Reviewerly/exHarmony huggingface dataset:
Perfectly Secure Oblivious Priority Queue

An Oblivious Priority Queue (OPQ) is a cryptographic primitive that enables a client to outsource its data to a dishonest server, and also to securely manage the data according to a priority queue algorithm. Though the first OPQ achieves perfect security, it supports only two operations; Inserting an element and extracting the top-priority element, which are the minimal requirement for a priority queue. In addition, this OPQ allows an adversary to observe operations in progress, which leaks the exact number of elements in the data structure. On the other hand, there are many subsequent works for OPQs that implement additional operations of a priority queue, hide the running operations, and improve efficiency. Though the recent works realize optimal efficiency, all of them achieve only statistical or computational security. Aiming to reconcile perfect security of the first OPQ with all functions (including the operation hiding) supported by recent OPQs, we construct a novel perfectly secure OPQ that can simulate the following operations while hiding which one is in progress; Inserting an element, extracting the top-priority one, deleting an element, and modifying the priority of an element. The efficiency of our scheme is O(log2 N), which is larger than that of the best known statistically secure OPQ but is the same as the known perfectly secure scheme.
Informative Integrity Frictions in Social Networks

Social media platforms such as Facebook and Twitter benefited from massive adoption in the last decade, and in turn facilitated the possibility of spreading harmful content, including false and misleading information. Some of these contents get massive distribution through user actions such as sharing, to a point that content removal or distribution reduction does not always stop its viral spread. At the same time, social media platforms efforts to implement solutions to preserve its integrity are typically not transparent, causing that users are not aware of any integrity intervention happening on the site. In this paper we present the rationale for adding what are now visible friction mechanisms to content share actions in the Facebook News Feed, its design and implementation challenges, and results obtained when applying them in the platform. We discuss effectiveness metrics for such interventions, and show their effects in terms of positive integrity outcomes, as well as in terms of bringing awareness to users about potentially making harmful content viral.
Multi-scale low-light image enhancement by fusing dense residual blocks

The existing low-light image enhancement algorithms could not process brightness, contrast, color, and other details at the same time. Thus, a multi-scale low-light image enhancement network which fused with dense residual blocks is proposed. The image is first generated with feature-rich input through an input module, then the features are fed into a multi-scale backbone enhancement network with dense residual blocks, and finally a refinement module is used to enrich image details and remove halos. The experimental results show that the proposed method better improves the contrast and brightness, actualizes the color, enriches the texture details, and decreases the noise and artifacts in low-light images, which quantitatively and qualitatively demonstrated its advantages comparing with other mainstream methods.
Lessons Learned from the Implementation of DOORS for the SUDA Instrument on Europa Clipper

Requirements management software can be useful, although the application of it on a project can be detrimental and time-consuming if not well implemented. The Dynamic Object-Oriented Requirements System (DOORS) software is one tool that is used for managing space-based systems requirements and their verifications. The SUrface Dust Analyzer (SUDA) team at the Laboratory for Atmospheric and Space Physics (LASP), part of the Europa Clipper mission being managed and built by the Jet Propulsion Laboratory (JPL), is using DOORS to manage this effort. This work details the SUDA Systems Engineering team&#x0027;s experience and lessons learned as they set up the requirements and verification database, managed the database contents, and worked around the limitations found with DOORS. In particular, decisions made early on can have significant unforeseen consequences that lead to large inefficiencies. Here we consider some of those decisions and their impacts as well as offer recommendations to inform future implementation of this software.
Robust U‐Net: Development of robust image enhancement model using modified U‐Net architecture

Summary The image dehazing stage is used significantly as a preprocessing step for various applications such as remote sensing and long range imaging and automatic driver assistance system. Images acquired under low illumination, fog and snow conditions frequently show qualities like low contrast and low brightness, which genuinely influence the enhanced abstract visualization on natural eyes and extraordinarily limit the exhibition of different machine vision frameworks. The images that are captured in low‐light or heavy fog might have salient features that cannot be extracted using standard computer vision systems. A good way to get the enhanced image is to determine the transmission map (haze density or low illumination parameters) of air‐light media from each pixel of the input image. In this article, an improved U‐Net architecture is proposed to enhance images and provide robust performance metrics against the existing methods. In this model, the pooling operations in generalized U‐Net architecture are replaced by discrete wavelet transform based on up and down samplings. An attention module is developed by fusing both up and down samples to identify the missing information of low‐level features in up‐samples. The proposed architecture for U‐Net tested with different datasets: See‐in‐the‐Dark (SID) dataset, Exclusively Dark Image Dataset (ExDark), Realistic Single Image Dehazing (RESIDE) dataset, and few real‐time images and achieves superior performance metrics in terms of PSNR, MSE, and SSI when compared to the other state‐of‐art methods.
Photon and speckle noise addressal in computed tomography and ultrasound imagery

