Reviewerly/exHarmony huggingface dataset:
Gaussian Information Entropy based band Reduction for Unsupervised Hyperspectral Video Tracking

Hyperspectral videos, which provide extra spectral characteristics besides spatial and temporal information, can improve the performance of object tracking using spectral signatures. However, there is a lack of labeled hyperspectral videos to support deep learning based model design. On the contrary, object tracking in the color space has been well developed in the past decade with many benchmark tracking models, e.g., SiamBAN. Therefore, how to transfer models designed in the color space to the hyperspectral space is of great importance. In this paper, hyperspectral videos are reduced into 3 bands using a band reduction algorithm, by which the existing well-trained trackers can be directly used. Specifically, Gaussian Information Entropy (GIE) is used to transform a hyperspectral video into a 3-band pseudo-color video, by which hyperspectral object tracking is conducted in an unsupervised mode. Experimental results demonstrate that object trackers designed in the color space can be transferred to hyperspectral videos using band reduction algorithms and the GIE based reduction is more effective than several well-known band reduction algorithms when using SiamBAN.
Optimal Shape Servoing with Task-focused Convergence Constraints

Most deformable object manipulation tasks still rely on skillful human operators. To automate such tasks, a robotic system should not only be able to deform an object to a desired shape but also servo its deformation along a specific path towards the desired shape. We propose a shape servoing control scheme to automate such tasks. Our scheme controls the deformation trajectory towards the desired shape by imposing task-focused convergence constraints. The constraints impose how fast the different regions of the object converge to the desired shape. Integrating such a behavior in shape servoing forms our main contribution. Experiments, carried out on rubber layer assembly tasks, show that our control scheme outperforms a state-of-the-art shape servoing scheme.
Investigation of Transfer Learning for End-to-End Russian Speech Recognition

End-to-end speech recognition systems reduce the speech decoding time and required amount of memory comparing to standard systems. However they need much more data for training, which complicates creation of such systems for low-resourced languages. One way to improve performance of end-to-end low-resourced speech recognition system is model’s pre-training by transfer learning, that is training the model on the non-target data and then transferring the trained parameters to the target model. The aim of the current research was to investigate application of transfer learning to the training of the end-to-end Russian speech recognition system in low-resourced conditions. We used several speech corpora of different languages for pre-training. Then end-to-end model was fine-tuned on a small Russian speech corpus of 60 h. We conducted experiments on application of transfer learning in different parts of the model (feature extraction block, encoder, and attention mechanism) as well as on freezing of the lower layers. We have achieved 24.53% relative word error rate reduction comparing to the baseline system trained without transfer learning.
Blockchain-based Configuration Management System

For SW development convenience, various development support systems like configuration management system, CI/CD, and Bugtrack system are required. Many open projects are underway using the connected environment advance of the internet. This open project uses a sharing configuration system. However, the source code history stored in the configuration system may be lost due to hw fault or manager’s mistake. In this study, we propose a blockchain-based configuration system to solve these problems. The proposed configuration system grafts the configuration unit function to the blockchain platform. In order to verify the effectiveness of the blockchain based configuration system proposed in this study, the functions to be provided by the configuration system and the functions provided by the blockchain platform were verified. As a result, it was confirmed that both functions were perfectly provided. If the configuration system proposed in this study is applied to an open project, data loss prevention and integrity can be guaranteed by applying the strengths of the blockchain platform while keeping the original purpose of the configuration management system. However, since this study used a blockchain platform, there is a limitation in not checking the performance. Therefore, further research on this is needed.
Deep Learning for Approximate Nearest Neighbour Search: A Survey and Future Directions

Approximate nearest neighbour search (ANNS) in high-dimensional space is an essential and fundamental operation in many applications from many domains such as multimedia database, information retrieval and computer vision. With the rapidly growing volume of data and the dramatically increasing demands of users, traditional heuristic-based ANNS solutions have been facing great challenges in terms of both efficiency and accuracy. Inspired by the recent successes of deep learning in many fields, substantial efforts have been devoted to applying deep learning techniques to ANNS for learning to index and learning to search, resulting in numerous algorithms that achieve state-of-the-art performance compared with conventional methods. In this survey paper, we comprehensively review the different types of deep learning-based ANNS methods according to two learning paradigms: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learning to index</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">learning to search</i> . We provide a comprehensive overview and analysis of these methods in a systematic manner. Based on the overview, we point out that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">end-to-end learning</i> will be a new and promising research direction for deep learning-based ANNS, i.e., applying deep learning techniques to jointly learn the indexing and searching together, such that the underlying knowledge learned from data can directly contribute to the final searching performance. Finally, we conduct experiments and provide general performance analyses for the representative deep learning-based ANNS algorithms.
YODA: Yet anOther agent Description lAnguage

