Reviewerly/exHarmony huggingface dataset:
Efficient and Interpretable Robot Manipulation with Graph Neural Networks

Manipulation tasks, like loading a dishwasher, can be seen as a sequence of spatial constraints and relationships between different objects. We aim to discover these rules from demonstrations by posing manipulation as a classification problem over a graph, whose nodes represent task-relevant entities like objects and goals, and present a graph neural network (GNN) policy architecture for solving this problem from demonstrations. In our experiments, a single GNN policy trained using imitation learning (IL) on 20 expert demos can solve blockstacking, rearrangement, and dishwasher loading tasks; once the policy has learned the spatial structure, it can generalize to a larger number of objects, goal configurations, and from simulation to the real world. These experiments show that graphical IL can solve complex long-horizon manipulation problems without requiring detailed task descriptions. Videos can be found at: https://youtu.be/POxaTDAj7aY.
Building Artificial Neural Networks for NLP Analysis and Classification of Target Content

The problems of analyzing texts in natural language (NLP) using artificial intelligence (AI) methods are caused by the semantic and lexicological diversity of texts.This circumstance causes the appearance of various machine learning (ML) metrics for neural network analysis.The problem of AI analysis is further complicated by the fact that the content under study often contains "information garbage", which is information noise, complicating the solution of a well-known problem of text classification.The lexicological diversity of Internet content requires improving the methods of neural network NLP analysis.The purpose of the research is to identify and solve problems that arise when analyzing information texts using artificial neural networks (ANN), using the example of socio-political content.Well-known NLP technologies include substantiation of the structure and formation of a subject-oriented database of text data bodies, construction of dictionaries based on frequency analysis, and digital vectorization of texts.To identify the latent semantic content, the expediency of using a dense vector representation of terms in a multidimensional space (the embedding model) is justified.In order to justify the choice of basic architectures developed by ANN to account for sequences and combinations of analyzed terms, modifications of convolutional (Conv1D) recurrent (CNN, LSTM, etc.) layers were selected that allow storing token sequences.Since such powerful layers contribute to the appearance of undesirable re-training of ANN, effective means of regularization are necessary, for example, dropout layers.The authors substantiate a modified NLP approach to identifying sociocultural and cyber threats contained in the information content of Internet resources.Based on the frequency analysis of the target Internet content, dictionaries of terms used for multi-class text analysis are pre-formed, as well as their markup.To justify and study the architecture and hyperparameters focused on the content of the analyzed subject area, the ANN family was built in Python using specialized libraries -Keras, ScikitLearn, and others.The ANN architecture included combinations of fully connected, convolutional, and/or recurrent layers.When training ANN in the Google Colaboratory environment, high-performance GPUs were used.Recommendations are given for selecting ANN hyperparameters that are invariant for various architectures of hidden layers of hybrid ANN focused on solving the problem of multiclass NLP analysis.The degree of correct text recognition in the test sample exceeded 80%.Recommendations for its improvment it are given.
Doubly Contrastive Deep Clustering

Deep clustering successfully provides more effective features than conventional ones and thus becomes an important technique in current unsupervised learning. However, most deep clustering methods ignore the vital positive and negative pairs introduced by data augmentation and further the significance of contrastive learning, which leads to suboptimal performance. In this paper, we present a novel Doubly Contrastive Deep Clustering (DCDC) framework, which constructs contrastive loss over both sample and class views to obtain more discriminative features and competitive results. Specifically, for the sample view, we set the class distribution of the original sample and its augmented version as positive sample pairs and set one of the other augmented samples as negative sample pairs. After that, we can adopt the sample-wise contrastive loss to pull positive sample pairs together and push negative sample pairs apart. Similarly, for the class view, we build the positive and negative pairs from the sample distribution of the class. In this way, two contrastive losses successfully constrain the clustering results of mini-batch samples in both sample and class level. Extensive experimental results on six benchmark datasets demonstrate the superiority of our proposed model against state-of-the-art methods. Particularly in the challenging dataset Tiny-ImageNet, our method leads 5.6\% against the latest comparison method. Our code will be available at \url{https://github.com/ZhiyuanDang/DCDC}.
Heuristic methods for data clustering

