Reviewerly/exHarmony huggingface dataset:
Security and Performance of Single Sign-on Based on One-Time Pad Algorithm

Single sign-on (SSO) techniques allow access control for multiple systems with a single login. The aim of our study is to construct an authentication algorithm that provides the authentication information of a user to a requester without requiring any specific token, thereby achieving domain-free access control. In this study, we propose an authentication algorithm for SSO based on a verifiable encryption (VE)-based authentication algorithm and implementation. VE is a kind of cryptosystem that allows calculation on cyphertexts, generating an encrypted result, which matches the distance between two plaintexts when decrypting. In our approach, we first construct the mathematical SSO algorithm based on the VE-based algorithm, and then implement the algorithm by applying the one-time pad to the algorithm and using sample data. We also consider robustness against theoretical attacks such as man-in-the-middle attack. In addition to that, our algorithm is robust against the well-known classical and theoretical attacks, the man-in-the-middle attack against the proposed algorithm is also impracticable. Furthermore, with security analysis using Proverif, the algorithm has been shown to be secure. The execution speed is less than 1 ms even with a text length of 8192 bits. Based on our results, it is evident that the computational burden of trusted third parties, such as a certificate authority, can be alleviated because the public key agreement is not required in our algorithm. Moreover, since only the authentication information is disclosed to the service provider, big tech such as GAFA cannot obtain personal information of the user without consent. As for the originality of our algorithm, any personal information, such as biometric information and non-contact magnetic IC cards in addition to the pair of ID and password, which is used for common SSO algorithms, is available.
Bootstrapping Techniques for Polysynthetic Morphological Analysis

Polysynthetic languages have exceptionally large and sparse vocabularies, thanks to the number of morpheme slots and combinations in a word.This complexity, together with a general scarcity of written data, poses a challenge to the development of natural language technologies.To address this challenge, we offer linguistically-informed approaches for bootstrapping a neural morphological analyzer, and demonstrate its application to Kunwinjku, a polysynthetic Australian language.We generate data from a finite state transducer to train an encoderdecoder model.We improve the model by "hallucinating" missing linguistic structure into the training data, and by resampling from a Zipf distribution to simulate a more natural distribution of morphemes.The best model accounts for all instances of reduplication in the test set and achieves an accuracy of 94.7% overall, a 10 percentage point improvement over the FST baseline.This process demonstrates the feasibility of bootstrapping a neural morph analyzer from minimal resources.
Legal and Ethical Challenges in Multimedia Research

Multimedia research has long moved beyond laboratory experiments and is being rapidly deployed in real-life applications including advertisements, search, security, automated driving, and healthcare. Hence, the developed algorithms now have a direct impact on the individuals using the abovementioned services and the society as a whole. While there is a huge potential to benefit the society using such technologies, there is also an urgent need to identify the checks and balances to ensure that the impact of such technologies is ethical and positive. For instance, if the multimedia technologies are being used to detect and protect pedestrians from accidents by autonomous vehicles, then the pedestrian detection performance needs to be equitable across demographic descriptors, such as gender and race of the pedestrians. Similarly, while logs of driving behaviors are important in many applications, making such information available to corporate entities and third parties could raise important privacy challenges. This position article aims to: first, increase the awareness of such concepts and existing legal constraints in the multimedia research community, second, initiate a discussion on community guidelines on how to conduct multimedia research in a lawful and ethical manner, and third, identify some important research directions to support a vision of lawful and ethical multimedia research.
Application of Logistic Regression in Natural Language Processing

Data Delicacy is one of the most important issue now a days, not only storing it requires a lot of storage space but even kills a lot of time.These generally happens in online discussion forums mostly, Quora is one of them.This paper gives an insight into handling the problem of actual duplication of questions.So, to overcome the problem Quora issued a public dataset in which users were asked to give a solution to their problem which should be time efficient and should categorize the dataset as duplicate or non-duplicate.NLP is the most important part to carry out this paper which helps in stemming of the question.Similarly, Tf-idf word vector helps in conversion of words and characters into computer understandable format.Since classifying characters by computers is not an easy task unless it's converted to binary.Then lastly applying Logistic Regression to train a model which will classify the next set of questions from itself.So, after this the forums will have cheaper data storagestoring less questions, Improved customer experiencefaster responses to questions, Re-use contentif a question has been answered before it is very efficient to use the same answer for a similar question.
Short text classification using feature enrichment from credible texts