Multi-Agent Systems (MASs) consist of a large number of entities, or agents, that cooperate and compete with each other to reach local and global goals. Agents operate immersed in an environment that can affect their behaviour. To design and predict the behaviour of MASs, it is crucial to use tools that allow not only to specify the behaviour of the single agent but allow to take into account the enclosing environment and its effect on the agents. In this paper, the framework YODA (Yet anOther agent Description lAnguage) is presented. The proposed specification language permits describing an agent in terms of the action it can perform and that are selected according to its state and a set of variables (sensors) that are observed from the environment. Action execution can change the agent state. The proposed language is also equipped with syntactic constructs that permits describing the enclosing environment and its evolution in time. At each step, the resulting model evolves as the consequence of the interaction among agents and the environment. In the paper, some case studies are used to show how our proposal works.
Smart Home Personal Assistants: Fueled by Natural Language Processor and Blockchain Technology

With the ever-increasing cost of livelihood, technological intervention is necessary to keep costs under control. Hence, smart home technology can be considered as a well-established and entrenched region of interest. In the current scenario, research subsidizes present-time houses, enabling the user to design and manage a smart home to conserve energy while allowing for more autonomous applications. A voice-activated home automation system integrates the Internet of Things, Artificial Intelligence, Blockchain, and Natural Language Processing to deliver a cost-effective and efficient method of interacting with household equipment. The primary objective of this paper is to assess SPAs using blockchain technology and a natural language processor (NLP), as well as the design of a SPA based on the internet of things. Applicable cyber threats and countermeasures are also addressed in this article. Using Yule's coefficient of association, a case study was undertaken to evaluate the association between privacy and security threats posed by this modern technology. The findings show that privacy and security are positively related.
Continual Learning for Document Image Binarization

In the field of Document Image Analysis (DIA), it is common to find great heterogeneity in terms of the possible graphic domains. In this sense, it is interesting to build neural models that can be sequentially adapted to new domains without losing the knowledge from the domains already learned. This learning paradigm is known as Continual (or Lifelong) Learning (CL). Although the adaptation comes along with a training set of the new domain, neural networks suffer what is known as "catastrophic forgetting". Therefore, assuming the constraint of not keeping data from the domains already addressed, this paradigm represents a challenge yet to be solved. This work presents an approach for CL in document image binarization, one of the most considered tasks within the DIA field. Our results report that it is indeed feasible to address CL in this field, given that the approach is successfully implemented and outperforms the baseline by a wide margin in most of the analyzed scenarios.
On the Development of an Optimized Web Usage Mining Tool

Web usage mining – a category of web mining and described as the process of extracting information from weblogs to extract patterns to make out some meaningful information therefrom. This paper reports on a new Optimized Web Usage Mining Tool (OWUMT). The web usage mining tool developed as a part of this research endeavour has incorporated newer classification algorithms namely Random Forest with Ant Colony Optimization, Random Forest with Genetic Algorithm, and Random Forest with Ant Colony Optimization and Genetic Algorithm have been incorporated in addition to traditional algorithms like Naïve Bayes and Decision Tree. The performance of three classification algorithms namely Random Forest, Naïve Bayes, and Decision Tree has been compared by executing these algorithms in OWUMT, Weka and Rapidminer, the latter two being available in the shareware domain. Selected algorithms have been used to classify the data contained in server log files. Experimental results of three classification algorithms have been evaluated on two parameters, namely, classification accuracy and error
Signature Algorithms on Non-commutative Algebras Over Finite Fields of Characteristic Two

The paper considers digital signature algorithms with a hidden group, security of which is based on the computational difficulty of solving a system of many quadratic equations with many unknowns. The attention is paid to an implementation of the said type of algorithms on finite non-commutative associative algebras set over the finite fields of characteristics two. The use of the latter type of algebraic support is aimed to improving the performance and reducing the hardware implementation cost. A new algebraic algorithm with a hidden group is introduced, in which a four-dimensional non-commutative algebra is used as algebraic support. In the used algebra the vector multiplication operation is defined by a sparse basis vector multiplication table. Decomposition of the non-commutative algebra into set of commutative subalgebras is studied. The formulas describing the number of the subalgebras of every type are also presented. It is shown that the factorization of the order of the hidden group is non-critical for the security of the signature algorithm, so one can apply the GF(2z) fields with a sufficiently large number of different values of the degree z, including those that are equal to a Mersenne exponent.
Face Shape-Guided Deep Feature Alignment for Face Recognition Robust to Face Misalignment

