Reviewerly/exHarmony huggingface dataset:
Classification of Abusive Comments in Social Media using Deep Learning

Social media has provided everyone to express views and communicate to masses, but it also becomes a place for hateful behavior, abusive language, cyber-bullying and personal attacks. However, determining comment or a post is abusive or not is still difficult and time consuming, most of the social media platforms still searching for more efficient ways for efficient moderate solution. Automating this will help in identifying abusive comments, and save the websites and increase user safety and improve discussions online. In this paper, Kaggle’s toxic comment dataset is used to train deep learning model and classifying the comments in following categories: toxic, severe toxic, obscene, threat, insult, and identity hate. The dataset is trained with various deep learning techniques and analyze which deep learning model is better in the comment classification. The deep learning techniques such as long short term memory cell (LSTM) with and without word GloVe embeddings, a Convolution neural network (CNN) with or without GloVe are used, and GloVe pretrained model is used for classification
Exploring Potential for Non-Disruptive Vertical Auto Scaling and Resource Estimation in Kubernetes

Cloud platforms typically require users to provide resource requirements for applications so that resource managers can schedule containers with adequate allocations. However, the requirements for container resources often depend on numerous factors such as application input parameters, optimization flags, input files, and attributes that are specified for each run. So, it is complex for users to estimate the resource requirements for a given container accurately, leading to resource over-estimation that negatively affects overall utilization. We have designed a Resource Utilization Based Autoscaling System (RUBAS) that can dynamically adjust the allocation of containers running in a Kubernetes cluster. RUBAS improves upon the Kubernetes Vertical Pod Autoscaler (VPA) system non-disruptively by incorporating container migration. Our experiments use multiple scientific benchmarks. We analyze the allocation pattern of RUBAS with Kubernetes VPA. We compare the performance of container migration for in-place and remote node migration and we evaluate the overhead in RUBAS. Our results show that compared to Kubernetes VPA, RUBAS improves the CPU and memory utilization of the cluster by 10% and reduces the runtime by 15% with an overhead for each application ranging from 5% to 20%.
Voice Assistants and Artificial Intelligence in Education

In recent years, Artificial Intelligence (AI) has shown significant progress and its potential is growing. An application area of AI is Natural Language Processing (NLP). Voice assistants incorporate AI using cloud computing and can communicate with the users in natural language. Voice assistants are easy to use and thus there are millions of devices that incorporates them in households nowadays. Most common devices with voice assistants are smart speakers and they have just started to be used in schools and universities. The purpose of this paper is to study the capabilities of voice assistants in the classroom and to present findings from previous studies.
Stream sediment geochemical data analysis for district-scale mineral exploration targeting: Measuring the performance of the spatial U-statistic and C-A fractal modeling

Recognition of mineralization-related geochemical footprints and modeling their multi-element dispersion patterns are important aspects to consider when “vectoring” toward undiscovered ore deposits. The collection, analysis, and interpretation of stream sediment geochemical data together make an exploration method that has proven to be successful at the district scale mineral exploration targeting. Identifying the possible sources of stream sediment geochemical anomalies and mapping evidence (i.e., footprints) of the underlying ore-forming processes, however, are not trivial tasks. This is because stream sediment samples represent transported material reflecting the entire geology upstream from the sample locations. Furthermore, indicator element distribution patterns are commonly strongly affected by local factors such as regolith, topographic gradient, vegetation density, and/or climate. Therefore, there is a need for finding a better geochemical anomaly separation method regarding the nature of geochemical data obtained from the area sampled. The main objectives of this study were (1) evaluating and comparing the spatial U-statistic and concentration–area fractal modeling methods of anomaly identification, both amenable to spatial analysis, for recognizing geochemical footprints of porphyry copper mineralization, and (2) measuring their performance in the context of district-scale exploration targeting in an area located in southeast Iran. Subsequently, the methods were first evaluated on a dataset of element contents to decompose anomalous populations. Finally, the geochemical model that proved more efficient with respect to predicting the known mineral deposits was integrated with additional evidence maps to delineate exploration targets. Our evaluation of the resulting geochemical targeting model demonstrated that the targets derived from this method are robust and worthy of follow-up exploration.
DeepRing: Protecting Deep Neural Network With Blockchain

Several computer vision applications such as object detection and face recognition have started to completely rely on deep learning based architectures. These architectures, when paired with appropriate loss functions and optimizers, produce state-of-the-art results in a myriad of problems. On the other hand, with the advent of "blockchain", the cybersecurity industry has developed a new sense of trust which was earlier missing from both the technical and commercial perspectives. Employment of cryptographic hash as well as symmetric/asymmetric encryption and decryption algorithms ensure security without any human intervention (i.e., centralized authority). In this research, we present the synergy between the best of both these worlds. We first propose a model which uses the learned parameters of a typical deep neural network and is secured from external adversaries by cryptography and blockchain technology. As the second contribution of the proposed research, a new parameter tampering attack is proposed to properly justify the role of blockchain in machine learning.
A baseline regularization scheme for transfer learning with convolutional neural networks

