Reviewerly/exHarmony huggingface dataset:
Combinatorial Analysis for Securing IoT-Assisted Industry 4.0 Applications From Vulnerability-Based Attacks

Modern Internet of Things (IoT)-assisted networks deploy many critical computing devices in the edge of the network so as to mitigate the bottleneck of network latency and bandwidth outage. Such computing devices have become attractive targets of attackers, as they derive critical insights that can often regulate the underlying industrial processes. Security administrators have to analyze vulnerable configurations, and evaluate feasible hardening options to secure such systems. In this article, we investigate the security threats to the edge devices in the IoT-assisted networks because of the inherent vulnerabilities present in the IoT devices, and formulate a novel graphical security model to conduct vulnerability-based risk assessment in such networks. We further propose a set of combinatorial optimization techniques for security hardening. For securing the target devices, we propose a randomized algorithm to enforce isolation of potential target devices from the attackers. The algorithm produces the least number of vulnerabilities, the removal of which protects the targets. For densely connected networks, we propose another algorithm to identify a minimal set of vulnerabilities to be hardened so as to keep potential targets below admissible levels of risk. The approach includes the formulation of an integer linear program, which is relaxed to solve in polynomial time, followed by a round-off technique to find the actual solution.
A semi-supervised sparse K-Means algorithm

We consider the problem of data clustering with unidentified feature quality and when a small amount of labelled data is provided. An unsupervised sparse clustering method can be employed in order to detect the subgroup of features necessary for clustering and a semi-supervised method can use the labelled data to create constraints and enhance the clustering solution. In this paper we propose a K-Means variant that employs these techniques. We show that the algorithm maintains the high performance of other semi-supervised algorithms and in addition preserves the ability to identify informative from uninformative features. We examine the performance of the algorithm on synthetic and real world data sets. We use scenarios with a different amount and types of constraints as well as different clustering initialisation methods.
A Lightweight Blockchain Scheme for a Secure Smart Dust IoT Environment

Since a smart dust Internet of Things (IoT) system includes a very large number of devices sometimes deployed in hard-access areas, it is very difficult to prevent security attacks and to alleviate bottleneck phenomena. In this paper, we propose a lightweight blockchain scheme that helps device authentication and data security in a secure smart dust IoT environment. To achieve our goals, (1) we propose the structure of the lightweight blockchain and the algorithm of processing the blockchain. In addition, (2) we reorganize the linear block structure of the conventional blockchain into the binary tree structure in such a way that the proposed blockchain is more efficient in a secure smart dust IoT environment. Experiments show that the proposed binary tree-structured lightweight blockchain scheme can greatly reduce the time required for smart dust device authentication, even taking into account the tree transformation overhead. Compared with the conventional linear-structured blockchain scheme, the proposed binary tree-structured lightweight blockchain scheme achieves performance improvement by up to 40% (10% in average) with respect to the authentication time.
A Comparative Study on Vectorization and Classification Techniques in Sentiment Analysis to Classify Student-Lecturer Comments

Sentiment analysis is one of the important fields in educational data mining. In this paper, a large dataset, more than 52 000 comments, was used during experiment to develop a state-of-art classification model. The correlation test was conducted on sentiment analysis results and scale-rated survey results, and the result (r(203)=.79, p<.001) shows that sentiment analysis can be accepted as reasonable method for course and lecturer evaluation. A comparative analysis was done between different vectorization and classification techniques. The results of the experiment show that classifier built using Random Forest was most optimal and efficient classification model with state-of-art prediction accuracy of 97% for 3-class classification. Moreover, to improve the diversity of the comments, a 5-class dataset was formed and experiment resulted with an efficient classification model with accuracy of 92%. The Tf-Idf vectorization technique performed better than Count (Binary) vectorization.
Lifelong learning as the way of modern personality development in Russia on the example of higher educational institution of technical and natural-scientific profile

Abstract The article shows the role of lifelong learning for the development of individuals, organizations and the state. Any state that is interested in its development gives special attention to the education system, which on the one hand trains personnel capable of moving science and technology forward, on the other hand, the development of science and technology requires more and more highly qualified personnel. The characteristics of the two main stages of the formation of the system of lifelong learning from Jan Amos Komenskiy to UNESCO and modern scientists and teachers are presented. It is noted that, although no country has created a coherent system of lifelong learning yet, universities around the world are working on its formation, based on the UNESCO strategy for 2014-2021. The article considers the experience of Krasnoyarsk state agrarian university, located in Russia, in the development of lifelong learning in an agricultural educational institution. The mission and tasks of the University in the light of the tasks of lifelong learning development, programs implemented at the Institute of Additional Professional Education (IAPE) of the University, and courses held at the Center for international relations and business, as well as other technologies for working with students, faculty and staff to form the desire for lifelong learning are presented.
Potential impacts of blockchain technology on business practices of bricks and mortar (B&amp;M) grocery stores

