Reviewerly/exHarmony huggingface dataset:
Stable Checkpoint Selection and Evaluation in Sequence to Sequence Speech Synthesis

Autoregressive Attentive Sequence-to-Sequence (S2S) speech synthesis is considered state-of-the-art in terms of speech quality and naturalness, as evaluated on a finite set of testing utterances. However, it can occasionally suffer from stability issues at inference time, such as local intelligibility problems or utterance incompletion. Frequently, a model’s stability varies from one checkpoint to another, even after the training loss shows signs of convergence, making the selection of a stable model a tedious and time-consuming task. In this work we propose a novel stability metric designed for automatic checkpoint selection based on incomplete utterance counts within a validation set. The metric is based solely on attention matrix analysis in inference mode and requires no ground-truth output targets. The proposal runs 125 times faster than real-time on a GPU (TeslaK80), allowing convenient incorporation during training to filter out unstable checkpoints, and we demonstrate, via objective and perceptual metrics, its effectiveness in selecting a robust model that attains a good trade-off between stability and quality.
Software License Audit and Security Implications

The typical purpose of software auditing is to assess the conformant of the developed software with the original plans, procedures, relevant regulations. Every audit involves several people with various roles in the auditing processes. The audit itself entails a number of preferable characteristics. In any audit engagement, the perceptions of the audit quality are directly related to the perceived reputation, credibility and objectivity of the auditor. This paper highlights and critically reviews the research works related to quality in audit, in particular, software license audit from the perspective of internal control and security. The paper examines existing studies in the field with a view to identifying future research opportunities in relation to software license auditing. Moreover, security implications and challenges in the context of software auditing, and a set of recommendations are provided.
Preliminary Annotation Results: Bargaining Roles for Bilateral Dialogues

Negotiations are common; however, mining important aspects from such dialogues is challenging because the lack of a proper strategy and the use of predetermined words, as well as sentences, could lead to on-going disputes or failure. In addition, existing studies have focused on bargaining flows but have neglected the semantic and negotiation roles of negotiations. As a first step in negotiation mining, this paper proposes an annotation scheme to obtain the maximum number of labeled segments in negotiation sessions. We focus on the unique negotiation roles used to come to agreements in bilateral bargaining. We define five labels in the negotiations: Greet, Inquiry, Statement, DialogAct, and Other. These labels include attributes and features that consider the roles of the labels. We conducted the annotation by recruiting four annotators, who performed the annotations after reading the guidelines and attending several training meetings. After the dataset was divided into 876 dialogues, each dialogue was annotated by two annotators. We evaluate the Cohen kappa measures of the agreement between two annotations and achieve a sufficient score to analyze the negotiation roles in the dialogues. In particular, DialogAct, which is important to detect negotiation actions, achieved a high score with respect to understanding the flow of the negotiations.
Research on the Characteristics of the Historical Evolution of China's Educational Purpose

Throughout the history of China's education development, the purpose of education in different periods has distinct characteristics of the times. To sort out the historical evolution of China's educational purpose and analyze its characteristics can better grasp the law of educational purpose evolution.
GCNs-Based Context-Aware Short Text Similarity Model

Semantic textual similarity is a fundamental task in text mining and natural language processing (NLP), which has profound research value. The essential step for text similarity is text representation learning. Recently, researches have explored the graph convolutional network (GCN) techniques on text representation, since GCN does well in handling complex structures and preserving syntactic information. However, current GCN models are usually limited to very shallow layers due to the vanishing gradient problem, which can not capture non-local dependency information of sentences. In this paper, we propose a GCNs-based context-aware (GCSTS) model that applies iterated GCN blocks to train deeper GCNs. Recurrently employing the same GCN block prevents over-fitting and provides broad effective input width. Combined with dense connections, GCSTS can be trained more deeply. Besides, we use dynamic graph structures in the block, which further extends the receptive field of each vertex in graphs, learning better sentence representations. Experiments show that our model outperforms existing models on several text similarity datasets, while also verify that GCNs-based text representation models can be trained in a deeper manner, rather than being trained in two or three layers.
Tabletop Interface Design: Developing and Using Usability Heuristics

