Reviewerly/exHarmony huggingface dataset:
A Secure Charging System for Electric Vehicles Based on Blockchain

Smart grids incorporating internet-of-things are emerging solutions to provide a reliable, sustainable and efficient electricity supply, and electric vehicle drivers can access efficient charging services in the smart grid. However, traditional electric vehicle charging systems are vulnerable to distributed denial of service and privileged insider attacks when the central charging server is attacked. The blockchain-based charging systems have been proposed to resolve these problems. In 2018, Huang et al. proposed the electric vehicle charging system using lightning network and smart contract. However, their system has an inefficient charging mechanism and does not guarantee security of key. We propose a secure charging system for electric vehicles based on blockchain to resolve these security flaws. Our charging system ensures the security of key, secure mutual authentication, anonymity, and perfect forward secrecy, and also provides efficient charging. We demonstrate that our proposed system provides secure mutual authentication using Burrows-Abadi-Needham logic and prevents replay and man-in-the-middle attacks using automated validation of internet security protocols and applications simulation tool. Furthermore, we compare computation and communication costs with previous schemes. Therefore, the proposed charging system efficiently applies to practical charging systems for electric vehicles.
New Characterization and Parametrization of LCD Codes

Linear complementary dual (LCD) cyclic codes were referred historically to as reversible cyclic codes, which had applications in data storage. Due to a newly discovered application in cryptography, there has been renewed interest in LCD codes. In particular, it has been shown that binary LCD codes play an important role in implementations against side-channel attacks and fault injection attacks. In this paper, we first present a new characterization of binary LCD codes in terms of their orthogonal or symplectic basis. Using such a characterization, we solve a conjecture proposed by Galvez et al. on the minimum distance of binary LCD codes. Next, we consider the action of the orthogonal group on the set of all LCD codes, determine all possible orbits of this action, derive simple closed formulas of the size of the orbits, and present some asymptotic results on the size of the corresponding orbits. Our results show that almost all binary LCD codes are odd-like codes with odd-like duals, and about half of q-ary LCD codes have orthonormal basis, where q is a power of an odd prime.
Manufacturing Knowledge Graph: A Connectivism to Answer Production Problems Query With Knowledge Reuse

Manufacturing knowledge (MK) is enjoying a “new golden age” in the academic domain, marked by vast reuse to support product-related production problems (PPs) solving decision making for manufacturing enterprises in the industry sector. However, the practice of MK reuse and research is fragmented and insufficient, which cannot be mature to provide a systemic solution for that a decision-maker has to consider the involving issues: how MK can be used earlier and rightly; what kind of practical problems can be solved? In order to answer those interconnecting issues, this paper firstly proposes a connectivism framework to clarify the compressive relationship of problem-to-problem, knowledge-to-knowledge and problem-to-knowledge with knowledge integration, knowledge matching, and problem-solving layers. Then, based on the framework, an ontology-based MK graph (MKG) is constructed with a unified MK-filter to collect and integrate multifactor and multilevel MK, and a graph-oriented meta-knowledge model (MKM) is proposed to represent the details between the knowledge entities (i.e., concept and instance), which also shows the contribution to knowledge reasoning. After that, driven by a structure temporal query (i.e., 5W2H), a semantics-based knowledge computation is developed to compute the intrinsic term similarity (IS) and relational term similarity (RS) between two knowledge entities in the MKG. Finally, a case study is taken to demonstrate the effectiveness and performance of the proposed methods.
Client-Controlled HECC-as-a-Service (HaaS)

Cloud computing is making big revolution in information technology field since it is advantageous than traditional systems. Outsourcing and remote data storage may stand as obstacle for security of data in cloud. Data security model (DSM) with safe data retrieval framework has been proposed which employs hyperelliptic curve cryptography for data encryption in order to avoid huge computation costs for keys involved. First approach is client-side encryption of data and then storing encrypted data in cloud. Registered users access data with valid username and password provided by client. Initially data categorization is performed with sensitivity levels being provided by clients, based on which data is placed in security sections. Various strategies utilized include 80-bit HECC encryption, SHA-3 (Secure Hash Algorithm-3) for integrity verification, data retrieval by using encrypted index and dividing data into three sections for storing in cloud. An algorithm to search files in server with multiple keywords has been proposed and implemented. We for first time implemented HECC and HECC with parallel executions in cloud with Openstack along with HECC-as-a-Service (HaaS). Detailed security and result analysis of model is provided along with comparison of scheme with existing methods which shows that model is much faster than any other model employing HECC in cloud.
Indistinguishability Obfuscation Without Multilinear Maps: New Paradigms via Low Degree Weak Pseudorandomness and Security Amplification

