Reviewerly/exHarmony huggingface dataset:
Cross-Network Learning with Partially Aligned Graph Convolutional Networks

Graph neural networks have been widely used for learning representations of nodes for many downstream tasks on graph data. Existing models were designed for the nodes on a single graph, which would not be able to utilize information across multiple graphs. The real world does have multiple graphs where the nodes are often partially aligned. For examples, knowledge graphs share a number of named entities though they may have different relation schema; collaboration networks on publications and awarded projects share some researcher nodes who are authors and investigators, respectively; people use multiple web services, shopping, tweeting, rating movies, and some may register the same email account across the platforms. In this paper, I propose partially aligned graph convolutional networks to learn node representations across the models. I investigate multiple methods (including model sharing, regularization, and alignment reconstruction) as well as theoretical analysis to positively transfer knowledge across the (small) set of partially aligned nodes. Extensive experiments on real-world knowledge graphs and collaboration networks show the superior performance of our proposed methods on relation classification and link prediction.
Weathering products at the historic Qulaan gold prospect, Eastern Desert, Egypt: Implication on the mobility and distribution of arsenic, gold and silver

Gold mineralization of the historic Qulaan prospect is hosted in hydrothermally altered rhyolite. A variety of supergene minerals that includes arsenic, gold and silver phases were formed as a consequence of superimposed weathering on primary mineralization hosted in the hydrothermally altered rhyolite at the prospect. The arsenic-bearing phases are represented mainly by arsenic-rich ferric oxyhydroxides and Ca–Fe arsenates (arseniosiderite). Gold occurs in the form of native platelet and irregular grains that are commonly associated with ferric oxyhydroxide, while the Ag-phase, represented by iodargyrite, occurs in sericite-rich cavities. The formation of these minerals is related to oxidation of primary arsenian pyrite during the weathering process. Although pyrite oxidation results in an acidic condition, the presence of arseniosiderite as well as iodargyrite and the association between ferric oxyhydroxide and gold indicate an increase in the pH during the weathering process. Relict feldspar as well as hydrothermal chlorite and calcite in the host rocks are responsible for neutralizing the earlier acidic condition during the weathering of rhyolite.
Tag-Aware Recommender System Based on Deep Reinforcement Learning

Recently, the application of deep reinforcement learning in the recommender system is flourishing and stands out by overcoming drawbacks of traditional methods and achieving high recommendation quality. The dynamics, long-term returns, and sparse data issues in the recommender system have been effectively solved. But the application of deep reinforcement learning brings problems of interpretability, overfitting, complex reward function design, and user cold start. This study proposed a tag-aware recommender system based on deep reinforcement learning without complex function design, taking advantage of tags to make up for the interpretability problems existing in the recommender system. Our experiment is carried out on the MovieLens dataset. The result shows that the DRL-based recommender system is superior than traditional algorithms in minimum error, and the application of tags have little effect on accuracy when making up for interpretability. In addition, the DRL-based recommender system has excellent performance on user cold start problems.
An Approach for Morse Code Translation from Eye Blinks Using Tree Based Machine Learning Algorithms and OpenCV

Abstract For ages, human beings have been communicating with one another through different modes of communication. Communication is a process through which a person can communicate his/her feelings and thoughts to the other person. To communicate we can do it through either speech or sign language. The spoken language is used by abled persons, While the differently abled persons (deaf and dumb) may find it difficult to understand the same. So, for effective communication between the differently abled and abled person sign language has been developed. For private communication between two people, morse code has been developed which is highly efficient to exchange secrets. It also helps in emergencies where a person cannot communicate through hand gestures. Different methods/modes are used in morse code, but our focus is on eye blinking. Our approach towards this area has been to implement morse code using eye blinks in real-time assistance using a webcam to provide predicting power based on machine learning’s tree algorithms.
Whitening for Self-Supervised Representation Learning

Most of the current self-supervised representation learning (SSL) methods are based on the contrastive loss and the instance-discrimination task, where augmented versions of the same image instance ("positives") are contrasted with instances extracted from other images ("negatives"). For the learning to be effective, many negatives should be compared with a positive pair, which is computationally demanding. In this paper, we propose a different direction and a new loss function for SSL, which is based on the whitening of the latent-space features. The whitening operation has a "scattering" effect on the batch samples, avoiding degenerate solutions where all the sample representations collapse to a single point. Our solution does not require asymmetric networks and it is conceptually simple. Moreover, since negatives are not needed, we can extract multiple positive pairs from the same image instance. The source code of the method and of all the experiments is available at: https://github.com/htdt/self-supervised.
Elastic Graph Neural Networks

