Reviewerly/exHarmony huggingface dataset:
Multilingual Phone Recognition: Comparison of Traditional versus Common Multilingual Phone-Set Approaches and Applications in Code-Switching

We propose a multilingual phone recognition system using common multilingual phone-set (Multi-PRS) derived from IPA based labelling convention, which offers seamless decoding of the code-switched speech. We show that this approach is superior to a more conventional front-end language-identification (LID)-switched monolingual phone recognition (LID-Mono) trained individually on each of the languages present in multilingual dataset. The state-of-the-art i-vectors are used to perform LID. We address the problem of efficient speech recognition for bilingual code-switching. We analyse the differences between LID-Mono and proposed Multi-PRS, by showing that the LID-Mono approach suffers due to a trade-off between two conflicting factors - the need for short windows for detecting code-switching at a high time resolution and the need for long windows needed for reliable language identification - which limits the overall performance of the LID-Mono system that suffers with high PERs at small windows (poor LID performance) and mismatched decoding conditions at long windows (due to poor code-switching detection time resolution). We show that the Multi-PRS, by virtue of not having to do a front-end LID switching and by using a multilingual phone-set, is not constrained by these conflicting factors and hence performs effectively on code-switched speech, offering low PERs than the LID-Mono system.
Anomaly Detection in IoT Frameworks Using Machine Learning

Rapid advancements in hardware programming and communication innovations have encouraged the development of internet-associated sensory devices that give perceptions and information measurements from the physical world. According to the internet of things (IoT) analytics, more than 100 IoT devices across the world connect to the internet every second, which in the coming years will sharply increase the number of IoT devices by billions. This number of IoT devices incorporates new dynamic associations and does not totally replace the devices that were purchased before yet are not utilized any longer. As an increasing number of IoT devices advance into the world, conveyed in uncontrolled, complex, and frequently hostile conditions, securing IoT frameworks displays various challenges. As per the Eclipse IoT Working Group's 2017 IoT engineer overview, security is the top worry for IoT designers. To approach the challenges in securing IoT devices, the authors propose using unsupervised machine learning model at the network/transport level for anomaly detection.
Analysis of the characteristics of English part of speech based on unsupervised machine learning and image recognition model

If there are more external interference factors in the process of intelligent recognition in English, the recognition accuracy will be greatly reduced. It is of great academic value and application significance to deeply study feature recognition of English part-of-speech and realize automatic image processing of English recognition. Based on unsupervised machine learning and image recognition technology, this study combines the actual factors of English recognition to set the corresponding influencing factors and proposes a reliable method to identify multi-body rotating characters. This method utilizes the principle of the periodic characteristics of the trajectory rotation on the feature space. Moreover, this study conducts a comparative analysis of recognition accuracy by comparative experiments. In addition, this paper analyzes the recognition principles of 4 fonts in detail. The research results show that the proposed method has certain effects and can provide theoretical reference for subsequent related research.
“Something Old, Something New” – Reflections on AI and the Just War Tradition

This chapter presents reflections and considerations regarding artificial intelligence (AI) and contemporary and future warfare. As "an evolving collection of computational techniques for solving problems," AI holds great potential for national defense endeavors (Rubin, Stafford, Mertoguno, & Lukos, 2018). Though decades old, AI is becoming an integral instrument of war for contemporary warfighters. But there are also challenges and uncertainties. Johannsen, Solka, and Rigsby (2018), scientists who work with AI and national defense, ask, "are we moving too quickly with a technology we still don't fully understand?" Their concern is not if AI should be used, but, if research and development of it and pursuit of its usage are following a course that will reap the rewards desired. Although they have long-term optimism, they ask: "Until theory can catch up with practice, is a system whose outputs we can neither predict nor explain really all that desirable?" 1 Time (speed of development) is a factor, but so too are research and development priorities, guidelines, and strong accountability mechanisms. 2
A Novel Fault-Detection Scheme for Nearest-Neighbor-Based Reversible Circuits

Since last couple of years, the field of reversible logic synthesis has progressed significantly. Starting from improved design methodologies to efficient testing algorithms, this field has witnessed several prominent research findings, and still research is going for developing more reliable design techniques. Inspired from existing testing works, here, we are proposing an efficient testing scheme that finds the missing of SWAP gates from nearest neighbor (NN) circuits using only single test pattern. Unlike conventional testing algorithms that highly rely on a set of test vectors, here, we test the circuit by finding a design for testability (DFT) model. This testing scheme is very capable to find not only single missing SWAP gates but also can detect multiple missing of SWAP gates in design using the single test pattern. In order to find the correctness of our approach, we have tested our algorithm over a large spectrum of benchmarks and have compared our obtained results with some peer-reviewed works.
Empirical mean curve decomposition with multiwavelet transformation for eye movements recognition using electrooculogram signals

