Reviewerly/exHarmony huggingface dataset:
COUNTERACTION TO THE SYSTEM AND ANTI-SYSTEM IN THE STRUGATSKY BROTHERS'STORY "SNAIL ON THE SLOPE"

Aspects of countering the system and anti-system are considered in the Strugatsky brothers ' novel "Snail on the slope". It is proved that science fiction writers managed to recreate quite complex aspects of the interaction of various systems within the framework of literary heritage. The broad palette of writers has absorbed an understanding of contradictions with underlying causes. In this article, some aspects of the interaction of the individual and the system, systems and anti-systems are demonstrated. Special attention is paid to the Soviet realities, which became the basis for the artistic picture of the fantastic world. This indicates the uniqueness of "Snail on the slope". One of the unique qualities is the influence on the story "Snail on the slope" by Franz Kafka (novels "The Process", "The Castle"). When analyzing the story, the focus of countering the system and the anti-system is transferred to the idea of the future. It is proved that the writers abandoned the continuous optimistic model of the future inherent in their earlier works. Strugatsky brothers have worked out in detail the mechanism of the inability to evolve into the future without radical changes in the essence of man. The literature of the second half of the twentieth century contains an extraordinary potential for understanding our current realities. Somewhere we are even in a more "winning" position – the authors and readers of that time had a more narrowed range of analysis, which was naturally inherent in "their time". At the same time, we are also partially vulnerable, especially in the historical and cultural context. Because the time of writing a work leaves an imprint on its content, the keys to its understanding are often in the social parallels of everyday life, which is fully understood by contemporaries, and "descendants" need to explain certain specific aspects of everyday life, psychological model or banal meaning of a particular word (they become anachronisms or acquire a different meaning). At the same time, it is worth paying attention to those questions that are transcendental in nature. Strugatsky brothers in "Snail on the Slope" experienced a corresponding experience – they tried to create a multi-faceted picture of the world with "their own" laws. At the same time, the task they set was not only to come up with, but also to "guess" the true concept, to bring the artistic fabric of the work closer to the "truth of life". Therefore, this work is fundamentally different from a significant part of their literary heritage, it is a kind of "experimental platform". They did not try to repeat this experience in their future work.
Applications of blockchain technology for smart cities in a nutshell

Advancement of technology has made humans use smartphones for transferring data. This has made an obvious requirement to have a record that tracks the modifications. Such an analysis provides a time-stamped record that is maintained by a user or by an algorithm. This time-stamped record is also called blockchain, which is distributed between computers. This is an immutable time series data for further access and research. Blockchain entirely depends on three pillars: transparency, decentralization, and immutability. This chapter deals with various applications of blockchain technology in smart cities. Applications of blockchain technology in smart cities include universal identification cards, smart property, smart power management, smart voting systems, etc. Such systems data will be of heterogeneous in nature and the introduction of blockchain provides a novel platform for sharing data with additional security as blockchain shares data through a distributed ledger that uses cryptography.
Enhancing Question Generation with Commonsense Knowledge

Question generation (QG) is to generate natural and grammatical questions that can be answered by a specific answer for a given context. Previous sequence-to-sequence models suffer from a problem that asking high-quality questions requires commonsense knowledge as backgrounds, which in most cases can not be learned directly from training data, resulting in unsatisfactory questions deprived of knowledge. In this paper, we propose a multi-task learning framework to introduce commonsense knowledge into question generation process. We first retrieve relevant commonsense knowledge triples from mature databases and select triples with the conversion information from source context to question. Based on these informative knowledge triples, we design two auxiliary tasks to incorporate commonsense knowledge into the main QG model, where one task is Concept Relation Classification and the other is Tail Concept Generation. Experimental results on SQuAD show that our proposed methods are able to noticeably improve the QG performance on both automatic and human evaluation metrics, demonstrating that incorporating external commonsense knowledge with multi-task learning can help the model generate human-like and high-quality questions.
A Trace Map Attack Against Special Ring-LWE Samples

