Reviewerly/exHarmony huggingface dataset:
Lightweight and Practical Privacy-Preserving Image Masking in Smart Community

Advances in the Internet of Things (IoT) and telecommunications technologies (e.g., 5G) have contributed to the development of smart cities and nations (collectively referred to as smart communities). In a smart community, IoT devices can collect significant information about urban residents (e.g., a large number of images collected by cameras containing sensitive information), and such information may be shared with intermediate nodes. In real-world deployment, intermediate nodes are not completely trusted, where the information collected may be used for commercial purposes (e.g., user profiling and advertising) or malicious activities (e.g., covert surveillance). In this paper, we introduce an approach to ensure privacy-preserving image masking. Specifically, before the image is transmitted to the camera owner or the monitoring cloud platform, only sensitive areas instead of the entire image will be processed according to the camera owner's settings, which allows to significantly reduce the computational cost. Then, in order to reduce the interactions between the community data center and the IoT camera, the monitoring cloud platform performs proxy re-encryption. This allows the community data center to recover the original image without relying on the IoT device's private key. Our evaluation indicates the utility and efficiency of our approach, as compared with similar schemes.
Improving Deep Learning Based Password Guessing Models Using Pre-processing

Passwords are the most widely used authentication method and play an important role in users' digital lives. Password guessing models are generally used to understand password security, yet statistic-based password models (like the Markov model and probabilistic context-free grammars (PCFG)) are subject to the inherent limitations of overfitting and sparsity. With the improvement of computing power, deep-learning based models with higher crack rates are emerging. Since neural networks are generally used as black boxes for learning password features, a key challenge for deep-learning based password guessing models is to choose the appropriate preprocessing methods to learn more effective features. To fill the gap, this paper explores three new preprocessing methods and makes an attempt to apply them to two promising deep-learning networks, i.e., Long Short-Term Memory (LSTM) neural networks and Generative Adversarial Networks (GAN). First, we propose a character-feature based method for encoding to replace the canonical one-hot encoding. Second, we add so far the most comprehensive recognition rules of words, keyboard patterns, years, and website names into the basic PCFG, and find that the frequency distribution of extracted segments follows the Zipf's law. Third, we adopt Xu et al.'s PCFG improvement with chunk segmentation at CCS'21, and study the performance of the Chunk+PCFG preprocessing method when applied to LSTM and GAN. Extensive experiments on six large real-world password datasets show the effectiveness of our preprocessing methods. Results show that within 50 million guesses: 1) When we apply the PCFG preprocessing method to PassGAN (a GAN-based password model proposed by Hitja et al. at ACNS'19), 13.83%–38.81% (26.79% on average) more passwords can be cracked; 2) Our LSTM based model using PCFG for preprocessing (short for PL) outperforms Wang et al.'s original PL model by 0.35%–3.94% (1.36% on average). Overall, our preprocessing methods can improve the attacking rates in four over seven tested cases. We believe this work provides new feasible directions for guessing optimization, and contributes to a better understanding of deep-learning based models.
TZ-IMA: Supporting Integrity Measurement for Applications with ARM TrustZone

With the development of cloud computing and distributed systems, the computer system becomes increasingly complicated and open. To protect the integrity of applications, Integrity Measurement Architecture (IMA) is applied in the Linux kernel. However, traditional operating systems are complex and may contain many potential vulnerabilities. If the sensitive data used in IMA is leaked or modified, the protection mechanism will lose effectiveness. This paper proposes TZ-IMA, a security-enhanced solution to verify the integrity of applications based on ARM TrustZone technology. The system saves the encrypted reference hash value of applications and the encryption key in the normal world and in TrustZone, respectively. Before an application is executed, the integrity of application is checked by the secure world. Moreover, a vPCR module is constructed in TrustZone to protect the security of the measurement list. Based on the trusted anchor provided by TrustZone, TZ-IMA enables a challenger to prove that the attesting platform has sufficient integrity to be used. TZ-IMA is implemented on ARMv8 development board, and the evaluation results demonstrate that the overhead is only approximately 5% compared with the original system.
Weak illumination image enhancement algorithm based on cyclic generation countermeasure network

To solve the problem of missing or distorted detail texture when manually adjusting image parameters, a weak illumination image enhancement algorithm based on cyclic generation game network is proposed. The image features are normalized by Gaussian distribution. Combined with homomorphic filtering theory and defogging operation, the image is generated and denoised according to the network brightness to enhance the weak illumination image. The experimental results show that after using this method to process the image, the image entropy increases by 6.8%, the contrast increases by 27.5%, and the noise content decreases by 24.1%. It has better contrast. It can not only meet the enhancement effect of weak light, but also ensure the details of the image, so that the image has richer details and good visual appearance.
Moving object detection and trajectory prediction based on image processing

