large-scale voice search task. Our experiments show significant improvements in WER compared to the state-of-the-art approaches.
Research on Network Database Laboratory Instrument Management Information System Based on Content Query

Abstract With the requirement of further opening of laboratories, it is increasingly important to carry out open management of laboratories on the basis of traditional laboratory management. In this paper, Windows operating system is used as the operating platform, ASP technology and databases such as Microsoft Access 2013 and Microsoft SQL Serv-er 2000 are used as development tools. This paper mainly discusses how to build an object-oriented image database based on content query based on the existing database management system, proposes a hierarchical model of the system, and presents a data structure model of the image database. A laboratory instrument management information system with B / S mode based on content query network database is developed. In the specific teaching, the laboratory should be fully open in the experiment content, experiment equipment, experiment time and experiment management, so as to make the laboratory open management scientific and improve the utilization rate of instruments and equipment.
Bengali Informative Chatbot

Bengali Informative Chatbot (BIC) is an effective technique that helps a user to trace relevant information by Natural Language Processing (NLP). In this research paper, we introduce an algorithmic Bengali Informative Chatbot (BIC) based on information that is significant mathematically and statistically. This paper is demonstrated by two algorithms for finding out the lemmatization of Bengali words such as Trie and Dictionary Based Search by Removing Affix (DBSRA) as well as compared with Edit Distance for the exact lemmatization. We present the Bengali Anaphora resolution system using the Hobbs’ algorithm to get the correct expression of information. As the actions of chatbot replying algorithms, the TF-IDF and Cosine Similarity are developed to find out the accurate answer from the documents. In this study, we introduce a Bengali Language Toolkit (BLTK) and Bengali Language Expression (BRE) that make the easiest implication of our task. We have also developed Bengali root word’s corpus, synonym word’s corpus, stop word’s corpus and gathered 672 articles as questions and answers form the popular Bengali newspapers ‘The Daily Prothom Alo’ is our inserted information. For testing this system, we have created 19334 questions from the introduced information and got 97.22% accurate answer by proposed BIC.
A Comparison of $$\chi ^2$$-Test and Mutual Information as Distinguisher for Side-Channel Analysis

Masking is known as the most widely studied countermeasure against side-channel analysis attacks. Since a masked implementation is based on a certain number of shares (referred to as the order of masking), it still exhibits leakages at higher orders. In order to exploit such leakages, higher-order statistical moments individually at each order need to be estimated reflecting the higher-order attacks. Instead, Mutual Information Analysis (MIA) known for more than 10 years avoids such a moment-based analysis by considering the entire distribution for the key recovery. Recently the $$\chi ^2$$-test has been proposed for leakage detection and as a distinguisher where also the whole distribution of the leakages is analyzed. In this work, we compare these two schemes to examine their dependency. Indeed, one of the goals of this research is to conclude whether one can outperform the other. In addition to a theoretical comparison, we present two case studies and their corresponding practical evaluations. Both case studies are masked hardware implementations; one is an FPGA-based realization of a threshold implementation of PRESENT, and the other is an AES implementation as a coprocessor on a commercial smart card.
Interpretable Deep Recurrent Neural Networks via Unfolding Reweighted &#120001; 1 -&#120001; 1 Minimization: Architecture Design and Generalization Analysis.

Deep unfolding methods---for example, the learned iterative shrinkage thresholding algorithm (LISTA)---design deep neural networks as learned variations of optimization methods. These networks have been shown to achieve faster convergence and higher accuracy than the original optimization methods. In this line of research, this paper develops a novel deep recurrent neural network (coined reweighted-RNN) by the unfolding of a reweighted $\ell_1$-$\ell_1$ minimization algorithm and applies it to the task of sequential signal reconstruction. To the best of our knowledge, this is the first deep unfolding method that explores reweighted minimization. Due to the underlying reweighted minimization model, our RNN has a different soft-thresholding function (alias, different activation functions) for each hidden unit in each layer. Furthermore, it has higher network expressivity than existing deep unfolding RNN models due to the over-parameterizing weights. Importantly, we establish theoretical generalization error bounds for the proposed reweighted-RNN model by means of Rademacher complexity. The bounds reveal that the parameterization of the proposed reweighted-RNN ensures good generalization. We apply the proposed reweighted-RNN to the problem of video frame reconstruction from low-dimensional measurements, that is, sequential frame reconstruction. The experimental results on the moving MNIST dataset demonstrate that the proposed deep reweighted-RNN significantly outperforms existing RNN models.
Non-Adversarial Video Synthesis with Learned Priors

Most of the existing works in video synthesis focus on generating videos using adversarial learning. Despite their success, these methods often require input reference frame or fail to generate diverse videos from the given data distribution, with little to no uniformity in the quality of videos that can be generated. Different from these methods, we focus on the problem of generating videos from latent noise vectors, without any reference input frames. To this end, we develop a novel approach that jointly optimizes the input latent space, the weights of a recurrent neural network and a generator through non-adversarial learning. Optimizing for the input latent space along with the network weights allows us to generate videos in a controlled environment, i.e., we can faithfully generate all videos the model has seen during the learning process as well as new unseen videos. Extensive experiments on three challenging and diverse datasets well demonstrate that our approach generates superior quality videos compared to the existing state-of-the-art methods.
ARDA: Automatic Relational Data Augmentation for Machine Learning