The learning with errors (LWE) problem is one of the hard problems supporting the security of modern lattice-based cryptography. Ring-LWE is the analog of LWE over the ring of integers of a cyclotomic field, and it has provided efficient cryptosystems. In this paper, we give cryptanalysis against ring-LWE using the trace map over the ring of integers of a cyclotomic field, without using any reduction to other structured lattice problems. Since it maps to a ring of a smaller degree, a trace map attack is expected to be able to decrease the hardness of ring-LWE. However, the trace map does not necessarily transform ring-LWE samples to samples over the smaller ring with a common secret. We give a sufficient and necessary condition on a pair of ring-LWE samples for which the trace map attack is applicable. We call such a pair of samples special. We demonstrate how efficiently the trace map attack can solve ring-LWE when a special pair of samples is given. Specifically, we compare blocksizes of the Blockwise Korkine-Zolotarev (BKZ) algorithm required for solving ring-LWE in the trace map attack and a standard attack. Moreover, we discuss the (in)feasibility of the trace map attack for random ring-LWE samples to evaluate how the trace map attack can give a threat against ring-LWE-based cryptosystems on a practical side.
Reversible Data Hiding in Encrypted Images Based on Bit-plane Rearrangement and Huffman Coding

In this paper, an improved reversible data hiding in encrypted image based on bit-plane rearrangement and Huffman coding is proposed. In the scheme, the eight bit-planes of the image are first rearranged using sixteen image-based rearrangement ways. After that, sixteen one-dimensional bit-streams are generated from each bit-plane, and then, the numbers of consecutive 0s or consecutive 1s in a bit-stream are used to form a sequence, the generated sequence is then encoded into a binary stream by Huffman coding, which is the compressed bit-stream. XOR-encryption and scrambling operation will be applied to the compressed image to ensure security. The experimental results show that our scheme can achieve large embedding capacity and high security.
Burstiness-Aware Web Search Analysis on Different Levels of Evidences

Personalizing the analysis for web search potentially improves the search experience. A good analytical model for web search should leverage not only collective wisdom but also individual characteristics. Most of the existing analytical models, however, focus on how to utilize the collective wisdom, from a crowd, for instance. In this paper, we address the problem of user-specific web search analysis by considering the so-called burstiness in web search, which captures the behavior of rare words appearing many times in a single document. We go beyond click graph and propose two probabilistic topic models, Topic Independence Model and Topic Dependence Model. The former adopts the assumption that the generation of query terms and URLs are topically independent, and the latter captures the coupling between search queries and URLs. We also capture the temporal burstiness of topics by utilizing continuous Beta distribution. Through a large-scale analysis of a real-life search query log, we observe that each user's web search trail enjoys multiple kinds of user-based unique characteristics. On a massive search query log, the new models achieve a better held-out likelihood than existing baselines, and they can also effectively reveal the latent evolution of topics on the corpus level and user-based level.
Product Feature Extraction from the Descriptions

Data provides information about accessories, equipment, ... related to cars. The purpose of the problem is to label data such as brand, model,... from the automobile description.
Identification of Criminal and Non-Criminal Face Using Deep Learning and Image Processing

This paper executes a criminal face affirmation using Deep learning Algorithm. The data is used to get ready and test using Image Processing count. The longing of this paper is to execute using a pretrained significant learning network called Convolution neural association. In this paper, we address a methodology for face acknowledgment using Densenet pre-trained of which is definite and less incidents count for picture face area. Now we use Densenet for classifiers faces. The accuracy of the face affirmation is high. The proposed structure can adequately see more than one face which is useful for quickly glancing through assumed individuals as the figuring time is low. In India, we have a system for seeing occupant called Aadhaar. Our technique works in future to perceive this as a citizenship data base we can isolate among resident and outsider and further inspect if the recognized individual is criminal.
ConvLSTM for Human Activity Recognition

The research in Human Activity Recognition (HAR) using wearable probes and pocket devices has intensified to further understand and inherently foresee human behavior and their intentions. The researchers are seeking a system to consume the least amount of allocated resources to identify the consumer’s activity being performed. In this paper, we propose a state-of-the-art deep learning-based activity recognition architecture, a Convolutional Long Short-Term Memory (ConvLSTM) network . This ConvLSTM approach significantly improves the accuracy of classification of the six activities from raw data without the use of any major aspect of feature engineering hence, reducing the complexity of the model with a very minor pre-processing procedure. Our proposed model is able to achieve a staggering 94% accuracy on the UCI HAR public dataset. During performance comparisons with earlier models, we were able to notice profitable improvements against Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) Network, Deep Neural Network (DNN) models, and also against linear and non-linear machine learning models which heavily depend upon manually manufactured featured data.
Identification of Scandinavian Languages from Speech Using Bottleneck Features and X-Vectors