The existence of secure indistinguishability obfuscators ( $$i\mathcal {O}$$ ) has far-reaching implications, significantly expanding the scope of problems amenable to cryptographic study. All known approaches to constructing $$i\mathcal {O}$$ rely on d-linear maps. While secure bilinear maps are well established in cryptographic literature, the security of candidates for $$d>2$$ is poorly understood. We propose a new approach to constructing $$i\mathcal {O}$$ for general circuits. Unlike all previously known realizations of $$i\mathcal {O}$$ , we avoid the use of d-linear maps of degree $$d \ge 3$$ . At the heart of our approach is the assumption that a new weak pseudorandom object exists. We consider two related variants of these objects, which we call perturbation resilient generator ( $$\varDelta $$ RG) and pseudo flawed-smudging generator ( $$\mathrm {PFG}$$ ), respectively. At a high level, both objects are polynomially expanding functions whose outputs partially hide (or smudge) small noise vectors when added to them. We further require that they are computable by a family of degree-3 polynomials over $$\mathbb {Z}$$ . We show how they can be used to construct functional encryption schemes with weak security guarantees. Finally, we use novel amplification techniques to obtain full security. As a result, we obtain $$i\mathcal {O}$$ for general circuits assuming:
Recurrent Convolutional Strategies for Face Manipulation Detection in Videos.

The spread of misinformation through synthetically generated yet realistic images and videos has become a significant problem, calling for robust manipulation detection methods. Despite the predominant effort of detecting face manipulation in still images, less attention has been paid to the identification of tampered faces in videos by taking advantage of the temporal information present in the stream. Recurrent convolutional models are a class of deep learning models which have proven effective at exploiting the temporal information from image streams across domains. We thereby distill the best strategy for combining variations in these models along with domain specific face preprocessing techniques through extensive experimentation to obtain state-of-the-art performance on publicly available video-based facial manipulation benchmarks. Specifically, we attempt to detect Deepfake, Face2Face and FaceSwap tampered faces in video streams. Evaluation is performed on the recently introduced FaceForensics++ dataset, improving the previous state-of-the-art by up to 4.55% in accuracy.
Collocating Clothes With Generative Adversarial Networks Cosupervised by Categories and Attributes: A Multidiscriminator Framework

The choice of which clothes to wear affects how one is perceived, as well as constitutes an expression of one's personal style. Based on the recent advances in image-to-image translation by the conditional generative adversarial network (cGAN), we propose a new framework with a multidiscriminator by incorporating different types of conditional information into the discriminator of cGAN for clothing matches. In contrast with most extant frameworks under cGAN, with one generator and one discriminator, the proposed framework investigates the potential of utilizing conditional information delivered by multidiscriminators to guide the generator. Under this framework, we propose an Attribute-GAN with two discriminators and a category-attribute GAN (CA-GAN) with three discriminators. In order to evaluate the performance of our proposed models, we built a large-scale data set that consists of 19 081 pairs of collocation clothing images with 90 manually labeled attributes. Experimental results demonstrate that with supervision of the additional attribute discriminator or category discriminator, the quality of the generated clothing images by GANs is consistently improved in comparison with the state-of-the-art methods.
Smart Contracts for Smart Supply Chains

A smart contract is an electronic transaction protocol intended to digitally facilitate, verify, or enforce the negotiation and execution of the terms of an underlying legal contract designed to fulfil common contractual conditions comprising payments, legal obligations, and enforcement without third parties. Thus, by following the traditional perception, smart contracts target to reduce transaction costs including arbitration and enforcement costs by realising trackable and irreversible transactions by using blockchain technology for distributed databases. However, the potential of smart contracts goes far beyond cost reductions by facilitating the entrepreneurial collaboration of cross-organisational business-processes that are characteristic for smart supply chains. A closer look to existing or ongoing smart contract projects reveals that the majority of smart-contract applications in business life are linked to supply chain management, Internet of Things and Industry 4.0 solutions. The author participated in several EU projects related to transnational entrepreneurial networks and smart supply chains. Thus, the paper discusses the research question of how and to which extent smart contracting and blockchain technology can facilitate the implementation of collaborative business structures for sustainable entrepreneurial activities in smart supply chains. The research is based on expert interviews, surveys and case studies, which took place in the context of the EU projects with a focus on the Baltic Sea Region.
The views, perspectives, and experiences of academic researchers with data sharing and reuse: A meta-synthesis

