Reviewerly/exHarmony huggingface dataset:
Strengthening the Security of AES Against Differential Fault Attack

AES, the NIST standard block cipher, is most widely used for cryptographic applications. Till date, AES is secured against all types of cryptographic attacks like algebraic or statistical attacks. Unfortunately, AES is vulnerable against side channel attacks. Differential Fault Attack is a powerful cryptanalytic technique against AES. The most successful fault attacks on AES form linear equations with a number of faulty and fault free ciphertexts to find out the key. Our proposal to protect AES from Differential Fault Attack is to add a new diffusion layer in each round to enlarge the exhaustive key-search space by generating a minimum number of equations. The existing result on AES key recovery using this kind of fault attack is $$2^{32}$$ , which is further improved to $$2^{8}$$ . With our approach, the security is enhanced by increasing key-search space to $$2^{84}$$ .
Learning With Sharing: An Edge-Optimized Incremental Learning Method for Deep Neural Networks

Incremental learning techniques aim to increase the capability of Deep Neural Network (DNN) model to add new classes in the pre-trained model. However, DNNs suffer from catastrophic forgetting during the incremental learning process. Existing incremental learning techniques try to reduce the effect of catastrophic forgetting by either using previous samples of data while adding new classes in the model or designing complex model architectures. This leads to high design complexity and memory requirements which make incremental learning impossible to implement on edge devices that have limited memory and computation resources. So, we propose a new incremental learning technique Learning with Sharing (LwS) based on the concept of transfer learning. The main aims of LwS are reduction in training complexity and storage memory requirements while achieving high accuracy during the incremental learning process. We perform cloning and sharing of full connected (FC) layers to add new classes in the model incrementally. Our proposed technique can preserve the knowledge of existing classes and add new classes without storing data from the previous classes. We show that our proposed technique outperforms the state-of-the-art techniques in accuracy comparison for Cifar-100, Caltech-101, and UCSD Birds datasets.
Mitigating Harmful Content on Social Media Using an Interactive User Interface

With the rapid growth in the number of users on social networking sites (SNS), harmful content has also been fueled enormously over time. The purpose of harmful content is usually to mislead or harm or deceive an individual or a group of users. This study focuses on two types of harmful content: hate speech and misinformation. Alongside existing methods to detect misinformation and hate speech, users are still not well informed about the content description. This study proposes an interactive user interface, ‘TweetInfo’, for analysing information consumption towards harmful content by providing metainformation about social media posts. The study aims to explore the consumption of harmful content by users by providing an interactive user interface which flags harmful content and provides metadata about the post by doing content analysis and standard platform without the above features. The effectiveness of the proposed TweetInfo is measured using a user study conducted with 30 participants. TweetInfo reduces the spread of harmful content compared to the standard platform. While there are still interactions with bots on both platforms, verified accounts are also involved in propagating harmful content.
Quantum Noise Secured Terahertz Communications

The terahertz communications display an important role in high-speed wireless communications, the security threat from the eavesdroppers in the terahertz communications has been gaining attention recently. The true randomness in the physical layer can ensure one-time-pad encryption for secured terahertz communications, however, physical layer security schemes like the quantum key distribution methods suffer from device imperfections that limit the desirable signal rate and link distance. Herein, we present the quantum noise secured terahertz wireless communications with photonic terahertz signal generation schemes. With the high-order diffusion algorithms, the signal is masked by the quantum noise ciphers to the eavesdroppers and cannot be detected because the inevitable randomness by quantum noise measurement will cause physical measurement errors. In the experiment, we demonstrate 16 Gbits <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−1</sup> quantum noise secured terahertz wireless communications with the conventional optical communication realms and devices, operating at 300 GHz terahertz frequency. This quantum noise secured terahertz communication approach is a significant step toward high-security wireless communications.
An Efficient and Secure Trading Framework for Shared Charging Service Based on Multiple Consortium Blockchains

