Reviewerly/exHarmony huggingface dataset:
Learning from others' mistakes: Avoiding dataset biases without modeling them

State-of-the-art natural language processing (NLP) models often learn to model dataset biases and surface form correlations instead of features that target the intended underlying task. Previous work has demonstrated effective methods to circumvent these issues when knowledge of the bias is available. We consider cases where the bias issues may not be explicitly identified, and show a method for training models that learn to ignore these problematic correlations. Our approach relies on the observation that models with limited capacity primarily learn to exploit biases in the dataset. We can leverage the errors of such limited capacity models to train a more robust model in a product of experts, thus bypassing the need to hand-craft a biased model. We show the effectiveness of this method to retain improvements in out-of-distribution settings even if no particular bias is targeted by the biased model.
High-speed 3D shape measurement using a rotary mechanical projector

In this paper, a fast rotary mechanical projector (RMP) is designed and manufactured for high-speed 3D shape measurement. Compared with the common high-speed projectors, RMP has a good performance in high-speed projection, which can obtain high quality projected fringes with shorter camera exposure time by using the error diffusion binary coding method and chrome plating technology. The magnitude, acceptability of systemic projection error is analyzed and quantified in detail. For the quantified error, the probability distribution function (PDF) algorithm is introduced to correct the error. Corrected projection error is reduced to more than one third of the original error. Subsequently, a monocular measurement system composed of the RMP and a single camera is constructed. The combination of the RMP device and PDF algorithm ensure the accuracy of a corresponding 3D shape measurement system. Experiments have demonstrated that the proposed solution has a good performance for the 3D measurement of high-speed scenes.
Video Captioning Based on Channel Soft Attention and Semantic Reconstructor

Video captioning is a popular task which automatically generates a natural-language sentence to describe video content. Previous video captioning works mainly use the encoder–decoder framework and exploit special techniques such as attention mechanisms to improve the quality of generated sentences. In addition, most attention mechanisms focus on global features and spatial features. However, global features are usually fully connected features. Recurrent convolution networks (RCNs) receive 3-dimensional features as input at each time step, but the temporal structure of each channel at each time step has been ignored, which provide temporal relation information of each channel. In this paper, a video captioning model based on channel soft attention and semantic reconstructor is proposed, which considers the global information for each channel. In a video feature map sequence, the same channel of every time step is generated by the same convolutional kernel. We selectively collect the features generated by each convolutional kernel and then input the weighted sum of each channel to RCN at each time step to encode video representation. Furthermore, a semantic reconstructor is proposed to rebuild semantic vectors to ensure the integrity of semantic information in the training process, which takes advantage of both forward (semantic to sentence) and backward (sentence to semantic) flows. Experimental results on popular datasets MSVD and MSR-VTT demonstrate the effectiveness and feasibility of our model.
RecPOID: POI Recommendation with Friendship Aware and Deep CNN

In location-based social networks (LBSNs), exploit several key features of points-of-interest (POIs) and users on precise POI recommendation be significant. In this work, a novel POI recommendation pipeline based on the convolutional neural network named RecPOID is proposed, which can recommend an accurate sequence of top-k POIs and considers only the effect of the most similar pattern friendship rather than all user’s friendship. We use the fuzzy c-mean clustering method to find the similarity. Temporal and spatial features of similar friends are fed to our Deep CNN model. The 10-layer convolutional neural network can predict longitude and latitude and the Id of the next proper locations; after that, based on the shortest time distance from a similar pattern’s friendship, select the smallest distance locations. The proposed structure uses six features, including user’s ID, month, day, hour, minute, and second of visiting time by each user as inputs. RecPOID based on two accessible LBSNs datasets is evaluated. Experimental outcomes illustrate considering most similar friendship could improve the accuracy of recommendations and the proposed RecPOID for POI recommendation outperforms state-of-the-art approaches.
TDMSci: A Specialized Corpus for Scientific Literature Entity Tagging of Tasks Datasets and Metrics

Tasks, Datasets and Evaluation Metrics are important concepts for understanding experimental scientific papers.However, most previous work on information extraction for scientific literature mainly focuses on the abstracts only, and does not treat datasets as a separate type of entity (Zadeh and Schumann, 2016;Luan et al., 2018).In this paper, we present a new corpus that contains domain expert annotations for Task (T), Dataset (D), Metric (M) entities on 2,000 sentences extracted from NLP papers.We report experiment results on TDM extraction using a simple data augmentation strategy and apply our tagger to around 30,000 NLP papers from the ACL Anthology.The corpus is made publicly available to the community for fostering research on scientific publication summarization (Erera et al., 2019) and knowledge discovery.
Automatic classification of plutonic rocks with deep learning

