Reviewerly/exHarmony huggingface dataset:
A Parallel Algorithm of Image Mean Filtering Based on OpenCL

The image will be contaminated by noise during the imaging process, which severely degrades the image quality.It is necessary to filter the collected image.With the increasing amount of image data, the traditional single-processor or multiprocessor computing equipment has been unable to meet the requirements of real-time data processing.In this paper, the computational model of weighted mean filtering and the characteristics of high performance computer architecture are studied.An efficient hierarchical image weighted mean filtering parallel algorithm for Open Computing Language (OpenCL) is designed and implemented, which can fully express the parallelism of the computing model.The parallel algorithm takes full account of the characteristics of image discrete convolution computing and the multi-layer logic architecture of high performance computer, deeply excavates the parallelism of the computing platform and computing model, and realizes the efficient task mapping from computing model to computing resources.The model is implemented in parallel with the two levels of work-group and work-item.The experimental results show that compared with the serial algorithm based on CPU, the parallel algorithm based on Open Multi-Processing (OpenMP) and the parallel algorithm based on Compute Unified Device Architecture (CUDA), the parallel algorithm of weighted mean filtering achieves 20.88 times, 18.52 times and 1.26 times acceleration ratio on the NVIDIA GPU computing platform based on OpenCL architecture, respectively.It realizes better computing performance and runs on different Graphic Processing Unit (GPU) computing platforms, and has good portability and scalability.
Efforts to Improve Reading Ability Using Picture Stories Media

This Classroom Action Research (CAR) is motivated by the fact that there are still many students who are not fluent in reading, do not understand how to read punctuation signs so that the pressure and intonation are not right, and do not understand the storyline, and do not understand the content and messages contained in the story. From the observations, it was found that the Minimum Completeness Criteria was still low, namely 62. This study used a John Elliot model PTK design. The location of this research was conducted at SD Negeri Majumulya, Sindangbarang District, Cianjur Regency. The research subjects were 32 students of grade IV SD Negeri 1 Kawunglarang, they came from the middle to lower socioeconomic level. The research was conducted in semester 1 of the 2012-2013 academic year for 4 months (October - December). The data collection technique is done by using observation sheets, field notes, interview sheets, evaluation sheets, and photo cameras. The data obtained were analyzed using descriptive qualitative methods and graphics. The results showed an increase in the learning process and students' abilities in learning to read stories and at the end of Cycle III there were no students who scored below the KKM. The increase in student learning outcomes can be seen from the average value of each cycle which continues to increase with the following details: the average value of Cycle I is 63.78, Cycle II is 70.37, and Cycle III is 75.85.n
The Exploration Model of Introduction and Concept Application (Epa) 5 In Learning In Beginning Reading

This action research was motivated by the existence of problems in learning to read early in elementary schools. Early reading learning is not creative. Students are only instructed to memorize letters, words, and sentences in reading books, so that when students read simple sentences on the blackboard, students are unable to read fluently. The research was conducted in class I SD Negeri with 26 students. This research was conducted using a classroom action research method (PTK). This research was conducted in three cycles. Each cycle consists of two actions. The instruments used were observation sheets, field note sheets, test sheets, interview sheets, and documentation sheets. Based on the research that has been done, in cycle I the average value of the students' initial reading activity was 57.69. In the second cycle, the average value of the students' initial reading activity was 74.61. In the third cycle the average value of the students' beginning reading activity was 89.23. Meanwhile, from the beginning reading ability, in the first cycle the average value of the students' beginning reading ability was 53.5. In the second cycle, the average value of the students' initial reading ability was 72.69. In the third cycle, the average value of the students' initial reading ability was 83.07. Thus, based on the two data analyzes, it shows that the EPA 5 model can improve students' activity and ability in pre-reading learning.
Robust Backdoor Attacks against Deep Neural Networks in Real Physical World

Deep neural networks (DNN) have been widely deployed in various applications. However, many researches indicated that DNN is vulnerable to backdoor attacks. The attacker can create a hidden backdoor in target DNN model, and trigger the malicious behaviors by submitting specific backdoor instance. However, almost all the existing backdoor works focused on the digital domain, while few studies investigate the backdoor attacks in real physical world. Restricted to a variety of physical constraints, the performance of backdoor attacks in the real physical world will be severely degraded. In this paper, we propose a robust physical backdoor attack method, PTB (physical transformations for backdoors), to implement the backdoor attacks against deep learning models in the real physical world. Specifically, in the training phase, we perform a series of physical transformations on these injected backdoor instances at each round of model training, so as to simulate various transformations that a backdoor may experience in real world, thus improves its physical robustness. Experimental results on the state-of-the-art face recognition model show that, compared with the backdoor methods that without PTB, the proposed attack method can significantly improve the performance of backdoor attacks in real physical world. Under various complex physical conditions, by injecting only a very small ratio (0.5 %) of backdoor instances, the attack success rate of physical backdoor attacks with the PTB method on VGGFace is 82%, while the attack success rate of backdoor attacks without the proposed PTB method is lower than 11%. Meanwhile, the normal performance of the target DNN model has not been affected.
Flex: High-Availability Datacenters With Zero Reserved Power

