Reviewerly/exHarmony huggingface dataset:
Tusom2021: A Phonetically Transcribed Speech Dataset from an Endangered Language for Universal Phone Recognition Experiments

There is growing interest in ASR systems that can recognize phones in a language-independent fashion. There is additionally interest in building language technologies for low-resource and endangered languages. However, there is a paucity of realistic data that can be used to test such systems and technologies. This paper presents a publicly available, phonetically transcribed corpus of 2255 utterances (words and short phrases) in the endangered Tangkhulic language East Tusom (no ISO 639-3 code), a Tibeto-Burman language variety spoken mostly in India. Because the dataset is transcribed in terms of phones, rather than phonemes, it is a better match for universal phone recognition systems than many larger (phonemically transcribed) datasets. This paper describes the dataset and the methodology used to produce it. It further presents basic benchmarks of state-of-the-art universal phone recognition systems on the dataset as baselines for future experiments.
314 Days of the COVID-19 Quarantine for the Scientific Library of Ukraine

The article describes the transition to remote work and online activities of the University Library of Ukraine during the COVID-19 pandemic.
CNN-Based Vehicle Classification Using Transfer Learning

This paper focuses on the classification of vehicles using Convolutional Neural Network (CNN) which is a class of deep learning neural network. This work makes use of transfer learning using the pre-trained networks to extract powerful and informative features and apply that to the classification task. In the proposed method, the pre-trained networks are trained on two vehicle datasets consisting of real-time images. The classifier performance along with the performance metrics such as accuracy, precision, false discovery rate, recall rate, and false negative rate is estimated for the following pre-trained networks: AlexNet, GoogLeNet, SqueezeNet, and ResNet18. The classification model is implemented on the standard vehicle dataset and also on a created dataset. The model is further used for the detection of the different vehicles using Regions with a Convolutional Neural Networks (RCNN) object detector on a smaller dataset. This paper focuses on finding the perfect network suitable for the classification problems which have only a limited amount of non-labeled data. The model makes use of limited pre-processing and achieves greater accuracy on continuous training of the networks on the vehicle images.
D* Lite-Based Navigation Algorithm for Multiple Spherical Underwater Robots Collaboration

This research investigates the multiple robotic systems navigation in an unknown environment problem. As an attempt to solve this challenge, a navigation strategy based on D* Lite search algorithm is proposed. To ensure an optimal path to the goal, we implemented the D* Lite algorithm and improved the safety of the algorithm by defining the non-reachable cells accordingly to the size and position of the obstacles. The collaborative navigation is ensured by a fault tolerance strategy based on a low computation cost fault detection and leader switching algorithm. The performed experiments in a water tank and the simulation demonstrated that the search algorithm can safely find the shortest path to the goal. The robustness of the path planning algorithm is demonstrated further with relatively few collisions occurrence. Also, the simulation shows that the robots can safely adapt their formation from V-shape to line shape when obstacles are present in the environment. However, the formation navigation presents some inefficiencies in terms of accuracy. The developed strategy will be extended in future works with more experiments.
Obstructing Classification via Projection

Machine learning and data mining techniques are effective tools to classify large amounts of data. But they tend to preserve any inherent bias in the data, for example, with regards to gender or race. Removing such bias from data or the learned representations is quite challenging. In this paper we study a geometric problem which models a possible approach for bias removal. Our input is a set of points P in Euclidean space ℝ^d and each point is labeled with k binary-valued properties. A priori we assume that it is to classify the data according to each property. Our goal is to obstruct the classification according to one property by a suitable projection to a lower-dimensional Euclidean space ℝ^m (m < d), while classification according to all other properties remains easy. What it means for classification to be easy depends on the classification model used. We first consider classification by linear separability as employed by support vector machines. We use Kirchberger’s Theorem to show that, under certain conditions, a simple projection to ℝ^{d-1} suffices to eliminate the linear separability of one of the properties whilst maintaining the linear separability of the other properties. We also study the problem of maximizing the linear inseparability of the chosen property. Second, we consider more complex forms of separability and prove a connection between the number of projections required to obstruct classification and the Helly-type properties of such separabilities.
WenLan: Efficient Large-Scale Multi-Modal Pre-Training on Real World Data