Igneous rocks form when molten magma is cooled and solidified, either within the Earth’s crust (plutonic rocks), or from lava extruded onto the Earth’s surface in the atmosphere or underwater (volcanic rocks). The classification of igneous rocks can be done using data from different instrumental techniques. However, these approaches tend to be expensive and time-consuming. In this research work, several models for the classification of granitoids, which are the most abundant plutonic rocks in the Earth’s crust, were created with a convolutional neural network developed with TensorFlow. Specifically, several combinations of gabbro, diorite, tonalite, granodiorite, monzodiorite, and granite image samples were used in the experiments. The best result was obtained in the model that classifies images of gabbro, diorite, granodiorite, and granite with an accuracy value of 95%, an average precision value of 96%, an average recall value of 95%, and an average F1 score value of 95%.
Efficiency aware scheduling techniques in cloud computing: a descriptive literature review

In the last decade, cloud computing becomes the most demanding platform to resolve issues and manage requests across the Internet. Cloud computing takes along terrific opportunities to run cost-effective scientific workflows without the requirement of possessing any set-up for customers. It makes available virtually unlimited resources that can be attained, organized, and used as required. Resource scheduling plays a fundamental role in the well-organized allocation of resources to every task in the cloud environment. However along with these gains many challenges are required to be considered to propose an efficient scheduling algorithm. An efficient Scheduling algorithm must enhance the implementation of goals like scheduling cost, load balancing, makespan time, security awareness, energy consumption, reliability, service level agreement maintenance, etc. To achieve the aforementioned goals many state-of-the-art scheduling techniques have been proposed based upon hybrid, heuristic, and meta-heuristic approaches. This work reviewed existing algorithms from the perspective of the scheduling objective and strategies. We conduct a comparative analysis of existing strategies along with the outcomes they provide. We highlight the drawbacks for insight into further research and open challenges. The findings aid researchers by providing a roadmap to propose efficient scheduling algorithms.
Towards an Unsupervised Feature Selection Method for Effective Dynamic Features

Dynamic features applications present new obstacles for the selection of streaming features. The dynamic features applications have various characteristics: a) features are processed sequentially while the number of instances is fixed; and b) the feature space does not exist in advance. For example, in a text classification task for spam detection, new features (e.g. words) are dynamically generated and therefore need to be mined to filter out the spams rather than waiting for all features to be collected in order to do so. Traditional feature selection methods, which are not designed for streaming features applications, cannot be used in such an environment, as they require the full feature space in advance in order to statistically determine the representative features. Existing methods that address feature selection in dynamic features applications require the class labels in order to select the representative features. However, most of the real-life data is unlabeled and it is costly to apply manual labeling. In this paper, an efficient unsupervised features selection method is proposed for streaming features applications where the number of features increases while the number of instances remains fixed. In particular, unsupervised Feature Selection for Dynamic Features (UFSSF) is developed to determine the representative streaming features without requiring prior knowledge about data class labels or representative features. The UFSSF extends the k-mean clustering to cumulatively determine whether the newly-arrived feature can be selected as a representative streaming feature, or discarded. Experimental results show significant accuracy results and efficient execution time compared to those of other benchmark methods.
Fermionic systems for quantum information people

Abstract The operator algebra of fermionic modes is isomorphic to that of qubits, the difference between them is twofold: the embedding of subalgebras corresponding to mode subsets and multiqubit subsystems on the one hand, and the parity superselection in the fermionic case on the other. We discuss these two fundamental differences extensively, and illustrate these through the Jordan–Wigner representation in a coherent, self-contained, pedagogical way, from the point of view of quantum information theory. Our perspective leads us to develop useful new tools for the treatment of fermionic systems, such as the fermionic (quasi-)tensor product, fermionic canonical embedding, fermionic partial trace, fermionic products of maps and fermionic embeddings of maps. We formulate these by direct, easily applicable formulas, without mode permutations, for arbitrary partitionings of the modes. It is also shown that fermionic reduced states can be calculated by the fermionic partial trace, containing the proper phase factors. We also consider variants of the notions of fermionic mode correlation and entanglement, which can be endowed with the usual, local operation based motivation, if the parity superselection rule is imposed. We also elucidate some other fundamental points, related to joint map extensions, which make the parity superselection inevitable in the description of fermionic systems.
Community evolution on Stack Overflow

