Reviewerly/exHarmony huggingface dataset:
Smart Contract-Based Refrigerated Container Traceability System

A large number of refrigerated container are moved everyday domestically and internationally.The information of refrigerated container is usually stored centrally. It is prone to being tampered by the responsible party, making it difficult to call to account. Due to its characteristics of decentralization, transparency and immutability, blockchain technology can be used to alleviate this problem. In this paper, we propose a blockchain-based refrigerated container traceability system that can trace the information of refrigerated container. We also design a smart contract to realize the information upload and query of refrigerated containers. The experimental results show that the blockchain traceability system and smart contract proposed can effectively monitor and trace refrigerated containers. It guarantees the integrity and reliability of traceability information.
Pronunciation-Based Language Processing Tool for Tai Ahom

Words with similar pronunciation may not be of same in its written form. Such similarities can be measured by means of computer algorithms. For English language, an algorithm is available, which may be utilized as a database function “Soundex”. This algorithm returns codes on the base of pronunciation of words. Same code signifies similar pronunciation, and dissimilar code indicates different pronunciation. This paper focuses on development of Soundex for Tai Ahom language. In some cases, a single word carries several different meanings. For such matters, exact meaning may be guessed on the basis of other words in the sentence. For this purpose, we have focused on developing another algorithm backed by sentiment analysis.
3rd Workshop on Patent Text Mining and Semantic Technologies (PatentSemTech2022)

Steadily increasing numbers of patent applications per year and large amounts of available patent data necessitate highly efficient and interactive next-generation information retrieval systems in the patent domain. AI and Machine Learning (ML) methods such as Deep Learning (DL) are successfully adopted in many domains, so patent researchers and practitioners start to employ AI-based approaches as well, to support experts in the patenting process or to automate patent analysis and retrieval processes. AI-enhanced Information Retrieval systems can improve patent search and analysis but also require millions of annotated sample data for training the ML models. When working with patent data, particular challenges arise that call for adaption of existing IR and AI methods as well as development of novel approaches suited for the patent domain. The focus of the 3rd edition of this workshop will be on two-way communication between industry and academia from all areas of Information Retrieval, such as Natural Language Processing (NLP), Text and Data Mining (TDM), and Semantic Technologies (ST). We want to bring together novel research results and the latest systems and methods employed by the Intellectual Property (IP) industry.
PTMOT: A Probabilistic Multiple Object Tracker Enhanced by Tracklet Confidence for Autonomous Driving

Abstract Real driving scenarios, due to occlusions and disturbances, provide disordered and noisy measurements, which makes the task of multi-object tracking quite challenging. Conventional approach is to find deterministic data association; however, it has unstable performance in high clutter density. This paper proposes a novel probabilistic tracklet-enhanced multiple object tracker (PTMOT), which integrates Poisson multi-Bernoulli mixture (PMBM) filter with confidence of tracklets. The proposed method is able to realize efficient and robust probabilistic association for 3D multi-object tracking (MOT) and improve the PMBM filter’s continuity by smoothing single target hypothesis with global hypothesis. It consists of two key parts. First, the PMBM tracker based on sets of tracklets is implemented to realize probabilistic fusion of disordered measurements. Second, the confidence of tracklets is smoothed through a smoothing-while-filtering approach. Extensive MOT tests on nuScenes tracking dataset demonstrate that the proposed method achieves superior performance in different modalities.
Joint Optimization of Ad Ranking and Creative Selection

In e-commerce, ad creatives play an important role in effectively delivering product information to users. The purpose of online creative selection is to learn users' preferences for ad creatives, and to select the most appealing design for users to maximize Click-Through Rate (CTR). However, the existing common practices in the industry usually place the creative selection after the ad ranking stage, and thus the optimal creative fails to reflect the influence on the ad ranking stage. To address these issues, we propose a novel Cascade Architecture of Creative Selection (CACS), which is built before the ranking stage to joint optimization of intra-ad creative selection and inter-ad ranking. To improve the efficiency, we design a classic two-tower structure and allow creative embeddings of the creative selection stage to share with the ranking stage. To boost the effectiveness, on the one hand, we propose a soft label list-wise ranking distillation method to distill the ranking knowledge from the ranking stage to guide CACS learning; and on the other hand, we also design an adaptive dropout network to encourage the model to probabilistically ignore ID features in favor of content features to learn multi-modal representations of the creative. Most of all, the ranking model obtains the optimal creative information of each ad from our CACS, and uses all available features to improve the performance of the ranking model. We have launched our solution in Taobao advertising platform and have obtained significant improvements both in offline and online evaluations.
Analisi <i>corpus-based</i> e didattica della lingua italiana. I risultati di una sperimentazione di linguistica educativa

