Reviewerly/exHarmony huggingface dataset:
CogKGE: A Knowledge Graph Embedding Toolkit and Benchmark for Representing Multi-source and Heterogeneous Knowledge

In this paper, we propose , a knowledge graph embedding (KGE) toolkit, which aims to represent the multi-source and heterogeneous knowledge.For multi-source knowledge, unlike existing methods that mainly focus on entity-centric world knowledge, CogKGE also supports the representations of eventcentric world knowledge, commonsense knowledge and linguistic knowledge.For heterogeneous knowledge, besides structured triple facts, CogKGE leverages additional unstructured information, such as text descriptions, node types and temporal information, to enhance the meaning of embeddings.Moreover, CogKGE aims to provide a unified programming framework for KGE tasks and a series of knowledge representations for downstream tasks.As a research framework, CogKGE consists of five parts, including core, data, model, knowledge and adapter module.As a knowledge discovery toolkit, CogKGE provides pretrained embedders to discover new facts, cluster entities and check facts.Furthermore, we construct two new benchmark datasets for further research on multi-source heterogeneous KGE tasks: EventKG240K and CogNet360K.We also release an online system 1 to discover knowledge visually.Source code, datasets and pre-trained embeddings are publicly available at GitHub 2 , with a short instruction video 3 .
My cyber self: identity presentation on TikTok

TikTok is a social network that has provided its users with a unique combination of communicative tools and, as a result, has laid the foundation for a new communication context for self-expression and identity presentation. The article examines how TikTokers' discourse, with the help of the app's technology, uses language means and other semiotic systems to present their self, to construct their online identities and to attract like-minded audiences. The study is based on 45 TikTok videos uploaded by different bloggers who were selected with the help of continuous sampling. The videos are analyzed by applying Critical Discourse Analysis and, in particular, Multimodal Discourse Analysis as frameworks for critical examination of verbal and nonverbal (images, soundtracks, clothes, etc.) means as well as contextual and intertextual structures of the TikTokers' texts aimed at online self-expression and identify-presentation. The research reveals that the TikTokers avail themselves with syntactic patterns typical of self-presentation in order to assert their preferred selves and construct their identities by purposefully breaking away from conventional master identities, e.g., they claim either to be sex-neutral, or to be able to combine stereotypically incompatible identities, or to deny having any identity at all. To reflect on their selves and convey their message to the audience, the TikTokers use texts that feature intertextuality and multimodality. The merge of multimodal semiotic means helps the bloggers to create a complex of techno-semio-linguistic materiality typical of social media. The bloggers succeed in combining these diverse means so as to develop the intended meaning, to mediate their identities to their audience and, finally, to create their own communities rallied around the constructed identities.
Overview of applications of heralded single photon source in quantum key distribution

In this paper, we mainly introduce the preparation and physical properties of the heralded single-photon source, the development history and its applications in three typical quantum key distribution protocols, including BB84, measurement-device-independent and twin-field quantum key distribution protocols. Moreover, we make comparisons of the above quantum key distribution protocols between using heralded single-photon source and using weak coherent sources, and analyze their advantages and disadvantages. Besides, according to the characteristics of single-photon interference in twin-field quantum key distributions, the limitations of separately applying heralded single-photon sources to twin-field quantum key distributions are revealed, and possible solutions are discussed. Therefore, this work may provide valuable references and help for the practical implementation of quantum secure communication in the near future.
Utilizing Cross-Modal Contrastive Learning to Improve Item Categorization BERT Model

Item categorization (IC) is a core natural language processing (NLP) task in e-commerce.As a special text classification task, fine-tuning pre-trained models, e.g., BERT, has become a main stream solution.To improve IC performance further, other product metadata, e.g., product images, have been used.Although multimodal IC (MIC) systems show higher performance, expanding from processing text to more resource-demanding images brings large engineering impacts and hinders the deployment of such dual-input MIC systems.In this paper, we proposed a new way of using product images to improve text-only IC model: leveraging crossmodal signals between products' titles and associated images to adapt BERT models in a self-supervised learning (SSL) way.Our experiments on the three genres in the public Amazon product dataset show that the proposed method generates improved prediction accuracy and macro-F1 values than simply using the original BERT.Moreover, the proposed method is able to keep using existing text-only IC inference implementation and shows a resource advantage than the deployment of a dual-input MIC system.
Cold-started Curriculum Learning for Task-oriented Dialogue Policy

