Reviewerly/exHarmony huggingface dataset:
A DWT-Based Blind Robust High Capacity and Secure Digital Watermarking and Information Hiding Scheme for Image Authentication, Tampering Localization and Automatic Self-Recovery

Due to the rise in many industrial environment applications involving distributed networked collaborative intelligent measurement systems, intelligent monitoring and multi-sensor information exchange of private information, digital image information security and hidden image authentication, tampering detection, correction, and automatic intelligent self-recovery have become critical concerns. Towards that end, there is a need for more secure, efficient, and robust-to-unauthorized-tampering digital image watermarking, information hiding and authentication schemes. In our prior work, we introduced non-blind watermarking schemes where the carrier and watermark images are available at the receiver's end. We also proposed a double-blind scheme where both the carrier and the watermark images are not available at the receiver's end, but at the expense of possible lower pixel tampering, authentication and automatic intelligent self-correction ability. This paper proposes a blind wavelet-based, high capacity, secure digital watermarking and information hiding scheme with higher tampering localization and authentication that nullifies the need to have the carrier image available at the receiving end. The proposed scheme also increases robustness to image authentication and automatic self-recovery and correction compared to our prior double-blind scheme. The scheme is high capacity and sustains hiding secret hidden and watermarking images of the same full size as an arbitrary-sized carrier image. One of the benefits of the blind watermarking scheme is that it allows usage of varying and arbitrary carrier images that are unknown and unavailable anywhere, including the receiver's end. This is to prevent hackers' recognition of a familiar carrier image and their potential unauthorized tampering. Here, in this paper, two grayscale images are inserted in a colored carrier image. Here the watermark image is used for authentication and tamper localization. The second image is used as ownership or encoded secret hidden message image. The Arnolds Transform is used to introduce added security to a hidden image. The proposed scheme uses an embedded correction matrices mechanism for the extracted hidden information self-recovery from various tampering and digital attacks by unauthorized hackers.
Nuclei Detection on Breast Cancer Histopathology Images Using RetinaNet

The number of breast cancer cases and mortality rate has been on the rise globally. For effective treatment and curbing the mortality rate, early diagnosis is paramount. To evaluate the effectiveness of treatment, a fundamental step is histopathological examination of the state of nuclei. However this is a challenging task for various reasons. Accordingly, we perform nuclei detection in breast cancer histopathology images using RetinaNet with a ResNet-152 as the feature extractor. The data used for training and validation of the model was carefully collected at our partner teaching hospital. A mean Average Precision (mAP) of 78% was achieved by optimizing the Focal Loss. Thus outperforming current state-of-the-art methods which have been reported in the literature.
E-Learning in Data Analytics on Basis of Rule Mining Prediction in DM Environment

Abstract Nowadays, most of the information science inquires about a spotlight on affiliation rule to decide explicit examples and rules from huge information. Affiliation guideline is worked by basic information duration device, for example, WEKA which incorporates arrangement, bunching, affiliation runs, etc. When all is said in done, the affiliation standard could be pertinent for enormous E-Learning datasets like they were pertinent towards the formless arrangement. Within this exam, the uses for affiliating rule after gathering under straight information for a considerable length of time from LMS have been explored. Intriguing quality measurements and other applicable understudy inclinations are caught in the wake of thinking about a few affiliation rule calculations. Some imagined introductions of rules and their applicable outcomes will be exhibited in wording reasonableness in eLearning conditions.
Automated Masking of Software Implementations on Industrial Microcontrollers

Physical side-channel attacks threaten the security of exposed embedded devices, such as microcontrollers. Dedicated countermeasures, like masking, are necessary to prevent these powerful attacks. However, a gap between well-studied leakage models and observed leakage on real devices makes the application of these countermeasures non-trivial. This work provides a gadget-based concept to automated masking covering practically relevant leakage models to achieve security on real-world devices. We realize this concept with a fully automated compiler that transforms unprotected microcontroller-implementations of cryptographic primitives into masked executables, capable of being executed on the target device. In a case study, we apply our approach to a bitsliced LED implementation and perform a TVLA-based security evaluation of its core component: the PRESENT s-box.
Playful Interactions for Representation Learning

