Reviewerly/exHarmony huggingface dataset:
RTIM Hashing: Robust and Compact Video Hashing With a Rotation- and Translation-Invariant Model

Abstract Video hashing is a popular research topic in the fields of multimedia information and security because its fast matching and low-cost storage characteristics are widely used in many applications (video copy detection, video retrieval, video authentication, etc.). This paper describes a compact video hashing method with a rotation- and translation-invariant model (RTIM). The key contribution of this approach is that it innovatively reconstructs an input video into a 3D RTIM by combining ring partition and a pipeline histogram; this is a first in video hashing and helps make video hashes resistant to rotation and translation. Then, the proposed model is decomposed via Tucker decomposition, and the generated core tensor is used to produce a compact hash. As the core tensor is a compressed version of the original tensor, hash construction with the core tensor makes RTIM hashing compact and achieves desirable discrimination ability. Different from existing video hashing algorithms, RTIM hashing can not only resist many commonly used digital operations, especially video rotation and cyclic frame shifting, but also achieve good discrimination ability. Various experiments demonstrate the effectiveness of our algorithm. Receiver operating characteristic curve comparisons show that compared with the state-of-the-art video hashing algorithms, RTIM hashing is more robust and compact.
Remote implementation of quantum operations in quantum multihop networks

Combining the method of the multihop and the idea of the remote implementation of quantum operations, the multihop quantum operation teleportation is proposed in this paper, that is, the remote implementation of quantum operations in quantum multihop networks. Here the source and destination node do not directly share entangled states at first, which can be established through the entanglement swapping of the intermediate nodes. In addition, taking the phase-damping and the bit-flipping noise as typical noisy channels, we use the fidelity to quantify the effect of our protocol in these two types of noise environments. Furthermore, compared with the protocol employing classical sequential entanglement swapping, our protocol can reduce the communication delay using simultaneous measurements.
Architectural Decisions in AI-Based Systems: An Ontological View

Architecting AI-based systems entails making some decisions that are particular to this type of systems. Therefore, it becomes necessary to gather all necessary knowledge to inform such decisions, and to articulate this knowledge in a form that facilitates knowledge transfer among different AI projects. In this exploratory paper, we first present the main results of a literature survey in the area, and then we propose a preliminary ontology for architectural decision making, which we exemplify using a subset of the papers selected in the literature review. In the discussion, we remark on the variety of decision types and system contexts, highlighting the need to further investigate the current state of research and practice in this area. Besides, we summarize our plans to move along this research area by widening the literature review and incorporating more AI-related concepts to this first version of the ontology.
An Educational Conversational Agent for GDPR

Large-scale learning scenarios as well as the ongoing pandemic situation underline the importance of educational technology in order to support scalability and spatial as well as temporal flexibility in all kinds of learning and teaching settings. Educational conversational agents build on a long research tradition in intelligent tutoring systems and other adaptive learning technologies but build for interaction on the more recent interaction paradigm of conversational interaction. In this paper, we describe a tutorial conversational agent, called GDPRAgent, which teaches a lesson on the European General Data Protection Regulation (GDPR). This regulation governs how personal data must be treated in Europe. Instructionally, the agent's dialogue structure follows a basic GDPR curriculum and uses Bloom's revised taxonomy of learning objectives in order to teach GDPR topics. This overall design of the dialogue structure allows inserting more specific adaptive tutorial strategies. From a learner perspective, the learners experience a completely one-on-one tutorial session in which they receive relevant content (is "being taught") as well as experiences active learning parts such as doing quizzes or summarising content. Our prototype, therefore, illustrates a move away from the dichotomy between content and the activity of teaching/learning in educational technology.
Evaluating semantic representations for extended association rules

In this work, we evaluate the impact of changing the semantic text representation on the performance of the AR-SVS (extended association rules in semantic vector spaces) algorithm on the sentiment polarity classification task on a paper reviews dataset. To do this, we use natural language processing techniques in conjunction with machine learning classifiers. In particular, we report the classification performance using the F1 and accuracy metrics. The semantic representations that we used in our evaluation were chosen based on a systematic literature review, leading to an evaluation of AR-SVS with FastText, GloVe, and LDA2vec representations, with word2vec providing the baseline performance. The results of the experiments indicate that the choice of semantic text representation does not have major effects on the performance of AR-SVS for polarity classification. Furthermore, the results resemble those obtained in the original AR-SVS article, both in quantitative and qualitative terms. Thus, while direct improvements in classification performance were not found, we discuss other aspects and advantages of using different semantic representations.
Generic conversions from CPA to CCA without ciphertext expansion for threshold ABE with constant-size ciphertexts

