Reviewerly/exHarmony huggingface dataset:
One-One Constrained Pseudorandom Functions.

We define and study a new cryptographic primitive, named One-One Constrained Pseudorandom Functions. In this model there are two parties, Alice and Bob, that hold a common random string K, where Alice in addition holds a predicate f:[N] → {0,1} and Bob in addition holds an input x ∈ [N]. We then let Alice generate a key K_f based on f and K, and let Bob evaluate a value K_x based on x and K. We consider a third party that sees the values (x,f,K_f) and the goal is to allow her to reconstruct K_x whenever f(x)=1, while keeping K_x pseudorandom whenever f(x)=0. This primitive can be viewed as a relaxation of constrained PRFs, such that there is only a single key query and a single evaluation query.We focus on the information-theoretic setting, where the one-one cPRF has perfect correctness and perfect security. Our main results are as follows. 1) A Lower Bound. We show that in the information-theoretic setting, any one-one cPRF for punctured predicates is of exponential complexity (and thus the lower bound meets the upper bound that is given by a trivial construction). This stands in contrast with the well known GGM-based punctured PRF from OWF, which is in particular a one-one cPRF. This also implies a similar lower bound for all NC1. 2) New Constructions. On the positive side, we present efficient information-theoretic constructions of one-one cPRFs for a few other predicate families, such as equality predicates, inner-product predicates, and subset predicates. We also show a generic AND composition lemma that preserves complexity. 3) An Amplification to standard cPRF. We show that all of our one-one cPRF constructions can be amplified to a standard (single-key) cPRF via any key-homomorphic PRF that supports linear computations. More generally, we suggest a new framework that we call the double-key model which allows to construct constrained PRFs via key-homomorphic PRFs. 4) Relation to CDS. We show that one-one constrained PRFs imply conditional disclosure of secrets (CDS) protocols. We believe that this simplemodel can be used to better understand constrained PRFs and related cryptographic primitives, and that further applications of one-one constrained PRFs and our double-key model will be found in the future, in addition to those we show in this paper.
Automating Container Deployments Using CI/CD

The biggest challenge faced by information technology (IT) industry is of the deployment and release, traditional methods used to release the final build as a part of deployment process, along with deployment of the database and code on the on premise infrastructure which was a long and cumbersome process, then with advent of cloud computing, things became easy, and the infrastructure was shifted from on premises to cloud, this made the on demand access of resources just a matter of clicks, but still deployment was a problem due to a major reason of “Code working on developer’s machine but not working properly in staging or production environments”. Primary reasons included versioning problems, software patches or bug fixes. It also had various problems such as scalability issues, the infrastructures were not fault tolerant as well as lack of disaster recovery strategy in place. With advent of containerization, the platform dependency issues were resolved, but again deployment release and rolling updates are a major issue with them as well. This paper focuses on high level overview about how CI/CD could solve the problem of deployment and rolling upgrades by automating the entire build and deployment process, as well as to handle the rolling updates process in an effective manner. It also provides a way of implementing the disaster recovery strategy regardless of the underlying cloud platform as well as fault tolerance mechanism if in case any failure occurs in staging or production environments.
Block Level Time Variant Dynamic Encryption Algorithm for Improved Cloud Security and Deduplication Using Block Level Topical Similarity

The problem of security and memory management has been well studied. A number of approaches has been discussed for the data security in cloud environment. However, the method does not produce higher security and introduces higher data duplication in the cloud which affects the data management performance. To overcome these issues, an efficient block level encryption and de-duplication scheme has been presented in this paper. The method represents the data into a number of blocks and stores them in the cloud storage. For each block considered, a dynamic encryption/decryption keys has been generated at each time window. The user has been validated with different public/private key approach. For the data access, the algorithm generates different keys for different blocks which restrict the access of users. Based on the key being generated, the data has been encrypted by the system to be decrypted by the cloud user upon access. The method maintains a block key table which contains the keys to be used for the particular time window. The method produces higher efficient results on data security and improves the performance of data management.
BLAPD: Biometric Liveness Authentication on Personalized Devices

The static password is as yet a foundation for confirmation of numerous sites, application and so on, it is the most prominent and the least secure validation technique.At the point when the aggressor gets the secret word of the client, he can utilize it inside its lifetime.At that point, the assailant can mimic the client for a boundless time.So this structure makes all-out security.On the off chance that one sign into a session, the username can be set as a live iris image.This can't break security and will be secure, likewise less tedious.In the event that there happen a circumstance that some other client expected to sign in, at that point can set the settings, to give username is the dynamic username.This gives access to control consent.On account of the password, an one-time password is sent to the gadget and will naturally bring from gadget to sign in.This case gives legitimacy time and access control authorization.The plan is to make a one of a kind username and secret key set for every session with the end goal that different security vulnerability in traditional, live iris image username and OTP password frameworks can be handled.
Organic Narrative Charts