Corpus linguistics has developed significantly as a branch of applied linguistics in recent times, in relation to the possibilities of employment in research both in linguistics and in related disciplines. This field of study, furthermore, aims at assaying and describing the salient characteristics of a language by questioning collected and stored data on computer support, in the most applicable way. Considering the proposed definition, it was decided to use the methodology of collection, archiving, and interrogation, specific of corpus-based analysis, within the language educational practices for teaching Italian as L1 and L2. For the purposes of experimentation, a working group of 30 Italian teachers was set up that was aimed at students from different educational orders, and specific training was proposed, within a course organised by Centro Interculturale della Città di Torino. At the end of the course, the teaching staff were able to use the operative modalities of the corpus linguistics with regard to the collection and analysis of the linguistic productions of the students, questioning the corpora with respect to the linguistic uses. In conclusion, the corpora were used as a tool for linguistic analysis and to suggest interventions on error analysis, proving effective both for teachers and for learners. Actually, all the linguistic productions have been used as a basis for the linguistic research proposed to the learners. Teachers were offered a questionnaire about experimentation and the results were positive, as these showed how corpus linguistics can contribute to a significant awareness of learners about the language and learning goals.
Application of Entropy Hierarchy Analysis and Deep Neural Network Algorithm Combination in Enterprise Economic Management

The efficiency and effect of enterprise economic management activities depend on whether the performance evaluation can be implemented and the evaluation results can effectively feedback and improve the enterprise knowledge management process. Allocate enterprise resources to create more value. The evaluation of enterprise economic management activities depends heavily on the weights of different evaluation indicators of enterprises. In order to solve this problem, this paper proposes an evaluation method that integrates entropy weight analytic hierarchy process and deep neural network algorithm. Through objective entropy weight and imitation, the deep neural network fusion of subjective judgment of experts combines the subjective and objective factors of the evaluation index, then uses the AHP method as the evaluation index of performance evaluation according to the index weight, and finally realizes the accurate evaluation of enterprise economic management.
Expression Syntax Information Bottleneck for Math Word Problems

Math Word Problems (MWP) aims to automatically solve mathematical questions given in texts. Previous studies tend to design complex models to capture additional information in the original text so as to enable the model to gain more comprehensive features. In this paper, we turn our attention in the opposite direction, and work on how to discard redundant features containing spurious correlations for MWP. To this end, we design an Expression Syntax Information Bottleneck method for MWP (called ESIB) based on variational information bottleneck, which extracts essential features of the expression syntax tree while filtering latent-specific redundancy containing syntax-irrelevant features. The key idea of ESIB is to encourage multiple models to predict the same expression syntax tree for different problem representations of the same problem by mutual learning so as to capture consistent information of expression syntax tree and discard latent-specific redundancy. To improve the generalization ability of the model and generate more diverse expressions, we design a self-distillation loss to encourage the model to rely more on the expression syntax information in the latent space. Experimental results on two large-scale benchmarks show that our model not only achieves state-of-the-art results but also generates more diverse solutions.
Application of the permutation method to the assessment of predictive ability of the models of spatial distribution of copper and iron concentrations in the topsoil

The article proposes the use of the permutation method for assessment of the predictive ability of models based on artificial neural networks. To test this method, three models based on artificial neural networks were implemented: a multilayer perceptron, a radial basis function network, and a generalized regression neural network. For modeling, data on the spatial distribution of copper and iron in the topsoil (depth 0.05 m) on the territory of the subarctic city of Noyabrsk, Yamalo-Nenets Autonomous Okrug, Russia, were used. A total of 237 soil samples were collected. For modelling, the copper and iron concentration data were divided into two subsets: training and test. The modelled spatial datasets were compared with the observed values of the test subset. To assess the performance of the constructed models, three approaches were used: 1) calculation of correlation coefficients, error or agreement indexes, 2) graphical approach (Taylor diagram), 3) randomization assessment of the probability of obtaining a divergence between the observed and modelled datasets, assuming that both of these datasets taken from the same population. For the randomization algorithm, two statistics were used: difference in means and correlation coefficient. The permutation method proved its productivity, as it allowed to assess the significance of the divergence between the observed and predicted datasets.
Tuxedo

