Reviewerly/exHarmony huggingface dataset:
Thinking While Moving: Deep Reinforcement Learning with Concurrent Control

We study reinforcement learning in settings where sampling an action from the policy must be done concurrently with the time evolution of the controlled system, such as when a robot must decide on the next action while still performing the previous action. Much like a person or an animal, the robot must and move at the same time, deciding on its next action before the previous one has completed. In order to develop an algorithmic framework for such concurrent control problems, we start with a continuous-time formulation of the Bellman equations, and then discretize them in a way that is aware of system delays. We instantiate this new class of approximate dynamic programming methods via a simple architectural extension to existing value-based deep reinforcement learning algorithms. We evaluate our methods on simulated benchmark tasks and a large-scale robotic grasping task where the robot must think while moving.''
Media life in the Anthropocene

This short article focuses on the interrelation of two seemingly invisible phenomena that have a growing influence on our daily lives: on the one hand, our intimate relationship with devices and practices mediated by technology, which make up our media life , and on the other, the great impact that our way of life has on the planet that we inhabit, which invites us to think about our role in the Anthropocene, a new geological era in which human activity is the main agent of change in the Earth’s ecosystems. Regarding these key trends as closely connected gives us the opportunity to find accessible solutions to our daily concerns on environmental issues. In other words, considering our media life in the Anthropocene means to be aware of the challenges that we face while asking ourselves how to claim a responsible use of technology that – in line with our humanity – will help us to confront them.
Interactive Visualization Interfaces for Big Data Analysis Using Combination of Dimensionality Reduction Methods: A Brief Review

The Big Data analysis allows to generate knowledge based on mathematical models that surpass human capabilities, and therefore it is necessary to have robust computer systems. In this connection, the dimensionality reduction (DR) allows to perform approximations to make data perceptible in a simple and compact way while also the computational cost is reduced. Additionally, interactive interfaces enable the user to work with algorithms involving complex mathematical and statistical processes typically aimed at providing weighting factors to each RD algorithm to find the best way to represent data at a low dimension. In this study, a bibliographic re-view of the different models of interactive interfaces for the analysis of Big Data using RD is presented, by considering different, existing proposals and approaches on how to display the information. Particularly, those approaches based on mental processes and uses of color along with an intuitive handling are of special interest.
Two-Stream Network Based on Visual Saliency Sharing for 3D Model Recognition

Shape representation for 3D models is an important topic in computer vision, multimedia analysis, and computer graphics. Recent multiview-based methods demonstrate promising performance for 3D model recognition and retrieval. However, most of the multiview-based methods focus on the visual information from the taken views and ignore correlation information among these views, which means the similarity and differentiation of multiple views have lost in their methods. In order to address this issue, we propose a novel two-stream network architecture for 3D model recognition and retrieval. The proposed network includes two sub-networks: a multi-view convolutional neural network (MVCNN) that extracts the view information from the taken views, and an Visual Saliency model that defines the weight of views based on the similarity and differentiation information of multiple views. Special, the weight of views defined by the Visual Saliency model can effectively be used to guide the visual information fusion in MVCNN model. This design can make the MVCNN model save visual information and the correlation information of these views in the learning step. Finally, we employ early-fusion method to fuse the feature vectors from MVCNN model and Visual Saliency model respectively, to generate the shape descriptor for 3D model recognition and retrieval. The experimental result on two public datasets, ModelNet40 and ShapeNetCore55, demonstrates the correlation information of multiple views is crucial for view-based 3D model recognition methods and the proposed method can achieve the state-of-the-art performance on both 3D object classification and retrieval.
Exploiting Motion Information from Unlabeled Videos for Static Image Action Recognition

Static image action recognition, which aims to recognize action based on a single image, usually relies on expensive human labeling effort such as adequate labeled action images and large-scale labeled image dataset. In contrast, abundant unlabeled videos can be economically obtained. Therefore, several works have explored using unlabeled videos to facilitate image action recognition, which can be categorized into the following two groups: (a) enhance visual representations of action images with a designed proxy task on unlabeled videos, which falls into the scope of self-supervised learning; (b) generate auxiliary representations for action images with the generator learned from unlabeled videos. In this paper, we integrate the above two strategies in a unified framework, which consists of Visual Representation Enhancement (VRE) module and Motion Representation Augmentation (MRA) module. Specifically, the VRE module includes a proxy task which imposes pseudo motion label constraint and temporal coherence constraint on unlabeled videos, while the MRA module could predict the motion information of a static action image by exploiting unlabeled videos. We demonstrate the superiority of our framework based on four benchmark human action datasets with limited labeled data.
Difference between Quantum Annealing by Imaginary-Time and Real-Time Schrödinger Equations of Grover’s Search

