Reviewerly/exHarmony huggingface dataset:
Image Learning at the Crossroads Between Human and Artificial Intelligence

Techniques for thought representation, facilitating brainstorming and thought-sharing – as for instance Venn-diagrams, flow-charts and mind-mapping – have been used since well before the current Digital Age. Now, image-based digital tools are being perfected to an unprecedented level, providing both humans and machines with a structured and synthetic way to analyse, simulate and design reality. They can even help humans and machines cooperate and learn from each other with enhanced transparency and mutual understanding, often within semantically structured processes. It is for instance the case of Knowledge-Graphs, on which some Artificial Intelligence (AI) algorithms can be based upon. Indeed, old techniques, new technologies and a series of innovative uses thereof are converging so that shared multi-dimensional images and maps of ideas may provide both humans and AI a faster way to grasp, assimilate and cognitively structure reality, and a very effective way to communicate and share thoughts. Moreover, AI is becoming an ideal playground to deepen in parallel – through analogies but also through differences – our knowledge about our own intelligence and learning processes; vice versa, a deeper comprehension of human intelligence can help us better structure AI. New epistemological paradigms may emerge from hybrid processes, and a deeper understanding of intelligence as such may stem therefrom. This paper describes such interplay of visual tools and thoughts, especially in the relationship between and among humans and machines, where images could be considered as the core of a basic common language of cognition, and for new hybrid humans/machines systems to learn about the world.
Animated Representations. Multimedia Techniques for Storytelling

The paper presents the results of two case studies that use multimedia language for the knowledge, appreciation and communication of works of art of different epochs, nature and location and that, through the tools of Representation, make use of digital methods of animation and visualisation. The final results concern the creation of multimedia products designed to narrate a conceptual path, through a reasoned animation of raster and vectorial products. Together with the necessary technical and IT skills, it is essential to reaffirm the role of representation, which is central to image processing, recognition of graphic analogies, study of proportions, knowledge of the laws of visual perception. It should be remembered that the act of seeing is a conscious creative process that involves an intentional selection of what one wants to express and therefore represent. The two case studies presented here aim to construct a precise and intentional narrative and reading perspective that integrates the physical experience of the work.
Differential-Aware Transformer for Partially Amended Sentence Translation

We propose a Transformer-based differential translation architecture that targets statutory sentences partially modified by amendments. In translating post-amendment statutory sentences, translation focality—modifying only the amended expressions in the translation and retaining the others—is important to avoid misunderstanding the amendment’s contents. To sharpen the translation’s focality, we introduce a neural network architecture called a Copiable Translation Transformer that can copy expressions in the pre-amendment translated sentence as needed and generate expressions from the post-amendment original sentence. In experiments, we showed that our method outperformed the naive Transformer with a training corpus of partially amended sentences.
On Complexity and Generality of Contrary Prioritized Defeasible Theory

This paper studies defeasible logic for representing legal theories. In defeasible logic, a literal can be ambiguous since a theory can support both the literal and its contrary. There have been several policies in handling ambiguities but choosing a policy is under judges’ discretion, which is unpredictable and possibly contradicts the legislators’ intentions. To prevent such a problem, this paper studies contrary prioritization, which restricts a defeasible theory to prioritize all pairs of rules in which heads are contrary to each other. This paper analyzes the complexity of contrary prioritization and the generality of contrary prioritized defeasible theories in the translations between such defeasible theories and stratified logic programs. This paper presents that contrary prioritization requires an effort of finding a correct assignment of the priorities between contrary pairs, and the effort makes the problem of contrary prioritization NP-complete. Furthermore, every contrary prioritized defeasible theory can be translated to a stratified logic program and vice versa. Hence, a contrary prioritized defeasible theory is as general as a stratified logic program.
Judgment method of subway carriage clearance based on gridded difference image

With the increase of railway mileage and people’s travel demand, the passenger volume of rail transit is growing year by year. In the context of the rail transit informatization, real-time monitoring of passenger behavior is not only conducive to enhance the operational efficiency and ensuring the safety of people’s lives and property, but also of great significance to improving the ability of rail companies to solve emergencies. In the case of normal train operation or emergency, carriage clearance and personnel evacuation are essential and important processes. However, at present, the judgment of carriage clearance in subway is still done by manual, which is inefficient and unable to adapt to the intelligent development. Due to the limited hardware support equipment that the train can carry, it is difficult for current mainstream deep learning methods to utilize low resources for distributed deployment in the face of synchronous video monitoring of multiple carriages. In view of the above, we propose a gridded image difference method, which combines background subtraction and temporal difference, and further reduces the complexity of the algorithm by dividing the region of interest. Applying this method, we can make real-time judgment of people and objects detained in the carriage, and provide an effective solution for intelligent carriage clearance.
Attack Graph Node Risk Probability Calculation For Level Systems Interconnection

