Reviewerly/exHarmony huggingface dataset:
A Blockchain and Machine Learning based IoT Framework to Improve Contract Farming

Farming sector forms a major part of the Indian economy. It makes a contribution of about 14% to the Gross Domestic Product (GDP) of the country. Despite holding such an essential place in the Indian economy, this sector has seen very little technology penetration. The farmers have been plagued with poor economic conditions. Contract Farming is seen as a viable solution for this problem, as it involves tie-up of corporate firms with farmers, which comes with its many benefits. In this work, we focus on facilitating Contract Farming so that both the firm and the farmer can reap benefits. Using Blockchain, IoT and Machine Learning techniques, we make it feasible for a corporate firm to supervise a farming operation according to specific needs while safeguarding the farmer from unfair exploitation.
Extended Reality Based Remote Learning Experience During Pandemic: Effectiveness and Barriers

The COVID-19 pandemic has impacted almost all sectors due to lockdown measures taken by governments across the globe to curtail the spread of virus. It has disrupted the regular teaching and learning process, as students miss face-to-face learning experience. However digital technologies have mitigated the negative impact these lockdowns would have otherwise caused. The challenges faced because of remote learning can be handled by incorporating XR related technologies to enhance the collaborative learning experience for remote learners. The XR technologies and their applications in educational institutions are still in their nascent stages but show great potential and are all set to create a paradigm shift in the way education and training is perceived. The chapter will provide an insight on how schools, universities, and enterprise learning can be made more engaging by reviewing different techniques adopted to effectively engage the remote learners using immersive technologies. It also focuses on advantages, adoption challenges and framework for using XR in education especially during these testing times.
Dynamic Educational Knowledge Graph Model via Information Entropy for Knowledge Building

Knowledge building is the production and continual improvement of ideas of value to a community, which attaches importance to conceptual engagement and contribution. However, knowledge building community will accumulate large and complex semi-structured educational data over time. It is not conducive to the continuation of in-depth knowledge building activities. To overcome these issues, we propose a dynamic educational knowledge graph with information entropy (IE-DEKG) model for knowledge building community. The model can construct dynamic knowledge graphs that contain instructional concepts and educational relations for learners. Specifically, it adopts the mutual information and adjacent information entropy to detect new terminologies on pedagogical data, and then the topic modeling algorithm is utilized to extract instructional concepts. Moreover, the model employs association rule mining to identify the prerequisite relations and uses pattern matching to obtain the inclusion relations. For the sake of satisfying the needs of educational applications and services, we design and implement the dynamic educational knowledge graph system. Experimental results demonstrate that the proposed IE-DEKG method outperforms the state-of-the-art methods.
Piecewise Simplification Approach for Accurate and Understandable Model

The scope of AI application is expanding to mission-critical domains such as safety, healthcare and finance. To introduce AI in such domains, understandability of the prediction logic is required. However typical accurate models such as deep neural networks are too complicated to understand. Although there exist methods for generating globally understandable models, their accuracy and applicability are often insufficient. To achieve high accuracy by understandable models for classification and regression tasks, we propose piecewise simple function (PSF) as a general understandable model and a method for training accurate PSF. PSF consists of a few local submodels and can be regarded as a generalization of the piecewise affine model. However, the output of the proposed method is expected to be more accurate since it utilizes information of accurate black-box AI. More concretely, we specify the partition of regions on the basis of information of black-box AI such as important features and thresholds. The proposed method is based on the hypothesis that black-box AI is often overly complicated in some regions and can be replaced with simpler models. Through numerical experiments, we demonstrate that PSF often achieves almost the same accuracy as original black-box AI.
A Multi-Class Blockchain Services Framework for Communication Networks

Blockchain-based applications have been recently introduced in communication networks. Different applications require different levels of service whereas existing blockchain-based applications mostly provide a single class of service. In this paper, we propose a multi-class blockchain services (MCBS) framework that prioritizes the processing of time-critical blockchain-based applications in communication networks. We model MCBS as a queueing system and derive the average service times for the different classes. We then formulate a multi-objective optimization problem to simultaneously optimize the average service time and aggregate transactions arrival rate. The numerical results show that existing blockchains may not satisfy the requirements of time-critical applications whereas a high priority queue in MCBS may meet the requirement. In addition, we analyze MCBS under double-spend attacks and show that with maximum attacker hash power, the blockchain system can still be stable if the aggregate transaction arrival rate is lower than half of its maximum achievable value.
FILTER PRUNING IN ORDER OF IMPORTANCE OF THE LAYER GROUPS TO ACCELERATE CONVOLUTIONAL NEURAL NETWORKS