Classifying Tweet's contents can become a useful feature for other application tasks. However, such classification can be quite challenging due to the short length and sparsity of tweet contents. Although individual tweets have limited length, their contents delve into different topics. Therefore, due to such diverse contents, achieving good coverage of content features remains a challenge. We adopt the expansion of keywords technique in this research and study the enrichment of tweet contents using text from credible sources, such as news sites. For evaluation, we conduct experiments on two Twitter datasets using four standard classifiers. The proposed approach has enhanced the performance of the classification task, with improvements in accuracy ranging from +0.05% to +3.54% for both datasets. Experimental results positively demonstrate that the proposed feature enrichment method can overcome the sparseness limitation of short text with improved classification performances when running on various classifiers.
On the Position Determination of Docking Station for AUVs Using Optical Sensor and Neural Network

Detecting the relative position of the docking station is a very important issue for the homing of AUVs (Autonomous Unmanned Vehicles). To detect the position of the light source, a pinhole camera model structure was proposed like the camera model. However, due to the sensor resolution and the distortion errors of the pinhole camera system, the application of the camera of docking the under turbid sea environments is almost impossible.&#x0D; In this paper, a new method detecting the position of the docking station using a light source is presented. Also, a newly developed optical sensor which makes it much easier to sense the light source than the camera system for homing of the AUV under the water is performed. In addition, to improve the system, a neural network (NN) algorithm constructing a model relating the light inputs and optical sensor which are developed in this study is proposed.&#x0D; To evaluate the performance of the NN algorithm, the experiments were performed in the air beforehand. The result shows that the NN algorithm with AUV docking system using the NN model is better than the pinhole camera model.
Translating openEHR Models to FHIR.

openEHR and FHIR are two competing clinical data modeling and data exchange standards, that are commonly seen as mostly incompatible. However, the two have quite much in common and bridging approaches between the two worlds can serve the benefit of both communities. In the presented work, the data models of openEHR are translated into FHIR data models.
How to Win Arguments

Abstract People make decisions every day or form an opinion based on persuasion processes, whether through advertising, planning leisure activities with friends or public speeches. Most of the time, however, subliminal persuasion processes triggered by behavioral cues (rather than the content of the message) play a far more important role than most people are aware of. To raise awareness of the different aspects of persuasion ( how and what ), we present a multimodal dialog system consisting of two virtual agents that use synthetic speech in a discussion setting to present pros and cons to a user on a controversial topic. The agents are able to adapt their emotions based on explicit feedback of the users to increase their perceived persuasiveness during interaction using Reinforcement Learning.
Case-Based Reasoning for Support of the Diagnostics of Cardiovascular Diseases.

In this paper we present a decision support system, which has been designed and implemented on the case-based reasoning principles. Our decision support system is being implemented in tight cooperation with the cardiologist, who represents the main future users of the system. Our system enables its user to find the most similar historical cases to a new patient, suggest the most probable result of the potential coronary angiography examination and also provide various useful visualizations to the cardiologist, who is responsible for the final decision about recommending the coronary angiography or not for the new patient. The first response from the cardiologist about our system is very promising.
Systemic Risk Infection and Control of Water Eco-Environmental Projects under the Mode of Government-Enterprise Cooperation

Wang, W.; Tang, D., and Xu, R., 2020. Systemic risk infection and control of water eco-environmental projects under the mode of government-enterprise cooperation. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journalof Coastal Research,Special Issue No.103,pp.447–452. Coconut Creek (Florida),ISSN0749-0208.It has become an important means to improve the ecological environment in China by introducing social capital into eco-environmental projects, which has made some achievements but also accumulated great risks. The social capitals who cooperate with the government form a complex network of relations by jointly participating in projects or cooperating with the same local government. The risk caused by the deterioration of the operation of some node enterprises in the network may have a negative impact on the affiliated enterprises. Based on scale-free network transmission dynamics and epidemiology theory, the risk contagion model of affiliated enterprises in government-enterprise cooperative network under complex network is constructed. The impact of risk contagion coefficient, government relief coefficient and relief success coefficient on network risk contagion is explored. An empirical analysis is made with the data of affiliated enterprises in the government-enterprise cooperative network in the field of ecological construction in China. The results show that the probability of risk contagion has a non-linear and non-monotone effect on the network risk contagion, and the government relief has no significant inhibition on the risk contagion. Also the intervention effect has a positive effect on the risk contagion, and there is a superposition effect with the intervention intensity.
Multi-label Classification for Clinical Text with Feature-level Attention