Training a satisfactory dialogue policy via Reinforcement Learning (RL) requires significant interaction costs because of delayed and sparse rewards in task-oriented dialogue tasks. Researching how to conduct efficient dialogue policy learning in environments where rewards are delayed and sparse is essential. Existing approaches obtain more positive rewards by incorporating an RL-based teacher model to customize a curriculum that matches the ability of dialogue policies for curriculum learning. However, such a teacher model still retains the disadvantage of reinforcement learning has expensive training costs. Therefore, we develop a novel framework, cold-start curriculum learning (CCL), for task-oriented dialogue policy learning that does not require any training cost for curriculum schedule. Besides, it adaptively adjusts the difficulty of the user goals and selects the next goal based on the feedback of students. Experiments show that the CCL significantly improves the effectiveness of dialogue tasks without any training cost for curriculum schedule.
An Improved Cuckoo Search Algorithm With Stud Crossover for Chinese TSP Problem

The travelling salesman problem (TSP) is an NP-hard problem in combinatorial optimization. It has assumed significance in operations research and theoretical computer science. The problem was first formulated in 1930 and since then, has been one of the most extensively studied problems in optimization. In fact, it is used as a benchmark for many optimization methods. This paper represents a new method to addressing TSP using an improved version of cuckoo search (CS) with Stud (SCS) crossover operator. In SCS method, similar to genetic operators used in various metaheuristic algorithms, a Stud crossover operator that is originated from classical Stud genetic algorithm, is introduced into the CS with the aim of improving its effectiveness and reliability while dealing with TSP. Various test functions had been used to test this approach, and used subsequently to find the shortest path for Chinese TSP (CTSP). Experimental results presented clearly demonstrates SCS as a viable and attractive addition to the portfolio of swarm intelligence techniques.
Incorporating External Knowledge Reasoning for Vision-and-Language Navigation with Assistant’s Help

Vision-and-Language Navigation (VLN) is a task designed to enable embodied agents carry out natural language instructions in realistic environments. Most VLN tasks, however, are guided by an elaborate set of instructions that is depicted step-by-step. This approach deviates from real-world problems in which humans only describe the object and its surroundings and allow the robot to ask for help when required. Vision-based Navigation with Language-based Assistance (VNLA) is a recently proposed task that requires an agent to navigate and find a target object according to a high-level language instruction. Due to the lack of step-by-step navigation guidance, the key to VNLA is to conduct goal-oriented exploration. In this paper, we design an Attention-based Knowledge-enabled Cross-modality Reasoning with Assistant’s Help (AKCR-AH) model to address the unique challenges of this task. AKCR-AH learns a generalized navigation strategy from three new perspectives: (1) external commonsense knowledge is incorporated into visual relational reasoning, so as to take proper action at each viewpoint by learning the internal–external correlations among object- and room-entities; (2) a simulated human assistant is introduced in the environment, who provides direct intervention assistance when required; (3) a memory-based Transformer architecture is adopted as the policy framework to make full use of the history clues stored in memory tokens for exploration. Extensive experiments demonstrate the effectiveness of our method compared with other baselines.
Time-Weighted Community Search Based on Interest

Community search aims to provide users with personalized community query services. It is a prerequisite for various recommendation systems and has received widespread attention from academia and industry. The existing literature has established various community search models and algorithms from different dimensions of social networks. Unfortunately, they only judge the representative attributes of users according to the frequency of attribute keywords, completely ignoring the temporal characteristics of keywords. It is clear that a user’s interest changes over time, so it is essential to select users’ representative attributes in combination with time. Therefore, we propose a time-weighted community search model (TWC) based on user interests which fully considers the impact of time on user interests. TWC reduces the number of query parameters as much as possible and improves the usability of the model. We design the time-weighted decay function of the attribute. We then extract the user’s time-weighted representative attributes to express the user’s short-term interests more clearly in the query window. In addition, we propose a new attribute similarity scoring function and a community scoring function. To solve the TWC problem, we design and implement the Local Extend algorithm and the Shrink algorithm. Finally, we conduct extensive experiments on a real dataset to verify the superiority of the TWC model and the efficiency of the proposed algorithm.
Prediction of optimum tilt angle of Flat-plate solar collector in Abia State Nigeria

