Reviewerly/exHarmony huggingface dataset:
Declarative Local Search for Predicate Logic

In this paper we introduce a framework built on top of the Knowledge Base System , which allows local search heuristics to be synthesized from their formal descriptions. It is introduced as a new inference to solve optimization problems in . To model a local search heuristic, users need to specify its components, among which neighbourhood moves are the most important. Two types of neighbourhood moves, namely standard moves and Large Neighbourhood Search moves, are supported. A set of built-in local search heuristics are provided, allowing users to combine neighbourhoods in different ways. We demonstrate how the new local search inference can be used to complement the existing solving mechanisms for logic programming.
Enhancing the Quality of Inserted 3D Object into a Video

Inserting 3d object into a real video is one of major task in an augmented reality system. To enhance the real quality, inserted object usually added with the environment effects. In this paper, we proposed a technique to enhance the quality of inserted object by adding its shadow. Experiments show the effectiveness of our proposal method.
How to be a maker: Switch to hot buttered toast

The ability to turn a device on and off creates myriad possibilities. That is where switches come in, as Hannah Joshua demonstrates
A Novel Single Target Auto-annotation Algorithm for SAR Images Based on Pre-trained CNN Classifier

Convolutional neural networks (CNNs) are extremely important building blocks for abstract deep learning algorithm constructs regarding visual interpretation especially when it comes to synthetic aperture radar (SAR) images. An ongoing research is being made in order to improve their accuracy forgetting about the undiscovered internals. CNNs are usually being used as black boxes that produce in a non-linear fashion abstract interpretations. In this paper, however, we propose a novel algorithm that shows where CNNs look in an image to provide the answer to the provided classification problem applied to SAR images. We provide also results as bounding boxes using only a pre-trained classification network and some post-processing. The algorithm uses a brute-force approach given a pre-trained neural network, it removes gradually lines of pixels and checks the effect on the resulting scores, and it post-processes the resulting scores to infer the most important region in a given input image. Although other attempts have been made in the literature to provide solutions to the problem, by reversing the convolutional map filters, they are limited in scope and generally fail to deal with a complex network such as the award winning Resnet. Our algorithm, in this category, is of significant usefulness, it bridges the gap between the object classification and object detection problems, opening new perspectives to eliminate the time-consuming task of manual object annotation.
ClassVector

We propose a parameterized prototype-based model for text classification, named ClassVector. Unlike prototype-based models, we parameterize the prototype vector of each class. We also apply convolutional neural network to map a text to a feature vector. Classification is then conducted by computing distances between the prototype vector of each class and the feature vector of a text. We test our model on four different datasets, involving tasks of topic classification and sentiment analysis. The results demonstrate that the proposed model has a competitive performance compared with the previous state-of-the-art methods.
Privacy-Aware Peak Load Reduction in Smart Homes

Smart meters record power consumption data at every minute or even every second. This fine-grained data on electricity usage exposes private information about the residents of the house like the number of occupants, times of occupancy, appliance information, and much more. A solution to obscure this data is to add a battery to each home and use it strategically to manipulate the readings observed at the smart meter. Deploying such a solution at a large scale can result in sudden peaks in the energy usage. This is an alarming concern for the electric utility companies as this may cause outages, making the grid unstable. This paper is the first to expose this shortcoming and propose algorithms to mitigate the problem while maintaining the privacy of the residents. Furthermore, this paper shows that the proposed algorithms are more effective in preserving privacy than existing ones while reducing the peak load.
Generation of Hindi Word Embeddings and Their Utilization in Ranking Documents Using Negative Sampling Architecture, t-SNE Visualization and TF-IDF Based Weighted Average of Vectors

Hindi is the official language of India and has over 500 million speakers worldwide. Being a dominant language with a widespread impact, implies the need for development of technologies that cater to its native speakers. In this paper, a text mining based information retrieval model has been developed to generate Hindi word embeddings and their application ranking documents in order of relevance to an input query. Word embeddings are multi-dimensional vectors that can be created by utilizing the linguistic context of words in a large corpus. To generate the embeddings, a corpus was created from the Hindi Wikipedia dump, on which the skip-gram approach was applied using a neural network based negative sampling-architecture. The weighted average of each word embedding along with its tf-idf score generated the embeddings for each individual document. The cosine-similarity was then calculated between each document vector and the query vector. Using these similarity scores, the documents were ranked in descending order of relevance to the query. Highly relevant rankings were obtained in response to a query input. The results of the model were visualized using the t-SNE visualization method. The accuracy of this method proves that in the process of conversion of words to numeric vectors, the semantic context of the words was preserved.
Access Controls in Internet of Things to Avoid Malicious Activity