Multi-label text classification, which tags a given plain text with the most relevant labels from a label space, is an important task in the natural language process. To diagnose diseases, clinical researchers use a machine-learning algorithm to do multi-label clinical text classification. However, conventional machine learning methods can neither capture deep semantic information nor the context of words strictly. Diagnostic information from the EHRs (Electronic Health Records) is mainly constructed by unstructured clinical free text which is an obstacle for clinical feature extraction. Moreover, feature engineering is time-consuming and labor-intensive. With the rapid development of deep learning, we apply neural network models to resolve this problem mentioned above. To favor multi-label classification on EHRs, we propose FAMLC-BERT (Feature-level Attention for Multi-label classification on BERT) to capture semantic features from different layers. The model uses feature-level attention with BERT to recognize the labels of EHRs. We empirically compared our model with other state-of-the-art models on real-world documents collected from the hospital. Experiments show that our model achieved significant improvements compared to other selected benchmarks.
Improving Sentence Retrieval Using Sequence Similarity

Sentence retrieval is an information retrieval technique that aims to find sentences corresponding to an information need. It is used for tasks like question answering (QA) or novelty detection. Since it is similar to document retrieval but with a smaller unit of retrieval, methods for document retrieval are also used for sentence retrieval like term frequency—inverse document frequency (TF-IDF), BM 25 , and language modeling-based methods. The effect of partial matching of words to sentence retrieval is an issue that has not been analyzed. We think that there is a substantial potential for the improvement of sentence retrieval methods if we consider this approach. We adapted TF-ISF, BM 25 , and language modeling-based methods to test the partial matching of terms through combining sentence retrieval with sequence similarity, which allows matching of words that are similar but not identical. All tests were conducted using data from the novelty tracks of the Text Retrieval Conference (TREC). The scope of this paper was to find out if such approach is generally beneficial to sentence retrieval. However, we did not examine in depth how partial matching helps or hinders the finding of relevant sentences.
Ordering Chaos: Memory-Aware Scheduling of Irregularly Wired Neural Networks for Edge Devices

Recent advances demonstrate that irregularly wired neural networks from Neural Architecture Search (NAS) and Random Wiring can not only automate the design of deep neural networks but also emit models that outperform previous manual designs. These designs are especially effective while designing neural architectures under hard resource constraints (memory, MACs, . . . ) which highlights the importance of this class of designing neural networks. However, such a move creates complication in the previously streamlined pattern of execution. In fact one of the main challenges is that the order of such nodes in the neural network significantly effects the memory footprint of the intermediate activations. Current compilers do not schedule with regard to activation memory footprint that it significantly increases its peak compared to the optimum, rendering it not applicable for edge devices. To address this standing issue, we present a memory-aware compiler, dubbed SERENITY, that utilizes dynamic programming to find a sequence that finds a schedule with optimal memory footprint. Our solution also comprises of graph rewriting technique that allows further reduction beyond the optimum. As such, SERENITY achieves optimal peak memory, and the graph rewriting technique further improves this resulting in 1.68x improvement with dynamic programming-based scheduler and 1.86x with graph rewriting, against TensorFlow Lite with less than one minute overhead.
Algebraic and Higher-Order Differential Cryptanalysis of Pyjamask-96

Cryptographic competitions, like the ongoing NIST call for lightweight cryptography, always provide a thriving research environment, where new interesting ideas are proposed and new cryptographic insights are made. One proposal for this NIST call that is accepted for the second round is Pyjamask. Pyjamask is an authenticated encryption scheme that builds upon two block ciphers, Pyjamask-96 and Pyjamask-128, that aim to minimize the number of AND operations at the cost of a very strong linear layer. A side-effect of this goal is a slow growth in the algebraic degree. In this paper, we focus on the block cipher Pyjamask-96 and are able to provide a theoretical key-recovery attack reaching 14 (out of 14) rounds as well as a practical attack on 8 rounds. We do this by combining higher-order differentials with an in-depth analysis of the system of equations gotten for 2.5 rounds of Pyjamask-96. The AEAD-scheme Pyjamask itself is not threatened by the work in this paper.
Directed Networks with a Differentially Private Bi-degree Sequence