Cloud providers, like Amazon and Microsoft, must guarantee high availability for a large fraction of their workloads. For this reason, they build datacenters with redundant infrastructures for power delivery and cooling. Typically, the redundant resources are reserved for use only during infrastructure failure or maintenance events, so that workload performance and availability do not suffer. Unfortunately, the reserved resources also produce lower power utilization and, consequently, require more datacenters to be built. To address these problems, in this paper we propose "zero-reserved-power" datacenters and the Flex system to ensure that workloads still receive their desired performance and availability. Flex leverages the existence of software-redundant workloads that can tolerate lower infrastructure availability, while imposing minimal (if any) performance degradation for those that require high infrastructure availability. Flex mainly comprises (1) a new offline workload placement policy that reduces stranded power while ensuring safety during failure or maintenance events, and (2) a distributed system that monitors for failures and quickly reduces the power draw while respecting the workloads’ requirements, when it detects a failure. Our evaluation shows that Flex produces less than 5% stranded power and increases the number of deployed servers by up to 33%, which translates to hundreds of millions of dollars in construction cost savings per datacenter site. We end the paper with lessons from our experience bringing Flex to production in Microsoft’s datacenters.
Multi-View Clustering Based on Invisible Weights

Multi-view clustering is a powerful tool for improving clustering results via integrating the heterogeneous features from different views. In this work, we develop a novel invisible weighted method to this issue. Specifically, our proposed method first considers the diversities among different features of each view with kernel function, and then fuses the multiple pre-embeddings of all the views attentively. As a result, a consensus embedding can be obtained with the adaptive invisible weighted model. Besides, we provide an efficient optimization approach to solve the involved optimization problem and provide the corresponding convergence analysis. Extensive experimental results on benchmark datasets validate the superiorities of the proposed method to the state-of-the-art methods.
Video Analytics Framework for Human Action Recognition

Human action recognition (HAR) is an essential but challenging task for observing human movements. This problem encompasses the observations of variations in human movement and activity identification by machine learning algorithms. This article addresses the challenges in activity recognition by implementing and experimenting an intelligent segmentation, features reduction and selection framework. A novel approach has been introduced for the fusion of segmented frames and multi-level features of interests are extracted. An entropy-skewness based features reduction technique has been implemented and the reduced features are converted into a codebook by serial based fusion. A custom made genetic algorithm is implemented on the constructed features codebook in order to select the strong and well-known features. The features are exploited by a multi-class SVM for action identification. Comprehensive experimental results are undertaken on four action datasets, namely, Weizmann, KTH, Muhavi, and WVU multi-view. We achieved the recognition rate of 96.80%, 100%, 100%, and 100% respectively. Analysis reveals that the proposed action recognition approach is efficient and well accurate as compare to existing approaches.
FoolHD: Fooling Speaker Identification by Highly Imperceptible Adversarial Disturbances

Speaker identification models are vulnerable to carefully designed adversarial perturbations of their input signals that induce misclassification. In this work, we propose a white-box steganography-inspired adversarial attack that generates imperceptible adversarial perturbations against a speaker identification model. Our approach, FoolHD, uses a Gated Convolutional Autoencoder that operates in the DCT domain and is trained with a multi-objective loss function, to generate and conceal the adversarial perturbation within the original audio files. In addition to hindering speaker identification performance, this multi-objective loss accounts for human perception through a frame-wise cosine similarity between MFCC feature vectors extracted from the original and adversarial audio files. We validate the effectiveness of FoolHD with a 250-speaker identification x-vector network, trained using VoxCeleb, in terms of accuracy, success rate, and imperceptibility. Our results show that FoolHD generates highly imperceptible adversarial audio files (average PESQ scores above 4.30), while achieving a success rate of 99.6% and 99.2% in misleading the speaker identification model, for untargeted and targeted settings, respectively.
Operation is the Hardest Teacher: Estimating DNN Accuracy Looking for Mispredictions

