Reviewerly/exHarmony huggingface dataset:
Breaking ACORN at Bitstream Level

Assuring the security of the Internet of Things (IoT) is much more challenging than assuring the security of centralized environments, like the cloud. A reason for this is that IoT devices are often deployed in domains that are remotely managed and monitored. Thus, they cannot be protected from physical attacks as reliably as data centers. Up till now, implementations of many established, standardized algorithms including AES and SNOW 3G have been broken by physical attacks. In this paper, we show that even the most recently designed algorithms are also vulnerable. We attack an SRAM-based FPGA implementation of ACORN v3 stream cipher, a finalist of CAESAR cryptographic competition for authenticated encryption. By modifying the content of several look-up tables directly in the bitstream, we inject faults which reduce the nonlinear feedback function of ACORN to a linear one. As a result, it becomes possible to extract the full key from 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">15.34</sup> bits of faulty keystream by an algebraic attack using 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">35.46</sup> operations. Our results, once again confirm the necessity to rethink the way cryptographic algorithms are implemented in FPGAs.
A scalable Blockchain based system for super resolution images manipulation

This work aims to solve the problem of “big” images manipulation up to the order of gigapixels ones. The sequential elaboration of these images is too complex and many times, impossible. This is a problem present in many study areas like forensics investigation or medical analysis and there are few solutions in literature. In this work, we will develop a distributed ledger and Smart Contracts solution in order to manipulate super resolution images in a decentralized and competitive way. The proposed system is highly dependable as the Blockchain infrastructure guarantee fault tolerance, high security and reliability. In the second part of the work, the solution has been tested in the context of demographic analysis. Thanks to this work, we can develop a peer to peer computational power exchange where scientists are able to offer their own digital token in exchange of a Gigapixel image elaboration, classification and recognition.
Frequent Itemset Mining and Multi-Layer Network-Based Analysis of RDF Databases

Triplestores or resource description framework (RDF) stores are purpose-built databases used to organise, store and share data with context. Knowledge extraction from a large amount of interconnected data requires effective tools and methods to address the complexity and the underlying structure of semantic information. We propose a method that generates an interpretable multilayered network from an RDF database. The method utilises frequent itemset mining (FIM) of the subjects, predicates and the objects of the RDF data, and automatically extracts informative subsets of the database for the analysis. The results are used to form layers in an analysable multidimensional network. The methodology enables a consistent, transparent, multi-aspect-oriented knowledge extraction from the linked dataset. To demonstrate the usability and effectiveness of the methodology, we analyse how the science of sustainability and climate change are structured using the Microsoft Academic Knowledge Graph. In the case study, the FIM forms networks of disciplines to reveal the significant interdisciplinary science communities in sustainability and climate change. The constructed multilayer network then enables an analysis of the significant disciplines and interdisciplinary scientific areas. To demonstrate the proposed knowledge extraction process, we search for interdisciplinary science communities and then measure and rank their multidisciplinary effects. The analysis identifies discipline similarities, pinpointing the similarity between atmospheric science and meteorology as well as between geomorphology and oceanography. The results confirm that frequent itemset mining provides an informative sampled subsets of RDF databases which can be simultaneously analysed as layers of a multilayer network.
Vger: a VRF based cross-chain mechanism for blockchains

Abstract As a new distributed computing paradigm, blockchain is rapidly evolving in areas such as digital finance and cryptocurrency. However, existing blockchain projects adopt different blockchain architectures and protocols, as a result it is difficult for different blockchain systems to flow value to each other or communicate information, and different blockchain systems become isolated islands. This has brought limitations to the usable range, function and scalability of blockchain technology. In order to solve this problem, and bulid trust between different blockchain networks, cross-chain technology has been proposed and received a lot of attention. In this paper we proposed a cross-chain mechanism based on verifiable random functions ( VRF ) and threshold cryptography, we name this scheme as Vger. In Vger, through VRF based cryptographic sortition to select management committee for escrow account and dynamic security setting mechanism, that optimize the security and efficiency about cross-chain transaction.
Performance Improvement of Convolutional Neural Network Using Random Under Sampling

With the advent of Convolutional Neural Networks, the image classification tasks have become easier to tackle. Numerous applications have been reported that successfully utilized image classification techniques based on CNNs. Recent studies reported that the presence of imbalanced classes in the training data might affect the performance of the CNN to a greater extent. Motivated by this, the current study proposes a Random Under Sampling (RUS) based method to overcome the problem of imbalanced classes. The existing RUS based method has been modified accordingly to make it suitable for the current application. To analyze the performance of the CNN, two modified well-known datasets have been used for training and testing. Further, different degrees of under sampling have been used to better understand the appropriate under sampling ratio required to achieve acceptable improvement. Results have indicated that the RUS based method has been able to significantly improve CNNs performance in terms of sensitivity, G-mean.
eHealth Solution for Cancer Patients Rehabilitation enabled by Optical Fiber Sensors