We confirmed the annealing time of Grover's search which is required to obtain desired success probability for quantum annealing by the imaginary-time and the real-time Schr\"{o}dinger equation with two kinds of schedulings; one linearly decreases the quantum fluctuation and the other tunes the evolution rate of the Hamiltonian based on the adiabatic condition. With linear scheduling, the required annealing time for quantum annealing by the imaginary-time Schr\"{o}dinger equation is of order $\log N$, which is very different from $O(N)$ required for the quantum annealing by the real-time Schr\"{o}dinger equation. With the scheduling based on the adiabatic condition, the required annealing time is of order $\sqrt{N}$, which is identical to the annealing time for quantum annealing by the real-time Schr\"{o}dinger equation. Although the scheduling based on the adiabatic condition is optimal for the quantum annealing by the real-time Schr\"{o}dinger equation, it is inefficient for the quantum annealing by the imaginary-time Schr\"{o}dinger equation. This result implies that the optimal scheduling for the quantum annealing by the imaginary-time and the real-time Schr\"{o}dinger equation is very different, and the efficient scheduling considered with the quantum Monte Carlo methods, which is based on imaginary-time Schr\"{o}dinger equation, is not necessarily effective to improve the performance of quantum annealing by the real-time Schr\"{o}dinger equation. We discuss the efficient scheduling for quantum annealing by the imaginary-time Schr\"{o}dinger equation with respect to the exponential decay of excited states.
Development and Implementation of Enhanced Shortest Path Algorithm for Navigation of Mobile Robot for Warehouse Automation (MRWA)

This paper presents development and implementation of a unique shortest path algorithm for navigation of mobile robot in a dynamic environment. A grid of passive RFID (Radio Frequency Identification) tags is utilized for the localization of swarm robots in the warehouse. The RFID tags, the mobile robots and the remote Control unit are the key components for the MRWA algorithm. On the desire of user, the robots are able to move objects in parallel from one place of the warehouse to another in an efficient and effective manner. Control unit controls the movement of all robots and navigate the selected robot to the desired location using shortest path MRWA algorithm. The algorithm also takes consideration of obstacles placed in the path, avoiding the collision of robot. The validity of the design was also tested and verified by practical implementation of a 5×5 gird in an experimental lab environment.
Efficient Algorithms for Generating Provably Near-Optimal Cluster Descriptors for Explainability

Improving the explainability of the results from machine learning methods has become an important research goal. Here, we study the problem of making clusters more interpretable by extending a recent approach of [Davidson et al., NeurIPS 2018] for constructing succinct representations for clusters. Given a set of objects S, a partition π of S (into clusters), and a universe T of tags such that each element in S is associated with a subset of tags, the goal is to find a representative set of tags for each cluster such that those sets are pairwise-disjoint and the total size of all the representatives is minimized. Since this problem is NP-hard in general, we develop approximation algorithms with provable performance guarantees for the problem. We also show applications to explain clusters from datasets, including clusters of genomic sequences that represent different threat levels.
Mechanistic and data-driven agent-based models to explain human behavior in online networked group anagram games

In anagram games, players are provided with letters for forming as many words as possible over a specified time duration. Anagram games have been used in controlled experiments to study problems such as collective identity, effects of goal-setting, internal-external attributions, test anxiety, and others. The majority of work on anagram games involves individual players. Recently, work has expanded to group anagram games where players cooperate by sharing letters. In this work, we analyze experimental data from online social networked experiments of group anagram games. We develop mechanistic and data-driven models of human decision-making to predict detailed game player actions (e.g., what word to form next). With these results, we develop a composite agent-based modeling and simulation platform that incorporates the models from data analysis. We compare model predictions against experimental data, which enables us to provide explanations of human decision-making and behavior. Finally, we provide illustrative case studies using agent-based simulations to demonstrate the efficacy of models to provide insights that are beyond those from experiments alone.
A Dutch coreference resolution system with an evaluation on literary fiction