Purpose This paper investigates the impact of blockchain technology on the Bricks and Mortar (B&amp;M) grocery sector from a technological and functional perspective. Design/methodology/approach The research adopted an exploratory research design and the data comprises 17 semi-structured interviews with personnel at the top grocery retail chains in the United States, for example, Wal-Mart, Tesco, Stop and Shop and Meijer. Additionally, two major US-based blockchain service providers are included – SumatoSoft and Accubits. Findings Blockchain technology affects the business processes of B&amp;M grocery retail by offering payment via tokens, secure payments and contracts between stakeholders, an end-to-end solution in the supply chain and secure management of the stock. However, this process is hampered by a number of challenges such as integrity and security concerns, difficulty in adapting sound logistics, lack of adequate skills and resistance to change by store managers and employees. This can be addressed by imparting education/training and creating awareness about the benefits of blockchain and generating industry-wide collaboration in which regulations can work. Practical implications The research has benefits for B&amp;M grocery stores, governments and the wider society. For example, the findings of this study will help B&amp;M grocery retailers to confront the competition by online retailers such as Amazon, AliExpress or eBay and promote the development of a systematic collaboration to achieve the changes they need. Originality/value The study is original and innovative in that no research to date has focused on how blockchain can help the B&amp;M grocery sector and address its challenges.
Proof of Evolution: leveraging blockchain mining for a cooperative execution of Genetic Algorithms

Proof of Work (PoW) is the consensus protocol introduced with Bitcoin, and is still one of the most used protocols, thanks to its security properties. However, it is very expensive in terms of energy consumption. For this reason, many other protocols have been designed in order to earmark part of the computations for useful tasks, or to reduce them, but few of these have the same properties of PoW.With this paper we propose a new consensus protocol for blockchains, called Proof of Evolution (PoE), that keeps the security features of PoW, and uses part of the mining computations for the execution of genetic algorithms (GAs) that some clients can submit. Moreover, PoE enables a form of cooperation among miners. During the mining process, in fact, miners have to maintain and evolve a population of solution candidates; PoE offers them the possibility of sharing their current best found solutions, that they can add to their population. This exchange seems to enhance the quality of the solutions they can achieve with the GAs in use. PoE is close to Proof of Search (PoS), which in turn extends PoW in order to solve optimization problems while mining. While PoS stimulates miners in submitting solutions for a problem of interest, the contribution of PoE is to encourage them to share their current best found solutions, allowing cooperation.
Analysing the HPKE Standard

The Hybrid Public Key Encryption (HPKE) scheme is an emerging standard currently under consideration by the Crypto Forum Research Group (CFRG) of the IETF as a candidate for formal approval. Of the four modes of HPKE, we analyse the authenticated mode $$\mathsf {HPKE}_\mathsf {Auth}$$ in its single-shot encryption form as it contains what is, arguably, the most novel part of HPKE. $$\mathsf {HPKE}_\mathsf {Auth}$$ 's intended application domain is captured by a new primitive which we call Authenticated Public Key Encryption (APKE). We provide syntax and security definitions for APKE schemes, as well as for the related Authenticated Key Encapsulation Mechanisms (AKEMs). We prove security of the AKEM scheme $$\mathsf {DH}\hbox {-}\mathsf {AKEM}$$ underlying $$\mathsf {HPKE}_\mathsf {Auth}$$ based on the Gap Diffie-Hellman assumption and provide general AKEM/DEM composition theorems with which to argue about $$\mathsf {HPKE}_\mathsf {Auth}$$ 's security. To this end, we also formally analyse $$\mathsf {HPKE}_\mathsf {Auth}$$ 's key schedule and key derivation functions. To increase confidence in our results we use the automatic theorem proving tool CryptoVerif. All our bounds are quantitative and we discuss their practical implications for $$\mathsf {HPKE}_\mathsf {Auth}$$ . As an independent contribution we propose the new framework of nominal groups that allows us to capture abstract syntactical and security properties of practical elliptic curves, including the Curve25519 and Curve448 based groups (which do not constitute cyclic groups).
A Novel Inference Algorithm for Large Sparse Neural Network using Task Graph Parallelism

