Reviewerly/exHarmony huggingface dataset:
Interdisciplinary integration in teaching foreign languages and culture for Master’s Degree Programme of “Economics” and “Management”

We consider the issue of interdisciplinary integration in teaching at Master’s Degree Programme of “Economics” and “Management”. It explains the important role of foreign languages and culture as the central element of the model of interdisciplinary integration at universities nowadays. It further proves that culture is a key to interdisciplinary integration in language learning of future Master’s Degree Student. Currently, a graduate of Master’s Degree Programme implies both being able to speak a modern language and use the knowledge of languages in working with up-to-date communication technologies when tackling professional tasks and collaborating with colleagues from other countries. Modern federal state education standard for Economics and Management require that Master’s Degree Programmes graduates are competent in the aspect of intercultural communication and know a foreign language at a high level. We suggest a Model of interdisciplinary integration in language teaching at Master’s Degree Programme, which includes target, content, organizational-methodic and perfomance-assessment components. This model implements the main language, culture, humane, innovation, environmental, axiological, valeological, acmeological, aesthetic and other orientations; principles integration and differentiation, continuity and consistency of education. The main purpose of interdisciplinary integration in foreing language teaching at Master’s Degree Programme is to form a well-founded, culturally full-fledged person able to speak a foreign language and operate modern communication technologies. Implementation of interdisciplinary integration in language teaching at Master’s Degree Programme demands coordinated actions of language teachers as well as major disciplines educators.
WDV: A Broad Data Verbalisation Dataset Built from Wikidata

Data verbalisation is a task of great importance in the current field of natural language processing, as there is a clear benefit in the transformation of our abundant structured and semi-structured data into human-readable formats. Verbalising Knowledge Graph (KG) data focuses on converting interconnected triple-based claims, formed of subject, predicate, and object, into text. Although KG verbalisation datasets exist for some KGs, there are still limitations in their applicability to many scenarios. This is especially true for Wikidata, where available datasets either loosely couple claim sets with textual information or heavily focus on predicates around biographies, cities, and countries. To address these gaps, we propose WDV, a large KG claim verbalisation dataset built from Wikidata, with a tight coupling between triples and text, covering a wide variety of entities and predicates. We also evaluate the quality of our verbalisations through a reusable workflow for measuring human-centred fluency and adequacy scores. Our data ( https://doi.org/10.6084/m9.figshare.17159045.v1 ) and code ( https://github.com/gabrielmaia7/WDV ) are openly available in the hopes of furthering research towards KG verbalisation.
Thing Ontologies for the Semantic Web of Things

The Web of Things (WoT) Thing Description (TD) of W3C is a JSON template representation of Thing properties (e.g. purpose, data types, and operations). In recent work, we proposed that Things can be treated as Web services, and be described using OpenAPI. OpenAPI specification provides a method for documenting RESTful services so that a user or another service can comprehend their purpose and reuse them in applications. The resulting descriptions by either approach (i.e. W3C and OpenAPI) can be semantically extended using ontologies or can be mapped to an ontology to allow a machine to better understand the inherent meaning of Thing descriptions and interact with them. Then, Things can be discovered (i.e. queried) using Semantic Web query languages (e.g. SPARQL) and checked by reasoners (e.g. Pellet) for consistency or, to infer new properties. In this work, both approaches are discussed and compared in terms of the completeness of their representation.
Deep Learning Generative Indonesian Response Model Chatbot for JKN-KIS

Jaminan Kesehatan Nasional- Kartu Indonesia Sehat (JKN-KIS) is a government program for the Ministry of Health Indonesia. Information of all the participant is the key to the sustainability of the JKN-KIS program. Lack of socialization and reading guidebooks desire are the main reasons why JKN-KIS services do not run properly and has many problem in the community. A question answering system (chatbot) has many benefits because it can automatically responds to questions and help the government with public communication. Unfortunately, most chatbot services today do not offer a completely automated solution. There are lots of chatbots out still static, so the resulting response must comply with certain rules. That chatbot is not scalable and less effective when applied to large JKN-KIS data. However, this research develops a generative model for an automatic question answering system (chatbot) in the Indonesian language with domain Indonesia JKN-KIS. The generative chatbot model is built with the Sequence to Sequence (Seq2Seq) model, encoder and decoder, with the LSTM Multiplicative Attention method. The dataset used is question and answer pairs from JKN-KIS guidebook. The results show that the chatbot able to solved the problem with a large and varied Indonesia dataset without certain rules. With a 20 thousand question and answer conversation pairs dataset, this research get the best chatbot results with the parameter at 6000 iterations for 15 batch sizes and 1000 hidden size architectures. The results show that the resulting loss value is 0.23 with a BLEU Score of unigram 0.86 and a BLEU Score of bigrams 0.85.
Multi-UAV Path Planning Model with Multiple Battery Recharge Points