Connecting and controlling the access to the Internet of Things (IoT) are crucial since it converges and evolves multiple technologies. Traditional embedded systems, wireless sensor networks, control systems, software-defined radio technologies, and smart systems contribute to the Internet of Things. The deep learning, data analytics, and consumer applications have an essential role to IoT. The challenges are to store, process, and develop a meaningful form so that it is useful to the business, government, and customers. The paper discusses computing the data in a cloud; current challenges to store, retrieve, and process; security requirements; and possible solutions. We further provide the trust framework for the user and access control algorithms for data processing in the cloud environment.
Analysis Of Interoperability In Cloud Computing

Today most of traditional forms of education are not suitable for requirements of educational development and can't keep up with changes in learning demand over time, but Computer networks have provided many opportunities for it. By Cloud Computing software and files are imported into cloud, so word processing, presentations, databases, and more can be accessed from a web browsers. Educational institutions can take advantage of cloud applications to provide students and teachers with free or cost-effective alternatives instead of expensive proprietary productivity tools. In this paper we use semantic models in cloud computing to provide platform-independent data presentation and improve the description of the service. We analyze and discuss how cloud computing can affect boundaries of educational resources in background of semantic web by providing a technological solution using experimental and semantic platforms and numerically cloud services.
Approximating earthquake source of Italy using Steepest Descent method

This paper describes the comparison of Newton's method and Steepest Descent method for determining the coordinates of earthquake source. Here, the Steepest Descent method is used because it is a Newton-based method. The earthquake case used in this research is Italian earthquake August 24, 2016, which has a seismic phase of Pg. The calculation was supported by Azimuth Coordinate equations to find the coordinates and Haversine formula to find the distance between five earthquake stations to the earthquake source. The final result of calculations was path's graph from the iteration of Steepest Descent method. Moreover, the results will be compared with the results of Newton's method that has been successfully approaching the point of earthquake source in the same case study of previous research. The result shows the number of final iterations of two methods using tolerance number 0.01, minimum velocity number 3093 m/s and three cases of initial guess in the form of city coordinate of Rome, Milan, and Palermo. Newton's method generates 12 iterations in every case, Steepest Descent method generate 7, 6, 5 iteration respectively. However, the final numerical errors for Rome, Milan and Palermo initial guess are 0.1598 by Newton's method, while Steepest Descent method are 0.1566, 0.1567 and 0.1567.
The Subjective Cost of Writing Reusable Code: The Case of Functions

Functions provide substantial benefits for software development, simplifying programming through decomposition, reusability and abstraction. In a previous study, our group identified a tendency of high-school students to not use functions, even in programming tasks where functions can be a good solution strategy. The current research extends this observation to university students and aims to provide an explanation for the factors underlying this tendency. We focus on the subjective cost of the cognitive effort required for writing functions. Our experiment examined how information systems students solved a set of programming tasks, which varied by the number of repetitive questions. The results showed that most of the students avoided using functions altogether. We further found that in the subgroup of students who used functions at least once, the likelihood of using functions was positively associated with (a) the number of repetitive questions in each task, and (b) the task order, i.e., the progress of the experiment. These results indicate that the subjective cost of writing functions is taken into account when making a decision on how to solve a task at hand and is compared with the cost of repetitive work without using function, and that the former cost is updated with experience.
Bidirectional LSTM Joint Model for Intent Classification and Named Entity Recognition in Natural Language Understanding

The aim of this paper is to present a Simple LSTM - Bidirectional LSTM in a joint model framework, for Intent Classification and Named Entity Recognition (NER) tasks. Both the models are approached as a classification task. This paper discuss the comparison of single models and joint models in the respective tasks, a data augmentation algorithm and how the joint model framework helped in learning a poor performing NER model in by adding learned weights from well performing Intent Classification model in their respective tasks. The experiment in the paper shows that there is approximately 44% improvement in performance of NER model when in joint model compared to when tested as independent model.
The Power of the “Pursuit” Learning Paradigm in the Partitioning of Data