Deep Neural Networks (DNN) are typically tested for accuracy relying on a set of unlabelled real world data (operational dataset), from which a subset is selected, manually labelled and used as test suite. This subset is required to be small (due to manual labelling cost) yet to faithfully represent the operational context, with the resulting test suite containing roughly the same proportion of examples causing misprediction (i.e., failing test cases) as the operational dataset. However, while testing to estimate accuracy, it is desirable to also learn as much as possible from the failing tests in the operational dataset, since they inform about possible bugs of the DNN. A smart sampling strategy may allow to intentionally include in the test suite many examples causing misprediction, thus providing this way more valuable inputs for DNN improvement while preserving the ability to get trustworthy unbiased estimates. This paper presents a test selection technique (DeepEST) that actively looks for failing test cases in the operational dataset of a DNN, with the goal of assessing the DNN expected accuracy by a small and "informative" test suite (namely with a high number of mispredictions) for subsequent DNN improvement. Experiments with five subjects, combining four DNN models and three datasets, are described. The results show that DeepEST provides DNN accuracy estimates with precision close to (and often better than) those of existing sampling-based DNN testing techniques, while detecting from 5 to 30 times more mispredictions, with the same test suite size.
Measuring entanglement of a rank-2 mixed state prepared on a quantum computer

We study the entanglement between a certain qubit and the remaining system in rank- 2 mixed states prepared on the quantum computer. The protocol, which we propose for this purpose, is based on the relation of geometric measure of entanglement with correlations between qubits. As a special case, we consider a two-qubit rank-2 mixed state and find the relation of concurrence with the geometric measure of entanglement. On the ibmq-melbourne quantum computer we measure the geometric measure of entanglement in the cases of 2- and 4-qubit mixed quantum states which consist of Schr\"odinger cat states. We study the dependence of the value of entanglement on the parameter which defines the weight of pure states. Finally, we determine the concurrence of 2-qubit mixed state.
FGN: Fusion Glyph Network for Chinese Named Entity Recognition

As pictographs, Chinese characters contain latent glyph information, which is often overlooked. In this paper, we propose the FGN (https://githup.com/AidenHuen/FGN-NER), Fusion Glyph Network for Chinese NER. Except for encoding glyph information with a novel CNN, this method may extract interactive information between character distributed representation and glyph representation by a fusion mechanism. The major innovations of FGN include: (1) a novel CNN structure called CGS-CNN is proposed to capture glyph information and interactive information between the neighboring graphs. (2) we provide a method with sliding window and attention mechanism to fuse the BERT representation and glyph representation for each character. This method may capture potential interactive knowledge between context and glyph. Experiments are conducted on four NER datasets, showing that FGN with LSTM-CRF as tagger achieves new state-of-the-art performance for Chinese NER. Further, more experiments are conducted to investigate the influences of various components and settings in FGN.
H-VECTORS: Improving the robustness in utterance-level speaker embeddings using a hierarchical attention model

In this paper, a hierarchical attention network is proposed to generate robust utterance-level embeddings (H-vectors) for speaker identification and verification. Since different parts of an utterance may have different contributions to speaker identities, the use of hierarchical structure aims to learn speaker related information locally and globally. In the proposed approach, frame-level encoder and attention are applied on segments of an input utterance and generate individual segment vectors. Then, segment level attention is applied on the segment vectors to construct an utterance representation. To evaluate the quality of the learned utterance-level speaker embeddings on speaker identification and verification, the proposed approach is tested on several benchmark datasets, such as the NIST SRE2008 Part1, the Switchboard Cellular (Part1), the CallHome American English Speech ,the Voxceleb1 and Voxceleb2 datasets. In comparison with some strong baselines, the obtained results show that the use of H-vectors can achieve better identification and verification performances in various acoustic conditions.
Coalitional Strategies for Efficient Individual Prediction Explanation

As Machine Learning (ML) is now widely applied in many domains, in both research and industry, an understanding of what is happening inside the black box is becoming a growing demand, especially by non-experts of these models. Several approaches had thus been developed to provide clear insights of a model prediction for a particular observation but at the cost of long computation time or restrictive hypothesis that does not fully take into account interaction between attributes. This paper provides methods based on the detection of relevant groups of attributes -named coalitions- influencing a prediction and compares them with the literature. Our results show that these coalitional methods are more efficient than existing ones such as SHapley Additive exPlanation (SHAP). Computation time is shortened while preserving an acceptable accuracy of individual prediction explanations. Therefore, this enables wider practical use of explanation methods to increase trust between developed ML models, end-users, and whoever impacted by any decision where these models played a role.
Counterfactual Graphs for Explainable Classification of Brain Networks

