Reviewerly/exHarmony huggingface dataset:
Reliable Constructions for the Key Generator of Code-based Post-quantum Cryptosystems on FPGA

Advances in quantum computing have urged the need for cryptographic algorithms that are low-power, low-energy, and secure against attacks that can be potentially enabled. For this post-quantum age, different solutions have been studied. Code-based cryptography is one feasible solution whose hardware architectures have become the focus of research in the NIST standardization process and has been advanced to the final round (to be concluded by 2022–2024). Nevertheless, although these constructions, e.g., McEliece and Niederreiter public key cryptography, have strong error correction properties, previous studies have proved the vulnerability of their hardware implementations against faults product of the environment and intentional faults, i.e., differential fault analysis. It is previously shown that depending on the codes used, i.e., classical or reduced (using either quasi-dyadic Goppa codes or quasi-cyclic alternant codes), flaws in error detection could be observed. In this work, efficient fault detection constructions are proposed for the first time to account for such shortcomings. Such schemes are based on regular parity, interleaved parity, and two different cyclic redundancy checks (CRC), i.e., CRC-2 and CRC-8. Without losing the generality, we experiment on the McEliece variant, noting that the presented schemes can be used for other code-based cryptosystems. We perform error detection capability assessments and implementations on field-programmable gate array Kintex-7 device xc7k70tfbv676-1 to verify the practicality of the presented approaches. To demonstrate the appropriateness for constrained embedded systems, the performance degradation and overheads of the presented schemes are assessed.
Enhancing Multi-View Stereo with Contrastive Matching and Weighted Focal Loss

Learning-based multi-view stereo (MVS) methods have made impressive progress and surpassed traditional methods in recent years. However, their accuracy and completeness are still struggling. In this paper, we propose a new method to enhance the performance of existing networks inspired by contrastive learning and feature matching. First, we propose a Contrast Matching Loss (CML), which treats the correct matching points in depth-dimension as positive sample and other points as negative samples, and computes the contrastive loss based on the similarity of features. We further propose a Weighted Focal Loss (WFL) for better classification capability, which weakens the contribution of low-confidence pixels in unimportant areas to the loss according to predicted confidence. Extensive experiments performed on DTU, Tanks and Temples and BlendedMVS datasets show our method achieves state-of-the-art performance and significant improvement over baseline network.
CNN Specific ISA Extensions Based on RISC-V Processors

The CNNs have achieved excellent performance in pattern recognition and target detection, which have a wide range of applications in industrial control, medical imaging, autonomous driving, and other fields. However, it is very inefficient to execute data-intensive CNN applications on edge devices with limited computing and power resources. It is necessary to add a domain-specific acceleration module on the edge devices to improve the performance when performing intensive calculations. In this work, we present ISA extensions based on the RISC-V ISA, including data operation instruction and data transfer instruction, aimed at boosting the computational efficiency of CNNs on edge devices. The microarchitecture supporting our proposed extensions is built on top of an open-source RISC-V core. In addition, extended instructions have been added to the GCC Binutils toolchain. To evaluate the effect of our extended instructions, we performed a set of workloads on the baseline and extended core, our proposed ISA extensions have a speed-up ratio of 1.5$\times$ when executing a CNN, and reaches 2.48$\times-2.82\times$ when only performing convolution calculations. The results show that our proposed ISA extensions can effectively improve the performance of CNNs.
Gaussian States: Evaluation of the Covariance Matrix from the Implementation with Primitive Component

Quantum Gaussian states play a fundamental role in quantum communications and in quantum information. This paper deals with the implementation of multimode, and particularly of two-mode Gaussian unitaries and Gaussian states with primitive components (phase shifters, single-mode real squeezers, displacements, and beam splitters). The architecture thus obtained allows one to obtain an insight into the physical meaning of each variable involved. Moreover, following the implementation architecture, it is possible to formulate an easy algebra (radical free) for the main operations and transformations of Gaussian states.
Stochastic Fractal Search Algorithm Improved with Opposition-Based Learning for Solving the Substitution Box Design Problem

The main component of a cryptographic system that allows us to ensure its strength against attacks, is the substitution box. The strength of this component can be validated by various metrics, one of them being the nonlinearity. To this end, it is essential to develop a design for substitution boxes that allows us to guarantee compliance with this metric. In this work, we implemented a hybrid between the stochastic fractal search algorithm in conjunction with opposition-based learning. This design is supported by sequential model algorithm configuration for the proper parameters configuration. We obtained substitution boxes of high nonlinearity in comparison with other works based on metaheuristics and chaotic schemes. The proposed substitution box is evaluated using bijectivity, the strict avalanche criterion, nonlinearity, linear probability, differential probability and bit-independence criterion, which demonstrate the excellent performance of the proposed approach.
Mind the Gap: Norm-Aware Adaptive Robust Loss for Multivariate Least-Squares Problems