Proof-of-Work~(PoW) based blockchains typically allocate only a tiny fraction (e.g., less than 1% for Ethereum) of the average interarrival time~$\mathbbI $ between blocks for validating smart contracts present in transactions. In such systems, block validation and PoW mining are typically performed sequentially, the former by CPUs and the latter by ASICs. A trivial increase in validation time~$(τ)$ introduces the popularly known Verifier's Dilemma, and as we demonstrate, causes more forking and hurts fairness. Large τ also reduces the tolerance for safety against a Byzantine adversary. Solutions that offload validation to a set of non-chain nodes (a.k.a. off-chain approaches) suffer from trust and performance issues that are non-trivial to resolve. In this paper, we present Tuxedo, the first on-chain protocol to theoretically scale τ/\mathbbI \approx 1$ in PoW blockchains. The key innovation in Tuxedo is to perform CPU-based block processing in \em parallel to ASIC mining. We achieve this by allowing miners to delay validation of transactions in a block by up to ζ blocks, where ζ is a system parameter. We perform security analysis of Tuxedo considering all possible adversarial strategies in a synchronous network with maximum end-to-end delay Δ and demonstrate that Tuxedo achieves security equivalent to known results for longest chain PoW Nakamoto consensus. Our prototype implementation of Tuxedo atop Ethereum demonstrates that it can scale τ without suffering the harmful effects of naïve scaling up of τ/\mathbbI $ in existing blockchains.
Fusing Speed Index during Web Page Loading

With conventional web page load metrics (e.g., Page Load Time) being blamed for deviating from actual user experiences, in recent years a more sensible and complex metric called Speed Index (SI) has been widely adopted to measure the user's quality of experience (QoE). In brief, SI indicates how quickly a page is filled up with above-the-fold visible elements (or crucial elements for short). To date, however, SI has been used as a metric for performance evaluation, rather than as an explicit heuristic to improve page loading. To demystify this, we examine the entire loading process of various pages and ascribe such incapability to three-fold fundamental uncertainties in terms of network, browser execution, and viewport size. In this paper, we design SipLoader, an SI-oriented page load scheduler through a novel cumulative reactive scheduling framework. It does not attempt to deal with uncertainties in advance or in one shot, but schedules page loading by "repairing" the anticipated (nearly) SI-optimal scheduling when uncertainties actually occur. This is achieved with a suite of efficient designs that fully exploit the cumulative nature of SI calculation. Evaluations show that SipLoader improves the median SI by 41%, and provides 1.43 times to 1.99 times more benefits than state-of-the-art solutions.
Publication and maintenance of RDB2RDF views externally materialized in enterprise knowledge graphs

Purpose Enterprise knowledge graphs (EKG) in resource description framework (RDF) consolidate and semantically integrate heterogeneous data sources into a comprehensive dataspace. However, to make an external relational data source accessible through an EKG, an RDF view of the underlying relational database, called an RDB2RDF view, must be created. The RDB2RDF view should be materialized in situations where live access to the data source is not possible, or the data source imposes restrictions on the type of query forms and the number of results. In this case, a mechanism for maintaining the materialized view data up-to-date is also required. The purpose of this paper is to address the problem of the efficient maintenance of externally materialized RDB2RDF views. Design/methodology/approach This paper proposes a formal framework for the incremental maintenance of externally materialized RDB2RDF views, in which the server computes and publishes changesets, indicating the difference between the two states of the view. The EKG system can then download the changesets and synchronize the externally materialized view. The changesets are computed based solely on the update and the source database state and require no access to the content of the view. Findings The central result of this paper shows that changesets computed according to the formal framework correctly maintain the externally materialized RDB2RDF view. The experiments indicate that the proposed strategy supports live synchronization of large RDB2RDF views and that the time taken to compute the changesets with the proposed approach was almost three orders of magnitude smaller than partial rematerialization and three orders of magnitude smaller than full rematerialization. Originality/value The main idea that differentiates the proposed approach from previous work on incremental view maintenance is to explore the object-preserving property of typical RDB2RDF views so that the solution can deal with views with duplicates. The algorithms for the incremental maintenance of relational views with duplicates published in the literature require querying the materialized view data to precisely compute the changesets. By contrast, the approach proposed in this paper requires no access to view data. This is important when the view is maintained externally, because accessing a remote data source may be too slow.
Cross-media retrieval of scientific and technological information based on multi-feature fusion