While electric vehicles are proliferating rapidly, the number of charging piles is only 1/16 of the number of electric vehicles, showing big tension between small supply of public charging piles and intense charging demand of electric vehicles. To alleviate the tension, it is highly desired to incorporate existing idle private charging piles into a shared charging service. However, existing solutions do not well implement the system due to some non-negligible issues, like poor consensus performance or lack of verification for outputs from blockchain networks. In the article, we propose an efficient and secure trading framework built atop multiple consortium blockchains. It devises a novel node voting mechanism so as to improve consensus efficiency. Meanwhile, it employs a publicly verifiable design based on the <inline-formula><tex-math notation="LaTeX">$(t,n)$</tex-math></inline-formula> Boneh-Lynn-Shacham (BLS) threshold signature technique, so as to enable entities outside the blockchain to verify the consistency between the output results with the valid consensus. Besides these novel system-level designs, it also introduces a fair and robust trading strategy running by smart contracts on the platform. Furthermore, we implemented a proof-of-concept system and conducted extensive experiments. The results show that the overall latency across chains is reduced by about 26% with the node voting mechanism and the transactions fees are comparable to recent popular cross-chain applications.
Comparative Analysis of Machine Learning Algorithms for Classification of Environmental Sounds and Fall Detection

In recent years, number of elderly people in population has been increased because of the rapid advancements in the medical field, which make it necessary to take care of old people. Accidental fall incidents are life-threatening and can lead to the death of a person if first aid is not given to the injured person. Immediate response and medical assistance are necessary in case of accidental fall incidents to elderly people. The research community explored various fall detection systems to early detect fall incidents, however, still there exist numerous limitations of the systems such as using expensive sensors, wearable sensors that are hard to wear all the time, camera violates the privacy of person, and computational complexity. In order to address the above-mentioned limitations of the existing systems, we proposed a novel set of integrated features that consist of melcepstral coefficients, gammatone cepstral coefficients, and spectral skewness. We employed a decision tree for the classification performance of both binary problems and multi-class problems. We obtained an accuracy of 91.39%, precision of 96.19%, recall of 91.81%, and F1-score of 93.95%. Moreover, we compared our method with existing state-of-the-art methods and the results of our method are higher than other methods. Experimental results demonstrate that our method is reliable for use in medical centers, nursing houses, old houses, and health care provisions.
Intelligent and Personalized Factoid Question and Answer System

The current era of information technology revolutions needs rapid progress of digital transformation. Question-answer system is one such remarkable intelligent application based on artificial intelligence that analyzes and processes human interactions, either spoken or written. Question-answer systems have been proven a very effective way of providing 24-hour interaction service that is interacting with end-users thru digital devices as if the user is interacting with a real human being. This application has found its major utilization in answering customers' queries in almost every business ranging from government to non-profit sectors and from very small to large organizations. Many chatbot frameworks are available that are either code-based or interface-based. However, they lack the usefulness of personal touch in communication. Thus, to have better customer interactions in the form of question-answers and improved personalization, this paper proposes a face recognition-based chatbot system utilizing the natural language processing toolkit with python. The proposed method implements a tailored user-centric approach to a simplistic version of the Question-answer system by implementing machine learning models for Face Recognition which rely on Local Binary patterns and HAAR Classifications. The method trains and maintains a unique dataset mapped to each user associated with the user's biometric identification. This chatbot focuses on speech synthesis and language processing to retrieve queries of each user based on their unique dataset.
Directional Self-supervised Learning for Heavy Image Augmentations

Despite the large augmentation family, only a few cherry-picked robust augmentation policies are beneficial to self-supervised image representation learning. In this paper, we propose a directional self-supervised learning paradigm (DSSL), which is compatible with significantly more augmentations. Specifically, we adapt heavy augmentation policies after the views lightly augmented by standard augmentations, to generate harder view (HV). HV usually has a higher deviation from the original image than the lightly augmented standard view (SV). Unlike previous methods equally pairing all augmented views to symmetrically maximize their similarities, DSSL treats augmented views of the same instance as a partially ordered set (with directions as SV↔SV, SV↔HV), and then equips a directional objective function respecting to the derived relationships among views. DSSL can be easily implemented with a few lines of codes and is highly flexible to popular self-supervised learning frameworks, including SimCLR, Sim-Siam, BYOL. Extensive experimental results on CIFAR and ImageNet demonstrated that DSSL can stably improve various baselines with compatibility to a wider range of augmentations. Code is available at: https://github.com/Yif-Yang/DSSL.
Can We Stabilize the Price of a Cryptocurrency? Understanding the Design of Bitcoin and Its Potential to Compete with Central Bank Money

Bitcoin (Nakamoto, 2008) is an electronic cash system designed to work without central management. Despite recent enthusiasm, Bitcoin (BTC) and other so-called cryptocurrencies are not ideal as means for payment, because of instability of their market prices against major currencies. This chapter explores the problem of such instability from the viewpoint of economics and proposes a new monetary policy for stabilizing the values of these cryptocurrencies. First, we begin by describing the institutional details of Bitcoin.
TSFNet: Triple-Steam Image Captioning