The ever-increasing size of modern deep neural network (DNN) architectures has put increasing strain on the hardware needed to implement them. Sparsified DNNs can greatly reduce memory costs and increase throughput over standard DNNs, if the loss of accuracy can be adequately controlled. However, sparse DNNs present unique computational challenges. Efficient model or data parallelism algorithms are extremely hard to design and implement. The recent effort MIT/IEEE/Amazon HPEC Graph Challenge has drawn attention to high-performance inference methods for large sparse DNNs. In this paper, we introduce SNIG, an efficient inference engine for large sparse DNN s. SNIG develops highly optimized inference kernels and leverages the power of CUDA Graphs to enable efficient decomposition of model and data parallelisms. Our decomposition strategy is flexible and scalable to different partitions of data volumes, model sizes, and GPU numbers. We have evaluated SNIG on the official benchmarks of HPEC Sparse DNN Challenge and demonstrated its promising performance scalable from a single GPU to multiple GPUs. Compared to the champion of the 2019 HPEC Sparse DNN Challenge, SNIG can finish all inference workloads using only a single GPU. At the largest DNN, which has more than 4 billion parameters across 1920 layers each of 65536 neurons, SNIG is up to 2.3x faster than a state-of-the-art baseline under a machine of 4 GPUs.
Extracting Explainable Assessments of Alzheimer’s disease via Machine Learning on brain MRI imaging data

A plethora of machine learning and deep learning methods are used for the assessment of Alzheimer's Disease (AD) from brain structural changes as seen in Magnetic Resonance Imaging (MRI) with highly satisfactory results. However, these models are black-box and lack an explicit declarative knowledge representation and thus there is a difficulty in generating the underlying explanatory imaging structures. The objective of this study was to investigate the usefulness of rule extraction in the assessment of AD using decision trees (DT) and random forests (RF) algorithms and integrating the extracted rules within an argumentation-based reasoning framework in order to make the results easy to interpret and explain. The DT and RF algorithms were applied on brain MRI images acquired from normal controls (NC) and AD subjects. The KNIME analytics platform was used to compute the DT and the R project was used for the RF. The argumentation model implemented in the Gorgias framework achieved an average accuracy of 91%, exhibiting improved results compared to the models of DT and RF. The overall performance of all models in this study is in agreement with other studies. In addition, the explanations given by our approach for the various possible predictions provide a more useful and complete assessment of the state of the patient/case at hand. This study demonstrated the usefulness of rule extraction in the assessment of AD based on MRI features and the positive results of the use of the argumentation based symbolic reasoning for composing and interpreting the ML results.
Generative Restricted Kernel Machines: A framework for multi-view generation and disentangled feature learning

This paper introduces a novel framework for generative models based on Restricted Kernel Machines (RKMs) with joint multi-view generation and uncorrelated feature learning, called Gen-RKM. To enable joint multi-view generation, this mechanism uses a shared representation of data from various views. Furthermore, the model has a primal and dual formulation to incorporate both kernel-based and (deep convolutional) neural network based models within the same setting. When using neural networks as explicit feature-maps, a novel training procedure is proposed, which jointly learns the features and shared subspace representation. The latent variables are given by the eigen-decomposition of the kernel matrix, where the mutual orthogonality of eigenvectors represents the learned uncorrelated features. Experiments demonstrate the potential of the framework through qualitative and quantitative evaluation of generated samples on various standard datasets.
TRFR: A ternary relation link prediction framework on Knowledge graphs

Artificial intelligence has been widely used in daily wireless networks for its flexibility and adaptability in solving extremely complex problems in real-time. These wireless network applications using artificial intelligence will generate a lot of valuable data. Knowledge graph is able to mine and store useful information from these large amounts of data and make data-driven wireless network applications more intelligent. Existing knowledge graph represents knowledge facts in triples whose examinations are restricted within two entities and binary relations. This nature induces the weakness of expanding to complex learning scenarios with multi-entity relations, which motivates the research insights into the n-ary relation. However, existing n-ary relation link prediction all embedding-based which are limited by their interpretability. We investigate the ternary relation link prediction task and propose a novel unified framework TRFR which is the first path-based model on n-ary relational data. TRFR incorporates the hierarchical attention mechanism and reinforcement learning technique. In addition, we release a newly constructed dataset NELL-995-3 to fill the shortage of learning resources for n-ary link prediction. Extensive experiments demonstrate the superiority of the proposed framework compared with a wide variety of state-of-the-art knowledge graph-based approaches in ternary relation link prediction task on one benchmark dataset and one newly constructed dataset.
Architectural Analysis of Deep Learning on Edge Accelerators

