Reviewerly/exHarmony huggingface dataset:
Societal Opinion Mining Using Machine Intelligence

The rapid growth in the chunk of social media messages has generated the dire need to architecture more reliable and robust techniques to mine the social opinion with the help of social media messages related to a particular realm. Machine learning techniques of recent scenarios can successfully be implemented for this pursuit. In this chapter, the authors presented a critical review and performance of different machine learning approaches for social opinion mining (SOM). Indeed, this chapter presents a beautiful amalgamation of different aspects related to SOM such as indispensable and inevitable concepts, implementation details, and efficiency to mine the opinion of society with the help of social media messages in the light of machine learning techniques. The basic discussion and theoretical background of this chapter attempt to explore the application of machine learning strategies in the phenomenon of SOM. Moreover, the chapter renders the discussion on the usual opinion mining process and the attempts performed by different researchers to tackle the pertinent problems of SOM with the help of social media and the implementation of machine learning techniques. Moreover, the chapter also discusses different challenges associated with this task. The mathematical models of social opinion mining are also proposed based on fuzzy concepts and linear algebra. Further, this chapter entails the algorithmic details of ten machine learning techniques for SOM. Finally, experimentations have been carried out for these ten machine learning techniques using Google embeddings and Amazon embeddings on two datasets and it has been observed that convolutional neural network (CNN) based deep learning outperforms other machine learning techniques in the pursuit of SOM.
Query Sub-intent Mining by Incorporating Search Results with Query Logs for Information Retrieval

Mining query sub-intents or sub-topics is one of the important task in information retrieval. It provides the user several potential queries to explore possible search intents of the user. With the development of Big Data and Natural Language Processing, pre-trained language models have been applied to model complex semantic information of different text resources for mining robust query sub-intents. These studies usually utilize search results and query logs independently as two important resources to generate query sub-intents. However, we deem that the contextual information contained in search results and user interest information contained in query logs can be incorporated together to enhance the effectiveness of user sub-intent mining, which can maximize the best of both resources. To generate high-quality sub-intents, we design a sequence-to-sequence pretrained language model which accepts search result texts and query suggestions extracted from query logs as the input, and outputs generated sub-intent phrases. For modeling the relation between search results and query logs, we design two information encoder and a novel attention mechanism at the decoder part. At each decoding step, the model weights the attention between the input search results and query logs to determine the output token. The experimental results on MIMICS dataset outperform strong baseline methods in almost all evaluation metrics, illustrating the effectiveness of our proposed methods. We also conduct removing studies to prove the effectiveness of search results and query logs individually, and then study and compare different generation paradigms of sub-intent with experiments. We finally show several generated examples to illustrate the quality of our generated sub-intents directly.
The Effectiveness of a Mobile Educational Platform for Engaging Students in Out-of-class Activities

Getting students engaged with out-of-class activities has been a long-standing challenge. With large class sizes, providing timely feedback to students is also challenging for faculty members. Students are spending fewer hours to study class content outside the classroom for several reasons, including working more hours because of rising tuition and living expenses. This paper describes a newly developed mobile learning system, Dysgu, which provides an engaging learning experience for students outside the classroom. Dysgu has interactive and auto-graded exercises to help students practice concepts. Students can see their progress and class standing as they work on the exercises. The mobile platform was deployed in two semesters at two different universities. We saw improved student grades, more on-time submissions, and the acknowledgment of useful features such as interactive activity and the ability to see the overall class status using this new mobile platform.
Artificial Neural Network for Multi-label Medical Text Classification

The Classification of medical reports is a crucial challenge, as they are usually presented in plain text, have a particular technical vocabulary, and are almost always unstructured. Document classification aims to assign the most appropriate label to a given document. Furthermore, among the significant issues of medical document classification is text representation in a numerical format. So, in this paper, we use artificial intelligence, proposing a model of multi-layer artificial neural networks for multi-label Classification. The transformation to numerical values of the medical documents relies on four encoding modes: Term Frequency (TF), Frequency-Inverse document frequency (TF-IDF), Bag-of- Words (BOW), and Document Term Matrix (DTM) models; in this study, we compared the four types of vectorizations. Experimental results demonstrated that the best results for our proposed neural network architecture for both models denoted Simple Neural Network (SNN) and Vocabulary Neural Network (VNN). We have used the local vocabulary of 7,400 documents in the SNN model; regarding the VNN model, we use the global terminology (Ohsumed_20000). The suggested models (VNN and SNN) performed well in classifying all four representations. Furthermore, the SNN results outperform the VNN findings. The accuracy of TF is 70.32 in time 3 with an epoch number of 64. For BOW, 68.16 is the accuracy reached with an epoch number 16 in time 1. Likewise, the accuracy of DTM with 32 epochs and in time three is 70.65, whereas the 71.08% value is the accuracy achieved by TF-IDF with 16 epochs in time 1, representing the best results obtained by SNN model.
Higher Education, Livability, and Sustainability

