Reviewerly/exHarmony huggingface dataset:
Generating Cooking Recipes from Cooking Videos Using Deep Learning Considering Previous Process with Video Encoding

Research on generating natural language captions to visual data such as images and videos has produced considerable results with deep learning methods and attracted attention in recent years. In this research, we aim to generate recipe sentences from cooking videos acquired from YouTube. We treat the task as image captioning. There are two aspects to be considered in order to do so. We believe that the semantics of each process should be taken into account to improve the captioning ' s accuracy. Furthermore, data processing, that is obtaining images from each process using several visual processing methods such as object detection should be important. We propose a captioning model where a sentence vector is embedded to consider the consistency of the recipe. From differences between generated recipes and the reference recipe, we can calculate recipe scores. We use three metrics that are used in previous studies to evaluate the image captioning model. We compare the scores to with ones from baseline models.
A Study on I/O Performance in Highly Consolidated Container-Based Virtualized Environment on OverlayFS with Optimized Synchronization

Virtualization has been utilized for a variety of purposes, such as server consolidation for saving many things like power consumption. A highly consolidated virtualized environment, in which many containers run simultaneously on a physical computer, can save these things remarkably. However, this environment may suffer from the large overhead of virtual machines and guest operating systems. In a virtualization technology field, Docker is attracting attention as a virtualization method without a large overhead. This is container-based virtualization and does not invoke the virtual machine and guest operating system. Our previous paper showed that Docker could not provide enough performance in I/O processing. The recent Docker is solving this performance issue by adopting OverlayFS, which causes less overhead. However, this filesystem has a performance problem on copy- up. We then proposed a method for solving this problem. In this paper, we demonstrate that our solving method can improve the container I/O performance also in a highly consolidated environment.
Speckle Noise Reduction Using Fuzzy Logic in Ultrasound Images

Speckle alludes to the granular models that occur in ultrasound images because of wave impedance. Speckle expulsion can extraordinarily enhance the deceivability of the basic structures in an ultrasound image and upgrade resulting post-handling. A structure for speckle removal dependent on fuzzy logic algorithm. The noise present in the image can coat and diminish the visibility of specific attributes of the items present inside the images. Subsequently making it unfit for further upgrades. There has been an examination on the de-noising filters for long to expel the noise from images. In this research, we propose a unique method which beats outstanding techniques.
Segmentation of Magnetic Resonance Brain Images Using the Advanced Ant Colony Optimization Technique

MR Brain Image Segmentation is an important step in brain image analysis. It facilitates the automatic interpretation or diagnosis that helps in surgical planning, estimating the changes in the brain’s volume for various types of tissues, and recognizing different neural disorders. Many neurological disorders like epilepsy, Alzheimer’s, tumor, and cancer can be effectively quantified and analyzed by finding the volume of the brain tissues such as White Matter (WM), Gray Matter (GM), and Cerebro Spinal Fluids (CSF). In manual segmentation of brain MRIs physicians manually determines the boundaries of different objects of interest and it is time-consuming and difficult. Thus, several accurate automatic brain MRI segmentation techniques with different levels of complexity have been proposed. This paper proposes an advanced thresholding technique for the segmentation of brain MRIs based on the biologically inspired Ant Colony Optimization (ACO) algorithm. Here the texture features are assumed as heuristic data. The experimental results for the T1-weighted brain MRIs have shown high accuracy than the conventional such as Fuzzy C-Means (FCM), Expectation-Maximization (EM), Improved Bacterial Foraging Algorithm (IBFA), and Improved Particle Swarm Optimization (IPSO).
Dynamic Updating of the Knowledge Base for a Large-Scale Question Answering System

Today, the knowledge base question answering (KB-QA) system is promising to achieve a large-scale high-quality reply in the e-commerce industry. However, there exist two major challenges to efficiently support large-scale KB-QA systems. On the one hand, it is difficult to serve tens of thousands of online stores (i.e., constrained by the tuning and deployment time), and it would perform poorly if the systems start without a sufficient number of chat records. On the other hand, current KB-QA systems cannot be updated in an efficient way due to the high cost of knowledge base (KB) updating. In this article, we propose an automatic learning scheme for KB-QA systems, called ALKB-QA , using a vector modeling method to address the preceding two main challenges. The ALKB-QA system provides online stores with basic KB templates that are suitable for many common occasions, and this feature enables the ability to deploy chatbots for a large number of online stores in a short time. Then, the KBs are further updated automatically to adapt to their own businesses (meet different specific needs), leading to increased reply accuracy. Our work has three main contributions. First, the proposed ALKB-QA system has a good business model in the e-commerce industry (serving tens of thousands of online stores with low cost), breaking the scalability limitations of existing KB-QA systems. Second, we assess the reply accuracy of the proposed ALKB-QA system using human evaluations, and the results show that it outperforms human annotation-base approaches. Third, we launched our ALKB-QA system as a real-world business application, and it supports tens of thousands of online stores.
Online Causal Structure Learning in the Presence of Latent Variables

