Reviewerly/exHarmony huggingface dataset:
MantissaCam: Learning Snapshot High-dynamic-range Imaging with Perceptually-based In-pixel Irradiance Encoding

The ability to image high-dynamic-range (HDR) scenes is crucial in many computer vision applications. The dynamic range of conventional sensors, however, is fundamentally limited by their well capacity, resulting in saturation of bright scene parts. To overcome this limitation, emerging sensors offer in-pixel processing capabilities to encode the incident irradiance. Among the most promising encoding schemes is modulo wrapping, which results in a computational photography problem where the HDR scene is computed by an irradiance unwrapping algorithm from the wrapped low-dynamic-range (LDR) sensor image. Here, we design a neural network-based algorithm that outperforms previous irradiance unwrapping methods and we design a perceptually inspired “mantissa,” or log-modulo, encoding scheme that more efficiently wraps an HDR scene into an LDR sensor. Combined with our reconstruction framework, MantissaCam achieves state-of-the-art results among modulo-type snapshot HDR imaging approaches. We demonstrate the efficacy of our method in simulation and show benefits of our algorithm on modulo images captured with a prototype implemented with a programmable sensor.
Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution Pipeline

Imaging is usually a mixture problem of incomplete color sampling, noise degradation, and limited resolution. This mixture problem is typically solved by a sequential solution that applies demosaicing (DM), denoising (DN), and super-resolution (SR) sequentially in a fixed and predefined pipeline (execution order of tasks), DM→DN→SR. The most recent work on image processing focuses on developing more sophisticated architectures to achieve higher image quality. Little attention has been paid to the design of the pipeline, and it is still not clear how significant the pipeline is to image quality. In this work, we comprehensively study the effects of pipelines on the mixture problem of learning-based DN, DM, and SR, in both sequential and joint solutions. On the one hand, in sequential solutions, we find that the pipeline has a non-trivial effect on the resulted image quality. Our suggested pipeline DN→SR→DM yields consistently better performance than other sequential pipelines in various experimental settings and benchmarks. On the other hand, in joint solutions, we propose an end-to-end Trinity Pixel Enhancement NETwork (TENet) that achieves the state-of-the-art performance for the mixture problem. We further present a novel and simple method that can integrate a certain pipeline into a given end-to-end network by providing intermediate supervision using a detachable head. Extensive experiments show that an end-to-end network with the proposed pipeline can attain only a consistent but insignificant improvement. Our work indicates that the investigation of pipelines is applicable in sequential solutions, but is not very necessary in end-to-end networks.
Threshold Based Load Balancing Algorithm in Cloud Computing

Cloud computing has become an emerging trend for the software industry with the requirement of large infrastructure and resources. The future success of cloud computing depends on the effectiveness of instantiation of the infrastructure and utilization of available resources. Load Balancing ensures the fulfillment of these conditions to improve the cloud environment for the users. Load Balancing dynamically distributes the workload among the nodes in such a way that no single resource is either overwhelmed with tasks or underutilized. In this paper we propose a threshold based load balancing algorithm to ensure the equal distribution of the workload among the nodes. The main objective of the algorithms is to stop the VMs in the cloud being overloaded with tasks or being idle for lack allocation of tasks, when there are active tasks. We have simulated our proposed algorithm in the Cloudanalyst simulator with real world data scenarios. Simulation results shows that our proposed threshold based algorithm can provide a better response time for the task/requests and data processing time for the datacenters compared to the existing algorithms such as First Come First Serve (FCFS), Round Robin(RR) and Equally Spread Current Execution Load Balancing algorithm(ESCELB).
Resource Usage Prediction Based on BILSTM-GRU Combination Model

With the rapid development of cloud computing, accurate resource usage prediction has become a key technology for the efficient utilization of cloud data center resources. Aiming at the problems of low prediction accuracy and long prediction time of the current load prediction model, a combined prediction model BILSTM-GRU based on bidirectional long short-term memory network (BILSTM) and gated recurrent unit (GRU) is proposed, which effectively combines BILSTM network with high prediction accuracy and short prediction time of the GRU network. It is compared and verified with various classical time series prediction algorithms on the Google cloud computing data set. Experimental results show that the mean square error (MSE) of BILSTM-GRU combined prediction model is reduced by about 5, and the prediction time is shortened by about 5% compared with the existing combined prediction model. The experimental results verify that BILSTM-GRU combined model has higher prediction accuracy and shorter prediction time, which provides an important scientific basis for automatic expansion and shrinkage of cloud computing containers using the prediction results of resource usage.
Improving scalability of multi-agent reinforcement learning with parameters sharing