Question and answer (Q&amp;A) websites are a medium where people can communicate and help each other. Stack Overflow is one of the most popular Q&amp;A websites about programming, where millions of developers seek help or provide valuable assistance. Activity on the Stack Overflow website is moderated by the user community, utilizing a voting system to promote high quality content. The website was created on 2008 and has accumulated a large amount of crowd wisdom about the software development industry. Here we analyse this data to examine trends in the grouping of technologies and their users into different sub-communities. In our work we analysed all questions, answers, votes and tags from Stack Overflow between 2008 and 2020. We generated a series of user-technology interaction graphs and applied community detection algorithms to identify the biggest user communities for each year, to examine which technologies those communities incorporate, how they are interconnected and how they evolve through time. The biggest and most persistent communities were related to web development. In general, there is little movement between communities; users tend to either stay within the same community or not acquire any score at all. Community evolution reveals the popularity of different programming languages and frameworks on Stack Overflow over time. These findings give insight into the user community on Stack Overflow and reveal long-term trends on the software development industry.
An Investigation into the Energy-Efficient Motion of Autonomous Wheeled Mobile Robots

In recent years, the use of electric Autonomous Wheeled Mobile Robots (AWMRs) has dramatically increased in transport of the production chain. Generally, AWMRs must operate for several hours on a single battery charge. Since the energy density of the battery is limited, energy efficiency becomes a key element in improving material transportation performance during the manufacturing process. However, energy consumption is influenced by the navigation stages, because the type of motion necessary for the AWMR to perform during a mission is totally defined by these stages. Therefore, this paper analyzes methods of energy efficiency that have been studied recently for AWMR navigation stages. The selected publications are classified into planning and motion control categories in order to identify research gaps. Unlike other similar studies, this work focuses on these methods with respect to their implications for the energy consumption of AWMRs. In addition, by using an industrial Self-Guided Vehicle (SGV), we illustrate the direct influence of the motion planning stage on global energy consumption by means of several simulations and experiments. The results indicate that the reaction of the SGV in response to unforeseen obstacles can affect the amount of energy consumed. Hence, energy constraints must be considered when developing the motion planning of AWMRs.
The Holdout Randomization Test for Feature Selection in Black Box Models

We propose the holdout randomization test (HRT), an approach to feature selection using black box predictive models. The HRT is a specialized version of the conditional randomization test (CRT) that uses data splitting for feasible computation. The HRT works with any predictive model and produces a valid p-value for each feature. To make the HRT more practical, we propose a set of extensions to maximize power and speed up computation. In simulations, these extensions lead to greater power than a competing knockoffs-based approach, without sacrificing control of the error rate. We apply the HRT to two case studies from the scientific literature where heuristics were originally used to select important features for predictive models. The results illustrate how such heuristics can be misleading relative to principled methods like the HRT. Code is available at https://github.com/tansey/hrt. Supplementary materials for this article are available online.
Domain Contrast for Domain Adaptive Object Detection

Despite of the substantial progress of visual object detection, models trained in one video domain often fail to generalize well to others due to the change of camera configurations, lighting conditions, and object person views. In this paper, we present Domain Contrast (DC), a simple yet effective approach inspired by contrastive learning for training domain adaptive detectors. DC is deduced from the error bound minimization perspective of a transferred model, and is implemented with cross-domain contrast loss which is plug-and-play. By minimizing cross-domain contrast loss, DC transfers detectors across domains while naturally alleviating the class imbalance issue in the target domain. DC can be applied at either image level or region level, consistently improving detectors’ discriminability while maintaining the transferability. Extensive experiments on commonly used benchmarks show that DC improves the baseline and state-of-the-art by significant margins, while demonstrating great potential for large domain divergence. Code is released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/PhoneSix/Domain-Contrast</uri> .
GLGE: A New General Language Generation Evaluation Benchmark