For moving object detection and trajectory prediction in video images, it is necessary to perform image processing, feature extraction, and localization of the object. Therefore, this paper designs an optimized Kalman-Elman (KE) algorithm for trajectory prediction. In order to remove the noise points on the measured values in the Kalman filter algorithm and to solve the problem of the random setting of the initial weights and thresholds of the Elman neural network, we encode the above parameters and improve the two algorithms by using Particle Swarm Optimization (PSO). Quantitative values of the object feature extraction are used as input parameters of the Elman neural network. After a large amount of training, we obtain the predicted position of the moving object finally. The experimental results show that the prediction error of this method is significantly smaller when it is compared with previous methods.
Parameter rollback averaged stochastic gradient descent for language model

Recently, AWD-LSTM (ASGD Weight-Dropped LSTM) has achieved good result in the language model, and many AWD-LSTM based models have obtained state-of-the-art perplexities. However, in fact, large-scale neural language models have been shown to be prone to overfitting. In AWD-LSTM original paper, the author decided to adopt the way of retraining calling finetune to get a better result. In this paper, we present a simple yet effective parameter rollback mechanism for neural language models. And we introduce the parameter rollback averaged stochastic gradient descent (PR-ASGD), wherein the parameter “step” in ASGD will decrease according to a certain probability. Using this strategy, we achieve better word level perplexities on Penn Treebank: 56.26 based on AWD-LSTM model and 53.57 based on AWD-LSTM-MoS (AWD-LSTM Mixture of Softmaxes) model.
Enhancing Botanical Knowledge Graphs with Machine Learning

Integrating sparse and incomplete biodiversity data into a global, coherent data space and generating machine-readable data infrastructures is a challenge in biodiversity informatics. In recent years, biodiversity data researchers have started proposing Knowledge Graphs (KGs) as one approach to connecting biodiversity data worldwide (Page 2019), representing the connections between the what, when, and where of objects in natural history collections. At the Natural History Museum (NHM) we have constructed a KG of botanical specimens and collectors, encoded into numerical representations, and using a Relational Graph Convolutional Network (RGCN) (Schlichtkrull et al. 2018) to infer gaps in the KG, forging new connections between nodes. The datasets involved in our botanical KG project are NHM Botany Collector database (105,780 entities) and NHM Indian Region Botanical Specimen Dataset (110,043 entities with geographical information). Our KG with RGCN enables the structured and contextual data to be reasoned across the knowledge content, allowing us to dynamically update its representation according to its closely related neighbours. Our work will explain why and how the KG with RGCN can offer a better way to link digitised botanical data. We use the prototype KG to demonstrate its potential for modelling botanical data and provide a graphical representation for other machine learning applications. For example, the combination of KG with RGCN and Metric Learning (Xing et al. 2002, a form of Machine Learning generally used to automatically construct task-specific distance metrics) supports data completion via entity classification and link prediction for a subset of botanical specimens within a geographic region. These data augmentation models with KGs allow us to identify gaps in specimen provenance, and fill in missing data. After phase one training, our model can achieve 88% accuracy in entity classification and report a reasonable Mean Reciprocal Rank (MRR) in raw ranking link prediction for the Indian Region Botanical Specimen Dataset. Our research also evaluates the use of the KG and RGCN to improve post-OCR (Optical Character Recognition) correction algorithms as part of automatic specimen digitisation pipelines. This improves the accuracy of entity recognition on specimen label text identification and transcription, as part of machine learning natural language processing and human-in-the-loop transcription. Human-based transcription can be aided and improved by an interpretation recommendation system predicated on the specimen unit’s RGCN-inferred location in the KG. This methodology can also be used to explore the alignment of KGs from different institutions within the global biodiversity network, to identify the relative importance of collectors or determine strengths or gaps in different geographic regions or ecosystems, duplicate items in collections, or objects in collections that have potentially been misidentified.
Performance Evolution of Software Engineering Development and Project Management: A Critical Evolution Software Engineering Process

Among the many pressing concerns in the realm of computers, software project development is among the highest. System development life cycle is a part of this (SDLC). A key goal of the software development life cycle (SDLC) is to reduce the likelihood of errors while improving the quality of the final result. Without a well-defined set of steps, the software development process is a pretty complicated affair. Method established to standardize and streamline software development. The introduction of the SDLC (Software Development Life Cycle) existence. What we have here is a methodical and organized approach to the process of creating software. Using the SDLC as a guide, entails a wide variety of steps and processes that must be completed before the final programmer is released. Software comes in many forms. Types of software development life cycles, each with their own benefits and drawbacks, are commonly employed in the software development process. Disadvantages Five of these software development life cycle (SDLC) models, including the waterfall model, the v-shaped model, and the prototype model, are presented in this study. Existing models are compared using a spiral and an iterative structure.
NFDI4Biodiversity: a German infrastructure for biodiversity data