Most solar collectors in Nigeria are mounted without consideration of optimum tilt angle for maximum solar radiation. To address the gap, a flat plate surface solar collector of area 0.15 m2, hinged on horizontal support for quick adjustment of inclination between 0 to 90o was fabricated marked out at 1o intervals on a telescopic leg graduated in degrees. Measurement of the solar radiation at varying degrees of inclination was taken between 12:00 noon and 2.00 pm for 4 days at clear sky hours, within the week of nth day of the year. The measurements were taken for each month of the year at Michael Okpara University of Agriculture, Umudike. Umuahia, Abia State, Nigeria, at each degree of inclination. The result showed that the optimum angle of inclination of a flat plate for maximum collection of solar radiation intensities for January to December were 9.5, 9.1, 3.0, 6.4, 9.9, 11.8, 9.0, 7.3, 3.1, 7.0, 10.2, and 11.8o respectively. The study also revealed that the average angle of inclination at which a flat surface solar collector can be mounted at a fixed position in Umudike is 8.14o. The analysis indicated that solar energy gain of 123,314.79 MJm-2, 39,041.24 MJm-2, and 25,610.84 MJm-2 for horizontal, Latitude angle of location and calculated fixed angle of inclination of the solar collector respectively. Comparison of the measured and calculated optimum values of angle of inclination of a flat plate surface for trapping maximum solar radiation intensity indicated a high correlation ( R2 ) of 0.97.
External validation of an AI-driven breast cancer risk prediction model in a racially diverse cohort of women undergoing mammographic screening

The aim of this retrospective case-cohort study was to perform additional validation of an artificial intelligence (AI)-driven breast cancer risk model in a racially diverse cohort of women undergoing screening. We included 176 breast cancer cases with non-actionable mammographic screening exams 3 months to 2 years prior to cancer diagnosis and a random sample of 4,963 controls from women with non-actionable mammographic screening exams and at least one-year of negative follow-up (Hospital University Pennsylvania, PA, USA; 9/1/2010-1/6/2015). A risk score for each woman was extracted from full-field digital mammography (FFDM) images via an AI risk prediction model, previously developed and validated in a Swedish screening cohort. The performance of the AI risk model was assessed via age-adjusted area under the ROC curve (AUC) for the entire cohort, as well as for the two largest racial subgroups (White and Black). The performance of the Gail 5-year risk model was also evaluated for comparison purposes. The AI risk model demonstrated an AUC for all women = 0.68 95% CIs [0.64, 0.72]; for White = 0.67 [0.61, 0.72]; for Black = 0.70 [0.65, 0.76]. The AI risk model significantly outperformed the Gail risk model for all women (AUC = 0.68 vs AUC = 0.55, p&lt;0.01) and for Black women (AUC = 0.71 vs AUC = 0.48, p&lt;0.01), but not for White women (AUC = 0.66 vs AUC = 0.61, p=0.38). Preliminary findings in an independent dataset suggest a promising performance of the AI risk prediction model in a racially diverse breast cancer screening cohort.
Int2IT: An Intent-based TOSCA IT Infrastructure Management Platform

The introduction and widespread adoption of cloud computing has opened the door to the possibility of designing and building large scale systems with tremendous raw computing capabilities for dealing with an ever-increasing volume of data. However, the infrastructure required to support these systems became too complex for manual efforts, so practices such as Infrastructure as Code (IaC) and Development and Operations (DevOps) methodologies with programmatic orchestration and provisioning of cloud infrastructures became increasingly common. This led to the creation of the OASIS Topology and Orchestration Specification for Cloud Applications (TOSCA) standard, which has seen extensive research by academia but infrequent adoption by the industry, as, despite simplifying partly the management of infrastructures, lacks the capacity of encapsulating the desired behaviour of the system and its business-level end-goals. The system only changes its configuration through manual intervention, which is a major source of error, or through pre-defined policies that instruct actions when a specific trigger occurs. Int2IT is presented here as a proof-of-concept solution to this specific problem, as an "intent-based" infrastructure management platform that incorporates autonomic computing concepts in order to manage cloud deployments autonomously, using a TOSCA-based cloud application description. The proposed solution will be able to capture the user’s "intents", which describe the system’s end-goals, and translate them into a TOSCA-based cloud application. As a result, it can then be deployed to the cloud and autonomously managed, by utilizing a Monitor-Analyze-Plan-Execute over a shared Knowledge (MAPE-K) autonomic deployment life-cycle capable of adapting to the outside environment, ensuring that the end-goals are met to the greatest extent possible.
Gambling for Success: The Lottery Ticket Hypothesis in Deep Learning-Based Side-Channel Analysis

Deep learning-based side-channel analysis (SCA) represents a strong approach for profiling attacks. Still, this does not mean it is trivial to find neural networks that perform well for any setting. Based on the developed neural network architectures, we can distinguish between small neural networks that are easier to tune and less prone to overfitting but could have insufficient capacity to model the data. On the other hand, large neural networks have sufficient capacity but can overfit and are more difficult to tune. This brings an interesting trade-off between simplicity and performance. This work proposes to use a pruning strategy and recently proposed Lottery Ticket Hypothesis (LTH) as an efficient method to tune deep neural networks for profiling SCA. Pruning provides a regularization effect on deep neural networks and reduces the overfitting posed by overparameterized models. We demonstrate that we can find pruned neural networks that perform on the level of larger networks, where we manage to reduce the number of weights by more than 90% on average. This way, pruning and LTH approaches become alternatives to costly and difficult hyperparameter tuning in profiling SCA. Our analysis is conducted over different masked AES datasets and for different neural network topologies. Our results indicate that pruning, and more specifically LTH, can result in competitive deep learning models.
The Usage of Augmented Reality Technology in the Educational Process of Higher Education Institutions