It is an effective method to implement risk assessment of level information systems interconnection using attack graph. However, correlation between nodes in the attack graph is very complex. For example, a node may be provided by a number of its predecessor nodes together, and these predecessor nodes are influenced by other nodes. Thus, It is not independent between these pre-nodes. For this correlation, the previous researches or assume the pre-nodes are independent of each other, or have the node of the largest risk probability to represent these pre-nodes. These studies didn’t actually handle such correlation in attack graphs objectively and correctly, which may lead to calculation error and affecting the final outcome of the risk assessment. To solve this problem, this article combines nodes risk probability calculation with CVSS, and correctly handles the node correlation with the use of Bayesian Networks-based directed-separation (D-separation) theorem. What’s more, A algorithm combining Accurate calculation and approximate calculation is proposed, which can reduce the running time and increase efficiency. The correction and effectiveness of the proposed method are verified by related experiments. Compared with the previous studies, this method have a better applicability in complex attack graphs.
Image Encryption and Decryption Algorithm Based on Fractional Order Simplified Lorenz Chaotic System and DNA Coding

Traditional information encryption techniques lead to inefficient encryption and affect image formats when encrypting images. Therefore, how to ensure the secure storage and transmission of digital images has become a great challenge in the current image processing research. Chaos, a relatively typical nonlinear system at present, has many advantages and meets the design requirements of image encryption systems. In this paper, we take the basic theory of chaos as the entry point and combine it with DNA coding operation to investigate the image encryption method. A new color image encryption and decryption algorithm are designed based on the research of scholars. Mainly, DNA encoding is performed first, followed by a permutation operation to solve the chaotic sequence, generate a DNA matrix and perform operations using DNA operation rules; finally, DNA decoding is performed to obtain the ciphertext image. In addition, to decrypt the image, the pixel average of each component is hidden in the last pixel of each component matrix, which further improves the security of the algorithm. The simulation experimental results show that this method of encryption and decryption works well.
Design and Implementation of an Ed25519 coprocessor

The special elliptic curve-Ed25519 is a digital signature algorithm with high performance of signature and verification. When used for Edwards-curve Digital Signature Algorithm (EdDSA), it can provides faster digital signatures than most of other curves. This paper presents a design of an ed25519 coprocessor. Field operations are based on Montgomery Multiplication, and inverse operation is based on extended Euclid algorithm. The coprocessor requires 40166 gates with 1Kbyte SRAM. It takes 0.62M clock cycles to complete an Eddsa scalar multiplication, which is more suitable for embedded systems and iot devices.
Multi-source data fusion for insider threat detection using residual networks

With the increased diversity of audit data sources and data heterogeneity within organizations, leveraging various data sources and fusing them is critical to improving insider threat detection. The data context between different sources is a part that is often overlooked by existing anomaly detection techniques. In this paper, we propose an insider threat detection method using residual convolutional neural networks for multisource data fusion to discover undetectable combinations of potential threats. The method first transforms tabular data into neighborhood-correlated feature maps, which allows for the contextual information fusion of the same layer by convolutional structure. Skip connections are then added between convolutional layers of different depths for multi-level feature fusion, which allows rich features at lower level to directly share weight information with highly aggregated features at higher levels. We simulated idealistic and realistic conditions for the experiments, and the results on the CERT dataset demonstrate the effectiveness of the proposed method. The insider detection rate is increased by 6% to 8% compared to 1D convolutional neural network models and by 8% to 14% compared to tree-based (Random Forest, XGBoost) models.
• Research on vector data encryption method based on SM4 algorithm

With the development of Internet technology, it is easier to obtain vector data, and it is necessary to ensure the security of data storage and transmission. This paper presents a method of vector data coordinate encryption in Shapefile format using domestic password SM4 algorithm, which can ensure data security without changing the vector data structure. The system clock is used as the random seed to generate the encryption key, and the coordinate sequence of each element of vector data is read. Firstly, the coordinates are preprocessed according to the ID of the element, and then the encryption key is used to encrypt the coordinate information to obtain the ciphertext data. The experiments verify the feasibility of the domestic cipher SM4 algorithm to encrypt vector data. The encryption efficiency can reach 90M/S, and the decryption efficiency can reach 92MB/S. The encryption method can protect the storage security of vector data, improve the theoretical system of vector data encryption, and has certain research significance.
A Deep Neural Network with Residual Skip Connections for Channel Estimation

