Reviewerly/exHarmony huggingface dataset:
Eliminating Broadband Covert Channels in DSA-Like Signatures

The Digital Signature Algorithm (DSA) is a representative of a family of digital signature algorithms that are known to have a number of subliminal channels for covert data transmission. The capacity of these channels stretches from several bits (narrowband channels) to about 256 or so bits (a broadband channel). There are a couple of methods described in the literature to prevent the usage of the broadband channel with the help of a warden. In the present paper, we discuss some weaknesses of the known methods and suggest a solution that is free of the weaknesses and eliminates the broadband covert channel. Our solution also requires a warden who does not participate in signature generation and is able to check any signed message for the absence of the covert communication.
Open Cultural Data and MediaWiki Software for a Museum: The Use Case of Musée Saint-Raymond (Toulouse, France)

Freely accessible online databases developed by cultural and artistic institutions (e.g., museums, libraries, universities, studios, etc.) enable the transnational dissemination of catalogues of cultural and creative works, exploiting the advantages of modern technologies. Intelligent tools, which use advanced algorithms to classify and contextualize data, can foster knowledge mainly in two ways: (1) providing a stable and accessible basis for large amounts of data; (2) promoting cultural heritage. A case of skillful use of such tools is the Saint-Raymond Museum, the archaeological museum of Toulouse. For several years it has been working on the open data front and on putting its catalogue online on Wikimedia platforms, in various forms.
Data Security Sharing Mechanism of Power Equipment Based on Federated Learning

In the context of the rapid development of power Internet of Things technology, the amount of power equipment data is increasing rapidly. How to realize the safe sharing of power data is a research focus. In order to improve the security of power equipment data sharing and use, this paper proposes a data security management model based on alliance chain. Based on this model, the power equipment registration process, power equipment data storage process, and data sharing process are designed. In the data sharing process, in order to improve the security of data use, a data security sharing mechanism is proposed based on federated learning and differential privacy encryption technology. In the experimental part, it is verified that the data security management and sharing mechanism proposed in this paper has better application performance in the process of data management and sharing.
Wikidata based Person Entity Linking in News Articles

Entity linking (EL) is a process of extracting entity mentions in documents and linking them to their corresponding actual entities in a Knowledge Base (KB) such as Wikipedia or Wikidata. This task is challenging due to name variations, incompleteness of the KB and high ambiguity of entity mentions. News articles generally contain mentions of entities like persons, organizations, locations, etc. Which are excellent resources for understanding readers’ news interest. However, an entity mention can refer to different real world entities. Furthermore, person entities are generally more ambiguous and critically important to understand the readers’ news interest. This paper aims to design EL system for disambiguating person mentions in news articles. Current EL methods do not focus on improving EL performance of person entity mentions. Wikidata KB is chosen based on accessibility, completeness of the relations and timeliness of the data. The proposed method includes three main steps. In the first step, a new approach is proposed to generate candidate entities based on name dictionary-based technique. Partial string matching technique is adopted when person names are referred part of their full names. In the second step, top N candidates are selected using features from previous studies: entity popularity, textual similarity and the contextual similarity between a mention and the KB entity. In the last step, the best-matched entity is chosen from the top N candidates based on the semantic relatedness between the entities in a news article. The performance of the proposed methods is evaluated over a manually annotated AIDA-CoNLL news dataset. Experimental results show that the proposed approach for candidate entity generation achieves the highest precision and the recall of 98.91% and the top-1 precision of 90.05% at best matched entity selection on AIDA-CoNLL testb dataset.
Multiattribute Regret: Theory and Experimental Study

The aim of this paper is two-fold. The first part is to generalize the simple regret model by Bell (1982) and Loomes and Sugden (1982) to cope with the situation in which decision outcomes are multi-attributed. We propose a model which combines the simple regret model for ex ante preferences and the additive difference representation for ex post preferences. Then we show a necessary and sufficient axiomatization of our model under Savage’s framework. The proposed model is composed of three types of functions. One is a value function for each attribute. The others are attribute-dependent and holistic regret-rejoicing functions which respectively capture trade-offs among value-differences of chosen and forgone outcomes for each attribute and among all attributes. We also discuss constructive procedures and their consistency. The second part is to conduct an experiment to measure two attributes case. Participants were asked to join in a work to carry over heavy bags at some varying distances. The data supports the framing effect, that is, participants tend to have convex value functions in each attribute and indicate regret averse for weights and regret seeking for distances in gain frame. On the other hand, participants exhibit concave value functions for both attributes and regret seeking for weights and regret averse (almost regret neutral) for distances in loss frame.
Comparing Deep Sentiment Models using Quantified Local Explanations

