Reviewerly/exHarmony huggingface dataset:
Knowledge Distillation from Multilingual and Monolingual Teachers for End-to-End Multilingual Speech Recognition

Attention-based encoder-decoder models significantly reduce the burden of developing multilingual speech recognition systems. By means of end-to-end modeling and parameters sharing, a single model can be efficiently trained and deployed for all languages. Although the single model benefits from jointly training across different languages, it should handle the variation and diversity of the languages at the same time. In this paper, we exploit knowledge distillation from multiple teachers to improve the recognition accuracy of the end-to-end multilingual model. Considering that teacher models learning from monolingual and multilingual data contain distinct knowledge of specific languages, we introduce multiple teachers including monolingual teachers of each language, and multilingual teacher to teach a same sized multilingual student model so that the multilingual student will learn various knowledge embedded in the data and intend to outperform multilingual teacher. Different from conventional knowledge distillation which usually relies on a linear interpolation for hard loss from true label and soft losses from teachers, a new random augmented training strategy is proposed to switch the optimization of the student model between hard or soft losses in random order. Our experiments on Wall Street Journal (English) and AISHELL-1 (Chinese) composed multilingual speech dataset show the proposed multiple teachers and distillation strategy boost the performance of the student significantly relative to the multilingual teacher.
Urgent Voicemail Detection Focused on Long-term Temporal Variation

This paper proposes a effective urgent speech detection for voicemails focused on speech rhythm. Previous techniques use short-term features with millisecond scale (such as fundamental frequency, loudness and spectral features), and conventional techniques for urgent speech detection use also features obtained from entire speech (such as average speech rate). However, the features obtained from entire speech are too over-smoothed to explain the difference between urgent and nonurgent speech. We found that there was a difference between urgent and non-urgent speech in temporal variability related to speech rhythm. To handle the temporal variability of speech rhythm, the proposal extracts long-term temporal features. The long-term temporal features are envelope modulation spectrum and temporal statistics of Mel-frequency cepstrum coefficient with 1 sec scale. To use both features with different time scales, the proposed method integrates the long-term temporal features and the short-term features on neural networks. Our proposal yields better accuracy than the conventional methods (which uses e features obtained from entire speech); it achieves a 50.0% reduction in the error rate.
Research on Video-Based Human Action Behavior Recognition Algorithms

Abstract Human action recognition is an important part of intelligent video processing, which has high research value and broad application prospects. This paper takes the video monitoring of the elderly living alone as an example to recognize human behavior, and chooses the video of indoor background taken by a single camera as the research object. The background subtraction method is used to extract moving objects from video, and the extracted moving objects are pretreated. Then, according to the overall outline of the object or the obvious features of a part of the object, the motion features are obtained to understand and identify the movement of the monitored object, such as walking and falling. Experiments show that the proposed algorithm has better processing effect for actual video.
QR Label for Handicapped Exhibition Visitors Queue Built-Up Avoidance

Abstract Most exhibitions such as in museums items are provided with printed labels that contain information of interest. A queue of visitors will be formed both to view the items and read the labels for information of interest. Visitors have limited time to read and view as a queue will built-up in due time. Handicapped visitors on wheel chair are very disadvantaged in enjoying exhibits due to the possibility of queue built-up that could cause inconvenience to other visitors. Current visitors most likely possess smartphones as communication device. Smartphones can be exploited to satisfy the need of information. QR labelling together with private cloud or network for information retrieval is presented as a viable solution. The system could cater the need of handicapped visitors. Visitors can conveniently collect information and study it at their convenience using their smartphones. Excessive queue forming can be avoided. This research is to find the position where a handicapped or any person can reliably scan the QR code label and the advisable size of label. The result is beneficial both for the exhibition organizer and the visitor. The parameters of interest are the distance and angle where a QR code label of certain can be reliably scanned.
Genetic Algorithm of the Mutual Selection Between Teachers and Students in Online Learning

With the development of education, computer application has penetrated into all aspects of people’s life. Computer makes the complicated data management work easy and efficient. In addition to the major, the current university courses are studied by students’ self selected subjects. This system can meet the choice wishes of teachers and students as much as possible, accelerate the speed of selection and matching, and change the shortcomings of the previous manual course arrangement. In this paper, when we study the system of teachers and students’ mutual selection, we focus on building a mathematical model of teachers and students’ mutual selection, combining with the theory of genetic algorithm, to explore a suitable algorithm to solve the problem of automatic combination and collocation in the process of teachers and students’ mutual selection. At the same time, we use MATLAB software to generate a system of teachers and students’ two-way selection based on genetic algorithm.
Ways of Drifting in Design Experiments

