Reviewerly/exHarmony huggingface dataset:
A Pre-Filtering Approach for Incorporating Contextual Information Into Deep Learning Based Recommender Systems

Depending on the Internet as the main source of information regarding all aspects of our life is becoming a trend.People seek relevant information, suggestions, and recommendations in an overloaded online world and through social ties regarding their daily activities, including places to visit and restaurants to try new food.The wide variety of choices that are available online causes information overloading, which thereby complicates the selection process.Traditional recommender systems are mostly dependent on a conventional model that is based on user-item-rating interaction without considering contextual information.We claim that new generations of recommendation systems able to exploit context in an innovative and efficient way is important and may statistically yield more significant rating predictions.However, only few research works have focused on how to effectively and efficiently exploit context metadata in Deep Learning (DL)-based recommendations.The main reason lies, perhaps most significantly, in the fact that most current DL algorithms are not intrinsically designed to incorporate contextual tags.In this paper, we provide a significant contribution for filling this gap by designing a hybrid algorithm that retrofits and repurposes a prefiltering contextual incorporation method and feeds the new dimension to a DL-based neural collaborative filtering method, thus preserving and recovering the benefits of both without their limitations.The paper also reports quantitative results that show that our method outperforms the baselines by statistically significant margins.
Simulation of transaction malleability attack for blockchain-based e-Voting

Blockchain has been adopted to address significant challenges, such as trust in diverse domains, including voting, logistics and finance. However, transaction malleability has been identified as a threat for blockchain, which can potentially lead to an inconsistent state that can result in further attacks such as double-spending. In this context, this paper is focused on investigating the feasibility of transaction malleability within a typical blockchain application aiming to identify scenarios that may lead to a successful transaction malleability attack. Our objective in doing so is to highlight conditions which cause such attack to facilitate the development of protection mechanisms for them. Specifically, this paper presents a successful simulation of transaction malleability attack within the context of blockchain-based electronic voting. The evaluation has identified the impact of parameters, such as network delay and block generation rate in achieving a successful transaction malleability attack, which highlights future directions of research.
New MDS self-dual codes over finite fields of odd characteristic

In this paper, we produce new classes of MDS self-dual codes via (extended) generalized Reed–Solomon codes over finite fields of odd characteristic. Among our constructions, there are many MDS self-dual codes with new parameters which have never been reported. When q is square of odd prime power, the total number of lengths of MDS self-dual codes over $$\mathbb {F}_q$$ presented in this paper is much more than those in all the previous results.
Static Analysis of Integer Overflow of Smart Contracts in Ethereum

In recent years, vulnerabilities of smart contracts have frequently break out. In particular, integer overflow of smart contracts, a high-risk vulnerability, has caused huge financial losses. However, most tools currently fail to detect integer overflow in smart contracts. In this paper, we summarize 11 types of integer overflow features for Solidity smart contracts in Ethereum and abstractly define 83 corresponding XPath patterns. And we design an extensible static analysis tool to detect common integer overflow vulnerabilities of Solidity smart contracts in Ethereum through the defined XPath patterns. To evaluate our tool, we tested 7,000 verified Solidity smart contracts and found that there were 430 smart contracts with vulnerabilities of integer overflow. Experimental results show that there are still high-risk vulnerabilities of integer overflow in verified smart contracts.
Toward Explainable Fashion Recommendation

Many studies have been conducted so far to build systems for recommending fashion items and outfits. Although they achieve good performances in their respective tasks, most of them cannot explain their judgments to the users, which compromises their usefulness. Toward explainable fashion recommendation, this study proposes a system that is able not only to provide a goodness score for an outfit but also to explain the score by providing reason behind it. For this purpose, we propose a method for quantifying how influential each feature of each item is to the score. Using this influence value, we can identify which item and what feature make the outfit good or bad. We represent the image of each item with a combination of human-interpretable features, and thereby the identification of the most influential item-feature pair gives useful explanation of the output score. To evaluate the performance of this approach, we design an experiment that can be performed without human annotation; we replace a single item-feature pair in an outfit so that the score will decrease, and then we test if the proposed method can detect the replaced item-feature pair correctly using the above influence values. The experimental results show that the proposed method can accurately detect bad items in outfits lowering their scores.
Arduino Based Automatic Irrigation System: Monitoring and SMS Controlling

