Reviewerly/exHarmony huggingface dataset:
CINS: Comprehensive Instruction for Few-Shot Learning in Task-Oriented Dialog Systems

As the labeling cost for different modules in task-oriented dialog (ToD) systems is high, a major challenge is to learn different tasks with the least amount of labeled data. Recently, pre-trained language models (PLMs) have shown promising results for few-shot learning in ToD. To better utilize the power of PLMs, this paper proposes Comprehensive Instruction (CINS) that exploits PLMs with extra task-specific instructions. We design a schema (definition, constraint, prompt) of instructions and their customized realizations for three important downstream tasks in ToD, ie. intent classification, dialog state tracking, and natural language generation. A sequence-to-sequence model (T5) is adopted to solve these three tasks in a unified framework. Extensive experiments are conducted on these ToD tasks in realistic few-shot learning scenarios with small validation data. Empirical results demonstrate that the proposed CINS approach consistently improves techniques that finetune PLMs with raw input or short prompt.
Towards a user-oriented adaptive system based on sentiment analysis from text

Sentiment analysis has known a big interest over recent years due to the expansion of data. It has many applications in different fields such as marketing, psychology, human-computer interaction, eLearning, etc. There are many forms of sentiment analysis, namely facial expressions, speech, and text. This article is more interested in sentiment analysis from the text as it is a relatively new field and still needs more effort and research. Sentiment analysis from text is very important for different fields, for eLearning it can be critical in determining the emotional state of students and therefore, putting in place the necessary interactions to motivate students to engage and complete their courses. In this article, we present different methods of sentiment analysis from the text that exist in the literature, beginning from the selection of features or text representation, until the training of the prediction model using either supervised or unsupervised learning algorithms and although there has been so much work done in this domain, there is still effort that can be done to improve the performance and to do that we first need to review the recent methods and approaches put in place on this field and then try to discuss improvements in certain approaches or even proposing new approaches.
Fostering Compositionality in Latent, Generative Encodings to Solve the Omniglot Challenge

The ability to develop representations of components and to recombine them in a new but compositionally meaningful manner is considered a hallmark of human cognition, which has not been reached by machines, yet. The Omniglot challenge taps into this deficit by posing several one-shot/few-shot generation and classification tasks of handwritten character trajectories. In contrast to the original approach of providing character components, we investigated how compositional representations can develop naturally within a generative LSTM model. The network’s performance and the underlying mechanisms are examined on the original Omniglot dataset and on our own more representative dataset. We show that solving the challenge becomes possible, because, during training, the designed LSTM network fosters the learning of compositional representations, which it can quickly reassemble into new, unseen but related character trajectories. Evidence is provided by several experiments, including an analysis of the latent states of the system, revealing the emergent compositional structures with t-SNE, and the evaluation of the network’s performance, when training and test alphabets do or do not share components. Overall, we show how compositionality can be fostered in latent, generative encodings, thus improving machine learning by further aligning technical methods to cognitive mechanisms in humans.
A framework for optimal quantum spatial search using alternating phase-walks

We present a novel methodological framework for quantum spatial search, generalising the Childs & Goldstone ($\mathcal{CG}$) algorithm via alternating applications of marked-vertex phase shifts and continuous-time quantum walks. We determine closed form expressions for the optimal walk time and phase shift parameters for periodic graphs. These parameters are chosen to rotate the system about subsets of the graph Laplacian eigenstates, amplifying the probability of measuring the marked vertex. The state evolution is asymptotically optimal for any class of periodic graphs having a fixed number of unique eigenvalues. We demonstrate the effectiveness of the algorithm by applying it to obtain $\mathcal{O}(\sqrt{N})$ search on a variety of graphs. One important class is the $n \times n^3$ rook graph, which has $N=n^4$ vertices. On this class of graphs the $\mathcal{CG}$ algorithm performs suboptimally, achieving only $\mathcal{O}(N^{-1/8})$ overlap after time $\mathcal{O}(N^{5/8})$. Using the new alternating phase-walk framework, we show that $\mathcal{O}(1)$ overlap is obtained in $\mathcal{O}(\sqrt{N})$ phase-walk iterations.
Development of Siberian Branch of the Russian Academy of Sciences scientific book publishing system (2001–2010)