Traditional Learning Automata (LA) work with the understanding that the actions are chosen purely based on the “state” in which the machine is. This modus operandus completely ignores any estimation of the Random Environment’s (RE’s) (specified as $$\mathbb {E}$$ ) reward/penalty probabilities. To take these into consideration, Estimator/Pursuit LA utilize “cheap” estimates of the Environment’s reward probabilities to make them converge by an order of magnitude faster. This concept is quite simply the following: Inexpensive estimates of the reward probabilities can be used to rank the actions. Thereafter, when the action probability vector has to be updated, it is done not on the basis of the Environment’s response alone, but also based on the ranking of these estimates. While this phenomenon has been utilized in the field of LA, until recently, it has not been incorporated into solutions that solve partitioning problems. In this paper (The second author gratefully acknowledges the partial support of NSERC, the Natural Sciences and Engineering Council of Canada), we will submit a complete survey of how the “Pursuit” learning paradigm can be and has been used in Object Partitioning. The results demonstrate that incorporating this paradigm can hasten the partitioning by a order of magnitude.
Learning Career Progression by Mining Social Media Profiles

With the popularity of social media, large amounts of data have given us the possibility to learn and build products to optimize certain areas of our existence. In this work, we focus on exploring methods by which we can model the career trajectory of a given candidate, with the help of data mining techniques applied to professional social media data. We first discuss our efforts to normalizing raw data in order to get good enough data for predictive models to be trained. We then report the experiments we conducted. Results show that we can predict job transitions with 67% accuracy when looking at the 10 top predictions.
Accurate and consistent depth estimation for light field camera arrays

In this paper, we propose a depth estimation framework for light field camera arrays. The goal of the proposed framework is to compute consistent depth information over the multiple cameras which is hardly achieved by conventional approaches based on the pairwise stereo matching. We first perform stereo matchings on adjacent image pairs using a convolutional neural network-based correspondence scoring model. Once the local disparity maps are estimated, we consolidate the disparity values to make them globally sharable over the multiple views. We finally refine the depth values in the image domain by introducing a novel image segmentation method considering edges in the image to obtain a semantic-aware global depth map. The proposed framework is evaluated on three different real world scenarios, and the experimental results validate that our proposed method produces accurate and consistent depth maps for images captured by the light field camera arrays.
A framework for context change detection and management in probabilistic models for context in fusion

In a prior paper, a probabilistic model for using context in fusion was developed. It was shown that context-based fusion could be represented by a Bayesian probabilistic model that contains situation and context data, as well as conditional probabilities for the random variables. In the same paper, a conceptual model of an adaptive real-time context management system was proposed to monitor fusion performance, and select the appropriate context in order to improve fusion performance. This paper represents an extension of the above paper by developing frameworks for an adaptive general real-time context management, with application to optimize the tracking performance of an airborne platform.
Improved Neural Networks Based on Mutual Information via Information Geometry

This paper presents a new algorithm based on the theory of mutual information and information geometry. This algorithm places emphasis on adaptive mutual information estimation and maximum likelihood estimation. With the theory of information geometry, we adjust the mutual information along the geodesic line. Finally, we evaluate our proposal using empirical datasets that are dedicated for classification and regression. The results show that our algorithm contributes to a significant improvement over existing methods.
AI – Challenges in application with bus data in the automotive sector

Artificial Intelligence (AI) is a scientific field which emerged in the 1950s shortly after the introduction of the first electronic and programable computers. Since then, the field has hit many astonishing milestones, like defeating the then world chess champion Garry Kasparow in 1997 (Deep Blue, IBM) or surpassing human abilities in areas like visual object recognition in images (ISBI [1], ICPR [2]). Today it is used widely in many different areas of our daily lives, from autonomous cars and drones to medical expert systems to recommender systems in online shops.
Gestural User Interfaces for Hearing and Speech Impaired People Using KINECT

A large Communication gap exist between Hearing and Speech Impaired People(HSIP) and normal hearing people because HSIP used sign language and normal people used spoken language. Sign language is the only way to communicate with HSIP. There are many system exists that translate signs into text of spoken language to make possible of this communication. Our proposed system Home Sign Language Tutor is divided into two main sections. One is home sign dictionary that helps to learn the home sign language and other section translates the signs performed by HSIP into plain text in real time. We used kinect sensor to get skeletal data from user. User Centered Design process is used to design the system which involves user in every stage of the design process. After developing the system we performed an experiment on 30 (Thirty) HSIP. We divided the participants in 2 equal groups in which one was Normal and other is Expert. Normal group has hearing parents and expert group has hard hearing parents. Home signs are used in home in early age when HSIPs are not exposed to formal learning in school. In this proposed system we translated 21 (Twenty one) home signs into text. Almost 10 signs were already in use or have little bit understanding in HSIPs while we have designed 11 new signs with help of sign language interpreter. Results shows that 75 % participants from normal group and 60 % from expert group performed 11 to 15 gestures successfully. The same experiment is conducted again with same groups and success rate for 95% is achieved with normal group participants for 16 to 20 gestures. On the other hand 60 % expert group performed all 21 signs successfully.
Enhanced Integrity Checking for Preserve Data Owner and User Level Privacy Using Dual Cryptography Approach

