Reviewerly/exHarmony huggingface dataset:
Complementary Blockchain-Based Privacy Protection for Covid-19 Contact Tracing

Fighting any pandemic outbreak begins with health authorities already behind time. They are required to locate, isolate and treat infected individuals while tracking possibly infected ones. In the case of Covid-19, the high reproductive number of the virus necessitates that all contacts of an infected individual be found within the shortest possible time to slow the rate of spreading. This presents multiple challenges because of the highly invasive nature of tracing activities which demand the mobility history of patients. Patients may be unwilling to cooperate or may be unable to communicate if the infection has advanced to the point where critical care is necessary. To speedily locate contacts of an infected individual, we propose using communications data logs from telecommunications operators. We employ a modified directed graph to determine which other individuals have been in close proximity to an infected individual in a specific frame of time. We then generate a contact graph and place it in a secure offline storage platform. We employ Smart Contracts to control access to the data while the blockchain keeps records of the provenance of all data and transactions. We find this method of conducting the contact tracing and protecting the resulting data more secure and pliant to the privacy laws that regulate the handling of sensitive personal data.
Oil palm empty bunches as a growing media for white oyster mushroom (Pleurotus ostreatus)

Abstract The media used to grow oyster mushrooms is sawdust waste, such as sengon, gmelina, and candlenut. Therefore, several researchers conducted research to find alternative media to replace sawdust, such as mulberry pulp, sago pulp, coconut fibre, and bamboo pulp. This research utilizes waste of oil palm empty bunches as a medium used to grow oyster mushrooms. In addition, this study aims to determine the duration of immersion of oil palm empty bunches as a pre-treatment before being used as a growing medium for white oyster mushroom (Pleurotus ostreatus). Before being used as a medium, empty fruit bunches of oil palm are cut into small pieces and then made into powder. The stages in this research are mixing raw materials, composting, making baglog, sterilization, inoculation, incubation, maintenance, and harvesting. There were seven treatments applied in this study, namely immersion in cold water for five days (P1), for seven days (P2), and nine days (P3); immersion in 100 °C hot water for 1 hour (P4), for 2 hours (P5), and 3 hours (P6); without immersion or control (P7). The results obtained are empty oil palm fruit bunches with cold soaking for five days (P1) and hot soaking at 100 °C for two hours can be used as a medium for oyster mushroom cultivation.
Watch, Listen, and Answer: Open-Ended VideoQA with Modulated Multi-Stream 3D ConvNets

We propose an open-ended multimodal video question answering (VideoQA) method that predicts textual answers by referring to multimodal information derived from videos. Most current open-ended VideoQA methods focus on motion and appearance features from videos and ignore the audio features that are useful for understanding video content in more detail. A few prior works that use motion, appearance, and audio features showed poor results on public benchmarks since they failed to (e.g., region or grid-level) multimodal features effectively fuse the features with details for video reasoning. We overcame these limitations with multi-stream 3-dimensional convolutional networks (3D ConvNets) and a transformer-based modulator for VideoQA. Our network represents detailed motion and appearance features as well as an audio feature on multiple 3D ConvNets and modulates each intermediate representation with question information to extract their relevant spatiotemporal features over the frames. Based on the question content, our network fuses the multimodal information of 3D ConvNets and predicts the final answers. Our VideoQA method, which effectively combined multimodal data yields, outperformed both a previous multimodal VideoQA method and a state-of-the-art method on standard benchmarks. Visualization suggests that our method can predict the correct answers by listening to the audio information, even when the motion and appearance features are inadequate for understanding the video constant.
Anomaly detection of high-dimensional sparse data based on Ensemble Generative Adversarial Networks

Anomaly detection has drawn public attentions in past decades. However, in a high-dimensional sparse data space, anomaly detection still faces big challenges. In this paper, the Generative Adversarial Network (GAN) combined with Ensemble Learning is introduced to anomaly detection in high-dimensional sparse data. On one hand, the generator of GAN can produce noise data to avoid the data space to be too sparse based on the potential data distribution patterns. On the other hand, the exchanges of the pairing of generators and discriminators can enable the model proposed to learn complex distribution of the data, which may be composed of some various distributions, and to avoid the training process to drop into over-fitting to some extent. Experiments on public datasets show that the proposed approach can improve AUC by 7% compared with traditional GAN based approaches, and by 7.5% to 21.8% compared with other representative anomaly detection approaches.
A Feature-Map-Based Large-Payload DNN Watermarking Algorithm