Digital data have become an indispensable basis for biodiversity research. Sustainable curation, archiving, accessibility and integrability according to the FAIR principles ("Findable, Accessible, Interoperable and Reusable", Wilkinson et al. 2016) are essential for re-use to answer pressing questions in a rapidly changing environment. As part of the German multidisciplinary National Research Data Infrastructure (NFDI), the NFDI4Biodiversity consortium with 49 partners, spanning a broad spectrum from academia, to agencies, learned societies, and citizen science, has set itself the goal of providing a sustainable data infrastructure for biodiversity research. NFDI4Biodiversity builds on the German Federation for Biological Data (GFBio) project (2014–2021) and the GFBio e.V. founded in it, both organisationally and in the provision of services. These include a data submission and archiving system, support for the creation of data management plans and certification, portal functions with extensive data visualization and terminology services flanked by helpdesk, support and outreach activities*1 (Diepenbroek et al. 2014). Within the framework of NFDI4Biodiversity, these services will be expanded, based on (and calibrated by) the requirements of 23 concrete use cases from manifold biodiversity research domains. A central new component is the development of a capable multi-cloud platform, the "Research Data Commons" (RDC) where data can be aggregated, semantically linked and enriched with external services (Glöckner et al. 2020). Alongside the development of the services, the potential for joint use of standards and service components will be exploited through cooperation with existing data infrastructures. In addition to other NFDI consortia, international infrastructures and comparable national initiatives will play a special role; this process was already started with a symposium during the 2019 Biodiversity Next conference.
Evaluation of digital panoramic images to support off-site bloodstain pattern analysis

Crime scene photography plays a fundamental role in forensic investigations. Its primary purpose is the recording of the entire crime scene, both the context and specific details, for later recollection, analysis and presentation. Bloodstain pattern analysis (BPA) relies extensively on the recording of context and detail. This study evaluated the use of panoramic imaging for the recording of impact patterns at crime scenes to support the off-site determination of area of origin (AO). This evaluation used a commercially available hardware system that consisted of a robotized, tripod-mounted platform, interfaced with a digital camera, to provide an automated panoramic image capture process. Software was subsequently used to construct a digital panorama. Bloodstain pattern analysis software was then used for the off-site determination of AO from the panoramic image. The results of this research demonstrated that the developed method can be employed to effectively capture a panoramic image of an impact pattern with sufficient detail for accurate AO determination. The approach can enable crime scene officers to record impact patterns without extensive training on BPA or the recording of blood spatter. The approach also enables BPA experts to digitally analyse spatter information from a single image as opposed to evaluating multiple photographs.
Vision to the Bi-Sionary

Abstract: Visual impairment is one of the biggest limitations for humanity, especially in this day and age when information is communicated a lot by text messages (electronic and paper based) rather than voice. Facial recognition is category of biometric software that maps an individual’s facial features mathematically and stores the data as a sprint The software uses deep learning algorithms to compare a live capture or digital image to be stored face print in order to verify an individual’s identity vbnnn This project aims to develop a device to help people with visual impairment. In this project, we manufactured a device that converts an image’s text to speech. The basic outline is an embedded system that captures an image, extracts only the area of interest (i.e. region of the image that comprises text) and changes that text to speech. It is incorporated using a Raspberry Pi 3 and a Raspberry Pi camera module. We have two phases in our project that are Text to Speech and Facial Recognition. Every Module for picture handling and voice preparing are available in the device. It likewise can play and stop the output while reading. The expectation is that it has less error rate and less processing time and less cost productivity.
Information Presentation Methods for Setting Achievable and Meaningful Goals on Fitness Apps

Exercise beginners often use recommender systems, such as fitness apps, to form new exercise habits. However, it is difficult for individuals with little training experience to estimate the costs and benefits of the recommended exercise goals and to set appropriate goals for themselves. In this paper, we propose several information presentation methods for recommender systems in the health field to help users accurately estimate the costs and benefits of their exercise goals. Based on Atkinson’s theory of achievement motivation, we designed three of information, – the amount of effort required to achieve a goal, the degree to which the goal is achievable, and the benefits obtained from achieving the goal – to encourage users to set effective goals and establish exercise habits. To determine how the proposed methods affect users’ motivation to exercise, we conducted an online user study using a Japanese crowdsourcing service. The results suggest that the proposed methods have the potential to motivate users to exercise and promote realistic goal-setting. The findings of this study contribute to the design of exercise goal–setting support systems tailored to individual users.
Addressing Popularity Bias in Citizen Science

