Reviewerly/exHarmony huggingface dataset:
Approaches to Modeling Development Scenarios of Multistep Social Engineering Attacks

Now Russian and foreign researchers are actively working on the problem of enhanced information security, specifically protect the organization from social engineering attacks. Which in turn can be direct and multistep. Multistep social engineering attacks are characterized by a chain of users become involved in the attack for obtain the greatest benefit by malefactors. The article focuses on improving approaches to modeling multistep social engineering attacks on the user. This study finds its application in the further design of complex information security management systems of an organization, namely, in the development of an automated complex for identifying the most critical nodes of an information system under social engineering attacks.
SCSF-Net: Single Class Scale Fixed Network for Object Detection in Optical Remote Sensing Images on Limited Hardware

The detection of objects such as vehicle, airplane and ship is a fundamental problem in optical remote-sensing(ORS) image process. Despite a great success has achieved by migrating nature image detection methods to the remote sensing field, some challenges in hardware limit environments still remain to be solved, e.g., space-borne hardware and UAV-borne hardware. We proposed a low-computational network by digging several prior knowledge in the remote sensing field. By focusing on certain ground sample distance(gsd) and single target class, the proposed method gains high performance with only less than 1% parameters and less than 1% computation used comparing with the state-of-the-art detection method. Detection result on public available vehicle dataset demonstrates the effectiveness of the proposed method. Meanwhile, the ship and airplane detection results of two private datasets are also shown. Our vehicle detection code on limited hardware is now available at https://github.com/minghuicode/scsf-detector.
A Generic Construction of Fuzzy Signature

Fuzzy signature is a signature scheme in which the signing key is no longer uniformly generated nor precisely reproducible but a fuzzy string with enough entropy such as biometric information. In this paper, we give a variant definition of fuzzy signature and propose a generic construction of fuzzy signature which uses a fuzzy extractor and a signature scheme with simple key generation process as building blocks. Meanwhile, we give two instantiations of our generic construction. The first instantiation results in a fuzzy signature scheme which is secure under the computational Diffie-Hellman (CDH) assumption over bilinear groups in the standard model. The second instantiation results in a fuzzy signature that is secure in the random oracle model under the worst-case hardness of the \(\widetilde{O}(n^{1.5})\)-SIVP problem in general lattices. Moreover, compared with previous work, our fuzzy signatures have weaker requirements for the fuzzy signing key, which makes our fuzzy signatures more practical.
Assessment of the Clusterability of Data Using a Multimodal Convolutional Neural Network

Clustering assigns data points into groups called clusters, which define the characteristics of similar data points. Our work defines a model to identify and assess the presence of a clusterable structure initially in a two-dimensional density grid of a data set which is respectively expanded into a multidimensional density grid according the dimensionality of the data set. Clusterability is defined as the tendency of a data set having a structure for successful clustering. Our approach consists of a multimodal, convolutional neural network to assess the clusterability of a data set. Multimodality is the utilization of multiple sources of information. The output of our approach, the created model, also identifies the type of the clusterable structure (none, centroid and density). Our approach does not require an initial clustering of the data to define its clusterability. In the assessment of the clusterability of high-dimensional data, we utilize random rotations accompanied with an ensemble approach. The multiple experiments of various clustering problems illustrate that our proposed approach is capable of assessing the clusterability of data and of identifying the type of the clusterable structure.
A Graph Symmetrization Bound on Channel Information Leakage Under Blowfish Privacy

Blowfish privacy is a recent generalisation of differential privacy that enables improved utility while maintaining privacy policies with semantic guarantees, a factor that has driven the popularity of differential privacy in computer science. This paper relates Blowfish privacy to an important measure of privacy loss of information channels from the communications theory community: min-entropy leakage. Symmetry in an input data neighbouring relation is central to known connections between differential privacy and min-entropy leakage. But while differential privacy exhibits strong symmetry, Blowfish neighbouring relations correspond to arbitrary simple graphs owing to the framework’s flexible privacy policies. To bound the min-entropy leakage of Blowfish-private mechanisms we organise our analysis over symmetrical partitions corresponding to orbits of graph automorphism groups. A construction meeting our bound with asymptotic equality demonstrates tightness.
Leveraging Compatibility and Diversity in Computational Music Mashup Creation