Online judge systems that enable the automatic evaluation of predictive models face the problem of choosing the best model among those with near equal accuracy measures. In such scenarios, interpreting competitive models may provide a better insight towards robustness. Sentiment analysis is a well- explored domain with many available approaches and libraries. Despite this, it is context-sensitive and poses many challenges to the research community and thus deems fit to our analysis. Selecting a model with the highest accuracy may not be satisfactory, especially when the decision margins are too narrow. Our comparative study on models with similar accuracies and f1-scores but distinct underlying architectures incorporate custom metrics and evaluation methods to assess their performance on a sentiment analysis task. We have proposed to include human judgment in an online judge system using a feedback acquisition mechanism, presenting explanations for model decisions on selected test cases. Our initial experimental findings indicate that evaluating model robustness by incorporating a well-defined human feedback mechanism, using model agnostic approaches encourages online judge systems to make explainable decisions. The p-value associated with a paired t-Test on the feedback collected for model preference indicates a significant preference for the model supported by our metrics.
Do not neglect related languages: The case of low-resource Occitan cross-lingual word embeddings

Cross-lingual word embeddings (CLWEs) have proven indispensable for various natural language processing tasks, e.g., bilingual lexicon induction (BLI).However, the lack of data often impairs the quality of representations.Various approaches requiring only weak crosslingual supervision were proposed, but current methods still fail to learn good CLWEs for languages with only a small monolingual corpus.We therefore claim that it is necessary to explore further datasets to improve CLWEs in low-resource setups.In this paper we propose to incorporate data of related high-resource languages.In contrast to previous approaches which leverage independently pre-trained embeddings of languages, we (i) train CLWEs for the low-resource and a related language jointly and (ii) map them to the target language to build the final multilingual space.In our experiments we focus on Occitan, a low-resource Romance language which is often neglected due to lack of resources.We leverage data from French, Spanish and Catalan for training and evaluate on the Occitan-English BLI task.By incorporating supporting languages our method outperforms previous approaches by a large margin.Furthermore, our analysis shows that the degree of relatedness between an incorporated language and the low-resource language is critically important.
Inhabiting the Uninhabitable: Encountering Atmospheres as Other Worlds

Abstract In this chapter I discuss a series of site-specific sound art works which are designed to act on our sensory system prior to our intellect. These artworks are created from my fascination with natural phenomena and systems coupled with contemporary and historical scientific ideas and discoveries. The artworks are immersive in that the participant enters into the atmosphere and ‘other worldliness’ of the work. These phenomena include the firing patterns of artificial neurons, the multiverse and the ionosphere; the meeting place between the sun and the earth. In this chapter, I draw on Juhanni Pallasmaa’s writing on sensing and perception where evoking multimodal or cross modal cues are a strategy to create atmospheres where artworks are felt rather than thought.
A Reinforcement Learning-Based Service Model for the Internet of Things

The Internet of Things (IoT) creates environments where devices and users interact. Service-oriented architectures (SOAs) encapsulate devices’ capabilities as IoT services which users can request. SOAs manage the scale of IoT services by placing services descriptions about appropriate services in distributed architectures (e.g., a network of IoT gateways). Such distribution increases the chances of responding to users in an efficient fashion as requests are attended locally. However, dynamic IoT environments can easily outdate the distribution of services descriptions, which in turn impacts SOAs efficiency when the required services descriptions are not in place. Current architectures use pre-defined knowledge to adapt the distribution of services descriptions reactively. However, such human intervention is not always available and may be error-prone in dynamic IoT environments. We propose a reinforcement learning model that IoT gateways use to automatically decide how to distribute services descriptions over time. We evaluate the model in a real IoT testbed and results show that its performance in different scenarios compares favourably against a reactive baseline.
Linear Congruence Generator and Chaos Based Encryption Key Generation for Medical Data Security in IoT Based Health Care System