Data clustering is a major research area having diverse applications in certain fields, which involves recognition of patterns, machine learning, and mining massive datasets. Also, it is a process of grouping the data, which are similar to each other. As the clustering issue on numerical databases can be modeled as a major combinatorial optimization problem, different research are done on the development of heuristic strategies for determining suboptimal solutions in a certain period of time. However, the majority of the heuristic clustering strategies suffered from the issue of being sensitive to the initialization and does not guarantee to offer qualitative outcomes. In this chapter, different heuristic clustering strategies are devised to provide improved results for clustering massive data. Also, the principles utilized in the heuristic methods are illustrated for getting a brief idea about heuristic methods. Here, the heuristic algorithms are splitted into five types that involve local search versus global search, single-solution versus population-based, hybridization, parallel metaheuristics, and nature-inspired techniques. These methods are briefly elaborated, and the application of the algorithm in data clustering is explained in the chapter. Moreover, algorithms and examples of different algorithms for data clustering are illustrated.
Transformation or modernization: what to choose for libraries

The paper presents a vision of the problem of changing modern libraries and defining their mission under the influence of the requirements of the emerging in-formation society. The main provisions of legislative acts in the field of culture, education, and librarianship in recent years allow us to assert that the mission of libraries is humanistic. However, the purpose of libraries is very vague in these documents. This leads to a discussion in the environment of the library community. Increasingly, there is a shift in library activity towards over-activation of cultural and leisure activities or digitalization. College libraries usually focus on the introduction of computer and telecommunication technologies, networking between libraries, the replenishment of electronic resources for information support of science and education, with-out paying due attention to the promotion and education process, which is humanistic in nature and is a part of education.It is emphasized that the radical transformation of libraries' activities proposed by V. K. Stepanov will lead to their disappearance. For further successful development of libraries, it is necessary to modernize, modernize, based on the understanding of the humanistic mission of the library. The list of priority measures that will help to increase the role of libraries in society and implement their humanistic mission is given.
Discrepancy-Based Active Learning for Domain Adaptation

The goal of the paper is to design active learning strategies which lead to domain adaptation under an assumption of Lipschitz functions. Building on previous work by Mansour et al. (2009) we adapt the concept of discrepancy distance between source and target distributions to restrict the maximization over the hypothesis class to a localized class of functions which are performing accurate labeling on the source domain. We derive generalization error bounds for such active learning strategies in terms of Rademacher average and localized discrepancy for general loss functions which satisfy a regularity condition. A practical K-medoids algorithm that can address the case of large data set is inferred from the theoretical bounds. Our numerical experiments show that the proposed algorithm is competitive against other state-of-the-art active learning techniques in the context of domain adaptation, in particular on large data sets of around one hundred thousand images.
Additional Security using Three-Pass Protocol and Pohlig-Hellman

Abstract Data and information protection is usually done by encrypting sensitive data and information using cipher keys or symmetric algorithms. This includes using what is known as “cipher keys,” which are used in systems that require two parts, a sender’s and receiver’s part, which is a special system that needs to be assembled, and a location where data is sent to or stored. The Three-Pass Protocol and Pohlig Hellman Algorithm solves a key distribution problem in symmetric and asymmetric cryptography, allowing senders and receivers to process encryption and decryption due to lack of key distribution processes.
Knowledge of Farmers Regarding Information and Communication Technology

ICT stands for Information and Communication Technology which can be broadly explained as technologies that facilitate communication, processing and transmission of information by electronic means, it can be considered as term that includes the use of any device from mobile phones& computer to ATMs.The present study on Attitude of farmers towards Information and Communication technology in Wardha district of Maharashtra state with a sample size of 120 farmers selected from 10 different villages of two tahsils.Vast majority of the selected respondents for the respective study had knowledge about Information and communication technology, As it is observed that 100 per cent respondents said Yes to the statement do you get agriculture information from television followed by 99.16 per cent of them said Yes to the statement Do you get agriculture information in mobile and majority of them said No the statement Documentaries, CDs/DVDs etc. provide you detailed information with clear audio and video.In overall majority of them 52.50 per cent of them were under medium category of knowledge about ICT.
Lifelong Learning and Task-Oriented Dialogue System: What Does It Mean?

The main objective of this paper is to propose a functional definition of lifelong learning systems adapted to the framework of task-oriented dialogue systems. We mainly identified two aspects where a lifelong learning technology could be applied in such systems: to improve the natural language understanding module and to enrich the database used by the system. Given our definition, we present an example of how it could be implemented in an existing task-oriented dialogue system that is developed in the LIHLITH project.
Study on Automobile Culture of Developed and Emerging Countries in Asia Using Text Mining Analysis on Social Media