This work deals with identification of the three main Scandinavian languages (Swedish, Danish and Norwegian) from spoken data. For this purpose, various state-of-the-art approaches are adopted, compared and combined, including i-vectors, deep neural networks (DNNs), bottleneck features (BTNs) as well as x-vectors. The best resulting approaches take advantage of multilingual BTNs and allow us to identify the target languages in speech segments lasting 5 s with a very low error rate around 1%. Therefore, they have many practical applications, such as in systems for transcription of Scandinavian TV and radio programs, where different persons speaking any of the target languages may occur. Within identification of Norwegian, we also focus on an unexplored sub-task of distinguishing between Bokmål and Nynorsk. Our results show that this problem is much harder to solve since these two language variants are acoustically very similar to each other: the best error rate achieved in this case is around 20%.
Leveraging Inter-step Dependencies for Information Extraction from Procedural Task Instructions

Written instructions are among the most prevalent means of transferring procedural knowledge. Hence, enabling computers to obtain information from textual instructions is crucial for future AI agents. Extracting information from a step of a multi-part instruction is usually performed by solely considering the semantic and syntactic information of the step itself. In procedural task instructions, however, there is a sequential dependency across entities throughout the entire task, which would be of value for optimal information extraction. However, conventional language models such as transformers have difficulties processing long text, i.e., the entire instruction text from the first step to the last one, since their scope of attention is limited to a relatively short chunk of text. As a result, the dependencies among the steps of a longer procedure are often overlooked. This paper suggests a BERT-GRU model for leveraging sequential dependencies among all steps in a procedure. We present experiments on annotated datasets of text instructions in two different domains, i.e., repairing electronics and cooking, showing our model’s advantage compared to standard transformer models. Moreover, we employ a sequence prediction model to show the correlation between the predictability of tags and the performance benefit achieved by leveraging inter-step dependencies.
An image watermarking framework based on PSO and FrQWT

This paper primarily explored the usefulness of singular value decomposition for the image watermarking in pursuit of overcoming its typically experienced issues. For this intention, an efficient watermarking scheme optimized by particle swarm optimization (PSO) is designed based on magic-matrix scrambling method and fractional quaternion wavelet transform (FrQWT). The central idea is to shuffle all picture elements of the watermark using magic-matrix scrambling method followed by its insertion into the fruit amplitude coefficients smartly. Another beauty of the designed proficiency is the usage of PSO algorithm to naturally optimize the parameters involved in watermarking strategy. Finally, the sturdyness of the designed watermarking algorithm is investigated against numerous intentional and unintentional attacks.
A New Semi-automatic Annotation Model via Semantic Boundary Estimation for Scene Text Detection

Manually annotating a data set for scene text detection is extremely time-consuming. In this paper, we propose a new semi-automatic annotation model to produce tight polygonal annotations for text instances in scene images, based on the input of manually annotated text center lines. Our approach first generates multiple candidate boundaries, which share the same input center line. Then, by training a fastidious content recognizer, optimal boundary selection is performed. The bounded text region, which achieves the smallest recognition loss, is selected as the tightest of the text. As this optimal boundary estimation is guided by semantic recognition, our method is called Semantic Boundary Estimation. Experiment results show that only half clicks compared to manually annotated polygon, are input to annotate center line, and precise polygon text region annotation is automatically produced. A high recall of more than 95% at IoU > 0.5 and 80% at IoU > 0.7 is achieved, demonstrating the high agreement with the original ground truth. In addition, using the generated annotations on benchmarks, such as Total-Text, CTW1500 and ICDAR2015, to train state-of-the-art detectors can achieve similar performance to those trained with manual annotations. This further verifies the good annotation performance. A annotation toolkit based on the proposed model is available at CenterlineAnnotation.
Hierarchical Stream Clustering Based NEWS Summarization System

News feed is one of the potential information providing sources which give updates on various topics of different domains. These updates on various topics need to be collected since the domain specific interested users are in need of important updates in their domains with organized data from various sources. In this paper, the news summarization system is proposed for the news data streams from RSS feeds and Google news. Since news stream analysis requires live content, the news data are continuously collected for our experimentation. The major contributions of this work involve domain corpus based news collection, news content extraction, hierarchical clustering of the news and summarization of news. Many of the existing news summarization systems lack in providing dynamic content with domain wise representation. This is alleviated in our proposed system by tagging the news feed with domain corpuses and organizing the news streams with the hierarchical structure with topic wise representation. Further, the news streams are summarized for the users with a novel summarization algorithm. The proposed summarization system generates topic wise summaries effectively for the user and no system in the literature has handled the news summarization by collecting the data dynamically and organizing the content hierarchically. The proposed system is compared with existing systems and achieves better results in generating news summaries. The Online news content editors are highly benefitted by this system for instantly getting the news summaries of their domain interest.
VitrAI: Applying Explainable AI in the Real World