In inductive transfer learning, fine-tuning pre-trained convolutional networks substantially outperforms training from scratch. When using fine-tuning, the underlying assumption is that the pre-trained model extracts generic features, which are at least partially relevant for solving the target task, but would be difficult to extract from the limited amount of data available on the target task. However, besides the initialization with the pre-trained model and the early stopping, there is no mechanism in fine-tuning for retaining the features learned on the source task. In this paper, we investigate several regularization schemes that explicitly promote the similarity of the final solution with the initial model. We show the benefit of having an explicit inductive bias towards the initial model. We eventually recommend that the baseline protocol for transfer learning should rely on a simple L2 penalty using the pre-trained model as a reference.
Non-Touch Sign Word Recognition Based on Dynamic Hand Gesture Using Hybrid Segmentation and CNN Feature Fusion

Hand gesture-based sign language recognition is a prosperous application of human– computer interaction (HCI), where the deaf community, hard of hearing, and deaf family members communicate with the help of a computer device. To help the deaf community, this paper presents a non-touch sign word recognition system that translates the gesture of a sign word into text. However, the uncontrolled environment, perspective light diversity, and partial occlusion may greatly affect the reliability of hand gesture recognition. From this point of view, a hybrid segmentation technique including YCbCr and SkinMask segmentation is developed to identify the hand and extract the feature using the feature fusion of the convolutional neural network (CNN). YCbCr performs image conversion, binarization, erosion, and eventually filling the hole to obtain the segmented images. SkinMask images are obtained by matching the color of the hand. Finally, a multiclass SVM classifier is used to classify the hand gestures of a sign word. As a result, the sign of twenty common words is evaluated in real time, and the test results confirm that this system can not only obtain better-segmented images but also has a higher recognition rate than the conventional ones.
Evaluating shallow and deep learning strategies for the 2018 n2c2 shared task on clinical text classification

Abstract Objective Automated clinical phenotyping is challenging because word-based features quickly turn it into a high-dimensional problem, in which the small, privacy-restricted, training datasets might lead to overfitting. Pretrained embeddings might solve this issue by reusing input representation schemes trained on a larger dataset. We sought to evaluate shallow and deep learning text classifiers and the impact of pretrained embeddings in a small clinical dataset. Materials and Methods We participated in the 2018 National NLP Clinical Challenges (n2c2) Shared Task on cohort selection and received an annotated dataset with medical narratives of 202 patients for multilabel binary text classification. We set our baseline to a majority classifier, to which we compared a rule-based classifier and orthogonal machine learning strategies: support vector machines, logistic regression, and long short-term memory neural networks. We evaluated logistic regression and long short-term memory using both self-trained and pretrained BioWordVec word embeddings as input representation schemes. Results Rule-based classifier showed the highest overall micro F1 score (0.9100), with which we finished first in the challenge. Shallow machine learning strategies showed lower overall micro F1 scores, but still higher than deep learning strategies and the baseline. We could not show a difference in classification efficiency between self-trained and pretrained embeddings. Discussion Clinical context, negation, and value-based criteria hindered shallow machine learning approaches, while deep learning strategies could not capture the term diversity due to the small training dataset. Conclusion Shallow methods for clinical phenotyping can still outperform deep learning methods in small imbalanced data, even when supported by pretrained embeddings.
Multi‐Scale 3D U‐Nets: An approach to automatic segmentation of brain tumor

Abstract Gliomas segmentation is a critical and challenging task in surgery and treatment, and it is also the basis for subsequent evaluation of gliomas. Magnetic resonance imaging is extensively employed in diagnosing brain and nervous system abnormalities. However, brain tumor segmentation remains a challenging task, because differentiating brain tumors from normal tissues is difficult, tumor boundaries are often ambiguous and there is a high degree of variability in the shape, location, and extent of the patient. It is therefore desired to devise effective image segmentation architectures. In the past few decades, many algorithms for automatic segmentation of brain tumors have been proposed. Methods based on deep learning have achieved favorable performance for brain tumor segmentation. In this article, we propose a Multi‐Scale 3D U‐Nets architecture, which uses several U‐net blocks to capture long‐distance spatial information at different resolutions. We upsample feature maps at different resolutions to extract and utilize sufficient features, and we hypothesize that semantically similar features are easier to learn and process. In order to reduce the computational cost, we use 3D depthwise separable convolution instead of some standard 3D convolution. On BraTS 2015 testing set, we obtained dice scores of 0.85, 0.72, and 0.61 for the whole tumor, tumor core, and enhancing tumor, respectively. Our segmentation performance was competitive compared to other state‐of‐the‐art methods.
Vulnerability to machine learning attacks of optical encryption based on diffractive imaging