Many research works are in progress in classification of the eye movements using the electrooculography signals and employing them to control the human–computer interface systems. This article introduces a new model for recognizing various eye movements using electrooculography signals with the help of empirical mean curve decomposition and multiwavelet transformation. Furthermore, this article also adopts a principal component analysis algorithm to reduce the dimension of electrooculography signals. Accordingly, the dimensionally reduced decomposed signal is provided to the neural network classifier for classifying the electrooculography signals, along with this, the weight of the neural network is fine-tuned with the assistance of the Levenberg–Marquardt algorithm. Finally, the proposed method is compared with the existing methods and it is observed that the proposed methodology gives the better performance in correspondence with accuracy, sensitivity, specificity, precision, false positive rate, false negative rate, negative predictive value, false discovery rate, F 1 score, and Mathews correlation coefficient.
Wavefront parallelization of recurrent neural networks on multi-core architectures

Recurrent neural networks (RNNs) are widely used for natural language processing, time-series prediction, or text analysis tasks. The internal structure of RNNs inference and training in terms of data or control dependencies across their fundamental numerical kernels complicate the exploitation of model parallelism, which is the reason why just data-parallelism has been traditionally applied to accelerate RNNs.
Application of Fuzzy Cognitive Maps in Critical Success Factors. Case Study: Resettlement of the Population of the Tres Cerritos Enclosure, Ecuador

Mining activity in Ecuador is one of the fundamental causes of environmental pollution and damage to the quality of life of the populations near the quarries. The high levels of noise, dust and gases are some of the causes of multiple illnesses suffered by the people who live in these places. In the present work, fuzzy cognitive maps are applied for the modeling, analysis and prioritization of critical success factors, taking into account the interrelationship among them with a systemic approach, for the decision making in the re-settlement plan of the inhabitants of the Tres Cerritos enclosure of the Taura Parish in the Naranjal County, which are affected by the mining exploitation of the Cerro Pelado quarries. The results achieved improved the decision-making process by giving priority to Basic information, Citizen engagement and Compensation factors to achieve success in the resettlement project.
Bridging Internet Coverage Gaps among Nomadic Pastoralists in Namibia

A number of initiatives have supported rural indigenous knowledge holders in digitizing their own cultural heritage using mobile technologies. In Namibia, members from the OvaHimba nomadic pastoralist communities have used mobile technologies to record their indigenous knowledge. In this paper we address the challenges of data safety caused by intermittent network coverage and absence of a reliable electricity grid in many of the areas inhabited and migrated to by the OvaHimba. We are proposing a low-cost technology solution, using Raspberry Pis, accounting for nomadic movement patterns, ensuring data transfer to cloud services.
Damping Factor Learning of BP Detection with Node Selection in Massive MIMO using Neural Network

In a massive multiple-input multiple-output (MIMO) system, belief propagation (BP) detection is known as a method to separate and detect received signals. In BP detection, a MIMO channel is represented by a factor graph and the transmitted symbols are estimated by message passing. However, the convergence property of BP deteriorates due to multiple loops included in the MIMO channel. As a method to improve the convergence property and the detection performance, the damped BP that averages two successive messages with a weighing factor (called damping factor) is known. To train the damping factors off-line for each antenna configuration, deep neural network-based damped BP (DNN-dBP) has been reported. The problem with DNN-dBP is that the detection performance deteriorates when there is a difference in the channel correlation between training and test. This is because the optimal damping factors vary with the channel correlation. In this paper, to solve this issue, we derive the damping factors of BP with the node selection (NS) method that selects nodes to be updated to lower spatial correlation using DNN-dBP. By applying the NS method, the channel correlation among the selected nodes in BP detection is lowered. Therefore, the proposed method can reduce the detection performance deterioration due to the mismatches of the channel correlations between training and test in DNN-dBP. In addition, the convergence property of BP is improved by applying the NS method. Therefore, the proposed method can improve the detection performance compared to the conventional DNN-dBP with the same computational complexity. By computer simulation, it is shown that the proposed method significantly reduces the bit error rate (BER) performance deterioration due to the mismatches of the channel correlations between training and test in DNN-dBP. The results also show that the proposed method has the better BER performance than the conventional DNN-dBP with the same computational complexity.
Optimized Entropy Based Image Segmentation

