Reviewerly/exHarmony huggingface dataset:
Uralic multimedia corpora: ISO/TEI corpus data in the project INEL

In this paper, we describe a data processing pipeline used for annotated spoken corpora of Uralic languages created in the INEL (Indigenous Northern Eurasian Languages) project.With this processing pipeline we convert the data into a lossless standard format (ISO/TEI) for long-term preservation while simultaneously enabling a powerful search in this version of the data.For each corpus, the input we are working with is a set of files in EXMARaLDA XML format, which contain transcriptions, multimedia alignment, morpheme segmentation and other kinds of annotation.The first step of processing is the conversion of the data into a certain subset of TEI following the ISO standard 'Transcription of spoken language' with the help of an XSL transformation.The primary purpose of this step is to obtain a representation of our data in a standard format, which will ensure its long-term accessibility.The second step is the conversion of the ISO/TEI files to a JSON format used by the "Tsakorpus" search platform.This step allows us to make the corpora available through a web-based search interface.As an addition, the existence of such a converter allows other spoken corpora with ISO/TEI annotation to be made accessible online in the future.
Indoor Activity Recognition by Using Recurrent Neural Networks

Because of the development of the ageing population, most countries are facing an increasingly serious pension resources problem. With the development of Internet of Things, the integration of smart home and smart retirement provides a new solution for the new smart home for the elderly, to achieve the elderly to intelligently support the elderly. This paper is based on the development of this background, mainly to solve the problem of indoor activity recognition of the elderly, so as to prepare for the construction of smart medical care. The specific research process is to process the sensor data collected from the smart environment, identify different activities using RNN, LSTM and GRU models with strong ability to process time series data, realize the target of activity recognition.
Blockchain Architectures for P2P Energy Trading Between Neighbors

We consider blockchain architectures for P2P energy trading of surpluses and demands between prosumers and consumers in the neighborhood without any intermediaries. We identify key elements comprising the P2P energy trading platform based on blockchain. The software architecture of our energy trading platform is provided with example codes. We build smart contracts on Ethereum for determination of trading price as well as matching between prosumers and consumers in such a way to balance the surpluses and the demands. At the start of each trading period, the prosumers and consumers send their respective amounts of surplus or demand. And the smart contract for price determination automatically evaluates the price in a predetermined way to balance surplus and demand within the neighborhood. Another smart contract performs matching between prosumers and consumers using this price. Authenticated and private messaging called `whisper' is employed for prosumers and DSO(Distributed System Operator)s to notify injection of surplus energy to the DSO and verify the ownership of the injected energy. Each participant interacts with Ethereum virtual machine through web server built with Node.js, Web3.js and Geth. We build a small experimental setup with 4 Raspberry-Pis in a private Ethereum network to provide proof-of-concept for our blockchain based P2P trading architecture between neighbors.
Seismic Random Noise Attenuation Based on PCC Classification in Transform Domain

Random noise attenuation of seismic data is an essential step in the processing of seismic signals. However, as the exploration environment is becoming more and more complicated, the energy of valid signals is weaker and the signal to noise (SNR) is much lower, which brings great difficulty to seismic data processing and interpretation. To this end, we propose an unconventional and effective seismic random noise attenuation method based on proximal classifier with consistency (PCC) in transform domain. Firstly, we analyze various transforms for seismic data from traditional wavelet transform and curvelet transform to emerging non-subsampled shearlet transform (NSST) and non-subsampled contourlet transform (NSCT). And, we select the excellent NSST to decompose the noisy seismic data into different sub-bands of frequency and orientation responses. Secondly, unlike traditional sparse transform based seismic denoising methods that often directly use a thresholding operator and corresponding inverse transform to denoise seismic data, our proposed method employs a superior performance PCC to classify the NSST coefficients of seismic data before thresholding operator. The added step can effectively divide the NSST coefficients into reflected useful signal coefficients and noise-related coefficients, which can preserve the edge of reflected signals and keep the information of events intact as much as possible. In addition, we also introduce an adaptive threshold computing method and a soft-thresholding method to achieve seismic data denoising better. Finally, the experimental results on the typical synthetic example and real seismic data show the superior performance of the proposed method.
Faster Subgraph Counting in Sparse Graphs