Improving the scalability of a multi-agent system is one of the key challenges for applying reinforcement learning to learn an effective policy. Parameter sharing is a common approach used to improve the efficiency of learning by reducing the volume of policy network parameters that need to be updated. However, sharing parameters also reduces the variance between agents’ policies, which further restricts the diversity of their behaviors. In this paper, we introduce a policy parameter sharing approach, it maintains a policy network for each agent, and only updates one of them. The differentiated behavior of agents is maintained by the policy, while sharing parameters are updated through a soft way. Experiments in foraging scenarios demonstrate that our method can effectively improve the performance and also the scalability of the multi-agent systems.
LAEG: Leak-based AEG using Dynamic Binary Analysis to Defeat ASLR

Address space layout randomization (ASLR) is a binary protection technique that randomizes a binary's loaded base addresses in every execution. It hardens binaries against exploitation by preventing attackers from reusing identified resources (e.g., code gadgets or stack buffers found at specific memory locations) in subsequent executions. As most modern compilers and operating systems enable ASLR by default, an effective automated exploit generation (AEG) system should be resilient to ASLR when constructing exploits. However, previ-ously proposed AEG systems either assume the absence of ASLR or only bypass it under limited circumstances, and thus cannot reliably exploit binaries running on modern operating systems. With the aim of improving AEG's practicality by developing an ASLR-resilient AEG system, we designed and implemented leak-based AEG (LAEG), a system that can recover randomized base addresses by leaking additional information at runtime. Specifically, given a proof-of-crash input, LAEG uses dynamic taint analysis to analyze the black-box binary, and identifies the input and output states relevant to the base address information. By doing so, LAEG can efficiently recover base addresses from uninitialized buffers and use them to construct an exploit that is resilient to ASLR. Moreover, our tests established that LAEG could successfully construct exploits that bypass state-of-the-art types of binary protection, including not only ASLR but PIE, NX, and stack canary. Besides that, LAEG exhibited better performance than an open-source AEG solution, Zeratool; and was between 6.46x and 45.15x faster at exploit generation than human experts were.
A Novel Chaos-based Light-weight Image Encryption Scheme for Multi-modal Hearing Aids

Multimodal hearing aids (HAs) aim to deliver more intelligible audio in noisy environments by contextually sensing and processing data in the form of not only audio but also visual information (e.g. lip reading). Machine learning techniques can play a pivotal role for the contextual processing of multimodal data, however, due to the low computational power of the HA devices, the data must be processed either on the edge or cloud which, in turn, poses privacy concerns for the users' sensitive data. Existing literature proposes several techniques for data encryption but their computational complexity is a major bottleneck to meet strict latency requirements for the development of future multi-modal hearing aids. To overcome this problem, this paper proposes a novel real-time audio/visual data encryption scheme based on chaos-based encryption using the Tangent-Delay Ellipse Reflecting Cavity-Map System (TD-ERCS) and Non-linear Chaotic (NCA) Algorithms. The results achieved against different security analysis parameters such as Correlation Coefficient, Unified Averaged Changed Intensity (UACI), Key Sensitivity Analysis, Number of Changing Pixel Rate (NPCR), Mean-Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Entropy test, and Chi-test, indicate that the proposed scheme is more secure with increased key-space against modern brute-force attacks and lightweight as compared to existing schemes.
A Digital Forensics Live Suspicious Activity Toolkit To Assist Investigators With Sexual Harm Prevention Order Monitoring

The National Society for the Prevention of Cruelty to Children (NSPCC) and the Internet Watch Foundation (IWF) report a growing number of child sexual abuse material within the UK, substantiated by the National Crime Agency (NCA). This paper will investigate the increasing burden, and time-consuming task placed upon police forces, by the required regular examination of digital devices, belonging to sentenced peadophiles and individuals, bound by a Sexual Harm Prevention Order (SHPO). By examining some of the motivations behind offenders and their desire to habitually offend, and by using the most common traits amongst them, indicators of suspicious behaviour will emerge. In this paper, a proof-of-concept digital forensic investigation toolkit is proposed to assist Public Protection Officers (PPO) in the analysis of digital devices belonging to these individuals. The proposed Live Suspicious Activity Toolkit (LiSA - T) will enable a time-efficient, up to date assessment of any suspicious activity and behaviour on a Windows 10 computer. By using specific modules that can be turned on and off, updated and have unique preferences assigned to them, LiSA-T will evaluate and then report the findings, assisting the PPO with an informed decision as to involve the Digital Forensic Unit (DFU), to further examine a device in a more in-depth forensic manner. The test results, demonstrated that the proposed LiSA- T techniques, showed low computational cost to successfully detect the targeted evidential artifacts for the defined suspicious activity.
Multi-task Learning Model based on Multiple Characteristics and Multiple Interests for CTR prediction