When using orthogonal frequency-division multiplexing (OFDM) for communication in channels with high resistance to inter-symbol interference (ISI), conventional channel estimation methods and neural networks with few layers perform poorly. To address this issue, the residual channel estimation network (ResCENet) is a deep neural network (DNN) containing residual skip connections that is suggested for channel estimation. ResCENet differs from previously investigated simple DNNs in that it is deeper and capable of detecting the link in sequential OFDM symbols, as well as performing estimation and demodulation tasks end-to-end. Specifically, ResCENet consists of convolutional neural networks (CNNs), bidirectional recurrent neural networks (Bi-RNNs), fully connected neural networks (FCNNs). To prevent the degradation problem caused by too many layers, residual skip connections were added, which can increase the number of layers in DNNs. Some regularization measures were also added. ResCENet receives a complex number-based digital signal and can directly restore the transmitted digital symbols. The simulation results show that ResCENet can achieve excellent channel estimation with only 4 out of 64 symbols as pilots, outperforming the conventional methods, as well as simple FCNNs.
Fine-grained Preference Learning for Next POI Recommendation

Next point-of-interest (POI) recommendation has become an important and challenging problem due to complex information and variety in user behavior patterns. Most of the prior studies utilized the RNN method to obtain their preference to various POIs. Recently, researchers integrate long- and short-term interests and achieve success. However, they fail to capture the influence of long-term preference on short-term preference. Besides, the granularity of the preference modeling is too coarse. To address the above limitations, we propose an end-to-end framework named Long- and Short-term Preference Learning with Transformer(LST), considering the user’s preference for various places at both long-term and short-term levels. Specifically, the multi-head self-attention mechanism in Transformer is utilized to extract long-term preference. To learn user’s short-term preference, we utilize spatial and temporal information of the POIs to model two different behavior patterns. In addition, our model incorporates long-term preference as background information into short-term preference to enhance the preference modeling ability. Results from extensive experiments performed on two Foursquare check-in datasets show that our model has advantages over the state-of-the-art baselines.
Comparison of Techniques with Speech Enhancement and Nonlinear Rectification for Robust Speaker Identificatio

Automatic speaker recognition is about the identification of a person based on his or her characteristic voice with the help of machines. However, this identification is drastically degraded due to adverse conditions. Our aim of this study is to evaluate the performance of the techniques Power- Normalized Cepstral Coefficients (PNCC) and Mel Frequency Cepstral Coefficients (MFCC) on NIST 2002 database, as well as using Wavelet Denoising and Cubic Law as techniques to speech enhancement and nonlinear rectification to improve speaker recognition rates. Results showed that combined Wavelet Denoising and Cubic Law get improved the recognition rates under noisy conditions.
Attribute-based searchable encryption with delegated equality test in cloud-assisted Internet of Things

With the popularity of cloud-assisted Internet of Things, many industries need to upload a large amount of data to cloud servers. In this paper, we combine the ideas of PE-ET and CP-ABE to propose an attribute-based equivalent testing model (ABE-ET), which can realize the outsourcing data and search. We allow the cloud server to perform equivalent testing on the ciphertext to judge whether the encrypted ciphertext of two different owners is consistent, and determine if the data owners have the same identity. During the search and equivalent-test of ciphertext, the cloud server cannot obtain any encrypted message. In the security and performance analysis, we prove that this scheme is under the adaptive chosen ciphertext attacks (CCA2) security, and it is suitable for cloud-assisted Internet environment the efficiency of our algorithm.
Some theoretical issues on the forms of community learning in school and network school in in-service training on professionals for school teachers to meet the requirements of the General Education Program 2018

Professional training development for teachers in general schools including students with disabilities has been carried out in recent years. In addition to the traditional forms of in-service training, a number of new forms have been used and shown to be advantageous and effective in meeting the requirements of teachers' professional development and implementation of the current general education program 2018. The content of the article focuses on studying some theoretical issues of the research topic, on that basis, giving instructions for the implementation of professional development for teachers through the forms of community learning and blended network schools for teachers in general schools including students with disabilities in order to effectively and qualitatively meet current practical requirements in schools of the country.
SNGN:Similar Neighbors Graph Transition Network for Session-based Recommendation

