Reviewerly/exHarmony huggingface dataset:
Keyword Spotting with Neural Networks Used for Image Classification

We might be living in a Screen Age, almost everyday a new object with a bright touch screen is invented. A possible antidote to our screen addiction is voice interface. The available voice assistants are activated by keywords such as “hey Siri” or “okay Google” [1]. For initial detection of these keywords, it is impractical to send the audio data over the Web from all devices all the time, as it would increase the privacy risks and would be costly to maintain. So, voice interfaces run a keyword detection module locally on the device. For independent makers and entrepreneurs, it is hard to build a simple speech detector using free, open data, and code. We have published the result as easy to train “Kaggle notebooks” [2]. With considerable improvement, these models can be used as a substitute for our keypads in touch screens. In this work, we have used convolutional neural networks (CNNs) for detection of the keywords, because of their ability to extract important features, while discarding the unimportant ones. This results in smaller number of parameters for the CNNs as compared to the networks with fully connected layers. The network that we have used on this work is derived from the CNNs that gave state-of-the-art results for image classification, e.g., dense convolutional network (DenseNet) [3], residual learning network (ResNet) [4], squeeze-and-excitation network (SeNet) [5], and VGG [6]. We have discussed the performance of these CNN architectures for keyword recognition. The method for reproducing the result had been suggested as well. These models achieve top one error of ~96–97%, with the ensemble of all achieving ~98%, on the voice command dataset [7]. We have concluded by analyzing the performance of all the ten models and their ensemble. Our models recognize some keywords that were not recognized by human. To promote further research ( https://github.com/xiaozhouwang/tensorflow_speech_recognition_solution ) contains the code.
Retraining Conditions: How Much to Retrain a Network After Pruning?

Restoring the desired performance of a pruned model requires a fine-tuning step, which lets the network relearn using the training data, except that the parameters are initialised to the pruned parameters. This relearning procedure is a key component in deciding the time taken in building a hardware-friendly architecture. This paper analyses the fine-tuning or retraining step after pruning the network layer-wise and derives lower bounds for the number of epochs the network will take based on the amount of pruning done. Analyses on the propagation of errors through the layers while pruning layer-wise is also performed and a new parameter named 'Net Deviation' is proposed which can be used to estimate how good a pruning algorithm is. This parameter could be an alternative to 'test accuracy' that is normally used. Net Deviation can be calculated while pruning, using the same data that was used in the pruning procedure. Similar to the test accuracy degradation for different amounts of pruning, the net deviation curves help compare the pruning methods. As an example, a comparison between Random pruning, Weight magnitude based pruning and Clustered pruning is performed on LeNet-300-100 and LeNet-5 architectures using Net Deviation. Results indicate clustered pruning to be a better option than random approach, for higher compression.
Forensics in the Cloud: A Literature Analysis and Classification

Although cloud, fog and edge computing have established their strengths and usefulness through characteristics such as low cost, reliability, location sensitivity, wireless connectivity, and geographical accessibility. These strengths and characteristics pose new problems in the area of privacy, security and forensics. In this paper, we present a literature analysis of works done in cloud, edge and fog forensics from 2010 through to 2018. We also present a classification of the works done during this period based on a scheme that consists of eight categories. These categories are models, tools and frameworks, case studies, evidence collection and preservation, evidence examination and analysis, evidence presentation, architecture, legality and law, and survey, comparative study and reviews. We also proposed nine criteria to be considered when designing a good and effective forensic system. Based on our proposed criteria, we evaluated some solutions that have been proposed by other researchers in cloud, fog and edge forensics. In all, we reviewed 281 articles from four publishers and presented statistical analysis of these articles based on their year of publication, the publishing outlet and our classification scheme. We also look at areas that can be considered by researchers for future work.
Modified Parallel Tracking and Mapping for Augmented Reality as an Alcohol Deterrent

Drug addiction among the population is a major social menace. This paper proposes an innovative method to offer a promising deterrent against alcohol abuse using Augmented Reality (AR) as a tool. The proposed method aims at traumatizing an alcoholic person by augmenting multiple multimedia objects in the vicinity of a person. A modified Parallel Tracking and Mapping (PTAM) technique has been proposed to create an AR workspace. For this, we have proposed an improvement in the Bundle Adjustment. A novel concept of map independence is incorporated in the PTAM, providing freedom of augmenting multimedia in the desired position. The major contribution of this paper is that a versatile AR platform is developed for a monocular camera-based system for educating alcoholic persons for overcoming alcohol addiction.
An advanced QER selection algorithm for 360-degree VR video streaming