We present two online causal structure learning algorithms which can track changes in a causal structure and process data in a dynamic real-time manner. Standard causal structure learning algorithms assume that causal structure does not change during the data collection process, but in real-world scenarios, it often does change. The algorithms proposed here can revise correlation values without reprocessing the entire dataset and use an existing model to avoid relearning the causal links in the prior model, which still fit data. Proposed algorithms are tested on synthetic datasets. The online causal structure learning algorithms outperformed standard FCI by a large margin in learning the changed causal structure correctly and efficiently when latent variables were present.
Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees

We consider the problem of enhancing user privacy in common data analysis and machine learning development tasks, such as data annotation and inspection, by substituting the real data with samples from a generative adversarial network. We propose employing Bayesian differential privacy as the means to achieve a rigorous theoretical guarantee while providing a better privacy-utility trade-off. We demonstrate experimentally that our approach produces higher-fidelity samples compared to prior work, allowing to (1) detect more subtle data errors and biases, and (2) reduce the need for real data labelling by achieving high accuracy when training directly on artificial samples.
Anomaly Classification in People Counting and Occupancy Sensor Systems

People counting and occupancy sensor systems are used in diverse smart building applications like lighting controls, HVAC controls and workspace management. Anomalies in people counting and occupancy sensor data can result in undesirable application behavior. We address the problem of anomaly classification of sensor data using 3-class random forest classifiers in this work. We specifically consider sensor deployment scenarios where sensor fields-of-view may overlap. Depending on the sensor type, we devise signal features in time, frequency and spatio-temporal domains. The proposed random forest classifiers are evaluated using people counting and binary occupancy sensor data in an office environment, and are shown to have higher true positive rate and a lower false positive rate in comparison to an unsupervised k -means method and a random forest classifier with a single signal energy feature.
Comparative Study of different Lazy Learning Associative Classification Methods

Lazy Learning Associative Classification (LLAC) is a promising approach in the field of data mining. It is one of the associative classification methods in which it delays the processing of training datasets until it receives the test instance for the class prediction. Lazy learning associative classification can be constructed in two phases. Subset generation is the first phase and the subset evaluation is the second phase. In the past decades, many lazy learning associative classification methods have been proposed. These algorithms utilize several different methods for subset generation and subset evaluation. This paper focuses on comparative study of different lazy learning associative classification methods.
Mask R-CNN End-to-End Text Detection and Recognition

Text detection and recognition have witnessed drastic improvements in the field of computer vision. This end-toend model comprising of the detection and recognition models scales to provide higher accuracy. The most important phase in this end-to-end approach is the detection phase, as it plays an important role to identify the text. To address this issue, different approaches have been proposed. However, most of the methods produce lower efficiency to detect and recognize real world text. In this paper, we propose a new approach to investigate the challenges that the existing models possess and improve the efficiency of the detection and in turn increases the accuracy of text recognition. The proposed method outperforms the state-ofthe-art approaches due to the use of deblurring and sharpening to reduce noise in the pre-processing stage, followed by the cascade region proposal network model to improve the detection of real world text using non max suppression. Experimentations on real word datasets highlight the effectiveness of our method.
On solving the 7,7,5-game and the 8,8,5-game

An mnk-game is a kind of k-in-a-row game played on an m×n board, where two players alternatively mark empty squares with their own colors and the first player who gets k-consecutive marks (horizontally, vertically, or diagonally) wins. In this paper, we present an AND/OR search tree algorithm specifically for proving mnk-games. We first propose three novel methods to reduce the branching factor of AND/OR search trees. We also propose a new method to find pairing strategies, which further accelerate the proof of mnk-games. The combined methods drastically speed up the proof for the 7,7,5-game, which is solved in 2.5 seconds. Moreover, this paper is the first to solve the 8,8,5-game, which is proven as a draw within 17.4 hours.
Energy-Efficient Shortest Path Planning on Uneven Terrains: A Composite Routing Metric Approach