Measurement outliers are unavoidable when solving real-world robot state estimation problems. A large family of robust loss functions (RLFs) exists to mitigate the effects of outliers, including newly developed adaptive methods that do not require parameter tuning. All of these methods assume that residuals follow a zero-mean Gaussian-like distribution. However, in multivariate problems the residual is often defined as a norm, and norms follow a Chi-like distribution with a non-zero mode value. This produces a "mode gap" that impacts the convergence rate and accuracy of existing RLFs. The proposed approach, "Adaptive MB," accounts for this gap by first estimating the mode of the residuals using an adaptive Chi-like distribution. Applying an existing adaptive weighting scheme only to residuals greater than the mode leads to more robust performance and faster convergence times in two fundamental state estimation problems, point cloud alignment and pose averaging.
EZEE: Epoch Parallel Zero Knowledge for ANSI C

Recent work has produced interactive Zero Knowledge (ZK) proof systems that can express proofs as arbitrary C programs (Heath et al., 2021, henceforth referred to as ZEE); these programs can be executed by a simulated ZK processor that runs in the 10KHz range. In this work, we demonstrate that such proof systems are amenable to high degrees of parallelism. Our epoch parallelism-based approach allows the prover and verifier to divide the ZK proof into pieces such that each piece can be executed on a different machine. These proof snippets can then be glued together, and the glued parallel proofs are equivalent to the original sequential proof. We implemented and we experimentally evaluate an epoch parallel version of the ZEE proof system. By running the prover and verifier each across 31 2-core machines, we achieve a ZK processor that runs at up to 394KHz. This allowed us to run a benchmark involving the Linux program bzip2, which would have required at least 11 days with the former ZEE system, in only 8.5 hours.
Assessment of Human-Robot Interaction between Householders and Robotic Vacuum Cleaners

The study presented in this paper investigates the application of the Hybrid Model, which is the combination of the two strategies of the Built-to-Order Model and the Dynamic Ecostrategy Explorer Model, to robotic vacuum cleaners.The Hybrid Model aims to switch the market power from seller-driven perception to buyer-driven one by creating an individual perspective from the eye of users rather than traditional customer segmentation.The human-centered approach established theoretically has been tested with a determined procedure that includes prototyping, testing, and evaluating the proposed customization system for robotic vacuum cleaners to increase the interaction degree with purchasers.In this case, robotic vacuum cleaners have been chosen to implement and assess the hypothesis.Firstly, the successful prototyping of the Hybrid Model requires well customer analysis and habits determination to build wellconstructed and coherent interaction between the purchaser and the robot.We utilized a content analysis of robotic vacuum cleaners and elaborative, conventional interviews with early adopters and early majority of this technology in Turkey to establish credible scenarios and product options during the phases of the Hybrid Model practice.The results of the interview were discussed, and the evaluations have been reported.
The effect of blockchain technology on supply chain management: its potential to prevent fraud and reduce risks to food safety and its effects on the relationships between supply chain actors in the Mexican food processing industry

In supply chain operations management, blockchain has tremendous deployment potential as it can enable dynamic connections between network stakeholders as well as mitigating threats to food safety and fraud by enabling goods to be traced across the supply chain in a way that is transparent by preserving supply chain data and making them impossible to alter. Our study establishes a theoretical framework for examining the adoption of blockchain in the Mexican food processing industry. Company managers’ attitudes are investigated by means of an online survey to understand their perceptions of the effects of blockchain technology on the relationship between actors in Mexican food industry supply chains; how they perceive the impact of blockchain technology on fraud and risks to food quality in the supply chain and to identify barriers to adopting blockchain.
Grasping Temperature: Thermal Feedback in VR Robot Teleoperation

This paper presents a proof-of-concept of a robotic teleoperation system, that provides the human operator a thermal sense in addition to the visual sense. With a sensor suite comprising a stereo camera, 360° camera and long-wave infra-red camera, our demonstrator pushes the boundaries of virtual-reality situational awareness by bringing not only 3D visual content but also a 360° thermal experience to the operator. The visual channel of our robotic teleoperation system is represented through a head-mounted-display and the thermal channel is displayed through directional heaters in the operator cockpit and a thermal glove. Initial tests showed that an operator successfully experienced a 360° remote environment, correctly distinguished between and interacted with hot and cold objects, and could notice the presence of nearby people outside her direct field-of-view, based on their emitted heat.
Technologically organized instructional process of higher education