In this paper, we experimentally demonstrate for the first time to our knowledge that optical encryption based on diffractive imaging is vulnerable to the attacks using learning methods. Using machine learning attack, an opponent is capable to retrieve unknown plaintexts from the given ciphertexts. The proposed method adopts end-to-end learning to extract a superior mapping relationship between the ciphertexts and the plaintexts. Without a direct retrieval or estimate of optical encryption keys, an unauthorised user can extract unknown plaintexts from the given ciphertexts by using the trained learning models. Simulations and optical experimental results demonstrate that the proposed learning method is feasible and effective to analyze the vulnerability of optical encryption schemes. The universality of the trained learning model is also illustrated, and it is verified that the machine learning model trained by using a database is robust to be used for attacking different databases. Compared with conventional cryptanalytic methods, the proposed machine learning attacks can retrieve unknown plaintexts from the given ciphertexts using the trained learning models without a direct usage of various different optical encryption keys, which provides a different strategy for the cryptanalysis of optical encryption systems. @ Elsevier, 2019.
Quantum video encryption based on qubit-planes controlled-XOR operations and improved logistic map

This paper proposes an efficient and secure quantum video encryption algorithm for quantum videos based on qubit-planes controlled-XOR operations and improved logistic map in multi-layer encryption steps. Three simple cryptosystem steps are presented in the proposed approach to accomplish the whole encryption process: inter-frame permutation, intra-frame pixel position geometric transformation, and high 4-intra-frame-qubit-planes scrambling. Firstly, the inter-frame positions of quantum video are permutated via inter-frame permutation that is controlled by the keys which are generated by improved logistic map. Secondly, intra-frame pixel positions are encrypted by intra-frame pixel position geometric transformation and improved logistic map. Finally, the high 4-intra-frame-qubit-planes are scrambled via quantum controlled-XOR operations and improved logistic map. Experiments conducted demonstrate that the proposed quantum video encryption approach has high efficiency with simple calculation, low complexity and strongly filtrates in security applications like video on demand, video conference.
Fast bridge deflection monitoring through an improved feature tracing algorithm

Abstract Completely non‐contact and fast measurements are still the focus of vision‐based monitoring in real‐bridge applications. Overall, a fast deflection measurement method, that traces the natural features of structures instead of artificial targets, was developed in this study. More specifically, due to the tiny scale change between images and the small deformation level of small and medium bridges, a simplified fast‐Hessian detector and a pre‐purification‐based RANdom Sampling Consistency (RANSAC) were proposed and verified by performing experiments under different illuminations. The non‐target deformation measurement method based on an improved algorithm was applied to field testing of an arch bridge with 100 m main girder and showed better processing speed and sub‐pixel accuracy than their originals. The optical results obtained by an industrial camera were consistent with other contact‐type sensors, such as level gauges and inclinometers.
Optimizing Validation Phase of Hyperledger Fabric

Blockchain technologies are on the rise, and Hyperledger Fabric is one of the most popular permissioned blockchain platforms. In this paper, we re-architect the validation phase of Fabric based on our analysis from fine-grained breakdown of the validation phase's latency. Our optimized validation phase uses a chaincode cache during validation of transactions, initiates state database reads in parallel with validation of transactions, and writes to the ledger and databases in parallel. Our experiments reveal performance improvements of 2x for CouchDB and 1.3x for LevelDB. Notably, our optimizations can be adopted in a future release of Hyperledger Fabric.
Single-Server Multi-Message Individually-Private Information Retrieval with Side Information

We consider a multi-user variant of the private information retrieval problem described as follows. Suppose there are D users, each of which wants to privately retrieve a distinct message from a server with the help of a trusted agent. We assume that the agent has a subset of M messages whose indices are unknown to the server. The goal of the agent is to collectively retrieve the users' requests from the server. For this problem, we introduce the notion of individual-privacy - the agent is required to protect the privacy only for each individual user (but may leak some correlations among user requests). We refer to this problem as Individually-Private Information Retrieval with Side Information (IPIR-SI).We first establish a lower bound on the capacity, which is defined as the maximum achievable download rate, of the IPIR-SI problem by presenting a novel achievability protocol. Next, we characterize the capacity of IPIR-SI problem for M = 1 and D = 2. In the process of characterizing the capacity for arbitrary M and D we present a novel combinatorial conjecture, that may be of independent interest.
Towards a Theory of Software Developer Job Satisfaction and Perceived Productivity