The study of scientific book publishing, due to its enormous social and state significance, is an urgent task of modern research. This article is the first to consider the system of scientific book publishing in the Siberian Branch of the Russian Academy of Sciences (SB RAS) in the period of 2001–2010 thus determining and adding to the history of regional and Russian scientific book publishing. The purpose of the study is to present the development of the system for publishing scientific literature in the SB RAS during this period. The source base of the study is representative: these are numerous documents of the current archives of the Siberian enterprise “Nauka” RAS (Novosibirsk Printing House No. 4) and the Siberian Publishing Company “Nauka” RAS, regulatory acts of the Presidium of SB RAS, Annotated thematic plans for the release of scientific literature for 2002-2010, Publishing houses of SB RAS, materials of the weekly newspaper SB RAS “Science in Siberia” for the same period. The revealed data allow reconstructing the picture of the scientific book publishing system development in SB RAS and its thematic orientation in the first decade of the XXI century. The results obtained show that the prevailing part of published scientific literature – every third edition – during this period was on archeology, history, philology, philosophy, law. The study refined quantitative data on the release of serial publications, revealed the role of the Presidium SB RAS in developing the system of scientific book publishing, expanded the idea of cooperation between SB RAS and SIF “Nauka” RAS. The results obtained provide an opportunity to redefine and assess the evolution of their relationship during the period under review. The continuing decrease in the share of CIF in the total volume of publications of SB RAS can be interpreted as the crisis increase, but the implementation of significant and complex publishing projects speaks for a new paradigm of relations between them: mutually beneficial, effective and promising.
An encryption algorithm for vector maps based on the Gaussian random and Haar transform

Security of vector maps is of importance in the community of geographic information sciences. Although some encryption techniques can protect vector maps from piracy, they need to decrypt the entire map in order to extract parts of the data, which obviously brings inconvenience to the users. To solve the problem, an encryption algorithm for vector maps based on Gaussian random and Haar transform is proposed in this paper. First, all vertices are randomized via the Gaussian random method to get the shuffled vector map coordinates. Then, the average coefficient and the differential coefficient generated by the Haar transform are encrypted. Lastly, the encrypted map data is obtained through performing the inverse Haar transformation. Experiments show that (1) the proposed algorithm guarantees authorized users can decrypt any parts (layers) of encrypted vector maps; and (2) the algorithm outperforms other existing encryption methods in security and efficiency.
Efficient Sampling of Transition Constraints for Motion Planning under Sliding Contacts

Contact-based motion planning for manipulation, object exploration or balancing often requires finding sequences of fixed and sliding contacts and planning the transition from one contact in the environment to another. However, most existing algorithms concentrate on the control and learning aspect of sliding contacts, but do not embed the problem into a principled framework to provide guarantees on completeness or optimality. To address this problem, we propose a method to extend constraint-based planning using contact transitions for sliding contacts. Such transitions are elementary operations required for whole contact sequences. To model sliding contacts, we define a sliding contact constraint that permits the robot to slide on the surface of a mesh-based object. To exploit transitions between sliding contacts, we develop a contact transition sampler, which uses three constraint modes: contact with a start surface, no contact and contact with a goal surface. We sample these transition modes uniformly which makes them usable with sampling-based planning algorithms. Our method is evaluated by testing it on manipulator arms of two, three and seven internal degrees of freedom with different objects and various sampling-based planning algorithms. This demonstrates that sliding contact constraints could be used as an elementary method for planning long-horizon contact sequences for high-dimensional robotic systems.
The Future of Edge Computing with Blockchain Technology: Possibility of Threats, Opportunities, and Challenges

In today’s world, people need tremendous improvement in each aspect of life and they saw so many changes that literally make their life much more simplistic. People need to access and exchange so many data continuously, and that is the concept of cloud computing. In this area, so many advancements happen, and that leads to edge computing. With the help of this, we can access with limited amounts of time and also with fewer errors. Blockchain technology, on the other hand, has also been polar in developing decentralized applications and pushing industries or sectors such as agriculture, finance, transport, manufacturing, logistics, etc., to use blockchain or distributed ledger technology to provide end users with trusted, safe services. When blockchain technology (integration of edge computing and blockchain) is used to protect data over edge or cloud computing, we may require a sufficient number of securities, power backups, regulation, standards, etc. This chapter therefore addresses numerous subjects of interest, such as the probability of risks, research opportunities (including existing research gaps), and challenges faced during the integration of blockchain with edge computing (with the perspective of the smart era).
An optimal weighted machine learning model for detecting financial fraud

The purpose of this study is to predict enterprises’ financial fraud. After collecting financial data and employing feature selection methods, totally eight features are selected. We select two best-performance machine learning models according to five indicators including accuracy, recall, specificity, AUC and the misclassification cost. Besides, an optimal weighted machine learning model, based on the two best-performance models, is proposed and the results confirm its good performance in forecasting enterprises’ financial fraud.
Universality of High-Strength Tensors