Breast cancer (BC) treatments are often aggressive for the patients, causing severe impairments to their physical condition. Physical supervised exercise has proven to be beneficial for the patients' physical and emotional health recovery. In this work, we present an eHealth solution based on optical fiber sensors (OFSs), to monitor the physical activity of BC patients, during their exercises. Such solution will allow the patients to exercise from the comfort of their home, with the continuous supervision of the physiotherapist, and with a possibility to connect remotely with other patients. The use case, presented in this work, is focused on the design of optical fiber based eHealth enablers to monitor the exercises prescribed in the recovery of the handgrip force, often affected by the treatments that BC patients are subjected to.
Fuzzy Cognitive Map-Based Knowledge Representation of Hazardous Industrial Operations

Hazardous industrial operations are highly stochastic, still human-dependent, and risky. Operators working in such an environment must understand the complex interrelation between several factors contributing to safe and effective operations. Therefore, being able to predict the effects of their actions on provoking or mitigating possible accidents is crucial. This study aims to utilize fuzzy cognitive maps (FCM) to model the expert’s reasoning about occupational health and safety (OHS) in confined space. This knowledge is used by operators to build their mental models. The developed FCM displays all the possible incidents of a confined space and links these incidents with all their causing and preventing factors. This approach may facilitate the development of simulation-based training solutions and allow operators to act proactively during the operation.
Analysis and compensation of lateral chromatic aberration of structured light 3D measurement system

Color coding greatly improves the measurement efficiency of structured light three-dimensional (3D) measurement system. However, chromatic aberration (CA) reduces the measurement accuracy of the system. In this study, the lateral chromatic aberration (LCA) of camera and projector is analyzed by experiments, and the optimization model of LCA is studied according to the analysis results. Based on the study, a compensation method of system LCA is proposed. The simulation results show that the compensation method is feasible, and the experimental results prove that the proposed method can effectively reduce the measurement error caused by LCA.
Utilization of the Batch Training Method for Predicting Natural Disasters and Their Impacts

Abstract Indonesia is one of the countries that often experiences natural disasters, including earthquakes, floods, tsunamis, etc. All of this causes losses, both casualties, Broken, and Anguishing for the population. Based on this, this paper is proposed, which aims to predict natural disasters in the coming years in Indonesia, casualties, Broken, and their consequences. This paper is an extension of previous research, which is still an architectural model to predict Indonesia’s natural disasters and their impacts. Model 4-10-1 is the best in this study, which produces 91% accuracy. Based on this architectural model, this paper will predict natural disasters that occur and their impacts for the years to come in Indonesia. The research dataset and algorithms used remain the same, namely the natural disaster dataset for 2008-2019. Resourced from its National Emergency Management Department and the Batch Training algorithm. Specifically, the results of this proposed paper are in the form of a prediction of natural disasters that will occur, dead and disappear, injured, Anguishing and displaced, houses severely Broken, moderately Broken, lightly Broken to submerged, and Broken to facilities and infrastructure such as health facilities, facilities. worship and educational facilities.
Progressive Supervision for Node Classification

Graph Convolution Networks (GCNs) are a powerful approach for the task of node classification, in which GCNs are trained by minimizing the loss over the final-layer predictions. However, a limitation of this training scheme is that it enforces every node to be classified from the fixed and unified size of receptive fields, which may not be optimal. We propose ProSup (Progressive Supervision), that improves the effectiveness of GCNs by training them in a different way. ProSup supervises all layers progressively to guide their representations towards the characteristics we desire. In addition, we propose a novel technique to reweight the node-wise losses, so as to guide GCNs to pay more attention to the nodes that are hard to classify. The hardness is evaluated progressively following the direction of information flows. Finally, ProSup fuses the rich hierarchical activations from multiple scales to form the final prediction in an adaptive and learnable way. We show that ProSup is effective to enhance the popular GCNs and help them to achieve superior performance on miscellaneous graphs.
Efficient Multi-Objective GANs for Image Restoration

