Reviewerly/exHarmony huggingface dataset:
The blockchain conundrum: humans, community regulation and chains

Blockchain can be used to build a human-centric future. This is a challenge to recent critical literature on blockchain that sees it as another manifestation of digital capitalism that is profoundly antisocial and anti-human. This argument is in three parts. The first part identifies in the hype and critical literatures about blockchain, the blockchain conundrum of the freedom/constraint dyad. While tempting to see these literatures as forming a sealed hermeneutic of over-positive meets over-negative, it is argued that the critical discourse in locating blockchain within digital capitalism provides an insight that could unravel the blockchain conundrum. The critical literature identifies regulation as essential for human blockchain futures. The second part unravels the blockchain conundrum through this focus on regulation–through two accounts of law, technology and society; Lessig’s notion of actors as ‘pathetic dots’ and Brownsword’s reimaging of regulation in technological societies. It is suggested that Brownsword’s emphasis provides a more nuanced way to make human-centric blockchain futures. The final part builds from Brownsword’s resolution of the blockchain conundrum, to examine a particular blockchain application in retail supply (BeefLedger) as representing assemblages including blockchains in building human-centric futures through trusted communities that enable, rather than restrict, meaningful human action.
Performance prediction strengths of noun and verb phrases in L2 writing: Comparison of density and complexity variables

This study aimed to compare different noun and verb phrase density and complexity variables regarding if and how strongly they could predict L2 writing performance. 183 short literary analysis essays with analytically-obtained scores, written in an English as a Foreign Language context, were used as the corpus of the study. The scores were divided as low, mediocre and high scores by K-means cluster analysis. Separate regression models were built for each group using Complex Nominal per T-unit, Complex Nominal per Clause, Noun Phrase per Sentence, Noun Phrase per 1000 Words, Verb Phrase per T-unit, Verb Phrase per Sentence and Verb Phrase per 1000 Word as predictor variables. A multivariate model of noun phrase complexity was also tested. The results showed that Complex Nominal per T-unit was the strongest predictor of high performance. Verb Phrase per 1000 Words was the only significant verb phrase-based (negative) predictor of high performance. Mediocre performance could only be predicted by Complex Nominal per Clause. Low performance could not be predicted by any of the noun or verb phrase-based variables. The results confirm that noun phrase complexity develops across performance levels; however, more traditional (length based) complexity indices may be more useful to explain low performance.
Analysis children with disabilities self-care problems based on selected data mining techniques

Children’s disabilities is a very important task. Lack of appropriate diagnosis and care increases children’s difficulties in adjusting to disability. Children with physical and motor disabilities have many problems in their lives related to basic daily activities. Therefore, it is very important to properly classify self-care problems. Such classification requires appropriately trained people and time. Data mining techniques can be helpful to support such diagnosis. Classification and selection of appropriate features can significantly support the work of diagnosticians and therapists. In this paper we attempted to build classification models based on all available instances as well as on instances obtained by feature selection. The most accurate model obtained, correctly classifies more than 85% of children with self-care problems, using only 19 characteristic features.
Attention Is Indeed All You Need: Semantically Attention-Guided Decoding for Data-to-Text NLG.

Ever since neural models were adopted in data-to-text language generation, they have invariably been reliant on extrinsic components to improve their semantic accuracy, because the models normally do not exhibit the ability to generate text that reliably mentions all of the information provided in the input. In this paper, we propose a novel decoding method that extracts interpretable information from encoder-decoder models' cross-attention, and uses it to infer which attributes are mentioned in the generated text, which is subsequently used to rescore beam hypotheses. Using this decoding method with T5 and BART, we show on three datasets its ability to dramatically reduce semantic errors in the generated outputs, while maintaining their state-of-the-art quality.
Design and implementation of dynamic teaching model of diesel engine based on 3D printing technology

Abstract In view of the shortage of models of practical installation and teaching AIDS in the teaching and training of traditional naval ship diesel engines, the 3D printing technology is integrated with the educational concept of this course, and a multi-functional diesel engine teaching AIDS model suitable for classroom teaching is designed and manufactured, and its design method and process are expounded. A set of models of diesel engine teaching AIDS are designed and manufactured by using this method and applied in classroom teaching. The teaching effect is good, the learners’ enthusiasm is stimulated, and the teaching and training quality is improved.
Paralingam: Parallel Causal Structure Learning for Linear Non-Gaussian Acyclic Models

