Reviewerly/exHarmony huggingface dataset:
Story Generation Using Knowledge Graph under Psychological States

Story generation, aiming to generate a story that people could understand easily, captures increasing researchers’ attention in recent years. However, a good story usually requires interesting and emotional plots. Previous works only consider a specific or binary emotion like positive or negative. In our work, we propose a Knowledge-Aware Generation framework under Controllable CondItions (K-GuCCI). The model assigns a change line of psychological states to story characters, which makes the story develop following the setting. Besides, we incorporate the knowledge graph into the model to facilitate the coherence of the story. Moreover, we investigate a metric AGPS to evaluate the accuracy of generated stories’ psychological states. Experiments exhibit that the proposed model improves over standard benchmarks, while also generating stories reliable and valid.
Development of flood inundation maps as an initial assessment for flood disaster mitigation - a case study of selagan river basin

Abstract Flood is a natural disaster that often occurs in Bengkulu Province. One of which occurred at the Selagan River Basin which caused in several sub-districts in Mukomuko District to be flooded. Goverment must be fast and precise in resolving future flooding problems in the Selagan River Basin. In solving the problem, initial assessment data is certainly needed, one of which is a flood inundation map, so that later the infrastructure that is built is right to solve the flooding problem that occurred in the Selagan River basin. This study was conducted to model section of the Selagan River for flood inundation forecasting with the help of digital elevation model (DEM) Data, hydrological data, river characteristics data, geographical information system(GIS). Those data is used to create a flood inundation model that is built with hydrodynamic modules which consist of rainfall runoff module, flood routing and inland flooding by using the MIKE software. Simulations of flood inundation that has been made are compared using inundation observation data at flood locations, so the results of flood inundation maps occur at this time can be a preliminary assessment for flood mitigation in the future.
Voice Conversion from Tibetan Amdo Dialect to Tibetan U-tsang Dialect Based on StarGAN-VC2

This paper proposes a conversion method from Tibetan Amdo dialect to Tibetan U-tsang dialect based on StarGAN-VC2. Recently, StarGAN-VC has gained increasing public attention due to use of a single build network. However, this method exits a difference between the converted result and the real speech. In order to alleviate this problem, the model proposes in this paper has been improved in the training part and the network architecture respectively. The training part proposes a source and target conditional adversarial loss, which allows all source domain data to be converted into target domain data. In the aspect of network architecture, a modulation-based dynamic approach is proposed, which can transform acoustic feature modulation in a specific way. Subjective and objective evaluations indicate that StarGAN-VC2 is superior to StarGAN-VC in naturalness and similarity.
What’s Wrong with ”Users”

The term “user” is quite pervasive in software engineering discussions and documents when referring to individuals that interact with a given software system. Unfortunately, since there can be many different categories of “users”, with different goals and different ways of interacting with software, this term tends to obscure these important distinctions. To a large part, this problem can be attributed to the unique culture of software programming – which emphasizes code to the exclusion of other important considerations. In this keynote talk, we explore the nature of this culture and overview some innovative software engineering methods and research initiatives designed to avoid these issues.
Parallel Computation of Event-Based Visual Features Using Relational Graphs

A graphical framework to approximate visual features from spike-based sensor data has been demonstrated. An event-based camera or dynamic vision sensor (DVS) provides the sensory input into the network which computes the intrascene optical flow, spatial gradient, and absolute intensity. The network uses the sparse, event-based input along with fundamental relations in parallel to converge upon quantities via incremental optimization. An event-based algorithm to compute optical flow was used to provide another stream of input into the network to aid convergence. A full network has been deployed in Python and parallelized to demonstrate its potential deployment on specialized hardware.
Vehicle target detection in special parkinglot based on feature clustering

At present, the management system of ordinary residential parkinglot for civilian use is relatively complete, but there is a lot of room for development in parkinglot management under special circumstances such as public security department, confidential department, and security department. Among them, in particular, the vehicle target detection still uses simple license plate recognition, which is the recognition and detection method used in ordinary parkinglots, but this has huge safety hazards for special occasions parkinglots with high safety requirements. In this regard, this article proposes a method for vehicle target detection in special parkinglots, specifically combining the bee colony algorithm with the clustering model, and inspired by team management, the method and team of honeybees in the bee colony clustering algorithm The advantages and disadvantages of the working methods of the members in management make up for each other, forming the parallel intelligent bee colony clustering under the team management mode of this article. A similarity measurement function is reconstructed and calculated for determining the degree of similarity to achieve the purpose of specific vehicle target detection. The simulation results show that the vehicle target detection method based on clustering analysis has good applicability. The average detection integrity rate and the average detection accuracy rate of the test set are 86.38% and 71.25%, respectively, which are higher compared to those of other methods. It can meet the parkinglot management under special occasions.
Enhancing Embedding via Two-Level Features for Machine Reading Comprehension

