Reviewerly/exHarmony huggingface dataset:
Proposal of a Comparative Framework for Face Super-Resolution Algorithms in Forensics

The number of images acquired by electronic devices is growing exponentially. This is partly due to an easier and more extended access to high-tech portable acquisition devices. These devices could record people of interest, either because they were involved in some type of criminal act, or they could be missing people, so their identification (and specifically the identification of their faces) is very relevant. On the other hand, image quality sometimes does not allow a positive identification of these individuals. This paper presents a framework for the comparative analysis of face super-resolution (hallucination) algorithms in forensics. The super-resolved images could be used to help identify a person of interest, and subsequently be admitted by the competent judicial authority in a criminal process, maintaining, like the rest of evidence, the so-called chain of custody, as required by the Spanish law and other similar legal systems.
A Low-Light Enhancement Network-LLENet

Low-light enhancement is one of the essential technologies for the construction of modern smart cities. This technology is particularly important for the improvement of urban real-time information capability. The unstable factors of the society will not be hidden in the dark, and it can add brick to the construction of smart cities. Inspired by the Retinex, this paper designs Low-Light Enhancement Network(LLENet) which designs two parts of image decomposition and enhancement In the enhancement part, we divide it into image restoration section and adjustment section. In the restoration section, we abandon the traditional interpolation method for recovery instead of U-Net and we add the attention mechanism before the U-Net enhancement channel at the same time. The nature of the network is still end-to-end trainable. The LOL dataset is used in the experiment. After a lot of experiments, it has been proved that this method is a very satisfactory low-light enhancement method and has good performance in noise removal. With the addition of U-Net, the image distortion is avoided by sampling on the reflectivity channel, and the real world is revealed before our eyes.
You Got Data‥‥ Now What: Building the Right Solution for the Problem

The demands placed upon the agri-food industry are becoming ever greater and ever more urgent, and food producers are turning to technology to provide solutions to maximizing production and productivity. In the last decade, there has been a rapid expansion in Information and Communications Technology that is now capable of answering these challenges and breaking free of dependence upon manual labor and levels of human skill and experience that would take years or even decades to develop. This expansion has taken place at two essential levels. At a first level, it is possible to design and engineer individual sensors that can supply accurate measurements wherever they are placed and whenever they are in place. However, it is only when an array of sensors is deployed in a spatial network over an extended period, does the power of technology become apparent as through these networks remote and automatic control of production processes becomes realized. At a second level, it is now possible to design and engineer computing hardware and software to process the enormous datasets that these sensor networks generate. Furthermore, advancements in machine learning have facilitated the creation of predictive models to divine accurate process control decisions from these datasets.
Towards Larger Receptive Field: Non-Local Reinforcement Learning

As a classical reinforcement learning algorithm, DQN combines traditional Q-learning with the deep neural network. When the input is an image, convolutional neural networks are frequently employed to extract features and the long-range relationships commonly occur in images. However, the receptive field of traditional convolutional neural networks is limited, failing to capture the long-range relationship. Hence, this work aims to address this problem and model the long-range relationship in the image by the proposed non-local reinforcement learning. Eventually, the performance of this method in the environment of angry Birds is evaluated, and the results demonstrate the superiority of this method.
Applications of Multi-agent Systems in Intelligent Health Care

Artificial intelligence has surged into human lives as an important requirement since the start of twenty-first century. Artificial intelligence (AI) can be understood as replications of human intelligence in systems that can be devised to ‘think and act’ like humans. Machine learning refers to the concept of programs or applications that ‘learn’ automatically and adapt fresh data without human support and is a subset of AI. One of the imperative areas in AI is distributed artificial intelligence (DAI) where complex problems can be dealt with the concept of multi-agent systems (MAS). MAS have become a bridge to come over difficult tasks and to bring out the best possible solution(s). Healthcare data is a domain of data science and can be considered as one of the most dynamically generated data. Multi-agent systems in healthcare data are the best combination possible to utilize the advantages that are available in both the sub-fields. Different diseases can be addressed through division of the task(s) as per the norms of MAS. Best results can be obtained here, and it can be truly helpful for the society and mankind. This chapter focuses on the applications that are possible through MAS in health care. Basics, important issues and algorithms are dealt with here in a comprehensible manner, and it is believed that the contents of this chapter can be immensely useful to the experts, researchers, data scientists, medicos and of course, to the society.
Research on Visual Analysis Method of Food Safety Big Data Based on Artificial Intelligence

