Reviewerly/exHarmony huggingface dataset:
User data privacy in multimedia domain using 3–3 LSB-based color image steganography with RC4 and Bernoulli map protection

Social media sites such as Facebook, Twitter, etc. have become commonplace in everyday life. With the emergence of the internet, the amount of data being created and communicated within social networks has increased exponentially. As much as we enjoy the exchange of information that occurs on social media, protecting and maintaining privacy demands a lot of effort. Users’ data that are to be kept private should be treated as confidential information. As a result, securing data exchange on the internet has become a critical and urgent issue. We have suggested a method for protecting user data which is encrypted by using the Rivest Cipher 4 (RC4) algorithm in multimedia images with the help of 3–3 least significant bits (LSB) techniques, with the added safety of Bernoulli map. We call this technique the “user data protection scheme.” 3–3 LSB is a message hiding process in which the first three stand for three LSB and the next three stand for all three channels (R, G, and B). In this method, we are using the Rivest Cipher 4 (RC4) encryption algorithm to encrypt the user’s data, which will then be embedded into the multimedia image in the next step. With the help of the Bernoulli map, we generate a random sequence of length similar to the size of the image. For every random number generated by the Bernoulli map, we determine the location in the plane and place three bits of secret message into the cover image using the LSB technique. Since we have used two different encryption techniques, i.e., RC4 for encryption of user data and Bernoulli map for embedding encrypted data at random locations over the cover image, our method is more secure than others. We performed this analysis to equate peak signal-to-noise ratio (PSNR) and mean square error. Our experimental result shows that 9 bpp can be embedded in color images with a PSNR higher than 42 dB, and for the gray scale image embedding capacity is 3 bpp with a PSNR higher than 42 dB, which provides better results. Apart from this, it produces less distorted stego-images than currently used methods (an average of 1.68 bpp).
Implementing Access Control in Chaincodes on Hyperledger Fabric with Attribute-Based Encryption

Hyperledger Fabric as a smart contract platform has been increasingly used to deploy many services. To protect stored data in a ledger from an adversary, encrypting the data is desirable before storing it on the ledger. In this paper, we propose an access control mechanism on Hyperledger Fabric smart contracts called chaincodes. Our approach for data protection is based on an attribute-based encryption scheme, which provides efficient key management for encryption and decryption by avoiding managing secret keys through using attributes. We describe how to implement the attribute-based encryption into chaincodes and then evaluate the performance with respect to execution time for data store and query.
LMS Content Evaluation System with Sentiment Analysis Using Lexicon-Based Approach

The emergence of information technology used in all factors of our everyday lives has exponentially increased the amount of unstructured data. This huge quantity of records is a great source for finding and thus, may be used for extracting actionable information. In the academe, for instance, teachers and school administrators can adjust their approach to teaching/learning by getting feedback from students through a Learning Management System that can automatically analyze the semantic orientation of words and contextual polarity of these feedbacks - categorizing them into positive and negative. Identifying and classifying words expressed in the students' feedback about learning materials can provide structured information that can guide the teacher, impact its design and target the students' needs. This study implemented a lexicon-based strategy for automatic sentiment analysis using VADER as a model. Student feedbacks are extracted from an LMS developed to demonstrate the usability and effectiveness of the adopted approach; among other features of LMS that will help teachers improve its implementation. Results of the LMS sentiment analysis are compared to human-annotated sentiments to verify and validate the output, as well as, check its accuracy using Confusion Matrix. It aims to create a structured representation of student sentiments through LMS to help teachers improve the design of learning materials.
Question and Answer Techniques for Financial Audits in Universities Based on Deep Learning

Financial auditing in universities is highly specialized, with a huge knowledge system and rapid updates. Auditors will encounter various problems and situations in their work and need to acquire domain knowledge efficiently and accurately to solve the difficulties they encounter. The existing audit information software, however, is mostly aimed at the management of audit affairs and lacks the relevant functions to acquire and retrieve knowledge of specific audit domains. In this study, we use deep learning theory as support to conduct an in-depth study on the key technologies of question and answer systems in the field of financial auditing in universities. In the question-answer retrieval stage, the local information and the global information of the sentence are first modelled using a two-way coding model based on the attentional mechanism, and then, an interactive text matching model is used to interact directly at the input layer, and a multilayer convolutional neural network model cable news network (CNN) is used to extract the fine-grained matching features from the interaction matrix; this study adopts two matching methods. We have conducted comparative experiments to verify the effectiveness and application value of the entity recognition algorithm based on this study’s algorithm and the question-answer retrieval model based on multi-granularity text matching in the university financial audit domain.
Context Attention Network for Skeleton Extraction