Various acceleration approaches have been studied to deploy convolutional neural networks in embedded devices. Among them, filter pruning is the most active research because it is easy to implement in hardware and keeps high accuracy while reducing the computational and memory cost. In this paper, we propose a method of grouping layers, finding the importance of each group, and groupwise pruning according to the order of importance to achieve high FLOPs reduction while retaining high accuracy. First, we divide the layers of the pre-trained network into groups according to the size of the output feature map. Next, we calculate the importance score per group using first-order Taylor expansion. Finally, filter pruning is performed in order from the group with the highest importance score. When pruning VGG and ResNet trained on CIFAR-10, our proposed method shows superior performance in accuracy and FLOPs compared to the state-of-art methods. Notably, on ResNet-50, we achieve 70.85% FLOPs reduction by removing 50% of the filters, with a slight loss of 0.41% in the baseline accuracy.
Optimal Las Vegas Approximate Near Neighbors in <i> ℓ <sub>p</sub> </i>

We show that approximate near neighbor search in high dimensions can be solved in a Las Vegas fashion (i.e., without false negatives) for ℓ p (1≤ p ≤ 2) while matching the performance of optimal locality-sensitive hashing. Specifically, we construct a data-independent Las Vegas data structure with query time O ( dn ρ ) and space usage O ( dn 1+ρ ) for ( r, c r )-approximate near neighbors in R d under the ℓ p norm, where ρ = 1/ c p + o (1). Furthermore, we give a Las Vegas locality-sensitive filter construction for the unit sphere that can be used with the data-dependent data structure of Andoni et al. (SODA 2017) to achieve optimal space-time tradeoffs in the data-dependent setting. For the symmetric case, this gives us a data-dependent Las Vegas data structure with query time O ( dn ρ ) and space usage O ( dn 1+ρ ) for ( r, c r )-approximate near neighbors in R d under the ℓ p norm, where ρ = 1/(2 c p - 1) + o (1). Our data-independent construction improves on the recent Las Vegas data structure of Ahle (FOCS 2017) for ℓ p when 1 &lt; p ≤ 2. Our data-dependent construction performs even better for ℓ p for all pε [1, 2] and is the first Las Vegas approximate near neighbors data structure to make use of data-dependent approaches. We also answer open questions of Indyk (SODA 2000), Pagh (SODA 2016), and Ahle by showing that for approximate near neighbors, Las Vegas data structures can match state-of-the-art Monte Carlo data structures in performance for both the data-independent and data-dependent settings and across space-time tradeoffs.
HIDMS-PSO Algorithm with an Adaptive Topological Structure

This paper presents a new variant of the state-of-the-art PSO (particle swarm optimisation) variant HIDMS-PSO (heterogeneous improved dynamic multiswarm PSO) algorithm. The proposed variant improves the fixed topology of the unit structure introduced in the HIDMS-PSO variant. The new master and slave dominated topologies significantly change the dynamics of the unit structure by fluctuating the behavioural heterogeneity in individual units via the use of adaptive topologies. In addition, several existing components in the standard HIDMS-PSO were trimmed down to simplify the algorithm. The efficacy of the adaptive HIDMS-PSO variant was tested by conducting three experiments on the CEC'05 and CEC'17 benchmark test suites at 30 and 50 dimensions using 11 baseline metaheuristics and 15 state-of-the-art PSO variants. The proposed algorithm outperformed all comparison algorithms in all of the conducted experiments. Additionally, adaptive HIDMS-PSO's convergence rate and population diversity maintenance capability were compared with the inertia weight PSO and the standard HIDMS-PSO. The empirical evidence suggests that the proposed algorithm is capable of converging faster to a better solution while mostly maintaining a better population diversity during the search.
Secure Federated Learning Based on Coded Distributed Computing

Federated learning (FL) enables multiple learning devices to exchange their training results and collaboratively develop a shared learning model without revealing their local data, thereby preserving data privacy. However, contemporary FL models have many drawbacks including limited security against malicious learning devices generating arbitrarily erroneous training results. Recently, a promising concept - coded distributed computing (CDC) has been proposed for maintaining security of various distributed systems by adding computational redundancy to the datasets exchanged in these systems. Although the CDC concept has already been adopted in several applications, it is yet to be applied to FL systems. Accordingly, in this paper, we develop the first integrated FL-CDC model that represents a low-complexity approach for enhancing security of FL systems. We implement the model for predicting the traffic slowness in vehicular applications and verify that the model can effectively secure the system even if the number of malicious devices is large.
A Mirror-based Compact Monocular Depth Estimation System for Environment Sensing