In the era of big data, People’s lives are filled with all kinds of information. Scientific and technological information is utilized for scholars to understand the current technology trends, and to think about the source of information for future development prospects. More and more scholars are no longer satisfied with single-modal retrieval methods. However, to get more intelligent cross-media retrieval results we should give higher requirements to the search engine. And how to span the semantic gap between different modalities is a key issue that needs to be solved. In response to the above problems, this paper proposes a Multi-feature Fusion based Cross-Media Retrieval (MFCMR) method. Our method is capable of integrating multiple features to promote semantic understanding, and adopting adversarial learning to further improve the accuracy of public subspace representation. Then we use similarity in the same space to sort the retrieval results. We conduct a lot of experiments on real datasets, and the results show that our method obtains better cross-media retrieval performance than other methods.
RDeF

Traditional database courses mainly focus on managing relational databases, but understanding NoSQL databases is becoming more relevant to computer science students. This also applies to Linked Data with the standards RDF and SPARQL where training material is scarce. Scarcity can be countered by involving students in the creation process. This paper introduces RDeF --- an open-source prosumer framework where students can consume and produce interactive stories to train their RDF and SPARQL skills. Stories are composed of scenes that are created with RDF triples from templates. Within stories, players must identify hidden RDF triples to build the knowledge base and query it by executing SPARQL requests to answer questions.
Novel Adaptive DNN Partitioning Method Based on Image-Stream Pipeline Inference between the Edge and Cloud

The cloud-only and edge-computing approaches have recently been proposed to satisfy the requirements of complex neural networks. However, the cloud-only approach generates a latency challenge because of the high data volumes that must be sent to a centralized location in the cloud. Less-powerful edge computing resources require a compression model for computation reduction, which degrades the model trading accuracy. To address this challenge, deep neural network (DNN) partitioning has become a recent trend, with DNN models being sliced into head and tail portions executed at the mobile edge devices and cloud server, respectively. We propose Edgepipe, a novel partitioning method based on pipeline inference with an image stream to automatically partition DNN computation between the edge device and cloud server, thereby reducing the global latency and enhancing the system-wide real-time performance. This method adapts to various DNN architectures, hardware platforms, and networks. Here, when evaluated on a suite of five DNN applications, Edgepipe achieves average latency speedups of 1.241× and 1.154× over the cloud-only approach and the state-of-the-art approach known as “Neurosurgeon”, respectively.
Seamless Runtime Transformations from Natural Language to Formal Methods – A usecase of Z-Notation

Requirements specification in a crucial activity in Software Development Life Cycle (SDLC). Traditional requirements specification in Natural Language (NL) is error prone activity which may leads to project failure. This scenario is more critical in Systems of Systems (SOS) domain as different stakeholder have different perspectives and requirements. Hence formal requirements specification is a popular domain since many years. This is because formal methods guarantees that the software must meet the standards by clear and unambiguous specification of requirements. However, formal methods are complex and not easy to implement. Therefore, we have proposed a comprehensive automated framework which takes requirements in natural language and provides their seamless transformation in the respective formal language. This paper aims to reduce the overhead of specifying requirements in formal methods and at the same time helps to reap maximum benefits offered by formal methods. We have validated the proposed framework by implementing a popular Z-Notation case study. We have used xtext framework to write a Domain Specific Language (DSL) and provided a luxurious interface for writing requirements in NL and then provided the seamless side-by-side transformation in Z-Notation.
ARC-SoISE: Towards a Reference Architecture for Constituents of Educational Systems-of-Information Systems

Systems of Information Systems (SoIS) are a class of complex systems that involve Information Systems (IS) as their constituents. To become SoIS constituents, systems should meet requirements, such as operational and managerial independence. However, several of the contemporary systems do not satisfy such requirements. In this sense, reference architectures (RA) can encapsulate these requirements and design decisions to guide architects on how to derive/evolve systems towards being able to become SoIS constituents. The educational domain is notably a relevant domain that could benefit from it. Virtual Learning environments (VLE) are often used in learning processes, but they have not been necessarily designed to be a constituent of an Educational SoIS. The main contribution of this paper is introducing a RA named ARC-SoISE (of the term in Portuguese that means Reference Architecture for a Constituent of an Educational SoIS). ARC-SoISE can support architects to derive constituents or evolve systems towards being part of a SoIS. The methodology used to create such a RA was PRoSA-RA, a state-of-the-art process to design RA. We evaluated ARC-SoISE by means of a survey with experts, one of the evaluation methods prescribed by PRoSA-RA. Results reveal that specialists considered ARC-SoISE fully or partially compliant with 92.2% of the essential criteria evaluated for a constituent to be able to contribute to the fulfillment of goals in a SoIS.