Tabletops enable the development of applications with high interactivity and simultaneous collaboration between users. Due to the size of these devices, they differ considerably from other touchscreen devices such as tablets and smartphones. Therefore, understanding these differences to define appropriate heuristics for the evaluation of tabletop interface designs is necessary. This paper presents a set of heuristics to be considered in tabletop interface design and a checklist composed of a set of questions for each proposed heuristic that aims to assist in the evaluation of interfaces for tabletops. Based on the literature, we analyze different proposals for heuristics for specific applications to identify the main concepts involved in defining a heuristic and how to write heuristics for specific applications. Also, we researched the particular features of tabletop applications that need to be considered in interaction design. From these characteristics, Nielsen’s ten heuristics were specifically adapted for use with tabletop devices and two new heuristics were defined. A case study was carried out in the SIS-ASTROS project, the proposed heuristics were considered to design a virtual tactical simulator, and an evaluation of the interface by the military was performed. Observing the militaries using them, we have gathered evidence that these heuristics can help designers to think about essential interface characteristics to support users to realize and understand the interface goal and how to interact with it.
Antarctic Expedition Security Guarantee Marine Information Collection and Analysis Forecasting

From the perspective of global level on digital Earth and Earth big data, this paper elaborated the development process of previous Antarctic expeditions on digitization and big data of products (services) provided by the National Center for Marine Environment Forecasting of China in the past 36 years, including marine route monitoring and forecasting guarantee, "sea-land investigation" monitoring and forecasting guarantee, traffic monitoring and forecasting guarantee for harbor unloading and "land-air storage and transportation" of investigation ship (team); in which, this paper elaborated the source and role of forecasting big data on cruising observation, localized observation, station area observation, mobile observation, as well as big data reanalysis of satellite remote sensing information, besides, the practical effects of various products were elaborated; moreover, the briefly introduction of the construction progress and work status of scientific investigation stations in Antarctica, especially meteorological observation and forecast stations in China was given. Main achievements: Firstly, creatively completion of 36 batches of marine and atmospheric forecast and guarantee tasks for Antarctic expeditions in China; secondly, establishment of the environmental monitoring and forecasting guarantee system of "sea-air" for several maritime routes of Antarctic expeditions in China, that of "sea-land-air" for the surrounding Antarctic continent, and that of "land-air" for the Antarctic bay to the inland depth hinterland.
Image clearness method based on improved Retinex

In order to improve the visibility of the aerial image with low contrast and lJow brightness, a changing scale Retinex algorithm based on scene depth information is studied in this paper. Firstly, the original image is converted from RGB color space to HIS color space, which is more comfortable for human eye features. Then the H component is ensured invariantly while the S component is stretched linearly by an adaptive stretching algorithm; the I component is adjusted by an improved Retinex algorithm, the scale of Retinex is changing according to the scene depth information of the image, which can provide significant improvement in contrast and brightness and enrich the detail information. Experiment result shows that the proposed algorithm has good clearness effect and fast speed, especially for the aerial images with greatly difference in scene depth.
Technology acceptance of a VR e-learning application addressing the cellular transport mechanisms

Over the last decades, research has shown that students of all ages experience difficulties in understanding cellular transport mechanisms, especially when it comes to processes on the molecular level such as osmosis and diffusion. One major difficulty in the comprehension of these basic concepts is their abstract nature along with the demand for high spatial ability. To address this problem a virtual reality-based application has been developed, which allows students to freely explore molecular transport mechanisms across the cell membrane by interacting with the environment and manipulating the molecule concentration or initiate transport mechanisms by actively moving molecules. An accompanying study showed an increase in the understanding of osmosis and diffusion after using the e-learning application.
The role of family in the formation of students’ labor values

The article discusses issues related to the influence of family on the formation of work attitudes of students. According to the data of the social research conducted by the author in 2020, the coincidence of the structures of basic values among students and their parents was exposed. An increase in the degree of influence of family on the selection of a profession by young people has been established. The results of the survey revealed the similarities and differences in the work attitudes of the two generations. For both students and their parents, the content aspect appeared to be a priority in the work. For the older generation, as compared with children, the social component of labor is more important. For young people, the opportunities for personal self-realization and the material side of working career are more important than for their parents. The analysis of the research findings allows for the conclusion that nowadays the importance of the educational influence of family on the formation of the value system of young people, as well as in the labor sphere, is still high.
Face Expression-Based Result Prediction in Talent Shows Using Deep Learning

In this world, where people try to keep their likes or dislikes to one self. It is human nature to tend to grow curious to know the opinions of their performance if it is a talent show or their attitude regarding proposal or if it is a business meeting, etc. These are a few basic ideas where we tend to grow impatient to know the result or judgement, there are various other areas which we encounter in our daily life where knowing the outcome beforehand would do no harm. From that thought only evolved, in our project, we use deep neural networks to study one’s facial expressions to predict the possible outcome. Here, a new dataset of facial expressions of a judge from a talent show is collected. This dataset is used to train the convolutional neural network, which can then be used to predict the result of a judge.
A Classifier to Predict Document Novelty Using Association Rule Mining