While many existing graph neural networks (GNNs) have been proven to perform $\ell_2$-based graph smoothing that enforces smoothness globally, in this work we aim to further enhance the local smoothness adaptivity of GNNs via $\ell_1$-based graph smoothing. As a result, we introduce a family of GNNs (Elastic GNNs) based on $\ell_1$ and $\ell_2$-based graph smoothing. In particular, we propose a novel and general message passing scheme into GNNs. This message passing algorithm is not only friendly to back-propagation training but also achieves the desired smoothing properties with a theoretical convergence guarantee. Experiments on semi-supervised learning tasks demonstrate that the proposed Elastic GNNs obtain better adaptivity on benchmark datasets and are significantly robust to graph adversarial attacks. The implementation of Elastic GNNs is available at \url{this https URL}.
The extended binary quadratic residue code of length 42 holds a 3‐design

The codewords of weight $10$ of the $[42,21,10]$ extended binary quadratic residue code are shown to hold a design of parameters $3-(42,10,18).$ Its automorphism group is isomorphic to $PSL(2,41)$. Its existence can be explained neither by a transitivity argument, nor by the Assmus-Mattson theorem.
Order-Agnostic Cross Entropy for Non-Autoregressive Machine Translation

We propose a new training objective named order-agnostic cross entropy (OaXE) for fully non-autoregressive translation (NAT) models. OaXE improves the standard cross-entropy loss to ameliorate the effect of word reordering, which is a common source of the critical multimodality problem in NAT. Concretely, OaXE removes the penalty for word order errors, and computes the cross entropy loss based on the best possible alignment between model predictions and target tokens. Since the log loss is very sensitive to invalid references, we leverage cross entropy initialization and loss truncation to ensure the model focuses on a good part of the search space. Extensive experiments on major WMT benchmarks show that OaXE substantially improves translation performance, setting new state of the art for fully NAT models. Further analyses show that OaXE alleviates the multimodality problem by reducing token repetitions and increasing prediction confidence. Our code, data, and trained models are available at https://github.com/tencent-ailab/ICML21_OAXE.
Research on the Construction of Student Ability Evaluation System Based on Computer Application

Abstract Cultivating the various abilities of college students is the focus of college education. The comprehensive evaluation of students’ abilities is also an important part of teaching design. This paper mainly describes the existing problems in the current student ability evaluation system, and it analyzes the construction and implementation of the student evaluation system.
A hybrid feature selection model based on improved squirrel search algorithm and rank aggregation using fuzzy techniques for biomedical data classification

Feature selection has gained its importance due to the voluminous nature of the data. Owing to the computational complexity of wrapper approaches, the poor performance of filtering techniques, and the classifier dependency of embedded approaches, hybrid approaches are more commonly used in feature selection. Hybrid approaches use filtering metrics to reduce the computational complexity of wrapper algorithms and are proved to yield better feature subset. Though filtering metrics select the features based on their significance, most of them are unstable and biased towards the metric used. Moreover, the choice of filtering metrics depends largely on the distribution of data and data types. Biomedical datasets contain features with different distribution and types adding to the complexity in the choice of filtering metric. We address this problem by proposing a stable filtering method based on rank aggregation in hybrid feature selection model with Improved Squirrel search algorithm for biomedical datasets. Our proposed model is compared with other well-known and state-of-the-art methods and the results prove that our model exhibited superior performance in terms of classification accuracy and computational time. The robustness of our proposed model is proved by conducting experiments on nine biomedical datasets and with three different classifiers.
Robust Question Answering Through Sub-part Alignment

Current textual question answering (QA) models achieve strong performance on in-domain test sets, but often do so by fitting surfacelevel patterns, so they fail to generalize to outof-distribution settings.To make a more robust and understandable QA system, we model question answering as an alignment problem.We decompose both the question and context into smaller units based on off-the-shelf semantic representations (here, semantic roles), and align the question to a subgraph of the context in order to find the answer.We formulate our model as a structured SVM, with alignment scores computed via BERT, and we can train end-to-end despite using beam search for approximate inference.Our use of explicit alignments allows us to explore a set of constraints with which we can prohibit certain types of bad model behavior arising in cross-domain settings.Furthermore, by investigating differences in scores across different potential answers, we can seek to understand what particular aspects of the input lead the model to choose the answer without relying on post-hoc explanation techniques.We train our model on SQuAD v1.1 and test it on several adversarial and out-of-domain datasets.The results show that our model is more robust than the standard BERT QA model, and constraints derived from alignment scores allow us to effectively trade off coverage and accuracy.
The Impact of Agile Transformations on Organizational Performance: A Survey of Teams, Programs and Portfolios