This study concerns issues, constraints, and opportunities for humanity in dealing with the global technological world. The purpose of this study is to unfold and divulge the aspects of the globalization of technology that matter for humanity in the system, process, and productivity of society at large. The study departs its critical exposition from the question of what is humanism. The question leads this study to explore the relationship between humanism and humanities in higher education. To what extent are the humanities still relevant to our society today? Regarding its service to society as labor and cultural capital, and the deliverables of humanities are convertible to be economic resources for society, this study argues that humanities are not in crisis, they’re a necessity for redefinition and realignment of humanities in higher education for the globally technological world.
Outlook 360 | A Practical Approach of Recruiting Talent Through Artificial Intelligence.

Intellectual capital acquired the Position of an asset in the fourth industrial revolution. An asset that would help companies to reach their targeted tier in the 'talent war' and help them maintain their bottom lines for significant setbacks. With the unforeseen consequences of Covid-19, this asset has become one benefit that needs to be retained. It implies that staying aware of the rapidly developing techniques for recruitment and implementing talented or skilled individual is one factor that assists companies in winning the talent war: the conversation objective, a potential employee to invest significant time and energy in a digitalized world. A better-performing e-recruitment application can bring employees to apply for the job. Thus, this paper sheds light on what upgrades and progressions are necessary for AI-based e-recruitment systems to draw and attain skilled individuals and help organizations achieve their target tier out in the corporate world.
Improving text classification using text summarization

The classification problem has been widely studied in data mining, machine learning, and information retrieval communities with applications in several domains, such as target marketing, medical diagnosis, newsgroup filtering, and document organization. In this work, we take up the challenge of improving Text Classification (TC) using Text Summarizing (TS).
Select-Them-Repair-Them: Automatically Optimize Pseudocode-to-Code Data

This paper considers the pseudocode-to-code generation task. Training the pseudocode-to-code generation model requires a set of labeled (pseudocode, code) pairs. However, manually annotated data pairs are costly. It is also timeconsuming to check and correct annotation errors in the annotation process. This paper proposes a novel Select-ThemRepair-Them (STRT) approach to select and repair pseudocode annotation errors to optimize data, which is able to explore pseudocode-to-code generation tasks better. This work has two key ideas: (1) although it is difficult to judge whether the pseudocode is correct, a critic is able to judge the correctness of the code corresponding to the pseudocode. (2) The pseudocode-to code generation model and code-to-pseudocode generation model are constructed to filter and correct data pairs. This paper applies the STRT approach to the SPoC dataset to obtain a new SPoC2022. The experimental results of the existing pseudocode-to-code generation methods on the SPoC2022 have increased by about 10% to 30%, which shows that the STRT is powerful.
Kobold: Simplified Cache Coherence for Cache-Attached Accelerators

The ever-increasing cost of data movement in computer systems is driving a new era of data-centric computing. common data-centric paradigms is near-data computing (NDC), where accelerators are placed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inside</i> the memory hierarchy to avoid the costly transfer of data to the core. potential to improve performance and energy efficiency. Unfortunately, adding accelerators into the memory hierarchy incurs significant complexity for system integration because accelerators often require cache-coherent access to memory. coherence protocols required to handle both cores and cache-attached accelerators result in significantly higher verification costs as well as an increase in directory state and on-chip network traffic. baseline processor performance. To simplify the integration of cache-attached accelerators, we present Kobold, a new coherence protocol and implementation which restricts the added complexity of an accelerator to its local tile. Kobold introduces a new directory structure within the L2 cache to track the accelerator's private cache and maintain coherence between the core and accelerator. A minor modification to the LLC protocol also enables accelerators to improve performance by bypassing the local L2. We verified Kobold's stable-state coherence protocols using the Murphi model checker and estimated area overhead using Cacti 7. Kobold simplifies integration of cache-attached accelerators, adds only 0.09% area over the baseline caches, and provides clear performance advantages vs. naïve extensions of existing directory coherence protocols.
Improving Self-Adaptation by Combining MAPE-K, Machine and Deep Learning