The 360-degree virtual reality (VR) video streaming faces many challenging issues since VR video contains a large volume of data and requires streaming omnidirectional view. For consuming VR video in a reasonable quality, the streaming system is required to efficiently use bandwidth and reduce the computational complexity of the client device. Quality Emphasized Region (QER) based streaming has been introduced as a kind of viewport adaptive streaming scheme for reducing waste of bandwidth. This scheme requires QER selection for providing the adaptive quality of QER. We propose an advanced QER selection algorithm consisting of two stages. The first stage is the calculation of the Quality Emphasis Center distance which employs preprocessed QER_ID_MAP for reducing the computational complexity. As the second stage, we propose an adaptive adjustment of the signaling interval between signaling messages. The simulations show that the proposed algorithm results in reduction of computational complexity and bandwidth waste.
Salient Object Detection via Distribution of Contrast

In human vision system, the contrast of image regions to their surroundings is highly sensitive. Motivated by this intuition, many contrast based salient object detection methods have been proposed in recent years. In these previous methods, saliency for one region is generally measured by sum of contrast from its surrounding. We find that the spatial distribution of contrast is one important cue of saliency. Salient region usually shows high contrast in the most of directions, while background region present high contrast in a few directions. Inspired by this phenomenon, we propose one salient object detection method via distribution of contrast. In proposed method, input image will be segmented into superpixels first, for each superpixel, contrast between this superpixel and its surrounding will be computed in every direction, relative standard deviation (RSD) of contrast in all directions is measured as cue of saliency. Experimental results on four benchmark dataset demonstrate the proposed method performs well when against the state-of-the-art methods.
A systematic approach as a basis for methodological training of the future Mathematics teachers

At the present stage of society development, Mathematical education is an extremely important indicator of the intellectual development of the young generation and its preparation for life. Accordingly, the problem of methodological training of the future mathematics teachers is of great urgency. In this context, there is a need to consider a systematic approach in the methodological preparation of students, as a basis for the achievement of pedagogical skills and orientation for continuous professional development throughout life. We will consider the systematic approach in the methodical preparation of the future Mathematics teacher as a methodological approach, in which the methodological preparation is considered as a system that is a set of interconnected structural elements, each of which has a clearly defined purpose and predictable end result, corresponding resources, interactive communication, connection with the environment. The concept of the system is the key notion of implementing a systems approach in the methodological training of the future Mathematics teachers. In reality, any system cannot exist (function) in isolation from its environment. The environment always influences some factors of the internal state of the system. In turn, the system cannot be neutral to the external environment. The influence of environmental factors on the system is characterized by input (exogenous) quantities, and the elements of the system that are affected are called "inputs" of the system. From the point of view of implementing a systematic approach in the methodological training of the future Mathematics teachers, it is necessary to first determine, on the basis of pedagogical research, the parameters of the “output” (end result), namely what competences should be formed in students and at any stage of preparation. The next step is to determine the "input" parameter: research the need for appropriate resources, the educational and organizational level of the proposed system of methodological training and environmental parameters is given a detailed study.
Performance Analysis of Optimization Process on Adaptive Group of Ink Drop Spread Algorithm Proficiency

Aims: Two evolutionary algorithms consist of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are being used for finding the best value of critical parameters in AGIDS which will affect the accuracy and efficiency of the algorithm. Background: Adaptive Group of Ink Drop Spread (AGIDS) is a powerful algorithm which was proposed in fuzzy domain based on Active Learning Method (ALM) algorithm. Objective: The effectiveness of AGIDS vs. artificial neural network and other soft-computing algorithms has been shown in classification, system modeling and regression problems. Methods: For solving a real-world problem a tradeoff should be taken between the needed accuracy and the available time and processing resources. Results: The simulation result shows that optimization approach will affect the accuracy of modelling being better, but its computation time is rather high. Conclusion: The simulation shows that AGIDS algorithm has a suitable efficacy in solving complex problems without using complex optimization algorithms. Other: The simulation shows that AGIDS algorithm has a suitable efficacy in solving complex problems without using complex optimization algorithms.
Learn by doing as a didactic strategy through the codecademy web portal in pedagogical practice to promote creative thinking and significant learning in students