Image captioning is a challenging task that generates a natural language description based on the visual understanding of the given image. Significant region representation is a milestone in image captioning. Despite the great success of existing region-based works, they only focus on the salient objects and encode these objects independently, still plagued by the lack of global contextual information and visual relationships. In fact, the global contextual information and structured visual relationships are exactly the merits of traditional grid features and emerging scene graph features. In this paper, we present a Triple-Steam Feature Fusion Network (TSFNet) to leverage the complementary advantages of the grid, region, and scene graph triple-steam visual representations in image captioning. Concretely, in our TSFNet, a novel Dual-level Attention (DA) mechanism is proposed to simultaneously explore visual intrinsic properties and word-related attributes uniformly of different features. Then attention enhanced features of different modalities are mapped into a joint representation to guide the caption generation. Moreover, we design a new global-aware decoder, which leverages the concatenated representation of triple-steam features and the joint attention representation to obtain global visual guidance information, further refine the complex multimodal reasoning. To verify the effectiveness of our feature fusion model, we perform extensive experiments on the highly competitive MSCOCO dataset to evaluate the model quantitatively and qualitatively. The results illustrate that the proposed framework outperforms many state-of-the-art image captioning approaches in various evaluation metrics, and generates more accurate and abundant captions.
Entropy of eye movements while reading code or text

A new gaze-based analysis method is presented based on word span entropy, suitable for comparison of eye movements collected during reading of code or text. Word span entropy is derived from gaze transition entropy but differs in that the transition matrix represents word span instead of gaze transition between Areas Of Interest (AOIs). Empirical evidence shows that, as expected, an increase in word span entropy is related to a shorter response time, especially when reading code, showcasing the metric's analytical utility.
Inpainting at Modern Camera Resolution by Guided PatchMatch with Auto-curation

Recently, deep models have established SOTA performance for low-resolution image inpainting, but they lack fidelity at resolutions associated with modern cameras such as 4K or more, and for large holes. We contribute an inpainting benchmark dataset of photos at 4K and above representative of modern sensors. We demonstrate a novel framework that combines deep learning and traditional methods. We use an existing deep inpainting model LaMa [27] to fill the hole plausibly, establish three guide images consisting of structure, segmentation, depth, and apply a multiply-guided PatchMatch [1] to produce eight candidate upsampled inpainted images. Next, we feed all candidate inpaintings through a novel curation module that chooses a good inpainting by column summation on an 8 $$\,\times \,$$ 8 antisymmetric pairwise preference matrix. Our framework's results are overwhelmingly preferred by users over 8 strong baselines, with improvements of quantitative metrics up to 7.4 times over the best baseline LaMa, and our technique when paired with 4 different SOTA inpainting backbones improves each such that ours is overwhelmingly preferred by users over a strong super-res baseline.
Analysis of Consensus Algorithms in context of the Blockchain based Applications

Redesigning the current procedures and incorporating blockchain technology into them, much can be accomplished. Blockchain has the potential to upend corporate models by increasing their efficiency. The blockchain is promising in many respects because of its immutability, transparency, provenance, and disintermediation in a distributed environment. In order to obtain the best results, it is crucial to understand when to use blockchain solutions to the processes and when to avoid doing so. This paper has contribution in three folds. Firstly, the evolution of blockchain, types of blockchain and their suitability based upon the use cases has been reviewed. Secondly, it provides the parametric review of consensus mechanisms based upon energy efficiency, throughput, finality of the transactions. Finally, this paper reviews the factors which can help determine the suitability for blockchain based applications.
Co-Learning: Consensus-based Learning for Multi-Agent Systems

One of the main advancements in distributed learning may be the idea behind Google’s Federated Learning (FL) algorithm. It allows a distributed deep learning process being made by different units while maintaining the privacy of the data used by each one of these units to train their model. This idea perfectly fits a Multi-Agent System, where the units learning and sharing the model are agents. FL is a centralized approach, where a server is in charge of receiving, averaging, and distributing back the models to the different units making the learning process. Based on this idea, we propose that the distributed learning process not be centralized, and all the agents have the same role in the system. We propose to use a Consensus-based Learning algorithm, which we call "Co-Learning," that uses a consensus algorithm (i) to share the models each agent is learning with its private data and (ii) to calculate the aggregated model of each agent with its local neighbors. As we are using a consensus algorithm, Co-Learning has the properties associated with this algorithm: the whole system calculates the average of the models shared by the agents with their local neighbors. The system adapts automatically to the change in the availability of the agents during its execution. Therefore, agents that fail during the execution and are not available anymore are excluded, or agents that become available during the execution are included and averaged with the rest of the participants.
Towards Autonomous Grading In The Real World

