Reviewerly/exHarmony huggingface dataset:
A hybrid classification method via keywords screening and attention mechanisms in extreme short text

Short text classification has provoked a vast amount of attention and research in recent decades. However, most existing methods only focus on the short texts that contain dozens of words like Twitter and Microblog, while pay far less attention to the extreme short texts like news headline and search snippets. Meanwhile, contemporary short text classification methods that extend the features via external knowledge sources always introduce lots of useless concepts, which may be detrimental to classification performance. Moreover, unlike traditional short text classification methods, the classification results of extreme short texts are often determined by a few even one or two keywords. To address these problems, we propose a novel hybrid classification method via Keywords Screening and Attention Mechanisms in extreme short text, called KSAM. More specifically, firstly, the attention-based BiLSTM is introduced in our method to enhance the role of keywords. Secondly, we screen the keywords in the extreme short text for obtaining the true class label, and the concepts concerning the keywords are retrieved from external open knowledge sources like DBpedia. Thirdly, the attention mechanisms are introduced to acquire the weight of these retrieved concepts. Finally, conceptual information is utilized to assist the classification of the extreme short text. Extensive experiments have demonstrated the effectiveness of our method compared to other state-of-the-art methods.
I2R: Intra and inter-modal representation learning for code search

Code search, which locates code snippets in large code repositories based on natural language queries entered by developers, has become increasingly popular in the software development process. It has the potential to improve the efficiency of software developers. Recent studies have demonstrated the effectiveness of using deep learning techniques to represent queries and codes accurately for code search. In specific, pre-trained models of programming languages have recently achieved significant progress in code searching. However, we argue that aligning programming and natural languages are crucial as there are two different modalities. Existing pre-train models based approaches for code search do not effectively consider implicit alignments of representations across modalities (inter-modal representation). Moreover, the existing methods do not take into account the consistency constraint of intra-modal representations, making the model ineffective. As a result, we propose a novel code search method that optimizes both intra-modal and inter-modal representation learning. The alignment of the representation between the two modalities is achieved by introducing contrastive learning. Furthermore, the consistency of intra-modal feature representation is constrained by KL-divergence. Our experimental results confirm the model’s effectiveness on seven different test datasets. This paper proposes a code search method that significantly improves existing methods. Our source code is publicly available on GitHub.1
A multi granularity information fusion text classification model based on attention mechanism

Existing research on Chinese text classification primarily focuses on classifying data information at different granularities, such as character, word, sentence, and chapter. However, this approach often fails to capture the semantic information embedded in these different levels of granularity. To enhance the extraction of the text’s core content, this study proposes a text classification model that incorporates an attention mechanism to fuse multi-granularity information. The model begins by constructing embedding vectors for characters, words, and sentences. Character and word vectors are generated using the Word2Vec training model, allowing the data to be converted into these respective vectors. To capture contextual semantic features, a bidirectional long and short-term memory network is employed for character and word vectors. Sentence vectors, on the other hand, are processed using the FastText model to extract the features they contain. To extract further important semantic information from the different feature vectors, they are fed into an attention mechanism layer. This layer enables the model to prioritize and emphasize the most significant information within the text. Experimental results demonstrate that the proposed model outperforms both single-granularity classification and combinations of two or more granularities. The model exhibits improved classification accuracy across three publicly available Chinese datasets.
Oversampling method based on GAN for tabular binary classification problems

Data-imbalanced problems are present in many applications. A big gap in the number of samples in different classes induces classifiers to skew to the majority class and thus diminish the performance of learning and quality of obtained results. Most data level imbalanced learning approaches generate new samples only using the information associated with the minority samples through linearly generating or data distribution fitting. Different from these algorithms, we propose a novel oversampling method based on generative adversarial networks (GANs), named OS-GAN. In this method, GAN is assigned to learn the distribution characteristics of the minority class from some selected majority samples but not random noise. As a result, samples released by the trained generator carry information of both majority and minority classes. Furthermore, the central regularization makes the distribution of all synthetic samples not restricted to the domain of the minority class, which can improve the generalization of learning models or algorithms. Experimental results reported on 14 datasets and one high-dimensional dataset show that OS-GAN outperforms 14 commonly used resampling techniques in terms of G-mean, accuracy and F1-score.
A fast and distributed C4.5 algorithm for urban big data

