Reviewerly/exHarmony huggingface dataset:
Usable Differential Privacy for the Internet-of-Things

Current implementations of Differential Privacy (DP) focus primarily on the privacy of the data release. The planned thesis will investigate steps towards a user-centric approach of DP in the scope of the Internet-of-Things (IoT) which focuses on data subjects, IoT developers, and data analysts. We will conduct user studies to find out more about the often conflicting interests of the involved parties and the encountered challenges. Furthermore, a technical solution will be developed to assist data subjects and analysts in making better informed decisions. As a result, we expect our contributions to be a step towards the development of usable DP for IoT sensor data.
Stitching Strip Determination for Optimal Seamline Search

To create a panorama image, various image stitching methods are available, which stitch two or more images together into a single image. However, often image stitching algorithms fail to stitch good panoramas. These failures in image stitching typically occur as the optimal seamline strategies in the image stitching framework are unable to avoid all the artifacts in their pathfinding. In our observation, working principles of optimal seamline strategies do not directly relate to the locating and avoiding artifacts. Thus, these principles often create errors in the construction of a panorama image. To overcome the issue, this paper introduces a new functional block, called Stitching Strip Determination, into existing image stitching framework. The new add-on block helps the existing optimal seamline strategies in identifying an artifact-free stitching strip/sub-region in the overlapping area which minimizes the possibility to incur artifacts in the image stitching process.
Identify risk factors and predict the postoperative risk of ESCC using ensemble learning

The prognosis of esophageal squamous cell carcinoma (ESCC) is very poor and the overall survival rate is very low, which is a major threat to human health. Using preoperative pathological information to predict prognostic risk is of great significance for improving prognosis evaluation. Aiming at the problem that the characteristic variables extracted by different modeling methods are different and the stability is not strong, this paper proposes a scheme that fuses the idea of ensemble learning with feature selection, establishes the final survival risk prediction model for ESCC. Five classification techniques are integrated to rank the importance of risk factors, including stochastic configuration network (SCN), Logistic regression (LR), support vector machine (SVM), decision tree (DT) and random forest (RF). The selected important risk factors as predictors of SCN, LR, SVM, DT and RF to construct ESCC prognostic risk assessment models. The results showed that INR, APTT and albumin are important factors affecting the prognosis of ESCC. The classification accuracy of SCN, LR, SVM, DT and RF models established by this scheme are all higher than that without using this scheme. Among them, the RF model established has the best performance (accuracy: 80.19%).
A Virtual Reality-based Spoken English Learning Platform

Under economic globalization, the exchange between different countries is becoming more frequent. Increasingly more people worldwide start or prepare to learn second (or foreign) languages. In particular, the English language plays a significant role in globalization progress, given that it has been used as a means for international commerce and communication. However, traditional methods for acquiring a second language (e.g., attending tutorial classes) are costly in terms of time, money, and effort to many people. Given the increasing demand for English language learning and traditional learning methods' pitfalls, this paper introduces a virtual-reality-based platform for learning English. The platform provides virtual scenes of daily life, such as hospitals, supermarkets, and schools. Users can instantly communicate with other online users in the virtual world. Besides, aiming to improve users' learning efficiency, every word uttered is translated into English and users' mother language in real-time. This proposed system has five advantages: 1) It is efficient because users can use it to learn a language at any time and space. They can chat with foreigners online whenever they want and wherever they are. 2) Communicating with online users helps practice one's oral English without the need to find a partner in reality. 3) Instant translation facilitates instant learning and acquisition of the phases and vocabularies. 4) The virtual 3D environment simulating real-life scenes creates an enjoyable atmosphere for users to practice their oral English. 5) The system is universal to learners of different languages since it supports English and many other languages.
METRIC-Bayes: Measurements Estimation for Tracking in High Clutter using Bayesian Nonparametrics

Robust tracking of a target in a clutter environment is an important and challenging task. In recent years, the nearest neighbor methods and probabilistic data association filters were proposed. However, the performance of these methods diminishes as number of measurements increases. In this paper, we propose a robust generative approach to effectively model multiple sensor measurements for tracking a moving target in an environment with high clutter. We assume a time-dependent number of measurements that include sensor observations with unknown origin, some of which may only contain clutter with no additional information. We robustly and accurately estimate the trajectory of the moving target in high clutter environment with unknown number of clutters by employing Bayesian nonparametric modeling. In particular, we employ a class of joint Bayesian nonparametric models to construct the joint prior distribution of target and clutter measurements such that the conditional distributions follow a Dirichlet process. The marginalized Dirichlet process prior of the target measurements is then used in a Bayesian tracker to estimate the dynamically-varying target state. We show through experiments that the tracking performance and effectiveness of our proposed framework are increased by suppressing high clutter measurements. In addition, we show that our proposed method outperforms existing methods such as nearest neighbor and probability data association filters.
OSINT Techniques Integration with Risk Assessment ISO/IEC 27001