Machine reading comprehension (MRC) is a challenging task in natural language processing domain. Mainstream methods such as BERT or XLNet create Language Model (LM) with rich contextual information, but suffer from lacking semantic and syntactic message for MRC. We present a novel model TLE-BERT to enhance text encoding with the token-level and sentence-level features. In token level, we add semantic representation to the BERT based embedding. The desired features such as POS, entity type, noun phrase and synonyms of tokens are chosen by the sentinel mechanism. In sentence level, we introduce the grammatical structure to text encoding with dependency relation of the sentence, which is converted into m-hop matrices and merged into the embedding with a multi-head attention transformer layer. Each feature improves the performance of the model in varying degrees, and experiments show that TLE-BERT which combines all features organically strengthen the expressive of text representation, achieving a significant improvement on the popular MRC datasets.
Multi-Scale Slanted O(1) Stereo Matching Algorithm

In this paper, a multi-scale slanted O(1) stereo (MSOS) matching algorithm is proposed. In the proposed MSOS, the concept of multi-scale propagation is introduced to handle the problem of different object size in practical applications. Moreover, the census feature, which is more robust than the original sum of absolute difference (SAD), is employed in the proposed MSOS to compensate the effect of practical situations such as radiometric change. Experimental results show that the matching performance of the proposed MSOS is greatly improved over the original SOS on the outdoor KITTI2015 dataset.
DCCH: Deep Continuous Center Hashing for Image Retrieval

Existing hash methods only compare the hash codes by the similarity of hash codes, which cannot fully express the relationship of between the hash codes corresponding to images. This work improves the quality of image retrieval by end-to-end representation learning. We propose a compact hash code learning method named DCCH (Deep Continuous Center Hashing), which learns hash representation with a well-specified loss function which adopts label information and spatial information. We train deep network model with proposed loss function to improve image hashing and video hashing. Experiments on image hashing and video hashing demonstrate that proposed DCCH method yields higher mean average precision than state-of-the-art hashing approaches.
Related-Key Analysis of Generalized Feistel Networks with Expanding Round Functions

We extend the prior provable related-key security analysis of (generalized) Feistel networks (Barbosa and Farshim, FSE 2014; Yu et al., Inscrypt 2020) to the setting of expanding round functions, i.e., n-bit to m-bit round functions with \(n<m\). This includes Expanding Feistel Networks( \(\mathsf {EFN}\text {s}\) ) that purely rely on such expanding round functions, and Alternating Feistel Networks( \(\mathsf {AFN}\text {s}\) ) that alternate expanding and contracting round functions. We show that, when two independent keys \(K_1,K_2\) are alternatively used in each round, (a) \(2\lceil \frac{m}{n}\rceil +2\) rounds are sufficient for related-key security of \(\mathsf {EFN}\text {s}\), and (b) a constant number of 4 rounds are sufficient for related-key security of \(\mathsf {AFN}\text {s}\). Our results complete the picture of provable related-key security of GFNs, and provide additional theoretical support for the \(\mathsf {AFN}\)-based NIST format preserving encryption standards FF1 and FF3.
THE MCKINSEY–TARSKI THEOREM FOR LOCALLY COMPACT ORDERED SPACES

Abstract We prove that the modal logic of a crowded locally compact generalized ordered space is $\textsf {S4}$ . This provides a version of the McKinsey–Tarski theorem for generalized ordered spaces. We then utilize this theorem to axiomatize the modal logic of an arbitrary locally compact generalized ordered space.
Knowledge Distillation with a Precise Teacher and Prediction with Abstention

Knowledge distillation, which aims to train model under the supervision from another large model (teacher model) to the original model (student model), has achieved remarkable results in supervised learning. However, there are two major problems with existing knowledge distillation methods. One is the teacher's supervision is sometimes misleading, and the other is the student's prediction is not accurate enough. To address the first issue, instead of learning a combination of both teachers and ground truth, we apply knowledge adjustment to correct teachers' supervision using ground truth. For the second problem, we use the selective classification framework to train the student model. In particular, the deep gambler loss is adopted to predict with reservation by explicitly introducing the ( m+1)-th class. We consider two settings of knowledge distillation: (1) distillation across different network structures (AlexNet, ResNet), and (2) distillation across networks with different depths (ResNet18, ResNet5 0) to evaluate the effectiveness of our method. The experimental results on benchmark datasets (i.e., Fashion-MNIST, SVHN, CIFAR10, CIFAR100) are reported with higher prediction accuracies and lower coverage errors.
Access control of blockchain based on dual-policy attribute-based encryption