Lack of novelty in documents concern knowledge workers, who have to read, analyze, and interpret numerous articles on a daily basis. We propose an unsupervised classifier which predicts the novelty of documents using the association rule mining technique. This classifier mines strongly associated terms with domain specific keywords based on a set of 200 training documents in the medical domain from the Newsgroups20 dataset. These terms along with a novelty threshold is then used to determine the novelty of 120 unseen test documents from the same dataset. Evaluation of this classifier against the assessment of matured human assessors indicate that the former has performed reasonably well to identify novel documents, despite the fact that every document always has some new content.
Design of Intelligent Fighting Robot Construction

Intelligent fighting robot competition is an intelligent competitive sport formed by the combination of Traditional Chinese martial arts and artificial intelligence. The fighting robot designed in this paper summarizes some relevant construction and design strategies of fighting robots through competition practice, including non-contact startup of fighting robots. Self-directed stage; Self-directed search for rivals; And the anti - fall design. The combat robot has good performance and high stability.
Security Analysis of the Transparent Image Moving Scheme

Graphical passwords provide an alternative to text-based passwords, and empirical studies show that the recall of pictures and objects are more than that of words. This paper presents a security analysis of a graphical password scheme called the Transparent Image Moving Scheme. While the scheme is more resistant to casual shoulder-surfing t han P IN-based and pattern-based authentications, we show that it is not resistant to persistent shoulder-surfing attacks.
FAN: A Lightweight Authenticated Cryptographic Algorithm

The wide application of the low-end embedded devices has largely stimulated the development of lightweight ciphers. In this paper, we propose a new lightweight authenticated encryption with additional data (AEAD) algorithm, named as Fan, which is based on a first non-Grain-like small-state stream cipher that adopts a novel block-wise structure, inspired by the 4-blade daily electric fan. It takes a 128-bit key, a 64-bit initial vector (IV), and a 192-bit state, promising 128-bit security and up to 72-bit authentication tag with the IV-respecting restriction. It consists of a nonlinear spindle, four linear blades and an accumulator, and updates by constant mutual feedbacks between the linear and nonlinear parts, which rapidly provides highly confused level by parallel diffusing the fastest-changing state of spindle. The key is used both in the initialization and generation phases as part of input and state respectively, making Fan suitable for resource-constrained scenarios with internal state diminishment but no security loss. A thorough security evaluation of the entire AEAD mode is provided, which shows that Fan can achieve enough security margin against known attacks. Furthermore, Fan can be implemented efficiently not only in hardware environments but also in software platforms, whose operations are carefully chosen for bit-slice technique, especially the S-box is newly designed efficiently implemented by logic circuit. The hardware implementation requires about 2327 GE on 90 nm technology with a throughput of 9.6 Gbps. The software implementation runs about 8.0 cycle/byte.
Terminology: Aboutness, Representation, and Metasemantics

Abstract This short chapter does two things. First, it shows that in fact workers in AI frequently talk as if AI systems express contents. We present the argument that the complex nature of the actions and communications of AI systems, even if they are very different from the complex behaviours of human beings, and the way they have ‘aboutness’, strongly suggest a contentful interpretation of those actions and communications. It then introduces some philosophical terminology that captures various aspects of language use, such as the ones in the title, to better make clear what one is saying—philosophically speaking—when one claims AI systems communicate, and to provide a vocabulary for the next few chapters.
Identification method of danger sources at infrastructure construction site based on neural network multi-layer feature information

Due to the complex site conditions of power infrastructure projects and the similar structural features of various danger sources, it is difficult to extract salient features suitable for classification. Although the neural networks with large numbers of available improved loss functions have good application effects in feature recognition, the single depth feature does not make full use of the complementarity between the multiple features of the field environment. In response to these problems, this paper proposes a method for classification and identification of danger sources at infrastructure construction sites based on neural network multi-layer feature information. It completes the image recognition of the infrastructure site by selecting the sparse self-encoding technology, extracts the multi-layer features in the neural network, and then sends the multi-layer features to the softmax classifier to solve the multi-classification problem of dangerous sources, and can extract more suitable based on the analysis of the characteristics of the danger sources on the infrastructure construction site. Through data analysis, the method illustrated in this paper effectively improves the identification rate of the danger sources and shows great significance to ensure the safety of infrastructure construction site.
LTE-R Communication System Safety Performance Analysis Based on Cloud Theory and Combined Weighting