Over the last few years, the amount of information that resides on the internet is quickly increasing especially due to the digital transformation era. Social media platforms, moving to the cloud, using the internet of things (IoT) are reasons for this transformation. However, taking advantage of publically available information related to companies and individuals can be useful in many ways. In this paper, an integration process between selected OSINT (Open source intelligence) techniques and ISO 27001 standard under some relevant domains for additional security, is proposed.
Application of Improved DBSCAN Clustering Algorithm on Industrial Fault Text Data

The industrial fault text data are the special type of short texts, and they come from the records of faults in the factory. Clustering the industrial fault text data can reduce the redundant data and find out the hidden information, which is of great significance to improve the utilization of the industrial fault text data. The industrial fault text data are unstructured and irregular, so the clustering faces quite a few challenges. This paper introduces some existing algorithms for the clustering of short texts, and the shortcomings of them are briefly analyzed. This paper indicates that the main problem of the clustering of the industrial fault text data is the contradiction between the requirements and the setup of parameters, and it leads to low accuracy when cluster the corpus of different sizes. To increase the accuracy of clustering, an improved clustering algorithm is proposed which can solve this contradiction. The results of the comparative experiments show that the improved clustering algorithm has better performance than DBSCAN in corpus of different sizes on the industrial fault text data.
Mind Map and Its Application in English Teaching

Based on the left and right brain theory and the characteristics of the mind map, the drawing method of the mind map is explained; the role of the mind map in the teaching of English writing is analyzed: stimulate the knowledge stored in the mind, open the mind to enrich the writing content, and enhance the English writing of the logic of mind mapping and the cultivation of students' divergent thinking and creative abilities; the application strategy of mind maps in English writing teaching is proposed: the application process of clear mind maps in English writing teaching, use mind maps to activate relevant background knowledge, and strengthen students cultivate the ability to draw mind maps. In the actual application process, teachers should design mind mapping teaching based on students' cognitive development level and English writing ability, and combine other writing teaching methods to achieve more efficient teaching effect.
Building a robust cyberthreat profile for institutions of higher education: An empirical analysis of external cyberattacks against a large University's computer network

This study investigated twelve categories of exploits targeting a large southern university to build a build a more robust cyberthreat profile for institutions of higher education. In one week, the university experienced 12.4 million external cyberattacks from 110 countries. Malware constituted 58% of all malicious activities. Attacks were statistically more likely to occur during the overnight hours of weekdays. Moreover, the majority of cyberattack activity was generated by a limited number of IP addresses originating from a handful of countries. Dutch-originating IP addresses were responsible for 71% of all cyberattacks in this study, and spyware attacks almost exclusively originated from a single IP address in the U.S.
VSumVis: Interactive Visual Understanding and Diagnosis of Video Summarization Model

With the rapid development of mobile Internet, the popularity of video capture devices has brought a surge in multimedia video resources. Utilizing machine learning methods combined with well-designed features, we could automatically obtain video summarization to relax video resource consumption and retrieval issues. However, there always exists a gap between the summarization obtained by the model and the ones annotated by users. How to help users understand the difference, provide insights in improving the model, and enhance the trust in the model remains challenging in the current study. To address these challenges, we propose VSumVis under a user-centered design methodology, a visual analysis system with multi-feature examination and multi-level exploration, which could help users explore and analyze video content, as well as the intrinsic relationship that existed in our video summarization model. The system contains multiple coordinated views, i.e., video view, projection view, detail view, and sequential frames view. A multi-level analysis process to integrate video events and frames are presented with clusters and nodes visualization in our system. Temporal patterns concerning the difference between the manual annotation score and the saliency score produced by our model are further investigated and distinguished with sequential frames view. Moreover, we propose a set of rich user interactions that enable an in-depth, multi-faceted analysis of the features in our video summarization model. We conduct case studies and interviews with domain experts to provide anecdotal evidence about the effectiveness of our approach. Quantitative feedback from a user study confirms the usefulness of our visual system for exploring the video summarization model.
Ranking kinematics for revising by contextual information