Environment sensing is one of the most challenging problem in robot sensing. Among the sensing systems, stereo camera systems provide dense information and usually is less expensive for environment sensing. Based on this, a monocular stereo system for robot environment sensing is proposed. The system combines a saccade mirror with an industrial camera and captures images of environment when the mirror rotates. With the prior of relative posture of cameras corresponding to each image, the stereo images can be easily rectified and the depth information of surroundings is estimated. The estimation accuracy is validated by experiments on small resin model and real-life indoor scene.
CESDAM: Centered subgraph data matrix for large graph representation

In this chapter, a space efficient graph representation scheme is proposed. Unlike the classical approaches where entire graph is represented in single entities such as adjacency matrix or incidence matrix, the proposed approach represents subgraphs of the graph into multiple entities. An algorithm is designed to generate the proposed representation from the raw data where relationships among objects are identified as edges. The time complexity of the algorithm is bounded by the best case Θ(m) and worst case O(mk), where m is the number of edges in the graph and k is the number of subgraphs generated for the graph. The methodology is further extended for generating the proposed representation with already available edge list or adjacency matrix. Theoretical, as well as empirical analysis on eight real-world graphs, show efficiency of the proposed approach in terms of memory requirement over existing representations.
Multimodal N-best List Rescoring with Weakly Supervised Pre-training in Hybrid Speech Recognition

N-best list rescoring, an essential step in hybrid automatic speech recognition (ASR), aims to re-evaluate the N-best hypothesis list decoded by the acoustic model (AM) and language model (LM), and selects the top-ranked hypotheses as the final ASR results. This paper explores the performance of neural rescoring models in scenarios where large-scale training labels are not available. We propose a weakly supervised neural rescoring method, WSNeuRescore, where a listwise multimodal neural rescoring model is pre-trained using labels automatically obtained without human annotators. Specifically, we employ the output of an unsupervised rescoring model, the weighted linear combination of the AM score and the LM score, as a weak supervision signal to pre-train the neural rescoring model. Our experimental evaluations on a public dataset validate that the pre-trained rescoring model based on weakly supervised data leads to an impressive performance. In the extreme scenario without any high-quality labeled data, it achieves up to an 11.90% WER reduction and a 15.56% NDCG@10 improvement over the baseline method in Kaldi, a well-known open-source toolkit in the ASR community.
An Introduction to Simulating Transitions to Agriculture in Prehistory

Most models or explanations of the past attempt to integrate a wide array of social and ecological processes while relying on modest amounts of evidence. This makes the validation of such models challenging, especially when explanations apply to wide geographical or chronological contexts, such as the transition to agriculture across Europe. Computational modeling allows for the formalization and testing of conceptual models while allowing other researchers to replicate that research. The formalization and open availability of such models is critical for a better understanding of this transition and large-scale complex processes in the past. We review successful attempts to use computational modeling in archaeology and outline the benefits of using computational models to understand the past.
Analyzing Code-mixing in Linguistic Corpora Using Kratylos

Code-switching, code-mixing, and, more generally, multilingualism pose technological challenges for language documentation, the sub-discipline of linguistics that deals with the annotation and basic analysis of field recordings and other primary data. We focus here on a case study involving code-mixing in the endangered Koda language, which poses special problems for morphosyntactic analysis. We offer a robust approach to multilingual annotations that involves a combination of the popular open source software FieldWorks Language Explorer (FLEx) with Kratylos, a web-based corpus tool for display and query. Kratylos exposes linguistic data from various formats to powerful regular-expression queries that can exploit tier structure and other aspects of interlinear glossed text. We show how Kratylos can target mixed structures in our FLEx database of Koda that cannot be easily identified within the original FLEx software itself.
Anomaly Detection for Early Warning in Object-oriented Programming Course

As one mainstream of current software development, Object-oriented programming has become one key course for undergraduate students in Computer Science. Since Object-oriented concepts are difficult to understand for students, small programming exercises are used to train and help the students, and the study performance is evaluated based on the quality of the submitted source code. The common practice of code assess-ment in programming courses is checking whether the submitted projects pass carefully-designed test cases. However, even some projects pass all test cases, they may have bad software design and do not use the knowledge of Object-oriented programming well, especially in the early stage of courses. Therefore, we propose an anomaly detection approach for early warning in Object-oriented programming courses, which can automatically find the abnormal application of Object-oriented knowledge. In our approach, we conduct static analysis on the code submitted by students. Typical Objected-oriented metrics are extracted, and students are divided into two groups by K-means clustering: being good at Object-objected knowledge or not, and finally detect anomalous students based on the distance from cluster centers. We evaluate our approach on the realistic data sets collected from our Object-oriented programming course, and experimental results show the effectiveness of our method.
Pendampingan Penerapan Aplikasi Pembelajaran Bahasa Inggris Berbasis IT bagi Guru Bahasa Inggris