To be successful in the automobile export business, it is essential to accurately identify consumer preferences in developed and emerging countries. In this study, big data were collected and compared using NodeXL to verify differences in automotive preferences caused by differences in living standards, regional characteristics and culture, and related regulations and policies between developed and emerging countries in Asia. The data to be analyzed were collected from Twitter big data on major Asian and emerging countries and cleaned up, and text mining techniques were applied for frequency analysis. Based on this, we predicted the demand for automobiles in emerging countries in the future and derived a way for automakers to efficiently approach the automobile markets of emerging countries. The analysis results are as follows: First, emerging economies are very fond of foreign vehicles that value their confidence and pride but are more price-sensitive and prefer medium-to-small car sedans rather than SUVs. Second, pride and price should be the first marketing considerations when an automotive business enters an emerging country. Third, in emerging economies, if small- and medium-sized cars that fit lower prices are marketed mainly as sedans rather than as SUVs, local consumers will respond well. This study's results are expected to be used as primary data for the localization sales factors necessary in understanding the car preferences of different countries for overseas marketing.
Lower Bounds on the Running Time of Two-Way Quantum Finite Automata and Sublogarithmic-Space Quantum Turing Machines.

The two-way finite automaton with quantum and classical states (2QCFA), defined by Ambainis and Watrous, is a model of quantum computation whose quantum part is extremely limited; however, as they showed, 2QCFA are surprisingly powerful: a 2QCFA with only a single-qubit can recognize the language $L_{pal}=\{w \in \{a,b\}^*:w \text{ is a palindrome}\}$ with bounded error in expected time $2^{O(n)}$, on inputs of length $n$. We prove that their result essentially cannot be improved upon: a 2QCFA (of any size) cannot recognize $L_{pal}$ with bounded error in expected time $2^{o(n)}$. To our knowledge, this is the first example of a language that can be recognized with bounded error by a 2QCFA in exponential time but not in subexponential time. Moreover, we prove that a quantum Turing machine (QTM) running in space $o(\log n)$ and expected time $2^{n^{1-\Omega(1)}}$ cannot recognize $L_{pal}$ with bounded error; again, this is the first lower bound of its kind. Far more generally, we establish a lower bound on the running time of any 2QCFA or $o(\log n)$-space QTM that recognizes any language $L$ in terms of a natural ``hardness measure of $L$. This allows us to exhibit a large family of languages for which we have asymptotically matching lower and upper bounds on the running time of any such 2QCFA or QTM recognizer.
On Attribution of Deepfakes

Progress in generative modelling, especially generative adversarial networks, have made it possible to efficiently synthesize and alter media at scale. Malicious individuals now rely on these machine-generated media, or deepfakes, to manipulate social discourse. In order to ensure media authenticity, existing research is focused on deepfake detection. Yet, the adversarial nature of frameworks used for generative modeling suggests that progress towards detecting deepfakes will enable more realistic deepfake generation. Therefore, it comes at no surprise that developers of generative models are under the scrutiny of stakeholders dealing with misinformation campaigns. At the same time, generative models have a lot of positive applications. As such, there is a clear need to develop tools that ensure the transparent use of generative modeling, while minimizing the harm caused by malicious applications. Our technique optimizes over the source of entropy of each generative model to probabilistically attribute a deepfake to one of the models. We evaluate our method on the seminal example of face synthesis, demonstrating that our approach achieves 97.62% attribution accuracy, and is less sensitive to perturbations and adversarial examples. We discuss the ethical implications of our work, identify where our technique can be used, and highlight that a more meaningful legislative framework is required for a more transparent and ethical use of generative modeling. Finally, we argue that model developers should be capable of claiming plausible deniability and propose a second framework to do so -- this allows a model developer to produce evidence that they did not produce media that they are being accused of having produced.
A novel adaptive learning deep belief network based on automatic growing and pruning algorithms

In this study, a novel adaptive learning deep belief network (ALDBN) with a series of growing and pruning algorithms is proposed to dynamically adjust its structure when ALDBN is utilized for extracting features. Specifically, a neuron growing algorithm is designed considering the individual and macroscopical impacts on each neuron to detect unstable hidden neurons, and a new hidden neuron will be added around each unstable neuron to compensate for the inadequacy of the local structure for feature extraction. Moreover, the relations of network depth and information entropy with respect to the normal distribution of each weight between hidden layers are revealed. On basis of the relations revealed, a layer growing algorithm is designed considering the obedience rate of the normal distribution to control the number of hidden layers. In addition, a neuron pruning algorithm using the standard deviation of neuron activation probability is integrated in ALDBN to prune the redundant neurons with low discriminative ability. We first give the theoretical proof for the convergence of ALDBN, which is crucial to its stability. To exhibit its performance, parameter sensitivity analysis is then provided to investigate the effects of two key parameters in ALDBN. Finally, we compare ALDBN with five state-of-art methods on three benchmark datasets, and the comparative experimental results demonstrate that ALDBN outperforms the other five competitors in terms of the accuracies of common test, cross-validated test and holdout test.
Digital Image Forgery Detection Approaches: A Review and Analysis