This work investigates the problem of energy-efficient shortest path planning for autonomous unmanned vehicles (AUVs) in outdoor natural terrain. We use an established energy-cost model for mobile robots, which integrates the terrain's angle of declination with the gravity and wheel-terrain friction forces, to calculate the AUV's energy consumption. We propose a novel composite routing metric that combines the energy consumption along the path with the traveled distance, to create a variant of the famous Dijkstra algorithm for path planning. Moreover, we obtain our results using a real-world terrain, which results in a graph with 10,000 nodes and 80,000 links. Our results indicate that the developed algorithm outperforms the conventional Dijkstra algorithm for energy-cost minimization in terms of the length of the paths obtained, with a negligible excessive energy penalty.
DeepCarotene -Job Title Classification with Multi-stream Convolutional Neural Network

In online recruitment, job title classification is a fundamental task that enables several downstream applications like job recommendation and ranking for job search. A special case of multi-class text classification, the job title classification problem takes as input two components from a job posting -a short job title and a lengthier job description, and normalizes the raw job title into its closest match from the given taxonomy. Typically, the job title, though shorter in length, contains more targeted signals than the job description, that can contain additional information irrelevant to the context. On the other hand, the job description often provides valuable information that helps steer the classification model towards choosing the best match. Achieving a balance between the two components is not a trivial task. In this paper, we propose a multi-stream CNN based model for job title classification, that learns semantic features on both character and word level. We collected about 15 million data points from one of the largest online job boards, Careerbuilder, to train the model. Due to the universal problem of getting massive labeled data, we adopt a weakly supervised method to efficiently generate noisy labels for this large data set. Compared with the current state-of-the-art job title classification systems, the proposed model, DeepCarotene, shows a significant improvement in performance. This model provides a new direction of CNN based end-to-end approach for job title classification.
A Low-Overhead Secure Image Compression over Wireless Multimedia Sensor Network

Due to inherent limitations in processing power, storage, and bandwidth, multimedia data processing is a challenge in wireless multimedia sensor network (WMSN). Protection of such data from unauthorized access is also of utmost importance. In this work we propose a lightweight image encryption scheme which works on compressed image captured by WMSN. It works in two steps. In the first step, the key is generated by picking a macroblock (key macroblock) dynamically from a template which is prepared by a set of video frames offline. This dynamic selection of key macroblock is done using a proposed address mapping mechanism. In the second step, each macroblock from compressed input image is encrypted by performing exclusive OR between the generated key and the intensities of the key macroblock. Performance of the scheme is evaluated through simulation in Cooja, the Contiki network simulator to make it readily implementable in real life mote e.g. MICAz. Performance is compared and the results show that the proposed securing scheme saves on an average 49% energy over its competitors while maintaining acceptable reconstruction quality. Finally, security analyses are carried out to prove system efficiency through histogram analysis, key sensitivity and key-space analyses. System robustness against noise and occlusion attacks has also been proved.
Native Language Identification from Raw Waveforms Using Deep Convolutional Neural Networks with Attentive Pooling

Automatic detection of an individual's native language (L1) based on speech data from their second language (L2) can be useful for informing a variety of speech applications such as automatic speech recognition (ASR), speaker recognition, voice biometrics, and computer assisted language learning (CALL). Previously proposed systems for native language identification from L2 acoustic signals rely on traditional feature extraction pipelines to extract relevant features such as mel-filterbanks, cepstral coefficients, i-vectors, etc. In this paper, we present a fully convolutional neural network approach that is trained end-to-end to predict the native language of the speaker directly from the raw waveforms, thereby removing the feature extraction step altogether. Experimental results using this approach on a database of 11 different L1s suggest that the learnable convolutional layers of our proposed attention-based end-to-end model extract meaningful features from raw waveforms. Further, the attentive pooling mechanism in our proposed network enables our model to focus on the most discriminative features leading to improvements over the conventional baseline.
RFID-Based Library Management System with Android Mobile Access Application

Manual processes in libraries fail to deal with the current demands of requests for information and find it difficult to comply with the amount of requests from users for books and other library services. Thus, this study presents the used of RFID in library management system, as well as the broader accessibility through the Android mobile application. It also offers handling and managing library routine processes that are generally not available in other library management systems, in particular, the capability of library consortium associated with different libraries to share resources. First, the framework of the study between the front end and back end of the system was synthesized. Then different evaluators were surveyed about the way the library management system functions. The system performance attributes are defined using the functionality, usability, reliability, performance and security (FURPS) model. The mean formula was used to analyze the data. The functionality, reliability, performance and security were evaluated perfectly acceptable with a weighted mean of 4.73, 4.58, 4.66 and 4.63 respectively. Furthermore, the usability was given an acceptable evaluation rating with a weighted mean of 4.50. The overall quality of the system was given a perfectly acceptable with a weighted mean of 4.62 thus proves that the used of RFID was successfully implemented in the library management system and the web and mobile application was successfully developed in managing library resources and tasks.
A chaotic microresonator structure for an optical implementation of an artificial neural network