In the background of today's big data era, the popularization of computer network and information technology helps us to understand and disseminate the hot information in the current society more quickly. By quickly understanding these hot issues, we can better supervise and prevent these problems in our life. In recent years, the problem of food safety appears frequently in our field of vision, which makes people have to regard food safety as a hot issue in today's social development. The state and relevant food safety supervision departments are also paying attention to the food safety problems. In order to better supervise food safety issues in this era of big data, this paper will analyze and study food safety issues with the help of popular technologies in the new era, such as artificial intelligence technology and big data technology, so as to formulate a new scheme to meet the needs of people in the new era for food safety supervision. Through the research, it can be found that a series of methods proposed in this paper can effectively provide new ideas for food safety big data visual analysis research method based on artificial intelligence.
Hamiltonian Properties of Bidimensional Compound Networks

With the development of Internet, data centers become more and more important. The scale of data center network expands rapidly, and the requirements for network cost, energy consumption and maintenance are also increasing. This paper studies the Bidimensional Compound Networks (BCN) based on server-centric nodes and cheaper two-port servers. BCN is a hierarchical dual-port server network structure with two dimensions. On each dimension, a high level uses a low level as a unit cluster and connects many clusters at the same level through a complete graph. It has the advantages of easy expansion, low cost, high efficiency, high security, large network scale, small network diameter, high bandwidth and high fault tolerance.
Developing an Adaptive AI Agent using Supervised and Reinforcement Learning with Monte Carlo Tree Search in FightingICE

Reinforcement Learning (RL) and Monte Carlo Tree Search (MCTS) are efficient algorithms for video game artificial intelligence (AI) agents, while Supervised Learning (SL) would make a video game AI agent visual-based. Combining SL and RL with MCTS has been tested in Computer Go, but this has yet to be thoroughly explored for fighting games. FightingICE, a 2D fighting game, serves as an ideal testing environment because of its complex action and observation spaces. In this paper, we use a Convolutional Neural Network (CNN) and Deep Q-Learning with MCTS (DQCN with MCTS) to create three models for FightingICE and compare their performance with an MCTS agent when playing against the same set of human testers. Our best performing model achieved a 58.57% game win-rate in 70 testing games after 7 training games. Although the model did not beat the MCTS agent's performance, it demonstrates the potential of combining SL, RL, and MCTS to develop an AI agent for fighting games.
Quantitative cooperation analysis among cross-chain smart contracts

In cross-chain scenarios, there are different blockchains that need cooperation. The cooperation between different blockchains is completed through smart contracts, which jointly complete cross-chain tasks. When numerous cooperative smart contracts are involved, smart contracts form a complex interactive network, which makes it difficult to evaluate the cooperation. A general model is needed to quantitatively analyze the cross-chain cooperation of associated smart contracts. In this paper, we model the cooperation among smart contracts as conditions and their corresponding actions, the quantitative condition-trigger model. Then, a method of calculating trigger probability by using graph weight is proposed. As the edge weight lacks the information of interaction probability, we introduce the dimension of the edge weight to calculate the interaction probability. The results show that the proposed method can effectively analyze the cross-chain cooperation between smart contracts.
A Case Study of Applying AI Educational Robot to Help Chinese Preschoolers Acquire Chinese and English Bilingualism

Preschool is a critical period for children's language development, but it is difficult for children to acquire English on their own due to the absence of an English background in China. This study proposes to improve this situation by applying AI educational robots and ESBR method to conduct a 12-week bilingual acquisition experiment with robots in Chinese preschool children. The post-test showed that the experimental group's reading time, reading amount, attention level to written words, bilingual recognition ability, and bilingual phonological standard were significantly improved, and they developed independent reading habits and promoted the development of school readiness level, which provided a new pathway for the multilingual acquisition of preschool children.
Research on Intelligent Recommendation of Learning Resources Based on Collaborative Filtering Algorithm