The article is devoted to one of the topical issues of professional and practical training of competitive specialists in higher education institutions of Ukraine. The focus is on improving the organization of the educational process and its technology. Using modeling as one of the relevant methods of pedagogical research, the paper presents and theoretically substantiates the author's model of organization of the educational process in higher education, which acts as a functional system for organizing the assimilation of knowledge by students of higher education institutions, the ways and means of this assimilstion in professional, practical and general cultural aspects. The structure of the proposed model of organization of the educational process in higher education consists of target, theoretical and methodological, content, technological, reflective-analytical, performance-evaluation blocks, each of which received a detailed description taking into account the specifics of their components.
Enhancing the Speed of the Learning Vector Quantization (LVQ) Algorithm by Adding Partial Distance Computation

Abstract Learning Vector Quantization (LVQ) is one of the most widely used classification approaches. LVQ faces a problem as when the size of data grows large it becomes slower. In this paper, a modified version of LVQ, which is called PDLVQ is proposed to accelerate the traditional version. The proposed scheme aims to avoid unnecessary computations by applying an efficient Partial Distance (PD) computation strategy. Three different benchmark datasets are used in the experiments. The comparisons have been done between LVQ and PDLVQ in terms of runtime and in result, it turns out that PDLVQ shows better efficiency than LVQ. PDLVQ has achieved up to 37% efficiency in runtime compared to LVQ when the dimensions have increased. Also, the enhanced algorithm (PDLVQ) shows clear enhancement to decrease runtime when the size of dimensions, the number of clusters, or the size of data becomes increased compared with the traditional one which is LVQ.
Joint Modality Synergy and Spatio-temporal Cue Purification for Moment Localization

Currently, many approaches to the sentence query based moment location (SQML) task emphasize (inter-)modality interaction between video and language query via transformer-based cross-attention or contrastive learning. However, they could still face two issues: 1) modality interaction could be unexpectedly friendly to modality specific learning that merely learns modality specific patterns, and 2) modality interaction easily confuses spatio-temporal cues and ultimately makes time cues in the original video ambiguous. In this paper, we propose a modality synergy with spatio-temporal cue purification method (MS2P) for SQML to address the above two issues. Particularly, a conceptually simple modality synergy strategy is explored to keep features modality specific while absorbing the other modality complementary information with both carefully designed cross-attention unit and non-contrastive learning. As a result, modality specific semantics can be calibrated progressively in a safer way. To preserve time cues in original video, we further purify video representation into spatial and temporal parts to enhance localization resolution by the proposed two light-weight sentence-aware filtering operations. Experiments on Charades-STA, TACoS, and ActivityNet Caption datasets show our model outperforms the state-of-the-art approaches by a large margin.
Progress and Analysis of Multimodal Knowledge Graphs

The important value of knowledge graphs in artificial intelligence applications is becoming increasingly evident. Super large-scale general knowledge graphs have been constructed and widely used in many products such as search, recommendation and intelligent interaction. At the same time, with the deepening of intelligent technologies such as text, speech and vision, knowledge graphs are facing new challenges and opportunities in complex knowledge representation, multimodal semantic understanding techniques and applications. In this paper, we will introduce the latest advances in understanding techniques and applications based on knowledge graphs, from text to multimodal combined knowledge graphs.
Ontology Driven High Performance Messaging System for Distributed Software Platforms

Async Messaging Systems tend to be ubiquitous nowadays. Their major drawback, however, lays in the limited usability with classic REST API’s, that cannot be easily integrated inside such a platform, but are readily available in all major programming languages. This article proposes an alternate approach, which is the design of an Ontology Driven Service Orchestration Layer, using Asynchronous POST HTTP calls, resulting in a complete, secure and fast messaging system. The system is based on a streamlined ontology, that is both flexible and lightweight, resulting in a simplified and improved architecture. This approach was successfully validated and can be used within any Service based system, without the need to switch languages, or to use third-party software components for any of the Services.
ForBackBench