In Chap. 4 , we presented a Knowledge-Relevance model (K-R) that maps design activities in terms of evaluation and hypothesis construction, and knowledge and relevance interests. We analyzed some of the ways in which hypotheses construction takes shape in the four traditions we have identified in Chap. 3 . In this chapter our focus will be at the very heart of the model: how design experiments articulate research interests, how drifting happens in experimentation, and how drifting happens between design experiments. Based on the corpus of PhD dissertations that form the foundations of this book, we provide a typology comprised of five types of design experiments. We will label these as accumulative, comparative, serial, expansive and probing.
Designing for Emergent Security in Heterogeneous Human-Machine teams

This work seeks to design decisionmaking rules for autonomous agents to jointly influence and optimize the behavior of teamed human decisionmakers in the presence of an adversary. We study a situation in which computational jobs are scheduled on servers by a collection of autonomous machines in concert with self-interested human decisionmakers, and the human and machine schedulers must react to an adversary's attack on one of the servers. We show a simple machine scheduling policy such that if all schedulers have permission to schedule jobs on all servers, increasing the penetration of machine schedulers always increases the level of security in the system, even when the machine schedulers have no explicit coordination or communication amongst themselves. However, we show a companion result in which simple constraints on server availability can nullify the machine schedulers' ability to effectively influence human schedulers; here, even if machine schedulers control an overwhelming majority of jobs, are socially-aware, and fully coordinated amongst themselves, they are incapable of influencing human decisionmakers to mitigate the harm of an attack.
Making Data FAIR Requires More than Just Principles: We Need Knowledge Technologies

Discussions regarding open science have circulated in the scientific community for many years. The articulation of the FAIR principles in 2016, however, led to a groundswell of excitement to make experimental data findable, accessible, interoperable, and reusable. The FAIR acronym is catchy and easy to remember. The 15 FAIR principles, however, are not. Efforts to enhance access to scientific datasets and to promote their reuse require intuitive tools that implement the FAIR principles as a side effect of their use. The CEDAR Workbench is one such tool that simplifies the authoring of standardized, comprehensive metadata to make datasets FAIR. Systems such as the CEDAR Workbench, which renders datasets FAIR in a transparent fashion, can enhance open science as a direct byproduct of their use. Current projects that have adopted the CEDAR Workbench provide an opportunity to assess how well knowledge technologies can facilitate the creation of FAIR data.
Non-Negative Feature Extraction using Conjugate Gradient Method

Non-negative matrix factorization (NMF) is an efficient approach for non-negative feature extraction and parts-based representation. Nevertheless, the optimization problem of NMF is usually resolved using gradient descent (GD) method, which leads to slow convergence. In this paper, we utilize conjugate gradient method (CGM) to develop a novel fast CGM-based NMF (CGM-NMF) algorithm for face recogition. The update rules of our CGM-NMF are acquired by searching conjugate directions under non-negativity constraint. The proposed CGM-NMF algorithm is theoretically proven to be convergent. Experimental results show that our CGM-NMF method has superior performance.
The Methodology of Dynamical Uncertain Causality Graph for Risk Assessment

With the development of technology, engineering systems are growing rapidly in their scale and complexity, which increases the severity of accidents and the urgency of risk assessment. Although lots of efforts have been done in risk analysis, their applications are limited when complex systems present the characteristic of multi-domain information synthesis and complete risk data is hard to obtain. To solve these problems, a risk assessment method based on DUCGs (Dynamical Uncertain Causality Graphs) is presented in this paper. With the advantage of DUCGs' flexibility, high tolerance for information's inaccuracy and incomplete information processing ability, the method based on DUCGs has lots of advantages: (1) Risk models in different fields can be established separately and the whole model can be obtained by merging the nodes shared by different models, which allow analysts to establish their risk model respectively. (2) Different from DBNs (Dynamic Bayesian Network) that need complete probability information, DUCGs only need probabilities related to risk status and they can alleviate the information incomplete problem to some extent. (3) The probability matrixes used in DUCGs are more consistent with people's cognitive habits, which makes it friendlier for analysts and promotes the integration of expert experience and data. Finally, the practicality and superiority of DUCGs are verified in a case study.
Teaching for Leadership, Innovation, and Creativity