Multi-modal pre-training models have been intensively explored to bridge vision and language in recent years. However, most of them explicitly model the cross-modal interaction between image-text pairs, by assuming that there exists strong semantic correlation between the text and image modalities. Since this strong assumption is often invalid in real-world scenarios, we choose to implicitly model the cross-modal correlation for large-scale multi-modal pre-training, which is the focus of the Chinese project 'WenLan' led by our team. Specifically, with the weak correlation assumption over image-text pairs, we propose a two-tower pre-training model called BriVL within the cross-modal contrastive learning framework [1]. We construct a large Chinese multi-source dataset of 650 million image-text pairs for pre-training our model. Extensive experiments demonstrate that WenLan on various downstream tasks and easy to build efficient applications based on searching between images and texts.
Exploring the Potential of Lexical Paraphrases for Mitigating Noise-Induced Comprehension Errors

Listening in noisy environments can be difficult even for individuals with a normal hearing thresholds. The speech signal can be masked by noise, which may lead to word misperceptions on the side of the listener, and overall difficulty to understand the message. To mitigate hearing difficulties on listeners, a co-operative speaker utilizes voice modulation strategies like Lombard speech to generate noise-robust utterances, and similar solutions have been developed for speech synthesis systems. In this work, we propose an alternate solution of choosing noise-robust lexical paraphrases to represent an intended meaning. Our results show that lexical paraphrases differ in their intelligibility in noise. We evaluate the intelligibility of synonyms in context and find that choosing a lexical unit that is less risky to be misheard than its synonym introduced an average gain in comprehension of 37% at SNR -5 dB and 21% at SNR 0 dB for babble noise.
An Intelligent 3D Printing Path Planning Algorithm 3D Printing Path Planning Algorithm

The path planning of large industrial parts in additive manufacturing directly affects manufacturing quality and efficiency. At present, the commonly used traditional path planning methods have many problems, such as turning and stacking of print head and the number of rise and fall of the print head, so they are not suitable for large industrial parts. Therefore, an intelligent path planning algorithm is proposed. Firstly, the two-dimensional plane obtained after slicing is concavely convexly decomposed into printing sub-areas, and then the sub-partition connection is treated as a traveling salesman problem (TSP) to reduce the number and length of printing paths. By comparing with traditional path planning algorithms, the proposed method significantly reduced the number of paths, the idle travel distance, and the number of print head lifts. The sub-partition-based intelligent path planning method provides a new idea for the additive manufacturing process of large industrial parts.
The Impact of ASR on the Automatic Analysis of Linguistic Complexity and Sophistication in Spontaneous L2 Speech

In recent years, automated approaches to assessing linguistic complexity in second language (L2) writing have made significant progress in gauging learner performance, predicting human ratings of the quality of learner productions, and benchmarking L2 development. In contrast, there is comparatively little work in the area of speaking, particularly with respect to fully automated approaches to assessing L2 spontaneous speech. While the importance of a well-performing ASR system is widely recognized, little research has been conducted to investigate the impact of its performance on subsequent automatic text analysis. In this paper, we focus on this issue and examine the impact of using a state-of-the-art ASR system for subsequent automatic analysis of linguistic complexity in spontaneously produced L2 speech. A set of 30 selected measures were considered, falling into four categories: syntactic, lexical, n-gram frequency, and information-theoretic measures. The agreement between the scores for these measures obtained on the basis of ASR-generated vs. manual transcriptions was determined through correlation analysis. A more differential effect of ASR performance on specific types of complexity measures when controlling for task type effects is also presented.
Research on the Extraction Method of Book Number Region Based on Bayesian Optimization and Deep Learning

The continuous development of artificial intelligence technology has promoted the construction of smart libraries and their intelligent services. In the process of intelligent access to books, the extraction of the requested book number region has become an important part of the process. The requested book number is generally affixed to the bottom of the spine of the book, which is small in size, and the height of the book is not always the same, so it’s difficult to identify. By the way, due to the images’ resolution, shooting angle and other practical problems, the difficulty of the extraction work will be increased. To improve the identification accuracy, in this paper, Bayesian Optimization (BO) and one kind of deep neural networks ‘Faster R-CNN’ are combined for the extraction work mentioned above. The data preparation, network training, optimization variable selection, establishment of BO objective function, optimization training, and network parameter evaluation have been introduced in detail. The performance of the designed algorithm has been tested with actual images of book spines taken in the academy library and compared with several other conventional recognition algorithms. The experimental results show that the requested book number region extraction method based on Bayesian optimization and deep neural network is effective and reliable, and its recognition rate can reach 91.82%, which has advantages in both recognition rate and extraction time compared with other algorithms.
Attentional Neural Factorization Machines for Knowledge Tracing