Ultrasound imaging (UI) is extensively used for the secure diagnose in medical procedures owing to its noninvasive characteristics, little price and capturing the real time image by using the sound waves having frequencies exceeding the human’s hearing threshold value. However, the availability of noise termed as speckle in medical ultrasound imaging degraded the functionality of diagnoses of biomedical implemented services. Furthermore, the design pattern of speckle depends upon the composition of tissue as well as other imaging specifications. The foremost important part in the UI is the suppression of speckle noise and for eliminating this, various image denoising schemes has been followed by many researchers such as by implementing filters in contemplation of improving medical ultrasound images. While enacting various despeckling schemes as an aid for visualization, it has to be acknowledged that particular speckle comprises of diagnostic details and it should be conserved. This paper encompasses various techniques which suppress the speckles from images.
Analysis and Research Based on the Crowdsourcing Corpus System in Guangdong-Hong Kong-Macao Greater Bay Area (GBA)

Objective. To sort out the application and status quo of some domestic crowdsourcing models, explore the factors affecting multilingual manual annotation through experiments and offer suggestions. Methodology. Crawling the government news texts in Mandarin, Cantonese, English, and Portuguese in Guangdong, Hong Kong, and Macao, and enter them into the database. Combine it with corpus tagging uses the established web platform to practice crowdsourcing and collect a large number of annotation results and behavior data. Results. Made assumptions about factors that may affect the quality of manual annotation, used SPSS and other data analysis software to evaluate the degree of interpretation of the assumptions, provided a regression formula for calculating the accuracy, and provided constructive suggestions for the corpus annotation quality assurance projects. Limitations. More corpus information in more languages and more professional annotators are needed. Conclusions. The study found that the accuracy of annotation is strongly related to the attributes of the corpus itself, such as the total number of vocabularies, the number of rare words, the complexity of parts of speech, etc., and the condition of languages are different from each other.
Hybrid Cryptosystem Ensuring CIA Triad

Any untoward incident occurs while transmitting data digitally may lead to threats whenever the data is transmitted to malicious destination unknowingly, there arises a question of data integrity. In this scenario cryptography plays a crucial role in ensuring the users both confidentiality and integrity while transmitting of data over various network platform, supporting with the algorithms like AES, hashing etc., to ensure end user safety. In this paper, an improved hybrid cryptography system which is a combination of message digest and symmetric key algorithm is being incorporated. This proposed system provides confidentiality as well as integrity.
Acoustic or Pattern? Speech Spoofing Countermeasure based on Image Pre-training Models

Traditional speech spoofing countermeasures (CM) typically contain a frontend which extract a two dimensional feature from the waveform, and a Convolutional Neural Network (CNN) based backend classifier. This pipeline is similar to an image classification task, in some degree. Pre-training is a widely used paradigm in many fields. Self-supervised pre-trained frontend such as Wav2Vec 2.0 has shown superior improvement in the speech spoofing detection task. However, these pre-trained models are only trained by bonafide utterances. Moreover, acoustic pre-trained frontends can also be used in the text-to-speech (TTS) and voice conversion (VC) task, which reveals that commonalities of speech are learnt by them, rather than discriminative information between real and fake data. Speech spoofing detection task and image classification task share the same pipeline. Based on the hypothesis that CNNs follow the same pattern in capturing artefacts in these two tasks, we apply image pre-trained CNN model to detect spoofed utterances, counterintuitively. To supplement the model with potentially missing acoustic features, we concatenate Jitter and Shimmer features to the output embedding. Our proposed CM achieve top-level performance on the ASVspoof 2019 dataset.
Recipe Recording by Duplicating and Editing Standard Recipe

The best way to ascertain the exact nutritional value of a user's food intake is to have the user record the recipe for that food himself/herself. However, writing a recipe from scratch is tedious and impractical. Therefore, we proposed a method that allows users to write their own recipe in a short time by duplicating and editing a standard recipe. We developed a smartphone application and conducted an experiment in which 19 participants were asked to write their own recipes for about 10 food items each. The results showed that the duplication method took 74% of the time compared to writing a recipe from scratch. The number of editing operations was also reduced to 45%. Future work is to construct a dataset of standard recipes that can be rewritten with little editing cost for any person's recipe.
Virtual Visits