The target of session-based recommendation is to predict the next item to be clicked by the user based on anonymous sequences. The current mainstream session recommendation model is to build the session into a graph to capture item transitions within it. However, this only gives information about the current session, ignoring complex project information in other sessions. If other sessions are blindly added to the model, noise may be introduced, so we propose a new method to calculate adjacent sessions. In this paper, we propose a novel method, namely a similar neighbors Graph Transition Network for Session-based Recommendation (SNGN), to model item transitions within not only the current session but also the neighbor sessions. Specifically, first, we get the neighbor session graph through our new neighbor session computing method. Then input the current session graph and neighbor session graph into the perceptron to get the corresponding representation. Finally, the two representations are fused to obtain the user preference representation. Experiments on real-world datasets demonstrate that SNGN outperforms other state-of-the-art methods.
Evolutionary Game study on manufacturing capacity Sharing based on block chain

In order to study the behavior of manufacturing capacity sharing in the context of blockchain and promote the transformation and upgrading of manufacturing industry, a new model of manufacturing capacity sharing is constructed. Combined with evolutionary game theory, the game model of manufacturing capacity sharing behavior is constructed and simulated with MATLAB software. The results show that the game behaviors of two parties are affected by different factors, and the direct benefit coefficient, synergistic benefit coefficient, trust loss coefficient, platform incentive coefficient and blockchain platform penalty cost have positive effects on interenterprise capacity sharing; while the direct cost coefficient and sunk cost coefficient have negative effects on interenterprise capacity sharing.
A Model Based On Multiple Intermediate Entity For Cross-Domain Authentication In Public Key Infrastructure and Blockchain System

In the cross-domain authentication architecture, the current method is based on public key infrastructure (PKI) and blockchain to enhance the security of data. However, most models need to be improved about the efficiency, robustness and security in the authentication phase, and in the revocation phase the most solutions cannot meet the real-time revocation or waste storage on-chain space. To solve the problem, this paper proposes a model based on multiple intermediate entities. Simulation experiments show that the model can improves the efficiency of cross-domain authentication, improves the robustness of the system, resists single-point attacks, meet the real-time in revocation, and saves space on the chain.
A CONTROLLABLE CROSS-GENDER VOICE CONVERSION FOR SOCIAL ROBOT

In this study, we propose a conversion intensity controllable model for voice conversion (VC) <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1.</sup> . In particular, we combine the CycleGAN and transformer module, and build a condition embedding network as a control parameter. The model is first pre-trained with self-supervised learning on the voice reconstruction task, with the condition set to male-to-male or female-to-female. Then, we retrain the model on the cross-gender voice conversion task after the pretraining is completed, with the condition set to male-to-female or female-to-male. In the testing procedure, the condition is expected to be employed as a controllable parameter (scale). The proposed method was evaluated on the Voice Conversion Challenge dataset and compared to two baselines (CycleGAN, CycleTransGAN) with objective and subjective evaluations. The results show that our proposed model is able to convert voice with competitive performance, with the additional function of cross-gender controllability.
A chat with Dr. Jekyll and Mr. Hyde - Intent in chatbot communication

What would happen if a chatbot tries to manipulate your emotions? Can a modern language model output stable sentiment oriented conversations which can manipulate the user? We propose a framework to explore chatbots which have hidden intentions in their interaction with the user.
Context-Dependent Deep Learning for Affective Computing

Deep-learning models have been widely employed for recognizing emotions from various modalities. Yet these models face a number of challenges such as generalizing to different test conditions and predicting fine-grained emotions to name a few. One possible way to tackle these challenges is to provide these deep-learning models with additional context which can be in the form of domain knowledge from external knowledge sources or inherent task properties in the form of task-specific auxiliary losses. We hypothesize that incorporating context can better guide deep-learning models to look at the right features. In this extended abstract, we specifically focus on the problem of fine-grained emotion recognition using text-based data. We explore how to augment state-of-the-art NLP models with context to improve their performance in detecting fine-grained classes. We also discuss the implications of our research and future directions.
Designing an Affective Interface for a Personal Digital Assistant

Integrating emotional understanding and empathetic response have become important for improving the naturalness of general purpose dialogue agents. With the aim of using dialogue systems as a way to interface with a personal digital assistant (PDA), the focus on empathy evolves over time, with the agent expected to maintain a memory of the user's state and their general emotion on a range of topics from previous interactions. Therefore, the goal of dialogue agents for PDAs is to be both empathetic and proactive in their use of prior information about the user, i.e. interact with compassion. This paper aims to lay the foundations, detail the key research questions, and present hypotheses aimed towards the design and architecture of an affective interface for a compassionate PDA.