With the rapid development of Internet and information technology, knowledge data information is exploding. How to quickly find out the learning resources that learners are interested in or most in need from massive data information and recommend these resources to learners according to their characteristics is of great practical significance for improving learners' learning efficiency and learning effect. Online learning is an important way to acquire knowledge at present. However, information overload makes it very difficult to find the required learning resources from a large number of resources of online learning platform. Learning is a step-by-step process, and there is a certain contextual relationship between learning resource objects. This relationship can't be marked by a large number of people alone, but it should be built independently and intelligently. In order to solve this problem, recommendation technology came into being. According to different recommendation algorithms, the recommendation system can be divided into content-based recommendation, collaborative filtering recommendation, hybrid recommendation and so on. Among them, collaborative filtering recommendation is the most widely used recommendation algorithm. Scholars' rapid and accurate access to the required literature information has a direct impact on the efficiency and success of scientific research and teaching. This paper describes the Learning Resource Recommendation of collaborative filtering algorithm.
How to Integrate Emotions in Dialogues With Pedagogic Conversational Agents to Teach Programming to Children

Pedagogic conversational agents are interactive systems that allow students to dialogue with them about a certain domain to learn. PCAs have been used in multiple domains from pre-primary education to university, in roles such as teacher, student, or companion. In this chapter, Alcody, a PCA to teach programming to children, is enhanced with a new proposal to manage emotions in the dialogue with students. The goal is that when children are learning to program, Alcody can help them with the emotions associated to the learning. Six emotions have been integrated into Alcody: happiness, anger, sadness, fear, surprise, and disgust. A description of how a PCA to teach programming can modify its face and verbal expressions according to the emotion detected in the student. This is given for any other researcher that would like to incorporate emotions in dialogues between PCAs and students.
Strong Electromagnetic Protection Circuit for RF Front End of Autodyne Radio Fuze

In order to solve the problem that the RF front end of autodyne radio fuze is weak in resisting high-power and energy-based jamming, a design method of electromagnetic protection circuit for RF front end is proposed. The method is to add an electromagnetic protection circuit composed of microwave limiter, RF band-pass filter, and single pole double throw switch, which between RF front-end circuit and transceiver antenna of the autodyne radio fuze. The electromagnetic protection of RF front-end core high-frequency devices is carried out by means of excess energy attenuation, selection of passing frequency, and transmission channel locking. Experimental verification shows that the electromagnetic protection circuit can protect the RF front end of the Autodyne radio fuze in both the frequency domain and the time domain, and improve the survivability of the fuze in the high EM environment.
A Novel Algorithm of Tube Association and Rearrangement in Multi-camera Video Synopsis

To solve the problems of storage and browsing through massive surveillance videos in the current society, the video synopsis was studied in depth. There are few optimal solutions to solve the following two problems of multi-camera video synopsis: 1) The association and stitch of image sequences (also known as tube) of the same object across space and time under different cameras. 2) Rearrangement of global tubes in the global context. In this paper, we propose a novel video synopsis algorithm for partially overlapping cameras. Our main contributions reside in two aspects: Firstly, our algorithm can accurately associate tubes across multi-camera through a hierarchical clustering algorithm based on an adaptive threshold. It is more suitable for tubes that are broken and objects which pace back and forth. Secondly, our algorithm uses a novel priority-based tube rearrangement to maintain the integrity of the global tubes activity of the synopsis video. We have demonstrated the effectiveness of our approach from the Toulouse campus surveillance dataset and another surveillance dataset captured by our camera network.
The Future Digital Battlefield and Challenges for Humanitarian Protection: A Primer

Novel digital technologies are set to revolutionise the ways wars are fought. Recent situations of armed conflict, both between states and between states and non-state actors, have revealed glimpses into this future of warfare. The increased employment of algorithmic decision-making systems that utilise vast amounts of data generated with the help of significant advances in unmanned aerial vehicle (UAV), space, and sensor technologies, fused in real time with further digital data sources such as social media activity, online behaviour, and other “publicly available information” as well as mobile communications data, have begun to create ecosystems of constant military surveillance. The far-reaching legal and ethical implications of these developments for affected civilian populations are still in the early stages of being properly analysed and understood.The paper first provides an overview of the various technologies that, taken together, constitute the “future digital battlefield”, with a specific focus on those areas that have not received as much attention to date. On the basis of these partially speculative descriptions, the second part highlights a few legal subject areas that entail some of the potentially most consequential implications for future humanitarian protection in armed conflict. Rather than presenting fully formed answers to the questions posed by novel digital technologies in the military or an in-depth legal analysis of each identified challenge, the purpose of this primer is to provide an informed and critical outline of the most urgent issues concerning the possible ramification of an evermore digitalised battlefield in order to frame the emerging debate among scholars and political decision-makers.To that end, in addition to original research conducted by the author, the paper draws on the findings and discussions of an online expert workshop conducted on 12 August 2021.
Agile Project Management in Logistics and Supply Chain Management