Background Funding agencies and research journals are increasingly demanding that researchers share their data in public repositories. Despite these requirements, researchers still withhold data, refuse to share, and deposit data that lacks annotation. We conducted a meta-synthesis to examine the views, perspectives, and experiences of academic researchers on data sharing and reuse of research data. Methods We searched the published and unpublished literature for studies on data sharing by researchers in academic institutions. Two independent reviewers screened citations and abstracts, then full-text articles. Data abstraction was performed independently by two investigators. The abstracted data was read and reread in order to generate codes. Key concepts were identified and thematic analysis was used for data synthesis. Results We reviewed 2005 records and included 45 studies along with 3 companion reports. The studies were published between 2003 and 2018 and most were conducted in North America (60%) or Europe (17%). The four major themes that emerged were data integrity, responsible conduct of research, feasibility of sharing data, and value of sharing data. Researchers lack time, resources, and skills to effectively share their data in public repositories. Data quality is affected by this, along with subjective decisions around what is considered to be worth sharing. Deficits in infrastructure also impede the availability of research data. Incentives for sharing data are lacking. Conclusion Researchers lack skills to share data in a manner that is efficient and effective. Improved infrastructure support would allow them to make data available quickly and seamlessly. The lack of incentives for sharing research data with regards to academic appointment, promotion, recognition, and rewards need to be addressed.
A Disentangling Invertible Interpretation Network for Explaining Latent Representations

Neural networks have greatly boosted performance in computer vision by learning powerful representations of input data. The drawback of end-to-end training for maximal overall performance are black-box models whose hidden representations are lacking interpretability: Since distributed coding is optimal for latent layers to improve their robustness, attributing meaning to parts of a hidden feature vector or to individual neurons is hindered. We formulate interpretation as a translation of hidden representations onto semantic concepts that are comprehensible to the user. The mapping between both domains has to be bijective so that semantic modifications in the target domain correctly alter the original representation. The proposed invertible interpretation network can be transparently applied on top of existing architectures with no need to modify or retrain them. Consequently, we translate an original representation to an equivalent yet interpretable one and backwards without affecting the expressiveness and performance of the original. The invertible interpretation network disentangles the hidden representation into separate, semantically meaningful concepts. Moreover, we present an efficient approach to define semantic concepts by only sketching two images and also an unsupervised strategy. Experimental evaluation demonstrates the wide applicability to interpretation of existing classification and image generation networks as well as to semantically guided image manipulation.
Exploring Unexplored Generalization Challenges for Cross-Database Semantic Parsing

We study the task of cross-database semantic parsing (XSP), where a system that maps natural language utterances to executable SQL queries is evaluated on databases unseen during training. Recently, several datasets, including Spider, were proposed to support development of XSP systems. We propose a challenging evaluation setup for cross-database semantic parsing, focusing on variation across database schemas and in-domain language use. We re-purpose eight semantic parsing datasets that have been well-studied in the setting where in-domain training data is available, and instead use them as additional evaluation data for XSP systems instead. We build a system that performs well on Spider, and find that it struggles to generalize to our re-purposed set. Our setup uncovers several generalization challenges for cross-database semantic parsing, demonstrating the need to use and develop diverse training and evaluation datasets.
Commonsense Reasoning for Natural Language Processing

Commonsense knowledge, such as knowing that "bumping into people annoys them" or "rain makes the road slippery", helps humans navigate everyday situations seamlessly (Apperly, 2010).Yet, endowing machines with such human-like commonsense reasoning capabilities has remained an elusive goal of artificial intelligence research for decades (Gunning, 2018).Commonsense knowledge and reasoning have received renewed attention from the natural language processing (NLP) community in recent years, yielding multiple exploratory research directions into automated commonsense understanding.Recent efforts to acquire and represent common knowledge resulted in large knowledge graphs, acquired through extractive methods (Speer et al., 2017) or crowdsourcing (Sap et al., 2019a).Simultaneously, a large body of work in integrating reasoning capabilities into downstream tasks has emerged, allowing the development of smarter dialogue (Zhou et al., 2018) and question answering agents (Xiong et al., 2019).Recent advances in large pretrained language models (e.g., Devlin et al., 2019; Liu et al., 2019b), however, have pushed machines closer to humanlike understanding capabilities, calling into question whether machines should directly model commonsense through symbolic integrations.But despite these impressive performance improvements in a variety of NLP tasks, it remains unclear whether these models are performing complex reasoning, or if they are merely learning complex surface correlation patterns (Davis and Marcus, 2015;Marcus, 2018).This difficulty in measuring the progress in commonsense reasoning using downstream tasks has yielded increased efforts at developing robust benchmarks for directly measuring commonsense capabilities in multiple
Using the Concept of Game-Based Learning in Education

