Reviewerly/exHarmony huggingface dataset:
Bridging Knowledge Gaps in Security Analytics

In a cyber-physical world, the number of links between corporate assets is growing and infrastructures are becoming more complex.This and related developments significantly enlarge the attack surface of organizations.Additionally, more and more attacks do not exploit technical vulnerabilities directly but gain a foothold through phishing or social engineering.Since traditional security systems prove to be no longer sufficient to detect incidents effectively, humans and their specialized knowledge are becoming a critical security factor.Therefore, it is vital to maintain an overview of the cybersecurity knowledge spread across the entire company.However, there is no uniform understanding of knowledge in the field of security analytics.We aim to close this gap by formalizing knowledge and defining a conceptual knowledge model in the context of security analytics.This allows existing research to be better classified and shows that individual areas offer much potential for future research.In particular, the collaboration between domain experts but also between machines and employees could enable the exploitation of previously unused but crucial knowledge.For example, this knowledge is of great value for defining security rules in current security analytics systems.We introduce a proof of concept implementation using visual programming to showcase how even security novices can easily contribute their knowledge to security analytics.
DIMY: Enabling privacy-preserving contact tracing

The infection rate of COVID-19 and the rapid mutation ability of the virus has forced governments and health authorities to adopt lockdowns, increased testing, and contact tracing to reduce the virus's spread. Digital contact tracing has become a supplement to the traditional manual contact tracing process. However, although several digital contact tracing apps are proposed and deployed, these have not been widely adopted due to apprehensions surrounding privacy and security. In this paper, we present a blockchain-based privacy-preserving contact tracing protocol,"Did I Meet You" (DIMY). The protocol provides full-lifecycle data privacy protection on the devices as well as the back-end servers to address most of the privacy concerns associated with existing protocols. We have employed Bloom filters to provide efficient privacy-preserving storage and have used the Diffie-Hellman key exchange for secret sharing among the participants. We show that DIMY provides resilience against many well-known attacks while introducing negligible overheads. DIMY's footprint on the storage space of clients' devices and back-end servers is also significantly lower than other similar state-of-the-art apps.
Solvable Model for the Linear Separability of Structured Data

Linear separability, a core concept in supervised machine learning, refers to whether the labels of a data set can be captured by the simplest possible machine: a linear classifier. In order to quantify linear separability beyond this single bit of information, one needs models of data structure parameterized by interpretable quantities, and tractable analytically. Here, I address one class of models with these properties, and show how a combinatorial method allows for the computation, in a mean field approximation, of two useful descriptors of linear separability, one of which is closely related to the popular concept of storage capacity. I motivate the need for multiple metrics by quantifying linear separability in a simple synthetic data set with controlled correlations between the points and their labels, as well as in the benchmark data set MNIST, where the capacity alone paints an incomplete picture. The analytical results indicate a high degree of “universality”, or robustness with respect to the microscopic parameters controlling data structure.
Modelling and simulation for cost optimization and performance analysis of transactional applications in hybrid clouds

In the design process of a hybrid cloud with the purpose of cost optimization, finding the optimal distribution between the resources to be deployed in both private and public clouds is a complex task. To help the cloud designer in this task, optimizer and simulation tools can be used. In the research presented in this paper, a state-of-the-art optimizer tool, designed for the optimal allocation of virtual machines in public clouds for cost optimization, is analyzed for its use in hybrid cloud scenarios. In addition, based on the system and workload model implemented by this tool, a simulator is developed and presented. The simulator provides detailed run-time performance information corresponding to the virtual machine allocations generated by the optimizer tool, offering essential analysis capabilities to the cloud designer. A motivating scenario is used to show how the combined utilization of the optimizer and simulation tools can offer fundamental help in the design and configuration process of hybrid clouds. The research questions posed in the motivating scenario are addressed in a set of experiments, which provide meaningful insights into the capabilities of the tools. The tools and all the experimental data carried out in this research have been made publicly available.
Novel algorithm for efficient privacy preservation in data analytics

