Reviewerly/exHarmony huggingface dataset:
Lab 21 – A Space for Learning, Sharing and Innovating

Societies everywhere are currently undergoing deep transformation, including the transition from traditional organizational structures to network structures where connected autonomous individuals are challenged to solve complex problems co-creatively and responsibly. Phenomena like the global health crisis, climate change, and the impact of digitalization are forcing governments, organizations, institutions of higher learning, and schools to re-think learning, teaching, and assessment practices in a way that they engage working learners, students, and pupils and prepare them for the future. The present paper aims to showcase a learner-centered, multi-method teaching approach, which is grounded in the principles of communities of practice, anchored in hyper-curricula for 21st century skills acquisition, and implemented in a hybrid hyper-structure for learning. This multi-method teaching approach, referred to as 2CG® method, aims to connect learners across hierarchical, cultural, and disciplinary boundaries and helps them unlock their creative potential by means of customized artistic impulses. In this paper, we look at how the 2CG® teaching approach has been implemented in Lab 21, a hybrid hyper-structure for learning that immerses learners in practice-based learning experiences so that they can develop 21st century skills while exploring new pathways of action. Practice examples demonstrate how communication skills programs, coding and media literacy programs, innovation and wellbeing programs in organizations, institutions of higher learning, and schools can serve as an effective context for this new way of learning, which lays out a human vision for education. Finally, the paper points to current limitations of the approach and makes suggestions for future research.
Overview of surficial geochemistry and indicator mineral surveys and case studies from the Geological Survey of Canada's GEM Program

The Geological Survey of Canada carried out reconnaissance-scale to deposit-scale geochemical and indicator-mineral surveys and case studies across northern Canada between 2008 and 2020 as part of its Geo-mapping for Energy and Minerals (GEM) program. In these studies, surficial geochemistry was used to determine the concentrations of up to 65 elements in various sample media including lake sediment, lake water, stream sediment, stream water, or till samples across approximately 1 000 000 km 2 of northern Canada. As part of these surficial geochemistry surveys, indicator mineral methods were also used in regional-scale and deposit-scale stream sediment and till surveys. Through this program, areas with anomalous concentrations of elements and/or indicator minerals that are indicative of bedrock mineralization were identified, new mineral exploration models and protocols were developed, a new generation of geoscientists was trained, and geoscience knowledge was transferred to northern communities. Regional- and deposit-scale studies demonstrated how transport data (till geochemistry, indicator mineral abundance) and ice-flow indicator data can be used together to identify and understand complex ice flow and glacial transport. Detailed studies at the Izok Lake Zn–Cu–Pb–Ag VMS, Nunavut, the Pine Point carbonate-hosted Pb–Zn in the Northwest Territories, the Strange Lake REE deposit in Quebec and Labrador as well as U–Cu–Fe–F and Cu–Ag–Au–Au IOCG deposits in the Great Bear magmatic zone, Northwest Territories demonstrate new suites of indicator minerals that can now be used in future reconnaissance- and regional-scale stream sediment and till surveys across Canada.
Adaptive Data Compression for Classification Problems

Data subset selection is a crucial task in deploying machine learning algorithms under strict constraints regarding memory and computation resources.Despite extensive research in this area, a practical difficulty is the lack of rigorous strategies for identifying the optimal size of the reduced data to regulate trade-offs between accuracy and efficiency.Furthermore, existing methods are often built around specific machine learning models, and translating existing theoretical results into practice is challenging for practitioners.To address these problems, we propose two adaptive compression algorithms for classification problems by formulating data subset selection in the form of interactive teaching.The user interacts with the learning task at hand to adapt to the unique structure of the problem at hand, developing an iterative importance sampling scheme.We also propose to couple importance sampling and a diversity criterion to further control the evolution of the data summary over the rounds of interaction.We conduct extensive experiments on several data sets, including imbalanced and multiclass data, and various classification algorithms, such as ensemble learning and neural networks.Our results demonstrate the performance, efficiency, and ease of implementation of the underlying framework.
Understanding the Effects of Visualizing Missing Values on Visual Data Exploration

When performing data analysis, people often confront data sets containing missing values. We conducted an empirical study to understand the effect of visualizing those missing values on participants’ decision-making processes while performing a visual data exploration task. More specifically, our study participants purchased a hypothetical portfolio of stocks based on a data set where some stocks had missing values for attributes such as PE ratio, beta, and EPS. The experiment used scatterplots to communicate the stock data. For one group of participants, stocks with missing values simply were not shown, while the second group saw such stocks depicted with estimated values as points with error bars. We measured participants’ cognitive load involved in decision-making with data with missing values. Our results indicate that their decision-making workflow was different across two conditions.
GANORCON: Are Generative Models Useful for Few-shot Segmentation?