A modern school deals with a generation of students who were born and grown up in a digital environment and require other teaching methods. Changes in the technological and social conditions of modern society need new professional skills, which are often called “21st century skills”. These skills should be devel-oped starting from the primary school, as they are psychological and behavioural, rather than practical. Game-based learning and gamification are effective means of such skills development. The use of video games in teaching practice is studied multifaceted, along with formats and requirements of educational game, methods for effectiveness assessment, and the effect of games on students. The controver-sial nature of game-based learning effect on students require deeper research, as the increase in motivation and learning efficiency cannot be disputed, as well as the negative impact of a long gaming on cognitive abilities, emotional state and social skills of students. The study tested both an increase in the motivation and attraction of school students for the learning process, and the impact on the prac-tical results of information assimilation. There was conducted a survey on the re-sults of the application of game training to determine the motivation and attraction of students. Likewise, we counted time, spent by the study and control group, for mastering the main educational material and the post-class study of game content and the educational material it presents. A comparative test was conducted on the results of educational material comprehension to determine the effectiveness of the methodology in mastering knowledge. The group, with game-based learning and gamification applied, showed an increase in motivation and attraction to learning, students paid additional efforts for assimilating the material studied, and also showed significantly higher learning outcomes compared to the control group.
Increasing generality in machine learning through procedural content generation

Procedural content generation (PCG) refers to the practice of generating game content, such as levels, quests or characters, algorithmically. Motivated by the need to make games replayable, as well as to reduce authoring burden and enable particular aesthetics, many PCG methods have been devised. At the same time that researchers are adapting methods from machine learning (ML) to PCG problems, the ML community has become more interested in PCG-inspired methods. One reason for this development is that ML algorithms often only work for a particular version of a particular task with particular initial parameters. In response, researchers have begun exploring randomization of problem parameters to counteract such overfitting and to allow trained policies to more easily transfer from one environment to another, such as from a simulated robot to a robot in the real world. Here we review existing work on PCG, its overlap with current efforts in ML, and promising new research directions such as procedurally generated learning environments. Although originating in games, we believe PCG algorithms are critical to creating more general machine intelligence. This review covers the history of procedural content generation (PCG) approaches for video games, and how these approaches are now used to generate training data and environments for machine learning models. The authors then discuss how PCG may be crucial for training agents which generalise well.
A Survey on Large-Scale Machine Learning

Machine learning can provide deep insights into data, allowing machines to make high-quality predictions and having been widely used in real-world applications, such as text mining, visual classification, and recommender systems. However, most sophisticated machine learning approaches suffer from huge time costs when operating on large-scale data. This issue calls for the need of Large-scale Machine Learning (LML), which aims to learn patterns from big data with comparable performance efficiently. In this paper, we offer a systematic survey on existing LML methods to provide a blueprint for the future developments of this area. We first divide these LML methods according to the ways of improving the scalability: 1) model simplification on computational complexities, 2) optimization approximation on computational efficiency, and 3) computation parallelism on computational capabilities. Then we categorize the methods in each perspective according to their targeted scenarios and introduce representative methods in line with intrinsic strategies. Lastly, we analyze their limitations and discuss potential directions as well as open issues that are promising to address in the future.
Hard Negative Mixing for Contrastive Learning

Contrastive learning has become a key component of self-supervised learning approaches for computer vision. By learning to embed two augmented versions of the same image close to each other and to push the embeddings of different images apart, one can train highly transferable visual representations. As revealed by recent studies, heavy data augmentation and large sets of negatives are both crucial in learning such representations. At the same time, data mixing strategies either at the image or the feature level improve both supervised and semi-supervised learning by synthesizing novel examples, forcing networks to learn more robust features. In this paper, we argue that an important aspect of contrastive learning, i.e., the effect of hard negatives, has so far been neglected. To get more meaningful negative samples, current top contrastive self-supervised learning approaches either substantially increase the batch sizes, or keep very large memory banks; increasing the memory size, however, leads to diminishing returns in terms of performance. We therefore start by delving deeper into a top-performing framework and show evidence that harder negatives are needed to facilitate better and faster learning. Based on these observations, and motivated by the success of data mixing, we propose hard negative mixing strategies at the feature level, that can be computed on-the-fly with a minimal computational overhead. We exhaustively ablate our approach on linear classification, object detection and instance segmentation and show that employing our hard negative mixing procedure improves the quality of visual representations learned by a state-of-the-art self-supervised learning method.