In the healthcare industry privacy and security of patient’s information is the most crucial issue at present. Considering current legal regulations, every healthcare organization should impose a prominent security technique to maintain a secure electronic health records system. On the other hand the realization of Internet of Things is the most notable advancement in the field of computer science and electronics. The healthcare services have increased with the help of IoT. Now-a-days security flaws on patient’s information are a significant issue in healthcare system. Electronics health record i.e., collection of health related information are sensitive in nature so, it is very significant to impose an advanced security techniques in the system. Here we have focused on security issue like technical safeguards. Our technique proposes a modified logistic map and linear congruence based security model for securing the diagnostics data of patients and an authentication technique. For encryption and decryption two keys are used which are intermediate key, and session key. The modified logistic map is the backbone of our work. This new approach of key generation provides beauty as well as extra robustness in our technique. The different types of experiments and their results conforms that our technique is very secure and efficient for data transmission in medical sector.
Project-Based Learning and Simulation Platform of Instructional Robot Control

Robots play an important role in the industrial field now, especially in intelligent manufacturing. This paper proposes a project-based MATLAB and Unity teaching simulation platform, which uses the graphical user interface (GUI) and 3D animation to provide an effective learning environment for college students to learn the basic principles of robot control. The experimental project of the training module includes six parts and the corresponding small experiment is designed and developed in each part. On the basis of the modular experiment, the challenging project and creative task of writing robot are completed. The effectiveness of the project is evaluated by the questionnaire data and grade anonymously. The results show that the students can avoid the interference of the subordinate problems by the simulation platform. Students’ interest is fully aroused through real-time animation and autonomous tasks. The teaching is geared to the needs of intelligent manufacturing and fits with engineering practice to develop students’ key skills, such as logical analysis and creative thinking, which lay the foundation for solving practical engineering problems in the future.
An Epistemological Study of Theory Change

Belief Revision is a well-established field of research that deals with how agents rationally change their minds in the face of new information. The milestone of Belief Revision is a general and versatile formal framework introduced by Alchourrón, Gärdenfors and Makinson, known as the AGM paradigm, which has been, to this date, the dominant model within the field. A main shortcoming of the AGM paradigm, as originally proposed, is its lack of any guidelines for relevant change. To remedy this weakness, Parikh proposed a relevance-sensitive axiom, which applies on splittable theories; i.e., theories that can be divided into syntax-disjoint compartments. The aim of this article is to provide an epistemological interpretation of the dynamics (revision) of splittable theories, from the perspective of Kuhn's inuential work on the evolution of scientific knowledge, through the consideration of principal belief-change scenarios. The whole study establishes a conceptual bridge between rational belief revision and traditional philosophy of science, which sheds light on the application of formal epistemological tools on the dynamics of knowledge.
Design of remote test system for structural reinforcement of high-rise buildings under strong earthquake environment

In recent years, geological disasters occur frequently in China, and earthquakes have seriously affected people's lives and property. In order to reduce the damage loss of high-rise buildings caused by earthquake, the design method of remote monitoring system for high-rise buildings under strong earthquake environment is proposed, aiming at the problem that the accuracy of traditional building reinforcement test system is insufficient in strong earthquake environment. According to the structural characteristics of high-rise buildings in strong earthquake environment, by optimizing the system structure and reasonably selecting the hardware configuration of the system, the software performance of the system is improved, so that the design of the remote test system for the reinforcement of high-rise buildings under strong earthquake environment is more scientific and reasonable. The experimental results show that the performance of the test system in the M = 5-8 earthquake environment is always above 70%, which makes it more accurate and stable in the actual operation process.
Path Planning Algorithm of Robot Dynamic Target Based on State Change

In the circuit, when the external voltage of the ideal inductor changes, the state changes. When the external voltage is stable, the state of the element quickly returns to the initial state. The obstacle can be regarded as the external excitation to the initial state of the robot from the starting point to the target point. Therefore, we can learn from this phenomenon to achieve obstacle avoidance. Based on this, a path planning algorithm for robot dynamic target based on state change is proposed. Firstly, the intermediate quantity of state of state detection based on the speed and direction of the robot and the effective obstacles is proposed; secondly, the real-time change of the middle value and direction of state detection is used to avoid obstacles; finally, the effective obstacles are determined by the detection of obstacles and target points to avoid the unreachable objects. By numerical simulation and analysis of experimental results, the algorithm can be effectively applied to robot path planning.
Low voltage ride through enhancement in DFIG based WECS by hybrid method