For the past decades, face recognition (FR) has been actively studied in computer vision and pattern recognition society. Recently, due to the advances in deep learning, the FR technology shows high performance for most of the benchmark datasets. However, when the FR algorithm is applied to a real-world scenario, the performance has been known to be still unsatisfactory. This is mainly attributed to the mismatch between training and testing sets. Among such mismatches, face misalignment between training and testing faces is one of the factors that hinder successful FR. To address this limitation, we propose a face shape-guided deep feature alignment framework for FR robust to the face misalignment. Based on a face shape prior (e.g., face keypoints), we train the proposed deep network by introducing alignment processes, i.e., pixel and feature alignments, between well-aligned and misaligned face images. Through the pixel alignment process that decodes the aggregated feature extracted from a face image and face shape prior, we add the auxiliary task to reconstruct the well-aligned face image. Since the aggregated features are linked to the face feature extraction network as a guide via the feature alignment process, we train the robust face feature to the face misalignment. Even if the face shape estimation is required in the training stage, the additional face alignment process, which is usually incorporated in the conventional FR pipeline, is not necessarily needed in the testing phase. Through the comparative experiments, we validate the effectiveness of the proposed method for the face misalignment with the FR datasets.
Document Database Watermark Algorithm Based on Connected Graph

With the wide application of Web technology and big data, non-relational database (NoSQL database) occupies an increasingly important position. Document database is the most widely used NoSQL database. In order to solve the problems such as copyright protection, data authentication and so on, a document database watermark scheme based on the connected graph is proposed. According to the real documents in the database, the pseudo documents are generated by genetic algorithm, and the watermark is embedded in the modifiable fields of the pseudo documents. The fitness function ensures the integration of real and pseudo documents. So that attackers cannot distinguish between real and pseudo documents, which increases the difficulty of attack. By forming pseudo documents into k-connected graphs with pointer fields, authorized users can distinguish between real and pseudo documents through connectivity. Experimental results show that the scheme achieves good invisibility and robustness.
Dynamic Objective Field for Marine Element Sampling Based on γ Discount Function

Due to the limitations of observation assets, adaptive sampling is widely used in the ocean. It works by allocating observed assets optimally. And setting the target field is the first and most important step in it. The target field directly affects the design of the sampling scheme by evaluating the sampling value of each location. However, most of the existing target fields are static, which lacks the evolution process over time. When operating a mobile platform, the marine environment and platform location are constantly changing. Obviously, static fields are not suitable for dynamic situations. Linking the series of prior information with time, we developed a method to design a dynamic field. Through the $\gamma$ discount reward strategy, the information sequence is constantly updated and integrated. This method achieves the maximum use of prior sequences in 3 types of environment. It provides some insight into the design of sampling schemes for mobile platforms, especially in long-term observation activities where the environment changes greatly.
Quadratic Multiparty Randomized Encodings Beyond Honest Majority and Their Applications

Multiparty randomized encodings (Applebaum, Brakerski, and Tsabary, SICOMP 2021) reduce the task of securely computing a complicated multiparty functionality f to the task of securely computing a simpler functionality g. The reduction is non-interactive and preserves information-theoretic security against a passive (semi-honest) adversary, also referred to as privacy. The special case of a degree-2 encoding g (2MPRE) has recently found several applications to secure multiparty computation (MPC) with either information-theoretic security or making black-box access to cryptographic primitives. Unfortunately, as all known constructions are based on information-theoretic MPC protocols in the plain model, they can only be private with an honest majority. In this paper, we break the honest-majority barrier and present the first construction of general 2MPRE that remains secure in the presence of a dishonest majority. Our construction encodes every n-party functionality f by a 2MPRE that tolerates at most $$t=\lfloor 2n/3\rfloor $$ passive corruptions. We derive several applications including: (1) The first non-interactive client-server MPC protocol with perfect privacy against any coalition of a minority of the servers and up to t of the n clients; (2) Completeness of 3-party functionalities under non-interactive t-private reductions; and (3) A single-round t-private reduction from general-MPC to an ideal oblivious transfer (OT). These positive results partially resolve open questions that were posed in several previous works. We also show that t-private 2MPREs are necessary for solving (2) and (3), thus establishing new equivalence theorems between these three notions. Finally, we present a new approach for constructing fully-private 2MPREs based on multi-round protocols in the OT-hybrid model that achieve perfect privacy against active attacks. Moreover, by slightly restricting the power of the active adversary, we derive an equivalence between these notions. This forms a surprising, and quite unique, connection between a non-interactive passively-private primitive to an interactive actively-private primitive.
Unseen Classes at a Later Time? No Problem