The amount of information nowadays is rapidly growing. Aside from valuable information, information that is unrelated to a target or is meaningless is also growing. Big data and broader digital technologies are considered the primary components of smart city governance and planning. Big data analysis is considered to define a new era in urban planning, research, and policy. Effective data mining and pattern detection techniques are becoming very important these days. Processing such a large amount of data entails the use of data mining, a technique that clarifies the association between valid information and excludes irrelevant data to implement a practical decision tree. A large amount of data affects processing time and I/O costs during data mining. This study proposes to distribute data among multiple clients and distribute a large amount of data computation equally to improve the resource cost problem of exploration. Following that, the main server consolidates the computation results and generates the survey results. Experiment results show that the proposed algorithm is superior, thus allowing a larger amount of data to be processed while producing high-quality results.
Mining skyline frequent-utility patterns from big data environment based on MapReduce framework

When the concentration focuses on data mining, frequent itemset mining (FIM) and high-utility itemset mining (HUIM) are commonly addressed and researched. Many related algorithms are proposed to reveal the general relationship between utility, frequency, and items in transaction databases. Although these algorithms can mine FIMs or HUIMs quickly, these algorithms merely take into account frequency or utility as a unilateral criterion for itemsets but the other factors (e.g., distance, price) could be also valuable for decision-making. A new skyline framework has been presented to mine frequent high utility patterns (SFUPs) to better support user decision-making. Several new algorithms have been proposed one after another. However, the Internet of Things (IoT), mobile Internet, and traditional Internet are generating massive amounts of data every day, and these cutting-edge standalone algorithms can not satisfy the new challenge of finding interesting patterns from this data. Big Data uses a distributed architecture in the form of cloud computing to filter and process this data to extract useful information. This paper proposes a novel parallel algorithm on Hadoop as a three-stage iterative algorithm based on MapReduce. MapReduce is used to divide the mining tasks of the whole large data set into multiple independent sub-tasks to find frequent and high utility patterns in parallel. Numerous experiments were done in this paper, and from the results, the algorithm can handle large datasets and show good performance on Hadoop clusters.
Design of an energy efficient dynamic virtual machine consolidation model for smart cities in urban areas

The growing smart cities in urban areas are becoming more intelligent day by day. Massive storage and high computational resources are required to provide smart services in urban areas. It can be provided through intelligence cloud computing. The establishment of large-scale cloud data centres is rapidly increasing to provide utility-based services in urban areas. Enormous energy consumption of data centres has a destructive effect on the environment. Due to the enormous energy consumption of data centres, a massive amount of greenhouse gases (GHG) are emitted into the environment. Virtual Machine (VM) consolidation can enable energy efficiency to reduce energy consumption of cloud data centres. The reduce energy consumption can increase the Service Level Agreement (SLA) violation. Therefore, in this research, an energy-efficient dynamic VM consolidation model has been proposed to reduce the energy consumption of cloud data centres and curb SLA violations. Novel algorithms have been proposed to accomplish the VM consolidation. A new status of any host called an almost overload host has been introduce, and determined by a novel algorithm based on the Naive Bayes Classifier Machine Learning (ML) model. A new algorithm based on the exponential binary search is proposed to perform the VM selection. Finally, a new Modified Power-Aware Best Fit Decreasing (MPABFD) VM allocation policy is proposed to allocate all VMs. The proposed model has been compared with certain well-known baseline algorithms. The comparison exhibits that the proposed model improves the energy consumption by 25% and SLA violation by 87%.
Region-Attention Prompt Learning for CLIP

Pre-trained Visual Language Models (VLMs) like CLIP have shown great potential in the multimodal domain. Among this, using different modal contexts and interaction features to construct prompt can stimulate the model’s prior knowledge circuit more accurately, thus generating better outputs. However, in CLIP, the formal mismatch of textual descriptions between the pre-training and inference phases results in a suboptimal representation ability of prompt, which is detrimental to model alignment learning. Therefore, Region-Attention Prompt (RAP) is proposed, which introduces region features to enrich the semantic representation of prompt. RAP is acquired by the Cross-Attention mechanism between images and texts, and it is essentially a region-level prompt with category-sensitive properties. For each category, RAP adaptively assigns greater attention weight to image regions that are more semantically relevant to the category. Besides, CLIP is equipped with RAP (called RA-CLIP) to improve image classification performance in generalization scenarios. Extensive experiments demonstrate that RA-CLIP outperforms the current SOTA CoCoOp 0.4% - 4.16% on base classes and 0.25% - 11.34% on new classes, across 7 datasets. In addition, we show that focusing on category-related regions to construct prompt can further improve the model’s alignment ability.
NonPC: Non-parametric clustering algorithm with adaptive noise detecting