CCA security is desirable when designing encryption schemes because it captures active attackers. One efficient approach for achieving CCA security is to use generic conversions. We first design a CPA secure scheme, and then transform it into a CCA secure scheme using the Fujisaki-Okamoto technique (CRYPTO ’99) in the random oracle model or the Canetti-Halevi-Katz technique (EUROCRYPT ’04) in the standard model if the CPA secure scheme satisfies some conditions. These transformations are generic so they can be applied to encryption primitives such as IBE and ABE . Nevertheless, both techniques result in an inevitable ciphertext expansion. Subsequent generic constructions mainly follow the idea surrounding these two techniques. The ciphertext expansion becomes a tradeoff in the existing conversions where it raises an inherently interesting question: do we have to sacrifice the size of the ciphertext during security transformation for all encryption primitives? In this work we will explore, for the first time, new generic conversions from CPA to CCA security without ciphertext expansion . By utilizing the properties of encryption in a novel way we will show that these generic conversions exist in the encryption primitive; they are called constant-size threshold ABE that satisfy verifiability or delegatability . Given a CPA secure threshold ABE that fulfills this requirement, we can convert it into a CCA secure threshold ABE without increasing the size of the ciphertext, albeit our conversions are restricted to the random oracle model. Our conversions are also applicable for transforming selective CPA to selective CCA security. We also show two instantiations of our conversions and obtain the shortest ciphertexts among all threshold ABE schemes with (selective) CCA security in the literature, even for other expressive access structures in the ABE setting. The ciphertext of the converted CCA secure scheme consists of three group elements only: two elements in group G 1 (or G in symmetric pairing) and one element in group G T .
Why Deeper Graph Neural Network Performs Worse? Discussion and Improvement About Deep GNNs

Many types of data can be represented by graphs consisting of nodes with features. The analysis of these graphs is facilitated by representing nodes as vectors, such that neighboring nodes and nodes with similar features have representations that are in close proximity in the vector space. Graph neural networks (GNNs) have been developed to learn such representations automatically, but their disadvantage is that the aggregation distance, which determines how many other nodes are considered relevant to each node, is fixed by the number of layers in the GNN. If the aggregation distance is too large, over-smoothing can result. This paper proposes TwinGNN for learning high-quality representations of nodes. Instead of using a fixed distance for the entire graph, TwinGNN learns the suitable distance for each node. We evaluated TwinGNN experimentally with several benchmark graph datasets. The results showed that TwinGNN successfully avoided over-smoothing. Its performance did not deteriorate as the number of GNN layers increased. Moreover, it performed better than the existing GNNs on almost all datasets.
Weight-Aware Graph Contrastive Learning

In contrastive learning, samples usually have different contributions to optimization. This inference applies to the specific downstream tasks of applying contrastive learning to graph learning. Nevertheless, the nodes, i.e., samples, are equally treated in the conventional graph contrastive learning approaches. To address such a problem and better model discriminative information from graphs, we propose a novel graph contrastive learning approach called Weight-Aware Graph Contrastive Learning (WA-GCL). WA-GCL first pairs up the node feature as positive and negative pairs. The weight factors of these pairs are then determined based on their similarities. WA-GCL utilizes a specific weight-aware loss to effectively and efficiently learn discriminative representations from the pairs with their corresponding weight factors. Empirically, the experiments across multiple datasets demonstrate that WA-GCL outperforms the state-of-the-art methods in graph contrastive learning tasks. Further experimental studies verified the effectiveness of different parts of WA-GCL.
Pheromone-inspired Communication Framework for Large-scale Multi-agent Reinforcement Learning

Being difficult to scale poses great problems in multi-agent coordination. Multi-agent Reinforcement Learning (MARL) algorithms applied in small-scale multi-agent systems are hard to extend to large-scale ones because the latter is far more dynamic and the number of interactions increases exponentially with the growing number of agents. Some swarm intelligence algorithms simulate the mechanism of pheromones to control large-scale agent coordination. Inspired by such algorithms, PooL, a pheromone-inspired indirect communication framework applied to large-scale multi-agent reinforcement learning is proposed in order to solve the large-scale multi-agent coordination problem. Pheromones released by agents of PooL are defined as outputs of value-based reinforcement learning algorithms, which reflect agents’ views of the current environment. The pheromone update mechanism can efficiently organize the information of all agents and simplify the complex interactions among agents into low-dimensional representations. Pheromones perceived by agents can be regarded as a summary of the views of nearby agents which can better reflect the real situation of the environment. Q-Learning is taken as our base model to implement PooL and PooL is evaluated in various large-scale cooperative environments. Experiments show agents can capture effective information through PooL and achieve higher rewards than other state-of-arts methods with lower communication costs.
Personalized Headline Generation with Enhanced User Interest Perception