Skeleton extraction is a task focused on providing a simple representation of an object by extracting the skeleton from the given binary or RGB image. In recent years many attractive works in skeleton extraction have been made. But as far as we know, there is little research on how to utilize the context information in the binary shape of objects. In this paper, we propose an attention-based model called Context Attention Network (CANet), which integrates the context extraction module in a UNet architecture and can effectively improve the network’s ability to extract the skeleton pixels. Meanwhile, we also use some novel techniques including distance transform, weight focal loss to achieve good results on the given dataset. Finally, without model ensemble and with only 80% of the training images, our method achieves 0.822 F1 score during the development phase and 0.8507 F1 score during the final phase of the Pixel SkelNetOn Competition, ranking 1st place on the leaderboard.
Intelligent Control of a Driverless Energy Vehicle Based on an Environment Sensing Sensor

In order to solve the complex environment in the process of vehicle driving and the complexity of self-vehicle structure, intelligent vehicles are prone to rear end collision, lateral collision, and other safety accidents in the presence of tall trees, mountains, and other road environments, endangering the safety of people on board. According to parameters such as the speed of the vehicle, the movement of the blind spot, and the relationship between the vehicle and the blind spot, the model is based on the safety mode of the preceding vehicle. Based on the static obstacles that may exist in the sensing blind area, a sensor sensing blind area safety distance model is established. Based on the possible dynamic obstacles, the active collision avoidance algorithm based on the sensor perceived blind area is studied and simulated. The experimental results show that the selected sensor sensing blind area active collision avoidance controller can well adapt to a variety of special and emergency working conditions, can accurately complete the accurate control of sensor sensing blind area active collision avoidance, and avoid collision accidents to the greatest extent. Compared with the control group, the system designed in this paper can avoid more than 80% of the collision scenes compared with the previous anticollision system. It provides a reference for the future research of sensor sensing blind area-related topics and sensor sensing blind area active collision avoidance system. To a certain extent, it can improve the ability of intelligent vehicle environmental perception and reduce the incidence of rear end collision accidents.
VM Failure Prediction based Intelligent Resource Management Model for Cloud Environments

This paper proposes a Virtual Machine (VM) failure prediction based intelligent cloud resource management (FP-IRM) model that estimates failure of VMs proactively and assorts all the available resources effectively. Specifically, a novel ensemble predictor is developed to determine any resource (CPU, storage) congestion prior to occurrence in real-time. Accordingly, the VM migration process is triggered proactively to proficiently manage the VM failures by reason of insufficient physical resources. FP-IRM model is implemented and evaluated by using a real-world benchmark Google Cluster VM traces dataset. The experimental simulation and comparison with state-of-the-arts confirms the influential performance of the proposed model which has reduced the number of active servers up to 51.2 % and an improved resource utilization up to 24.3 % over the comparative approaches.
Survey on Image Authentication and Privacy in Public Networks

The number of people using social networking sites such as Facebook, Twitter, Instagram, and LinkedIn over the last couple of years has increased dramatically. On the one hand, these online communication sites have offered a lot of options for users to communicate and interact with each other. On the other hand, social networks raise concerns about privacy and security among internet users. In gaining an understanding of the unknown risks associated with the exposure of their content and information in this space as a result of its unchecked publication and sharing, users can better protect themselves against various forms of online attacks and be able to moderate its influence and adapt to evolving technologies. As people have become more dependent on social media and the Internet, they have developed negative traits that have led some to commit crimes. Due to multiple shortcomings, information security has become a major issue in today's society. While classifying the images, the main obstacles will be on achieving intra-class variation, size variation, occlusion, lighting, and background clutter. This research paper intends to present a detailed survey about various methodology implementations by previous researches for OSN privacy management and control using AI algorithms.
CAMA: Class activation mapping disruptive attack for deep neural networks