Advances in generative modeling based on GANs has motivated the community to find their use beyond image generation and editing tasks. In particular, several re-cent works have shown that GAN representations can be re-purposed for discriminative tasks such as part segmen-tation, especially when training data is limited. But how do these improvements stack-up against recent advances in self-supervised learning? Motivated by this we present an alternative approach based on contrastive learning and compare their performance on standard few-shot part seg-mentation benchmarks. Our experiments reveal that not only do the GAN-based approach offer no significant per-formance advantage, their multi-step training is complex, nearly an order-of-magnitude slower, and can introduce ad-ditional bias. These experiments suggest that the inductive biases of generative models, such as their ability to dis-entangle shape and texture, are well captured by standard feed-forward networks trained using contrastive learning.
Meltdown-type attacks are still feasible in the wall of kernel page-Table isolation

Meltdown released in 2018 is a hardware vulnerability primarily affecting Intel modern processors. It allows a rogue process to read the kernel data in CPU L1D cache. To defend against the Meltdown attack in legacy processors, the most effective software-only mitigation approach is to unmap kernel memory from user processes, known as kernel page-table isolation (KPTI). In this paper, we present a novel Meltdown-type attack, named KPTImew, that can defeat KPTI in Linux and reliably dump all the target data in the kernel address space. We observe that there still exists kernel memory mapped in a user process, indicating that the mapped memory content can still be leaked through the Meltdown attack. However, the Meltdown attack is limited to leaking data that must be resident in CPU L1D cache. To lift the limitation, we propose a new technique, called reDump, as a part of our contribution. reDump exploits speculative execution to load data in the mapped memory into L1D cache and thus reliably dump the data using the Meltdown attack. To further leak data from the whole kernel including the above mapped memory, KPTImew first establishes data dependency between the mapped memory and any target kernel memory, and then exploits the data dependency to bring certain mapped kernel data into L1D cache that is dependent on targeted kernel data. When the mapped kernel data is leaked, the targeted kernel data can be leaked through the data-dependency. We modify an open-source tool, called smatch, to find such gadgets in recent kernels (i.e., 4.17.3 and 5.8.7) for loading the kernel mapped data into L1D cache and establishing the data-dependency, respectively. Specifically, dozens of potential gadgets are found in default kernel compile configuration while hundreds of gadget candidates are available for all-yes compile configuration. Our experiments show that reDump leaks 32 B of the mapped data within 6 seconds on average. With the assistance of reDump, KPTImew leaks any 32 B of kernel data within 12 seconds on average. In comparison, KPTImew can also work independently and requires 218 seconds on average to leak 32 B without reDump.
User Demand Awareness and Analysis of Online Music——Take NetEase Cloud Music Platform as an Example

Music is one of the most critical objects of entertainment and spiritual consumption to human beings. It is also an essential part of the cultural industry and creative industry, which plays a vital role in stimulating consumption and realizing economic growth. With the rapid development of the Internet and information technology, online music platforms have rapidly emerged and become the mainstream listening channel for users. Accurately perceiving users' needs has become the key to the sustainable development and competitiveness of online music platforms. First, This paper uses the web crawler to obtain the data of Netease cloud music, including online reviews and user data in the hot song list. Second, uses text analysis method and LDA theme model, combined with "uses and gratifications theory" to analyze user's demand awareness. The results show that Netease cloud music meets users' needs in entertainment, information, social, psychological benefits, and immersion. But it also has some problems in music copyright, UGC community management, empathy of platform functions, etc., then puts forward some suggestions for the development of Netease cloud music platform based on the demanded awareness of users. This paper also has reference value for similar online user demand perception research.
Analysis and Contributions of Classical Techniques for Path Planning