Watermarking has recently been proposed as a solution to protect the Intellectual Property Rights (IPR) of Deep Neural Networks (DNN). Dynamic DNN watermarking refers to a particular class of watermarking algorithms according to which the watermark message is associated to the behaviour of the network in correspondence to some specific inputs. When the observed behaviour is defined at the level of the network output, the watermark can be retrieved even without accessing the internal status of the network (black-box watermarking). Despite their attractiveness, most black-box dynamic watermarking algorithms proposed so far have a very small payload, due to the low entropic content of the network output. In this paper, we propose a new, multi-bit, white-box, dynamic DNN watermarking algorithm embedding the watermark message into the feature maps of the host DNN, in correspondence to a set of predefined inputs. Thanks to the high-entropy content of the maps hosting the watermark, the new approach can achieve a very high payload, with acceptable robustness. The experiments we carried out on a variety of host networks, demonstrate the capability of the proposed method to provide a very high payload with a low impact on network accuracy.
A practical implementation of mask detection for COVID-19 using face detection and histogram of oriented gradients

Wearing a face mask is one of the effective barriers against the coronavirus COVID-19 pandemic. It offers protection according to the World Health Organization and many medical papers. This paper proposes a method for masked face recognition in order to force the population to put on masks and reduce the COVID-19 pandemic in the world. The Viola-Jones algorithm is used to detect the face, and the Histogram of Oriented Gradients (HOG) technique was used to extract the relevant features from face images. The performance of the proposed algorithm is analysed for different data using two common image classification methods, including support vector machines and K Nearest Neighbor (KNN) algorithm for machine learning, which are used to classify the feature vectors. Their performance was compared and evaluated using accuracy. In this case, the experimental result shows that the support vector machine classifier achieved the highest accuracy and surpasses the KNN method in mask detection with an accuracy of 99.43%.
Perceptual Evaluation of Pre-processing for Video Transcoding

Recently, the pre-processed video transcoding has attracted wide attention and has been increasingly used in practical applications for improving the perceptual experience and saving transmission resources. However, very few works have been conducted to evaluate the performance of pre-processing methods. In this paper, we select the source (SRC) videos and various pre-processing approaches to construct the first Pre-processed and Transcoded Video Database (PTVD). Then, we conduct the subjective experiment, showing that compared with the video sent to the codec directly at the same bitrate, the appropriate pre-processing methods indeed improve the perceptual quality. Finally, existing image/video quality metrics are evaluated on our database. The results indicate that the performance of the existing image/video quality assessment (IQA/VQA) approaches remain to be improved. We will make our database publicly available soon.
Knowledge Based Training Derived from Risk Evaluation Concerning Failure Mode, Effects and Criticality Analysis in Autonomous Railway Systems

When building digital solutions security and safety are essential, especially in critical industrial applications. Considering this in connection with people who operate them, we investigate here how such an extension can be applied by using the FMECA analysis. We derived a meta-model for an autonomous railway system. This meta-model includes all relevant components to apply an FMECA assessment including adequate technical and sociological requirements as well as the assessment of measures for a safety process from the design phase. Before working out the social aspects in the FMECA, methods from Design Thinking were used to combine them with the technical aspects in a structured way. Methods used in Design Thinking processes help to create ideas and to prototype new (technical) products with general know-how about user aspects. This research combines market research, technical development and sociological aspects. Assessing safety aspects at the interface between humans and machines, we applied the Persona-model and developed it further to consider the users’ diversity categories and aspects of organizational learning within an industrial unit. The result provides answers to questions about the avoidance of errors, discussions on safety aspects and an outlook on tools that can be used to prevent accidents, such as organizational development tools and knowledge based training materials. At the same time, they offer starting points for creating trust in digital solutions and cyber security for employees.
Exemplar-based image inpainting using adaptive two-stage structure-tensor based priority function and nonlocal filtering

For the exemplar-based image inpainting problem, the filling order and local intensity smoothness are two crucial factors that should be considered carefully. This work gives a new exemplar-based image inpainting method, preventing geometric structures from being destroyed and reconstructing textures well to obtain elegant-looking outputs. For a better filling order, we define a new adaptive two-stage structure-tensor based priority function. To promote the local intensity smoothness, we adopt a non-local way, and at the same time, propose a weighted filter based on a Gaussian-like function to generate the ideal filling patch by combining non-local patches. We compare the proposed method with some recent state-of-the-art image inpainting approaches on different tasks, such as texture and structure synthesis, object removal, and remote sensing images inpainting. Experimental results demonstrate the superiority of the proposed method, both visually and quantitatively.
Replication Based Fault Tolerance Approach for Cloud