In this paper, we advance a multimodal optimization music mashup creation model for loop recombination at scale. The motivation to pursue such a model is to 1) tackle current scalability limitations in state-of-the-art (brute force) models while enforcing the 2) compatibility, i.e., recombination quality, of audio loops, and 3) a pool of diverse solutions that can accommodate personal user preferences or promote different musical styles. To this end, we adopt the Artificial Immune System (AIS) opt-aiNet algorithm to efficiently compute a population of compatible and diverse mashups from loop recombinations. Optimal mashups result from local minima in a feature space that objectively represents harmonic and rhythmic compatibility. We implemented our model as a prototype application named Mixmash-AIS, and conducted an objective evaluation that tackles three dimensions: loop recombination compatibility, mashups diversity, and computational model efficiency. The conducted evaluation compares the proposed system to a standard genetic algorithm (GA) and a brute force (BF) approach. While the GA stands as the most efficient algorithm, its poor results in terms of compatibility reinforce the primacy of the AIS opt-aiNet in efficiently finding optimal compatible loop mashups. Furthermore, the AIS opt-aiNet showed to promote a diverse mashup population, outperforming both GA or BF approaches.
Alpha Skew Gaussian Naïve Bayes Classifier

The main goal of this paper is to introduce a new procedure for a naïve Bayes classifier, namely alpha skew Gaussian naïve Bayes (ASGNB), which is based on a flexible generalization of the Gaussian distribution applied to continuous variables. As a direct advantage, this method can accommodate the possibility to handle with asymmetry in the uni or bimodal behavior. We provide the estimation procedure of this method, and we check the predictive performance when compared to other traditional classification methods using simulation studies and many real datasets with different application fields. The ASGNB is a powerful alternative to classification tasks when lie the presence of asymmetry of bimodality in the data and outperforms well when compared to other traditional classification methods in most of the cases analyzed.
The HURRIER process for experimentation in business‐to‐business mission‐critical systems

Abstract Continuous experimentation (CE) refers to a set of practices used by software companies to rapidly assess the usage, value, and performance of deployed software using data collected from customers and systems in the field using an experimental methodology. However, despite its increasing popularity in developing web‐facing applications, CE has not been studied in the development process of business‐to‐business (B2B) mission‐critical systems. By observing the CE practices of different teams, with a case study methodology inside Ericsson, we were able to identify the different practices and techniques used in B2B mission‐critical systems and a description and classification of the four possible types of experiments. We present and analyze each of the four types of experiments with examples in the context of the mission‐critical long‐term evolution (4G) product. These examples show the general experimentation process followed by the teams and the use of the different CE practices and techniques. Based on these examples and the empirical data, we derived the HURRIER process to deliver high‐quality solutions that the customers value. Finally, we discuss the challenges, opportunities, and lessons learned from applying CE and the HURRIER process in B2B mission‐critical systems.
Romadoro: Leveraging Nudge Techniques to Encourage Break-Taking

Excessive screen-time has negative impacts on mental and physical well-being, and taking breaks is important to keeping creativity, interest, and productivity high. We developed Romadoro, a Chrome extension that uses the Pomodoro Technique and technology-mediated nudges to promote better break-taking practices. Nudges involve designing choices to predictably alter the behavior of users. To test the effectiveness of using technology mediated nudges together with the Pomodoro Technique on break-taking, we conducted a mixed design user study with 36 participants. The findings from our study indicate that nudge techniques have a significant impact on motivating users to take breaks. Our work demonstrates potential avenues for designing time-management apps that could be more beneficial to the users than the classic Pomodoro approach.
Epistemic Planning with Attention as a Bounded Resource

When information grows abundant, attention becomes a scarce resource. As a result, agents must plan wisely how to allocate their attention in order to achieve epistemic efficiency. Here, we present a framework for multi-agent epistemic planning with attention, based on Dynamic Epistemic Logic (DEL, a powerful formalism for epistemic planning). We identify the framework as a fragment of standard DEL, and consider its plan existence problem. While it is undecidable in the general case, we show that when attention is required for learning, all instances of the problem are decidable.
Delurking and Influence Maximization in Online Social Networks

The concept of lurking is becoming increasingly popular these days. Lurkers (also known as silent users) are members of Online Social Networks (OSNs) who intentionally choose to observe rather than engage. There is a growing need to delurk these users and make use of their skills in computer science. In this paper, we discuss the concept of lurkers in online social networks, as well as the different categories of delurking mechanisms, including influence maximization. We first introduce the notion of lurking and the importance of transforming these users into active ones. Then, we explore the reasons behind this phenomenon and the motives that make people act in this way in OSNs. In the last chapter, we present ways of delurking, focusing on methods such as rewarding mechanisms and influence maximization.
IoT-Based Pervasive Sentiment Analysis: A Fine-Grained Text Normalization Framework for Context Aware Hybrid Applications