Digital images have an inevitable role in almost all areas like clinical imaging, media broadcasting, crime analysis and scientific analysis etc. The phrase, “One image is equal to a thousand words”, is exactly fit as an image has more expressive capability than text messages. In all investigation, a photograph was always perceived as ‘proof of occurrence of an event’, due to a strong observation that, ‘seeing is believing ‘. Hence, images were considered as a ‘piece of truth’. Normally, an image is authentic if it is an originally recorded or captured from an actual scene or situation using any image capturing device. The captured image is expected to convey the original situation or scene at the source while capturing it in a real sense. In recent decades, due to the accessibility of modern photograph content altering software tools, it is very easy to transform the content of the image and therefore authenticity and integrity of the image is meager and tampering in digital images therefore does not require any specialist skills. This paper presents the modern methodological assessment and analysis of recent image forgery detection techniques. The various methods used in each stage of forgery detection techniques are also briefed. For an immediate reference, the tables of comparison are provided. The subject review paper is intended to help researchers to deliver useful understandings and modernized info about ongoing progress in forgery detection.
Training Large-Scale News Recommenders with Pretrained Language Models in the Loop

News recommendation calls for deep insights of news articles' underlying semantics. Therefore, pretrained language models (PLMs), like BERT and RoBERTa, may substantially contribute to the recommendation quality. However, it's extremely challenging to have news recommenders trained together with such big models: the learning of news recommenders requires intensive news encoding operations, whose cost is prohibitive if PLMs are used as the news encoder. In this paper, we propose a novel framework, {SpeedyFeed}, which efficiently trains PLMs-based news recommenders of superior quality. SpeedyFeed is highlighted for its light-weighted encoding pipeline, which gives rise to three major advantages. Firstly, it makes the intermedia results fully reusable for the training workflow, which removes most of the repetitive but redundant encoding operations. Secondly, it improves the data efficiency of the training workflow, where non-informative data can be eliminated from encoding. Thirdly, it further saves the cost by leveraging simplified news encoding and compact news representation. Extensive experiments show that SpeedyFeed leads to more than 100$\times$ acceleration of the training process, which enables big models to be trained efficiently and effectively over massive user data. The well-trained PLMs-based model from SpeedyFeed demonstrates highly competitive performance, where it outperforms the state-of-the-art news recommenders with significant margins. SpeedyFeed is also a model-agnostic framework, which is potentially applicable to a wide spectrum of content-based recommender systems; therefore, the whole framework is open-sourced to facilitate the progress in related areas.
Time Wavelength Interleaving Perceptron at 12 Giga-Ops/s with a Kerr Soliton Crystal Microcomb for Optical Neural Networks

Optical artificial neural networks (ONNs) have significant potential for ultra-high computing speed and energy efficiency. We report a novel approach to ONNs that uses integrated Kerr optical micro-combs. This approach is programmable and scalable and is capable of reaching ultra-high speeds. We demonstrate the basic building block ONNs &amp;mdash; a single neuron perceptron &amp;mdash; by mapping synapses onto 49 wavelengths to achieve an operating speed of 11.9 x 109 operations per second, or Giga-OPS, at 8 bits per operation, which equates to 95.2 gigabits/s (Gbps). We test the perceptron on handwritten-digit recognition and cancer-cell detection &amp;mdash; achieving over 90% and 85% accuracy, respectively. By scaling the perceptron to a deep learning network using off-the-shelf telecom technology we can achieve high throughput operation for matrix multiplication for real-time massive data processing.
Multi-Agent Path Planning based on MPC and DDPG

The problem of mixed static and dynamic obstacle avoidance is essential for path planning in highly dynamic environment. However, the paths formed by grid edges can be longer than the true shortest paths in the terrain since their headings are artificially constrained. Existing methods can hardly deal with dynamic obstacles. To address this problem, we propose a new algorithm combining Model Predictive Control (MPC) with Deep Deterministic Policy Gradient (DDPG). Firstly, we apply the MPC algorithm to predict the trajectory of dynamic obstacles. Secondly, the DDPG with continuous action space is designed to provide learning and autonomous decision-making capability for robots. Finally, we introduce the idea of the Artificial Potential Field to set the reward function to improve convergence speed and accuracy. We employ Unity 3D to perform simulation experiments in highly uncertain environment such as aircraft carrier decks and squares. The results show that our method has made great improvement on accuracy by 7%-30% compared with the other methods, and on the length of the path and turning angle by reducing 100 units and 400-450 degrees compared with DQN (Deep Q Network), respectively.