Abstract The work developed is action research type, framed in the qualitative paradigm. It designs a didactic strategy for the development of a different pedagogical practice to the traditional one that allows a greater autonomy in the student during the learning process, since the apprehension of knowledge is done in a more personal and practical way. The didactic proposal is developed from the diagnosis of the student’s creativity and their ability to interact with academic platforms. Its implementation allows the student to learn by doing in a process of trial, error and correction in order to promote creative thinking while achieving meaningful learning.
A Study on Design and Key Technology of Intelligent Power Grid Dispatching Visualization System

Abstract The design principle of smart grid and the operation mechanism of smart grid dispatching system are studied. A visual system design framework of Smart Grid Dispatching Based on interactive operation of visual interface is proposed. The intelligent grid dispatching visualization model was constructed from four aspects: early warning, analysis, control and observation. The key technology research including system intelligent intelligence and rapid simulation modeling was analyzed in detail. It is designed to ensure the safe and stable operation of the system while improving the efficiency of the dispatcher.
Optimization of the allocation of academic schedules through artificial intelligence techniques

Abstract The programming or assignment of academic schedules in educational institutions has become an important global problem, this is due to the fact that it implies great efforts in time and resources for the staff a burden of this task due to the amount of restrictions and conditions that exist properly involved from the institution. Based on this problem and considering that common algorithms do not offer an optimal solution in this situation, it is necessary to resort to artificial intelligence techniques because they offer methods and algorithms that reduce the time and resources needed to generate an optimal program. This document details different artificial intelligence techniques used for the allocation of schedules, in order to find the technique that best suits the needs of the institution under study Universidad Francisco de Paula Santander, Ocaña located in Colombia, and which will be implemented more forward as a possible solution for the allocation of academic schedules in the institution.
Geochemical Interpretations Using Multiple Remote Datasets

The advent of multiple orbital and in situ missions to planetary bodies beyond Earth has enabled characterization of extraterrestrial shallow crustal processes. We describe examples of interpreting geochemical, isotopic, and radar properties from multiple remote datasets, supplemented with in situ observations from rovers and landers, meteorites, and lunar samples. Given the availability of distinct data types and the relevance to bulk-silicate bodies in the Solar System, we present five case studies for the Moon and Mars. The first involves lunar magmatic processes in relation to TiO2 and radargram-derived physical properties. Next, O and Fe isotope variations relative to the Mg number provide insight into the degree of fractional crystallization in lunar lava flows. Physical mixing of endmembers and chemical weathering processes in Gusev crater soil on Mars are discussed. Effective use of the Chemical Index of Alteration (CIA) is also considered by comparing mineralogic observations across Mars with terrestrial references. Lastly, the nature of bulk soil hydration on Mars is described by assessing chemical variations with Principal Component Analysis (PCA). This chapter describes in situ analyses and mapping across local and regional scales. Data synthesis also involves contrasting depth scales from tens of microns to multiple kilometers.
Saliency Detection Based on Foreground and Background Propagation

In recent years, image saliency detection has become a research hotspot in the field of computer vision. Although significant progress has been witnessed in visual saliency detection, several existing saliency detection methods still cannot highlight the complete salient object when under complex background. For the purpose of improving the robustness of saliency detection, we propose a novel salient detection method via foreground and background propagation. In order to take both foreground and background information into consideration, we obtain a background-prior map by computing the dissimilarity between superpixels and background labels. A foreground-prior map is obtained by calculating the difference of superpixels between the inner and outer of a convex hull. Then we use label propagation algorithm to propagate saliency information based on foreground and background prior maps. Finally, the two saliency maps are integrated to generate an accurate saliency map. The experimental results on two public available data sets MSRA and ECSSD demonstrate that the proposed method performs well against the state-of-the-art methods.
Automatic Classification Based on Features Fusion for Upper Gastrointestinal WCE Images

Wireless Capsule Endoscopy (WCE) is an important clinical application which suffers a time-consuming review procedure. A WCE images automatic classification algorithm which fuses color and texture features is proposed to alleviate the burdensome task. A pre-process that aims to label shadow and highlight is implemented via a new automatic tuning algorithm based on superpixel-level. Hue-Saturation (HS) histograms and a new texture feature named colour scale invariant local ternary pattern (CSILTP) are extracted as local descriptors. A strategy of feature fusion that utilizes discrimination power analysis (DPA) is applied to reduce the dimension of features. The random forest (RF) classifier is then used to discriminate WCE images. The experiment results indicate a better performance of proposed method compared with some existing approaches.
A Noise Robust Batch Mode Semi-supervised and Active Learning Framework for Image Classification