In scenarios where multiple parties such as the Internet of Things and Supply Chains participate in data sharing and computing, when accessing data, users not only need to accept the forward access control of the data owner, but also needs to perform reverse access control on the data, so as to realize two-way access control. In the traditional blockchain, all users can participate in accounting and view transaction data, and only protect user privacy through “pseudo-anonymity”. The access rights of different users cannot be distinguished, which cannot meet the user's two-way access control needs. However, most of the existing blockchain-based access control schemes are one-way access control, which cannot meet the needs of users for two-way access control in scenarios such as the Internet of Things and Supply Chains. Therefore, it is particularly important to design a two-way access control mechanism suitable for application in the blockchain. On this basis, this paper proposes a dual strategy attribute-based encryption (ABE) scheme for distributed outsourcing. This scheme combines two existing schemes, ciphertext-policy ABE and key-policy ABE, and proposes two access structures and a structure of attribute sets. The primary access structure and the secondary attributes are stored in the ciphertext, and the secondary access structure and the primary attributes are stored in the user's private key. Only when the primary attribute set satisfies the primary access structure and the secondary attribute set satisfies the secondary access structure can the user unlock the ciphertext. This scheme has no single authorization center; instead, blockchain nodes jointly participate in authorization. In addition, the proposed scheme outsources the encryption and decryption of the ciphertext to blockchain nodes to reduce the computing pressure on users and can adapt to the decentralized environment of the blockchain and provide users with two-way access control services. Finally, security analysis shows that the proposed scheme is polynomial-safe under plaintext attack.
Social Network Analysis using Knowledge-Graph Embeddings and Convolution Operations

Link prediction and node classification in social networks remain open research problems with respect to Artificial Intelligence (AI). Innate representations about social network structures can be effectively harnessed for training AI models in a bid to predict ties; and detect clusters via classification of actors with regard to a given social network. In this paper, we have proposed a distinct hybrid model: Representation Learning via Knowledge-Graph Embeddings and Convolution Operations (RLVECO), which hybridizes the strengths of Knowledge-Graph Embeddings (VE) and Convolution Operations (CO) in extracting and learning meaningful features from social graphs via Representation Learning (RL). RLVECO utilizes an edge sampling approach for exploiting features of a social graph via learning the context of each actor with respect to its neighboring actors.
Developing the P.O.L.E Framework for User Subjective Experience

Thomas Nagel in his paper, ‘What is it like to be a bat?’ highlights the problem of subjective experience. He talks about the problem of subjectivity, of how an observer, if not a bat, can never fully understand what it is like to be a bat. This paper takes on the above problem and attempts to utilize it to theoretically deduce that the problem of user subjectivity can be logically dismantled and the information gained can help in the domain of design processes. The P.O.L.E framework tries to utilize on the social and biological aspect of the user’s subjectivity and attempts to break it down by engaging in the pre-design stage of task-clarification. It aims at providing a more holistic perception of the user, by piecing together the various influences that might affect an individual’s subjectivity. The proposed framework is in its adolescence, but shows the potential to establish a base of information before the design process takes place. The research concludes with tentative future plans of how this framework will be further deployed and tested.
Decision Support System for Purchase of Used Cars using Electre Method

- This study aims to build a decision support system that can help facilitate the selection of the right used car for potential buyers. To be able to buy a used car that suits the needs and funds owned by consumers, buyers must consider the many criteria and factors of each used car which consists of various brands that exist today. This study uses the ELECTRE (Elimination and Choice Translation Reality) method. The criteria in the comparison used in this decision support system are documents, cylinder volume, year of production and car price. The ELECTRE method is used in conditions where alternatives that do not meet the criteria are eliminated, and suitable alternatives can be generated. Of the 8 samples of used cars studied, the best recommendation was Ayla (A4) with a total aggregate of 4 and an alternative that was not recommended was Agya (A3) with a total aggregate of 0.
Lightweight Security Protocols for Securing IoT Devices in Smart Cities