Objective: To address the modern privacy threats in data analytics by designing an efficient privacy preserving data analytics technique.Methods: The method applied is a non anonymized method that uses the concepts of synthesizing quasi identifiers and application of differential privacy.The proposed method was applied to three data sets viz.Adult data set, Statlog data set and Indian Liver Patient data set.All the data sets are freely available in the UCI repository.Findings: The study presents "Synthesize Quasi Identifiers and apply Differential Privacy" (SQIDP) which is proved to be a more efficient and scalable algorithm.Compared to anonymity based algorithms SQIDP is not prone to similarity attacks, background knowledge attacks, attribute disclosure, and inference attacks.Anonymization, cryptographic, SWARM, and randomization methods will reduce data utility whereas SQIDP offers 100% data utility.Hence it is more efficient than other techniques.SQIDP was applied on three different data sets with 270, 583, and 48842 records but the execution time of the algorithm remained the same for all three data sets.SQIDP is proved to be a better privacy preservation technique with 100% data utility because it is not anonymized that abides by the recommendation in many privacy legislations like GDPR (General Data Protection Regulation) of the European Union and PDP (Personal Data Protection bill) of India.
List decoding with double samplers

We develop the notion of double samplers, first introduced by Dinur and Kaufman [DK17], which are samplers with additional combinatorial properties, and whose existence we prove using high dimensional expanders.We show how double samplers give a generic way of amplifying distance in a way that enables efficient list-decoding. There are many error correcting code constructions that achieve large distance by starting with a base code C with moderate distance, and then amplifying the distance using a sampler, e.g., the ABNNR code construction [ABN+92] is such. We show that if the sampler is part of a larger double sampler then the construction has an efficient list-decoding algorithm and the list decoding algorithm is oblivious to the base code C (i.e., it runs the unique decoder for C in a black box way).Our list-decoding algorithm works as follows: it uses a local voting scheme from which it constructs a unique games constraint graph. The constraint graph is an expander, so we can solve unique games efficiently. These solutions are the output of the list decoder. This is a novel use of a unique games algorithm as a subroutine in a decoding procedure, as opposed to the more common situation in which unique games are used for demonstrating hardness results.Double samplers and high dimensional expanders are akin to pseudorandom objects in their utility, but they greatly exceed random objects in their combinatorial properties. We believe that these objects hold significant potential for coding theoretic constructions and view this work as demonstrating the power of double samplers in this context.
Theoretical and experimental analysis of co-designed binary phase masks for enhancing the depth of field of panchromatic cameras

We investigate the depth of field (DoF) enhancing capacity of binary annular phase masks embedded in panchromatic imaging systems. We first demonstrate with numerical simulations and real-world imaging experiments that phase masks optimized for monochromatic illumination are somewhat robust to their use under wide spectrum illumination: they provide images that are slightly less sharp but less affected by deconvolution artifacts due to spectral averaging. Then, we show that masks specifically optimized for wide spectrum illumination perform better under this type of illumination than monochromatically optimized phase masks under monochromatic illumination, especially when the targeted DoF range is large. This interesting effect comes from the fact that deconvolution artifacts are significantly reduced by wide spectrum illumination. These results show that it is useful to take into account the illumination spectrum together with the scene characteristics and the targeted DoF range for effective co-design of DoF enhancing imaging systems.
Machine Translation of Mathematical Text

We have implemented a machine translation system, the PolyMath Translator, for LaTeX documents containing mathematical text. The current implementation translates English LaTeX to French LaTeX, attaining a BLEU score of 53.6 on a held-out test corpus of mathematical sentences. It produces LaTeX documents that can be compiled to PDF without further editing. The system first converts the body of an input LaTeX document into English sentences containing math tokens, using the pandoc universal document converter to parse LaTeX input. We have trained a Transformer-based translator model, using OpenNMT, on a combined corpus containing a small proportion of domain-specific sentences. Our full system uses this Transformer model and also Google Translate with a custom glossary, the latter being used as a backup to better handle linguistic features that do not appear in our training dataset. Google Translate is used when the Transformer model does not have confidence in its translation, as determined by a high perplexity score. Ablation testing demonstrates that the tokenization of symbolic expressions is essential to the high quality of translations produced by our system. We have published our test corpus of mathematical text. The PolyMath Translator is available as a web service at http://www.polymathtrans.ai.
Controlled quantum teleportation of an unknown single-qutrit state in noisy channels with memory*

This paper proposes a three-dimensional (3D) controlled quantum teleportation scheme for an unknown single-qutrit state. The scheme is first introduced in an ideal environment, and its detailed implementation is described via the transformation of the quantum system. Four types of 3D-Pauli-like noise corresponding to Weyl operators are created by Kraus operators: trit-flip, t-phase-flip, trit-phase-flip, and t-depolarizing. Then, this scheme is analyzed in terms of four types of noisy channel with memory. For each type of noise, the average fidelity is calculated as a function of memory and noise parameters, which is afterwards compared with classical fidelity. The results demonstrate that for trit-flip and t-depolarizing noises, memory will increase the average fidelity regardless of the noise parameter. However, for t-phase-flip and trit-phase-flip noises, memory may become ineffective in increasing the average fidelity above a certain noise threshold.
Ecosystem designing for the development of universities in a digital environment