Graph-based clustering performs efficiently for identifying clusters in local and nonlinear data Patterns. The existing methods face the problem of parameter selection, such as the setting of k of the k-nearest neighbor graph and the threshold in noise detection. In this paper, a non-parametric clustering algorithm (NonPC) is proposed to tackle those inherent limitations and improve clustering performance. The weighted natural neighbor graph (wNaNG) is developed to represent the given data without any prior knowledge. What is more, the proposed NonPC method adaptively detects noise data in an unsupervised way based on some attributes extracted from wNaNG. The algorithm works without preliminary parameter settings while automatically identifying clusters with unbalanced densities, arbitrary shapes, and noises. To assess the advantages of the NonPC algorithm, extensive experiments have been conducted compared with some classic and recent clustering methods. The results demonstrate that the proposed NonPC algorithm significantly outperforms the state-of-the-art and well-known algorithms in Adjusted Rand index, Normalized Mutual Information, and Fowlkes-Mallows index aspects.
Data management scheme for building internet of things based on blockchain sharding

As an important part of digital building, building internet of things (BIoT) plays a positive role in promoting the construction of smart cities. Existing schemes utilize blockchain to achieve trusted data storage in BIoT. However, the full-copy storage mechanism of blockchain and the management requirements of massive data have brought computing and storage challenges to edge nodes with limited resources. Therefore, a data management scheme for BIoT based on blockchain sharding is proposed. The scheme proposes a hybrid storage mechanism, which uses inter-planetary file system (IPFS) to ensure the integrity and availability of data outside the chain, and reduces the storage overhead of edge nodes. Based on the hybrid storage mechanism, the sharding algorithm is designed to divide the blockchain into multiple shards, and the storage overhead and computing overhead are offloaded to each shard, which effectively balances the computing and storage overhead of edge nodes. Finally, comparative analysis was made with existing schemes, and effectiveness of proposed scheme was verified from the perspectives of storage overhead, computation overhead, access delay and throughput. Results show that proposed scheme can effectively reduce storage overhead and computing overhead of edge nodes in BIoT scenario.
Linear logic in a refutational setting

Abstract Sequent-style refutation calculi with non-invertible rules are challenging to design because multiple proof-search strategies need to be simultaneously verified. In this paper, we present a refutation calculus for the multiplicative–additive fragment of linear logic ($\textsf{MALL}$) whose binary rule for the multiplicative conjunction $(\otimes )$ and the unary rule for the additive disjunction $(\oplus )$ fail invertibility. Specifically, we design a cut-free hypersequent calculus $\textsf{HMALL}$, which is equivalent to $\textsf{MALL}$, and obtained by transforming the usual tree-like shape of derivations into a parallel and linear structure. Next, we develop a refutation calculus $\overline{\textsf{HMALL}}$ based on the calculus $\textsf{HMALL}$. As far as we know, this is also the first refutation calculus for a substructural logic. Finally, we offer a fractional semantics for $\textsf{MALL}$—whereby its formulas are interpreted by a rational number in the closed interval [0, 1] —thus extending to the substructural landscape the project of fractional semantics already pursued for classical and modal logics.
Impact of Social Media Usage on Organisation Performance: Lesson From Covid-19 Pandemic

Companies have turned to digital and social media advertising due to the government's widespread limitation strategy implemented in response to the COVID-19 outbreak.Due to its potency and efficiency, social media may help intelligent businesses increase their chances of success and gain a competitive edge.Yet nuances of grey persist.This research focuses on how social media usage affects corporate performance.As part of this research, we break down the ways in which people use social media into three distinct types: social media for "customer relations and services", "marketing", and "information accessibility".Through purposive sampling, data was collected through an online questionnaire using 5 Likert scales to organisational informants such as managers and senior staff in the Small Medium Enterprise (SMEs).Data were analysed using IBM SPSS, with descriptive, normality, reliability, and correlation analysis.The finding demonstrates the significant impact that social media may have on business operations, particularly in the areas of brand awareness, customer service, and information dissemination.
Features of the functioning of the article in comedies of Italian authors of the 16th century