The usage of cloud computing has been increasing everyday and in almost every organization. It is now an integral part of the information technology market. And this rapidly growing need gives rise to consider two main criteria namely reliability and availability to sustain growing demand. To ensure SLAs, and gain customer confidence, cloud architectures use fault tolerant strategies. The work in the literature towards fault tolerant cloud has not considered aspects such as using resource subsets to be fault tolerant. In this paper, we have used a replication based fault-tolerance strategy which considers cost and fault proneness of VMs. Simulation of the proposed algorithm has been carried out using CloudSim and the result shows an improvement in average total execution time and average delay time.
Towards a classification of sustainable software development process using manifold machine learning techniques

With the evaluation of the software industry, a huge number of software applications are designing, developing, and uploading to multiple online repositories. To find out the same type of category and resource utilization of applications, researchers must adopt manual working. To reduce their efforts, a solution has been proposed that works in two phases. In first phase, a semantic analysis-based keywords and variables identification process has been proposed. Based on the semantics, designed a dataset having two classes: one represents application type and the other corresponds to application keywords. Afterward, in second phase, input preprocessed dataset to manifold machine learning techniques (Decision Table, Random Forest, OneR, Randomizable Filtered Classifier, Logistic model tree) and compute their performance based on TP Rate, FP Rate, Precision, Recall, F1-Score, MCC, ROC Area, PRC Area, and Accuracy (%). For evaluation purposes, We have used an R language library called latent semantic analysis for creating semantics, and the Weka tool is used for measuring the performance of algorithms. Results show that the random forest depicts the highest accuracy which is 99.3% due to its parametric function evaluation and less misclassification error.
KannadaPado: Mobile-based recognition and cross-lingual transcription of camera captured text in Kannada

We report our recent work on the recognition of scene text captured by mobile cameras, which we have named Kannada Pado. The text region is currently manually cropped using a user-friendly interface, which permits repeated croppings from the captured image in a hierarchical fashion. The scene text segment is then binarized using the algorithm, midline analysis, and propagation for segmentation. The segmented binary text image is recognized using Lipi Gnani Kannada OCR. The recognized text can be transcribed in Roman, Devanagari, and other principal Indian scripts. Such tools will be of immense use in metropolitan cities such as Bengaluru for business visitors and tourists to be able to read important textual information using their mobile itself. The entire implementation is of low computational complexity and hence, runs fully on the mobile itself, without any backend computation. Currently, text recognition accuracy is the bottleneck, which, when improved, will make the app immediately usable by people. Then, it will be made available to the public from Google Playstore.
Recognizing Abnormal Activity Using MultiClass SVM Classification Approach in Tele-health Care

Anomalies are cases of an active class that vary in their performance from the standard or planned sequence of events. Early identification of body sensor networks is an essential and difficult role and has many possible benefits. Both these tasks are very important for the monitoring of suspicious behavior to capture human activities and evaluate these results. Data collection is important if the true evidence of human behavior is to be discovered. Since it uses the technique of state table transformation to find suspicious behaviors, identify them and track them from a distant location for elderly or children. This article offers an automatic activity surveillance system that identifies common activities that usually occur in a human routine. SVM is used here E-MultiClass (Support Vector Machine). It is used to rate two data groups with a binary score. This paper describes our task of data acquisition, recording them in the field of imagery processing and validating our algorithms on data obtained from remote sensors. It is also used to detect improvements in human routine as well as everyday life across these capabilities. It describes our activities in the field of image processing.
MC-OCR Challenge 2021: Simple approach for receipt information extraction and quality evaluation

This challenge organized at the RIVF conference 2021 [12], with two tasks including (1) image quality assessment (IQA) of the captured receipt, and (2) key information extraction (KIE) of required fields, our team came up with a solution based on extracting image patches for task 1 and Yolov5 + VietOCR for task 2. Our solution achieved 0.149 of the RMSE score for task 1 (rank 7) and 0.219 of the CER score for task 2 (rank 1). Our code is available at https://github.com/cuongngm/RIVF2021.
Some Chess-Specific Improvements for Perturbation-Based Saliency Maps