Segmentation performs a very important role in image processing and computer vision. The automatic thresholding methods are very popular because of their ease of implementation. Depending upon the nature of histogram, thresholding can be used to divide an image into many classes. The decision of threshold value is slightly difficult and has to be done keeping in view the maximization of certain metric. Here in this paper, we have used two entropies, namely Tsallis entropy and Renyi’s entropy and we have combined them linearly and maximized this entropic function using genetic algorithm. We have applied this computation over certain number of sub windows in the image. We have seen that this method is useful for segmenting images taken in low light conditions. We have compared our proposal against some standard methods and found out that it performs better both qualitatively and quantitatively.
The Performance Evaluation of Recommendation Algorithm Using Mahout Framework

In order to achieve a better recommendation effect, the optimization and improvement of the recommendation algorithm has been the research hotspot of the recommendation system. Similarity is the core problem of recommendation algorithm, so in this paper, a novel method of calculating similarity in collaborative filtering recommendation was proposed to make the recommendation better. We used the weighted average method to combine various similarity algorithms on the calculation of similarity, so as to improve the accuracy of recommend results and the stability of the algorithm. In order to test the weighted coefficient of similarity and tuning, the experiment is conducted on open source data sets and Mahout framework. Finally, an effective way to improve the collaborative filtering algorithm is presented.
Novel Chaotic Elephant Herding Optimization for Multilevel Thresholding of Color Image

Chakraborty, Falguni Roy, Provas Kumar Nandi, DebashisElephant herding optimization (EHO) is a newly developed metaheuristic algorithm which is inspired from the herding behavior of elephant in nature. However, slow convergence is the main disadvantage of the basic EHO algorithm. To improve the global convergence speed as well as the performance of the basic EHO, we propose a new variant of elephant herding optimization by introducing the chaos theory into it. This new variant of EHO algorithm is called chaotic elephant herding optimization algorithm (CEHO). The CEHO algorithm uses a set of chaotic maps that generate chaotic numbers for tuning the control parameters of the basic EHO. The chaotic maps generate different sets of non-repetitive random numbers in a specified range, which are suitable for increasing the searching domain of the algorithm. The performance of the proposed CEHO is applied to a set of images collected from "Berkeley segmentation dataset" to find the optimal threshold values for multilevel image thresholding. The performance of the proposed algorithm is compared with the basic EHO, cuckoo search (CS), and artificial bee colony (ABC) quantitatively and qualitatively. The simulation outputs show that the proposed algorithm supersedes the others.
Undecidable, Unrecognizable, and Quantum Computing

Quantum computing allows us to solve some problems much faster than existing classical algorithms. Yet, the quantum computer has been believed to be no more powerful than the most general computing model—the Turing machine. Undecidable problems, such as the halting problem, and unrecognizable inputs, such as the real numbers, are beyond the theoretical limit of the Turing machine. I suggest a model for a quantum computer, which is less general than the Turing machine, but may solve the halting problem for any task programmable on it. Moreover, inputs unrecognizable by the Turing machine can be recognized by the model, thus breaking the theoretical limit for a computational task. A quantum computer is not just a successful design of the Turing machine as it is widely perceived now, but is a different, less general but more powerful model for computing, the practical realization of which may need different strategies than those in use now.
Contingent Scaffolding for System Safety Analysis

System safety analysis is a creative process that can often be undertaken by people who are not experts in the system under analysis whilst also learning the analysis methodology. With the increase of system complexity, the high demand for analyses conducted at a scale and the potentially catastrophic consequences of inadequate analysis, there is an urgent need for supporting the development of system analysis skills. Technological solutions can effectively scaffold this ill-defined domain. We propose a generic framework for Contingent Scaffolding capable of providing flexible learning support while conducting system safety analysis. This has been implemented into an intelligent agent, Oswin, which offers Ontology-driven scaffolding with interactive nudges.
Timing Prediction of Facilitating Utterance in Multi-party Conversation

Supporting consensus-building in multi-party conversations is a very important task in intelligent systems. To conduct smooth, active, and productive discussions, we need a facilitator who controls a discussion appropriately. However, it is impractical to assign a good facilitator to each group in the discussion environment. The goal of our study is to develop a digital facilitator system that supports high-quality discussions. One role of the digital facilitator is to generate facilitating utterances in the discussions. To realize the system, we need to predict the timing of facilitating utterances. To apply a machine learning technique to our model, we construct a data set from the AMI corpus, first. For the construction, we use some rules based on the annotation of the corpus. Then, we generate a prediction model with verbal and non-verbal features extracted from discussions. We obtained 0.75 on the F-measure. We compared our model with a baseline method. Our model outperformed the baseline (0.7 vs. 0.5 on the AUC value). In addition, we introduce additional features about the role of participants in the AMI corpus. By using the additional features, the F measure increased by 2 points. The experimental results show the effectiveness of our model.
The Prospective of Library Spurred by Artificial Inteligence-China Survey