Recent progress towards learning from limited supervision has encouraged efforts towards designing models that can recognize novel classes at test time (generalized zeroshot learning or GZSL).GZSL approaches assume knowledge of all classes, with or without labeled data, beforehand.However, practical scenarios demand models that are adaptable and can handle dynamic addition of new seen and unseen classes on the fly (i.e continual generalized zero-shot learning or CGZSL).One solution is to sequentially retrain and reuse conventional GZSL methods, however, such an approach suffers from catastrophic forgetting leading to suboptimal generalization performance.A few recent efforts towards tackling CGZSL have been limited by difference in settings, practicality, data splits and protocols followed -inhibiting fair comparison and a clear direction forward.Motivated from these observations, in this work, we firstly consolidate the different CGZSL setting variants and propose a new Online-CGZSL setting which is more practical and flexible.Secondly, we introduce a unified feature-generative framework for CGZSL that leverages bi-directional incremental alignment to dynamically adapt to addition of new classes, with or without labeled data, that arrive over time in any of these CGZSL settings.Our comprehensive experiments and analysis on five benchmark datasets and comparison with baselines show that our approach consistently outperforms existing methods, especially on the more practical Online setting.
Self-supervised Information Retrieval Trained from Self-generated Sets of Queries and Relevant Documents

Large corpora of textual data such as scientific papers, patents, legal documents, reviews, etc., represent precious unstructured knowledge that needs semantic information retrieval engines to be extracted. Current best information retrieval solutions use supervised deep learning approaches, requiring large labelled training sets of queries and corresponding relevant documents, often unavailable, or their preparation is economically infeasible for most organizations. In this work, we present a new self-supervised method to train a neural solution to model and efficiently search large corpora of documents against arbitrary queries without requiring labelled dataset of queries and associated relevant papers. The core points of our self-supervised approach are (i) a method to self-generate the training set of queries and their relevant documents from the corpus itself, without any kind of human supervision, (ii) a deep metric learning approach to model their semantic space of relationships, and (iii) the incorporation of a multi-dimensional index for this neural semantic space over which running queries efficiently. To better stress the performance of the approach, we applied it to a totally unsupervised corpus with complex contents of over half a million Italian legal documents.
Weakly Supervised Foreground Object Detection Network Using Background Model Image

In visual surveillance, deep learning-based foreground object detection algorithms are superior to classical background subtraction (BGS)-based algorithms. However, deep learning-based methods are limited because detection performance deteriorates in a new environment different from the training environment. This limitation can be solved by retraining the model using additional ground-truth labels in the new environment. However, generating ground-truth labels for visual surveillance is time-consuming and expensive. This paper proposes a method that does not require foreground labels when adapting to a new environment. To this end, we propose an integrated network that produces two kinds of outputs a background model image and a foreground object map. We can adapt to the new environment by retraining using a background model image. The proposed method consists of one encoder and two decoders for detecting foreground objects and a background model image. It is designed to enable real-time processing with desktop GPUs. The proposed method shows 14.46% improved FM in a new environment different from training and 11.49% higher FM than the latest BGS algorithm.
Accident Detection through CCTV Surveillance

Traffic has become an integral part of people's life in recent years, influencing a wide range of human activities and services. Accidents have been a leading cause of death worldwide, particularly in India. Approximately more than 80% of accident related deaths cases are caused by lack of timely assistance to accident victims rather than the accident itself. One of the basic reason for the vehicle accident is the vehicle speed and there are many others too. Many lives can be saved if there was a proper emergency service that could get accident information in time. Computer vision-based accident detection on traffic video surveillance is a advantageous but also a challenging task. This paper proposes an automated accident detection from traffic videos. Here the frames are extracted from the video shots. Key-frames are identified using the histogram difference, later features are extracted from it using the VGGNET and finally classified as accident or non-accident. The proposed methodology has been compared with the literature methods and found out to be the best.