Storyline visualizations display the interactions of groups and entities and their development over time. Existing approaches have successfully adopted the general layout from hand-drawn illustrations to automatically create similar depictions. Ward Shelley is the author of several diagrammatic paintings that show the timeline of art-related subjects, such as Downtown Body, a history of art scenes. His drawings include many stylistic elements that are not covered by existing storyline visualizations, like links between entities, splits and merges of streams, and tags or labels to describe the individual elements. We present a visualization method that provides a visual mapping for the complex relationships in the data, creates a layout for their display, and adopts a similar styling of elements to imitate the artistic appeal of such illustrations. We compare our results to the original drawings and provide an open-source authoring tool prototype.
Should Machines Feel or Flee Emotions? User Expectations and Concerns about Emotionally Aware Chatbots

As chatbots are becoming increasingly popular, we often wonder what users perceive as natural and socially accepted manners of interacting with them. While there are many aspects to this overall question, we focused on user expectations of their emotional characteristics. Some researchers maintain that humans should avoid engaging in emotional conversations with chatbots, while others have started building empathetic chatting machines using the latest deep learning techniques. To understand if chatbots should comprehend and display emotions, we conducted semi-structured interviews with 18 participants. Our analysis revealed their overall enthusiasm towards emotionally aware agents. The findings disclosed interesting emotional interaction patterns in daily conversations and the specific application domains where emotionally intelligent technology could improve user experience. Further, we identified key user concerns that may hinder the adoption of these chatbots. Finally, we summarized a few guidelines useful for the development of emotionally intelligent conversational agents and identified further research opportunities.
Hierarchical Reinforcement Learning with Integrated Discovery of Salient Subgoals

Hierarchical Reinforcement Learning (HRL) is a promising approach to solve more complex tasks which may be challenging for the traditional reinforcement learning. HRL achieves this by decomposing a task into shorter-horizon subgoals which are simpler to achieve. Autonomous discovery of such subgoals is an important part of HRL. Recently, end-to-end HRL methods have been used to reduce the overhead from offline subgoal discovery by seeking the useful subgoals while simultaneously learning optimal policies in a hierarchy. However, these methods may still suffer from slow learning when the search space used by a high level policy to find the subgoals is large. We propose LIDOSS, an end-to-end HRL method with an integrated heuristic for subgoal discovery. In LIDOSS, the search space of a high level policy can be reduced by focusing only on the subgoal states that have high saliency. We evaluate LIDOSS on continuous control tasks in the MuJoCo Ant domain. The results show that LIDOSS outperforms Hierarchical Actor Critic (HAC), a state-of-the-art HRL method, in the fixed goal tasks.
River Segmentation Based on Visual Salience Calculation of Spectral Residual and Region Growing in SAR Images

In the problem of the SAR image river segmentation, the threshold method and the region growing method are two widely used segmentation methods based on water pixel information. In view of the fact that the precision of the segmentation result for the threshold method is low and the segmentation result of the region growing method has voids which lead to the problem of the high missing alarm; for the first time, this paper introduces the visual salience detection theories into the SAR image river segmentation and presents a method that combines visual salience calculation of spectral residual and region growing. This method first binarizes the preprocessed SAR image, then extracts the saliency map of the binary image by utilizing the spectral residual model, and finally segments the river region by using the region growing method on the saliency map. Compared with the threshold method, the region growing method and the method that combines the threshold method and the region growing method, the experiments demonstrate that the method proposed by this paper has much better precision and comprehensive segmentation performance. Apart from that, it also effectively solves the problem of the voids existing in the segmentation results for the traditional region growing method. Therefore, the method presented by this paper can be applied to the SAR image river segmentation in the applications such as water resources planning and flood disaster prevention.
MALARIA DISEASE RECOGNITION THROUGH ADAPTIVE DEEP LEARNING MODELS