Nowadays, there is speed and diversity in scientific innovations that enter into the way of an individual's life and make the life easier. There are many inventors of irrigation systems that rely on the control system which facilitates the way towards a solid system of irrigation. In this research, a new system is designed to monitor and operate the irrigation system. The system uses the Arduino mega 2560 enhanced with Global System for Mobile communication (GSM) technology so that Arduino platform allows to receive/send SMS to/from the mobile of farms/homeowner according to soil needed for water or the instructions sent by the user. The system is equipped with moisture sensors that are inserted in the soil to irrigate the plants automatically if the ground is dry or controlled by mobile using SMS messaging. The mobile of farms/homeowner can inform him if soil needs watering and in turn the farmer/householder sends an SMS message to the controller to start irrigation, and then send another SMS message to stop the irrigation after receiving an SMS message from the microcontroller that the soil saturated with water. There are probabilities to take into consideration, one of them is if the water tank is empty, which in this situation the moisture sensor inserted in the tank to inform the homeowner there is no water if empty and in turn the homeowner has sent the SMS message to microcontroller to turn ON the water pump to provide the tank with water. Another probability is rain fall; in this state, the rain drop sensor module is added to the system to stop the irrigation via SMS sent by homeowner.
Plug-and-Play Gesture Control Using Muscle and Motion Sensors

As the capacity for machines to extend human capabilities continues to grow, the communication channels used must also expand. Allowing machines to interpret nonverbal commands such as gestures can help make interactions more similar to interactions with another person. Yet to be pervasive and effective in realistic scenarios, such interfaces should not require significant sensing infrastructure or per-user setup time. The presented work takes a step towards these goals by using wearable muscle and motion sensors to detect gestures without dedicated calibration or training procedures. An algorithm is presented for clustering unlabeled streaming data in real time, and it is applied to adaptively thresholding muscle and motion signals acquired via electromyography (EMG) and an inertial measurement unit (IMU). This enables plug-and-play online detection of arm stiffening, fist clenching, rotation gestures, and forearm activation. It also augments a neural network pipeline, trained only on strategically chosen training data from previous users, to detect left, right, up, and down gestures. Together, these pipelines offer a plug-and-play gesture vocabulary suitable for remotely controlling a robot. Experiments with 6 subjects evaluate classifier performance and interface efficacy. Classifiers correctly identified 97.6% of 1,200 cued gestures, and a drone correctly responded to 81.6% of 1,535 unstructured gestures as subjects remotely controlled it through target hoops during 119 minutes of total flight time.
Detecting hate speech from tweets for sentiment analysis

In the era of the popularity of Social Networking Service (SNS), people became increasingly inseparable from mobile phones and computers. People want to get information and real-time updates from social media, and they want to know how many Internet citizens have comments and opinions on many dynamic news. The interaction among users on social networking platforms is usually positive, advisory and motivating and influential. However, sometimes people will also reveal objectionable content, such as hate speech, abusive and bullying or discriminatory words. According to multiple methods, we will find out which method has the best accuracy of detecting hate speech from tweets. Many papers using types of data for experimentation. The major innovation of this article is that we used different ratios of data to compare with multiple methods at the same time. As a result, good performance is obtained by using machine learning when data is small. The good results can be obtained by using deep learning when we use more data for our experiments. Using BiRNN can get the best results, compared with other methods we used. Even if this method is superior to other models, we have to consider the type of data set in the future.
The Impact of Software Fault Prediction in Real-World Application

Software fault prediction and proneness has long been considered as a critical issue for the tech industry and software professionals. In the traditional techniques, it requires previous experience of faults or a faulty module while detecting the software faults inside an application. An automated software fault recovery models enable the software to significantly predict and recover software faults using machine learning techniques. Such ability of the feature makes the software to run more effectively and reduce the faults, time and cost. In this paper, we proposed a software defect predictive development models using machine learning techniques that can enable the software to continue its projected task. Moreover, we used different prominent evaluation benchmark to evaluate the model's performance such as ten-fold cross-validation techniques, precision, recall, specificity, f 1 measure, and accuracy. This study reports a significant classification performance of 98-100% using SVM on three defect datasets in terms of f1 measure. However, software practitioners and researchers can attain independent understanding from this study while selecting automated task for their intended application.
Analysis of Proof-of-Work-Based Blockchains Under an Adaptive Double-Spend Attack

