Reviewerly/exHarmony huggingface dataset:
Extended missing data imputation via GANs for ranking applications

We propose Conditional Imputation GAN, an extended missing data imputation method based on Generative Adversarial Networks (GANs). The motivating use case is learning-to-rank, the cornerstone of modern search, recommendation system, and information retrieval applications. Empirical ranking datasets do not always follow standard Gaussian distributions or Missing Completely At Random (MCAR) mechanism, which are standard assumptions of classic missing data imputation methods. Our methodology provides a simple solution that offers compatible imputation guarantees while relaxing assumptions for missing mechanisms and sidesteps approximating intractable distributions to improve imputation quality. We prove that the optimal GAN imputation is achieved for Extended Missing At Random and Extended Always Missing At Random mechanisms, beyond the naive MCAR. Our method demonstrates the highest imputation quality on the open-source Microsoft Research Ranking Dataset and a synthetic ranking dataset compared to state-of-the-art benchmarks and across various feature distributions. Using a proprietary Amazon Search ranking dataset, we also demonstrate comparable ranking quality metrics for ranking models trained on GAN-imputed data compared to ground-truth data.
Profiling Attack against RSA Key Generation Based on a Euclidean Algorithm

A profiling attack is a powerful variant among the noninvasive side channel attacks. In this work, we target RSA key generation relying on the binary version of the extended Euclidean algorithm for modular inverse and GCD computations. To date, this algorithm has only been exploited by simple power analysis; therefore, the countermeasures described in the literature are focused on mitigating only this kind of attack. We demonstrate that one of those countermeasures is not effective in preventing profiling attacks. The feasibility of our approach relies on the extraction of several leakage vectors from a single power trace. Moreover, because there are known relationships between the secrets and the public modulo in RSA, the uncertainty in some of the guessed secrets can be reduced by simple tests. This increases the effectiveness of the proposed attack.
Single qubit neural quantum circuit for solving Exclusive-OR

The main goal of this work was designing a single qubit neural quantum circuit for performing Exclusive-OR, a concrete example of an operation requiring multiple layers in classical neural networks. The corresponding Qiskit code was tested on both simulators and IBM's "ibmqx2" five-qubit quantum processor. Further analyses along training the proposed neural quantum circuit are currently in progress. • A single qubit neural quantum circuit for performing XOR was tested on IBM Q Experience • XOR is a concrete example of an operation requiring multiple layers in classical neural networks.
Multi-BERT-wwm Model Based on Probabilistic Graph Strategy for Relation Extraction

As the core work of information extraction, relation extraction aims to find medical entity pairs with relations from the medical records. The current methods of relation extraction either ignore the relevance of entity extraction and relation classification, or fail to solve the problem of multiple relations or entities in one sentence. To handle those problems, this paper proposes a cascading pointer Multi-BERT-wwm model based on the probabilistic graph strategy. The model selects an entity randomly from all the predicted entities each time, then predicts other entities and their relations according to that entity. Meanwhile, the pointer labeling network helps to solve the problem of overlapping entities. The Multi-BERT-wwm model is improved based on BERT, which connects a layer of self-attention to the multi-head attention layer in the first six Encoder modules to strengthen the feature extraction ability. In addition, we add the adversarial training to improve the robustness and generalization ability of the model. The experimental results tested on the CMeIE dataset show that compared with the traditional CNN+Attention and BERT methods, our method improves the F1-score by 4.42% and 1.91% respectively in relation extraction task.
Multi-UAVs Collaboration Routing Plan Based on Modified Artificial Bee Colony Algorithm

In this paper, the multi-UAVs collaboration routing plan problem is formally defined, and the route penalty degree is used to evaluate the multi-UAVs routes. A Modified Artificial Bee Colony (M-ABC), which includes collaboration strategy and slow communication style, is proposed to solve the multi-UAVs collaboration real-time routing plan problem. The simulation results show that M-ABC has great real-time and stability in solving multi-UAVs collaboration real-time route planning, which can quickly find collaboration routes of satisfying multi-UAVs’ requirements.
An approach for combining multimodal fusion and neural architecture search applied to knowledge tracing