To promote the quality and intelligence of online education systems, knowledge tracing becomes a fundamental and crucial task. It models knowledge state and predicts performance based on student’s learning records. Recently, factorization machine based approaches have been proposed to fit student’s learning behavior by generating interactions between underlying features. However, there are two major unresolved issues: (1) quantities of interactions are introduced along with different features involved in. Nevertheless, the redundant interactions do not effectively contribute to the model training. There is a need to extract the most important and relevant interactions. (2) The widely employed factorization machines simply process with second order interactions, leading to insufficiently expressive representations, which is not beneficial to prediction. To this end, we propose a novel Attentional Neural Factorization Machines (ANFMs) to address the above problems. First, we leverage attention mechanisms to suppress the interference of interactions redundancy by distinguishing the importance of different interactions. To be specific, explicit and implicit attention strategies are utilized respectively. Secondly, to facilitate expressive capability, we apply neural networks to the transformed interactions after attention propagation, which is able to capture high order representations. Extensive experiments on two real-world datasets clearly show that ANFMs outperforms baselines with significant margins, which demonstrates the effectiveness of our work.
Extreme Visual Languages

Extreme Visual Languages are visual languages that can communicate an integrated/holistic meaning of an application, a philosophy or even the entire spatial/temporal dimensions of a universe in a single or a few visual expressions.An example is the aliens' visual language in the movie "The Arrival" where each visual sentence expresses the continuum of space and time.Another example is a visual diagram illustrating the spread of Covid-19 in the world.A third example is the Ying/Yang icon that appears in the national flag of the Republic of Korea (and many other places).A fourth example is the red flag.A natural extension to include sound and other media leads to Extreme Multimedia Languages.In this talk I will discuss extreme visual and multimedia languages, both abbreviated as XVLs, including empirical and comparative studies of existing XVLs and the investigation of syntax, semantics, dynamics and application integration of existing and/or new XVLs and related topics.
Exploring Nonnegative and Low-Rank Correlation for Noise-Resistant Spectral Clustering

Clustering has been extensively explored in pattern recognition and data mining in order to facilitate various applications. Due to the presence of data noise, traditional clustering approaches may become vulnerable and unreliable, thereby degrading clustering performance. In this paper, we propose a robust spectral clustering approach, termed Non-negative Low-rank Self-reconstruction (NLS), which simultaneously (a) explores the nonnegative low-rank properties of data correlation as well as (b) adaptively models the structural sparsity of data noise. Specifically, in order to discover the intrinsic correlation among data, we devise a self-reconstruction approach to jointly consider the nonnegativity and low-rank property of data correlation matrix. Meanwhile, we propose to model data noise via a structural norm, i.e., $$\ell _{p,2}$$-norm, which not only naturally conforms to genuine patterns of data noise in real-world situations, but also provides more adaptivity and flexibility to different noise levels. Extensive experiments on various real-world datasets illustrate the advantage of the proposed robust spectral clustering approach compared to existing clustering methods.
The Evolution of Hardware-Assisted Security

In this chapter, we study the security mechanisms implemented in modern hardware architectures. In essence, hardware-based security can be more effective against powerful attacks, while at the same time minimize the imposed runtime overheads of the security checks themselves.
Explaining Evolutionary Agent-Based Models via Principled Simplification

Abstract Understanding how evolutionary agents behave in complex environments is a challenging problem. Agents can be faced with complex fitness landscapes derived from multi-stage tasks, interaction with others, and limited environmental feedback. Agents that evolve to overcome these can sometimes access greater fitness, as a result of factors such as cooperation and tool use. However, it is often difficult to explain why evolutionary agents behave in certain ways, and what specific elements of the environment or task may influence the ability of evolution to find goal-achieving behaviours; even seemingly simple environments or tasks may contain features that affect agent evolution in unexpected ways. We explore principled simplification of evolutionary agent-based models, as a possible route to aiding their explainability. Using the River Crossing Task (RCT) as a case study, we draw on analysis in the Minimal River Crossing (RC-) Task testbed, which was designed to simplify the original task while keeping its key features. Using this method, we present new analysis concerning when agents evolve to successfully complete the RCT. We demonstrate that the RC- environment can be used to understand the effect that a cost to movement has on agent evolution, and that these findings can be generalised back to the original RCT. Then, we present new insight into the use of principled simplification in understanding evolutionary agents. We find evidence that behaviour dependent on features that survive simplification, such as problem structure, are amenable to prediction; while predicting behaviour dependent on features that are typically reduced in simplification, such as scale, can be invalid.
Analisis Indikator Pendidikan: Partisipasi Pendidikan di Indonesia Periode 1994-2018