In recent years, multi-UAV mission scheduling has become a very active research area. In this paper, a scenario is established in which multiple-access battery recharge points are set up to solve the problem of UAV enlistment in the current logistics scenario. The study constructs a mixed integer programming model based on UAV battery replacement as well as UAV cargo demand and UAV enlistment and flight characteristics, and designs solution cases based on the model and solves them using an exact solver, and experiments prove that the model built in this paper has good solution effectiveness and compatibility with the scenario.
Assuring Referential Integrity in Blockchain Applications

Blockchain technology is increasingly used for applications beyond cryptocurrencies and asset management. Taking advantage of its immutability, security, trustworthiness, auditability, reliability, and decentralized authority, these applications store rich data with nuanced internal structures, relationships, and constraints in the blockchain. Popular blockchain platforms provide very limited support for assuring logical data integrity. An important integrity constraint is the foreign key referential integrity (RI) originated from relational databases. This paper is among the first efforts on specifying and assuring RI in blockchain applications by proposing an effective JSON-based model and framework. It generalizes the concept of RI to the JSON object model, which can then be applied to document NoSQL databases as well as blockchain. The paper provides a typed graph model formulation for specifying blockchain assets and RI, and accompanying algorithms for RI assurance. A permeating application example is presented with an implementation using the proposed model in Hyperledger's Fabric, a permissioned blockchain platform.
Kajian Implementasi Building Information Modeling (BIM) di Dunia Konstruksi Indonesia

One of the most common obstacles encountered in the construction industry is the fragmented delivery process and relies on paper-based documentation and communication. To overcome these problems a good integration system is needed. Building Information Modeling (BIM) is a construction technology innovation that can help stakeholders to collaborate, visualize, and manage construction work better. However, the adoption rate of BIM in Indonesia is still behind compared to other countries. This research was conducted to gather information about the BIM adoption experience and the challenges and benefits faced by construction actors in Indonesia. This study uses the literature review method for data collection. Based on the results of the study it was found that in general the level of adoption of BIM by construction actors in Indonesia is still low, the main challenge faced is the high investment costs of BIM applications, while time efficiency is the dominant benefit of using BIM.
Revisiting the Streaming Video Patron-Driven Acquisition Program

In 2020, shortly after campus closure mid-pandemic, the Pollak Library at California State University, Fullerton mediated their Kanopy streaming video patron-driven acquisition (PDA) program, which had been running for six years. There were pros and cons to the timing of this decision given that the demand for streaming video would rise with the move to remote instruction. After the return to campus and weighing the impact of PDA mediation on acquisitions workload and streaming video expenditures, the library decided to reimplement an unmediated Kanopy Smart PDA that would improve access and workflow but provide a sustainable and predictable budget.
Hyperspherical Learning in Multi-Label Classification

Learning from online data with noisy web labels is gaining more attention due to the increasing cost of fully annotated datasets in large-scale multi-label classification tasks. Partial (positive) annotated data, as a particular case of data with noisy labels, are economically accessible. And they serve as benchmarks to evaluate the learning capacity of state-of-the-art methods in real scenarios, though they contain a large number of samples with false negative labels. Existing (partial) multi-label methods are usually studied in the Euclidean space, where the relationship between the label embeddings and image features is not symmetrical and thus can be challenging to learn. To alleviate this problem, we propose reformulating the task into a hyperspherical space, where an angular margin can be incorporated into a hyperspherical multi-label loss function. This margin allows us to effectively balance the impact of false negative and true positive labels. We further design a mechanism to tune the angular margin and scale adaptively. We investigate the effectiveness of our method under three multi-label scenarios (single positive labels, partial positive labels and full labels) on four datasets (VOC12, COCO, CUB-200 and NUS-WIDE). In the single and partial positive labels scenarios, our method achieves state-of-the-art performance. The robustness of our method is verified by comparing the performances at different proportions of partial positive labels in the datasets. Our method also obtains more than 1% improvement over the BCE loss even on the fully annotated scenario. Analysis shows that the learned label embeddings potentially correspond to actual label correlation, since in hyperspherical space label embeddings and image features are symmetrical and interchangeable. This further indicates the geometric interpretability of our method. Code is available at https://github.com/TencentYoutuResearch/MultiLabel-HML .
Efficient Integrity Checking and Secured Data Sharing in Cloud