Artificial intelligence (AI) is known as one of cutting-edge technologies in recent years, and many countries have taken its development as a national strategic planning. The changes that AI brings to social will be universal; and libraries are no exception. Changes brought about by AI development to the library will mainly be embodied in three aspects, i.e., environment, resources and services. As a result, the future library will be of intelligent library space, intelligent resource system and intelligent customer service. The current research on the application of AI to Chinese libraries covers all aspects of library management and service. At present, the intelligent consulting robots and the book inventory robot have been applied in some libraries of China.
Linear Quadratic Regulator Method in Vision-Based Laser Beam Tracking for a Mobile Target Robot

SUMMARY This paper proposes a novel laser beam tracking mechanism for a mobile target robot that is used in shooting ranges. Compared with other traditional tracking mechanisms and modules, the proposed laser beam tracking mechanism is more flexible and low cost in use. The mechanical design and the working principle of the tracking module are illustrated, and the complete control system of the mobile target robot is introduced in detail. The tracking control includes two main steps: localizing the mobile target robot with regards to the position of the laser beam and tracking the laser beam by the linear quadratic regulator (LQR). First of all, the state function of the control system is built for this tracking system; second, the control law is deduced according to the discretized state function; lastly, the stability of the control method is proved by the Lyapunov theory. The experimental results demonstrate that the Hue, Saturation, Value feature-extracting method is robust and is qualified to be used for localization in the laser beam tracking control. It is verified through experiments that the LQR method is of better performance than the conventional Proportional Derivative control in the aspect of converge time, lateral error control, and distance error control.
Adaptive Weighted Loss Makes Brain Tumors Segmentation More Accurate in 3D MRI Volume

Accurately segmenting brain tumors in Magnetic Resonance Imaging (MRI) volume can benefit the diagnosis, monitoring, and surgery planning of the disease. Manual delineation practices require anatomical knowledge, are expensive, time consuming and can be inaccurate due to human error. In the data era, machines with learning capabilities can achieve automatic brain tumor segmentation in MRI volume with promising performance on large region. However, it is not very effective for tumor segmentation on small region. Although more and more methods use pixel-level loss to guide algorithms to pay more attention to accurate segmentation of small regions, this problem still exists. In this paper, we propose an adaptive weighted loss, which can automatically adjust the proportion of loss generated by different region segmentation, thereby making small region segmentation more accurate. We added the adaptive weighted loss to a 3D MRI brain tumor segmentation network using auto-encoder regularization (3D-AE), and performed extensive validation on the MICCAI Brain Tumor Segmentation Challenge 2018 dataset (BRATS 2018). The achieved dice score is 0.769 for core tumor, 0.904 for the whole tumor and 0.887 for enhanced tumor. The overall results show better performance than the state-of-the-art in both dice score and precision on BRATS 2018.
The Development of Self-Service Application at Martabak Air Mancur Restaurant

Abstract The amount of time spent in requesting and waiting for the meal in Martabak Air Mancur restaurant may permanently multiply the number of customers in the line waiting especially during peak hours and on weekend. It will affect the quality service of the restaurant and the loyalty of the customer. From the observation, most of the customers spent a great deal of time making an order when faced with many proportionate choices. The study proposed a system by using self-service technology and the waterfall model. In the beginning, the time needed from making an order to finishing the payment is 15 minutes. The system built can reduce the transaction time to 10 minutes.
User-Defined Gestures for Taking Self-portraits with Smartphone Based on Consistency

Contrary to most general studies focusing on applicable methods of gesture recognition and system actualization, this paper is aimed at obtaining gestures for taking self-portraits from a distance, which should be consistent with user intuition, to enhance user experience. The study was conducted through two experiments: 1) Gesture set acquisition: Participants were encouraged to propose five different gestures to trigger the gesture shot function, which would be collected as an original gesture set; 2) Consistency verification and evaluation: Participants subjectively evaluated the level of consistent experience of the representative gestures selected from the first experiment. Three user characteristics were presented in results, that was, a preference for dynamic gestures, a preference for one-hand gestures, and a preference for gestures with definite meanings. The results also showed two optimal gestures that met participants’ preferences and were verified to have the best consistency with high significance levels. This study can be a reference for the researches on gestural interaction behaviors and also a reference for the exploration of user-centered research methods.