Abstract While many organizations embark on agile transformations, they can lack insight into the actual impact of these transformations across organizational layers. In this paper, we collect new and study existing evidence on the impact of agile transformations on organizational performance across teams, programs and portfolios. We conducted an international survey collecting the perceptions of agile coaches, transformation leads and other relevant roles, and we correlated levels of agile maturity to the perceptions on dimensions of organizational performance. Based on 134 responses from 29 countries across 16 industries, (1) we consolidated understanding of the benefits of agile transformations based on prior evidence and our data from a more diverse and larger sample, (2) we identified the dimensions impacted by agile transformations as being productivity, responsiveness, quality, workflow health and employee satisfaction &amp; engagement and (3) we traced specific benefits on those dimensions to individual organizational layers of teams, programs and portfolios, showing the magnitude of impact of each dimension per layer. Overall, we can conclude that agile transformations have a variety of strong organizational benefits. This aggregated evidence allows reflection on transformation trends, but also enables organizations to optimize their agile transformation efforts.
Domain Generalization using Causal Matching

Learning invariant representations has been proposed as a key technique for addressing the domain generalization problem. However, the question of identifying the right conditions for invariance remains unanswered. In this work, we propose a causal interpretation of domain generalization that defines domains as interventions under a data-generating process. Based on a general causal model for data from multiple domains, we show that prior methods for learning an invariant representation optimize for an incorrect objective. We highlight an alternative condition: inputs across domains should have the same representation if they are derived from the same base object. In practice, knowledge about generation of data or objects is not available. Hence we propose an iterative algorithm called MatchDG that approximates base object similarity by using a contrastive loss formulation adapted for multiple domains. We then match inputs that are similar under the resultant representation to build an invariant classifier. We evaluate MatchDG on rotated MNIST, Fashion-MNIST, and PACS datasets and find that it outperforms prior work on out-of-domain accuracy and learns matches that have over 25% overlap with ground-truth object matches in MNIST and Fashion-MNIST. Code repository can be accessed at https://github.com/microsoft/robustdg
A robust tracking system for COVID-19 like pandemic using advanced hybrid technologies

In order to track patients in coronavirus (COVID-19) like pandemic, this paper proposes a novel model based on hybrid advance technologies, which is capable to trace and track COVID-19 affectees with high accuracy. The hybrid technologies include, cellular, cyber and low range wireless technologies. This technique is capable to trace patients through call data record using cellular technology, voice over Internet protocol calls using cyber technology and physical contact without having a call history using low range wireless technologies. The proposed model is also capable to trace COVID-19 suspects. In addition to tracking, the proposed model is capable to provide surveillance capability as well by geo tagging the patients. In case of any violation by the patients an alert is sent to the concerned department. The proposed model is cost effective and privacy preserved as the entire process is carried out under the umbrella of a concerned government department. The potential outcomes of the proposed model are tracking of COVID-19 patients, monitoring of isolated patients, tracking of suspected ones and inform the mass about the safest path to use.
Data Poisoning Attacks Against Outcome Interpretations of Predictive Models

The past decades have witnessed significant progress towards improving the accuracy of predictions powered by complex machine learning models. Despite much success, the lack of model interpretability prevents the usage of these techniques in life-critical systems such as medical diagnosis and self-driving systems. Recently, the interpretability issue has received much attention, and one critical task is to explain why a predictive model makes a specific decision. We refer to this task as outcome interpretation. Many outcome interpretation methods have been developed to produce human-understandable interpretations by utilizing intermediate results of the machine learning models, such as gradients and model parameters.
Reducing magnetic resonance image spacing by learning without ground-truth