Citizen science projects rely on volunteers to contribute time and effort to solve scientific problems, but the majority of citizen science users typically contribute to only one or two projects. Recommender systems have been recently used in citizen science to motivate people to contribute to additional projects. However, these systems often recommend the more popular projects at the expense of less popular projects which need people’s contributions more critically. In this work we develop a post processing approach for enhancing “long-tail” item recommendation that can be applied to any recommendation system. We propose a novel re-ranking model, based on the lift measure used in machine learning, which considers item co-occurrence as well as popularity for enhancing long tail recommendations. We demonstrate the efficacy of our approach in the citizen science domain on two data sets and three state of the art recommendation algorithms, comparing hit rate before and after applying lift boosting. Additionally, we compare our approach to two predetermined re-ranking baselines. Results show that our proposed approach significantly improves performance for tail item recommendation without a substantial loss in head item and overall item recommendation performance. Our approach is general and can be naturally applied to existing recommendation systems in citizen science that personalize project suggestions to users, potentially leading to an increase in efficiency and performance.
Medical Data Bench-Marking for Recommender Systems and Care of Discordant Chronic Comorbidities

Discordant Chronic Comorbidities (DCCs) are health conditions in which patients have multiple, often unrelated, chronic illnesses that may need to be addressed concurrently but may also be associated with conflicting treatment instructions. Patients with DCCs often experience multiple obstacles when prioritizing treatment plans and prescriptions. Machine learning and Deep learning algorithms can provide these treatment recommendations and also identify drug interactions and conflicts. Although algorithms are increasingly commonly used in classification, and diagnosis of chronic diseases, we have yet to see a good documentation of how conflicting recommendations and evolving patient’s needs could be addressed using machine learning techniques. Further, there are no recommendations for algorithms suitable for addressing complex needs for those patients with DCCs experience. In this work, we distributed an online survey to understand patients' and healthcare providers' strategies for setting DCCs treatment plans. The participants' responses were collected to form a data-set which we used to create our benchmarks using a selected list of state-of-the-art machine learning algorithms.
Missing-Edge Aware Knowledge Graph Inductive Inference Through Dual Graph Learning and Traversing

Knowledge graph (KG) is a kind of structured human knowledge of modeling the relations between real-world entities. This paper studies the KG inductive inference problem, i.e., predicting the relations for out-of-KG entities. However, due to the incomplete nature of the KGs, the connections of some relations are missing. This makes existing differentiable rule learning methods unable to represent some possible rule candidates, which will further affect the inductive inference result. To solve this challenge, our research hypothesis is that the semantics of relation's argument can be well used to reflect the possible connections between relations. We propose a KG inductive inference model, RuleNet, which consists of two parts. Firstly, a query-dependent dual graph construction method is proposed, which is able to learn the relation connections using the information of the relation's argument. Secondly, a dual graph traversing method is proposed, which is able to traverse all possible rule candidates even if some rules cannot be formed due to the missing edges. Performance of the proposed methods is evaluated using the FB15K237 (10%-20%), WN18RR (10%-20%) and YAGO3-10 (10%-20%) benchmarks. Experimental results show that RuleNet achieves a superior performance compared with many strong baselines. Ablation studies have verified the effectiveness of the proposed network components. Qualitative analysis shows that RuleNet can learn meaningful dual graph and logic rules.
The Comparative Economics of Globalisation and Governance in Sub-Saharan Africa

This study investigates the effect of globalisation on governance in 40 Sub-Saharan African countries for the period 2000-2019, with particular emphasis on income levels (low income versus middle income), legal origins (English common law versus French civil law), landlockedness (landlocked versus unlandlocked), resource wealth (oil-rich versus oil-poor) and political stability (stable versus unstable). The empirical evidence is based on Fixed Effects in order to control for the unobserved heterogeneity. Political, economic, social, and general globalisation variables are used, while three bundled governance indicators are also employed to assess five main hypotheses. From baseline findings, while all globalization dynamics negatively affect political governance, only political and social globalisation have a negative incidence on economic governance. Social and general globalisation dynamics positively affect institutional governance. The hypotheses that higher income, English common law, unlandlocked, oil poor, and politically-stable countries are associated with higher levels of globalisation-driven governance, are valid, invalid, and partially valid contingent on the globalisation and governance dynamics.