Abstract Probability kinematics is a leading paradigm in probabilistic belief change. It is based on the idea that conditional beliefs should be independent from changes of their antecedents’ probabilities. In this paper, we propose a re-interpretation of this paradigm for Spohn’s ranking functions which we call Generalized Ranking Kinematics as a new principle for iterated belief revision of ranking functions by sets of conditional beliefs with respect to their specific subcontext. By taking into account semantical independencies, we can reduce the complexity of the revision task to local contexts. We show that global belief revision can be set up from revisions on the local contexts via a merging operator. Furthermore, we formalize a variant of the Ramsey-Test based on the idea of local contexts which connects conditional and propositional revision in a straightforward way. We extend the belief change methodology of c-revisions to strategic c-revisions which will serve as a proof of concept.
IoT Thing To Service Semantic Matching

We propose an automated IoT data source (Thing) to service semantic matching mechanism that selects an optimal set of available IoT data sources in order to satisfy service requirements and needs. The main motivation is to provide a better use of data captured via IoT data sources. To achieve this goal, we propose a three-fold approach: (i) modelling service requirements based on existing service descriptions; (ii) automating the selection of an optimal set of Things that best meet the service requirements; (iii) selecting data processing services that provide additional service requirements.
Rational Chebyshev Graph Filters

This paper proposes rational Chebyshev graph filters to approximate step graph spectral responses with arbitrary precision, which are of interest in graph filter banks and spectral clustering. The proposed method relies on the well-known Chebyshev filters of the first kind and on a domain transform of the angular frequencies to the graph frequencies. This approach identifies in closed-form the filter coefficients, hence it avoids the costs of solving a nonlinear problem. Rational Chebyshev graph filters improve the control on the ripples in the pass- and stop-band and on the transition decay. Numerical experiments show the proposed approach approximates better ideal step responses than competing alternatives and reaches the performance of the ideal filters in compressive spectral clustering.
Computational Intelligence for Automatic Object Recognition for Vision Systems

Computer vision combines cameras, edge or cloud computing, software and artificial intelligence (AI) to enable systems to automatically recognise and identify objects. In this context, this paper details machine intelligence and data analysis algorithms that can be used for real-time object recognition. This task is essential to design an automatic vision system that can be integrated in multiple applications in future sustainable smart cities, for example, self-driving cars, robots for home help/assistance or security. Therefore, this paper is firstly a bibliographical study of algorithms for real time object recognition, and secondly a comparison of these different approaches. The comparison will consist in highlighting the specificities of each algorithm and the common points or similarities that may exist between them. Also, the results of a concrete comparison in terms of time and recognition rate were also reported. The algorithms that were included in this study are: Convolutional Neural Network (CNN), Region-based CNN (R-CNN), Fast R-CNN, Faster R-CNN for region-based algorithms, and YOLO, Tiny-YOLO, Nano-YOLO, Mini-YOLO, Slim-YOLO, MobileNet, Single Shot Multibox Detector (SSD), and RetinaNet for non-region-based algorithms. Furthermore, this review addresses in its different sections the issue of implementing AI algorithms, whether in software or hardware architectures, or in a co-design approach which is considered an appropriate method capable of simultaneously taking advantage of software and hardware advantages.
Path planning of robotic arm based on deep reinforcement learning algorithm

Abstract Robotic Arms are used in many fields due to their high accuracy. A robotic arm has the advantage of solving the same tasks as a human arm because of its similar structure and working repetitively and independently of any human. Setting up a robotic arm's task requires the engineer to plan a path for solving the task. Using an approach with reinforcement learning to make the robotic arm learn the path independently has not been feasible due to robotic arm tasks' sparse rewards. A recently successful method of using Reinforcement Learning is the concept of Hindsight Experience Replay. This article extends the difficulty of existing tasks to two new tasks and examines Hindsight Experience Replay's performance on these tasks. In the first experiment, the robotic arm has to move a ball towards a point that is far outside of its reach, similar to golf. In the second experiment, the task is to toss a ball into a box outside its reach, similar to basketball. Results show that vanilla Hindsight Experience Replay performs poorly on these tasks. Further research on solving the tasks with improvements to Hindsight Experience Replay, like Hindsight Goal Generation and Energy‐Based Hindsight Experience Prioritization, is required to solve them further.
Lattice‐based revocable attribute‐based encryption with decryption key exposure resistance