Developer satisfaction and work productivity are important considerations for software companies. Enhanced developer satisfaction may improve the attraction, retention and health of employees, while higher productivity should reduce costs and increase customer satisfaction through faster software improvements. Many researchers and companies assume that perceived productivity and job satisfaction are related and may be used as proxies for one another, but these claims are a current topic of debate. There are also many social and technical factors that may impact satisfaction and productivity, but which factors have the most impact is not clear, especially for specific development contexts. Through our research, we developed a theory articulating a bi-directional relationship between software developer job satisfaction and perceived productivity, and identified what additional social and technical factors, challenges and work context variables influence this relationship. The constructs and relationships in our theory were derived in part from related literature in software engineering and knowledge work, and we validated and extended these concepts through a rigorously designed survey instrument. We instantiate our theory with a large software company, which suggests a number of propositions about the relative impact of various factors and challenges on developer satisfaction and perceived productivity. Our survey instrument and analysis approach can be applied to other development settings, while our findings lead to concrete recommendations for practitioners and researchers.
A chaotic image encryption algorithm based on improved Joseph traversal and cyclic shift function

This paper proposes an encryption algorithm that uses the initial value and parameters of the chaotic system as the key, and mainly uses the improved Joseph traversal method, and the cyclic shift function encrypts the image. Traditional image scrambling algorithms, such as cat mapping, geometric transformation, and E-curve transformation, have been shown to have the limitations of periodicity and regularity of scrambling effects. In the stage of image scrambling, this paper mainly uses the improved Joseph traversal method to solve the problem of periodicity and regularity of the scrambling effect. Therefore, safety is higher. Compared with other algorithms, the correlation value between adjacent elements of the encrypted image is closer to zero, so the scrambling effect is more random. The basic steps of the algorithm are as follows: (1) firstly, the image is scrambled mainly by using the improved Joseph traversal method; (2) secondly, uses the cyclic shift function to initially diffuse the image; (3) finally uses the dynamic exclusive-OR method to re-spread the image. The experiment proves that the algorithm has the advantages of high security and high sensitivity. It can effectively resist exhaustive attacks and differential attacks, and has good statistical characteristics.
Word2Vec for Indonesian Sentiment Analysis towards Hotel Reviews: An Evaluation Study

Hand-crafted features engineering is a labor-intensive and highly-cost task. In this paper, we implement Word2Vec as an alternative solution of hand-crafted features for sentiment analysis of hotel reviews in the Indonesian language. To obtain the highest performance of sentiment analysis, we evaluate three parameters of Word2Vec include Word2Vec model architecture, evaluation method, and vector dimension. This evaluation process was implemented towards our proposed corpus for a specific domain, i.e. hotel reviews, consists of 2500 hotel reviews in the Indonesian language (1250 positive reviews and 1250 negative reviews). The result shows that the highest accuracy values are obtained under the combination of the following parameters, namely the architecture of Word2Vec Model is Skip-gram model, the evaluation method is Hierarchical Softmax, as well as the vector dimension is 100. The Skip-gram model results highest accuracy for words that rarely appear, such as in sentiment analysis task, whereas the Hierarchical Softmax provides better results since during the training process using a binary tree model to represent all of the words in the vocabulary and leaf nodes representing rare words so that rarely appearing words will inherit vector representations in it. Furthermore, to obtain the optimal value of accuracy, then we should increase the vector dimensions and amount of data simultaneously.
T-HSAB: A Tunisian Hate Speech and Abusive Dataset

Since the "Jasmine Revolution" at 2011, Tunisia has entered a new era of ultimate freedom of expression with a full access into social media. This has been associated with an unrestricted spread of toxic contents such as Abusive and Hate speech. Considering the psychological harm, let alone the potential hate crimes that might be caused by these toxic contents, automatic Abusive and Hate speech detection systems become a mandatory. This evokes the need for Tunisian benchmark datasets required to evaluate Abusive and Hate speech detection models. Being an underrepresented dialect, no previous Abusive or Hate speech datasets were provided for the Tunisian dialect. In this paper, we introduce the first publicly-available Tunisian Hate and Abusive speech (T-HSAB) dataset with the objective to be a benchmark dataset for automatic detection of online Tunisian toxic contents. We provide a detailed review of the data collection steps and how we design the annotation guidelines such that a reliable dataset annotation is guaranteed. This was later emphasized through the comprehensive evaluation of the annotations as the annotation agreement metrics of Cohen's Kappa (k) and Krippendorff's alpha ( $$\alpha $$ ) indicated the consistency of the annotations.