In this article, we study the performance of blockchains by analyzing the common prefix depth, chain quality coefficient, and chain growth speed coefficient. These three parameters characterize the liveness and consistency of transactions which are important for the proper operation of the blockchain. We examine how these three parameters are affected under an adaptive double-spend attack (ADSA). To maintain the performance of a blockchain against ADSA, the user nodes can use a larger number, z, of confirmation blocks for validating a transaction. A comparison of the values of z needed to achieve a given target probability of successful attack is provided for ADSA and the traditional double-spend attack with different system models. The results indicate that a larger value of z is required under ADSA. A more realistic reward model for attackers is also introduced. It is found that the expected reward of an attacker decreases rapidly to zero as z is increased.
Depth Completion via Deep Basis Fitting

In this paper we consider the task of image-guided depth completion where our system must infer the depth at every pixel of an input image based on the image content and a sparse set of depth measurements. We propose a novel approach that builds upon the strengths of modern deep learning techniques and classical optimization algorithms and significantly improves performance. The proposed method replaces the final 1 × 1 convolutional layer employed in most depth completion networks with a least squares fitting module which computes weights by fitting the implicit depth bases to the given sparse depth measurements. In addition, we show how our proposed method can be naturally extended to a multi-scale formulation for improved self-supervised training. We demonstrate through extensive experiments on various datasets that our approach achieves consistent improvements over state-of-the-art baseline methods with small computational overhead.
Block Annotation: Better Image Annotation With Sub-Image Decomposition

Image datasets with high-quality pixel-level annotations are valuable for semantic segmentation: labelling every pixel in an image ensures that rare classes and small objects are annotated. However, full-image annotations are expensive, with experts spending up to 90 minutes per image. We propose block sub-image annotation as a replacement for full-image annotation. Despite the attention cost of frequent task switching, we find that block annotations can be crowdsourced at higher quality compared to full-image annotation with equal monetary cost using existing annotation tools developed for full-image annotation. Surprisingly, we find that 50% pixels annotated with blocks allows semantic segmentation to achieve equivalent performance to 100% pixels annotated. Furthermore, as little as 12% of pixels annotated allows performance as high as 98% of the performance with dense annotation. In weakly-supervised settings, block annotation outperforms existing methods by 3-4% (absolute) given equivalent annotation time. To recover the necessary global structure for applications such as characterizing spatial context and affordance relationships, we propose an effective method to inpaint block-annotated images with high-quality labels without additional human effort. As such, fewer annotations can also be used for these applications compared to full-image annotation.
Weakly Supervised Gaussian Networks for Action Detection

Detecting temporal extents of human actions in videos is a challenging computer vision problem that requires detailed manual supervision including frame-level labels. This expensive annotation process limits deploying action detectors to a limited number of categories. We propose a novel method, called WSGN, that learns to detect actions from weak supervision, using only video-level labels. WSGN learns to exploit both video-specific and dataset-wide statistics to predict relevance of each frame to an action category. This strategy leads to significant gains in action detection for two standard benchmarks THU-MOS14 and Charades. Our method obtains excellent results compared to state-of-the-art methods that uses similar features and loss functions on THUMOS14 dataset. Similarly, our weakly supervised method is only 0.3% mAP behind a state-of-the-art supervised method on challenging Charades dataset for action localization.
An Efficient Human Activity Recognition Framework Based on Wearable IMU Wrist Sensors

Lately, Human Activity Recognition (HAR) using wearable sensors has received extensive research attention for its great use in the human health performance evaluation across several domain. HAR methods can be embedded in a smart home healthcare model to assist patients and enhance their rehabilitation process. Several types of sensors are currently used for HAR amongst them are wearable wrist sensors, which have a great ability to deliver Valuable information about the patient's grade of ability. Some recent studies have proposed HAR using Machine Learning (ML) techniques. These studies have included non-invasive wearable wrist sensors, such as Accelerometer, Magnetometer and Gyroscope. In this paper, a novel framework for HAR using ML based on sensor-fusion is proposed. Moreover, a feature selection approach to select useful features based on Random Forest (RF), Bagged Decision Tree (DT) and Support Vector Machine (SVM) classifiers is employed. The proposed framework is investigated on two publicly available datasets. Numerical results show that our framework based on sensor-fusion outperforms other methods proposed in the literature.
Figure Captioning with Relation Maps for Reasoning