In the era of big data, the acquisition and utilization of information becomes difficult with the skyrocketing amount of data. It is often difficult for ordinary users to find the in-formation or items they need, and personalized recommendation systems can solve this problem well. Currently, recommendation systems increasingly adopt models based on deep learning. The most critical issue in using deep learning for recommendation systems is how to use neural networks to accurately learn user representation vectors and item representation vectors. Many deep learning models used a single vector to represent users, but users' interests were often diverse. Therefore, some researchers consider using multiple vectors to represent user interests, and each interest vector corresponds to a category of items. This method sounds more scientific. However, these models still have problems. Their interpretation of user interests stays at the item level, and does not go deep into the item feature level. In order to solve this problem, we consider user interests from the perspective of item characteristics, and propose 3M (Multi-task, Multi-interest, Multi-feature) model. The 3M model trains multiple interest vectors for each user and extracts multiple characteristic vectors for each item at the same time, then uses a multi-task learning model to connect the characteristic vectors with the interest vectors and train them to obtain multiple interest scores. According to the multiple interest scores, the user click probability can be obtained. Experiments show that our model performs significantly better than the classic CTR(Click - Through Rate) prediction model on the experimental dataset.
Show Me Your Attach Request and I'll Tell You Who You Are: Practical Fingerprinting Attacks in 4G and 5G Mobile Networks

Both attacks are valid for 4G and 5G NSA. 4G will most likely relevant for many years to come. Even if 4G networks will be deactivated in several of years (as it is with GSM or UMTS networks right now), the baseband chips on the UE side will still support 4G and will be prone to 4G-based attacks in the future. In this paper, we leverage a previously introduced vulnerability for 4G mobile communications and present new means for its exploitation. Based on the vulnerability, we introduce a fingerprinting technique and two new attacks to demonstrate how the privacy of mobile devices may be compromised during the initialization procedure of 4G and 5G NSA mobile commu-nications. For this, we exploit information that is exposed in the attach request of the attach procedure sent from a mobile device to the network. This is particularly critical because the confidentiality of this information is not cryptographically protected. In our experiments, we evaluate our attacks against a set of approximately 110 mobile phones from 22 different vendors. Please note that we use pseudonyms (Vendor A etc.) to refer to device vendors to not disadvantage vendors. We demonstrate that our attacks enable to re-identify previously observed mobile devices for tracking purposes and to identify the device vendor and model, respectively, to derive potential sensitive information for tracking their owners.
Application of blockchain technology in energy digital economy

The use of blockchain technology has increased dramatically in the past decade. Blockchain continues to develop, and new functions and capabilities continue to develop. Although the use of this technology began with cryptocurrency, it has been extended to other areas that can benefit from a shared and secure ledger. As an important technology of energy digitization, blockchain has the characteristics of distributed computing, storage, level data sharing, sharing, openness, transparency, tamperability and continuity, and can provide technical support for energy business digitization. Put forward the concept of blockchain breaking the Bureau of energy digital economy, and analyze the role of blockchain in building a new production relationship of energy digital economy from three aspects: ownership of means of production, new production relationship and product distribution form.
360RAT: A Tool for Annotating Regions of Interest in 360-degree Videos

This paper introduces the software 360RAT as a tool for annotating regions of interest (RoIs) in 360-degree videos. These regions represent the portions of the video content that are important for telling a story throughout the video. We believe that this software is an invaluable tool for studying different aspects of 360-degree videos, including what viewers consider relevant and interesting to the experience. As part of this work, we conducted a subjective experiment in which 9 human observers used the proposed software to annotate 11 360-degree videos. As a result, we created a dataset containing a set of annotated 360-degree videos, i.e., videos with marked RoIs and their semantic classification. We present a simple analysis of the annotations gathered with the experiment for a subset of the videos. We noticed that there is a higher agreement of annotations among participants for videos with fewer objects. We also compared the RoI maps with saliency maps computed with the Cube Padding saliency model. We found a strong correlation between RoI maps and computed saliency models, indicating a link between the annotated RoI and the saliency properties of the content.
ST-VTON: Self-supervised vision transformer for image-based virtual try-on