Generative adversarial networks (GANs) have been widely adopted in many image processing tasks including restoration. In order to further improve quality of generated images, the training objective function needs to incorporate more constraints in addition to the adversarial loss. It can be straightforward to combine various losses in a linear fashion. However, hyperparameter fine-tuning and non-convex loss optimization are challenging problems when combining cost functions in such a manner. Here, we propose an efficient formulation of multiple loss components for training GANs. The proposed method, termed HypervolGAN, not only provides an efficient alternative for simultaneous cost optimization, but also boosts model performance in terms of improving generated image quality without excess computation. We further introduce two image-quality-measure based loss components to the GANs specifically for image restoration. Extensive evaluations and results on various benchmark datasets validate the effectiveness of the proposed methods.
Joint Learning for Document-Level Threat Intelligence Relation Extraction and Coreference Resolution Based on GCN

In order to help researchers quickly understand the connection between new threat events and previous threat events, threat intelligence document-level relation extraction plays a very important role in threat intelligence text analysis and processing. Because there is no public document-level threat intelligence dataset, we create APTERC-DOC, an APT intelligence entities, relations and coreference dataset. We treat the relation extraction as a multi-classification task. Treating the coreference relation as a kind of predefined relations, we develop a joint learning framework called TIRECO, a model which can simultaneously complete threat intelligence relation extraction and coreference resolution. In order to solve the problem of document-level text being too long to extract feature, we propose the concept of sentence set, which transforms document-level relation extraction into inter-sentence relation extraction. To incorporate relevant information with maximally removing irrelevant content in sentence set, we further apply a novel pruning strategy (SDP-VP-SET) to the input trees considering that verbs are crucial in determining the relation between entities in sentence set. With retaining the shortest path and nodes that are K hops away from the shortest path, we give the edge connected to the verb nodes a weight of w times. Experimental results show that our model not only performs well in the extraction of inter-sentence relations, it is also effective in intra-sentence relations, and the F1 value has increased by 15.694%.
A method for UAV monitoring road conditions in dangerous environment

Abstract This paper analyzes the scene of UAV executing path monitoring task in dangerous environment, and puts forward a method for UAV path selection in dangerous environment. In the proposed method, the operator mode and the agent operator mode are provided, and the model checking tool prism is used to model and verify the path selection scene of UAV. In normal environment, the UAV performs the path selection under the operator mode, and counts the number of successful and failed photographing of UAV. When the UAV reaches the harsh environment area and loses contact with the operator, the UAV path in the past is analyzed through the agent operator mode, the judgment of the operator is simulated to enable the UAV to continue to carry out the path monitoring task. The UAV road condition monitoring scene in dangerous environment is analyzed and verified from different scales and different operator characteristics.
Differential Privacy for Government Agencies—Are We There Yet?

Government agencies typically need to take potential risks of disclosure into account whenever they publish statistics based on their data or give external researchers access to collected data. In this context, the promise of formal privacy guarantees offered by concepts such as differential privacy seems to be the panacea enabling the agencies to quantify and control the privacy loss incurred by any data release exactly. Nevertheless, despite the excitement in academia and industry, most agencies -- with the prominent exception of the U.S. Census Bureau -- have been reluctant to even consider the concept for their data release strategy. This paper discusses potential reasons for this. We argue that the requirements for implementing differential privacy approaches at government agencies are often fundamentally different from the requirements in industry. This raises many challenges and questions that still need to be addressed before the concept can be used as an overarching principle when sharing data with the public. The paper does not offer any solutions to these challenges. Instead, we hope to stimulate some collaborative research efforts, as we believe that many of the problems can only be addressed by interdisciplinary collaborations.
An Image Encryption Algorithm with Hermite Chaotic Polynomials and Scan Pattern

Abstract In today’s digital world, massive amount of sensitive information is being generated in the form of image and further it is shared via Internet. Maintaining the confidentiality of these data has become a major issue due to security vulnerability present in the network and transmission media. Image encryption algorithms are developed by various researchers to permit only authorized users to access and view sensitive images. This paper presents a novel image encryption scheme based on Hermite polynomials chaos function. First, random key stream is generated by using Hermite polynomial with different initial and incremental values of the variables. Next, the original image is scrambled with scan pattern to rearrange the pixels position. The scrambled image is XORed with the random key stream to get the encrypted image. The proposed method is experimented and observed that it is efficient and confirms the confusion and diffusion properties of a secure image encryption method. Large key space, low relation between the pixels of encrypted image, and sensitivity to initial seed value of the key are the strength of the proposed image encryption method.
The Effect of the Launch of Bitcoin Futures on the Cryptocurrency Market: An Economic Efficiency Approach

We analyze the economic efficiency of the cryptocurrency market after the launch of Bitcoin futures by means of the Data Envelopment Analysis and Malmquist Indexes. Our results show that the introduction of Bitcoin futures did not affect the economic efficiency of the cryptocurrency market. However, we observe that Bitcoin obtained the highest risk-return trade-off due to its liquidity compared to the rest of cryptocurrencies. Therefore, our paper underlines the support of investors on Bitcoin to the detriment of the rest of cryptocurrencies.
Trace Element Geochemistry of Pyrite from Bitumen-Bearing Stratabound Cu–(Ag) Deposits, Northern Chile