Ahstract- One of the key challenges in visual imitation learning is collecting large amounts of expert demonstrations for a given task. While methods for collecting human demonstrations are becoming easier with teleoperation methods and the use of low-cost assistive tools, we often still require 100–1000 demonstrations for every task to learn a visual representation and policy. To address this, we turn to an alternate form of data that does not require task-specific demonstrations - play. Playing is a fundamental method children use to learn a set of skills and behaviors and visual representations in early learning. Importantly, play data is diverse, task-agnostic, and relatively cheap to obtain. In this work, we propose to use playful interactions in a self-supervised manner to learn visual representations for downstream tasks. We collect 2 hours of playful data in 19 diverse environments and use self-predictive learning to extract visual representations. Given these representations, we train policies using imitation learning for two downstream tasks: Pushing and Stacking. We demonstrate that our visual representations generalize better than standard behavior cloning and can achieve similar performance with only half the number of required demonstrations. Our representations, which are trained from scratch, compare favorably against ImageNet pretrained representations. Finally, we provide an experimental analysis on the effects of different pretraining modes on downstream task learning.
Research on Risk Management of Digital Currency Based on Blockchain Technology in Mobile Commerce

In the mobile era of blockchain 3.0, blockchain will no longer only exist in the virtual world, but will also exist in the actual environment of people’s lives, and mobile commerce is the best aid to realize connection between virtual and reality. However, this kind of financial innovation also brings impact on financial security and challenges to the current financial regulatory system. As the underlying technology of digital currency, blockchain is essentially a decentralized database. Due to its special and ingenious design, blockchain has the characteristics of decentralization, immutability of information and anonymity. Therefore, digital currency based on blockchain has relatively high security, which can not only effectively reduce the high cost of issuing and circulating traditional banknotes, but also promote the transparency and convenience of transactions. At present, there are still some risks and challenges in the aspects of technology, law and financial system of digital currency, such as network security risk, lack of supporting laws and policies, impact on the current financial system, etc. This paper first briefly introduces the related concepts of blockchain and digital currency. Then it analyzes the advantages of blockchain-based digital currency and its application in the financial field. Next, it analyzes the risk characteristics of digital currency and points out the current major problems in supervision and gives corresponding risk management strategies with a view to providing help for the future sustainable development of blockchain technology and digital currency.
Automated Code Repair to Ensure Spatial Memory Safety

We present a technique for repairing C code to protect against potential violations of spatial memory safety. Many existing techniques can harden software against memory bugs as part of a compiler pass. However, this creates dependencies on the compiler and makes it difficult to fine-tune or even inspect the repairs. We propose an automated technique for repairing the source code to eliminate spatial memory vulnerabilities. Performing the repair at the source-code level introduces a new challenge: analysis and transformation are most easily done on an intermediate representation (IR), but existing techniques using IRs have fundamental limitations in regards to translating changes back to the level of source code. We break this challenge into two parts: (1) translating changes at the level of the IR to the abstract syntax tree (AST) level, and (2) translating changes at the AST level back to the original source-code text. Preemptively repairing potential memory bugs leads to a tradeoff between performance overhead and memory safety. While for safety-critical applications this trade-off may be acceptable, for other applications we can reduce the performance overhead by only repairing suspicious locations. We implemented our approach in a tool called ACR and show that it can repair spatial memory vulnerabilities on buggy programs from the Software Verification Competition. Additionally, we also ran ACR on medium-size programs and preliminary results show the scalability of ACR for thousands of lines of code. Finally, we integrated ACR with static analysis tools and show that the performance overhead is small when repairing only locations that are flagged by a static analyzer.
Data Driven Solar Forecasting Model for Northern Ireland

Solar forecasting is an increasing problem for power system operators due to its variability from day to day. This inconsistent power generation effects not only load forecasting, but can cause increased network stability problems due to changing system inertia. Ample research has been carried out surrounding wind generation, with several accurate forecast methodologies created and used across the world. However, as the solar industry is only now beginning to take off in Northern Ireland (NI), with its capacity increasing exponentially every year, forecasting methods have not yet been accurately implemented by the System Operator for Northern Ireland (SONI). This report documents the creation of a NI specific photovoltaic (PV) forecasting model, based on meteorological and historic PV generation data, from data collection to testing. It was found that partial least squares out-performed multiple linear regression and therefore was used as the forecast base. A large scale model was created, achieving an average Root Mean Square Error (RMSE) of 11.9MW and MAE of 7.7MW. The forecast also improved accuracy with the addition of temperature, wind speed and cloud cover data. A small scale forecast was also built and up-scaled to its representative capacity on the NI system. The model was tested using NI System Demand data, achieving positive results.
Analysis of the Semantic Distance of Words in the RuWordNet Thesaurus