Training graph classifiers able to distinguish between healthy brains and dysfunctional ones, can help identifying substructures associated to specific cognitive phenotypes. However, the mere predictive power of the graph classifier is of limited interest to the neuroscientists, which have plenty of tools for the diagnosis of specific mental disorders. What matters is the interpretation of the model, as it can provide novel insights and new hypotheses. In this paper we propose counterfactual graphs as a way to produce local post-hoc explanations of any black-box graph classifier. Given a graph and a black-box, a counterfactual is a graph which, while having high structural similarity with the original graph, is classified by the black-box in a different class. We propose and empirically compare several strategies for counterfactual graph search. Our experiments against a white-box classifier with known optimal counterfactual, show that our methods, although heuristic, can produce counterfactuals very close to the optimal one. Finally, we show how to use counterfactual graphs to build global explanations correctly capturing the behaviour of different black-box classifiers and providing interesting insights for the neuroscientists.
SGL: Speaking the Graph Languages of Semantic Parsing via Multilingual Translation

Graph-based semantic parsing aims to represent textual meaning through directed graphs.As one of the most promising general-purpose meaning representations, these structures and their parsing have gained a significant interest momentum during recent years, with several diverse formalisms being proposed.Yet, owing to this very heterogeneity, most of the research effort has focused mainly on solutions specific to a given formalism.In this work, instead, we reframe semantic parsing towards multiple formalisms as Multilingual Neural Machine Translation (MNMT), and propose SGL, a many-to-many seq2seq architecture trained with an MNMT objective.Backed by several experiments, we show that this framework is indeed effective once the learning procedure is enhanced with large parallel corpora coming from Machine Translation: we report competitive performances on AMR and UCCA parsing, especially once paired with pre-trained architectures.Furthermore, we find that models trained under this configuration scale remarkably well to tasks such as cross-lingual AMR parsing: SGL outperforms all its competitors by a large margin without even explicitly seeing non-English to AMR examples at training time and, once these examples are included as well, sets an unprecedented state of the art in this task.We release our code and our models for research purposes at https: //github.com/SapienzaNLP/sgl.
Variant of Data Particle Geometrical Divide for Imbalanced Data Sets Classification by the Example of Occupancy Detection

The history of gravitational classification started in 1977. Over the years, the gravitational approaches have reached many extensions, which were adapted into different classification problems. This article is the next stage of the research concerning the algorithms of creating data particles by their geometrical divide. In the previous analyses it was established that the Geometrical Divide (GD) method outperforms the algorithm creating the data particles based on classes by a compound of 1 ÷ 1 cardinality. This occurs in the process of balanced data sets classification, in which class centroids are close to each other and the groups of objects, described by different labels, overlap. The purpose of the article was to examine the efficiency of the Geometrical Divide method in the unbalanced data sets classification, by the example of real case-occupancy detecting. In addition, in the paper, the concept of the Unequal Geometrical Divide (UGD) was developed. The evaluation of approaches was conducted on 26 unbalanced data sets-16 with the features of Moons and Circles data sets and 10 created based on real occupancy data set. In the experiment, the GD method and its unbalanced variant (UGD) as well as the 1CT1P approach, were compared. Each method was combined with three data particle mass determination algorithms-n-Mass Model (n-MM), Stochastic Learning Algorithm (SLA) and Bath-update Algorithm (BLA). k-fold cross validation method, precision, recall, F-measure, and number of used data particles were applied in the evaluation process. Obtained results showed that the methods based on geometrical divide outperform the 1CT1P approach in the imbalanced data sets classification. The article’s conclusion describes the observations and indicates the potential directions of further research and development of methods, which concern creating the data particle through its geometrical divide.
No-reference screen content video quality assessment

• A no-reference VQA model designed specifically for screen content videos is proposed. • In this model, video quality is aggregated from a multi-scale approach with extraction of several groups of features. • Through training with labeled videos, the model map the frame feature vectors to quality scores by SVR. • Experiments show that our proposed model outperforms the existing full- and no-reference quality evaluation metrics. How to effectively and accurately measure the degradation of media content is an important research topic in the field of image or video processing. Application scenarios such as online meetings, distance learning, and live game streaming make screen content video become a hot spot in Video Quality Assessment (VQA) research. However, to the best of our knowledge, there is currently no no-reference VQA model designed specifically for screen content videos. In this paper, we propose a blind VQA model for screen content videos. This model first uses a multi-scale approach to extract several groups of features, including gradient features, relative standard deviation features, compression features, frequency domain features and inter-frame features. Through training with labeled videos, the model then uses support vector regressor to map the frame feature vectors to video quality scores. We validate the model on the CSCVQ database. Experiments show that our proposed model outperforms the existing full- and no-reference quality evaluation metrics and is also competitive in terms of stability and computational efficiency.