Coreference resolution is the task of identifying descriptions that refer to the same entity. In this paper we consider the task of entity coreference resolution for Dutch with a particular focus on literary texts. We make three main contributions. First, we propose a simplified annotation scheme to reduce annotation effort. This scheme is used for the annotation of a corpus of 107k tokens from 21 contemporary works of literature. Second, we present a rule-based coreference resolution system for Dutch based on the Stanford deterministic multi-sieve coreference architecture and heuristic rules for quote attribution. Our system (dutchcoref) forms a simple but strong baseline and improves on previous systems in shared task evaluations. Finally, we perform an evaluation and error analysis on literary texts which highlights difficult cases of coreference in general, and the literary domain in particular. The code of our system is made available at https://github.com/andreasvc/dutchcoref/
A New Efficient Algorithm Based on Multi-Classifiers Model for Classification

Classification is one of the most important problems in data mining and machine learning. The quality and quantity of classification rules are two factors to influence the accuracy of classification. In this paper, we propose a new algorithm to enhance the final classification accuracy, called CMCM (Classification based on Multiple Classifier Models), which consists of two classification models. Model1 centers on the improvement of quality. The optimal attribute values are obtained as the first item of a classification rule from both the items and their complements. While in Model2, quantity is taken into consideration, so it constructs two candidate sets and uses the one-versus-many strategy to generate several rules at one time. The experiment results demonstrate that CMCM can achieve higher classification accuracy than the proposed classification approaches. CMCM can extract sufficient high-quality rules for imbalanced data. Meanwhile, it can also obtain sufficient latent information for classification.
Efficient Reconfigurable Integrated Cryptosystems for Cybersecurity Protection

Modern cryptosystems comprise various cryptographic mechanisms designed to defend cyber-attacks which target different application areas. For successful protection of the attacks, selection of suitable crypto algorithms and optimized implementations are required pertaining to the applications and attack types. To effectively utilize the advantages of the existing cryptographic mechanisms, various methods have been proposed for enhanced performance and better security. Cryptosystem implementations for high performance and constrained environments must be different as these platforms differ in terms of resource, performance, and security requirements. If the application incorporates both platforms for sensitive information exchange like healthcare IoT, a cryptosystem fulfilling the specific requirements of each platform and providing end-to-end security is needed. It is challenging to implement a cryptosystem which simultaneously meets the requirements of both environments using the same cryptosystem. In this chapter, efficient FPGA-based integrated cryptosystems are proposed for the security of high-performance platforms, constrained devices, and secure information exchange between them focusing on the security of healthcare IoT to address the challenges. Fewer number of algorithms are used achieving better throughput and smaller area in accordance with the specific implementations while saving extra space, key management, and key storage requirements compared to existing reported outcomes.
International trade and blockchain technologies: implications for practice and policy

Abstract International trade is an important aspect of business. Exchanging goods on a global scale allows states to produce and export goods that they are more efficient in and import the ones that they are not. As a result, countries reduce the cost of production, price of commodities, and expand their markets. Even so, international trade is a complex process that is facilitated by different financial, socio-political, and technological factors. The blockchain technology has revolutionized international trade and has the potential to continue doing so as this smart technology is still at its infancy. This paper analyzes the role and impacts of the blockchain in international trade, including the technology’s benefits, problems and challenges, and recommendations for improving its usefulness. The work finds that blockchain plays a significant role in international trade with numerous benefits. However, a few challenges should be addressed to ensure organizations in international trade benefits fully from blockchain technology.
Sentiment Extraction from Bilingual Code Mixed Social Media Text

In social media, code mixed data is getting exceptionally mainstream due to which there is an enormous development in noisy and inadequate multilingual content. Automation of noisy social media text is one of the existing research areas. This work focuses on extracting sentiments at word level for movie-related code mixed English–Telugu bilingual Roman script data extracted from Twitter. Initially, data was cleaned and slang words were replaced. Next, named entities and language of each word were identified. Further Telugu words were back transliterated to the native script. Finally, the text was classified either positive, negative or neutral sentiments based on the count of corresponding positive and negative lexicons present in each sentence and achieved an average accuracy of 79.9%.
Cognitive work of hypothesis exploration during anomaly response

A look at how we respond to the unexpected.
Unified deep neural network for segmentation and labeling of multipanel biomedical figures