Attribute-based encryption (ABE) is a promising management method that enables fine-grained access control in large-scale systems. Revocable ABE (RABE) can support a key revocation mechanism in an ABE system. With the advent of the Internet of Things, users may need to delegate their decryption capacity to other devices, which requires that RABE meet a necessary feature called decryption key exposure resistance (DKER). Although many constructions about RABE from bilinear maps have been proposed, the situation of lattice-based constructions with DKER is less satisfactory. In order to narrow this gap, this paper propose the first lattice-based RABE with DKER. First, a formal description of RABE with DKER and the corresponding security models is proposed. Subsequently, a lattice-based RABE scheme without DKER is constructed and it is proved to be selective indistinguishability under chosen-plaintext attack (IND-CPA) security based on Learning with Errors (LWE). To achieve DKER, this paper construct a RABE scheme by using the RABE scheme without DKER and a key extension mechanism as its building blocks. Finally, this paper show that this scheme is selective IND-CPA security, with the DKER based on LWE.
Contact Tracing Solution for Global Community

Abstract There are several Contact Tracing solutions since the outbreak of SARS COVID-19. All these solutions are localized—specific to a country. The Apps supported by these solutions do not interwork with each other. There are no standards to the proximity data collected by these Apps. Once the international travel restrictions are relaxed, this will become an issue. This paper explores this issue, by addressing one of the key requirements of Contact Tracing solutions. All the current solutions use an Identifier, Proximity Identifier (PID), that anonymously represents the user in the proximity data exchanged. The PID used in these applications varies in their structure, management and properties. This paper first identifies the common desirable properties of PID, including some non-obvious ones for its global application. This identification is essential for the design and development of the Contact Tracing solution that can work across boundaries seamlessly. The paper also evaluates representative solutions from two different design classes against these properties.
Mutual cooperative effects between the mode components of two-photon and Raman induced cavity lasing processes

The paper proposes to unify the two-photon laser and induced cooperative scattering Raman lasing in which the flying ensemble of two-level excited radiators are coupled with the bimodal cavity field in nonlinear two-photon and scattering interactions. It discusses the situation in which the induced two-quantum lasing takes place together with the cooperative scattering conversion of photons between the pump, Stokes, and anti-Stokes cavity modes. The proposed master equation describes cooperative energy exchanges between Raman and two-photon lasing processes by portions of energies multiple to the energy of the entangled photon pair. The generators of such emission and absorption processes belonging to the symmetries of Raman conversion and two-photon cooperative generation are unified. The first symmetry belongs to su(2) algebra connected to the scattering conversion in the Stokes, pump, and anti-Stokes modes of cavity (Opt. Commun.,285(5) (2012) 686–692). The second one belongs to multi-mode su(1,1) symmetry which quantifies the cooperative process belonging to two-photon emission in cavity/fiber mode components of field (see Opt. Commun. 247(4–6) (2005) 381–392). The bimodal collective photon operators are introduced describing the emission or absorption acts of the fixed portion of energy from the cavity equal to the transition energy between the states of each excited atom. A key impact of the study focuses on the statistical properties of the bimodal field and their detection possibilities are proposed for the description of the time evolution of quantum correlations between the field components of Raman conversion and two-photon emission.
Differentiable Pattern Set Mining

Pattern set mining has been successful in discovering small sets of highly informative and useful patterns from data. To find good models, existing methods heuristically explore the twice-exponential search space over all possible pattern sets in a combinatorial way, by which they are limited to data over at most hundreds of features, as well as likely to get stuck in local minima. Here, we propose a gradient based optimization approach that allows us to efficiently discover high-quality pattern sets from data of millions of rows and hundreds of thousands of features.
Distance Learning—Challenges and New Opportunities Under COVID-19 Pandemic

The research is devoted to distance learning in Russian under the new coronavirus infection (COVID-19). The article presents the results of polling students and professors of universities of Kazan, Magnitogorsk, Penza, and Ryazan. The respondents were over 100 students and 40 professors. The research showed that 67% of students and 91.7% of professors believe that higher education cannot be completely transferred into the online format. The professors consider a combination of online and offline regimes to be an optimal form of learning. The polling allowed evaluating the strengths and weaknesses of organization and content of distance learning at the local level, the difficulties and problems for professors and students, and the prospects of the phenomenon under study. The first and foremost of them is the insufficient preparedness of the professors and students for transiting to the distance learning regime. Many organizational problems were due to an unstable functioning of the digital educational environment, educational portals, and information-communication networks. The research showed that interaction between professors and students should be made more efficient. In general, both students and professors acknowledge that distance learning complements the existing traditional educational system and only certain forms of teaching can work in distant format. In this regard, the full transition of the Russian universities to distance format in spring 2020 is a forced necessity caused by the COVID-19 pandemic.