Malaria is a disease from blood generated by the Plasmodium parasites spread through the bite of female Anopheles mosquito. Inspection thruspecialists is done on the dense, fine blood slurs to make a diagnosis and perform parasitemia calculations. Nevertheless, precision testing varies onslur characteristics and technical skills in tallying and rating infected cells. It could be laborious for a massive population in conducting assessmentgiven the poor conditions and locality.Convolutional Neural Networks, a division of deep learning (DL) models has dominance and extensible result in terms of feature extraction andclassification. Automatic screening of malaria with deep learning techniques is an effective tool in analyzing the malaria. The author has evaluatedthe implementations of the pre-trained convolutional neural network-based Deep Learning models as feature extractors in categorizing andimproving the examination of cells of infected and not-infected with malaria.
Polysemous Language in Child Directed Speech

Polysemous Language in Child Directed Speech Learning the meaning of words is one of the fundamental building blocks of verbal communication. Models of child language acquisition have generally made the simplifying assumption that each word appears in child-directed speech with a single meaning. To understand naturalistic word learning during childhood, it is essential to know whether children hear input that is in fact constrained to single meaning per word, or whether the environment naturally contains multiple senses.In this study, we use a topic modeling approach to automatically induce word senses from child-directed speech. Our results confirm the plausibility of our automated analysis approach and reveal an increasing rate of using multiple senses in child-directed speech, starting with corpora from children as early as the first year of life.
Handwritten Text Recognition and Linguistic Research.

This paper presents ongoing work with automatic transcription of handwritten, phonetically precise dialect texts from the south-west of Sweden (collected in the 1890s). Using a SAMPA-based transcri ...
Boolean Games: Inferring Agents' Goals Using Taxation Queries

In Boolean games, each agent controls a set of Boolean variables and has a goal represented by a propositional formula. We initiate a study of inference in Boolean games assuming the presence of a PRINCIPAL who has the ability to control the agents and impose taxation schemes. Previous work used taxation schemes to guide a game towards certain equilibria. We show how taxation schemes can also be used to infer agents' goals. In our formulation, agents' goals are assumed to be unknown and the objective of the PRINCIPAL is to infer the goals of all the agents using appropriate taxation queries. Using an undirected graph (called the goal overlap graph) associated with a Boolean game, we establish necessary and sufficient conditions for the existence of a Nash equilibrium for any taxation query. Using these conditions, we develop an algorithm that uses taxation queries to learn agents' goals. Using a valid node coloring of the goal overlap graph, we show that goals of many agents can be inferred simultaneously. We also present more efficient (in terms of number of queries) goal inference algorithms for two special classes of Boolean functions, namely threshold and symmetric functions.
A robust low frequency integer wavelet transform based fractal encryption algorithm for image steganography

Image steganography is one of the emerging research areas in the field of information technology. In today's scenario, steganography is widely utilised in communication systems that send secret information through appropriate carriers. In this scenario, the secret information that embeds in the cover image is secret image. The primary objective of this paper is to develop a high level secure transmission technique to transmit messages through lossless channel. In this paper, an effective transform domain image steganography approach is proposed by employing lower frequency integer wavelet transform (IWT) and fractal encryption with the combination of L-shaped tromino theorem for enhancing the performance of information hiding method. The proposed methodology: IWT-fractal encryption delivers the advantage of better embedding capacity with low computational complexity and also provides better secret image quality. Experimental outcome shows that the proposed approach improved peak signal-to-noise ratio (PSNR) value up to 30 dB and entropy value up to 0.02-0.4 compared to the existing methods: genetic algorithm and discrete cosine transform (DCT)-Arnold transform. This experimental outcome confirms that the proposed technique delivers a high security level network with low computational complexity compared to other existing approaches.
Public Diplomacy in the Context of Cross-Border Knowledge-Based Cooperation Between Universities

At present, Russian universities are increasingly involved in the processes of globalization of education and science, which, in our opinion, are components of public diplomacy. The education system has become the most important institution of modernity in the state. The main forms of contacts between Russian teachers and foreign colleagues are participation in joint conferences, and the implementation of joint research projects. Such forms of scientific cooperation are typical for teachers of almost all specialties. A prerequisite for participation in the cross-boarder university cooperation and support of the process is the full awareness of all stakeholders on the entire spectrum of issues (principles, goals, objectives, expected results). Questions of forecasting and exclusion, or at least compensation of the possible negative consequences of choosing specific options for the implementation of cross-border university cooperation, also require an answer. The paper analyzes the existing strategies of cross-border cooperation at universities in terms of implementing issues of public diplomacy.
Linear Block Codes

As usualBlock codes, in this chapter we consider that p denotes a prime number, q is a prime power and $${\mathbb F}_{q}$$ is the finite field with q elements. We begin by defining the general concept of a code over $${\mathbb F}_{q}$$; after this, we present the concept of a linear code that is the more important class of codes in coding and information theory due to its vector space structure. In this subsection, the vectors are written in bold.
Do quê se trata? Documento, indexação e produção de sentido nos arquivos de universidade