The Coastal Cordillera metallogenic province of northern Chile comprises several styles of mineralization, including stratabound Cu–(Ag), iron oxide–copper–gold (IOCG), iron oxide–apatite (IOA), and porphyry Cu deposits. Stratabound Cu–(Ag) or “Manto-type” deposits from the Coastal Cordillera of northern Chile formed during two main periods, i.e., the Late Jurassic and Early Cretaceous. These deposits are hosted in volcanic and volcano sedimentary rocks, and some of them are characterized by the presence of solid hydrocarbons (bitumen), which are closely associated with pyrite and Cu–(Fe) sulfides. Although pyrite is ubiquitous and in some cases abundant in this deposit type, its trace element content and mineralogical form of incorporation, i.e., nanoscale inclusions or solid solution, remains largely unknown. In this study, we focus on pyrite from bitumen-bearing stratabound Cu–(Ag) deposits from northern Chile to assess the formation conditions of these deposits by using a combination of electron probe microanalysis (EPMA) and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). Our results show that pyrite from stratabound Cu–(Ag) deposits hosts significant concentrations of Cu, As, Mn, Pb, Tl, Co, Ni, V, and Mo that range from a few parts per million (ppm) to weight percent (wt %) levels. Among all elements analyzed, Cu, As, Mn, and Tl are the most abundant, reaching concentrations up to ∼2.8 (Cu), ∼0.25 (As), ∼2.4 (Mn), and ∼0.09 wt % (Tl). These elements occur dominantly in solid solution, although LA-ICP-MS depth profiles suggest the presence of micro- to nanosized mineral inclusions. Pyrite from stratabound Cu–(Ag) deposits is characterized by distinctively lower Co/Ni (∼0.1 to 10) and Co/Cu ratios (∼0.0001 to 10) and higher Ag/Co values (∼0.001 to 10) than pyrite from Cretaceous iron oxide-apatite (IOA) and IOCG deposits from the Coastal Cordillera metallogenic province. We interpret these variations as resulting from differences in physicochemical parameters of the pyrite-forming fluid, i.e., temperature, redox conditions, and source of metals/sulfur. Based on our data we conclude that pyrite from the studied stratabound Cu–(Ag) deposits formed at relatively low temperatures (100 to ∼300 °C), reducing conditions, and with a strong sedimentary component as reflected in its trace element geochemistry. Therefore, our data supports the use of the trace element content of pyrite to fingerprint the formation conditions of ore mineralization in different deposit types with possible applications as a vectoring tool within the Coastal Cordillera metallogenic province and elsewhere.
Research on Optimized Storage and Analysis System of Web Log Based on Django’s MVC Framework

Abstract Association rule analysis algorithm is widely used in Web log analysis, but the existing association rule analysis algorithm will significantly reduce the analysis and mining performance when the amount of Web log is relatively large. This paper proposes an improved clustering algorithm, which first clusters users with the same interests and hobbies, and then mines association rules for users in the same category, thereby reducing data dispersion. Based on Django’s MVC framework, it optimizes the storage and storage of Web logs. In the analysis part, users can configure the support and confidence of association rule mining through the front-end, and at the same time query the results of mining through Hive, and use encryption algorithms in the data transmission process to ensure data security.
Artificial Intelligence and Its Impact on Public Management and Decision-Making

Artificial Intelligence (AI) is a high-speed technology that influences our everyday lives. It is traditional to mean an artificial development of an intelligence that enables the learning, planification, perception, or process of natural language to create vast and ethical and socio-economic opportunities. As AI is a technology activated by the Internet, the Internet Society recognizes that an Internet with which people can trust is important to the creation of an opportunity and challenge associated with AI. At the same time, in this dynamic area, AI or machine learning problems are more common and involve concerns when it comes to users’ trust in the internet. This happens most frequently in goods and services. A variety of problems, including the socio-economic effects, concerns on openness, partiality, and accountability, new uses of data, health, ethical issues and how AI makes it easier to build new ecosystems, have to be addressed when it comes to dealing with AI. We plan to give policymakers and other players in the broader Internet ecosystem help in this chapter. Nonetheless, if it continues unabated some people find AI to be a threat to mankind. Others claim AI would create the possibility of mass unemployment, as opposed to past technological revolutions. This chapter describes the basics of the AI system, described key technology issues and challenges, examined the AI impacts on the performance of organizations and finally provided some guidelines for coping with this technology.