"It is to protect data in a cloud server from third-party access". Most present systems suffer from a complex key managing issue since they depends on expensive public key infrastructure (PKI),it may make RDIC implementation challenging in practice. Remote data integrity checking (RDIC) in the proposed system allows a data storage server used to approve and a verifier which is accurately storing the data of the owner. Here we introduce a new identity-based RDIC protocol that uses key which is the same form of cryptographic primitives which are introduced to reduce the system performance, complex and expense of initiating and oversee public key authentication substructure which is in PKI-based Remote data integrity checking (RDIC) methods. The Identity based RDIC that is replica of hostage, which includes safety, are formalized opposed to a rogue cloud server, as well as providing a zero-knowledge required privacy from an identified third-party verifier.
Unifying Visual Perception by Dispersible Points Learning

We present a conceptually simple, flexible, and universal visual perception head for variant visual tasks, e.g., classification, object detection, instance segmentation and pose estimation, and different frameworks, such as one-stage or two-stage pipelines. Our approach effectively identifies an object in an image while simultaneously generating a high-quality bounding box or contour-based segmentation mask or set of keypoints. The method, called UniHead, views different visual perception tasks as the dispersible points learning via the transformer encoder architecture. Given a fixed spatial coordinate, UniHead adaptively scatters it to different spatial points and reasons about their relations by transformer encoder. It directly outputs the final set of predictions in the form of multiple points, allowing us to perform different visual tasks in different frameworks with the same head design. We show extensive evaluations on ImageNet classification and all three tracks of the COCO suite of challenges, including object detection, instance segmentation and pose estimation. Without bells and whistles, UniHead can unify these visual tasks via a single visual head design and achieve comparable performance compared to expert models developed for each task. We hope our simple and universal UniHead will serve as a solid baseline and help promote universal visual perception research. Code and models are available at https://github.com/Sense-X/UniHead .
FocusTR: Focusing on Valuable Feature by Multiple Transformers for Fusing Feature Pyramid on Object Detection

The feature pyramid, which is a vital component of the convolutional neural networks, plays a significant role in several perception tasks, including object detection for autonomous driving. However, how to better fuse multi-level and multi-sensor feature pyramids is still a significant challenge, especially for object detection. This paper presents a FocusTR (Focusing on the valuable features by multiple Transformers), which is a simple yet effective architecture, to fuse feature pyramid for the single-stream 2D detector and two-stream 3D detector. Specifically, FocusTR encompasses several novel self-attention mechanisms, including the spatial-wise boxAlign attention (SB) for low-level spatial locations, context-wise affinity attention (CA) for high-level context information, and level-wise attention for the multi-level feature. To alleviate self-attention's computational complexity and slow training convergence, Fo-cusTR introduces a low and high-level fusion (LHF) to reduce the computational parameters, and the Pre- Ln [1]to accelerate the training convergence.
Analysis of error rate for various attributes to obtain the optimal decision tree

The competitiveness and computational intelligence are required to increase the gross profit of the product in a market. The classification algorithm rpart is applied on retail market dataset. The regression rpart decision tree algorithm is implemented with principal component analysis to impute data in the missing part of the dataset. The objective is to obtain an optimal tree by analysing cross validation error, standard deviation error, and number of splits and relative error of various attributes. The results of various attributes by ANOVA method are compared to choose the best optimal tree. The tree with minimum error rate is considered for the optimal tree.
Robust pedestrian detection using scale and illumination invariant Mask R-CNN

In this paper, we address the challenging difficulty of detecting pedestrians with variation in scale and the illumination of the images. Occurrences of pedestrians with such variations exhibit diverse features. Therefore, it intensely affects the performance of recent pedestrian detection methods. We propose a new robust approach for overcoming the antecedent challenges. We proposed a scale and illumination invariant Mask R-CNN (SII Mask-RCNN) framework. The first phase of the proposed framework corrects illumination variations by performing a logarithmic transformation and adaptive illumination enhancement. In addition, the non-subsampled contourlet transform is used to decompose the image into multi-resolution components. Finally, we convolved the image with the multi-scale masks to find corresponding points that are illumination and scale-invariant. Extensive evaluations on pedestrian benchmark databases illustrate the effectiveness and robustness of the proposed framework. The experimental results contribute the notable performance improvements in pedestrian detection as compared to the state-of-the-art approaches.
A Delegated Offline Private Set Intersection Protocol for Cloud Computing Environments