The Internet of Things (IoT) integrates physical objects in a networked real-world environment utilizing sensor-based software technologies in order to connect, share and exchange data with other relevant devices or platforms over the Internet. The contemporary business intelligence solutions encourage the IoT industry to reap benefits of big data dependent pervasive semantic orientation of public moods and relevant information shared in textual form using context aware integrated information management, by means of opinion mining through social media-based pervasive hybrid applications. Opinion mining (OM), also referred as pervasive sentiment analysis, is a process of extracting user orientation regarding products, services, businesses and other entities. It aims to classify an opinionative clue into positive or negative based on the sense and semantic category, which remains hidden from the human eye and non-pervasive applications. The microblog and social media-based information is intrinsically hybrid in nature, which may comprise an ample amount of ubiquitous noise. The public opinions can be classified through supervised, semi-supervised, or unsupervised classifiers. Effective sentiment analysis is common to use in data science research, but unfortunately microblogging services like Twitter allows short text for communication, which compels online publishers to post unstructured and short form of opinions towards the target entities. Unstructured and short form of tweets makes it difficult to extract meaningful and accurate sentiment orientation. This limitation can be tackled through an effective preprocessing of text. Text normalization or preprocessing is the process of removing undesired symbols, tags and conversion of unstructured data into valuable information in order to make quality input for efficient sentiment analysis. It is observed that existing preprocessors and text normalizers ignored the informal nature of text especially slangs and subject tags due to scarcity of linguistic resources, which sometimes affects the sentiment accuracy. Therefore, this research proposes a framework for effective preprocessing of text in order to generate quality input. The proposed framework for an effective text preprocessing involves the following: Informal text (a.k.a. slang) and tag identification are the major contributions of proposed framework. Slangs and Tags play a significant role in the semantic orientation of sentiments and emotions. The performance of proposed framework is evaluated over Twitter dataset using Python natural language toolkit. Experimental setup revealed that the proposed system achieved promising results with an average F1-Measure of 71% and accuracy of 72.4%.
Towards Adjoint Sensing and Acting Schemes and Interleaving Task Planning for Robust Robot Plan

Robots operating in open environments expect to have robust plans to achieve tasks successfully under environment uncertainties. However, both partial observability and dynamics of environment states have significantly decreased the robustness of task achievement, making robot task planning much more challenging. The partially observable states require the robot to obtain observations for optimally acting of the task goal. Also, state dynamics expects the robot to continuously observe surroundings for acting safely. Both challenges practically demand the purposeful and tight interactions between robot state-changing actuating actions and sensor-based observation actions. This paper proposes a novel model of Adjoint Sensing and Acting (ASA) that explicitly defines two parallel and sequential interaction schemes between actuating and observation actions, as well as an extended Behavior Tree for a concrete implementation of above schemes. We further propose an interleaving task planning approach for planning ASA-style plans, which integrates a deliberative POMDP planner for pursuing task goals, and a reactive Behavior Tree executive for fast responding to unexpected events. We experimentally demonstrate that ASA interaction schemes are practical and applicable to model and plan the open environment robot tasks. The plans from the interleaving task planning approach are both reactive in run-time response and efficient in task achievement.
Financial Early Warning System Model Combining Hybrid Semantic Hierarchy with Group Method of Data Handling Neural Network for Detection of Banks’ Risks

Banks, financial, and credit institutions encountering the weakening financial system and increased risk factors cause high inflation and great losses for an economy. Detecting financial risks in advance could help financial institutions avoid losses, and the financial system could be eventually affected less. Early warning systems for banks could be helpful to identify financial risks and take measures to deal with hazardous situations. Various approaches have already been put forward. However, inaccuracy issues in risk detection are one of the main issues. Combining semantic hierarchy with the GMDH neural network to predict financial risks is proposed. A semantic hierarchy approach based on converting risk-related values and picking influential variables could be practical in risk detection. Besides, the GMDH algorithm utilizing neural networks based on available data has the capability of predicting possible risks that could occur in the future. The outcomes of the proposed method when compared to non-data mining methods suggest that it improves accuracy by almost 20%.
Mesh Based Analysis of Low Fractal Dimension Reinforcement Learning Policies