With recent progress in the field of Explainable Artificial Intelligence (XAI) and increasing use in practice, the need for an evaluation of different XAI methods and their explanation quality in practical usage scenarios arises. For this purpose, we present VitrAI, which is a web-based service with the goal of uniformly demonstrating four different XAI algorithms in the context of three real life scenarios and evaluating their performance and comprehensibility for humans. This work highlights practical obstacles to the use of XAI methods, and also shows that various XAI algorithms are only partially consistent with each other and unsystematically meet human expectations.
On the Capacity of Latent Variable Private Information Retrieval

In latent-variable private information retrieval (LV-PIR), a user wishes to retrieve one out of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> messages (indexed by θ) without revealing any information about a sensitive latent attribute (modeled by a latent variable <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$S$</tex> correlated with θ). While conventional PIR protocols, which keep θ2private, also suffice for hiding S, they can be too costly in terms of the download overhead. In this paper, we characterize the capacity (equivalently, the optimal download cost) of LV-PIR as a function of the distribution P <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S|</inf> <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">θ</inf> . We present a converse proof that yields a lower bound on the optimal download cost, and a matching achievable scheme. The optimal scheme, however, involves an exhaustive search over subset queries and over all messages, which can be computationally prohibitive. We further present two low-complexity, albeit sub-optimal, schemes that also outperform the conventional PIR solution.
Outsourcing Secure MPC to Untrusted Cloud Environments with Correctness Verification

With the increasing interest in Secure Multi-Party Computation protocols (MPC), there have been several works such as the SPDZ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> protocol that tackled this problem under a malicious security with dishonest majority attack model. However, most of these MPC efforts assume that the nodes running the computations are also supplying the inputs, which is not a realistic assumption for many real-life applications. In this paper, we extend the SPDZ protocol to enable clients outsource data and computation to the clouds while ensuring the correctness of the results, in addition to integrity and confidentiality of the input and output. We guarantee that the computation among nodes is done correctly by verifying their output’s Message Authentication Codes (MACs) at the end. Specifically, we delegate this task to an honest server. Our approach strives to minimize the burden on clients while enabling cheating detection even when assuming a malicious attack model with dishonest majority.
Inscription Segmentation Using Synthetic Inscription Images for Text Detection at Stone Monuments

Stone monuments have historical value, and the inscriptions engraved on them can tell us about the events and people at the time of their installation. Photography is an easy way to record inscriptions; however, the light falling on the monument, the resulting shadows, and the innate texture of the stone can make the text in the photographs unclear and difficult to recognize. This paper presents a method for inferring pixel-wise text areas in a stone monument image by developing a deep learning network that can deduce the shape of kanji characters. Our method uses pseudo-inscription images for training a deep neural network, which is generated by synthesizing a shaded image representing the engraved text and stone texture image. Through experiments using a High Resolution Net (HRNet), we confirm that the HRNet achieves high accuracy in the task of inscription segmentation and that training with pseudo-inscription images is effective in detecting inscriptions on real stone monuments. Thus, synthetic inscription images can facilitate efficient and accurate detection of text on stone monuments, thereby contributing to further history research.
Self-taught cross-domain few-shot learning with weakly supervised object localization and task-decomposition

The shift between the source and target domains is the main challenge in cross-domain few-shot learning (CD-FSL) tasks. However, the target domain is unknown during training in the source domain, which results in a lack of directed guidance for target tasks. We observe that, owing to similar backgrounds in the target domains, self-labelled samples can be applied as prior tasks to transfer knowledge onto target tasks. Accordingly, we propose a task-expansion-decomposition framework for CD-FSL called the self-taught (ST) approach, which alleviates the problem of non-target guidance by constructing task-oriented metric spaces. Specifically, weakly supervised object localization (WSOL) and self-supervised technologies are employed to enrich task-oriented samples by exchanging and rotating discriminative regions, which generates a more abundant task set. Then, these tasks are decomposed into several tasks to complete few-shot recognition and rotation classification tasks. Transferring the source knowledge onto the target tasks and focusing on discriminative regions is beneficial to the task completion. We conducted extensive experiments under a cross-domain setting, including eight target domains: CUB, Cars, Places, Plantae, CropDieases, EuroSAT, ISIC, and ChestX. The experimental results demonstrate that the proposed ST approach is applicable to various metric-based models and provides promising improvements to CD-FSL.