We are amidst a digital world wherein the Internet and advanced technological advancements have ushered smart solutions to our every requirement, and have imparted an interconnected environment for a hassle-free life altogether. We have become so accustomed to a smart handheld device as if it controls, manages, and records even the simplest and most straightforward task of our daily routine. The miniaturization of hardware and Internet-powered consumer appliances and services have solved diverse problems not only for an individual but also related to the community. The smart city project is effectively governing a city which was a dream a decade ago. The healthcare services, clean city drives, power and water supply departments, traffic control, surveillance, and many similar initiatives within the region of a municipal corporation have become IoT-enabled. The smart city services we enjoy may be vulnerable to attacks such as data interception over the communication channel, hacking the devices, stealing database records and consumer credentials, and financial frauds, etc. A consumer is not always aware of such attempts but can be a probable victim of such criminal activities. For a smart device manufacturer and a service provider, it is challenging to claim that their products and services are robust enough to combat all existing attacks. Since the IoT environment consists of small battery-powered devices, the security mechanisms generally employed to secure conventional devices and data within a typical Internet environment are not suitable for IoT infrastructure. Hence we have lightweight solutions to limit the security overhead of data storage and data communication between IoT nodes. The lightweight security protocols targeted towards securing IoT infrastructure are strong enough to mitigate well-known attacks while consuming less memory and resource footprint on the device. This chapter introduces the lightweight security protocols specifying their need in different smart city services. We need these protocols to perform user authentication, access control, payment mechanisms, and encrypting data during transmission, inventory management, traffic control, etc. The chapter introduces Singapore as a smart city model and aims to provide insight into existing security schemes for IoT-enabled smart city services. Lightweight cryptographic initiatives contributed significantly to assure the integrity of data in a constrained environment. We discuss lightweight primitives under block cipher, stream cipher, and hash function category. However, there are incidences where some of these schemes proved susceptible to certain cryptanalysis attempts. The chapter further presents a glimpse of such lightweight ciphers and their respective vulnerabilities. The chapter's contents will benefit the readers in having a clear vision of the security schemes explicitly designed for IoT applications in smart city projects.
ICE

Clustering methods are the most used algorithms for unsupervised learning. However, there is no unique optimal approach for all datasets since different clustering algorithms produce different partitions. To overcome this issue of selecting an appropriate technique and its corresponding parameters, cluster ensemble strategies are used for improving accuracy and robustness by a weighted combination of two or more approaches. However, this process is often carried out almost in a blind manner, testing different combinations of methods and assessing if its performance is beneficial for the defined purpose. Thus, the procedure for selecting the best combination tests many clustering ensembles until the desired result is achieved. This paper proposes a novel analytic tool for clustering ensemble generation, based on quantitative metrics and interactive visual resources. Our approach allows the analysts to display different results from state-of-the-art clustering methods and analyze their performance based on specific metrics and visual inspection. Based on their requirements/experience, the analysts can interactively assign weights to the different methods to set their contributions and manage (create, store, compare, and merge), such as for ensembles. Our approach's effectiveness is shown through a set of experiments and case studies, attesting to its usefulness in practical applications.
The Complex Evolution of Technologies and Pedagogies for Learning about Complex Systems

It was the mid 1990s [...]
GreedyBigVis – A greedy approach for preparing large datasets to multidimensional visualization

Structured visualization offers an efficient way to explore and analyze data. Some of the latter need a hierarchical visualization to be more understandable. However, with a large amount of big heterogeneous data, several issues regarding visualization are challenged. The numerous patterns cannot be visualized simultaneously together, and they must be presented incrementally and in an effective manner. On the other hand, the visualization must be done in low latency, while respecting the real-time and scalability constraints. This paper proposes a new approach to prepare large data-sets for multidimensional interactive visualizations. Based on the greedy algorithm, it offers mechanisms to prioritize the potential hierarchical one, select the first patterns to be visualized, and manage the multidimensional visualization cases. The proposed approach was implemented using Apache Spark to ensure the parallel computing tasks. The experimentation shows its efficiency regarding the considered factors such as scalability constraints, complexity, multidimensionality, and hierarchy.
Validation of Fresnel–Kirchhoff Integral Method for the Study of Volume Dielectric Bodies

In this work, we test a nondestructive optical method based on the Fresnel–Kirchhoff integral, which could be applied to different fields of engineering, such as detection of small cracks in structures, determination of dimensions for small components, analysis of composition of materials, etc. The basic idea is to apply the Fresnel–Kirchhoff integral method to the study of the properties of small-volume dielectric objects. In this work, we study the validity of this method. To do this, the results obtained by using this technique were compared to those obtained by rigorously solving the Helmholtz equation for a dielectric cylinder of circular cross-section. As an example of the precision of the method, the Fresnel–Kirchhoff integral method was applied to obtain the refractive index of a hair by fitting the theoretical curve to the experimental results of the diffraction pattern of the hair measured with a CCD camera. In a same manner, the method also was applied to obtain the dimensions of a crack artificially created in a piece of plastic.