Abstract Recent efforts in biomedical visual question answering (VQA) research rely on combined information gathered from the image content and surrounding text supporting the figure. Biomedical journals are a rich source of information for such multimodal content indexing. For multipanel figures in these journals, it is critical to develop automatic figure panel splitting and label recognition algorithms to associate individual panels with text metadata in the figure caption and the body of the article. Challenges in this task include large variations in figure panel layout, label location, size, contrast to background, and so on. In this work, we propose a deep convolutional neural network, which splits the panels and recognizes the panel labels in a single step. Visual features are extracted from several layers at various depths of the backbone neural network and organized to form a feature pyramid. These features are fed into classification and regression networks to generate candidates of panels and their labels. These candidates are merged to create the final panel segmentation result through a beam search algorithm. We evaluated the proposed algorithm on the ImageCLEF data set and achieved better performance than the results reported in the literature. In order to thoroughly investigate the proposed algorithm, we also collected and annotated our own data set of 10,642 figures. The experiments, trained on 9,642 figures and evaluated on the remaining 1,000 figures, show that combining panel splitting and panel label recognition mutually benefit each other.
Stability of Nonlinear Feedback Shift Registers with Periodic Input

The stability of Non-Linear Feedback Shift Registers (NFSRs) plays an important role in the cryptographic security. Due to the complexity of nonlinear systems and the lack of efficient algebraic tools, the theorems relate... | Find, read and cite all the research you need on Tech Science Press
Efficient backward private searchable encryption

Dynamic Searchable Symmetric Encryption (DSSE), apart from providing support for search operation, allows a client to perform update operations on outsourced database efficiently. Two security properties, viz., forward privacy and backward privacy are desirable from a DSSE scheme. The former captur es that the newly updated entries cannot be related to previous search queries and the latter ensures that search queries should not leak matching entries after they have been deleted. These security properties are formalized in terms of the information leakage that can be incurred by the respective constructions. Existing backward private constructions either have a non-optimal communication overhead or they make use of heavy cryptographic primitives. Our main contribution consists of two efficient backward private schemes ΠBP and ΠWBP that aim to achieve practical efficiency by using light weight symmetric cryptographic components only. In the process, we also revisit the existing definitions of information leakage for backward privacy [Bost et al. (In ACM CCS (2017) 1465–1482 ACM Press)] and propose a relaxed formulation. ΠBP is the first construction to achieve backward privacy in the general setting with optimal communication complexity. Our second construction, ΠWBP, is the first single round-trip scheme achieving backward privacy in a restricted setting with optimal communication complexity using light weight symmetric cryptographic primitives. The prototype implementations of our schemes depict the practicability of the proposed constructions and indicate that the cost of achieving backward privacy over forward privacy is substantially small. The performance results also show that the proposed constructions outperform the currently most efficient scheme achieving backward privacy.
Research progress and Application of Behavior Tree Technology

Artificial intelligence will not only change the production and life of human beings, but also fundamentally change the winning mechanism and fighting mode of modern wars, and give birth to new fighting means and ideas. Behavior tree will be a good medium for the application of artificial intelligence in the military field. This paper first introduces the basic principle of Behavior Tree (BT), and then elaborates and analyzes the development status of Behavior Tree from the aspects of implementation and design application. Secondly, the characteristics of behavior tree, finite state machine and hierarchical finite state machine are compared from the aspects of hierarchy, maintainability, code coupling, expansibility and reusability. Finally, the development trend of behavior tree combined with machine learning and expert system and its application prospects in interactive software platform, unmanned autonomous systems and combat simulation systems are analyzed.
A Novel Approach to Image Steganography Based on the Image Colorization

This paper proposes a novel image steganography algorithm for color image. Recently, colorization-based image coding technique has been studied. In order to compress the color image effectively, this technique transform the chrominance image to a vector in a low-dimensional subspace via the colorization matrix. This paper utilizes the colorization-based image coding for steganography algorithm, where the secret data is embedded into the null space of the colorization matrix. Because the null space is high dimension enough, a large capacity data can be embedded. Numerical examples show that the proposed algorithm embeds large capacity secret data such as grayscale image into color image effectively.
Margin based knowledge distillation for mobile face recognition

With the rapid progress of face recognition it has more and more applications in everyday life. Although its backbone, very deep neural networks, also show improvement both in terms of accuracy and efficiency their computational cost and memory usage is still a limiting factor for deploying these models on a hardware with limited computational and power resources, such as mobile or embedded devices. Here arises the task of learning fast and compact deep neural networks which have a comparable accuracy to the complex model as requirement of real-life applications. Another issue is that sometimes face recognition system may run models of different complexity depending of the devices used for biometric template extraction (i.e. desktop with GPU or mobile phone), so the compatibility between the face descriptors is desirable. Our paper considers both this cases: we propose a new method for learning fast and compact face recognition model which has a similar performance to a much more complex model used for transferring its knowledge and we also show that both these models can be used for verification in a single face recognition system. To the best of our knowledge such evaluation of a compatibility between 2 different models for face recognition was never done before our work.