The digitalization of economic relations determines the particular importance of personnel training on a qualitatively new technological basis, which is inextricably linked with the study of the problem of ecosystem designing for the development of universities in a digital environment. The foundations of the systems theory, the theory of education economics, the methods of system analysis, factor analysis, and economic and mathematical modeling were used for studying the problem of ecosystem development for universities. The conducted studies made it possible to obtain the following main research results: the ecosystem principles for organizing the provision of higher education services, representing the integrity of the university system based on network interactions of a set of various participants, as well as objects, projects, processes, and environments, were proposed. An approach to designing a strategy for the development of universities, based on the scenario forecasting of the receptivity of digital economy competencies by students, teachers, and partners of universities in the medium term, is proposed based on the organigram for the implementation of the project of matrix digital transformation of the adhocratic university hierarchy. The conclusions stating that the design of the ecosystem of the development of universities should be aimed at the implementation of the social mission of the university in the region and the transformation of the ecosystem of the region as a whole are formulated. As prospects for further research, the study of the prerequisites for generating the effects of designing the development of a university ecosystem based on the network platform interaction of the economic agents of the region/territory, as well as the implementation of digital transformation projects of the university management system, is considered.
Competition among Universities: Conceptual Analysis

The transition to the knowledge economy focused attention on the higher education system development and its problems, and put the universities in the situation of competition for limited resources. Free and controlled competition brings a chain of interrelated, sometimes contradictory concepts applied to higher education as a socially significant area. The authors of this research article by means of conceptual analysis aim at identifying the relationship between the concepts of «competition», «competitiveness», «efficiency», and «cooperation» regarding the higher education system. Competition and cooperation are considered to be factors which increase the efficiency of universities’ functioning. At the same time, there are contradictions in the justifications for the need to create competitive conditions for universities and in the definitions of the concept of efficiency, University is understood to be a commercial organization, its indicators being locked in its internal environment, whereas its main functions (reproduction of intellectual capital, improving the quality of human resources, orientation to the needs of the labor market) come to be out of sight. No less limited is the concept of competition, which is represented through the index of monopolization, calculated mostly through the proportion of students enrolled, beyond taking into account the peculiarities of the Russian higher education system. However, the works on competition and financing draw attention to the public good created by universities, which is not always tangible and requires the financial participation of the government. Thus, the discussions on universities’ competition and effectiveness in the context of their association with the commercial sector omit a number of socially significant elements of higher education and science.
A Hybrid Multi-Task Learning Approach for Optimizing Deep Reinforcement Learning Agents

Driven by recent technological advancements within the field of artificial intelligence (AI), deep learning (DL) has been emerged as a promising representation learning technique across different machine learning (ML) classes, especially within the reinforcement learning (RL) arena. This new direction has given rise to the evolution of a new technological domain named deep reinforcement learning (DRL) that combines the high representational learning capabilities of DL with existing RL methods. Performance optimization achieved by RL-based intelligent agents designed with model-free-based approaches was majorly limited to systems with RL algorithms focused on learning a single task. The aforementioned approach was found to be quite data inefficient, whenever DRL agents needed to interact with more complex, data-rich environments. This is primarily due to the limited applicability of DRL algorithms to many scenarios across related tasks from the same distribution. One of the possible approaches to mitigate this issue is by adopting the method of multi-task learning. The objective of this research paper is to present a hybrid multi-task learning-oriented approach for the optimization of DRL agents operating within different but semantically similar environments with related tasks. The proposed framework will be built with multiple, individual actor-critic models functioning within independent environments and transferring knowledge among themselves through a global network to optimize performance. The empirical results obtained by the hybrid multi-task learning model on OpenAI Gym based Atari 2600 video gaming environment demonstrates that the proposed model enhances the performance of the DRL agent relatively in the range of 15% to 20% margin.
Learning Latent Multi-Criteria Ratings From User Reviews for Recommendations