A theorem due to Kazhdan and Ziegler implies that, by substituting linear forms for its variables, a homogeneous polynomial of sufficiently high strength specialises to any given polynomial of the same degree in a bounded number of variables. Using entirely different techniques, we extend this theorem to arbitrary polynomial functors. As a corollary of our work, we show that specialisation induces a quasi-order on elements in polynomial functors, and that among the elements with a dense orbit there are unique smallest and largest equivalence classes in this quasi-order.
Recent Advances in Counting and Sampling Markov Equivalent DAGs

Counting and sampling directed acyclic graphs (DAGs) from a Markov equivalence class are fundamental tasks in graphical causal analysis. In this paper, we discuss recently proposed polynomial-time algorithms for these tasks. The presented result solves a long-standing open problem in graphical modelling. Experiments show that the proposed algorithms are implementable and effective in practice. Our paper is an extended abstract of the work [], honored as an AAAI-21 Distinguished Paper at the 35th AAAI Conference on Artificial Intelligence.
Readiness to Learn English for Early Childhood

Teachers need to understand the factors that influence the readiness of early childhood to learn their new language, especially English, to avoid inappropriate stimulation practices. This study aimed to examine early childhood English learning readiness factors based on relevant theories and research results. This research method was a qualitative literature review study. The literature review consists of 70 references related to the topic of early childhood English. This research method is a qualitative literature review study. As a result, children's readiness to learn is influenced by internal and external factors. Internal factors include cognitive abilities, mastery of the initial language, mastery of a new language, namely English, emotional maturity, and intrinsic motivation. At the same time, the external factors that influence include extrinsic motivation, family, social culture environment, and stimulation by playing.The novelty is to identify the influence of children's readiness factors in learning English both inside and outside the child. As a result, the teacher can consider the readiness of the child to avoid mistakes in introducing English as a new language for early childhood.
COVID-19 Pandemic and Internet Traffic in Poland: Evidence from Selected Regional Networks

The COVID-19 pandemic has forced governments all over the world to impose lockdowns keeping citizens at home in order to limit the virus spread rate.The paper compares weekly traffic samples captured in the selected nodes of the network managed by NASK -National Research Institute during the pre-lockdown period, i.e. between January 27 and February 3, 2020, with those captured between March 30 and April 6, 2020, i.e. after the lockdown was announced.The presented results show changes in network traffic observed during the periods of time in question and illustrate the evolution in the popularity of top network services.
Deep Sparse Learning for Automatic Modulation Classification Using Recurrent Neural Networks

Deep learning models, especially recurrent neural networks (RNNs), have been successfully applied to automatic modulation classification (AMC) problems recently. However, deep neural networks are usually overparameterized, i.e., most of the connections between neurons are redundant. The large model size hinders the deployment of deep neural networks in applications such as Internet-of-Things (IoT) networks. Therefore, reducing parameters without compromising the network performance via sparse learning is often desirable since it can alleviates the computational and storage burdens of deep learning models. In this paper, we propose a sparse learning algorithm that can directly train a sparsely connected neural network based on the statistics of weight magnitude and gradient momentum. We first used the MNIST and CIFAR10 datasets to demonstrate the effectiveness of this method. Subsequently, we applied it to RNNs with different pruning strategies on recurrent and non-recurrent connections for AMC problems. Experimental results demonstrated that the proposed method can effectively reduce the parameters of the neural networks while maintaining model performance. Moreover, we show that appropriate sparsity can further improve network generalization ability.
Structure-from-Sherds: Incremental 3D Reassembly of Axially Symmetric Pots from Unordered and Mixed Fragment Collections

Re-assembling multiple pots accurately from numerous 3D scanned fragments remains a challenging task to this date. Previous methods extract all potential matching pairs of pot sherds and considers them simultaneously to search for an optimal global pot configuration. In this work, we empirically show such global approach greatly suffers from false positive matches between sherds inflicted by indistinc-tive sharp fracture surfaces in pot fragments. To mitigate this problem, we take inspirations from the field of structure-from-motion (SfM), where many pipelines have matured in reconstructing a 3D scene from multiple images. Motivated by the success of the incremental approach in robust SfM, we present an efficient reassembly method for axially symmetric pots based on iterative registration of one sherd at a time. Our method goes beyond replicating incremental SfM and addresses indistinguishable false matches by embracing beam search to explore multitudes of registration possibilities. Additionally, we utilize multiple roots in each step to allow simultaneous reassembly of multiple pots. The proposed approach shows above 80% reassembly accuracy on a dataset of real 80 fragments mixed from 5 pots, pushing the state-of-the-art and paving the way towards the goal of large-scale pot reassembly. Our code and preprocessed data is available at https://github.com/SeongJong-Yoo/structure-from-sherds.
Key Point Extraction Hardware Using SIFT Algorithm in FPGA