One of the key objectives in many fields in machine learning is to discover causal relationships among a set of variables from observational data. In linear non-Gaussian acyclic models (LiNGAM), it can be shown that the true underlying causal structure can be identified uniquely from merely observational data. DirectLiNGAM algorithm is a well-known solution to learn the true causal structure in high dimensional setting. DirectLiNGAM algorithm executes in a sequence of iterations and it performs a set of comparisons between pairs of variables in each iteration. Unfortunately, the runtime of this algorithm grows significantly as the number of variables increases. In this paper, we propose a parallel algorithm, called ParaLiNGAM, to learn casual structures based on DirectLiNGAM algorithm. We propose a threshold mechanism that can reduce the number of comparisons remarkably compared with the sequential solution. Moreover, in order to further reduce runtime, we employ a messaging mechanism between workers and derive some mathematical formulations to simplify the execution of comparisons. We also present an implementation of ParaLiNGAM on GPU, considering hardware constraints. Experimental results on synthetic and real data show that the implementation of proposed algorithm on GPU can outperform DirectLiNGAM by a factor up to 4600 X.
Text-based NP Enrichment

Abstract Understanding the relations between entities denoted by NPs in a text is a critical part of human-like natural language understanding. However, only a fraction of such relations is covered by standard NLP tasks and benchmarks nowadays. In this work, we propose a novel task termed text-based NP enrichment (TNE), in which we aim to enrich each NP in a text with all the preposition-mediated relations—either explicit or implicit—that hold between it and other NPs in the text. The relations are represented as triplets, each denoted by two NPs related via a preposition. Humans recover such relations seamlessly, while current state-of-the-art models struggle with them due to the implicit nature of the problem. We build the first large-scale dataset for the problem, provide the formal framing and scope of annotation, analyze the data, and report the results of fine-tuned language models on the task, demonstrating the challenge it poses to current technology. A webpage with a data-exploration UI, a demo, and links to the code, models, and leaderboard, to foster further research into this challenging problem can be found at: yanaiela.github.io/TNE/.
Automatic Hate Speech Detection: A Literature Review

Hate speech has been an ongoing problem on the Internet for many years. Besides, social media, especially Facebook, and Twitter have given it a global stage where those hate speeches can spread far more rapidly. Every social media platform needs to implement an effective hate speech detection system to remove offensive content in real-time. There are various approaches to identify hate speech, such as Rule-Based, Machine Learning based, deep learning based and Hybrid approach. Since this is a review paper, we explained the valuable works of various authors who have invested their valuable time in studying to identifying hate speech using various approaches.
Solidity : Smart Contract Language or Legal Contract Language

This article primarily focuses on how to set up a smart contract with Solidity. Considering that the usage and examples of smart contracts are increasing day by day, the number of contracts written with Solidity is increasing at the same rate. These contracts, in which computers describe the will of the parties in codes, have a relieving effect for the legal sector as well. Although “language” is the basis of the law, smart contracts are perfect for contracts that can be put forward concretely without human emotions. In this article, I started by defining what a smart contract is. Based on the definition of smart contract, I wrote in which sectors and in which projects smart contracts are used. Finally, I introduced the Solidity programming language, which is the main purpose of this article, and I wrote and explained the Inheritance Contract with Solidity.
Unsupervised Semantic Segmentation by Contrasting Object Mask Proposals

Being able to learn dense semantic representations of images without supervision is an important problem in computer vision. However, despite its significance, this problem remains rather unexplored, with a few exceptions that considered unsupervised semantic segmentation on small-scale datasets with a narrow visual domain. In this paper, we make a first attempt to tackle the problem on datasets that have been traditionally utilized for the supervised case. To achieve this, we introduce a two-step framework that adopts a predetermined mid-level prior in a contrastive optimization objective to learn pixel embeddings. This marks a large deviation from existing works that relied on proxy tasks or end-to-end clustering. Additionally, we argue about the importance of having a prior that contains information about objects, or their parts, and discuss several possibilities to obtain such a prior in an unsupervised manner. Experimental evaluation shows that our method comes with key advantages over existing works. First, the learned pixel embeddings can be directly clustered in semantic groups using K-Means on PASCAL. Under the fully unsupervised setting, there is no precedent in solving the semantic segmentation task on such a challenging benchmark. Second, our representations can improve over strong baselines when transferred to new datasets, e.g. COCO and DAVIS. The code is available.
Leveraging Information and Communication Technology for Higher Education amidst the COVID-19 Pandemic

