Reviewerly/exHarmony huggingface dataset:
Cryptanalyzing Merkle-Hellman Public Key Cryptosystem with Artificial Neural Networks

Analyzing any cryptographic algorithm is in general a herculean task but smart use of Artificial Intelligence(A.I.) can deliver some positive results. Various attempts regarding cryptanalysis till now have been focused on either exploiting a mathematical loop hole in the algorithm or any way out in the implementation of the algorithm. Work has been done in the field of cryptanalysis using Neural Networks and other pattern recognition techniques to identify the type of algorithm used. In this paper, we attempt to cryptanalyze the Merkle-Hellman Knapsack Public Key Cryptosystem using Shallow and Deep-multi-layered Artificial Neural Network (ANN) to know how good it is in replicating the working of the algorithm. Here we will try to investigate a third front where an ANN tries deconstruct the cryptosystem into its fundamental parts in its own way and then replicate it by learning from any relationship between cipher texts and the message or any other useful patterns it can find. Supervised learning has been used in order to check the accuracy of finding patterns and the level of classification the proposed model can perform.
Collaborative Semantic Data Fusion with Dynamically Observable Decision Processes

This work presents novel techniques for tightly integrated online information fusion and planning in human-autonomy teams operating in partially known environments. Motivated by dynamic target search problems, we present a new map-based sketch interface for online soft-hard data fusion. This interface lets human collaborators efficiently update map information and continuously build their own highly flexible ad hoc dictionaries for making language-based semantic observations, which can be actively exploited by autonomous agents in optimal search and information gathering problems. We formally link these capabilities to POMDP algorithms for optimal planning under uncertainty, and develop a new Dynamically Observable Monte Carlo planning (DOMCP) algorithm as an efficient means for updating online sampling-based planning policies for POMDPs with non-static observation models. DOMCP is validated on a small scale robot localization problem, and then demonstrated with our new user interface on a simulated dynamic target search scenario in a partially known outdoor environment.
Online processing for speech-driven gesture motion generation in android robots

Hand gestures commonly occur in daily dialogue interactions, and have important functions in communication. In this study, we proposed and implemented an online processing for a speech-driven gesture motion generation in an android robot dialogue system. Issues on motion overlaps and speech interruptions by the dialogue partner were taken into account. We then conducted two experiments to evaluate the effects of occasional dis-synchrony between the generated motions and speech, and the effects of holding duration control after speech interruptions. Evaluation results indicated that gestures should not be delayed by more than 400ms relative to the speech utterances. Evaluation of the second experiment indicated that gesture holding durations around 0.5 to 2 seconds after an interruption look natural, while longer durations may cause impression of displeasure by the robot.
An Accelerated Cue Combination Principle Accounts for Multi-cue Depth Perception

For the visual world in which we operate, the core issue is to conceptualize how its three-dimensional structure is encoded through the neural computation of multiple depth cues and their integration to a unitary depth structure. One approach to this issue is the full Bayesian model of scene understanding, but this is shown to require selection from the implausibly large number of possible scenes. An alternative approach is to propagate the implied depth structure solution for the scene through the &#x201C;belief propagation&#x201D; algorithm on general probability distributions. However, a more efficient model of local slant propagation is developed as an alternative.The overall depth percept must be derived from the combination of all available depth cues, but a simple linear summation rule across, say, a dozen different depth cues, would massively overestimate the perceived depth in the scene in cases where each cue alone provides a close-to-veridical depth estimate. On the other hand, a Bayesian averaging or &#x201C;modified weak fusion&#x201D; model for depth cue combination does not provide for the observed enhancement of perceived depth from weak depth cues. Thus, the current models do not account for the empirical properties of perceived depth from multiple depth cues.The present analysis shows that these problems can be addressed by an asymptotic, or hyperbolic Minkowski, approach to cue combination. With appropriate parameters, this first-order rule gives strong summation for a few depth cues, but the effect of an increasing number of cues beyond that remains too weak to account for the available degree of perceived depth magnitude. Finally, an accelerated asymptotic rule is proposed to match the empirical strength of perceived depth as measured, with appropriate behavior for any number of depth cues.
A Unified Access to Heterogeneous Big Data through Ontology-Based Semantic Integration

A tremendous amount of heterogeneous data is produced frequently in different areas due to the rise of Big Data technologies. Such data characteristics might hinder the process of acquiring the utmost value from them. This is because different technologies are used to separately store data that are different in type, yet closely related. Moreover, data may be represented inconsistently even within individual data stores. This is due to different data producers and frequent additions over time. Therefore, there is an urgent need to access big data in a unified and consistent manner to extract the maximum value from them. This paper provides an overview of different approaches proposed to integrate big data sources through a unified semantic model, followed by a proposed approach to semantically integrate graph Big Data.
Knowledge Base Collecting Using Natural Language Processing Algorithms