A fundamental graph problem asks to compute the number of induced copies of a k-node pattern graph H in an n-node graph G. The fastest algorithm to date is still the 35-years-old algorithm by Nesetril and Poljak [Nesetril and Poljak, 1985], with running time f(k) * O(n^{omega floor[k/3] + 2}) where omega <=2.373 is the matrix multiplication exponent. In this work we show that, if one takes into account the degeneracy d of G, then the picture becomes substantially richer and leads to faster algorithms when G is sufficiently sparse. More precisely, after introducing a novel notion of graph width, the DAG-treewidth, we prove what follows. If H has DAG-treewidth tau(H) and G has degeneracy d, then the induced copies of H in G can be counted in time f(d,k) * O~(n^{tau(H)}); and, under the Exponential Time Hypothesis, no algorithm can solve the problem in time f(d,k) * n^{o(tau(H)/ln tau(H))} for all H. This result characterises the complexity of counting subgraphs in a d-degenerate graph. Developing bounds on tau(H), then, we obtain natural generalisations of classic results and faster algorithms for sparse graphs. For example, when d=O(poly log(n)) we can count the induced copies of any H in time f(k) * O~(n^{floor[k/4] + 2}), beating the Nesetril-Poljak algorithm by essentially a cubic factor in n.
Practical Analysis on Running Speed for Efficient CNN Architecture Design

Convolutional neural networks (CNNs) have shown significant performance in solving various artificial intelligence tasks in recent years. However, the increasing model size has raised challenges in adopting them in limited-resource applications. Recently, many research works try to build efficient networks which are as small as possible and have small computation time while still have acceptable performance. The state-of-the-art architectures are ShuffleNetV2 and MobileNet. They use Depthwise Separable Convolution (DWConvolution) in place of standard Convolution to reduce the model size. Their designs are very efficient which follows many practical guild-lines. However, the ShuffleNetV2 has higher memory access cost (MAC) than the MobileNet. This paper evaluates these two networks on GPU and CPU with and without tuning step to show the advantages of each network. The experiments show that the MobileNet is faster on high-computational-optimization devices like GPU, whereas the ShuffleNetV2 is faster on low-computational-optimization devices like CPU when they have similar FLOPs.
American and Russian Sign Language Dactyl Recognition and Text2Sign Translation

Sign language is the main way to communicate for people from deaf community. However, common people mostly do not know sign language. In this paper, we overview several real-time sign language dactyl recognition systems using deep convolutional neural networks. These systems are able to recognize dactylized words gestured by signs for each letter. We evaluate our approach on American (ASL) and Russian (RSL) sign languages. This solution may help fasten the process of communication for deaf people. On the contrary, we also present the algorithm for generating sign animation from text information using text-to-sign video vocabulary, which helps to integrate sign language in dubbed TV and combining with speech recognition tool provide full translation from natural language to sign language.
The Reduced PC-Algorithm: Improved Causal Structure Learning in Large Random Networks

We consider the task of estimating a high-dimensional directed acyclic graph, given observations from a linear structural equation model with arbitrary noise distribution. By exploiting properties of common random graphs, we develop a new algorithm that requires conditioning only on small sets of variables. The proposed algorithm, which is essentially a modified version of the PC-Algorithm, offers significant gains in both computational complexity and estimation accuracy. In particular, it results in more efficient and accurate estimation in large networks containing hub nodes, which are common in biological systems. We prove the consistency of the proposed algorithm, and show that it also requires a less stringent faithfulness assumption than the PC-Algorithm. Simulations in low and high-dimensional settings are used to illustrate these findings. An application to gene expression data suggests that the proposed algorithm can identify a greater number of clinically relevant genes than current methods.
Visual Domain Adaptation by Consensus-Based Transfer to Intermediate Domain

We describe an unsupervised domain adaptation framework for images by a transform to an abstract intermediate domain and ensemble classifiers seeking a consensus. The intermediate domain can be thought as a latent domain where both the source and target domains can be transferred easily. The proposed framework aligns both domains to the intermediate domain, which greatly improves the adaptation performance when the source and target domains are notably dissimilar. In addition, we propose an ensemble model trained by confusing multiple classifiers and letting them make a consensus alternately to enhance the adaptation performance for ambiguous samples. To estimate the hidden intermediate domain and the unknown labels of the target domain simultaneously, we develop a training algorithm using a double-structured architecture. We validate the proposed framework in hard adaptation scenarios with real-world datasets from simple synthetic domains to complex real-world domains. The proposed algorithm outperforms the previous state-of-the-art algorithms on various environments.
Real Time Face Expression Classification Using Convolutional Neural Network Algorithm