The emergence of adversarial examples has aroused widespread attention to the safety of deep learning. Most recent research focuses on how to obtain adversarial examples which make networks’ predictions wrong, and rarely observe the changes in feature embedding space from the perspective of interpretability. In addition, researchers have proposed various attack algorithms for a single task, but there are few general methods that can perform multiple tasks at the same time, such as image classification, object detection, and face recognition. To resolve these issues, we propose a new attack algorithm CAMA for deep neural networks (DNNs). CAMA perturbs each feature extraction layer through adaptive feature measurement function, thereby disrupting the predicted class activation mapping of DNNs. Experiments show that CAMA is good at creating white-box adversarial examples on classification networks and has the highest attack success rate. To solve the problem of the disappearance of aggression caused by image transformation, we propose spread-spectrum compression CAMA, which achieve a better attack success rate under various defensive measures. In addition, we successfully attack face recognition networks and object detection networks using CAMA, and achieve excellent performance. It verifies that our algorithm is a general attack algorithm for attacking different tasks.
Ownership vs Contribution: Investigating the Alignment Between Ownership and Contribution

Software development is a collaborative endeavour. Organisations that develop software assign modules to different teams, i.e., teams own their modules and are responsible for them. These modules are rarely isolated, meaning that there exist dependencies among them. Therefore, other teams might often contribute to developing modules they do not own. The contribution can be, among other types, in the form of code authorship, code review, and issue detection. This research presents a model to investigate the alignment between module ownership and contribution and the preliminary results of an industrial case study to evaluate the model in practice. Our model uses seven metrics to assess teams’ contributions. Initial results suggest that the model correctly identifies misalignment between ownership and contribution. The detection of misalignment between ownership and contribution is the first step towards investigating the impact it might have on the faster accumulation of Technical Debt.
Classroom Roll Call System Based on Face Detection Technology

The students in universities are relatively free and have a rich after-school life in this time, so the absenteeism rate is getting higher and higher, which brings new challenges and opportunities to college classroom teaching. How to use the latest artificial intelligence technology, talk about the application of face detection technology to the classroom, you can quickly and accurately count the attendance status of each student. This article first analyzes the advantages and disadvantages of the new naming methods in university classrooms, and then designs and implements a classroom naming system based on face detection technology. Face detection technology is based on the latest deep learning algorithm Faster-R-CNN, which can quickly and accurately detect face targets. Besides, the system also needs simple Internet of Things technology, which uses a camera as a sensor to transfer real-time photos of the classroom to the system. This system aims to reduce the time taken for roll call, increase university classroom attendance, and improve university classroom teaching effects. The practice has shown that this roll call system increases the university classroom attendance rate by 15.3%, the accuracy rate is comparable to other best roll call systems, and the time spent is reduced by more than 10 times.
Pure Vision-Based Motion Tracking for Data-Driven Design – A Simple, Flexible, and Cost-Effective Approach for Capturing Static and Dynamic Interactions

Abstract This paper presents an exploratory case study where video-based pose estimation is used to analyse human motion to support data-driven design. It provides two example use cases related to design. Results are compared to ground truth measurements showing high correlation for the estimated pose, with an RMSE of 65.5 mm. The paper exemplifies how design projects can benefit from a simple, flexible, and cost-effective approach to capture human-object interactions. This also entails the possibility of implementing interaction and body capturing in the earliest stages of design, at minimal effort.
Research on Classification Label Denoising Algorithm Based on Granular Ball

This paper presents a granular ball denoising method (GBD) which can effectively improve the accuracy and robustness of classification algorithm. GBD method first uses the self-adaptive hypersphere to cover the data space, then eliminates the data not contained in the hypersphere and the noise data in the sphere, and finally uses only the hypersphere data for training, so as to reduce the sample data and improve the data quality. Experiments show that using the data obtained by this method for training can greatly improve the accuracy of the classification algorithm. In addition, because the hypersphere has good adaptive ability, GBD also has excellent robustness. The experiment shows that GBD sampling data training can still get good results after adding noise to the data set. Therefore, GBD is an efficient and robust denoising method.
Developer load balancing bug triage: Developed load balance