Natural language processing (NLP) is one of the most complicated and fast developing area in Computer Science. There are solutions in this area for special cases, but developing one general solution is impossible due to variety of grammatical, syntactic and semantic forms in different languages. The NLP algorithms and methods are used in speech recognition, text analyzing and understanding, speech generation. This paper is focused on application of NLP approaches to understand quasi-structured or unstructured data with subsequent inclusion in a knowledge base. The article covers the usage of a graph database as a knowledge base, that allows to show and visualize relationships between different pieces of text according to specified data patterns.
Deep model with neighborhood-awareness for text tagging

In recent years, many efforts based on deep learning have been made to address the issue of text tagging. However, these work generally neglect to consider the neighborhood effect which may help improve the accuracy of predictions. For this, we present a neighborhood-aware deep model for text tagging (NATT). A neural component which combines bi-directional recurrent neural network and self-attention mechanism, is firstly selected as the text encoder to encode the target document into one feature vector. Then, k-nearest-neighbor documents of the target document are identified and encoded into feature vectors one by one with the same text encoder. Simultaneously, an independent attention module is introduced to aggregate these neighboring documents into a special feature vector, which will represent features of the neighborhood. Finally, the two feature vectors are fused to match the embedding vectors of tags. To optimize the NATT model, we build the objective function with pairwise hinge loss and specially develop a neighborhood-aware negative sampling strategy to form training data. Experimental results on four datasets demonstrate that NATT outperforms some state-of-the-art neural models. Additionally, NATT is economical on achieving the best results with less training epochs and a smaller number of nearest neighbors.
A REST Framework on IoT Streams using Apache Spark for Smart Cities

The proliferation in the sensory hardware and the generation of voluminous data in Internet of Things (IoT) necessitate the development of effective methodologies to process the data to create actionable knowledge. Existing works tend to train cognitive models on data and derive insights from it. The insights may or may not be in real-time. This proposed work illustrates a real-time streaming analytics framework to predict congestions on multivariate IoT data streams in a smart city scenario. With an increase in the connected devices and Machine to Machine (M2M) communications, there is also a rise in the volumes of data generated. To handle such voluminous data, the frameworks needs to address scalability and reliability aspects. In this research, we present an architecture with the help of open source components that simulate an IoT streaming scenario in a persistent way and a batch analytics engine that processes the data inputs in order to produce higher order, granular insights. For the proposed work, we have used unsupervised learning approaches to identify congestions in traffic scenarios in smart cities.
SDFS: A Scalable Data Feed Service for Smart Contracts

Smart Contract (SC) is distributed, trustless, programmable, and tamper-resistant, which can realize secure and efficient information exchange, value transfer and asset management. However, SCs can only access data on their own blockchains (BCs), and can't get external information by themselves. Data feed service is a third-party data source provider that supports a broad range of data requests will thus be critical to SC ecosystems. In this paper, we propose a novel scalable data feed service for SCs, SDFS, that not only ensures that the data acquisition process is reasonable, but also guarantees that the system service is scalable. In addition, we also designed a reputation evaluation strategy for nodes to determine whether each node is a malicious node. After each feedback, the reputation value of each node will be updated. Besides that, SDFS keeps content providers auditable by using BC to preserve evidence of data processing processes. As a data feed service for SCs, it can provide the data of off-chain into the BC truly and accurately, so as to ensure the authenticity of the data on the BC and realize the SC to interact with the outside world.
Adversarial Analysis of Natural Language Inference Systems

The release of large natural language inference (NLI) datasets like SNLI and MNLI have led to rapid development and improvement of completely neural systems for the task. Most recently, heavily pre-trained, Transformer-based models like BERT and MT-DNN have reached near-human performance on these datasets. However, these standard datasets have been shown to contain many annotation artifacts, allowing models to shortcut understanding using simple fallible heuristics, and still perform well on the test set. So it is no surprise that many adversarial (challenge) datasets have been created that cause models trained on standard datasets to fail dramatically. Although extra training on this data generally improves model performance on just that type of data, transferring that learning to unseen examples is still partial at best. This work evaluates the failures of state-of-the-art models on existing adversarial datasets that test different linguistic phenomena, and find that even though the models perform similarly on MNLI, they differ greatly in their robustness to these attacks. In particular, we find syntax-related attacks to be particularly effective across all models, so we provide a fine-grained analysis and comparison of model performance on those examples. We draw conclusions about the value of model size and multi-task learning (beyond comparing their standard test set performance), and provide suggestions for more effective training data.
Enhancement of Website Password Security by Using Access Log-based Salt