This research is about the indexing process in university archive systems when the records are in production phase. The intend is analying the indexing relations with the record uses by work office units. It is also considerate the indexing implications in information retrieval. In record production phase, the information uses are associated with the producer activities. This uses may help the producers to make strategic decisions, to control finances, to improve functions, to inform abouthistorical actions and to identify business transactions. The universities as producers have specific organizational dynamics with particular hierarchical structures and decision-making practices. The business trasactions are more independent than other institutions. The managers most part of timesrespond only one department, institute or school. This reality is also presented by this study. The approach is about the nature and the history of indexing concept. It also presents different tradictions of indexing theory in Science Information field. A literature review of indexing process, archive systems, records, production of meaning are made, besides that a large research about registry systems, federal universities, archives and information acess is produced . It is also investigated the new electronic registry system, called Sistema Protocolo Integrado, a governmentiniciative, and the accession by Universidade Federal Fluminense, the empirical field of this research. The preliminary results provide that the indexing records are social practices characterized by language use. Moreover, in strictu sensu, the indexing is a techinique able to recognize and identify concepts inside the records. Although, in lato sensu, the indexing may dignose a knowledge domain and offer a instrumental domain analysis whose knowledge domain of discursivecommunity can be recognized. The registry systems make the control of the workflow and inform about business transactions in university archive systems, but they also became a potential power instance for the managers. They can provide all kind of crucial information to the administrators make strategic decisions and plan important action for the university development.
The Common Data Service

The Common Data Service is a single place for apps. Data is stored in the Common Data Service that is served to many applications to be viewed and updated. Sounds like a basic definition, but really when it comes down to it, that is what is at the core of the Common Data Service, or CDS. The CDS is a data store, but it does more than simply store data in the same way as a SQL database, for example. The CDS reviews what data type is being stored, such as image data, and stores it in the correct type of storage based on the data type. Model-Driven Applications using Power Apps are built on top of the CDS, as the UI layer, or the “presentation” layer, and are designed for the user of the applications to interact with the data stored in the CDS.
A Universal Framework to Construct a Huffman-Code-Mapping-based Reversible Data Hiding Scheme for JPEG Images.

Huffman code mapping (HCM) is a recent technique for reversible data hiding (RDH) in JPEG images. The existing HCM-based RDH schemes cause neither file-size increment nor visual distortion for the marked JPEG image, which is the superiority compared to the RDH schemes that use other techniques, such as histogram shifting (HS). However, the embedding capacity achieved by the HCM-based RDH schemes is much lower than the HS-based RDH schemes. Moreover, the existing HCM-based schemes are only applicable to the JPEG images coded with the default Huffman table. In this paper, we propose a universal framework to design the HCM-based RDH scheme. Under this framework, the key issue of HCM-based schemes, i.e., construct the optimal code mapping relationship, is converted to solve a combinatorial optimization problem. The high embedding capacity can be achieved with a slight increase in the file-size of the marked JPEG image. In addition, the problem of applicability is also solved by customizing the Huffman table. As a realization, we construct a new HCM-based scheme by employing the genetic algorithm to search the nearly optimal solution. Experiments show that the performance on the file-size preservation, visual quality, and computational complexity is superior to recent HS-based RDH schemes under the identical payload.
Automatic Synthesis of Generalized Winning Strategy of Impartial Combinatorial Games

One of the challenging problems of impartial combinatorial games (ICGs) is to construct generalized winning strategies for possibly infinitely many states.In this paper, we investigate synthesizing generalized winning strategies for ICGs.To this end, we first propose a logical framework to formalize ICGs based on linear integer arithmetic. We then propose an approach to generating the winning strategy for ICGs.Experimental results on several games demonstrate that our approach is effective in most of these games.
An augmented reality application to engage students in STEM education

This paper proposes a Mobile Augmented Reality application to engage students in STEM education. With a design research methodology, the SolarSystemGO game, initially designed to promote learning about our Solar System, is being developed by undergraduate higher education students supervised by higher education teachers from a Portuguese polytechnic. After several cycles of implementation of the game (design research cycles), which took place over two academic years, we argue that the SolarSystemGO game can be a resource to develop mathematical contents in formal and informal learning environments. Based on our research and experience in the field, we sustain the importance of developing Mobile Augmented Reality games that engage students and motivate them to learn interdisciplinary subject matters adequate to primary school syllabus.