The development of additional reality (AR-technology) and its application in the educational process is considered. Definitions of AR technology are given, its advantages are given, as well as the limitations associated with its use in the educational process. An overview of platforms for creating AR-applications is given, their functional capabilities are considered, the expediency and efficiency of application in the educational process of free economic zones are assessed. Aspects of the use of augmented reality technology in the higher education system are considered. One of the most important features of augmented reality in terms of pedagogy is that it provides a space focused on the student and flexible for learning opportunities. The advantages and limitations of using this technology are given. The general scheme for creating augmented reality is always as follows: the camera of the AR device captures an image of a real object; the software (software) of the device identifies the received image, selects or calculates the corresponding image visual complement, combines the real image with its complement and outputs the final image to the visualization device. To work with AR, it is enough to use a smartphone or tablet and the appropriate software. There are the following main areas of augmented reality technology in the education system: support for research and experimental approach; checking the model for adequacy; modeling environments that combine the possibilities of teaching, learning, communication with game elements; acquisition of technological skills. AR-technology has the following potential pedagogical advantages: accessibility, involvement, cooperation, interactivity. Despite these benefits, there are certain factors that need to be considered when working with educational technologies using AR. Such factors include: lack of necessary teacher training; dependence on hardware (not all students may have smartphones that support AR applications); content mobility issues across all platforms and devices. Directions and examples of application of augmented reality technology in the educational environment are offered.
Entanglement quantification enhanced by dark count correction

In this letter, we propose a method of dark count correction in quantum state tomography of entangled photon pairs. The framework is based on a linear model of dark counts, which is imposed on the measurement formalism. The method is tested on empirical data derived from an experiment on polarization-entangled photons. We demonstrate that the numerical approach to dark count correction guarantees more reliable state reconstruction compared with standard estimation. Most importantly, however, the conceptually simple theoretical approach proves to be more efficient at entanglement quantification than experimental techniques.
Blockchain Consensus Mechanism for Distributed Energy Transactions

In order to reduce the cost of grid dispatching and increase the transparency of energy transactions, the distributed energy transaction model based on blockchain is constructed. At the same time, in order to improve the high communication overhead and low throughput of the traditional PBFT algorithm in the consortium blockchain, an efficient Byzantine fault-tolerant consensus mechanism (DE-BFT) for the energy blockchain is designed. The algorithm improves from two aspect: node election and main chain consensus. In the stage of node election, the model uses a health score evaluation and a verifiable random function to improve the security and randomness of node selection. In the stage of main chain consensus, the efficient data consistency interaction protocol decreases the complexity of the communications between nodes, down to a constant term level from exponential one. The result shows that, compared with other consensus algorithm, the DE-BFT algorithm performs better in terms of consensus delay, communication overhead, throughput, and consensus node reliability.
Analytical Review of Methods for Solving Data Scarcity Issues Regarding Elaboration of Automatic Speech Recognition Systems for Low-Resource Languages

In this paper, principal methods for solving training data issues for the so-called low-resource languages are discussed, regarding elaboration of automatic speech recognition systems. The notion of low-resource languages is studied and a working definition is coined on the basis of a number of papers on this topic. The main difficulties associated with the application of classical approaches to automatic speech recognition to the material of low-resource languages are determined, and the principal methods used to solve these problems are outlined. The paper discusses the methods for data augmentation, transfer learning and collection of new language data in detail. Depending on specific tasks, methods for audio material and text data augmentation, transfer learning and multi-task learning are distinguished. In Section 4 of the paper the current information support methods, databases and the basic principles of their architecture are discussed with regard to low-resource languages. Conclusions are drawn about the justification of augmentation and knowledge transfer methods for languages with low information support. In the case of unavailability of language data or structurally similar parent models, the preferred option is to collect a new database, including the crowdsourcing technique. Multilanguage learning models are effective for small datasets. If big language data are available, the most efficient method is transfer learning within a language pair. The conclusions made in the course of this this review will be applied to the data of the low-resource Karelian language, for which an automatic speech recognition system has been being created by the authors of this paper since the beginning of the year 2022.