Using Cloud Storage, users can tenuously store their data and enjoy the on-demand great quality applications and facilities from a shared pool of configurable computing resources, without the problem of local data storage and maintenance. However, the fact that users no longer have physical possession of the outsourced data makes the data integrity protection in Cloud Computing a formidable task, especially for users with constrained dividing resources. From users’ perspective, including both individuals and IT systems, storing data remotely into the cloud in a flexible on-demand manner brings tempting benefits: relief of the burden for storage management, universal data access with independent geographical locations, and avoidance of capital expenditure on hardware, software, and personnel maintenances, etc. To securely introduce an effective Sanitizer and third party auditor (TPA), the following two fundamental requirements have to be met: 1) TPA should be able to capably audit the cloud data storage without demanding the local copy of data, and introduce no additional on-line burden to the cloud user; 2) The third party auditing process should take in no new vulnerabilities towards user data privacy. In this project, utilize and uniquely combine the public auditing protocols with double encryption approach to achieve the privacy-preserving public cloud data auditing system, which meets all integrity checking without any leakage of data. To support efficient handling of multiple auditing tasks, we further explore the technique of online signature to extend our main result into a multi-user setting, where TPA can perform multiple auditing tasks simultaneously. We can implement double encryption algorithm to encrypt the data twice and stored cloud server in Electronic Health Record applications.
Using Interactive Mouthguard as Alternative Control Method and Improve Mobile Gaming Experience with Self-adaptive Human-Computer Interface

This research attempts to introduce a new way for users to interact with their devices in gaming session. Using interactive mouthguard as a new method of input, the new user interface system will be able to react to the user without them physically tapping the screen, adapt to the user, reduce the amount of direct hand control, making the control more intuitive and streamline the whole user experience.
Proposal for a Model of a Computer Systems Implantation Process (MoProIMP)

From the analysis of software development methodologies, it was observed that many of them do not contemplate the process of system implantation or, if they do, they do not develop or manage them in a comprehensive manner. As an answer to the identified gaps, MoProIMP, a model that allows to systematize the process of software implantation, is proposed. This model can be used together with the development methodology of the software producer organization. MoProIMP identifies phases, activities, tasks (inputs and outputs) and existing dependencies in a software implantation, systematically managing each of them. Finally, a case study is presented as an initial validation to the proposed model.
On the Cyclic Regularities of Strings

Regularities in strings are often related to periods and covers, which have extensively been studied, and algorithms for their efficient computation have broad application. In this paper we concentrate on computing cyclic regularities of strings, in particular, we propose several efficient algorithms for computing: (i) cyclic periodicity; (ii) all cyclic periodicity; (iii) maximal local cyclic periodicity; (iv) cyclic covers.
Region-Semantics Preserving Image Synthesis

We study the problem of region-semantics preserving (RSP) image synthesis. Given a reference image and a region specification R, our goal is to train a model that is able to generate realistic and diverse images, each preserving the same semantics as that of the reference image within the region R. This problem is challenging because the model needs to (1) understand and preserve the marginal semantics of the reference region; i.e., the semantics excluding that of any subregion; and (2) maintain the compatibility of any synthesized region with the marginal semantics of the reference region. In this paper, we propose a novel model, called the fast region-semantics preserver (Fast-RSPer), for the RSP image synthesis problem. The Fast-RSPer uses a pre-trained GAN generator and a pre-trained deep feature extractor to generate images without undergoing a dedicated training phase. This makes it particularly useful for the interactive applications. We conduct extensive experiments using the real-world datasets and the results show that Fast-PSPer can synthesize realistic, diverse RSP images efficiently.
Metaheuristics in Physical Processes Optimization

The subject of this work is applying the artificial neural network (ANN) taught using two metaheuristics - the firefly algorithm (FA) and properly prepared evolutionary algorithm (EA) - to find the approximate solution of the Wessinger’s equation, which is a nonlinear, first order, ordinary differential equation. Both methods were compared as an ANN training tool. Then, application of this method in selected physical processes is discussed.