The article presents the analyses of analogues in the RuWordNet thesaurus regarding their semantic distance. Consideration of analogues is by nature a comprehensive analysis for identifying of possible routes for each pair of words with application of both traditional linguistic methods and approaches of cognitive and corpus linguistics. The lack of semantic connections between some members of the series, as well as the inconsistency of their routes with the general linguistic representations established in the dictionaries serves the basis for distance spacing between words in the RuWordNet thesaurus. Methods for bringing analogues, which are remote in the RuWordNet thesaurus, closer together, were proposed basing on a comparative analysis of the data in the explanatory dictionaries and in the New Explanatory Dictionary of Synonyms of the Russian Language, taking into account semantic relations of inclusion or meanings intersection. Basing on the obtained data, recommendations to reduce the distances between analogues were formulated for RuWordNet; and general principles of analysis were proposed, which could be useful for RuWordNet verifying.
Rule Extraction From Binary Neural Networks With Convolutional Rules for Model Validation

Classification approaches that allow to extract logical rules such as decision trees are often considered to be more interpretable than neural networks. Also, logical rules are comparatively easy to verify with any possible input. This is an important part in systems that aim to ensure correct operation of a given model. However, for high-dimensional input data such as images, the individual symbols, i.e. pixels, are not easily interpretable. Therefore, rule-based approaches are not typically used for this kind of high-dimensional data. We introduce the concept of first-order convolutional rules, which are logical rules that can be extracted using a convolutional neural network (CNN), and whose complexity depends on the size of the convolutional filter and not on the dimensionality of the input. Our approach is based on rule extraction from binary neural networks with stochastic local search. We show how to extract rules that are not necessarily short, but characteristic of the input, and easy to visualize. Our experiments show that the proposed approach is able to model the functionality of the neural network while at the same time producing interpretable logical rules. Thus, we demonstrate the potential of rule-based approaches for images which allows to combine advantages of neural networks and rule learning.
SVSNet: An End-to-End Speaker Voice Similarity Assessment Model

Neural evaluation metrics derived for numerous speech generation tasks have recently attracted great attention.In this paper, we propose SVSNet, the first end-to-end neural network model to assess the speaker voice similarity between converted speech and natural speech for voice conversion tasks.Unlike most neural evaluation metrics that use hand-crafted features, SVSNet directly takes the raw waveform as input to more completely utilize speech information for prediction.SVSNet consists of encoder, co-attention, distance calculation, and prediction modules and is trained in an end-to-end manner.The experimental results on the Voice Conversion Challenge 2018 and 2020 (VCC2018 and VCC2020) datasets show that SVSNet outperforms well-known baseline systems in the assessment of speaker similarity at the utterance and system levels.
Subgraph matching on temporal graphs

Temporal graphs are graphs whose edges are associated with timestamps. Subgraph matching on temporal graphs retrieves temporal subgraphs whose edge timestamps satisfy user-specified temporal orders. In this paper, a temporal query pattern is composed of a query graph with an arbitrary structure and a partial order on the edge set of the query graph. Moreover, the time span between the minimum and the maximum edge timestamps in a matching is required to be less than or equal to a specified threshold. This paper proposes a temporal subgraph matching algorithm based on two key techniques. First, a memory-efficient index structure called TO-tree is designed to compactly store all necessary information required for finding all temporal subgraph matchings. The TO-tree constructed for a temporal query pattern is much smaller than the temporal graph because unnecessary information is mostly excluded from the TO-tree by three powerful filters. The second technique is a temporal subgraph matching enumeration method that runs on the TO-tree instead of on the temporal graph. This enumeration method expands temporal subgraph matchings in an edge-by-edge manner. Since the TO-tree can fit into the main memory, the enumeration method runs very fast on the TO-tree. An extensive experimental evaluation has been carried out. The experimental results show that the TO-tree index structure is memory-efficient, which in turn enables a fast temporal subgraph matching enumeration. Overall, our algorithm is at least 3X faster than the baseline algorithm adapted from the state-of-the-art non-temporal subgraph matching algorithm CECI and is at least 4X faster than the temporal subgraph matching algorithm HASSE.
Rowhammering Storage Devices

Peripheral devices like SSDs are growing more complex, to the point they are effectively small computers themselves. Our position is that this trend creates a new kind of attack vector, where untrusted software could use peripherals strictly as intended to accomplish unintended goals. To exemplify, we set out to rowhammer the DRAM component of a simplified host-side FTL, issuing regular I/O requests that manage to flip bits in a way that triggers sensitive information leakage. We conclude that such attacks might soon be feasible, and we argue that systems need principled approaches for securing peripherals against them.
Beautiful Formalizations in Isabelle/Naproche