Surface grading is an integral part of the construction pipeline. Here, a bulldozer, which is a key machinery tool at any construction site, is required to level an uneven area containing pre-dumped sand piles. In this work, we aim to tackle the problem of autonomous surface grading on real-world scenarios. We design both a realistic physical simulation and a scaled real-world prototype environment mimicking real bulldozer dynamics and sensory information. In addition, we establish heuristics and learning strategies in order to solve the problem. Through extensive experiments, we show that although heuristics are capable of tackling the problem in a clean and noise-free simulated environment, they fail catastrophically when facing real-world scenarios. However, we show that the simulation can be leveraged to guide a learning agent, which can generalize and solve the task both in simulation and in a scaled prototype environment.
Decentralized Multi-Agent Path Finding in Warehouse Environments for Fleets of Mobile Robots with Limited Communication Range

Mobile robots have already made their way into warehouses, and significant effort has consequently been devoted to designing effective algorithms for the related multi-agent path finding (MAPF) problem. However, most of the proposed MAPF algorithms still rely on centralized planning as well as simplistic assumptions, such as that robots have full observability of the environment and move at equal and constant speeds. The resultant plans thus cannot be executed directly on physical robots where these assumptions generally do not hold. To mitigate these issues, we consider the decentralized partially observable multirobot setting where robots do not have access to the full state of the world. Instead, each robot coordinates with neighbors within a limited communication range. In the proposed approach, each robot independently plans its own path using A* without taking into account other robots, and the robots then solve potential conflicts locally as they occur. Experimental results obtained in various benchmark scenarios confirm that the proposed decentralized approach is effective and scales well to large numbers of robots.
From Animals to Artificial Intelligence: Non-Human Beings’ Intellectual Property Protection by “Judicial Capacity for Copyrights”

Since artificial intelligence has completed the process from the auxiliary tool of human creation to the independent creation completion of works with formal appearance, it has brought many legal issues that have caused widespread controversy.Among them, whether artificial intelligence has the qualification of legal subject and whether the products of artificial intelligence should be protected by law is the focus of the problem.In the legal circle, the involvement of the theme of "non-anthropocentrism" can be traced back to the debate between animal legal personality and non-human ecological rights.The Naruto v. Slater Monkey selfie case and the Pigcasso light people's debating about animal copyright, and artificial intelligence provides a new research perspective and reinvigorates the research on animal copyright.By means of the analogy research of animals, humans and artificial intelligence, this paper explores the rationality, necessity and feasibility of investing nonhuman beings with quasi-legal subject qualification in the special subdivision field of law-copyright.Quasi-legal subject qualification means that artificial narrow intelligence and animals are endowed with judicial capacity for copyrights and limited capacity to act.At the same time, the designers of artificial intelligence, animal breeders and the government and so on serve as the quasi-guardian of artificial intelligence and animals.In addition, artificial general intelligence and artificial super general intelligence are endowed with completely independent legal capacity to act, and the quasi-guardian system is terminated.The quasi-guardian system is perfectly compatible with the existing legal framework from the perspective of development.It protects the ownerless intellectual property from the free lift, thereby helping avoid the tragedy of the commons.Furthermore, it solves the problem that animals and artificial narrow intelligence cannot independently safeguard their rights and provides a forward-looking theoretical model for the system construction of How to cite this paper:
The First RE100 University in Japan-Responsible Consumption and Production of Energy

Proactive use of renewable energy is required to create a sustainable energy society, which is related to the various goals of the SDGs such as SDG7 on energy, SDG13 on climate action and SDG12 on responsible consumption and production. Addressing the energy issue as a university is significant in two ways. First, universities can influence other entities, such as businesses, local governments etc. Second, universities have a mission to educate students who can transform society into a sustainable one. In a press release of 2014, I announced the intention of making CUC the first 100% renewable energy university in Japan, which was a voluntary action. After becoming the president in 2017, this became an official project of the university, enabling investment in the necessary facilities. From the experience of having achieved our goal, I will explain the factors leading to our success and the lessons applicable to other universities.Keyword: SDGs, responsible university, zero carbon campus, renewable energy, heart-ware