Background: Distance learning has been a major source of learning since the covid-19 pandemic hit the world. Unfortunately, more research shows that distance learning faces many obstacles, so learning goals are not achieved. This community service aims to assist English teachers in implementing IT-based English learning applications, namely Chatbot and Smart Rector. The teachers chosen as partners are teachers of Lamongan State MTs 2. Method: The implementation of this devotion follows the steps, that is; conduct preliminary research, conduct needs analysis, hold seminars, hold workshops, and then conduct monitoring and evaluation. Results: Teachers understand and master the concept and application of e-learning methods by using mobile devices such as smart phones, tablets, and laptops. Conclusion: The knowledge of English teachers at MTS Negeri 2 Lamongan has significantly improved in terms of application knowledge and e-learning methods using mobile devices.
Research on Path Planning Algorithm of Intelligent Wheeled Robot Based on ROS

Aiming at some limitations of the A-star algorithm, the algorithm is improved and optimized, robot path planning experiment is carried out. In the traditional A-star algorithm, there are too many turning points of the path and the path is not smooth enough, the car deviates from the path when navigating due to the arc at the corner is too large. This paper combines the conjugate gradient and Gaussian process principle to optimize the A-star algorithm and eliminate the invalid path information to jump points, to implement a path with less angle and smoother. The results show that the hybrid algorithm has a shorter and smoother path than the A-star algorithm.
An Optimized Training Dynamic for Data Streams

There are severe computational restrictions that are seen in Online Learning events. Nowadays, many researchers have engaged in this learning mostly due to the significance of real-world applications that deal with context changes over time on data streams. The adaptation of the current model is a strategy to deal with concept drifts in data streams, so at every concept drift, the old model is replaced by a new one. In this article, an optimized training dynamic that can choose if it is better to use or ignore detectors' Warning signals is presented to ensure a minimum amount of training or the traditional Warning instances to improve the manner of change's adaption. The strategy admits a fair evaluation connecting improvements in Prequential accuracy and concept drift signals, in addition, increases the concept drift detection methods' performance rate. The proposal algorithm's results indicate an improvement contribution to the Prequential accuracy. As well, this paper allows us to notice a better performance with Warning signals application if compared to not Warning signals application, showing their critical role to these contexts. Here, it could be seen the evaluation with DDM, FHDDM, and RDDM methods, the evaluations frequently have statistical superiorities to the optimized dynamic and its choices power if compared to the traditional approach that only contemplates the Warning signals. Although, in some cases, RDDM did not have the same behavior.
Learning to Reweight Samples with Offline Loss Sequence

Deep neural networks (DNNs) provide the best of class solutions to many supervised tasks due to their powerful function fitting capabilities. However, it is challenging to handle data bias, such as label noise and class imbalance, when applying DNNs to solve real-world problems. Sample reweighting is a popular strategy to tackle data bias, which assigns higher weights to informative samples or samples with clean labels. However, conventional reweighting methods require prior knowledge of the distribution information of data bias, which is intractable in practice. In recent years, meta-learning-based methods have been proposed to learn to assign weights to training samples adaptively by using their online training loss or gradient directions. However, the latent bias distribution cannot be adequately characterized in an online fashion. The online loss distribution changes over the training procedure, making it even harder to perform the sample weight learning. In contrast to past methods, we propose a two-stage training strategy to tackle the above problems. In the first stage, the loss sequences of samples are collected. In the second stage, a subnet with convolutional layers is utilized to learn the mapping from offline sample loss sequence to sample weight adaptively. Guided by a small unbiased meta dataset, this subnet is optimized iteratively with the main classifier network in a meta-learning manner. Empirical results show that our method, called Meta Reweighting with Offline Loss Sequence (MROLS), outperforms state-of-the-art reweighting techniques on most benchmarks. Moreover, the weights of training samples learned via MROLS can be well utilized by other classifiers, which can directly enhance the standard training schema. Our source code is available at https://github.com/Neronjust2017/MROLS.
Single Document Extractive Summarization Model Based on Heterogeneous Graph Transformer

Abstract At present, the graph model-based summary model has problems such as insufficient semantic fusion between nodes and lack of location information. Therefore, this paper proposes a single-document extraction text summary model based on a heterogeneous graph attention neural network, using HGT (Heterogeneous Graph Transformer), Heterogeneous Graph Attention Neural Network to solve the defect of insufficient deep semantic fusion of nodes, and use trainable position coding to solve the defect of missing position information. Experiments show that the model in this paper has improved on the three evaluation indicators of R_1, R_2 and R_L, and the abstracts extracted have better generality.