Monitoring, Analyzing, Planning, and Execution share knowledge and build a favorable approach in the form of a loop (MAPE-K). However, this proposed reference model is not efficient for large self-adaptations. Moreover, the failure of the analyzer component to keep up with the current expansion of data is one of the reasons that making the MAPE-K loop consumes a lot of time and resources. We suggest a hybrid learning dataflow design for the analysis phase that combines Machine and Deep Learning techniques to enhance the accuracy of the Analyzer component in less time.
360° Virtual reality video tours generation model for hospitality, tourism and education using case-based reasoning

This article proposes a software architecture focused on the generation of tours based on 360° immersive virtual reality (IVR), using a Case-Based Reasoning (CBR) component, which will allow users to enjoy personalized experiences based on a profile analysis. At the time of developing this model, 360° videos and images of hotels in the Arequipa region were captured; immersive application user interfaces were designed and prototyped, a visualization layer was implemented as an immersive application in Unity, focused on immersive VR goggles, and finally, based on a CBR engine and an API Rest, the intelligent layer was implemented, this was validated using the case library subset test (CLST) technique. Finally, the conclusions and recommendations are established.
Big Data Oriented Graph Division and Storage

Parameters of machinery parts have dependency relationships between each other. Given a part, our task is to compute all the combinations of all its parameters constrained by the dependency relationships between them. A directed graph can be used to represent the dependency relationships between those parameters. However, simply using the width-first extension to solve the combinations would lead to the problem of combinatorial explosion, even with the constraint imposed by the dependency relationships. This problem causes consequences of too long the computing time, too large the required computer memory, and too slow the subsequent data querying. To solve the problem, we propose a method of graph division to reduce the computation scale. Based on specific characteristics of our task, the method divides the directed graph into various regions, and further divides complex ones of the regions into various sub-graphs. By the strategy of divide-and-conquer, processing of the entire graph is decomposed into processing of simple sub-graphs. Even with the application of above graph division, rows number of tables used to store the result of the extension can still be as large as one billion. Conventional DBMSs would perform poorly at such large a data scale. To combat this problem, we use a tool of persistent memory programming, Metall, to process the big data. Utilizing the tool gains a high I/O throughout. Our experiments show that, among 3,581 machinery parts being processed, for 97% of them, each part can be processed within 6 hours. Moreover, the technique of graph division improves the efficiency of subsequent data querying by 77%.
A Data Engineering Method for Filtering and Identifying Open Source Software Supply Chain

Filtering and identifying open source supply chain software are the front conditions for the security of the software supply chain, and it is a necessary means to help users and enterprises choose reliable softwares. At the same time, identifying the supply chain of the entire ecology is a vital way to explore the ecological characteristics and find hidden dangers. By tracing the development history of external dependencies in different programming language management, this article summarizes the four common external dependencies management methods today and proposes a universal open source software supply chain construction algorithm. Finally, the Linux distribution version is used as a case of large software systems, and its supply chain is analyzed.
Experimentation for Decentralized Resource-based Multi-pool Mining in Ethereum Blockchain

The potential of having multiple distributed applications across multiple domains such as healthcare, finance, supply chain management and many more have made blockchain very popular among both academia and industries. Blockchain provides the much-needed mechanism for decentralization of systems, where the need of trusted central authority is eliminated. Proof of Work (PoW) is heavily adopted in both Bitcoin and Ethereum based blockchain, where many miners (or mining pools) compete to mine each block generated by solving a cryptographic puzzle that uses all of the previous information of the blockchain, before tying the block to the blockchain with the nonce. Recently there is a shift in having two or more mining pools for the PoW consensus in Ethereum-based blockchain. Majority of the studies conducted for multiple mining pool techniques in the literature are verified with simulation experiments. Therefore, in this paper, we implement a testbed for Ethereum blockchain with multiple nodes that simulate two mining pools using PoW consensus in a centralized and decentralized fashion. Two miner-nodes were deployed with different computational power (in term of CPU threads) and transactions executed. We evaluate the contribution of each miner node in the blockchain system and the assignment of transactions with respect to the computational resources available.
Evaluation of Local Binary Pattern for Osteoporosis Classification