State-of-the-art game playing programs do not provide an explanation for their move choice beyond a numerical score for the best and alternative moves. However, human players want to understand the reasons why a certain move is considered to be the best. Saliency maps are a useful general technique for this purpose, because they allow visualizing relevant aspects of a given input to the produced output. While such saliency maps are commonly used in the field of image classification, their adaptation to chess engines like Stockfish or Leela has not yet seen much work. This paper takes one such approach, Specific and Relevant Feature Attribution (SARFA), which has previously been proposed for a variety of game settings, and analyzes it specifically for the game of chess. In particular, we investigate differences with respect to its use with different chess engines, to different types of positions (tactical vs. positional as well as middle-game vs. endgame), and point out some of the approach's down-sides. Ultimately, we also suggest and evaluate a few improvements of the basic algorithm that are able to address some of the found shortcomings.
Power and Pitfalls of Generic Smart Contracts

Generics are a powerful feature of programming languages that allows one to write highly reusable code. More specifically, they are based on the use of type placeholders in order to produce parametrized code, that can be instantiated for each concrete type provided for them. In many programming languages, such as Java, they are implemented by erasure, ie. replaced by their upper bound type during compilation into bytecode. This paper originated from a real security issue that we found while using generics for writing smart contracts for blockchain, in order to implement a contract for shared entities (such as a company shared by its shareholders), for the Hotmoka blockchain, whose contracts are written in Java. The considered case study is particularly important since the validators&#x2019; set of the blockchain itself is a special case of shared entities. The analysis shows that the power of generics comes at the risk of a too permissive typing of the compiled code, due to the erasure mechanism, with a consequent possible attack to the validators&#x2019; set. This paper proposes a solution that forces the compiler to generate more precise type information than those arising by erasure.
Improving deep label noise learning with dual active label correction

Label noise is now a common problem in many applications, which may lead to significant learning performance degeneration. To deal with the label noise, Active Label Correction (ALC) was proposed to query the true labels for a small subset of instances. As the true labels costs can be high, the focus of ALC is to maximally improve the learning performance with minimal query costs. Existing ALC methods mainly proceed by querying the most likely mislabeled instances, or using criteria derived from standard active learning. In this paper, we focus on deep neural network models and show that due to their intrinsic memorization effect, the true labels of a large proportion of mislabeled instances can be correctly predicted with early stopped training, even under severe noise. Inspired by this, we propose to train deep label noise learning models robustly with dual ALC (DALC): on one hand, we select the most useful instances for classifier improvement and query their true labels from external experts; on the other hand, due to the active data sampling bias, the label noise model estimation can be highly biased, which may in turn hurt the classifier learning. To alleviate this issue, we propose to identify the instances that are most likely predicted with true labels by the classifier, and take the predictions as their true labels. By integrating the two sources of true labels, we experiment on multiple benchmark datasets with various label noise rate and show the effectiveness of the proposed DALC on both the classification accuracy and the label noise model estimation. The code is available at https://github.com/lilylisy/mlj21DALC .
On the relationship between similar requirements and similar software

Abstract Recommender systems for requirements are typically built on the assumption that similar requirements can be used as proxies to retrieve similar software. When a stakeholder proposes a new requirement, natural language processing (NLP)-based similarity metrics can be exploited to retrieve existing requirements, and in turn, identify previously developed code. Several NLP approaches for similarity computation between requirements are available. However, there is little empirical evidence on their effectiveness for code retrieval. This study compares different NLP approaches, from lexical ones to semantic, deep-learning techniques, and correlates the similarity among requirements with the similarity of their associated software. The evaluation is conducted on real-world requirements from two industrial projects from a railway company. Specifically, the most similar pairs of requirements across two industrial projects are automatically identified using six language models. Then, the trace links between requirements and software are used to identify the software pairs associated with each requirements pair. The software similarity between pairs is then automatically computed with JPLag. Finally, the correlation between requirements similarity and software similarity is evaluated to see which language model shows the highest correlation and is thus more appropriate for code retrieval. In addition, we perform a focus group with members of the company to collect qualitative data. Results show a moderately positive correlation between requirements similarity and software similarity, with the pre-trained deep learning-based BERT language model with preprocessing outperforming the other models. Practitioners confirm that requirements similarity is generally regarded as a proxy for software similarity. However, they also highlight that additional aspect comes into play when deciding software reuse, e.g., domain/project knowledge, information coming from test cases, and trace links. Our work is among the first ones to explore the relationship between requirements and software similarity from a quantitative and qualitative standpoint. This can be useful not only in recommender systems but also in other requirements engineering tasks in which similarity computation is relevant, such as tracing and change impact analysis.