Automatic machine learning (\AML) is a family of techniques to automate the process of training predictive models, aiming to both improve performance and make machine learning more accessible. While many recent works have focused on aspects of the machine learning pipeline like model selection, hyperparameter tuning, and feature selection, relatively few works have focused on automatic data augmentation. Automatic data augmentation involves finding new features relevant to the user's predictive task with minimal ``human-in-the-loop'' involvement. We present \system, an end-to-end system that takes as input a dataset and a data repository, and outputs an augmented data set such that training a predictive model on this augmented dataset results in improved performance. Our system has two distinct components: (1) a framework to search and join data with the input data, based on various attributes of the input, and (2) an efficient feature selection algorithm that prunes out noisy or irrelevant features from the resulting join. We perform an extensive empirical evaluation of different system components and benchmark our feature selection algorithm on real-world datasets.
Cultural IoT Framework Focusing on Interactive and Personalized Museum Sightseeing

Museum visitors are very focused and demanding. Immersive technologies as virtual and augmented reality, interactive haptics, 3D scanning and plotting, content digitization, and personalized automatic navigation must be exploited by museums in order to stimulate museum visitors and extract their attention. The authors of this work propose an open source IoT InteRactive Museum Experience (IRME) framework. IRME offers information classified in thematic sections. The visitors have the opportunity to explore specific thematic sections of interest. Navigation instructions and artwork guidelines are obtained with the help of a smart phone application. Data-mining, artificial intelligence and cognitive services offer the ability to learn from visitor’s preferences and respond more accurately to future requests and in this way enhance visitor’s experience in the museum. IRME provides a real-time, responsive and personalized navigation to museum visitors. It includes indoor positioning technology, IoT sensors and actuators, haptic devices orchestrated over cloud services. Wherever possible, IRME uses low power technology such as Bluetooth Low Energy devices, led plates-spots-cubes and 3D printing modeling capabilities, in order to promote museum artifacts and to enhance the visitors’ knowledge acquisitions and entertainment. Moreover, the reflection of such recreational improvements to the visitors is also measured using IoT sensors and the results are used as feedback for future thematic land planning, and IoT illustration techniques.
Ensuring Query Completeness in Outsourced Database Using Order-Preserving Encryption

Nowadays database outsourcing has become business owners' preferred option and they are benefiting from its flexibility, reliability, and low cost. However, because database service providers cannot always be fully trusted and data owners will no longer have a direct control over their own data, how to make the outsourced data secure becomes a hot research topic. From the data integrity protection aspect, the client wants to make sure the data returned is correct, complete, and up-to-date. Previous research work in literature put more efforts on data correctness, while data completeness is still a challenging problem to solve. There are some existing works that tried to protect the completeness of data. Unfortunately, these solutions were considered not fully solving the problem because of their high communication or computation overhead. The implementations and limitations of existing works will be further discussed in this paper. From the data confidentiality protection aspect, order-preserving encryption (OPE) is a widely used encryption scheme in protecting data confidentiality. It allows the client to perform range queries and some other operations such as GROUP BY and ORDER BY over the OPE encrypted data. Therefore, it is worthy to develop a solution that allows user to verify the query completeness for an OPE encrypted database so that both data confidentiality and completeness are both protected. Inspired by this motivation, we propose a new data completeness protecting scheme by inserting fake tuples into databases. Both the real and fake tuples are OPE encrypted and thus the cloud server cannot distinguish among them. While our new scheme is much more efficient than all existing approaches, the level of security protection remains the same.
A Study on Imbalanced Data Streams

Data are growing fast in today’s world and great portion of that is in the form of stream. In many situations, data streams are imbalanced making it difficult to use with classical data mining methods. However, mining these special kinds of streams is one of the most attractive research area. In this paper, we propose two algorithms for learning from imbalanced regression data streams. Both methods are based on Chebychev’s inequality but in a different way. The first method, under-samples from the frequent target value examples while the second method over-samples the rare and extreme target value examples. This way, the learner will focus in the rare and more difficult cases. We applied our methods to train regression models using two benchmark datasets and two well-known regression algorithms: Perceptron and FIMT-DD. Our obtained results from the simulations indicate the usefulness of our proposed methods.
Vortex Swarm Optimization: New Metaheuristic Algorithm

This paper proposes a new swarm intelligence algorithm called vortex swarm optimization, inspired from vortex collective behavior where particles move around a common known center, they cover all the directions so they play as alarm system where any individual at any direction see the target (resources, predator…etc.) it informs other individuals to take an action. This type of collective behavior was seen in many creatures like (Bats and fishes). They use this type of collective behavior for feeding, access to resources, moving, information transmission, decision making, and protection from predators. This type is different from straight-ahead collective behavior (polarized collective behavior) where individuals move forward. The majority of swarm intelligence algorithms inspired their techniques from straight-ahead collective behavior, but vortex collective behavior had a few pieces of research so the importance of this research appears. The algorithm performs the two basics functions that any searching algorithm had (exploitation and exploration) by circling phase the algorithm will know the direction of the promising region, By swimming phase the algorithm will go to this region (exploration)and by attacking phase individuals will reduce their interaction range and reduce their velocity to get much closer to each other and best region found so far (exploitation), Algorithm skip local minimum by eliminating some individuals to a new regions to explore them and to change vortex direction to explore new directions. The algorithm was tested over ten benchmark functions and it competes with some other powerful algorithms.
Practical Solutions for Machine Learning Safety in Autonomous Vehicles.

Autonomous vehicles rely on machine learning to solve challenging tasks in perception and motion planning. However, automotive software safety standards have not fully evolved to address the challenges of machine learning safety such as interpretability, verification, and performance limitations. In this paper, we review and organize practical machine learning safety techniques that can complement engineering safety for machine learning based software in autonomous vehicles. Our organization maps safety strategies to state-of-the-art machine learning techniques in order to enhance dependability and safety of machine learning algorithms. We also discuss security limitations and user experience aspects of machine learning components in autonomous vehicles.