Abstract During the software development process, numerous bugs are reported daily in the software bug repositories. Bug triage assigned these bugs to the most relevant and expert developer for resolution. Moreover, assigning bugs to an incompetent or an over‐engaged developer causes repeated reassignment to other developers until it is resolved. This problem can be solved by devising a triage process that assigns bugs to not only expert developers but also to those who are either under‐engaged or reasonably engaged but are not over‐engaged in work. This paper has designed and implemented work engagement sensitive bug triage that resolves the issue of assigning bugs to developers considering their due work engagement, expertise as well as the current state of activity. For this purpose, a developer profile is built by using metrics to generate three types of scores: technical skill , work engagement and work experience . Metadata features like developer‐name, email, developer‐work‐experience in bug resolution, last and present‐work‐activity, timestamp, component and priority are used for it. A multi‐criteria‐based Henry–Garret technique is used to generate a single ranked list of developers from three ranked lists. The performance of the proposed approach is evaluated on four large OOS projects: Mozilla, Eclipse, Netbeans, and Open Office covering 895,439 bug reports accumulated for 33 years of development. The overall system accuracy of the proposed triage using all four datasets is 91.96 ± 0.05% which is 5.87% better than previously published work. The proposed method is achieved up to 90.30 ± 0.05 and 97.1 ± 0.05 MRR and accuracy of reassignment respectively that indicates how significantly it re‐assigns the bug to the relevant developers. The results demonstrate improvement in the accuracy of software‐bugs triaging as well as a reduction in the probability of bug‐tossing.
Comparison of Hash Functions for Network Traffic Acquisition Using a Hardware-Accelerated Probe

In this article we address the problem of efficient and secure monitoring of computer network traffic. We proposed, implemented, and tested a hardware-accelerated implementation of a network probe, using the DE5-Net FPGA development platform. We showed that even when using a cryptographic SHA-3 hash function, the probe uses less than 17% of the available FPGA resources, offering a throughput of over 20 Gbit/s. We have also researched the problem of choosing an optimal hash function to be used in a network probe for addressing network flows in a flow cache. In our work we compared five 32-bit hash functions, including two cryptographic ones: SHA-1 and SHA-3. We ran a series of experiments with various hash functions, using traffic replayed from the CICIDS 2017 dataset. We showed that SHA-1 and SHA-3 provide flow distributions as uniform as the ones offered by the modified Vermont hash function proposed in 2008 (i.e., with low means and standard deviations of the bucket occupation), yet assuring higher security against potential attacks on a network probe.
Global–Local Enhancement Network for Short Text Classification

Because of the limited context information, it is a challenging task to classify short texts. Most existing methods only focus on extracting high-quality local features or global features from text to construct text representations, which is not comprehensive enough. This article proposes a global–local enhancement network (GLEN), which can construct a high-quality text representation by integrating the global and local features of the text. To improve the quality of global and local features, a group-wise enhancement mechanism is introduced, which can effectively enhance the important features while weakening the unimportant features. For the information-loss problem of traditional pooling operation, we designed a global–local pooling mechanism, which can filter out local features that are more relevant to the whole information of the text. Seven benchmark datasets of text classification were selected to test the performance of the model, and GLEN obtained the best results on most datasets. The ablation experiment on GLEN shows that both the group-wise enhancement mechanism and the global–local pooling mechanism can effectively improve the performance of the model.
KNN based Entertainment Enhancing System

Entertainment is the one thing every individual strives to have besides the common or basic needs in one's life. In the other requirements of humanity, that can be widely generalized. When thirsty one seeks for the same water as any other person in the world. But when it comes to entertainment sector, every individual has their own requirement in what they seek. There are huge number of sources of entertainment in the current technological era. So, one obviously expects the movies or shows are tailored for his own diversified taste. There are already filters available for filtering the content on the basis of the genre like collaborative filtering. But that single parameter is not always sufficient for delivering most of the time. So, in this paper a movie recommendation system is developed using one of the most powerful, well known and widely being used machine learning algorithm KNN to enhance the prediction of likeliness of particular digital content to a user whose likeliness is previously analyzed.
Joint self-supervised and reference-guided learning for depth inpainting

Abstract Depth information can benefit various computer vision tasks on both images and videos. However, depth maps may suffer from invalid values in many pixels, and also large holes. To improve such data, we propose a joint self-supervised and reference-guided learning approach for depth inpainting. For the self-supervised learning strategy, we introduce an improved spatial convolutional sparse coding module in which total variation regularization is employed to enhance the structural information while preserving edge information. This module alternately learns a convolutional dictionary and sparse coding from a corrupted depth map. Then, both the learned convolutional dictionary and sparse coding are convolved to yield an initial depth map, which is effectively smoothed using local contextual information. The reference-guided learning part is inspired by the fact that adjacent pixels with close colors in the RGB image tend to have similar depth values. We thus construct a hierarchical joint bilateral filter module using the corresponding color image to fill in large holes. In summary, our approach integrates a convolutional sparse coding module to preserve local contextual information and a hierarchical joint bilateral filter module for filling using specific adjacent information. Experimental results show that the proposed approach works well for both invalid value restoration and large hole inpainting.