Private set intersection (PSI) has a wide range of application scenarios in our daily life like contact matching. With the rise of cloud computing, people hope to use the effective computations and storage capabilities of cloud servers to help complete the PSI computations to reduce their own computing and storage costs. In this paper, we design an efficient PSI protocol based on oblivious transfer (OT), oblivious pseudo-random function (OPRF), and cuckoo filter (CF) for the cloud environments. Our protocol allows clients to upload encrypted private data sets to the cloud server, and the server can act as its agent to perform intersection calculations even when the client is offline. The protocol ensures that the result of the intersection is completely confidential to the server and can only be known by the computing party. Our protocol is efficient and it can be easily extended to multiple parties. A formal detailed security analysis of the protocol is also provided.
Learning Phase Mask for Privacy-Preserving Passive Depth Estimation

With over a billion sold each year, cameras are not only becoming ubiquitous, but are driving progress in a wide range of domains such as mixed reality, robotics, and more. However, severe concerns regarding the privacy implications of camera-based solutions currently limit the range of environments where cameras can be deployed. The key question we address is: Can cameras be enhanced with a scalable solution to preserve users’ privacy without degrading their machine intelligence capabilities? Our solution is a novel end-to-end adversarial learning pipeline in which a phase mask placed at the aperture plane of a camera is jointly optimized with respect to privacy and utility objectives. We conduct an extensive design space analysis to determine operating points with desirable privacy-utility tradeoffs that are also amenable to sensor fabrication and real-world constraints. We demonstrate the first working prototype that enables passive depth estimation while inhibiting face identification.
Multi-Agent Task Allocation Based on Discrete DEPSO in Epidemic Scenarios

Multi-Agent Task Allocation is an emerging technology that changes the world in the epidemic scenario through its power to serve the needs of any hospital that requires unmanned operation. In this environment, the end user may want to have a better quality of unmanned service at low loss and high efficiency. We defined a new multi-agent task allocation problem (MATAP) in the epidemic scenario, and then MATAP was formulated. This paper presents a novel hybrid discrete approach that is based on the Differential Evolution Algorithm (DE) and Partial Swarm Optimization (PSO), namely D-DEPSO, for handling this problem. First, the initial personal population was handled by "mutation operation". Modulus operations in the "mutation operation" modify the numerical overflow of a variable. Second, when updating the speed matrix, the speed matrix is discretized using the "round" function we have defined. Then, a random permutation was used to delete repeated numbers and to reinsert integers in the "crossover operation". The diversity of the population was expanded by introducing the discrete mutation operation of the DE into the PSO and preserving the optimal solution for each generation using the properties of PSO. It can be used for optimizing a single objective function. Experimental results are compared with other existing metaheuristic algorithms, such as discrete DE, discrete PSO, improved discrete DE, improved discrete PSO, and improved discrete genetic algorithm, in terms of running time and loss. The experiments show that the optimal solutions obtained by D-DEPSO are better than those obtained by other five algorithms. For the actual problem, D-DEPSO can generate an optimal solution by optimal parameter setting to allocate tasks rationally. It can achieve a rational distribution of tasks in the prevention of disease.
Waveform Defence Against Deep Learning Generative Adversarial Network Attacks

Physical layer (PHY) in communications is open to public and therefore it is highly vulnerable to over-the-air attacks such as spoofing, which will cause malicious communication resource occupation and result in reduced spectral efficiency. Traditional countermeasures rely on physical layer authentication (PLA) where unique hardware features and propagation channel features from legitimate users are difficult for an attacker to replicate. However, due to the advancement of artificial intelligence (AI), an intelligent attack method, based on generative adversarial network (GAN), is becoming more challenging. The GAN framework can use deep learning to impersonate a legitimate user including its signal pattern, hardware impairments and wireless channel characteristics. Therefore, traditional PLA methods will not work well in such GAN attack scenarios. This work will deal with the GAN attack challenge from a waveform design perspective. We investigate three waveform candidates with unique signal features such as orthogonality, non-orthogonality and windowing. Simulation results reveal that GAN attackers can successfully learn waveforms with either orthogonal or nonorthogonal features while it is difficult to learn the windowing characteristic. Therefore, this work reveals a robust waveform defence solution to combat with intelligent GAN attacks.
Load Balancing in Cloud Computing

Nowadays, many organizations are using cloud-based applications and platforms because of its on-demand service and quick response. The biggest concern of cloud computing is overloading of the system for any individual, group or organization. So, nowadays, load balancing is gaining popularity, its algorithms and solutions are also getting better day by day. This review article gives a brief overview that how the traditional load balancing was done, and the updates made in it with time. This paper also provides comparison table of the major research papers published on load balancing, various algorithms of load balancing, context on which load balancing is used like round robin, throttled, honey bee, max min, min-min, OLB, etc. Also, cloud service providers in CC who deal with powerful computational structures which are entirely determined on usage and offer their solutions in a stable manner. In addition, this research article provides extensive list of areas for research under load balancing and its future scopes.