According to privacy policy, the password of the website should be stored by one-way encryption using a hash function to prevent decryption. National information security agencies over various countries recommend hash functions as the safe encryption. However, as the computer's performance improved, MD5 and SHA-1 hash functions' collision occurred, indicating a hash function such as SHA-256 that was designated as safe one would be decrypted. To address this, the Salt, a way to secure the hash function, is randomly given to the user, and is put in the hash function by appending it at the beginning or end of the password. In the previous Salt usage method, even if the Salt is randomly assigned to each user, it is stored in the database as a fixed value after generated once. The vulnerability is that if the database is hacked, the Salt can be easily identified and used to crack the password. Access-based Salts are continuously changing, making passwords more secure and hiding what the Salt is.
Application of Metaverses as an Evaluation Tool in the Baccalaureate

Based on the technological development and the possibility to take advance of the capabilities that it offers on education, the purpose of this article is to study the usage of virtual worlds (Metaverses) as evaluational tool on students cursing the unified general bachelorship, appraisal applied to the third-year pupils at (Unidad Educativa Santa Mariana de Jesús) in the subjects of Mathematics, Language-Literature, and Chemistry, considering the stratified groups with parallels A, B, and C, performing the evaluations in the classroom, as well in the Metaverse, through a theoretical conceptualization of the main aspects and the creation of a virtual world in "OpenSim" with its own server both for the Metaverse and for "Moodle" and its connection with "Sloodle", in this way contributing with statistical data on the obtained results.
Software Application for Biometrical Person’s Identification by Portrait Photograph Based on Wavelet Transform

The paper is focuses on creation of software application for biometrical person’s identification by portrait photograph based on wavelet transform. During the research the experiments for determining system methods were performed. The results of experimentation indicated that the most accurate identification rate of 97,5% was obtained with the use of reverse biorthogonal wavelets for the image processing, standard deviation for the image feature extraction and Manhattan distance for the image classification.
An ontological architecture for principled and automated system of systems composition

A distributed system's functionality must continuously evolve, especially when environmental context changes. Such required evolution imposes unbearable complexity on system development. An alternative is to make systems able to self-adapt by opportunistically composing at runtime to generate systems of systems (SoSs) that offer value-added functionality. The success of such an approach calls for abstracting the heterogeneity of systems and enabling the programmatic construction of SoSs with minimal developer intervention. We propose a general ontology-based approach to describe distributed systems, seeking to achieve abstraction and enable runtime reasoning between systems. We also propose an architecture for systems that utilize such ontologies to enable systems to discover and `understand' each other, and potentially compose, all at runtime. We detail features of the ontology and the architecture through two contrasting case studies: one on controlling multiple systems in smart home environment, and another on the management of dynamic computing clusters. We also quantitatively evaluate the scalability and validity of our approach through experiments and simulations. Our approach enables system developers to focus on high-level SoS composition without being constrained by deployment-specific implementation details.
An Unsupervised Incremental Virtual Learning Method for Financial Fraud Detection

Financial fraud detection is an important topic in business intelligence, and neural networks have been effectively applied to construct detection models for this problem. Nevertheless, the timeliness of fitted models degrades over time when they are deployed in online detection systems. To maintain model performance when the labels of new transactions are not available, we propose an incremental virtual learning (IVL) method to update neural networks continually. The basic idea of IVL is to make the output distribution of neural networks smoother in the adversarial direction by unlabeled data. IVL uses local distribution smoothness (LDS) as the loss function at the unsupervised incremental learning stage. When updating a neural network by IVL, unlabeled data can be fed into the neural network periodically, so IVL can be implemented in a real online system without violating time constraints. To evaluate the effectiveness of IVL, experiments are conducted on a real dataset, demonstrating that neural networks augmented by IVL (IVL-NN) perform better than unoptimized neural networks in term of area under the curve (AUC). Furthermore, the standard deviation of the daily AUCs improves up to 19.09%, which indicates that IVL improves the stability of neural networks.
Clinical Trial Drug Safety Assessment With Interactive Visual Analytics