Lockdown was seen as the only choice in view of increasing fears regarding the spread of the Novel Coronavirus (Covid-19) and the attempts to suppress the virus. With that, all higher learning institutions have to face global shutdowns. The virus revealed emerging vulnerabilities of higher learning institutions around the globe. It has now become evident that a resilient education system is needed as we head to face an unpredictable future. In the unfolding global crisis, the explosion of emerging modes of Information and Communication Technology (ICT) has flooded students’ learning habits at all levels, and especially at the higher education level. The damage created by the announcement of the pandemic and subsequent public health interventions placed in effect has had a direct influence on the willingness of teachers to promote the interest of students in technology education. Despite all these challenges, the Higher Education Institutions (HEIs) have reacted positively and managed to ensure the continuity of teaching–learning, research and service to the society with some tools and techniques during the pandemic. Many different ways of learning, new insights, new patterns have arisen as a consequence of the Covid-19 pandemic, and the same may continue as we move on to a new tomorrow. To encourage their involuntary transfer to virtual classrooms and to preserve acceptable online learning habits and protocols, some methods have been created for students. Several innovations have been built in the educational field to provide distance learning to students at “any time anywhere” environments. In order to assess potential priorities and development strategies, it is important to consider how certain tools have been utilized to handle the pandemic.
Privacy-Preserving Federated Learning

In this chapter, we introduce the emerging application of privacy-preserving federated learning in a coordinated way among multi-party. We suggest the function specific Privacy-Preserving Federated Learning (PPFL) to provide fairness,integrity, correctness, adaptiveness and flexibility. Application specific PPFL includes mobile devices, medical imaging, traffic flow prediction and healthcare, Android malware detection, and edge computing.
Design and Implementation of Crawler Program Based on Python

With the development of computer and network technology, we often get information through the Internet. However, it is difficult for us to obtain valuable information from massive amounts of data because of the large amount of network data and complex formats. At present, researches found that web crawler technology can be automatically obtained information from internet. In this paper we takes the crawling of second-hand housing information of Anjuke Xi'an as an example. According to the crawler principle and process, the structure of Anjuke's page is first analyzed, using requests to obtain web pages, lxml to analyze web pages and SQL Server 2017 to store data to design and implement a network. The crawler program collects and stores housing information in some cities in East China through this program, and finally analyzes the housing price trend through the collected data through Excel. The results show that this program can automatically obtain housing information from the Internet, which provides a data source for later data analysis.
Multi-agent Conformant Planning with Distributed Knowledge

In this paper, we study the evolution of knowledge in multi-agent conformant planning over transition systems. We propose a dynamic epistemic logical framework with modalities of distributed knowledge to handle the epistemic reasoning in such scenarios, and we reduce a problem of multi-agent conformant planning to a model checking problem. We prove that multi-agent conformant planning is Pspace-complete on the size of the dynamic epistemic model.
Potential Fault Diagnosis Method and Classification Accuracy Detection of IGBT Device Based on Improved Single Hidden Layer Feedforward Neural Network

Insulated Gate Bipolar Transistor (IGBT) is a high-power switch in the field of power electronics. Its reliability is closely related to system stability. Once failure occurs, it may cause irreparable loss. Therefore, potential fault diagnosis methods of IGBT devices are studied in this paper, and their classification accuracy is tested. Due to the shortcomings of incomplete data application in the traditional method of characterizing the device state based on point frequency parameters, a fault diagnosis method based on full frequency threshold screening was proposed in this paper, and its classification accuracy was detected by the Extreme Learning Machine (ELM) algorithm. The randomly generated input layer weight ω and hidden layer deviation lead to the change of output layer weight β and then affect the overall output result. In view of the errors and instability caused by this randomness, an improved Finite Impulse Response Filter ELM (FIR-ELM) training algorithm is proposed. The filtering technique is used to initialize the input weights of the Single Hidden Layer Feedforward Neural Network (SLFN). The hidden layer of SLFN is used as the preprocessor to achieve the minimum output error. To reduce the structural risk and empirical risk of SLFN, the simulation results of 300 groups of spectral data show that the improved FIR-ELM algorithm significantly improves the training accuracy and has good robustness compared with the traditional extreme learning machine algorithm.
Triple-O for SHL Recognition Challenge: An Ensemble Framework for Multi-class Imbalance and Training-testing Distribution Inconsistency by OvO Binarization with Confidence Weight of One-class Classification