Knowledge Tracing is the process of tracking mastery level of different skills of students for a given learning domain. It is one of the key components for building adaptive learning systems and has been investigated for decades. The empirical success of deep neural networks in the past few years has encouraged researchers in the learning science community to take similar approaches. However, most existing deep learning based knowledge tracing models have the following limitations: (1) Only use the correct/incorrect response, ignoring useful information from other modalities. (2) For works do consider multimodality use simple concatenation, which might not be the best way for modality fusion. (3) Design their network architectures manually via trial and error. To solve these problems, we propose Multimodal Fusion and Neural Architecture Search (MFNAS) in this paper. The commonly used neural architecture search technique could be considered as a special case of our proposed approach when there is only one modality involved. We further propose to use a new metric called time-weighted Area Under the Curve (weighted AUC) to measure how a sequence model performs with time. Our proposed approach MFNAS allows more efficient design of Knowledge Tracing models. Besides, evaluated on two public real datasets, the discovered model is able to achieve around 12% improvement in coefficient of determination, 2% improvement in AUC and weighted AUC compared with state of the art models. What’s more, unlike most existing works, we conduct McNemar’s test on the model predictions and the results are statistically significant.
Non-Functional Requirements Elicitation Based on Domain Knowledge Graph for Automatic Code Generation of Industrial Cyber-Physical Systems

As an important part of Industry 4.0, industrial software development must be highly flexible and reconfigurable to deal with a large number of requirements and random failures during the manufacturing process. Requirements understanding errors that occur in the early stages of software development often cannot be discovered prior to the testing phase. Therefore, the analysis and understanding of requirements are particularly crucial in the software development process. In this paper, a knowledge-driven functional-oriented requirements elicitation and analysis method are proposed to extract both functional and non-functional requirements from design documents. Nonfunctional requirements which are implicit in the documents will be elicited according to the domain knowledge graphs and predefined rules. A domain glossary is also constructed for the elicitation process, especially from unstructured sources. Domain knowledge graphs are modified periodically according to the feedback information to form a closed loop from requirements to code. The feasibility of the proposed method is demonstrated under an automobile welding line.
Seeds of SEED: H<sup>2</sup>Cache: Building a Hybrid Randomized Cache Hierarchy for Mitigating Cache Side-Channel Attacks

Cache side-channel attacks can leak critical information from the target programs. The cache randomization methodology has proven to be an efficient way to mitigate such attacks. However, existing works do not take the cache hierarchy into consideration, failing to address the issue that different levels of caches have different performance and security requirements. In this work, we propose and implement a hybrid randomization scheme, named H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Cache, to mitigate cache side-channel attacks. H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Cache leverages two randomization approaches and applies them to different levels of caches. It strengthens the security of cache modules, while satisfying the performance and resource utilization requirements. Specifically, we design a table-based randomization method for the L1 cache, which uses a hashed virtual index to look up the actual cache set index. The L2 cache in H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Cache takes a computation-based randomization function to calculate the cache set index. We have implemented a prototype of H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Cache and extensively evaluated it using a self-designed RISC-V processor on the FPGA platform. We demonstrate the security of H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Cache through simulated attack programs and quantitative analysis. Meanwhile, the evaluation results of performance and resource utilization have shown its efficacy.
Decentralized Peer-Review Research Solution

The current conference and journal management systems for scientific research are centralized and suffer from single-point-of-failures. Moreover, these systems lack transparency and require their participants to trust a third-party. In this paper, we design a decentralized alternative for conference and journal management systems by leveraging decentralized technologies like blockchain and IPFS. We conceptualized a blockchain-based portal for sharing research articles with organizers of a scientific research publication venue. Additionally, we implement symmetric key encryption of research articles using a data encryption key to ensure the confidentiality and authenticity of intellectual property. A novel contribution of this research work is the design of a secure approach to storing conflict of interests information and, ensuring confidentiality, integrity, and non-repudiation of the author's conflict of interests. This solution has been prototyped and analyzed for overall cost-effectiveness and the results show that an organizer incurs an estimated expense of up to $300 for hosting a conference (considering 200 submissions and 2 peer-reviewers assigned to each submission). The authors and reviewers also incur transaction costs estimated at $1.54 and $0.56 for each submission and review, respectively. Additionally, the core concept of our solution can be applied to other use cases where confidential document sharing in a trustless environment is desirable, like patent applications and grant applications.
A credible transfer method of cross-chain assets based on DID and VC

The distributed network structure of blockchain coincides with the market-oriented structure of distributed renewable energy. Blockchain technology can be used to synchronize the real-time price of grid services with real-time phasor control systems to balance microgrid operation, distributed power generation system access, and wholesale market operation. Distributed shared energy networks constructed in different regions and regions carry digital assets of different values. How to realize the credible exchange of energy digital assets across regions is an urgent need facing the current energy interconnection network. At present, there have been many researches on cross-chain asset exchange methods in the blockchain, such as: hash time lock, notary, side chain and so on. However, these asset exchange methods lack proper supervision (KYC&AML) and are inefficient. This article combines distributed digital identities (hereinafter referred to as DID) and verifiable credentials (VC) to propose a DID-based trusted cross-chain asset transfer method.
Towards a Cognitive Framework for Multimodal Person Recognition in Multiparty HRI