Unmanned aerial vehicles have been increasingly used to solve problems in several areas. These problems require the UAV to move between two nodes precisely to perform the tasks. In this way, path planning algorithms are adopted to provide this path. The classic algorithms can confirm whether a result exists or not and be great for flying in real-time. However, there are many classical algorithms, and each of them has better performance in different environments. Therefore, this paper presents a comparison and statistical analysis between the classical algorithms, emphasizing the best algorithms, showing the advantages and disadvantages of each algorithm in indoor and outdoor environments. The algorithms that presented better performance are tested in a 3D environment. The analyzed algorithms are A*, Artificial Potential Field, Probabilistic Random Map, Rapid Random Tree, and Rapid Random Tree Connect. We validate the results in 2D and 3D simulations, in Python, and on Gazebo.
Combined scaling for zero-shot transfer learning

Recent developments in multimodal training methodologies, including CLIP and ALIGN, obviate the necessity for individual data labeling. These approaches utilize pairs of data and corresponding textual information found online as a form of weak supervision signal. However, models employing this kind of weak supervision are not as competitive as their supervised and semi-supervised counterparts when sufficient labeled data is accessible. This performance gap constrains the applicability of weekly supervised models. In this paper, we narrow the gap by proposing a combined scaling method, named BASIC, that achieves 85.7% top-1 accuracy on the ImageNet ILSVRC-2012 validation set without learning from any labeled ImageNet example. This accuracy surpasses best-published similar models, CLIP and ALIGN, by 9.3%. Our BASIC model also shows significant improvements in robustness benchmarks. For instance, on 5 test sets with natural distribution shifts such as ImageNet-{A,R,V2,Sketch} and ObjectNet, our model achieves 84.3% top-1 average accuracy, only a small drop from its original ImageNet accuracy. To achieve these results, we first develop a theoretical framework which shows that larger contrastive batch sizes lead to smaller generalization gaps for image-text models such as CLIP and ALIGN. Based on this theoretical result, we scale up the contrastive learning framework of CLIP and ALIGN in three dimensions (data size, model size, and batch size) by proposing a new method using gradient checkpointing and model parallelism. As a result, our dataset has 6.6B noisy image-text pairs, which is 4x larger than ALIGN, and 16x larger than CLIP. Our largest model has 3B weights, which is 3.75x larger in parameters and 8x larger in FLOPs than ALIGN and CLIP. Finally, our batch size is 65536 which is 2x more than CLIP and 4x more than ALIGN.
Securing Sensitive Data Through Corner Filters, Chaotic Maps and LSB Embedding

This paper proposes 2 multiple layer message security schemes. Information security is carried out through the implementation of cryptography, steganography and image processing techniques. In both schemes, the sensitive data is first encrypted by employing a chaotic function. In the first proposed scheme, LSB steganography is then applied to 2D slices of a 3D image. In the second proposed scheme, a corner detection filter is first applied to the 2D slices of a 3D image, then LSB embedding is carried out in those corner-detected pixels. The number of neighboring pixels used for corner detection is varied and its effect is noted. Performance of the proposed schemes is numerically evaluated using a number of metrics, including the mean squared error (MSE), the peak signal to noise ratio (PSNR), the structure similarity index measure (SSIM), the normalized cross-correlation (NCC), the image fidelity (IF), as well as the image difference (ID). The proposed schemes exhibit superior payload capacity and security in comparison to their counterparts from the literature.
Issues and Challenges associated with Blockchain in Smart Cities

Blockchain technology now has applications in many areas, such as cryptocurrencies and smart contracts. For example, in an IoT system, sensors from different devices transfer data collected in cloud computing on a central server. The issues that arise relate to security and privacy matters that occur mainly due to difficulties in upgrading the software of millions of smart devices and the management of servers in the cloud by other third-party companies. By enabling the blockchain, various security options are offered by storing transactions in a decentralized and transparent system. However, a lot of effort is needed to integrate blockchain systems and their application in smart cities. In this work, we review how blockchain systems can be integrated into smart cities.
How Distributed Ledger Technologies affect business models of manufacturing companies

Industry 4.0 is characterized by the transformation and networking of production systems as a result of utilizing digital technologies and processing huge amounts of data. The features of Distributed Ledger Technologies have the potential to revolutionize production by creating trust and transparency in stored data, eliminating dependence on single entities and performing automated transactions in real time. Due to the disruptive change coming with an implementation, manufacturers remain skeptical about the technology. To raise awareness of the potential of Distributed Ledger Technology, this paper describes valid use cases and examines the interactions with business models in real world manufacturing scenarios.
Dual images in reversible data hiding with adaptive color space variation using wavelet transforms