Deterioration of the bone’s microarchitecture and low bone mineral density, which results in increased fragility of bone, are symptoms of the disease osteoporosis, which decreases bone mass. Early osteoporosis identification can prevent the disease and predict fracture risk. Usually, the diagnosis is based on the analysis of X-ray images. However, the healthy and osteoporotic subject radiography shows a great resemblance. This study aims to develop an evaluation of an automatic osteoporosis identification system based on texture analysis. This paper proposes a Local Optimal Oriented Pattern (LOOP) to address some of the shortcomings of existing feature descriptors such as Local Binary Pattern (LBP) and Local Directional Pattern (LDP). Ensemble and SVM learning algorithms were used for the classification task. The obtained results were compared with some state-of-art methods used in the literature. Experimental results show that the proposed approach outperforms the previous binary descriptor in terms of recognition accuracy proving that the proposed approach is efficient for real clinical applications.
maplib: Interactive, Literal RDF Model Mapping for Industry

Knowledge graphs are important for industrial digitalization. Industrial knowledge graphs are often mapped from multiple existing large data sources, and creating a mapping requires the time of scarce subject matter experts (SME). Interactive, literal programming for large scale mapping would allow mapping engineers to make good use of SME time, and document their work. Currently, there are no open source tools supporting such a process. To solve this problem, we implement maplib, which leverages existing tooling from data science. In data science, there is widespread use of literate programming using frameworks such as Jupyter notebooks to interactively prepare data and create analyses using in-memory tables called DataFrames. Maplib is implemented in Rust using Polars DataFrames and has Python bindings, allowing us to leverage tooling used in data science. Maplib implements the OTTR mapping language, which is highly suited for industrial use cases. Maplib features a SPARQL engine defined directly on DataFrames, making querying possible immediately after mapping. We evaluate our approach by comparing mapping and querying performance with Morph-KGC and SPARQL Anything on the GTFS Madrid benchmark. Our approach materializes the graph and is ready to query 47x-182x faster, and scales to models that are over twice as large. Morph-KGC and SPARQL Anything perform better for most, but not all of the queries once the graph has been constructed. On the end-to-end task of mapping and querying however, which is very important for interactive mapping, maplib performs better for most queries.
Work in progress: Agile Methodologies for Online Software Engineering Education under the Pandemic

The Agile process is the major software development process. It aims at simplicity and efficiency based on team communication. In this paper, we propose Agile methodologies to deliver online Software Engineering classes which heavily depend on group projects. The methodologies are designed to promote students' communication quality when students have difficulties performing a group project with peers, most of whom they never met. With the students' survey results and academic grades, we show the proposed methodologies are highly effective. We also analyze the effectiveness of each methodology: The online chat tool (e.g., discord) and the student's team building activities strongly correlate with academic performance. The results suggest that instructors can detect possible team problems early from these methodologies and provide the proper intervention. We plan to apply the online methodologies for in-person classes and compare the results. It will find the optimum teaching modality to balance in-person and online classes.
Hashing-based Undersampling for Large Scale Histopathology Image Classification

The early diagnosis of cancer based on histopathology images plays an important role in medical science. Existing techniques generally partition the original histopathology image into small pieces for further classification. However, due to the fact that the number of benign (majority) samples is much larger than that of malignant (minority) samples, the classification is significantly imbalanced which adversely affects classification performance. Undersampling is commonly used to address the class-imbalance problem. However, existing methods are typically time consuming so they are not suitable to handle large-scale and high-dimensional data. In this paper we propose a fast and scalable undersampling method, hashing-based undersampling (HBU), to address class imbalance in large-scale medical image classification. Benign images are hashed and then placed into different buckets according to their locations in the input space. Undersampling is achieved by proportionally selecting benign images from the hash buckets. The HBU method is experimentally evaluated on two real histopathology image datasets, CAMELYON16 and ACDC@LUNGHP, by comparison with existing methods. Experimental results show that the HBU method outperforms six state-of-the-art methods and is scalable and fast.
Deep learning based sequence detection with natural redundancy for memory sources

Abstract The current wireless communication system has higher performance demands for receivers. This paper considers exploiting the natural redundancy (NR) that is widely existed in the transmission sources to improve the receiver's performance of sequence detection. The type of NR discussed in this paper is the inherent redundancy in data caused by the correlation between symbols in the memory sources. Since the correlation between transmitted symbols is too challenging to obtain, deep learning (DL) is introduced into sequence detection, which has a powerful ability to extract complex patterns from data. Specifically, intended for a specific memory source that is convenient for performance analysis, namely the Markov source, an iterative sequence detection algorithm based on the long short‐term memory network is proposed. Simulation results demonstrate that the receiver's performance can be improved by exploiting the NR and the proposed DL‐based sequence detection scheme can obtain optimal bit error rate performance with blind state transition probability of the transmitted Markov source.