SHL Recognition Challenge provides phone sensor data for recognizing eight modes of locomotion and transportation(activities). Most motion sensor data is user-dependent, so the model’s generalization ability requires various data at the user level. The SHL dataset is collected only by three users, and category imbalance and distribution gap between training data and testing data can be significant obstacles for this challenge. In comparison to exploring the large and deep model structure, some model-free tricks play a more critical role in the previous challenges, such as transfer learning, re-sampling and anti-overfit trick. SHL challenge also notices that problem and provides user-independent sensor data in 2021. This paper analyses the data and finds out that category imbalance and distribution inconsistency are still the obstacles. This paper focuses on evaluating different methods to improve the general predicting ability at the setting of category imbalance and training-testing distribution inconsistency. Besides, this paper puts forward a new ensemble framework called triple-O, using OvO binarization and one-class classification. The results show that the OvO binarization ensemble gets better results on the hard-to-distinguish class than re-sampling and re-weighting. One-class classification can be an anomaly detection to re-weight the meta learners to tackle the distribution gap. Triple-O can be plug-and-play and pave the way for exploring complex model structures. This paper introduces the solutions from the team GoodGoodDriveDayDayUp.
Instances As Queries

Recently, query based object detection frameworks achieve comparable performance with previous state-of-the-art object detectors. However, how to fully leverage such frameworks to perform instance segmentation remains an open problem. In this paper, we present QueryInst (Instances as Queries), a query based instance segmentation method driven by parallel supervision on dynamic mask heads. The key insight of QueryInst is to leverage the intrinsic one-to-one correspondence in object queries across different stages, as well as one-to-one correspondence between mask RoI features and object queries in the same stage. This approach eliminates the explicit multi-stage mask head connection and the proposal distribution inconsistency issues inherent in non-query based multi-stage instance segmentation methods. We conduct extensive experiments on three challenging benchmarks, i.e., COCO, CityScapes, and YouTube-VIS to evaluate the effectiveness of QueryInst in instance segmentation and video instance segmentation (VIS) task. Specifically, using ResNet-101-FPN backbone, QueryInst obtains 48.1 box AP and 42.8 mask AP on COCO test-dev, which is 2 points higher than HTC in terms of both box AP and mask AP, while runs 2.4 times faster. For video instance segmentation, QueryInst achieves the best performance among all online VIS approaches and strikes a decent speed-accuracy trade-off. Code is available at \url{this https URL}.
Passive Video Forgery Detection Techniques to Detect Copy Move Tampering Through Feature Comparison and RANSAC

With the advancement of innovation, these days respectability of digitized information has been addressed from various perspectives. With the assistance of innovation, the integrity level of advanced video can be disturbed from various perspectives. Video tempering performs with two different ways: one is frame level and other is changing the sequence of frames to hide or highlight the specific aim of the original video. In some cases, the original information of video frames is altered and afterward pasted at some other area of the same video. The proposed framework completed two stages of calculation to distinguish the doctored frames. Feature extraction is executed as the initial steps of the proposed framework. Correlation of separated features will assist with distinguishing the altered frames. In the subsequent advance, each frame will be compared with one before and after frames with the assistance of extricated highlights to check the measure of changes in the tempered frames all through the video stream. To check the uprightness of a video, include correlation and RANSAC strategies are utilized and it shows huge outcomes distinctive when checked with the video which altered and not altered. With the utilization of this strategy, we can ensure the originality of the advanced video when it assumes a significant part as proof or verification in specific conditions.
Weight and Gradient Centralization in Deep Neural Networks

Batch normalization is currently the most widely used variant of internal normalization for deep neural networks. Additional work has shown that the normalization of weights and additional conditioning as well as the normalization of gradients further improve the generalization. In this work, we combine several of these methods and thereby increase the generalization of the networks. The advantage of the newer methods compared to the batch normalization is not only increased generalization, but also that these methods only have to be applied during training and, therefore, do not influence the running time during use. https://atreus.informatik.uni-tuebingen.de/seafile/d/8e2ab8c3fdd444e1a135/?p=%2FWeightAndGradientCentralization&mode=list.
University students’ perceptions of the effects of online lecture types and its media types

This study examined undergraduate students’ perceptions of effects of online course types such as face-to-face classes, online classes, and blended classes using survey data from 331 students during three semesters from 2020 to 2021. There were differences in effects using ‘PPT video’, ‘voice only in PPT’, and ‘real-time zoom’ between theoretical courses and practice courses. In terms of media types for online classes, ‘PPT video’ was perceived as the most effective type by students, followed by ‘voice only in PPT’, ‘real-time video’, and ‘blended media use’ meaning a 2-hour-course mixed with 1-hour PPT video and 1-hour-real-time video. The effectiveness of ‘real-time video’ and ‘blended media use’ was gradually decreased as time went by. Students had difficulties in academic achievement and daily life with online learning. They were eager to return to face-to-face classes after COVID-19 passes. Some online learning media types were preferable to use for face-to-face classes after COVID-19.