Supervised learning with convolutional neural networks has made a great contribution to computer vision largely due to massive labeled samples. However, it is far from adequate available labeled samples for training in many applications. Realistically, annotation is a tedious, time consuming, and costly task while a strong need for specialty-oriented knowledge and skillful expert. Therefore, in order to take full advantage of limited resources to observably reduce the cost of annotation, we propose a noise robust batch mode semi-supervised and active learning framework which named NRMSL-BMAL. When querying labels in an iteration, firstly, a convolutional autoencoder cluster based batch mode active learning strategy is used for querying worthy samples from annotation experts with a cost. Then, a noise robust memorized self-learning is successively used for extending training samples without any annotation cost. Finally, these labeled samples are added to the training set for improving the performance of the target model. We perform a thorough experimental evaluation in image classification tasks, using datasets from different domains, including medical image, natural image, and a real-world application. Our experimental evaluation shows that NRMSL-BMAL is capable to observably reduce the annotation cost range from 44% to 95% while maintaining or even improving the performance of the target model.
6DoF Vehicle Pose Estimation Using Segmentation-Based Part Correspondences

Over the last years, pixel-wise analysis of semantic segmentation was established as a powerful method in scene understanding for autonomous driving, providing classification and 2D shape estimation even with monocular camera systems. Despite this positive resonance, a way to take advantage of this representation for the extraction of 3D information solely from a single-shot RGB image has never been presented.In this paper we present a full-fledged six degree-of-freedom vehicle pose estimation algorithm, demonstrating that a segmentation representation can be utilized to extract precise 3D information for non-ego vehicles. We train a neural network to predict a multi-class mask from segmentation, defining classes based on mechanical parts and relative part positions, treating different entities of a part as separate classes. The multi-class mask is transformed to a variable set of key points, serving as a set of 2D-3D correspondences for a Point-n-Perspective-solver. Our paper shows not only promising results for 3D vehicle pose estimation on a publicly available dataset but also exemplifies the high potential of the representation for vehicle state analysis. We present detailed insight on network configuration as well as correspondence calculation and their effect on the quality of the estimated vehicle pose.
The SEC meditates on The DAO: tracing the initial arc of cryptosecurities regulation

Purpose The purpose of this paper is to review the first two years of the US Securities and Exchange Commission (SEC) efforts to regulate cryptosecurities to assess the trends of that regulation. Design/methodology/approach The authors review the SEC’s official pronouncements and informal statements about, and its enforcement actions against participants in, various early experiments in cryptosecurities. Findings The SEC has been evolving how to apply the US securities laws to cryptosecurities since its report on The DAO two years ago. When “coins” on a blockchain meet the traditional Howey Test, it is easy to categorize them as “securities.” However, the bedrock regulatory principle that some person must account for violations is frustrated by automated blockchain transactions, where no human is in control. This tension risks a “moral crumple zone” arising around cryptosecurities, in which persons might become liable for violations that they cannot fairly be said to have caused. Originality/value This paper provides valuable information and insights about the beginnings of US regulation of cryptosecurities and how the evolution of that regulation is trending after two years.
Multimodal Logical Architecture for Emergency Transportation Toward Better Decision Making in Humanitarian Logistics

Humanitarian logistics has emerged as a vital tool to reduce and alleviate the harmful impacts and suffering caused by extreme events. A significant task of planners and decision makers involved in humanitarian logistics is planning for and satisfying the vital needs of the people during highly stochastic disaster/catastrophe conditions. To accomplish this, there is a clear research need to describe and evaluate a multi-modal logical architecture of efficient emergency transportation operations that will support decision makers to generate and evaluate decision alternatives for solving the problems related to transporting vital supplies during highly stochastic disaster conditions. To be more specific, the humanitarian multi-modal logical architecture described in this paper is created as a comprehensive needs assessment effort and knowledge base that can be used in the creation of software tools for the movement of emergency supplies. This paper carefully describes the steps needed to create such a logical architecture for multi-modal humanitarian logistics with an emphasis on the sustainability and resiliency of the emergency relief system in the event of a disaster/catastrophe. During the extensive process of evaluation of the proposed logical architecture, a thorough study and general assessment of the transportation network and infrastructure based on system profiles, availability, allocation, and optimal assignment of critical resources and database requirements are also conducted, followed by a case study applied to the NY-NJ-CT-PA CSA region. Finally, future directions on the usage of this comprehensive logical architecture are discussed.