The Agile Manifesto from 2001 represents the initial spark of agile project management. It was adopted by software developers and advocates that project planning should not be carried out at the beginning of the project, but rather iteratively. With the help of new roles in the project (product owner, scrum master and scrum team) as well as sprint workshops, projects should be implemented more efficiently and faster. Decision makers need to be ready for surprises and keep an open mind and be flexible to cope with changes and uncertainties. (Participant’s voice in the context of a survey of Lison and Hartel). The Agile Manifest (Beck et al., 2001) from 2001 represents the initial spark of agile project management. It was adopted by software developers and advocates that project planning should not be carried out at beginning of the project, but rather iteratively. With the help of new roles in the project (product owner, scrum master and scrum team) as well as sprint workshops, projects should be implemented more efficiently and faster.
Improving Deep-Feature Image Similarity Calculation: A Case Study on an Ukiyo-e Card Matching Game Lottery

The purpose of this study is to improve AI players in Lottery. Lottery is a card matching game designed based on the concept of Audience Participation Game With a Purpose. Namely, it lets live streaming game audiences take part in gameplay and collects image similarity data perceived by these audiences. The game employs two AI players that initially calculate image similarities based on deep features (deep-feature similarities). In our previous study, it was found that similarities, between a certain pair of images, perceived by machines or computers – calculated based on deep features – were different from similarities perceived by humans. This would make gameplay by the AI players unbelievable, in other words, non-human-like. This study, therefore, proposes to use a linear model, built based on pre-collected human data, for improving the deep-feature similarities. The amount of human data required to make the model stable is also discussed. Experimental results show that the linear model only requires small quantities of human data to greatly improve the deep-feature similarities. At the same time, our results also show that the game Lottery is indispensable. This is because the linear model can only make the calculated similarity closer to that of humans, but there is still discernible difference; in order to obtain accurate similarity between images, it is necessary to collect a certain amount of human-perceived similarity data.
Automation of the Process for Generation and Publishing Release Notes for Software Applications

The software development lifecycle contains a list of steps to be performed to build software application. It includes requirement preparation, high level design, implementation, testing and preparing the final distribution for the end user and customers. Every step has their own efforts for the team of developers and managers. That efforts are worth reducing by automation approaches to reduce final cost of software product. The paper is devoted to reducing the efforts and time on preparation and delivering software releases to the customer by automation of the process for generation and publishing release notes for software applications. The paper contains research and comparative analysis of existing approaches for release notes generation in various existing tools like: ARENA, Chronicler, Atlassian and other. Based on the research new approach is suggested, and its automation workflow is presented in the paper as well as technical implementation details and integration into continuous delivery tool. The results and metrics that shows benefits of suggested approach usage in the process of mobile application development are listed.
Blur Parameters Detection for No-Reference Local-Motion Blurred Images

Blur parameters (blur angle and blur scale) are the key parameters for blurred images restoration. A detection method of blur parameters for no-reference local-motion blurred images was proposed to the problem that the inconsistency problem of local-motion blur parameters. First, the test image was re-blurred by a Gaussian low-pass filter, the discrete cosine transform was adopted to segmented the blur region from the test image, and then motion blur parameters was obtained from the blur region; To solve the interference of cross brightness in spectrum image center of blur image, the method of obtaining blur angle using dark stripe of blur spectrum image was adopted: the spectrum image was thinning transformed after it was binary converted, to narrow the width of dark stripes; Finally the radon transform was adopted to acquire blur parameters of the blur region. Experiment results demonstrate that the proposed approach can avoid the interference of cross brightness in spectrum images, the accuracy, the stability, detection range of blur parameters detection are improved.
Multi-layer optimization algorithm

The optimization of experimental results has repeatedly posed major challenges for scientist and engineers. In this work, a systematic multi-layer optimization scheme is proposed in conjunction with particle swarm optimization algorithm to locate a global optimum that fits a cost function. The technique utilizes SP-lines to form three-dimensional patches surfaces from experimental data in multi-layer fashion and incorporates a multi-layer search using particle swarm optimization. The novel technique is illustrated and verified over two layers of experimental data to show its effectiveness.