In previous work, using a process we call meshing, the reachable state spaces for various continuous and hybrid systems were approximated as a discrete set of states which can then be synthesized into a Markov chain. One of the applications for this approach has been to analyze locomotion policies obtained by reinforcement learning, in a step towards making empirical guarantees about the stability properties of the resulting system. In a separate line of research, we introduced a modified reward function for on-policy reinforcement learning algorithms that utilizes a "fractal dimension" of rollout trajectories. This reward was shown to encourage policies that induce individual trajectories which can be more compactly represented as a discrete mesh. In this work, we combine these two threads of research by building meshes of the reachable state space of a system subject to disturbances and controlled by policies obtained with the modified reward. Our analysis shows that the modified policies do produce much smaller reachable meshes. This shows that agents trained with the fractal dimension reward transfer their desirable quality of having a more compact state space to a setting with external disturbances. The results also suggest that the previous work using mesh based tools to analyze RL policies may be extended to higher dimensional systems or to higher resolution meshes than would have otherwise been possible.
Overview of Argumentative Text Understanding for AI Debater Challenge

In this paper we present the results of the Argumentative Text Understanding for AI Debater Challenge held by the 10th CCF International Conference on Natural Language Processing and Chinese Computing (NLPCC2021), and introduce the related datasets. We organize three tracks to handle the argumentative texts in different scenarios, namely, supporting material identification (track 1), argument pair identification from online forum (track 2) and argument pair extraction from peer review and rebuttal (track 3). Each track is equipped with its distinct dataset and baseline model respectively. In total, 110 competing teams register for the challenge, from which we received 54 successful submissions. In this paper, we will present the results of the challenge and a summary of the systems, highlighting commonalities and innovations among participating systems. Datasets and baseline models of the Argumentative Text Understanding for AI Debater Challenge have been already released and can be accessed through the official website (http://www.fudan-disc.com/sharedtask/AIDebater21/index.html) of the challenge.
Research on Vehicle Detection Model based on Attention Mechanism

With the continuous development of artificial intelligence technology, innovative car technology has become an important direction for a new round of technological changes in the driverless scene. As an essential part of uncrewed vehicles, vehicle detection is of great significance for improving the reliability and safety of intelligent vehicles in the unmanned environment. Therefore, achieving efficient and real-time vehicle detection has become one of the most popular research contents. However, traditional deep learning-based vehicle detection algorithms still have the problems of losing high-resolution features of the target object and insufficient feature fusion. Because of the above issues, this paper proposes a multiscale parallel vehicle detection model based on an attention mechanism. The algorithm adopts a multi-channel neural network structure. On this basis, a weighted feature fusion method is used. The weighted fusion is carried out according to the contribution of the multi-channel features to the network. The simulation results show that, compared with mainstream vehicle detection models, the model proposed in this paper achieves better detection results.
Reverse-Engineering EFSMs with Data Dependencies

EFSMs provide a way to model systems with internal data variables. In situations where they do not already exist, we need to infer them from system behaviour. A key challenge here is inferring the functions which relate inputs, outputs, and internal variables. Existing approaches either work with white-box traces, which expose variable values, or rely upon the user to provide heuristics to recognise and generalise particular data-usage patterns. This paper presents a preprocessing technique for the inference process which generalises the concrete values from the traces into symbolic functions which calculate output from input, even when this depends on values not present in the original traces. Our results show that our technique leads to more accurate models than are produced by the current state-of-the-art and that somewhat accurate models can still be inferred even when the output of particular transitions depends on values not present in the original traces.
Platform building and application of substation digital control based on BIM

Through the combination of Internet, big data, networking and the construction process, a deep and real digital information model which is decomposed by construction control procedures of the whole substation is built. The digital information model technology is applied in a Shanghai 110kV substation construction project. According to the Gantt chart schedule, the digital model could be implemented growth. The QR code scan mechanism of the model is established to associate the digital model and the real construction nodes. At the same time, through building appropriate control platform and the development of APP function module, the life cycle management of power transmission projects is achieved. Through BIM technology, the project quantity could be precisely calculated, which strengthens the control of cost to improve the efficiency and save costs.
Deep Global Feature-Based Template Matching for Fast Multi-Modal Image Registration

Due to the different imaging mechanisms, there is a significant non-line difference between multi-modal images, which brings difficulties to multi-modal image registration. The traditional methods based on grayscale and handcraft features are difficult with obtain common features between different source images. The performances of deep local features matching methods rely on the quality and quantity of the detected keypoints, which can be quite time-consuming to register images. To achieve fast and accurate multi-modal image registration, we propose a deep global feature-based template matching method (GFTM) which uses a deep convolutional network to extract common global deep features from multi-modal images. Then, fast template matching is performed on global deep features to search the position with maximal similarity. Additionally, we build a similarity label map and design three losses to optimize our network, including contrast loss, error loss and peak loss. Extensive experimental results on optical and SAR images demonstrated that our proposed method is effective on multi-modal image registration.