Purpose Steganography is a data hiding technique used in the data security. while transmission of data through channel, no guarantee that the data is transmitted safely or not. Variety of data security techniques exists such as patch work, low bit rate data hiding, lossy compression etc. This paper aims to increase the security and robustness. Design/methodology/approach This paper describes, an approach for multiple images steganography that is oriented on the combination of lifting wavelet transform (LWT) and discrete cosine transform (DCT). Here, we have one cover image and two secret images. The cover image is applied with one of the different noises like Gaussian, Salt &amp; Pepper, Poisson, and speckle noises and converted into different color spaces of YCbCr, HSV, and Lab. Findings Due to the vast development of Internet access and multimedia technology, it becomes very simple to hack and trace secret information. Using this steganography process in reversible data hiding (RDH) helps to prevent secret information. Originality/value We can divide the color space converted image into four sub-bands of images by using lifting wavelet transform. By selecting lower bands, the discrete cosine transform is computed for hiding two secret images into the cover image and again one of the transformed secret images is converted by using Arnold transform to get the encrypted/embedded/encoded image. To extract the Stego image, we can apply the revertible operation. For comparing the results, we can calculate PSNR, SSIM, and MSE values by applying the same process for all color spaces of YCbCr, HSV, and Lab. The experimental results give better performance when compared to all other spaces.
Advances in Geo-Time Series Modelling

Recent advances in geo-time series modelling are briefly presented. These progressive developments and imminent applications in the data-driven research have come across three main categories of investigation efforts (i) Classical to recent advances in spectral analyses and their applications on some very significant geophysical/geological time series. Specially, applications of modern multi-taper methods of spectral analysis (MTM) and singular spectral analysis (SSA) techniques based filtering are discussed. Applications on 3D seismic reflection data de-noising using multi-channel SSA along with significant results are demonstrated. (ii) Methods of nonlinear time series analyses and physical concept of fractal and chaos are enumerated and relevance of chaos in complex geophysical and geological time series modelling are discussed (iii) The upcoming field of research in Machine learning (ML) based artificial neural network (ANN) and Deep Learning (DL) along with pertinent applications on variety of geophysical data, such well-log, ground water, gravity, seismic/seismological data etc., are presented and discussed.
The Role of Linguistic Relativity on the Identification of Sustainability Requirements: An Empirical Study

Linguistic-Relativity-Theory states that language and its structure influence people’s world view and cognition. We investigate how this theory impacts the identification of requirements in practice. To this end, we conducted two controlled experiments with 101 participants. We randomly showed participants a set of requirements dimensions (i.e. a language structure) either with a focus on software quality or on sustainability and asked them to identify the requirements for a grocery shopping app according to these dimensions. Participants of the control group were not given any dimensions. The results show that the use of requirements dimensions significantly increases the number of identified requirements in comparison to the control group. Furthermore, participants who were given the sustainability dimensions identified more sustainability requirements. In follow up interviews with 16 practitioners, the interviewees reported benefits of the dimensions such as a holistic guidance but were also concerned about the customers acceptance. Furthermore, they stated challenges of implementing sustainability dimensions in the daily business but also suggested solutions like establishing sustainability as a common standard. Our study indicates that carefully structuring requirements engineering along sustainability dimensions can guide development teams towards considering and ensuring software sustainability.
Face Recognition in the Wild for Secure Authentication with Open Set Approach

In everyday life, authentication is an indispensable process of human activities. Bio-metric authentication system is one of the effective solutions, because it uses human-based features, instead of other traditional features, such as pin, password, etc. However, to apply a face authentication system in practical applications, we need to ensure that the system must not try to recognize the face of an unknown person into known categories, meaning we need to reject faces of unknown people in our application. In this paper, we present the limitations of recent Deep Learning based methods in Face Recognition tasks. We then propose two methods helping Face Recognition system have the ability to reject faces from unknown people by using Open-Set concepts. We conduct the experiments on a subset of CASIA-WebFace dataset, with a train set that includes 7000 images of 100 known people and a test set that includes both known and unknown people. Without rejecting unknown faces, the regular face recognition, i.e. the baseline method, yields the accuracy of only 45.9%, as the method tries to classify all face photos into known classes. Our proposed methods, which are combined deep network of Facenet system with recent Open Set methods, are called Learning Placeholder on Facenet (P-Facenet) and Facenet with OpenMax (O-Facenet). They achieve the accuracy of 83.6% and 88.5% respectively. This is a potential approach for authentication with face recognition to decrease the error rate of the model when recognizing faces of unknown people in the wild.