The problems of Data Integration/Exchange (DE) and Ontology Based Data Access (OBDA) have been extensively studied across different communities. The underlying problem is common: using a number of differently structured data-sources mapped to a mediating schema/ontology/knowledge-graph, answer a query posed on the latter. In DE, forward-chaining algorithms, collectively known as the chase, transform source data to a new materialised instance that satisfies the ontology and can be directly queried. In OBDA, backward-chaining algorithms rewrite the query over the source schema, taking the ontology into account, in order to execute the rewriting directly on the sources. These two reasoning approaches have seen an individual rise in algorithms, practical implementations, and benchmarks. However, there has not been a principled methodology to compare solutions across both areas. In this paper we provide an original methodology and a benchmark infrastructure - a set of test scenarios, generator and translator tools, and an experimental infrastructure - to allow the translation and execution of a DE/OBDA scenario across areas and among different chase and query-rewriting systems. In the process, we also present a syntactic restriction of linear Tuple Generating Dependencies that precisely captures DL-Lite R , a correspondence previously uninvestigated. We perform cross-approach experiments under a wide range of assumptions, such as the use of different source-to-target mapping languages, shedding light to the interplay between forward-and backward-chaining. Our preliminary results show that, indeed, chase can compete and might overcome query rewriting even in the face of large data especially for complex mapping languages.
Sparse Trigger Pattern Guided Deep Learning Model Watermarking

Watermarking neural networks (NNs) for ownership protection has received considerable attention recently. Resisting both model pruning and fine-tuning is commonly considered to evaluate the robustness of a watermarked NN. However, the rationale behind such a robustness is still relatively unexplored in the literature. In this paper, we study this problem to propose a so-called sparse trigger pattern (STP) guided deep learning model watermarking method. We provide empirical evidence to show that trigger patterns are able to make the distribution of model parameters compact, and thus exhibit interpretable resilience to model pruning and fine-tuning. We find the effect of STP can also be technically interpreted as the first layer dropout. Extensive experiments demonstrate the robustness of our method.
Tunnel Parsing with the Token’s Lexeme

Abstract The article describes a string recognition approach, engraved in the parsers generated by Tunnel Grammar Studio that use the tunnel parsing algorithm, of how a lexer and a parser can operate on the input during its recognition. Proposed is an addition of the augmented Backus-Naur form syntax that enables the formal language to be expressed with a parser grammar and optionally with an additional lexer grammar. The tokens outputted from the lexer are matched to the phrases in the parser grammar by their name and optionally by their lexeme, case sensitively or insensitively.
Fashion Style-Aware Embeddings for Clothing Image Retrieval

Clothing image retrieval is becoming increasingly important as users on social media grow to enjoy sharing their daily outfits. Most conventional methods offer single query-based retrieval and depend on visual features learnt via target classification training. This paper presents an embedding learning framework that uses novel style description features available on users' posts, allowing image-based and multiple choice-based queries for practical clothing image retrieval. Specifically, the proposed method exploits the following complementary information for representing fashion styles: season tags, style tags, users' heights, and silhouette descriptions. Then, we learn embeddings based on a quadruplet loss that considers the ranked pairings of the visual features and the proposed style description features, enabling flexible outfit search based on either of these two types of features as queries. Experiments conducted on WEAR posts demonstrated the effectiveness of the proposed method compared with several baseline methods.
Web-Based News Straining and Summarization Using Machine Learning Enabled Communication Techniques for Large-Scale 5G Networks

In recent times, text summarization has gained enormous attention from the research community. Among the many uses of natural language processing, text summarization has emerged as a critical component in information retrieval. In particular, within the past two decades, many attempts have been undertaken by researchers to provide robust, useful summaries of their findings. Text summarizing may be described as automatically constructing a summary version of a given document while keeping the most important information included within the content itself. This method also aids users in quickly grasping the fundamental notions of information sources. The current trend in text summarizing, on the other hand, is increasingly focused on the area of news summaries. The first work in summarizing was done using a single-document summary as a starting point. The summarizing of a single document generates a summary of a single paper. As research advanced, mainly due to the vast quantity of information available on the internet, the concept of multidocument summarization evolved. Multidocument summarization generates summaries from a large number of source papers that are all about the same subject or are about the same event. Because of the content duplication, the news summarization system, on the other hand, is unable to cope with multidocument news summarizations well. Using the Naive Bayes classifier for classification, news websites were distinguished from nonnews web pages by extracting content, structure, and URL characteristics. The classifier was then used to differentiate between the two groups. A comparison is also made between the Naive Bayes classifier and the SMO and J48 classifiers for the same dataset. The findings demonstrate that it performs much better than the other two. After those important contents have been extracted from the correctly classified newscast web pages. Then, extracted relevant content is used for the keyphrase extraction from the news articles. Keyphrases can be a single word or a combination of more than one word representing the news article’s significant concept. Our proposed approach of crucial phrase extraction is based on identifying candidate phrases from the news articles and choosing the highest weight candidate phrase using the weight formula. Weight formula includes features such as TFIDF, phrase position, and construction of lexical chain to represent the semantic relations between words using WordNet. The proposed approach shows promising results compared to the other existing techniques.