This study aims to examine indicators of education in Indonesia based on the School Participation Rate (SPR), Rough Participation Rate (RPR), and Pure Participation Rate (PPR). This research used quantitative methods. The type of data used was secondary data obtained from the Central Statistics Agency (CSA) during the 1994-2018 period. The instrument used by BPS to collect data was a questionnaire through the National Survey and Census program (Susenas). The population involved in this study was all Indonesians based on age and education level. The data were processed using quantitative statistics with explorative descriptive analysis. The results showed that the SPR, RPR, and PPR in Indonesia based on age and level of education had an increasing trend. However, based on the average SPR, RPR, and PPR scores, educational participation has decreased with increasing age and level of education taken.
Compression by Contracting Straight-Line Programs

In grammar-based compression a string is represented by a context-free grammar, also called a straight-line program (SLP), that generates only that string. We refine a recent balancing result stating that one can transform an SLP of size g in linear time into an equivalent SLP of size 𝒪(g) so that the height of the unique derivation tree is 𝒪(log N) where N is the length of the represented string (FOCS 2019). We introduce a new class of balanced SLPs, called contracting SLPs, where for every rule A → β₁ … β_k the string length of every variable β_i on the right-hand side is smaller by a constant factor than the string length of A. In particular, the derivation tree of a contracting SLP has the property that every subtree has logarithmic height in its leaf size. We show that a given SLP of size g can be transformed in linear time into an equivalent contracting SLP of size 𝒪(g) with rules of constant length. This result is complemented by a lower bound, proving that converting SLPs into so called α-balanced SLPs or AVL-grammars can incur an increase by a factor of Ω(log N).We present an application to the navigation problem in compressed unranked trees, represented by forest straight-line programs (FSLPs). A linear space data structure by Reh and Sieber (2020) supports navigation steps such as going to the parent, left/right sibling, or to the first/last child in constant time. We extend their solution by the operation of moving to the i-th child in time 𝒪(log d) where d is the degree of the current node.Contracting SLPs are also applied to the finger search problem over SLP-compressed strings where one wants to access positions near to a pre-specified finger position, ideally in 𝒪(log d) time where d is the distance between the accessed position and the finger. We give a linear space solution for the dynamic variant where one can set the finger in 𝒪(log N) time, and then access symbols or move the finger in time 𝒪(log d + log^(t) N) for any constant t where log^(t) N is the t-fold logarithm of N. This improves a previous solution by Bille, Christiansen, Cording, and Gortz (2018) with access/move time 𝒪(log d + log log N).
Method of Combining Cryptography and LDPC Coding for Enhanced Privacy

This paper describes a method of combining cryptographic encoding and low density parity check (LDPC) encoding for the purpose of enhancing privacy. This method uses pseudorandom number generators (PRNGs) to create parity check matrices that are constantly updated. The generated cyphertext is at least as private as a standard additive (XORing) cryptosystem, and also has error correcting capability. The eavesdropper, Eve, has the expanded burden of having to perform cryptanalysis and error correction simultaneously.
Direct Multimodal Few-Shot Learning of Speech and Images

We propose direct multimodal few-shot models that learn a shared embedding space of spoken words and images from only a few paired examples. Imagine an agent is shown an image along with a spoken word describing the object in the picture, e.g. pen, and eraser. After observing a few paired examples of each class, the model is asked to identify the book in a set of unseen pictures. Previous work used a two-step indirect approach relying on learned unimodal representations: speech-speech and image-image comparisons are performed across the support set of given speech-image pairs. We propose two direct models which instead learn a single multimodal space where inputs from different modalities are directly comparable: a multimodal triplet network (MTriplet) and a multimodal correspondence autoencoder (MCAE). To train these direct models, we mine speech-image pairs: the support set is used to pair up unlabelled in-domain speech and images. In a speech-to-image digit matching task, direct models outperform indirect models, with the MTriplet achieving the best multimodal five-shot accuracy. We show that the improvements are due to the combination of unsupervised and transfer learning in the direct models, and the absence of two-step compounding errors.
A Proposal of Learning Feedback System for Children to Promote Self-directed Learning

Since after COVID-19 pandemic, many schools promoted ICT-based learning activities such as online classes, online Project Based Learning (PBL) and active learning and learning portfolios, etc. The authors have been studying the development of children’s independence, especially, classifying and defining skills required to promote self-directed learning, and developing a learning support system for children. In this paper, the authors proposed a learning feedback system for children with clustering of the learning-data. Training data for clustering features of learners have collected in community activities in 2020. The authors planned a new community relationship event “Walk Adventure” in which online and onsite activities are linked to keep social distance. A feedback system based on the learning-data collected in this event and simple clustering was developed.