In this article, we provide guidance on how statisticians can use interactive visual analytics to assist medical personnel through the entire clinical safety review process. For the general assessment of safety data (e.g., adverse events, laboratory measurements), we recommend a review flow with the first step using a display that leverages statistical methods to identify events with stronger evidence of a treatment difference. In combination with clinical knowledge, reviewers can identify events that need further scrutiny. Next, clinical reviewers will be provided with displays that show additional details on these events or patient level information to aid their decision making. For safety topics of interest (e.g., suicidal ideation and behavior), we propose a tailored approach to each topic both at the summary level as well as the patient level. Displays will show only information relevant to the specific topic and for the clinical questions of interest. We also discuss some challenges to fully implement and leverage interactive displays and tools. We encourage broad use of interactive visual analytics in the analysis and display of clinical data.Study sponsor: Eli Lilly and Company.
Privacy-Preserving Substring Search on Multi-Source Encrypted Gene Data

Substring searching on gene sequence data is widely used for analyzing the association between a list of gene mutations and a specific disease. As substring search usually has a high computational cost, deploying it in the cloud has become a popular solution. Moreover, the cloud allows easier data sharing among medical organizations. Since the gene data contains private information, medical organizations usually outsource encrypted gene data to the cloud and selectively share with others. Most existing solutions for the privacy-preserving substring search problem are based on searchable encryption. However, they mainly focus on single-source gene data, not suitable for handling multi-source gene data because of some practical weaknesses. In this paper, we propose a cryptographic scheme that supports privacy-preserving substring search on multi-source encrypted gene data. The cloud can authorize queriers with access control and perform substring searches over the multi-source encrypted gene data for them. Despite the outsourced gene data is encrypted with different keys, but the authorized querier can issue a substring search only uses its own key. We adopt the composite order bilinear map as the primary underlying cryptographic primitive of our scheme. We mainly focus on protecting the privacy of the outsourced gene data and the substring queries, and we provide a security analysis under the honest-but-curious model. We also perform experiments on different datasets and analyze the experimental results in terms of computation cost and communication cost. The analyses show that our scheme is secure and efficient for substring search on multi-source encrypted gene data.
Error Detection and Correction Capability for BCH Encoder using VHDL

The BCH code is the essential class of multiple-error-correcting linear cyclic code. In actuality, Bose-Chaudhuri-Hocqunghem code is an abstraction of the cyclic hamming code for multiple-error correction. The finding of errors made by noise during transmission from the sender to the recipient is called as Error Detection. The finding of errors and renewal of the original data is called as Error Correction. The error can be detected and corrected for the fixed value of n and k. So that, the system efficiency and correcting capability are lower. In this paper, we are implementing a system in which we can detect and correct the flexible block length and message length. When the input data is applied to Galois field, the generator matrix H is performing the XOR operation with bits of the input register and formed the output.
A decentralized computational system built on blockchain for educational institutions

An educational institute demands significant computational resources for which it mostly relies on centralized servers, networks and storage facilities. Currently these systems are built around the centralized model which exposes it to risks of data breach, low fault tolerance and the numerous pitfalls of a centralized system. Furthermore, the maintenance of these systems incur significant costs for the institute. The purpose of this research is to introduce a decentralized model of computational system built on blockchain for educational institutions which can mitigate the several drawbacks of a centralized system and further explore the possibilities of introducing a cryptocurrency within the network of the institute. This model proposes to utilize the computational power of devices of the members of the institute by allowing them to be participant nodes in the network. Using the Ethereum blockchain, this study proposes a Smart Contract based system and introduces a cryptocurrency to incentivize the participants of the network.
Intentional Control of Type I Error Over Unconscious Data Distortion: A Neyman–Pearson Approach to Text Classification

This paper addresses the challenges in classifying textual data obtained from open online platforms, which are vulnerable to distortion. Most existing classification methods minimize the overall classification error and may yield an undesirably large type I error (relevant textual messages are classified as irrelevant), particularly when available data exhibit an asymmetry between relevant and irrelevant information. Data distortion exacerbates this situation and often leads to fallacious prediction. To deal with inestimable data distortion, we propose the use of the Neyman-Pearson (NP) classification paradigm, which minimizes type II error under a user-specified type I error constraint. Theoretically, we show that the NP oracle is unaffected by data distortion when the class conditional distributions remain the same. Empirically, we study a case of classifying posts about worker strikes obtained from a leading Chinese microblogging platform, which are frequently prone to extensive, unpredictable and inestimable censorship. We demonstrate that, even though the training and test data are susceptible to different distortion and therefore potentially follow different distributions, our proposed NP methods control the type I error on test data at the targeted level. The methods and implementation pipeline proposed in our case study are applicable to many other problems involving data distortion.