Multi-criteria recommender systems have been increasingly useful for helping consumers identify the most relevant items based on different dimensions of user experiences and highlighting their most valued features. Therefore, researchers have proposed various multi-criteria models to improve recommendation performance. However, most of the existing methods utilize only multi-criteria ratings explicitly provided by the users. Note that explicit multi-criteria ratings are sparse and have the problem of missing values. User reviews, on the other hand, contain richer information of user experiences and reveal multi-dimensional user preferences. Therefore, we propose to use latent multi-criteria ratings generated from user reviews, as opposed to explicit multi-criteria ratings, to provide recommendations and capture latent complex heterogeneous user preferences. Specifically, we propose two novel models for the latent multi-criteria rating generation process: the one-stage model LatentMC-1S that utilizes document hashing method to directly compute latent ratings and the two-stage model LatentMC-2S that uses GRU and Gumbel-Softmax for indirect rating generation. Extensive experiments show that the proposed latent multi-criteria rating approaches outperform explicit ratings across different datasets and performance measures. We also show that latent multi-criteria ratings could be used for imputing missing explicit multi-criteria ratings and thus further improving multi-criteria recommender systems.
Recursive Algorithms of Maximum Entropy Thresholding on Circular Histogram

Circular histogram thresholding is a novel color image segmentation method, which makes full use of the hue component color information of the image, so that the desired target can be better separated from the background. Maximum entropy thresholding on circular histogram is one of the exist circular histogram thresholding methods. However, this method needs to search for a pair of optimal thresholds on the circular histogram of two-class thresholding in an exhaustive way, and its running time is even longer than that of the existing circular histogram thresholding based on the Otsu criteria, so the segmentation efficiency is extremely low, and the real-time application cannot be realized. In order to solve this problem, a recursive algorithm of maximum entropy thresholding on circular histogram is proposed. Moreover, the recursive algorithm is extended to the case of multiclass thresholding. A large number of experimental results show that the proposed recursive algorithms are more efficient than brute force and the existing circular histogram thresholding based on the Otsu criteria.
The k-NN algorithm for compositional data: a revised approach with and without zero values present

In compositional data, an observation is a vector with non-negative components which sum to a constant, typically 1.Data of this type arise in many areas, such as geology, archaeology, biology, economics and political science among others.The goal of this paper is to extend the taxicab metric and a newly suggested metric for com-positional data by employing a power transformation.Both metrics are to be used in the k-nearest neighbours algorithm regardless of the presence of zeros.Examples with real data are exhibited.
Reevaluating pragmatic reasoning in language games

The results of a highly influential study that tested the predictions of the Rational Speech Act (RSA) model suggest that (a) listeners use pragmatic reasoning in one-shot web-based referential communication games despite the artificial, highly constrained, and minimally interactive nature of the task, and (b) that RSA accurately captures this behavior. In this work, we reevaluate the contribution of the pragmatic reasoning formalized by RSA in explaining listener behavior by comparing RSA to a baseline literal listener model that is only driven by literal word meaning and the prior probability of referring to an object. Across three experiments we observe only modest evidence of pragmatic behavior in one-shot web-based language games, and only under very limited circumstances. We find that although RSA provides a strong fit to listener responses, it does not perform better than the baseline literal listener model. Our results suggest that while participants playing the role of the Speaker are informative in these one-shot web-based reference games, participants playing the role of the Listener only rarely take this Speaker behavior into account to reason about the intended referent. In addition, we show that RSA’s fit is primarily due to a combination of non-pragmatic factors, perhaps the most surprising of which is that in the majority of conditions that are amenable to pragmatic reasoning, RSA (accurately) predicts that listeners will behave non-pragmatically. This leads us to conclude that RSA’s strong overall correlation with human behavior in one-shot web-based language games does not reflect listener’s pragmatic reasoning about informative speakers.
Sentiment Analysis: Predicting Product Reviews’ Ratings using Online Customer Reviews

In the present era, most of the data on the internet is in the form of raw text. These gold mines of data are invaluable since it contains lots of underlying information which can be extracted using natural language processing or text analytics techniques. The data from these text-based documents disclose users’ sentiments and opinions about a particular subject. In this paper, customer reviews from Amazon.com are pre-processed, analyzed using our proposed framework, and how these textual reviews justify the star ratings is studied. Features derived from textual reviews are used to predict its corresponding star ratings. To accomplish it, the prediction problem is transformed into a multi-class classification task to classify reviews to one of the five classes corresponding to its star rating. The performances of various classifiers used are evaluated and compared. Evaluation results on ground-truth data set show that Logistic Regression Classifier outperformed other models. Our study also reveals that among the various factors, polarity of the review and length of the review showed a significant effect on its rating.