As the world enters the age of digitalization, it is demanded by time that the education curriculum be updated to the latest version. However, in the contemporary research literature, many different notable researchers and authors have established that there is a disparity which exists in the education curriculum and the educational practices especially in the business sector. This disparity is characterized by gaps in technology and knowledge which must be bridged for the business sector to reap the fruits of a global education in the contemporary timeframe. In this connection, the role of the teachers and the educators is of critical importance primarily due to the fact that they are assigned the heavy responsibility of interacting with students.
Crime Suspect Profiling (CSP) for Forensic Investigation on Smartphone

Recently, crime cases have been highly reported due to the increased of the utilization of technology. The development of system aims to identify the probable suspects based on social network messages. The study focuses on certain features extracted from the content of social media text messages. There were four features identified namely location, time, object and person. These four features help to manage the system based on specified crime messages. The implementation of features for crime suspect profiling (CSP) utilizes text mining through named entity recognition to identify the word or phrase for particular features. In this study, the suspect measure was obtained through the integrated similarity. Results obtained from the experiments conducted have revealed the similarity value that reflects the possibility of a contact as a suspect to the crime. The results of this study affirmed that the suspect profiling through the messages content is promising to identify the suspects.
Intelligent Active and Passive Learning for Integrated Semantic Computing for Vision Data Annotation

This paper proposes an intelligent control, which generates semantic meanings in natural language descriptions from media data, and discusses its application for semantic image retrieval. The new intelligent control for active and passive learning in the integrated semantic space works in a semantic space that is dynamically formed by the combinations of meaning space attained from the semantic associative calculation and the learned visual feature space derived by deep learning over the media data. Our model is inspired by the human ability to understand the semantic meaning of information by using active and passive learning. To get the query image results with similar semantic meaning, we define two types of knowledge representations: known and unknown semantic. Known semantic, is a set of the comprehensive concepts of meaning that is embedded in the system and used for the semantic associative calculation to construct the meaning space. We associate it with the passive learning. On the other hand, the unknown semantic knowledge is a set of comprehensive concepts of meaning that is not discovered in the system and has to be sought from the pattern of media through statistic and/or learned patterns. This deep active learning can be associated with the active human learning. We describe how the system chooses the subspace from the semantic space through the intelligent control for active and passive learning in the integrated semantic space by slicing, dicing and pivoting dimensions to identify and retrieve the meaning of images. The proposed system is an experimental modular model for database developed to answer the given semantic query in which the axis is represented in the columnar model database. We performed an experimental study on a large image dataset for deep learning. Our model outperforms the state-of-art in annotation. In segmentation, it compares favorably with other methods that use significantly more labeled training data.
FRISP

Projection-based augmented reality (AR) is a promising medium for realizing pervasive computing environment, and yet the problem of determining projection-suitable regions and where to project remains. To tackle this problem, we introduce FRISP, a projection-based augmented reality (AR) Framework designed for Registering Interactive Spatial Projection. The FRISP framework utilizes a pantilt projection-camera (pro-cam) system for capturing geometry and projection mapping. The framework scans and analyzes the geometric properties of a room, in order to determine projection-suitable regions and generate multi-window layouts. Once the multi-windows are registered to the real world, they can be interacted with by the users. The users can assign various widgets or applications to the multi-windows, which are then finally augmented onto the real world and can serve as a base units for realizing the pervasive AR environment.
Confidence Modeling for Neural Machine Translation

Current methods of neural machine translation output incorrect sentences together with sentences translated correctly. Consequently, users of neural machine translation algorithms do not have a way to check which outputted sentences have been translated correctly without employing an evaluation method. Therefore, we aim to define the confidence values in neural machine translation models. We suppose that setting a threshold to limit the confidence value would allow correctly translated sentences to exceed the threshold; thus, only clearly translated sentences would be outputted. Hence, users of such a translation tool can obtain a particular level of confidence in the translation correctness. We propose some indices; sentence log-likelihood, minimum variance, and average variance. After that, we calculated the correlation between each index and bilingual evaluation score (BLEU) to investigate the appropriateness of the defined confidence indices. As a result, sentence log-likelihood and average variance calculated by probability have a weak correlation with the BLEU score. Furthermore, when we set each index as the threshold value, we could obtain high quality translated sentences instead of outputting all translated sentences which include a wide range of quality sentences like previous work.
Differentiable Programming based Step Size Optimization for LMS and NLMS Algorithms