Figures, such as line plots, pie charts, bar charts, are widely used to convey important information in a concise format. In this work, we investigate the problem of figure caption generation where the goal is to automatically generate a natural language description for a given figure. While natural image captioning has been studied extensively, figure captioning has received relatively little attention and remains a challenging problem. A successful solution to this task has many potential applications, such as: 1) automatic parsing large amount of figures in PDF document; 2) improving user experience by allowing figure content to be accessible to those with visual impairment. To solve this problem, we introduce a dataset FigCAP and propose novel attention mechanism. In order to solve the exposure bias issue, we further train the captioning model with sequence-level policy based on reinforcement learning, which directly optimizes evaluation metrics. Extensive experiments show that the proposed method outperforms the baselines, thus demonstrating a significant potential for automatic generating captions for figures.
A Detection and Prevention Technique on SQL Injection Attacks

With the web advancements are rapidly developing, the greater part of individuals makes their transactions on web, for example, searching through data, banking, shopping, managing, overseeing and controlling dam and business exchanges, etc. Web applications have gotten fit to numerous individuals' day by day lives activities. Dangers pertinent to web applications have expanded to huge development. Presently a day, the more the quantity of vulnerabilities will be diminished, the more the quantity of threats become to increment. Structured Query Language Injection Attack (SQLIA) is one of the incredible dangers of web applications threats. Lack of input validation vulnerabilities where cause to SQL injection attack on web. SQLIA is a malicious activity that takes negated SQL statement to misuse data-driven applications. This vulnerability admits an attacker to comply crafted input to disclosure with the application's interaction with back-end databases. Therefore, the attacker can gain access to the database by inserting, modifying or deleting critical information without legitimate approval. The paper presents an approach which detects a query token with reserved words-based lexicon to detect SQLIA. The approach consists of two highlights: the first one creates lexicon and the second step tokenizes the input query statement and each string token was detected to predefined words lexicon to prevent SQLIA. In this paper, detection and prevention technologies of SQL injection attacks are experimented and the result are satisfactory.
Interactive Rule Attention Network for Aspect-Level Sentiment Analysis

Aspect-level sentiment analysis is a fundamental task in NLP, and it aims to predict the sentiment polarity of each specific aspect term in a given sentence. Recent researches show that the fine-grained sentiment analysis for aspect-level has become a research hotspot. However, previous work did not consider the influence of grammatical rules on aspect-level sentiment analysis. In addition, attention mechanism is too simple to learn attention information from context and target interactively. Therefore, we propose an interactive rule attention network (IRAN) for aspect-level sentiment analysis. IRAN not only designs a grammar rule encoder, which simulates the grammatical functions at the sentence by standardizing the output of adjacent positions, but also constructs an interaction attention network to learn attention information from context and target. Experimental results on SemEval 2014 Dataset and ACL 2014 Twitter Dataset demonstrate IRAN can learn effective features and obtain superior performance over the baseline models.
A Framework for Mixed Reality Free-Choice, Self-Determined Learning

In this article, we present a theoretical framework for mixed reality (MR/XR) self-determined learning to enhance ecological literacy in free-choice educational settings. The framework emerged from a research study in New Zealand which aimed to explore how learning experiences which incorporate mobile technologies within free-choice learning settings can be designed to enhance learner development of marine ecological literacy. An understanding of how mobile technology can be integrated into the teaching and learning of sustainability education that incorporates free-choice learning contexts, such as visitor centres, is of strategic importance to both education outside the classroom and adult learning. Following a design-based research methodology, the framework is presented in the form of a set of design principles and guidelines, informed by key theories in ecological literacy and free-choice learning, heutagogy, bring your own device and self-determined learning. We briefly describe how the framework provided the foundation for an educational intervention. This paper aims to assist researchers and developers of MR/XR immersive learning environments to consider design principles and processes that can enhance learning outcomes within free-choice settings, such as museums and visitor centres. This article is part of the special collection Mobile Mixed Reality Enhanced Learning edited by Thom Cochrane, James Birt, Helen Farley, Vickel Narayan and Fiona Smart. More articles from this collection can be found here.
Single Image Dehazing Based on Adaptive Histogram Equalization and Linearization of Gamma Correction

Visibility of outdoor images is usually limited due to haze, dust, smoke and other particles in air. Visibility limit can cause many difficulties for activities of transport, rescue, oceanography etc. Hence, image dehazing is very necessary. In this paper, we propose a single image dehazing method based on combination of adaptive histogram equalization, HSV color model and linearization of Gamma correction. In the experiments, we test the proposed method on hazy images of the TAU dataset. To assess dehazing quality, we utilize NIQE metric and compare to other dehazing methods. The results confirm that the proposed method dehazes effectively and can compete with other state-of-the-art dehazing methods.