Although a lot of approaches are developed to release network data with a differentially privacy guarantee, inference using noisy data in many network models is still unknown or not properly explored. In this paper, we release the bi-degree sequences of directed networks using the Laplace mechanism and use the $p_0$ model for inferring the degree parameters. The $p_0$ model is an exponential random graph model with the bi-degree sequence as its exclusively sufficient statistic. We show that the estimator of the parameter without the denoised process is asymptotically consistent and normally distributed. This is contrast sharply with some known results that valid inference such as the existence and consistency of the estimator needs the denoised process. Along the way, a new phenomenon is revealed in which an additional variance factor appears in the asymptotic variance of the estimator when the noise becomes large. Further, we propose an efficient algorithm for finding the closet point lying in the set of all graphical bi-degree sequences under the global $L_1$ optimization problem. Numerical studies demonstrate our theoretical findings.
Multi-agent cooperation Q-learning algorithm based on constrained Markov Game

Multi-Agent system has broad application in real world, whose security performance, however, is barely considered. Reinforcement learning is one of the most important methods to resolve Multi-Agent problems. At present, certain progress has been made in applying Multi-Agent reinforcement learning to robot system, man-machine match, and automatic, etc. However, in the above area, an agent may fall into unsafe states where the agent may find it difficult to bypass obstacles, to receive information from other agents and so on. Ensuring the safety of Multi-Agent system is of great importance in the above areas where an agent may fall into dangerous states that are irreversible, causing great damage. To solve the safety problem, in this paper we introduce a Multi-Agent Cooperation Q-Learning Algorithm based on Constrained Markov Game. In this method, safety constraints are added to the set of actions, and each agent, when interacting with the environment to search for optimal values, should be restricted by the safety rules, so as to obtain an optimal policy that satisfies the security requirements. Since traditional Multi-Agent reinforcement learning algorithm is no more suitable for the proposed model in this paper, a new solution is introduced for calculating the global optimum state-action function that satisfies the safety constraints. We take advantage of the Lagrange multiplier method to determine the optimal action that can be performed in the current state based on the premise of linearizing constraint functions, under conditions that the state-action function and the constraint function are both differentiable, which not only improves the efficiency and accuracy of the algorithm, but also guarantees to obtain the global optimal solution. The experiments verify the effectiveness of the algorithm.
Deriving QoE in systems: from fundamental relationships to a QoE-based Service-level Quality Index

Abstract With Quality of Experience (QoE) research having made significant advances over the years, service and network providers aim at user-centric evaluation of the services provided in their system. The question arises how to derive QoE in systems. In the context of subjective user studies conducted to derive relationships between influence factors and QoE, user diversity leads to varying distributions of user rating scores for different test conditions. Such models are commonly exploited by providers to derive various QoE metrics in their system, such as expected QoE, or the percentage of users rating above a certain threshold. The question then becomes how to combine (a) user rating distributions obtained from subjective studies, and (b) system parameter distributions, so as to obtain the actual observed QoE distribution in the system? Moreover, how can various QoE metrics of interest in the system be derived? We prove fundamental relationships for the derivation of QoE in systems, thus providing an important link between the QoE community and the systems community. In our numerical examples, we focus mainly on QoE metrics. We furthermore provide a more generalized view on quantifying the quality of systems by defining a QoE-based Service-level Quality Index. This index exploits the fact that quality can be seen as a proxy measure for utility. Following the assumption that not all user sessions should be weighted equally, we aim to provide a generic framework that can be utilized to quantify the overall utility of a service delivered by a system.
Recognition of Facial Emotions Relying on Deep Belief Networks and Quantum Particle Swarm Optimization

Classification of human face images into emotion categories is a hard challenge.Deep learning is mostly effective for this task.In this paper, we propose a facial emotions recognition system based on Deep Belief Networks (DBNs) and Quantum Particle Swarm Optimization (QPSO).The proposed methodology is composed of four phases: first, the input image is preprocessed by cropping the Region Of Interest (ROI) in order to get the desired region and discard non-significant parts.Second, the ROI is divided into many blocks and then the integral image is utilized to determine the superior (most efficient) blocks.Third, image down sampling algorithm is adapted to reduce the size of the new sub image in order to improve the system performance.Fourth, the emotion's class is identified using the DBN.Instead of adapting DBN parameters manually, QPSO is used to automatically optimize DBN parameters' values.The proposed algorithm has been applied to the Japanese female facial expression (JAFFE) and FER-2013 datasets.By applying the proposed algorithm, the computation time is reduced effectively by about 62% on the whole JAFFE and 82% on the whole FER-2013 datasets.Whereas, the accuracy is retained at 97.2% and 68.1% on JAFFE and FER-2013 datasets respectively.