High-quality magnetic resonance (MR) image, i.e., with near isotropic voxel spacing, is desirable in various scenarios of medical image analysis. However, many MR images are acquired using good in-plane resolution but large spacing between slices in clinical practice. In this work, we propose a novel deep-learning-based super-resolution algorithm to generate high-resolution (HR) MR images of small slice spacing from low-resolution (LR) inputs of large slice spacing. Notice that real HR images are needed in most existing deep-learning-based methods to supervise the training, but in clinical scenarios, usually they will not be acquired. Therefore, our unique goal herein is to design and train the super-resolution network without real HR ground-truth. Specifically, two-staged training is used in our method. In the first stage, HR images of reduced slice spacing are synthesized from real LR images using variational auto-encoder (VAE). Although these synthesized HR images of reduced slice spacing are as realistic as possible, they may still suffer from unexpected morphing induced by VAE, implying that the synthesized HR images cannot be paired with the real LR images in terms of anatomical structure details. In the second stage, we degrade the synthesized HR images to generate corresponding LR-HR image pairs and train a super-resolution network based on these synthesized pairs. The underlying mechanism is that such a super-resolution network is less vulnerable to anatomical variability. Experiments on knee MR images successfully demonstrate the effectiveness of our proposed solution to reduce the slice spacing for better rendering.
Vehicle and Pedestrian Detection Based on Improved YOLOv4-tiny Model

Abstract Target detection is the basic technology of automatic driving system. Deep learning has gradually become the mainstream target detection algorithm because of its powerful feature extraction ability and adaptive ability. How to ensure accuracy and speed is a great challenge in the field of target detection. In order to solve the problems of high miss detection rate of small target and difficult to realize embedded real-time detection in the process of complex environment detection by deep learning method, this paper adds two auxiliary remaining network blocks in the backbone network. So that the backbone network can extract the global and local features of the detected object, and carry out feature extraction based on the feature pyramid network Fusion, adding a scale to form a three scale prediction, to improve the problem of poor detection accuracy of yolov4-tiny network. The simulation results show that: Compared with yolov4-tiny, the accuracy of the improved network structure is improved by 3.3%, and the detection speed is 251 fps, which ensures the requirements of real-time detection. This algorithm has good detection effect in the case of lack of illumination and target occlusion, and its detection accuracy on the mixed data set is better than that of the contrast algorithm, which meets the real-time detection conditions and is suitable for deployment on the embedded system carried by the car.
Exploring Philippine Presidents’ speeches: A sentiment analysis and topic modeling approach

This study analyzed the annual obligatory and traditional speeches, referred to as State of the Nation Address (SONA), of the 13 past Philippine presidents. The study determined the sentiments, as well as the emergent topics, expressed in these materials. It is found that these SONAs generally expressed positive sentiments while the lowest negative sentiment, on the other hand, was during the martial law period in 1974. Also, it is shown that "development" is the most frequently appeared word among these speeches. The study also revealed that the sentiments of the incoming presidents were lower than that of the outgoing. Moreover, it is shown that these SONAs mainly focused on the following concerns of the country: (a) economic development; (b) enhancement of public services; and (c) addressing challenges. The results of the study translate into the importance of SONA as a venue to discuss and to engage with its people the nation's state and direction.
ADES: A New Ensemble Diversity-Based Approach for Handling Concept Drift

Beyond applying machine learning predictive models to static tasks, a significant corpus of research exists that applies machine learning predictive models to streaming environments that incur concept drift. With the prevalence of streaming real-world applications that are associated with changes in the underlying data distribution, the need for applications that are capable of adapting to evolving and time-varying dynamic environments can be hardly overstated. Dynamic environments are nonstationary and change with time and the target variables to be predicted by the learning algorithm and often evolve with time, a phenomenon known as concept drift. Most work in handling concept drift focuses on updating the prediction model so that it can recover from concept drift while little effort has been dedicated to the formulation of a learning system that is capable of learning different types of drifting concepts at any time with minimum overheads. This work proposes a novel and evolving data stream classifier called Adaptive Diversified Ensemble Selection Classifier (ADES) that significantly optimizes adaptation to different types of concept drifts at any time and improves convergence to new concepts by exploiting different amounts of ensemble diversity. The ADES algorithm generates diverse base classifiers, thereby optimizing the margin distribution to exploit ensemble diversity to formulate an ensemble classifier that generalizes well to unseen instances and provides fast recovery from different types of concept drift. Empirical experiments conducted on both artificial and real-world data streams demonstrate that ADES can adapt to different types of drifts at any given time. The prediction performance of ADES is compared to three other ensemble classifiers designed to handle concept drift using both artificial and real-world data streams. The comparative evaluation performed demonstrated the ability of ADES to handle different types of concept drifts. The experimental results, including statistical test results, indicate comparable performances with other algorithms designed to handle concept drift and prove their significance and effectiveness.