This paper proposes a hybrid technique for the enhancement of Low Voltage Ride Through (LVRT) capacity in a Doubly Fed Induction Generator (DFIG) based Wind Energy Conversion System (WECS). The proposed hybrid technique is the joint execution of S-shaped binary version of Whale Optimisation Algorithm (S-bWOA) and Random Decision Forest (RDF), hence it called S-bWOA-RDF method. The major objective is to ensure the LVRT capacity in the WECS at voltage drop including failure stages. S-bWOA model is used to identify the optimal solutions for offline mode as an available search location under objective function and generates the training dataset. In light of proficient dataset, RDF performs and predicts better feasible machine side with grid side converter control signals. The proposed technique is activated in MATLAB/Simulink site, and the efficiency of the proposed S-bWORDF method is compared with the existing methods, such as Random Decision Forest and Whale Optimisation Algorithm. The IAE and ITAE of the proposed and existing methods are also analysed. The IAE and ITAE of the proposed hybrid technique are identified as 5.0071 and 54.0475. The comparison results demonstrate the efficiency of the S-bWORDF method and confirm its potential for enhancing the LVRT of DFIG based WECS.
Deep State Inference: Toward Behavioral Model Inference of Black-Box Software Systems

Many software engineering tasks, such as testing, debugging, and anomaly detection can benefit from the ability to infer a behavioral model of the software. Most existing inference approaches assume access to code to collect execution sequences. In this paper, we investigate a black-box scenario, where the system under analysis cannot be instrumented in this fashion. This scenario is particularly common when it comes to the analysis of control system logs, which often take the form of continuous signals. In this situation, an execution trace amounts to a multivariate time-series of input and output signals, where different states of the system correspond to different “phases” in the time-series. From an inference perspective, the challenge is to detect when these phase changes take place. Unfortunately, most existing solutions are either univariate, make assumptions about the data distribution, or have limited learning power. In this paper we propose a hybrid deep neural network that accepts as input a multivariate time series and applies a set of convolutional and recurrent layers to learn the non-linear correlations between signals and the patterns over time. We show how this approach can be used to accurately detect state changes, and how the inferred models can be successfully applied to transfer-learning scenarios, to accurately process traces from different products with similar execution characteristics. Our experimental results on two UAV autopilot case studies (one industrial and one open-source) indicate that our approach is highly accurate (over 90% F1 score for state classification) and significantly improves baselines (by up to 102% for change point detection). Using transfer learning we also show that up to 90% of the maximum achievable F1 scores in the open-source case study can be achieved by reusing the trained models from the industrial case and only fine tuning them using as low as 5 labeled samples, which reduces the manual labeling effort by 98%.
A Spatial Calibration Method for Mixed Reality Devices

In this study, we present a spatial calibration method for mixed reality equipment, which greatly facilitates the interactive display of mixed reality devices in a specified position in space. In our calibration method, firstly, the formula <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$AX=XB$</tex> is used to establish the relation between the calibrator and the projection coordinate system of the mixed reality device. Then the actual position of the projected object in reality is determined by the known spatial position relationship between the calibrator and the sensor in reality. Ultimately, mixed-reality devices can better display objects at specified locations in real space using this calibration method. The spatial positioning accuracy of this calibration method can reach the level of millimeters. At the same time, we have carried out spatial positioning tests on three different types of mixed reality devices.
Elastic CRFs for Open-Ontology Slot Filling

Slot filling is a crucial component in task-oriented dialog systems that is used to parse (user) utterances into semantic concepts called slots. An ontology is defined by the collection of slots and the values that each slot can take. The most widely used practice of treating slot filling as a sequence labeling task suffers from two main drawbacks. First, the ontology is usually pre-defined and fixed and therefore is not able to detect new labels for unseen slots. Second, the one-hot encoding of slot labels ignores the correlations between slots with similar semantics, which makes it difficult to share knowledge learned across different domains. To address these problems, we propose a new model called elastic conditional random field (eCRF), where each slot is represented by the embedding of its natural language description and modeled by a CRF layer. New slot values can be detected by eCRF whenever a language description is available for the slot. In our experiment, we show that eCRFs outperform existing models in both in-domain and cross-domain tasks, especially in predicting unseen slots and values.
Diversity-promoting human motion interpolation via conditional variational auto-encoder

In this paper, we present a deep generative model based method to generate diverse human motion interpolation results. We resort to the conditional variational auto-encoder (CVAE) to learn human motion conditioned on a pair of given start and end motions, by leveraging the recurrent neural network (RNN) structure for both the encoder and the decoder. Additionally, we introduce a regularization loss to further promote sample diversity. Once trained, our method is able to generate multiple plausible coherent motions by repetitively sampling from the learned latent space. Experiments on the publicly available dataset demonstrate the effectiveness of our method, in terms of sample plausibility and diversity.