The study of facial expression has been exist since the Aristotelian erain which it explains the expression into a complicated nonverbal research. Expression signals are instinctive behaviors, not learning outcomes, so machines will not be easy to recognize the varied expressions of people. In this study, the detection of accuracy levels in the form of predictions of class expressions of pleasure, sadness, surprise, disgust, neutral, anger and fear in real time with captured face images from the camera of an Android mobile phone isconducted. This research is important as the first step in detecting individual lies in terms of non-verbal communication and how accurately it detects someone's expression. Broadly speaking, the research process is divided into 2, an approach to detect face position with Viola Jones and extraction and classification with deep learning Convolutional Neural Network to produce predictions of expressions from machine learning. In CNN there are several layers for the filtering process by conducting the learning process to find the best representation. Datasets used face image capture in real time using a mobile camera with a total of 41 try expression detection.The result of the study shows that the performance of the Viola Jones and CNN method produces the highest level of accuracy, that is 98,7% for happiness expression with 9 times detection.
Scalable Decision-Theoretic Planning in Open and Typed Multiagent Systems

In open agent systems, the set of agents that are cooperating or competing changes over time and in ways that are nontrivial to predict. For example, if collaborative robots were tasked with fighting wildfires, they may run out of suppressants and be temporarily unavailable to assist their peers. We consider the problem of planning in these contexts with the additional challenges that the agents are unable to communicate with each other and that there are many of them. Because an agent's optimal action depends on the actions of others, each agent must not only predict the actions of its peers, but, before that, reason whether they are even present to perform an action. Addressing openness thus requires agents to model each other's presence, which becomes computationally intractable with high numbers of agents. We present a novel, principled, and scalable method in this context that enables an agent to reason about others' presence in its shared environment and their actions. Our method extrapolates models of a few peers to the overall behavior of the many-agent system, and combines it with a generalization of Monte Carlo tree search to perform individual agent reasoning in many-agent open environments. Theoretical analyses establish the number of agents to model in order to achieve acceptable worst case bounds on extrapolation error, as well as regret bounds on the agent's utility from modeling only some neighbors. Simulations of multiagent wildfire suppression problems demonstrate our approach's efficacy compared with alternative baselines.
The Design of Not-so-everyday Things: Designing for Emerging Experiences

In this paper, we explore how emerging technologies and experiences challenge previous theories and practices to grow and adopt and, thus, address new and unique challenges, such as designing across macro-level ecosystems, new devices, and interaction models that enable user control of data in an increasingly complex digital world. We discuss these topics with respect to real and future examples, the unique challenges they present, and how academia and industry must collaborate to adapt current frameworks and develop new methods to address these challenges. This partnership will ensure both parties better understand the problem space for designing emerging experiences in today's digital economy. Further, this partnership enables scholars and practitioners to more effectively explore the solution space for designing novel products and developing advanced theories that help craft meaningful user experiences. Finally, we argue that the partnership between academia and industry can develop future talent and upskill current practitioners, which is paramount in successfully meeting the challenges inherent in the design of emerging technologies.
A Quantized Neural Network Library for Proper Implementation of Hardware Emulation

Deep neural networks (DNNs) have recently shown outstanding performance in many domains. However, running DNN applications on mobile devices with limited hardware resources is difficult because DNN requires heavy computations. Quantization is one of network compression techniques to reduce the hardware requirements because it uses fewer bits, such as the 8-bit fixed points instead of 32-bit floating-point numbers. DNN libraries use floating-point numbers after quantization because they assume that all data to be calculated in floating-point format. Therefore, a mismatch occurs in running hardware emulation. In this paper, we propose a method of calculation for hardware emulation and developed a new DNNs library based on our method. We show that our library is capable of exact hardware emulation.
Virtual Reality in Tourism: Centennials Acceptance

Widespread access to and heavy investment in the development of virtual reality underline the importance of understanding its acceptance by consumers and its possible implications in the promotion of tourist destinations. This study was carried out to evaluate the acceptance of virtual reality applied to tourism, and its capacity to change attitudes towards the viewed destination among 889 centennial virtual reality users. The results serve to highlight the validity of the proposed model, where personal innovation, the attitude towards virtual reality, and performance expectations are the main antecedents of intention to use. Furthermore, the influence of performance expectations as the main antecedent of the change in attitude towards the tourism destination is evident. The practical implications and future lines of research are discussed in detail.
Birds vs. Fish: Visualizing Out-of-View Objects in Augmented Reality using 3D Minimaps

Despite recent technological advanced in Augmented Reality (AR), the majority of state-of-the-art head-mounted displays (HMDs) still suffers from a limited field of view. Hence, there is a high probability that virtual objects are located outside of the user's view. Several approaches to mitigate the problem of out-of-view objects have been proposed in the past by using visualization techniques to guide the user's attention towards such objects. However, none of these techniques provides any context about the real environment. Current state-of-the-art HMDs are equipped with various sensors to create a 3D map of the environment. In this work, we leverage such 3D reconstructions and developed a set of different 3D minimap visualizations for conveying information about out-of-view objects in AR. We propose a novel kind of minimap using stereographic fisheye projection and compare it to more traditional, bird's-eye view minimaps. Preliminary results show that our stereographic fisheye minimap offers a set of distinct advantages over bird's-eye view minimaps that facilitate the localization of virtual objects in AR environments.
Low‐power and area‐efficient design of AES S‐Box using enhanced transformation method for security application