Tuneable all-optical signal processing has been the holy grail of information photonics; it has been pursued for many years but has proven to be very challenging. In this contribution, we present our recent work in developing an all-optical signal processing device called a photonic reservoir computer (PhRC) which can be tuned to perform a bespoke task. The PhRC is inspired by how the brain handles and process information. We demonstrate that a chaotic micro-resonator is a suitable platform for the optical implementation of such an artificial neural network.
Through STEM education and OER to attract the girls in ICT career

The paper reflects the problem of increasing the engagement of women in the Information and Communication technologies (ICT) profession and how to attract them to make better use of women's potential. The role of STEM education in this process is analysed. A model of the key drivers influencing girls' interest in STEM-related subjects is presented. The authors evaluate the results of a survey among students after two-year campaign to attract more girls to follow ICT career. The role of Open Educational Resources (OER) in STEM education is explored. The survey and the results done under a project for development of OER is analysed. Conclusions and future work for increasing women numbers in ICT sector are made.
Constructing lattice-free gradient polyhedra in dimension two

Lattice-free gradient polyhedra can be used to certify optimality for mixed integer convex minimization models. We consider how to construct these polyhedra for unconstrained models with two integer variables under the assumption that all level sets are bounded. In this setting, a classic result of Bell, Doignon, and Scarf states the existence of a lattice-free gradient polyhedron with at most four facets. We present an algorithm for creating a sequence of gradient polyhedra, each of which has at most four facets, that finitely converges to a lattice-free gradient polyhedron. Each update requires constantly many gradient evaluations. Our updates imitate the gradient descent algorithm, and consequently, it yields a gradient descent type of algorithm for problems with two integer variables.
High-Frequency Drilling Data Analysis to Characterize Water-Ice on the Moon

Abstract This paper discusses the evolution of a pattern recognition algorithm that utilizes the high-frequency drilling data to characterize the water-ice on the Moon. The algorithm developed here can estimate the moisture content of a grout sample by analyzing the trend of drilling data. Such an algorithm can be used by NASA and private organizations in near-future to identify and produce water from Lunar Poles. An auger based rotary drilling rig with a drilling data acquisition system was designed and fabricated. The data acquisition system records drilling parameters like RPM, drilling depth, torque, and weight on bit at 1000Hz. The data is then filtered to remove electromagnetic interference. The drilling tests were conducted on meticulously designed grout block samples which contained the lunar soil simulant with specific geotechnical properties, replicating lunar subsurface at a specific pressure, temperature condition, and moisture contents. Simultaneously, the UCS of the grout block was tested in a lab to correlate the drilling data to specific grout strength. The drilling data recorded in the homogenous samples and the data collected during UCS tests were used to develop and validate a pattern recognition algorithm. The algorithm was tested on layered grout samples containing layers with varying strength. The UCS estimated by this algorithm was then correlated to the moisture content using the lab observations and published literature. The algorithm could distinctly detect the boundaries between different layers, estimate the UCS, and moisture content in real-time. The high-frequency drilling data was also used to identify different dysfunctions and optimize drilling operations. Trends of MSE, RPM, and torque were analyzed to detect auger choking, inefficient cuttings transport, and drilling vibrations. On the Moon, this algorithm can be invaluable in optimizing the drilling operations, estimating the spatial distribution of various subsurface layers improving our understanding of the subsurface lunar stratigraphy. It can also be used to estimate the quantity of water available on the lunar poles, which will be essential in planning the manned and robotic lunar missions in the coming years.
Commitment of Traders Report: Angular-Based Graph Representation (Agriculture Contracts)

In finance, a futures contract is a generalized legal agreement to buy or sell something at a predetermined price at a specified time later in the future, between parties anonymous to each other. The asset transacted is a commodity or financial instrument (in a general rule). The preordained price in which the assets are bought or sold is called forward price. The specified time when payment and delivery occurs is known as the delivery date. The Commodity Futures Trading Commission publishes the COT reports for the better understanding of the futures market. Particularly, the COT reports provide a breakdown of each Tuesday’s open interest for futures and options on futures markets in which 20 or more traders hold positions equivalent to or over the reporting levels established by the CFTC. COT reports released by CFTC are in either text or in spreadsheet format which are complicated and take some time to read and understand. Hence, here is an idea of developing an interactive graphical representation of all this data which will be filtered on various parameters such as desirable commodity, viewing date, different properties of the commodities, simple, understandable yet containing everything desired and also with a platform-independent way.