Social communication is increasingly performed through video communication (VC) tools such as Teams or Zoom. Although VC is an effective way to maintain relationships remotely, it is still limited in creating a sense of social connectedness since the interaction does not feel as natural as a physical encounter. We evaluated the user experience of a new photorealistic VC tool based on augmented reality (AR) compared with a popular VC tool (MS Teams), focusing on the feeling of social presence they provide. Participants experienced one of the systems and answered a questionnaire about their experience. In the AR condition, participants perceived a 3D image of the remote person projected in their local background (Fig.1). In the "regular video call" condition, using MS Teams, participants perceived a 2D image of the remote person in the remote background. The variables measured were social presence, interpersonal closeness, global user experience, duration of calls and perception of time, behaviour, and attitude toward the AR tool. No significant differences were found in subjective measures of social presence, interpersonal closeness, and global UX. There were also no significant differences in the duration of calls or time perception per condition. However, behavioural analysis showed that with the AR tool participants used significantly more hand gestures (non-verbal communication), showed a more relaxed attitude and laughed more frequently. Although we did not find any evidence that it provides more social presence, the AR communication tool appears to induce more natural behaviour. Both conditions received high scores on social presence and global UX. Qualitative analysis of open questions and comments showed that the human-sized image of the communication partner was the main factor driving these scores and suggested improvements for future system design.
A Framework Model to Support A/B Tests at the Class and Component Level

The amount of data collected from software use facilitated systematic inquiries about users' expectations and reactions to software systems. The most well-known practice for continuous experimentation is A/B tests, in which two or more versions of a feature are compared based on users' metrics when exposed to these options. The literature shows that A/B tests are spread in the industry, but companies usually rely on in-house software platforms based on traffic split. However, these approaches are better suitable for feature tests and not implementation options, besides increasing technical debt and maintenance effort. We aim to develop a model as a guide to frameworks for introducing experiments at class and component levels without coupling this concern to business rules. To reach this goal, we followed an iterative approach based on Design Science Research. In this process, we developed a reference im-plementation of this model by creating a framework for the Java language. We evaluated the experimentation framework usage in a web project that simulates a realistic scenario for applying A/B tests. The evaluation study showed that the framework provides the functional features needed to perform A/B tests at component and class level, collecting relevant information for decision-making. Moreover, it was possible to instantiate the framework without any coupling to code related to business rules. The proposed model could guide the development of experimentation frameworks that perform A/B tests at class and component levels, even for other languages and platforms. It captured the main elements of the experimentation domain, proposing appropriate extension points and a decoupled approach to be plugged into the application.
Experience of rational understanding of life and death (biological and social plans of evolutionary development)

The article proposes to consider the phenomena of life and death within the framework of philosophical and scientific discourse. The author does not aim to explain the origin of life, he seeks to conceive of life and death on the basis of the methodology developed by him and the research conducted. The main way of understanding these phenomena is the hypothesis about the nature of the mechanism of life (the assumption of a "vital organization" responsible for activity and other life reactions is introduced), as well as cultural–historical and semiotic analysis of the evolution of life. In evolution, the author distinguishes three main stages: the formation of life, biological evolution and the evolution of social life. An important role in the latter is assigned, on the one hand, to the use of signs denoting not observable realities, but necessary for the management of the "primary team", on the other hand, technology. The technique performed two main functions: it expanded the capabilities of hominids in terms of creating the right environment (fire, tools, clothing, housing, etc.) and confirmed the use of signs, since it created a reality corresponding to their meanings. In social life, as well as in biological, two spheres need to be distinguished in the vital organization: one belongs to individuals (psyche, activity, corporeality, etc.), and the other, actually social (culture, sociality with its institutions, communication, collectives that include individuals), encompassing, without which the first cannot exist and function. Having asked the provisions indicated here, the author characterizes what death is and considers the cognitive dissonance that occurs when we try to rationally think about these phenomena.
Research on a Chinese Character Recognition Framework based on Multi-dimensional Image Information

This paper conducts the research on a Chinese character recognition framework based on the multi-dimensional image information. Since the font data storage structure is designed by the user and is not in the directly addressable space of the code, it is necessary to call the external memory access interface function. Therefore, it is designed as a callback function to copy the font to the buffe. Hence, the pattern analysis model is then integrated. Therefore, the method of fuzzy mathematics is proposed and studied here, that is, the method of fuzzy pattern recognition is used to select the best expression of spatial entities. Furthermore, the novel image recognition algorithm is designed for the scenario of the Chinese character recognition. Through the experimental result, the performance is validated.
A Lévy Flight Based Probabilistic Motion Planning for UAVs in Constricted Environments