Multi-task benchmarks such as GLUE and Su-perGLUE have driven great progress of pretraining and transfer learning in Natural Language Processing (NLP).These benchmarks mostly focus on a range of Natural Language Understanding (NLU) tasks, without considering the Natural Language Generation (NLG) models.In this paper, we present the General Language Generation Evaluation (GLGE), a new multi-task benchmark for evaluating the generalization capabilities of NLG models across eight language generation tasks.For each task, we continue to design three subtasks in terms of task difficulty (GLGE-Easy, GLGE-Medium, and GLGE-Hard).This introduces 24 subtasks to comprehensively compare model performance.To encourage research on pretraining and transfer learning on NLG models, we make GLGE publicly available and build a leaderboard with strong baselines including MASS, BART, and Prophet-Net 1 .
TreeCaps: Tree-Based Capsule Networks for Source Code Processing

Recently program learning techniques have been proposed to process source code based on syntactical structures (e.g., abstract syntax trees) and/or semantic information (e.g., dependency graphs). While graphs may be better than trees at capturing code semantics, constructing the graphs from code inputs through the semantic analysis of multiple viewpoints can lead to inaccurate noises for a specific software engineering task. Compared to graphs, syntax trees are more precisely defined on the grammar and easier to parse; unfortunately, previous tree-based learning techniques have not been able to learn semantic information from trees to achieve better accuracy than graph-based techniques. We have proposed a new learning technique, named TreeCaps, by fusing together capsule networks with tree-based convolutional neural networks to achieve a learning accuracy higher than some existing graph-based techniques while it is based only on trees. TreeCaps introduces novel variable-to-static routing algorithms into the capsule networks to compensate for the loss of previous routing algorithms. Aside from accuracy, we also find that TreeCaps is the most robust to withstand those semantic-preserving program transformations that change code syntax without modifying the semantics. Evaluated on a large number of Java and C/C++ programs, TreeCaps models outperform prior deep learning models of program source code, in terms of both accuracy and robustness for program comprehension tasks such as code functionality classification and function name prediction. Our implementation is publicly available at: https://github.com/bdqnghi/treecaps.
Privacy-Preserving Decision Trees Training and Prediction

In the era of cloud computing and machine learning, data has become a highly valuable resource. Recent history has shown that the benefits brought forth by this data driven culture come at a cost of potential data leakage. Such breaches have a devastating impact on individuals and industry, and lead the community to seek privacy preserving solutions. A promising approach is to utilize Fully Homomorphic Encryption ( \( \mathsf {FHE } \) ) to enable machine learning over encrypted data, thus providing resiliency against information leakage. However, computing over encrypted data incurs a high computational overhead, thus requiring the redesign of algorithms, in an “ \( \mathsf {FHE } \) -friendly” manner, to maintain their practicality. In this work we focus on the ever-popular tree based methods, and propose a new privacy-preserving solution to training and prediction for trees over data encrypted with homomorphic encryption. Our solution employs a low-degree approximation for the step-function together with a lightweight interactive protocol, to replace components of the vanilla algorithm that are costly over encrypted data. Our protocols for decision trees achieve practical usability demonstrated on standard UCI datasets encrypted with fully homomorphic encryption. In addition, the communication complexity of our protocols is independent of the tree size and dataset size in prediction and training, respectively, which significantly improves on prior works. 1
THE IMPLEMENTATION OF PROJECT-BASED ASSIGNMENT IN ONLINE LEARNING DURING COVID-19

By implementation of online systems strategy indirectly forces teachers and students to master the uses of technology in learning process. Online learning is needed to facilitate the teacher delivering the material to students during the pandemic situation. From this condition, it is suitable for teachers to implement Project Based Learning as it allows students to design, plan and carry out an extended project that produces a publicly exhibited output such as a product, publication or presentations Project Based Learning improves the students’ media research skills provides a real-world connection to context. The students conduct research using multiple information resources. By locating the resources themselves, their research skills develop and improve. Project Based Learning increases the students‟ collaboration. In the processing stages, students create and organize their own groups. They share knowledge and collaboratively construct their products. Through collaboration, they develop social communication skills and obtain multiple perspectives.
Amazigh spell checker using Damerau-Levenshtein algorithm and N-gram

Natural language processing (NLP) is a rapidly growing research field in computer science and cognitive science. Automatic correction of lexical errors is one of the applications of NLP, which aims to detect errors in the text and suggest possible corrections based on computer and linguistic models. After a state of the art and a comparison of spelling correction approaches, we developed a spelling error correction system for the Amazigh language combining the Damerau-Levenshtein algorithm and N-gram. This tool will propose possible corrections for each misspelled word in the text. Successful tests have been carried out using an Amazigh corpus.