We present short example formalizations of basic theorems from number theory, set theory, and lattice theory which ship with the new $$\mathbbm {N}$$ aproche component in Isabelle 2021. The natural proof assistant $$\mathbbm {N}$$ aproche accepts input texts in the mathematical controlled natural language ForTheL. Some ForTheL texts that proof-check in $$\mathbbm {N}$$ aproche come close to ordinary mathematical writing. The formalization examples demonstrate the potential to write mathematics in a natural yet completely formal language and to delegate tedious organisatorial details and obvious proof steps to strong automated theorem proving so that mathematical ideas and the “beauty” of proofs become visible.
Towards a firmware TPM on RISC-V

To develop the next generation of Internet of Things, Edge devices and systems which leverage progress in enabling technologies such as 5G, distributed computing and artificial intelligence (AI), several requirements need to be developed and put in place to make the devices smarter. A major requirement for all the above applications is the long-term security and trust computing infrastructure. Trusted Computing requires the introduction inside of the platform of a Trusted Platform Module (TPM). Traditionally, a TPM was a discrete and dedicated module plugged into the platform to give TPM capabilities. Recently, processors manufacturers started integrating trusted computing features into their processors. A significant drawback of this approach is the need for a permanent modification of the processor microarchitecture. In this context, we suggest an analysis and a design of a software-only TPM for RISC-V processors based on seL4 microkernel and OP-TEE.
RETRACTED ARTICLE: Active components of mountain soil based on clustering algorithm and evaluation of Internet English teaching

According to the ICPP report, by the end of this century, global temperature will rise, leading to sea level rise and frequent extreme weather conditions, and ecosystem services and biodiversity decline, threatening global food supply and human safety and health. Soil organic carbon is the largest carbon pool in terrestrial ecosystems and plays an important role in global greenhouse gas balance. Enzymatic reaction is the rate-limiting step of SOC degradation. Therefore, it is necessary to understand the heating characteristics and the binding characteristics of SOC and soil enzymes. This relationship is very important for soil carbon sequestration and emission reduction. On this basis, combined with the adaptive weighted data fusion technology, this paper uses the clustering algorithm to optimize the dual sensor cluster to study the active components of mountain soil. With the development and popularization of the Internet, the Internet has become an important part of students’ study and life. A network learning platform can make smartphones become an important learning tool, so that students can learn new methods and teachers can provide new ideas and models of English teaching, and combine information technology to improve the efficiency of English teaching, thus greatly improving the students’ interest in learning. In this context, the traditional teaching evaluation method should be changed, and students should be regarded as the core part of classroom evaluation, so that teachers can have a more complete understanding of students, pay attention to students’ personal needs, and achieve the goal of obtaining high-quality education. Therefore, this paper analyzes how to effectively evaluate English teaching according to informatization, and puts forward several effective strategies.
PiPoMonitor: Mitigating Cross-core Cache Attacks Using the Auto-Cuckoo Filter

Cache side channel attacks obtain victim cache line access footprint to infer security-critical information. Among them, cross-core attacks exploiting the shared last level cache are more threatening as their simplicity to set up and high capacity. Stateful approaches of detection-based mitigation observe precise cache behaviors and protect specific cache lines that are suspected of being attacked. However, their recording structures incur large storage overhead and are vulnerable to reverse engineering attacks. Exploring the intrinsic non-determinate layout of a traditional Cuckoo filter, this paper proposes a space efficient Auto-Cuckoo filter to record access footprints, which succeed to decrease storage overhead and resist reverse engineering attacks at the same time. With Auto-Cuckoo filter, we propose PiPoMonitor to detect Ping-Pong patterns and prefetch specific cache line to interfere with adversaries' cache probes. Security analysis shows the PiPoMonitor can effectively mitigate cross-core attacks and the Auto-Cuckoo filter is immune to reverse engineering attacks. Evaluation results indicate PiPoMonitor has negligible impact on performance and the storage overhead is only 0.37%, an order of magnitude lower than previous stateful approaches.
Trust and Governance in Collective Blockchain Treasuries

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“You AIn’t Seen Nothing yet” – Arguments against the Protectability of AI-generated Outputs by Copyright Law

Artificial Intelligence (AI) has become a part of our daily life, and “algorithmic creativity” and the protection of it by copyright law has similarly gained a spotlight recently. This article collects the key arguments against the proposal of such protection. The core elements of copyright law – namely the concept of authorship, originality and moral rights, as well as copyright’s history and incentives – are deeply rooted in an anthropocentric (although not only author-centric) world. Unless paradigm shift in copyright law, the lack of direct human element of an AI-generated output shall lead to the unavailability of copyright protection for these outputs. The paper is an updated version of “From Leonardo to the Next Rembrandt – The Need for AI-Pessimism in the Age of Algorithms”.