The use of the article is often connected with difficulties for those people who study the Italian language, especially if it is absent in their native language. It is necessary to study the article comprehensively to understand its functioning better. For example, it is important to considerate the historical aspect: its formation, as well as the principles and contexts of its using during early stages of development. That's why the subject of the study is the functioning of the definite, indefinite and partitive article in the texts of comedies written by Italian authors of the 16th century. Particular attention is paid to the use of the partitive article in prepositional constructions because this structure, according to F. Sabatini, is considered an innovative trend in the modern Italian language of informal communication. The scientific novelty is in the material chosen for this research. Previously, Italian comedies were not used as a source of value for obtaining new knowledge about the language, in particular, the aspect concerning the functioning of the article. It is important that their texts imitate informal everyday communication, and therefore serve as valuable material for analyzing the language of the 16th century. The examples given in this research give reason to believe that the use of the partitive article with prepositions is not an innovation of italiano neostandard. Thus, at the present moment, the Italian language does not develop innovative structures, but rather undergoes a process of restandartization.
Monitoring Peringatan Dini Kebakaran Pada Sistem Smart Home Menggunakan NodeMcu Berbasis IoT

Abstract – One of the most frequent disasters in Indonesia is fire. There are situations where homeowners are not aware that their house is on fire, such as when the owner is not home or at night when the owner is sleeping. Lack of information regarding the condition of a house that has caught fire can result in enormous material losses or can endanger a person's life if the fire is not resolved immediately. In this case, it is necessary to have a monitoring system that can provide information regarding the presence of early fire symptoms that can be accessed anytime and anywhere. This system will later be able to display fire, smoke, and temperature values from sensor readings that have been stored in the Firebase Realtime Database which will later be displayed on Mobile Apps. If there is a fire there will be a warning in the form of a buzzer and notification on the smartphone and the system will spray water through the waterpump. The results of this study are a fire early warning monitoring system on a smart home system using Internet of Things (IoT) based NodeMcu. It is hoped that this system can reduce or minimize the occurrence of fires and losses due to fires.
Klasterisasi Hasil Belajar Matematika dengan Algoritma K-Means Clustering

Mathematics has an important role in the computer field and provides a theoretical foundation for people working in the computer field. The facts show that in the Informatics Engineering study program, Unisba Blitar, the selection of specializations is carried out without considering the grades of courses that teach basic mathematical skills. In fact, mathematical ability is needed by a computer expert. For this reason, research was conducted that aimed to cluster student mathematics learning outcomes. Clustering was carried out on 51 students in semester 4, through the application of the K-means clustering algorithm. The attributes used are school origin data, majors currently in high school, and student learning outcomes in informatics logic, statistics, computational mathematics, and advanced computational mathematics courses. The results show that through clustering with the K-Means Clustering algorithm, 5 clusters are obtained, starting from the highest average score, namely cluster 2 with a value of 86.81 and the lowest average value is cluster 5 with a value of 76.50. In cluster 2, it is dominated by students from SMK graduates majoring in TKJ. Meanwhile, cluster 5 was dominated by students from high school graduates majoring in natural sciences. In addition, there are findings indicating that vocational high school graduates do not always have lower mathematical abilities than high school graduates, because intrinsic motivation also influences the level of learning outcomes.
Electronic Invigilation Inclusion in Curbing Examination Malpractices Among Postgraduate Students in Selected Public Tertiary Institutions In Rivers State

The study investigated electronic invigilation inclusion in curbing examination malpractices among postgraduate students in selected public tertiary institutions in Rivers State. The study adopted the descriptive survey research design. Three research questions and three hypotheses guided the study. The population of the study consisted of all 9,510 postgraduate students of three academic sessions of three tertiary institutions. Taro Yamane Formula was used for the sample size. The multi-stage sampling techniques were adopted in selecting the sample size of 400. A structured questionnaire titled electronic invigilation inclusion in curbing examination malpractice questionnaire (EIICEMQ) was used as an instrument for data collection. Three experts validated the instrument and Cronbach Alpha Method was used to obtain an average reliability coefficient of 0.83. 400 copies of the questionnaire were administered to the respondents but only 365 were retrieved and used for the study. The items were rated on a four (4) point rating scale; mean and standard deviation were used to answer the research questions while z-test was used in testing the formulated hypotheses. The findings reviewed that there is a high extent of close-circuit television (CCTV) camera, biometric system and signal jamming devices inclusion in curbing examination malpractices among postgraduate students in selected public tertiary institutions in Rivers State. The researcher recommended that tertiary institutions should adopt the use of CCTV camera for surveillance and the recording of examination offenses because the mere presence of CCTV cameras alone is capable of scaring potential malpractice candidates, since they are well aware of being watched, with the possibilities of recorded clips being filed as evidence of malpractice endeavors, tertiary institutions should adopt the use of biometric system in registering admitted students and use same process in identifying the registered students during examinations and tertiary institutions should also deploy signal jamming devices in order to temporarily stop the functionality of cell phones and its usage for unnecessary communications during examinations.