In this paper, FPGA (Field-Programmable Gate Array) implementation of SIFT (Scale Invariant Feature Transform) algorithms is presented. SIFT algorithm extracts key points from the image. For implementation, basic study for SIFT algorithm and hardware design method is shown including pipeline line buffer and simplified gradient calculation. The SIFT key point extraction hardware was designed with Verilog HDL and implemented to Xilinx Vertex-7.
BI-RADS Classification of Calcification on Mammograms

Calcification is one of the most common and important lesions in mammograms, and a higher BI-RADS category indicates a higher cancer risk. In this paper, we present the first deep learning-based six-class BI-RADS classification for each individual calcification in mammograms. We propose an attention ROI generation strategy to highlight calcification features. Moreover, by incorporating malignancy information, the designed new loss function effectively boosts the performance of the model. We also design a novel evaluation metric for BI-RADS classification, which considers the severity of malignancy. Experimental results have demonstrated the superior classification performance of the proposed approach to the competing methods.
Enhanced quantum signature scheme using quantum amplitude amplification operators

Quantum signature is the use of the principles of quantum computing to establish a trusted communication between two parties. In this paper, a quantum signature scheme using amplitude amplification techniques will be proposed. To secure the signature, the proposed scheme uses a partial diffusion operator and a diffusion operator to hide/unhide certain quantum states during communication. The proposed scheme consists of three phases, preparation phase, signature phase and verification phase. To confuse the eavesdropper, the quantum states representing the signature might be hidden, not hidden or encoded in Bell states. It will be shown that the proposed scheme is more secure against eavesdropping when compared with relevant quantum signature schemes.
Learning from Partially Overlapping Labels: Image Segmentation Under Annotation Shift

Scarcity of high quality annotated images remains a limiting factor for training accurate image segmentation models. While more and more annotated datasets become publicly available, the number of samples in each individual database is often small. Combining different databases to create larger amounts of training data is appealing yet challenging due to the heterogeneity as a result of differences in data acquisition and annotation processes, often yielding incompatible or even conflicting information. In this paper, we investigate and propose several strategies for learning from partially overlapping labels in the context of abdominal organ segmentation. We find that combining a semi-supervised approach with an adaptive cross entropy loss can successfully exploit heterogeneously annotated data and substantially improve segmentation accuracy compared to baseline and alternative approaches.
Pragmatic annotation of a domain-restricted English-Spanish comparable corpus

This paper explores the multi-layer annotation of a written domain-restricted English-Spanish comparable corpus (CLANES – Controlled LANguage English Spanish), focusing on pragmatic annotation. The annotation scheme draws on part of speech tagging and a semantic annotation scheme, i.e. the UCREL Semantic Analysis System, with some added categories to fit the food-and-drink domain represented in CLANES. These are used to build significant (pragmatic) metapatterns. Seven different pragmatic functions have been identified in our corpus, namely &lt;STATE&gt;, &lt;DIRECT&gt;, &lt;SUGGEST&gt;, &lt;RECOMMEND&gt;, &lt;PRAISE&gt;, &lt;EVIDENCE&gt; and &lt;RELATE TO READER&gt;. Computer scripts translate this linguistic information into regular expressions to be used in unsupervised annotation. Partial results indicate that applying lexical restrictors boosts the success rate considerably. However, metadata is preferred because of increased replicability and generality. Replicability issues and limitations encountered during testing are also addressed.
Heterogeneous face detection based on multi‐task cascaded convolutional neural network

Facial target detection is an important task in computer vision. Because heterogeneous face detection shows broad prospects, it has attracted extensive attention from the academic community. In recent years, with the rise of deep learning and its applications in computer vision, face detection technology has made great strides. This paper uses multi-task cascaded convolutional neural network (MTCNN) for heterogeneous face feature detection. This algorithm makes full use of the advantages of image pyramid, boundary regression, fully convolutional attention networks and non-maximum suppression. The main idea of this paper is to use candidate frame plus classifier for fast and efficient face detection. Specifically, the candidate window is generated by the proposal network (P-Net), and the high-precision candidate window is filtered and selected by the reduced network (R-Net), and the final bounding box and facial key points are generated by the output network (O-Net). In order to prove the effectiveness of this method in visible light, near-infrared and sketch face recognition scenes, it was verified in the datasets of CUFS, CUFSF and CASIA NIR-VIS 2.0. Experiments show that this method is effective for face images in heterogeneous face and is better than the latest algorithms.