Personalized news headline generation aims to model users interests through their news browsing history, and then generate news headlines based on their preferences. Using the user representation of existing news recommendation systems to personalize news headlines has been very successful, but there are still many problems to be solved. Firstly, news recommendation and headline generation tasks have different requirements for user interest modelling. Secondly, the user interest embedding approach does not consider the redundancy of user interests and the complementarity between user interests and candidate news texts. In this paper, we propose a new personalized headline generation framework to enhance the user interest perception. It can highlight user interests related to candidate texts for accurate embedding. We further offer to improve user interest representation using entity words in the news to meet the demand for news headline personalization. Extensive experiments show that our approach achieves better performance in headline generation tasks.
CKEN: Collaborative Knowledge-Aware Enhanced Network for Recommender Systems

Knowledge graph are widely used as a kind of side information to alleviate the data sparsity in collaborative filtering. However, a majority of existing recommendation methods incorporating knowledge graph emphasize how to encode knowledge associations in the knowledge graph, while ignoring the key collaboration information implied in the user-item history interactions and the shared information between the item and the knowledge graph entities, resulting in insufficient embedding representation of users and items. To that end, we propose a collaborative knowledge-aware enhanced network (CKEN). Specifically, CKEN uses a collaborative propagation approach to explicitly encode user-item collaborative information and then allows it to be propagated in the knowledge graph, and applies an attention mechanism to obtain contributions from neighbors with different knowledge during knowledge propagation. In addition, considering the association relationship between items and knowledge graph entities, this paper designs a feature interaction layer to facilitate feature sharing between items in the recommender systems and entities in the knowledge graph in order to further enrich the latent vector representation of items. Numerous experiments on several real-world scenario datasets have shown that CKEN’s recommendations are significantly better than several other best-of-class baselines.
Height deviation detection of rail bearing platform on high-speed railway track slab based on digital image correlation

The security and convenience of high-speed railways depend on the high reliability and high stability of ballastless tracks. To ensure track regularity, it is necessary to measure the height deviation of each rail bearing platform before laying the steel rail. By adjusting the fastening system, the rail could reach the design height and be at the same level. Conventionally, the height deviation of the bearing platform is measured through the contact walking measurement system. However, there are shortcomings such as few measurement points, long preparation time and low efficiency. In this paper, a fusion measurement method based on digital image correlation method and total station is proposed to solve these shortcomings. A measuring equipment consisting of dual cameras and reflecting prisms is assembled to measure the height deviation of bearing platforms (675 mm × 275 mm × 28 mm) on the high-speed railway track slab (5600 mm × 2500 mm). The three-dimensional contour of each bearing platform is measured using three-dimensional digital image correlation method and unified by locating the position of the reflecting prisms using the total station. In real applications, the height deviation of each bearing platform is measured with high accuracy, the contour measurement accuracy of a single bearing platform is better than 0.1 mm. More importantly, the range of ballastless tracks with bearing platforms to be detected in a single day is increased from the original 500 m to 5000 m without increasing the measurement error. The efficiency is increased by 10 times. The paper achieved the height deviation detection of rail bearing platform with high precision and high efficiency, contributing to improving the construction efficiency.
Steganographic algorithm in video with message encryption

One of the modern approaches to information protection is the use of steganographic methods. With the advancement of technology, the different types of information are replaced by digital, in order to facilitate storage, processing and accessibility. In computer systems and the Internet, a huge amount of digital information circulates every second, which provides conditions for developing steganographic algorithms for sending or hiding classified information. This article proposes an algorithm for steganographic manipulation of video data obtained from streaming video with message encryption. The tests of the algorithm show good results both in terms of the selected indicators and the stability.
Algorithm for visualizing substation areas in electric power systems