We propose TLMS (Trainable Least Mean Squares) and TNLMS (Trainable Normalized LMS) algorithms, which use different step size parameter at each iteration determined by machine learning approach. It has been known that LMS algorithm can achieve fast convergence and small steady-state error simultaneously by dynamically controlling the step size compared as a fix step size, however, in conventional variable step size approaches, the step size parameter has been controlled in rather heuristic manners. In this study, based on the concept of differential programming, we unfold the iterative process of LMS or NLMS algorithms, and obtain a multilayer signal-flow graph similar to a neural network, where each layer has a step size of each iteration of LMS or NLMS algorithm as an independent learnable parameter. Then, we optimize the step size parameters of all iterations by using a machine learning approach, such as the stochastic gradient descent. Numerical experiments demonstrate the performance of the proposed TLMS and TNLMS algorithms under various conditions.
Conceptual specifications of a cooperative inter-machines dialogue

Beside the universe which enhances the interaction of its different components and the alive world which boots its inner interactions with a certain intelligence related to reproduction and communication, it is obvious that the art of invention, manipulation and interpretation are the main features of human intelligence. How is the cooperative dialogue playing a key role in all of that? And precisely between, in one hand, a dominated sense environment conceived with a huge sophisticated concepts, in other hand, a billions of bits reaching, within any executive task, two possible states, in order to rich at the end an amazing interpretable responses. In this paper we are going to stress on those dialogue mechanisms which are based essentially on a philosophical, logical and scientific concept of induction which is tied to deduction or also inference but which in all case lead to action. We will therefore present a general model of dialogue between any type of participants (humans, machines or other) that guarantees both the structural coherence of the conversations and the cognitive coherence of the dialogical acts.
The What of Data: Defining Which Scientific Research Is Appropriate to Share

Increasingly, scientists are releasing research data to the public for potential (re)use. Yet, the what of data–what gets shared (or kept private), by whom, and why–is difficult for data curators and stewards to determine. Scientific field-specific norms play an important role in decision-making processes to define what data are deemed acceptable to release. I explore the framework of contextual integrity (CI), which operationalizes appropriate flows of information that reflect context-dependent norms. CI is essentially a theoretical framework for privacy in data; however, in this work, appropriate data sharing surrounds the data. In this paper, CI methods are applied to a case study in astronomy and show how CI can guide an understanding of which data can be shared by tracing how people move information within contexts. The aim is to provide both researchers and repository maintainers an approach to make data available in an appropriate way that does not violate rapidly evolving sharing norms.
Starting from Scratch

The lesson plan in this chapter offers an experiential, hands-on approach to learning common beat (simple, compound) and meter (duple, triple, quadruple) types. Students begin by creating metrical effects in a simple programming environment (Scratch) using only changes in pitch, duration, or emphasis. Students then proceed to study how different pieces combine these three elements to produce their unique metrical effects. Having constructed their own examples of each beat and meter type, they finally learn how to represent the music they composed using musical time and durational symbols.
Learning Static and Dynamic Features for Collaborative Filtering

User preferences are influenced by the purchased products, and ratings of products are also related to theirs public praises. Dynamic latent representations can be learned from these sequence information. Researches show that learning such dynamic features is helpful to build model-based collaborative filtering. However, static features also play an irreplaceable role in recommendations by reason of inherent characteristics of users/items. Ratings of users on products directly represent user preferences and qualities of products. A neural network model for learning both static and dynamic features is proposed in this paper. Autoencoder is adopted as a static model focusing on explicit feedback i.e. ratings, and gated recurrent unit is adopted as a dynamic model focusing on implicit feedback i.e. sequences. Features learned from static and dynamic models are combined to make predictions. Experiments on two real-word datasets i.e. Baby of Amazon dataset and MovieLens 10M show improvement of our proposed model over the state-of-the-art methods.
Research on Classification Method of Vehicle Body Surface Defects Based on BP Neural Network

Abstract In order to classify the surface defects of the vehicle body, a classification method based on BP (back propagation) neural network is proposed. According to the characteristics of vehicle body defects, the gray, morphological and geometric features of various defects were extracted, and the defect feature database was established. The defect classifier was designed by using improved BP neural network algorithm. The experimental results show that the method is very feasible and effective in the identification of vehicle body defects.