Summary In implementing well‐known advanced encryption standard (AES) algorithm, the main part for constructing the hardware is S‐Box, which is abbreviated from substitution box. This is the basic component of symmetric key cryptographic algorithm, and it requires in AES because of its nonlinear organization with multiplicative inversion. This S‐Box is designed by two transformations, ie, inversing the multiplication part in Galois field directed with modified affine transformation. The limitation of S‐Box is more time and power consumption. This limitation has to be overcome by doing slight modification in affine and inverse affine transform. The time consumption for AES S‐Box is decreased by the proposed transformation method. The proposed system achieved 39% power when compared with the existing system; the proposed system is efficient.
The Multifaceted Concept of Context in Sentiment Analysis

The contemporary web is about communication, collaboration, participation, and sharing. Currently, the sharing of content on the web ranges from sharing of ideas and information which includes text, photos, videos, audios, and memes to even gifs. Moreover, the language and linguistic tone of user-generated content are informal and indistinct. Analyzing explicit and clear sentiment is challenging owing to language constructs which may intensify or flip the polarity within the posts. Context-based sentiment analysis is the domain of study which deals with comprehending cues which can enhance the prediction accuracy of the generic sentiment analysis as well as facilitate fine grain analysis of varied linguistic constructs such as sarcasm, humor, or irony. This work is preliminary to understand the what, how and why of using the context in sentiment analysis. The concept of ‘context in use’ is described by exemplifying the types of context. A strength–weakness–opportunity–threat (SWOT) matrix is made to demonstrate the effectiveness of context-based sentiment analysis.
Review of FMEA

Purpose: Failure Mode and Effects Analysis (FMEA) is a widely used technique to assess product reliability by identifying potential failure modes and their effects at the early stages of product development. FMEA techniques have been consistently evolving. In this study, we examine the past and present trends of FMEA, and make recommendations for the future.BRMethods: Through our Literature Review, we classified FMEA techniques into seven categories (excluding application studies) as follows: FMEA Types; FMEA Format; Implemented Subject; Studies for Identifying and Preventing Omission of Failure Mode; Studies for Inferring Failure Mechanism; Automation for Performing FMEA; and Risk Priority Number (RPN) Assessment.BRResults: Though FMEA has two primary approaches (hardware and functional), the required industrial standards of FMEA have been developed based only on the functional approach. This approach may not be proper for sub-system or component manufacturers that need to improve their failure analyses through identifying all potential failure modes.BRConclusion: We propose applying different analyses approaches depending on the objectives of FMEA implementation. We also propose that concrete procedures for System FMEA, Design FMEA, and Process FMEA be required.
Constant delay traversal of grammar-compressed graphs with bounded rank

We present a pointer-based data structure for constant time traversal of the edges of an edge-labeled (alphabet Σ) directed hypergraph (a graph where edges can be incident to more than two vertices, and the incident vertices are ordered) given as hyperedge-replacement grammar G. It is assumed that the grammar has a fixed rank κ (maximal number of vertices connected to a nonterminal hyperedge) and that each vertex of the represented graph is incident to at most one σ-edge per direction (σ∈Σ). Precomputing the data structure needs O(|G||Σ|κrh) space and O(|G||Σ|κrh2) time, where h is the height of the derivation tree of G and r is the maximal rank of a terminal edge occurring in the grammar.
Weighted Extreme Learning Machine for Dengue Detection with Class-imbalance Classification

Dengue is a disease caused by mosquitoes that may even be lethal to some patients. It is important to detect this disease as soon as possible to decrease the death toll. In this research, we use machines to classify patients as Dengue patients and Non-Dengue patients. The dataset is the treatment data from the patients with fever, cold, flu, pneumonia, and Dengue, from Sarapee Hospital, Chiangmai province, Thailand, during September 2015 to September 2017. The dataset includes 248 records of Dengue patients and 4,960 records of Non-Dengue patients including patient with fever, cold, flu, and pneumonia. We use the text of symptoms of the patients for input data. Weighted Extreme Learning Machine (WELM) is used to solve the class imbalance problems. It was compared for accuracy with neural network and Extreme Learning Machine (ELM). The result shows, that if the number of records of Non-Dengue patients are increasing, the accuracy of the neural network and ELM are decreasing, but the accuracy of WELM is stable.