As computer architectures continue to integrate application-specific hardware, it is critical to understand the relative performance of devices for maximum app acceleration. The goal of benchmarking suites, such as MLPerf for analyzing machine-learning (ML) hardware performance, is to standardize a fair comparison of different hardware architectures. However, there are many apps that are not well represented by these standards that require different workloads, such as ML models and datasets, to achieve similar goals. Additionally, many devices feature hardware optimized for data types other than 32-bit floating-point numbers, the standard representation defined by MLPerf. Edge-computing devices often feature app-specific hardware to offload common operations found in ML apps from the constrained CPU. This research analyzes multiple low-power compute architectures that feature ML-specific hardware on a case study of handwritten Chinese character recognition. Specifically, AlexNet and a custom version of GoogLeNet are benchmarked in terms of their streaming latency for optical character recognition. Considering these models are custom and not the most widely used, many architectures are not specifically optimized for them. The performance of these models can stress devices in different, yet insightful, ways that generalizations of the performance of other models can be drawn from. The NVIDIA Jetson AGX Xavier (AGX), Intel Neural Compute Stick 2 (NCS2), and Google Edge TPU architectures are analyzed with respect to their performance. The design of the AGX and TPU devices showcased the lowest streaming latency for AlexNet and GoogLeNet, respectively. Additionally, the tightly-integrated N CS2 design showed the best generalizability in performance and efficiency across neural networks.
Formal Foundations for Intel SGX Data Center Attestation Primitives

Intel has recently offered third-party attestation services, called Data Center Attestation Primitives (DCAP), for a data center to create its own attestation infrastructure. These services address the availability concerns and improve the performance as compared to the remote attestation based on Enhanced Privacy ID (EPID). Practical developments, such as Hyperledger Avalon, have already planned to support DCAP in their roadmap. However, the lack of formal proof for DCAP leads to security concerns. To fill this gap, we propose an automated, rigorous, and sound formal approach to specify and verify the remote attestation based on Intel SGX DCAP under the assumption that there are no side-channel attacks and no vulnerabilities inside the enclave. In the proposed approach, the data center configuration and operational policies are specified to generate the symbolic model, and security goals are specified as security properties to produce verification results. The evaluation of non-Quoting Verification Enclave-based DCAP indicates that the confidentiality of secrets and integrity of data is preserved against a Dolev-Yao adversary in this technology. We also present a few of the many inconsistencies found in the existing literature on Intel SGX DCAP during formal specification.
Fine-grained classification of social science journal articles using textual data: A comparison of supervised machine learning approaches

Abstract We compare two supervised machine learning algorithms—Multinomial Naïve Bayes and Gradient Boosting—to classify social science articles using textual data. The high level of granularity of the classification scheme used and the possibility that multiple categories are assigned to a document make this task challenging. To collect the training data, we query three discipline specific thesauri to retrieve articles corresponding to specialties in the classification. The resulting data set consists of 113,909 records and covers 245 specialties, aggregated into 31 subdisciplines from three disciplines. Experts were consulted to validate the thesauri-based classification. The resulting multilabel data set is used to train the machine learning algorithms in different configurations. We deploy a multilabel classifier chaining model, allowing for an arbitrary number of categories to be assigned to each document. The best results are obtained with Gradient Boosting. The approach does not rely on citation data. It can be applied in settings where such information is not available. We conclude that fine-grained text-based classification of social sciences publications at a subdisciplinary level is a hard task, for humans and machines alike. A combination of human expertise and machine learning is suggested as a way forward to improve the classification of social sciences documents.
Role of Blockchain Technology in Digitization of Land Records in Indian Scenario

Abstract Blockchain is a way of passing data (such as records, events, or transactions) from one party to another in a very secure way. It is an electronic record of information that requires digital security. All data stored in the blockchain is immutable; once a piece of data enters into a blockchain, it is practically impossible to alter its value. The Blockchain has changed the model from a centralized way of traditional business to the decentralized model of the blockchain, that means there it can run without any central authority. It works on Peer to Peer Model rather than Peer-Mediator-Peer. The Blockchains makes the process easier, fastest, and trustworthy to deal with businesses as it follows Peer to Peer nature Blockchain. It has become the most used business model in different industries, such as construction industries, as it is the safest, fastest, transparent, and it also is more comfortable to implement. The critical feature of the blockchain that makes it today’s most potential technologies are: decentralized, self-control, peer- to- peer relationship, fixed record and time stamping. Thus, this chapter focuses on the pivotal role and application of BlockChain technology on the digitization of land records of the Indian Scenario.
Cross-class generative network for zero-shot learning

Zero-shot learning (ZSL) aims to recognize unseen classes when no training samples are provided for these classes. A traditional approach to solving ZSL is to generate samples of unseen classes, transforming it into a supervised task. However, the quality of these pseudo-samples is crucial for model performance. In this paper, we propose a novel network, called cross-class generative network, which includes two novel end-to-end models, to generate high-quality samples for unseen classes. Unlike previous work, our proposed models directly generate samples of unseen classes via samples of seen classes. As a result, generated samples are distributed more similarly to real samples. In addition, we propose an intra-class entropy to measure the discrepancy degree for selecting suitable source–target pairs. To the best of our knowledge, this intra-class entropy is proposed in ZSL for the first time. Our models include two versions, non-adversarial and adversarial ones, to support and explore different scenarios. We conduct extensive experiments on five benchmark datasets. A comprehensive comparison with state-of-the-art methods shows the superiority of our proposed models.