Aerial Inspection robotics often demands navigating through complex environments with constricted regions. Such narrow passage scenarios appear, for example, as an unmanned aerial vehicle (UAV) tries to navigate through windows or tunnels, or navigating through a maze. Such UAV motion planning scenario demands 6-DOF motion planning manoeuvres and often suffers computational complexity is-sues. Aerial manipulators exhibit even higher dimensionality. Sampling based probabilistic roadmap planners (PRM) have been successful in motion planning of robots with higher degrees of freedom, but may fail to capture the connectivity of the configuration space in scenarios with a critical narrow passage. This remains as a central drawback for sampling based motion planning algorithms. The primary reason for failure is generally associated with the problem of very low probability of generating candidate sampling configurations within a narrow region. The low Lebesgue volume measure of the narrow passage region acts as adversary to obtain critical sample points inside the region of manoeuvre and thus affects the connectivity of the random graph structure. Several heuristic based sampling strategies can remove this difficulty to a great extent, but a satisfactory answer remains elusive. In this paper, we show a novel technique based on Levy Flights to generate key samples in the narrow regions of the configuration space of UAV motion planning. The technique is based on Random Walk to surface approach for sample generation, it however extends the concept with the support of a heavy tail distribution. The paper shows a complete integration of the proposed sampling scheme into a PRM like planner and shows the UAV motion trajectories for various complex scenarios. The technique substantially improves sample quality at the expense of a minimal additional computation when compared with pure random walk based methods. However, it still outperforms state of the art random bridge building method, in terms of number of collision calls, computational overhead and sample quality. The method is robust to the changes in the parameters related to the structure of the narrow passage, thus giving an additional generality. A number of 2D &#x0026; 3D motion planning simulations are presented which shows the effectiveness of the method and are compared with the state of the art results.
Efficient Dual Batch Size Deep Learning for Distributed Parameter Server Systems

Distributed machine learning is essential for applying deep learning models with many data and parameters. Current researches on distributed machine learning focus on using more hardware devices powerful computing units for fast training. Consequently, the model training prefers a larger batch size to accelerate the training speed. However, the large batch training often suffers from poor accuracy due to poor generalization ability. Researchers have come up with many sophisticated methods to address this accuracy issue due to large batch sizes. These methods usually have complex mechanisms, thus making training more difficult. In addition, powerful training hardware for large batch sizes is expensive, and not all researchers can afford it. We propose a dual batch size learning scheme to address the batch size issue. We use the maximum batch size of our hardware for maximum training efficiency we can afford. In addition, we introduce a smaller batch size during the training to improve the model generalization ability. Using two different batch sizes in the same training simultaneously will reduce the testing loss and obtain a good generalization ability, with only a slight increase in the training time. We implement our dual batch size learning scheme and conduct experiments. By increasing 5% of the training time, we can reduce the loss from 1.429 to 1.246 in some cases. In addition, by appropriately adjusting the percentage of large and small batch sizes, we can increase the accuracy by 2.8% in some cases. With the additional 10% increase in training time, we can reduce the loss from 1.429 to 1.193. And after moderately adjusting the number of large batches and small batches used by GPUs, the accuracy can increase by 2.9%. Using two different batch sizes in the same training introduces two complications. First, the data processing speeds for two different batch sizes are different, so we must assign the data proportionally to maximize the overall processing speed. In addition, since the smaller batches will see fewer data due to the overall processing speed consideration, we proportionally adjust their contribution towards the global weight update in the parameter server. We use the ratio of data between the small and large batches to adjust the contribution. Experimental results indicate that this contribution adjustment increases the final accuracy by another 0.9%.
On the (in)security of Memory Protection Units : A Cautionary Note

The Memory Protection Unit (MPU) is an optional component in many embedded devices where it can be used to protect the device memory from unwanted access by potentially malicious software. Unfortunately, the correct configuration of the MPU relies entirely on the application programmer, who has to make security-aware executive decisions to offer the desired level of security guarantees. This task is complicated and error-prone, and any configuration mistake could allow malicious software to completely breach the system, e.g., disabling the MPU. The MPU protection could also be voided by malicious software leveraging specific device features, e.g., interrupts.In this paper, we share our experiences in analyzing the security of MPUs. We first highlight the bad practices when configuring the MPU for securing the device memory. We then demonstrate the possibility of exploiting the raised issues, targeting two well-known architectures, namely MSP430 and ARMv7-M. We conclude with a set of recommendations for the correct usage of MPUs.