Virtual try-on techniques are in the spotlight due to the promise of enhancing the user experience of online shopping in the apparel industry. Among these technologies, image-based virtual try-on methods have received more attention due to their efficiency and low cost. These methods commonly warp the specific clothing item to the desired shape and then fuse it with the target person image. However, existing approaches demand the laborious paired dataset of clothes and persons. While recent works alleviate the dependence on paired datasets by transferring clothing from one person to another, the pose information is still a rigid demand to guide the synthesis. To solve the above-mentioned problems, we propose ST-VTON, a two-stage self-supervised try-on approach that can synthesize photo-realistic try-on results with only person images and a standard ViT structure. Specifically, we first utilize massive person and clothing images to pre-train a powerful ViT encoder following the MAE approach to enhance the generalization ability and accelerate model training. Secondly, we propose the Clothing Recovery Module (CRM) to reconstruct in-shop clothes from person images and use the paired generated clothing and corresponding person images to train a new ViT decoder while optimizing the pre-trained encoder. Noted that, the CRM, as an independent module, can be used to effectively expand existing paired try-on datasets and enhance other try-on approaches in terms of training data. Extensive experiments conducted on the VITON dataset and UnPaired virtual Try-on (UPT) dataset with both paired and unpaired approaches demonstrate that try-on results rendered by our model can match or even outperform supervised methods.
Biologically inspired image classifier based on saccadic eye movement design for convolutional neural networks

We propose a model for image classification by attentional search. Analogous to how humans scan an image by a sequence of saccades, in this model, an attentional window of size much smaller than the target size scans the target by a sequence of “saccades”, integrates the information acquired, and makes a classification decision. In order to process a sequence of attended image segments, the network must have memory, which is incorporated through 3 kinds of recurrent elements in the network architecture: Elman connections, Jordan connections, and Flip-flop neurons. The architecture of the model is designed as three separate channels labeled as – classifier network, eye-position network, and saccade network. Multiple attentional windows with different resolutions and a common center are given as input to the classifier network and the saccade network. The heat-map representation of the location of the attentional windows is given as input to the eye-position network. The saccade network predicts the next jump of the attention windows with the help of reward signals received by the classifier network. The output features of all the three channels are concatenated, before finally terminating in two output layers representing class prediction and next saccade prediction. The model is trained using deep Q-learning algorithm. Attentional search model is evaluated on MNIST handwritten digit, Kannada MNIST, Medical-MNIST, OCTMNIST, and QuickDraw datasets. Translated and Cluttered Translated versions of each dataset are generated to perform the task of classification based on local target search. Original datasets are used to show the task of classification based on search with global target integration. We also evaluate the saccade performance on Extended Yale Face B database. In various problem cases, the model exhibits comparable or superior performance to a state-of-the-art recurrent attention model. Demo code is available in this link.
Role of Accounting Information Systems (AIS) Applications on Increasing SMES Corporate Social Responsibility (CSR) During COVID 19

Main aim was to highlight role of AIS applications in increasing CSR during COVID 19. (132) individuals responded to an online questionnaire. Results indicated that AIS supported CSR among SMEs in Jordan attributed to credibility and reliability of AIS outcomes. In other words, main hypothesis was accepted and there was a positive impact of AIS applications on CSR in the fields of legal, economic, ethical and philanthropy and indicated that organizations were to support the community during the pandemic through organizing their own finances, avoid corruption and support the society through funding, donations and charity works as they were aware of their profits and abilities.
The Impact of the COVID 19 Shock on Intention to Adopt Social Commerce

This study aims to explore the impact of the COVID 19 shock on the intention to adopt social commerce. It also examines the impact of the pandemic shock on Electronic Word of Mouth (EWOM) and the perceived usefulness of social commerce. Data were collected from Facebook users in Jordan and analyzed using smart PLS software. The results show a significant impact of the COVID 19 shock on EWOM, perceived usefulness, and intention to adopt social commerce. The findings also show a significant mediating impact of EWOM and perceived usefulness on the relationship between the COVID 19 shock and intention to adopt social commerce.
Metaverse

Metaverse combines the virtual and physical, synthesizing the internet, the web, and extended reality into a world where digital and physical elements blend in differing degrees. The core purpose of the metaverse is social connection. This consists of users coexisting in an ecosystem that includes cultures, laws, economies, societies, and related obligations. Therefore, it will demand standards of how humans should behave and what is considered right and wrong. Ethics is based on these standards within any given universe. The ethical challenges faced in developing and deploying a metaverse are explored, and ethical design considerations of metaverse environments are defined. The metaverse ecosystem is described and critical technologies that support it are included. Examples of current metaverses are provided so that the reader can explore and experience a metaverse and further assess the ethics and benefits.
Privacy Preserving in the Modern Era

The development of data communication technologies promotes large-scale sensitive data collection and transmission in various application areas. The sensitivity and criticism of the exchanged data raise several privacy issues. A lack of privacy may cause moral and emotional damage and discrimination. It can even create an unequal society. To fill this gap, a better understanding of privacy concept and its requirements is required. This chapter presents a comprehensive survey of privacy-preserving in the modern era. It deals with this concept of privacy-preserving from all perspectives, classifying its requirements into content-oriented and context-oriented ones. Based on the taxonomy, privacy attacks are described, and approaches and mechanisms for privacy protection are reviewed. A future research direction about privacy preserving in various fields is finally exposed.