The ability to recognize human partners is an important social skill to build personalized and long-term Human-Robot Interactions (HRI). However, in HRI contexts, unfolding in ever-changing and realistic environments, the identification problem presents still significant challenges. Possible solutions consist of relying on a multimodal approach and making robots learn from their first-hand sensory data. To this aim, we propose a framework to allow robots to autonomously organize their sensory experience into a structured dataset suitable for person recognition during a multiparty interaction. Our results demonstrate the effectiveness of our approach and show that it is a promising solution in the quest of making robots more autonomous in their learning process.
Tackling the DM Challenges with cDMN: A Tight Integration of DMN and Constraint Reasoning

Abstract Knowledge-based AI typically depends on a knowledge engineer to construct a formal model of domain knowledge – but what if domain experts could do this themselves? This paper describes an extension to the Decision Model and Notation (DMN) standard, called Constraint Decision Model and Notation (cDMN). DMN is a user-friendly, table-based notation for decision logic, which allows domain experts to model simple decision procedures without the help of IT staff. cDMN aims to enlarge the expressiveness of DMN in order to model more complex domain knowledge, while retaining DMNs goal of being understandable by domain experts. We test cDMN by solving the most complex challenges posted on the DM Community website. We compare our own cDMN solutions to the solutions that have been submitted to the website and find that our approach is competitive. Moreover, cDMN is able to solve more challenges than any other approach.
Assamese Dialect Identification From Vowel Acoustics

Sarmah, Priyankoo Dihingia, LeenaAssamese language is spoken in the state of Assam in a geographical area spread over 400 km. The Assamese language, spoken in different areas of the state, is known to differ by geographically defined dialects. In this work, we collect speech samples of Assamese speakers from five different geographical regions in Assam, producing the Assamese vowels in various contexts and examine the acoustic characteristics of the vowels. The regional variations of Assamese language were captured in terms of formant frequencies, their discrete cosine transforms, F0 and duration of the vowels. A random forest was trained to classify an vowel as belonging to one of the five regions. The accuracy of classifying unseen test data was 94.0%. The confusion matrix shows that geographically close regions have greater tendency to be confused. The results show that a reasonable accuracy in dialect identification can be attained only by using vowel specific acoustic properties.
On interoperability of metadata within RNPLS&amp;T’s Single Open Information Archive

Russian National Public Library for Science and Technology has been developing the Single Open Information Archive (UOIA) to merge all digital full-text resources created or acquired by the Library. The authors examine the issues of interoperability when exchanging metadata between UOIA built on library automation software and open archives using OAI-PMH technology for metadata acquisition. Interoperability in information exchange between different ALIS is provided, for example, through applying SRU/SRW protocol and metadata scheme, while metadata exchange between OA repositories is provided mainly within Dublin Core (DC) scheme. ALIS – OA metadata transmission with transformation into DC results in information loss and prevents unambiguous reverse transformation.For a long time, DSpace has been the most popular software for open digital repositories. This product enables OAI-PMH metadata acquisition in DC and Qualified DC (QDC) formats, and supports Object Reuse and Exchange (ORE) standard, which enables to describe aggregated resources. ORE in DSpace enables to collect not only metadata but also connected files and to receive other connected data provided by importing source. DSpace uses rather simple ORE format based on Atom XML that allows binding several files of different functionality with RDF-triplets.The OAI-PMH software connector is designed for RNPLS&amp;T SOIA and enables to present metadata in DC, QDC, MARC21, and ORE formats, which supports interoperability in information exchange with OA repositories with DSpace software. Beside metadata transmission, transmission of various data types is possible, e. g. document text or license information. Further development is to expand format structure to represent associated data, in particular using RDF.
Integration of the Helpdesk System with Messaging Service: A Case Study Approach

As IT organization handles information systems and technology receives many services request in the field of operational or implementation of systems and information technology, they had to deal with these problems: a simple ticketing service, IT management mechanism in order to provide services to users of the system, to boost the information service which enabled the process with positive influences. This paper proposes a service desk solution integrated with Messaging services, using Short Messaging Services (SMS) and a push notification application. Here we use the WhatsApp application as a notification media to assist the design of the helpdesk. Analysis and adjustment were made to the ITSM concept using the ITIL V4 framework at the Service Management Practices stage. The results of this study are implementing the ITIL V4 framework to get better helpdesk services and utilizing the Messaging services to increase the promptness of sending tickets.
Distributed Ledgers for Distributed Edge