To solve the problem of single evaluation method of LTE-R communication system safety performance, a cloud theory and combined weighting method was proposed. Firstly, the index system of LTE-Railway (LTE-R) communication system was established. Secondly, combining with the dynamic weighting method, the subjective & objective weighting values of the evaluation system calculated by 3-scale AHP and CRITIC were fused. Thirdly, by determining the cloud digital characteristics of the comprehensive cloud theory, the cloud evaluation system was established, and then the cloud parameters of each layer of the evaluation system were calculated from bottom to top by the reverse cloud theory. An example showed that the method was feasible and effective.
The Effect of Using Masked Language Models in Random Textual Data Augmentation

Powerful yet simple augmentation techniques have significantly helped modern deep learning-based text classifiers to become more robust in recent years. Although these augmentation methods have proven to be effective, they often utilize random or non-contextualized operations to generate new data. In this work, we modify a specific augmentation method called Easy Data Augmentation or EDA with more sophisticated text editing operations powered by masked language models such as BERT and RoBERTa to analyze the benefits or setbacks of creating more linguistically meaningful and hopefully higher quality augmentations. Our analysis demonstrates that using a masked language model for word insertion almost always achieves better results than the initial method but it comes at a cost of more time and resources which can be comparatively remedied by deploying a lighter and smaller language model like DistilBERT.
Multi-modal Data Analysis and Fusion for Robust Object Detection in 2D/3D Sensing

Multi-modal data is useful for complex imaging scenarios due to the exclusivity of information found in each modality, but there is a lack of meaningful comparisons of different modalities for object detection. In our work, we propose three contributions: (1) Release of a multi-modal, ground-based small object detection dataset, (2) A performance comparison of 2D and 3D imaging modalities using state-of-the-art algorithms, and (3) a multi-modal fusion framework for 2D/3D sensing. The new dataset encompasses various small objects for detection in EO, IR, and LiDAR modalities. The labeled data has comparable resolutions across each modality for better performance analysis. The modality comparison conducted in this work uses advanced deep learning algorithms, such as Mask R-CNN for 2D imaging and PointNet++ for 3D imaging. The comparisons are conducted with similar parameter sizes and the results are analyzed for specific instances where each modality performed the best. To complement the effectiveness of different data modalities, we developed a fusion strategy to combine detection networks operating on different modalities into a single detection output for accurate object detection and region segmentation. Our fusion strategy utilized the state of the art networks listed above as backbone networks to obtain a confidence score from each modality. The network then determines which modality to base the object detection off of based on those confidences. The effectiveness of the proposed fusion method is being evaluated on the multi-modal dataset for object detection and segmentation and we observe superior performance when compared to single-modality algorithms.
Large-Scale Evolutionary Strategy Based on Gradient Approximation

For large-scale optimization, CMA-ES has the disadvantages of high complexity and premature stagnation. An improved CMA-ES algorithm called GI-ES was proposed in this paper. For the problem of high complexity, the method in this paper replaces the calculation of a covariance matrix with the modeling of expected fitting degrees for a given covariance matrix. At the same time, to solve the problem of premature stagnation, this paper replaces the historical information of elite individuals with the historical information of all individuals. The information can be seen as approximate gradients. The parameters of the next generation of individuals are generated based on the approximate gradients. The experimental results were tested using CEC 2010 and CEC2013 LSGO benchmark test suite, and the experimental results verified the effectiveness of the algorithm on a number of different tasks.
Personality Formation And Innovation Of Thinking Of Students In Russia And Mongolia

The article was prepared on the basis of summarizing the results of a field empirical study of the characteristics of the general and particular in the sociocultural, environmental and educational spaces that form the personality, lifestyle and incentives of students to learn in Russia, Yakutia, and Mongolia.The article contains a comparative analysis of the values and value orientations of teachers and students of pedagogical universities of Russia and Mongolia.The analysis of the values and value orientations of teachers and students is supplemented by a study of the main fears and apprehension of the surveyed contingent, its attitude to the profession and social activities.Differences are defined between the basic values of teachers of Yakutia, Mongolia and Central Russia, which must be taken into account when forming the worldview of a modern teacher.According to the study results and field expeditions in Mongolia to the rural settlements of Yakutia, questionnaires among school teachers, students, teachers of higher educational institutions of Russia and Mongolia identified the general (priority of Eurasian values), particular (traditionalism, paternalism, stability, ethnocultural stereotypes of behavior), and single ("nomadic" schools) peculiarities in the socio-cultural and educational spaces of Russia and Mongolia.