Electric power distribution systems can be represented with mathematical graphs in which the hub nodes are substations receiving electric power from the transmission system and delivering it to the consumers via feeders consisting of transformers, junctions, cables and power lines. High-quality diagrams depicting these systems are key elements of efficient monitoring and control. Existing automatic diagram generation solutions are usually limited to drawing single feeders or contain significant simplifications of the underlying distribution system e.g., visualize multiple feeders with their interconnections removed. The goal of this research was to develop an algorithm capable of generating visually-pleasing diagrams of substation areas consisting of a single hub node and a complex network of thousands of interconnected elements. The resulting algorithm was defined as a sequence of transformations which takes a mathematical graph as input and generates a one-line diagram as output. Multiple novel approaches are incorporated to optimize the algorithm thereby allowing it to run efficiently on regular desktop computers. Also, a novel diagram quality evaluation procedure was developed to automatically measure and compare the usability of each generated diagram. The algorithm was tested on 2510 different substation areas of various complexity extracted from the network models of real-life, European distribution systems. The quality of the generated diagrams was measured with the novel evaluation procedure and verified with a human domain experts on a subset of the resulting diagrams. Based on these analyses, it was concluded that the generated diagrams are usable in newly built supervisory and control systems with minimum human intervention. • Electric power systems are monitored and controlled via one-line diagrams. • The algorithm generates diagrams of weakly-meshed distribution substation areas. • Includes a rich dataset of 2500+ real-life substation areas as graph adjacency lists. • Proposes a quality assessment formula for qualifying any substation area diagram. • Less error prone and automates the manual labor of expert one-line diagram editors.
Stream-Based Lossless Compression of Sensor Signals Using an Integer Adaptive Predictor

Real-time sensor signal acquisition poses new challenges in data transmission and storage. Therefore, it is beneficial to insert a lossless compression system between the sensor signal’s generation and subsequent data transmission. Additionally, the compression algorithm must consider both the compression ratio and throughput. This brief proposes a two-stage lossless compression system using an integer adaptive predictor to address the inherent data diversity in digital signals and improve the compression ratio. Meanwhile, high-throughput hardware architecture is implemented for the compression system. The experimental results show that our stream-based lossless compression system improves the compression ratio by 10.5%-40.0% compared with the traditional compressor; up to 16.9% compared with the reported stream-based compressor and can operate at a speed of 800MB/s on a field-programmable gate array (FPGA).
A Study of Transfer Learning in an Ant-Based Generation Construction Hyper-Heuristic

Generation construction hyper-heuristics have proven to be effective in solving discrete optimization problems. Previous work has shown the effectiveness of an ant colony optimization hyper-heuristic for solving scheduling and packing problems. One of the challenges with generation construction hyper-heuristics is the high processing times associated with creating new construction heuristics. While there has been research into using transfer learning to reduce the computational cost of genetic programming generation constructive hyper-heuristics, this has not been investigated for ant colony optimization generation construction hyper-heuristics. In fact to the knowledge of the authors transfer learning has not previously been investigated for ant colony optimization. In this study the knowledge transferred is the pheromone map. The maps are transferred from the source domain to the target domain, with the target domain being more complicated problem instances and the source domain simpler problem instances, which do not take as long to solve. The approach was evaluated on the movie scene scheduling problem, the one dimensional bin packing problem and the quadratic assignment problem. The study has shown that the use of transfer learning has reduced the computational cost drastically while maintaining the same performance for the more complex problems for the movie scene scheduling problem and the quadratic assignment problem. However, for the one dimensional bin packing problem while there is a reduction in computational cost, the quality of the solutions is worse. Future research will investigate the reason for this and evaluate transferring different types of knowledge at various points in the life cycle of ant colony optimization generation construction hyper-heuristics.
Time-Frequency Component-Aware Convolutional Neural Network for Wireless Interference Classification

In wireless communication systems, wireless interference classification (WIC) is considered as one of the most effective technologies to address the challenges brought by electromagnetic interference in military and civilian scenarios. Recently, deep learning (DL) based methods have dominated progress in the field of WIC. However, the most existing methods do not consider the redundancy of input samples, nor have the ability to adaptively allocate computational resources conditioned on the inputs. To this end, we propose time-frequency component-aware convolutional neural network (TFCCNN), and it allows the convolution calculation to be performed only at the locations where time-frequency components or important parts exist in the time-frequency image of interference signals, leading to reduce the superfluous computation. Furthermore, to further reduce the computational complexity, we introduce a novel adaptive forward propagation (AFP) algorithm, and the network can determine the depth of forward propagation according to the difficulty of the sample during inference. Experimental results demonstrate that the proposed method reduces the computational complexity by about 75% when the recognition accuracy is slightly improved compared to the traditional CNNs.