Edge computing has received significant attention from both academic and industrial research circles. The paradigm aims to decentralize the existing cloud infrastructure by incorporating resources co-located alongside its client. Researchers have also proposed solutions for a fully decentralized crowdsourced compute paradigm enabled by Distributed Ledger Technologies (DLTs). This paper investigates the rationale behind DLTs over crowdsourced resource marketplaces to support the requirements of latency-critical applications targeted by edge computing. We develop a fully configurable NEtworked Blockchain emULAtor, or NEBULA, to scrutinize the internal performance bottlenecks of DLTs. We evaluate two blockchain categories - proof-based (popularly used in Bitcoin, Ethereum) and hybrid consensus and find that the enabling factor of DLTs -scale - is also its primary latency contributor. We show that, in reality, the latency overheads due to DLT operation far exceed the operational requirements of edge applications.
Scalable Quasi-Bayesian Inference for Instrumental Variable Regression

Recent years have witnessed an upsurge of interest in employing flexible machine learning models for instrumental variable (IV) regression, but the development of uncertainty quantification methodology is still lacking. In this work we present a scalable quasi-Bayesian procedure for IV regression, building upon the recently developed kernelized IV models. Contrary to Bayesian modeling for IV, our approach does not require additional assumptions on the data generating process, and leads to a scalable approximate inference algorithm with time cost comparable to the corresponding point estimation methods. Our algorithm can be further extended to work with neural network models. We analyze the theoretical properties of the proposed quasi-posterior, and demonstrate through empirical evaluation the competitive performance of our method.
Museum, School and Augmented Reality, a Way to Preserve the Ethnic Identity of an Ancestral Culture

Museums are an inexhaustible source of knowledge of past and present cultures; therefore, they can be considered as intangible heritage of humanity, and through them, the cultural identity of a region is strengthened. This research sought to visualize the archaeological pieces of the Zenú Ethnic through augmented reality with support of a web platform to enrich the experience of visitors to the museum and, thus, have an experience of meeting their ancestral or ethnic roots, for thus preserve the culture of an extinct people. The type of study was ethnographic in depth. The project resorted to historical sources that safeguard the information of the Zenú ethnic group, as well as to the knowledge of the indigenous reservations to reconstruct ancestral knowledge about the rhythms, beliefs and the bases of their goldsmith's work. From the software engineering, the process model based on prototypes was applied for the construction of the web platform that allowed the development of a model of the product to verify its functionality and obtain an evaluation to make improvements to it. The research was carried out in four phases: characterization of archaeological objects; modelling of archaeological objects; design and evaluation of the web platform. The project concludes with the design of quick response codes, tagged augmented reality tags and evaluation of the web platform using the web content accessibility guidelines specified from the World Wide Web consortium and application of a metric for an integral analysis.
Secure Digitization of Land Record using Blockchain Technology in India

The traditional way of selling and buying land has lots of problems exists as its takes time, the verification process is lengthy and final processing is also time-consuming. With the development of Information and Communication Technologies (ICTs), the records are converted from file to digital. Still, there are several challenges are there to make the land management system effective and trustworthy. During the buying or selling process, different middlemen exist at various levels which makes the process complex and risky. There is a chance of duplication or forge of digital documents by the fraudulent person. To eliminate the above challenges in this paper Blockchain-based approach is applied to make the land record management system secure. The paper initially described the overall challenges that exist in the land record system in the India scenario. The authors in this paper proposed a Secure distributed architecture for land record management. Ethereum platform is used for the implementation of the land record digitization. The results analysis show that the system becomes faster, transparent, records are immutable by the use of Blockchain technology.
Infrared Image Colorization Network using Variational AutoEncoder

This paper presents a novel method for colorizing the near-infrared (NIR) images. Recent deep learning based approaches for NIR colorization utilized an auto-encoder structure to map a single channel to the color domain. However, these methods only provide latent codes of a NIR image using an encoder structure, which simply vectorizes an input image to a latent vector. In this vectorization, all semantic information is mutually considered although each latent code represents independent information. To tackle this issue, we propose a NIR colorization network using variational auto-encoder. This network encodes a NIR image into latent codes with probabilistic distributions. Moreover, we embed a novel correlation module to interactively consider luminance and chrominance features. It facilitates the proposed network to generate better textures and color information. Our model achieves comparable performances on the large-scale dataset: video surveillance in a day (VSIAD).