Reviewerly/exHarmony huggingface dataset:
A Survey of Intellectual Property Rights Protection in Big Data Applications

Big Data applications have the potential to transform any digital business platform by enabling the analysis of vast amounts of data. However, the biggest problem with Big Data is breaking down the intellectual property barriers to using that data, especially for cross-database applications. It is a challenge to achieve this trade-off and overcome the difficulties of Big Data, even though intellectual property restrictions have been developed to limit misuse and regulate access to Big Data. This study examines the scope of intellectual property rights in Big Data applications with a security framework for protecting intellectual property rights, watermarking and fingerprinting algorithms. The emergence of Big Data necessitates the development of new conceptual frameworks, security standards, and laws. This study addresses the significant copyright difficulties on cross-database platforms and the paradigm shift from ownership to control of access to and use of Big Data, especially on such platforms. We provide a comprehensive overview of copyright applications for multimedia data and a summary of the main trends in the discussion of intellectual property protection, highlighting crucial issues and existing obstacles and identifying the three major findings for investigating the relationship between them.
Clustering Gene Expressions Using the Table Invitation Prior

A prior for Bayesian nonparametric clustering called the Table Invitation Prior (TIP) is used to cluster gene expression data. TIP uses information concerning the pairwise distances between subjects (e.g., gene expression samples) and automatically estimates the number of clusters. TIP’s hyperparameters are estimated using a univariate multiple change point detection algorithm with respect to the subject distances, and thus TIP does not require an analyst’s intervention for estimating hyperparameters. A Gibbs sampling algorithm is provided, and TIP is used in conjunction with a Normal-Inverse-Wishart likelihood to cluster 801 gene expression samples, each of which belongs to one of five different types of cancer.
Learning Mixed Strategies in Quantum Games with Imperfect Information

The quantization of games expand the players strategy space, allowing the emergence of more equilibriums. However, finding these equilibriums is difficult, especially if players are allowed to use mixed strategies. The size of the exploration space expands so much for quantum games that makes far harder to find the player’s best strategy. In this work, we propose a method to learn and visualize mixed quantum strategies and compare them with their classical counterpart. In our model, players do not know in advance which game they are playing (pay-off matrix) neither the action selected nor the reward obtained by their competitors at each step, they only learn from an individual feedback reward signal. In addition, we study both the influence of entanglement and noise on the performance of various quantum games.
Handwritten Geez Digit Recognition Using Deep Learning

Amharic language is the second most spoken language in the Semitic family after Arabic. In Ethiopia and neighboring countries more than 100 million people speak the Amharic language. There are many historical documents that are written using the Geez script. Digitizing historical handwritten documents and recognizing handwritten characters is essential to preserving valuable documents. Handwritten digit recognition is one of the tasks of digitizing handwritten documents from different sources. Currently, handwritten Geez digit recognition researches are very few, and there is no available organized dataset for the public researchers. Convolutional neural network (CNN) is preferable for pattern recognition like in handwritten document recognition by extracting a feature from different styles of writing. In this work, the proposed model is to recognize Geez digits using CNN. Deep neural networks, which have recently shown exceptional performance in numerous pattern recognition and machine learning applications, are used to recognize handwritten Geez digits, but this has not been attempted for Ethiopic scripts. Our dataset, which contains 51,952 images of handwritten Geez digits collected from 524 individuals, is used to train and evaluate the CNN model. The application of the CNN improves the performance of several machine-learning classification methods significantly. Our proposed CNN model has an accuracy of 96.21% and a loss of 0.2013. In comparison to earlier research works on Geez handwritten digit recognition, the study was able to attain higher recognition accuracy using the developed CNN model.
Cyber Security Threats to Educational Institutes: A Growing Concern for the New Era of Cybersecurity

Background: The outbreak of the Covid-19 pandemic has significantly affected the operations of higher education institutions. Due to the limited use of video conferencing and cloud computing in these facilities, distance learning became the only option available to them. Objective: The study focused on identifying the most common types of attacks that can affect e-learning assets. Results: There was a lack of clear cybersecurity policies for educational institutes and universities in 2020, according to a report by Microsoft Security Intelligence. The report showed that the education industry was the most targeted sector for malware attacks in the last 30 days. Conclusion: The recommendations for improving the security of e-learning systems. Some of these include implementing policies that restrict access to the resources and applications, updating security patches, and using cryptographic protocols.
A low-code approach to support method engineering

Method engineering is defined as a discipline to design, construct and adapt methods, techniques and tools for the development of information systems. A method formalizes and structures the steps needed in order to achieve a specific goal. By executing a method step by step, users follow a systematic and well defined way to attain the results which the method was created for. However, method creation or the adaptation of existing methods to a specific domain or organization is challenging if one does not have method engineering skills. In addition, the adoption of a method may also be difficult if there is no tool to support these activities. Method engineers may not have the programming skills to implement such a tool. In this context, we propose an approach inspired by the low-code and end-user development paradigms for method engineering. The advantages are twofold. Firstly, it helps method engineers in creating and configuring new methods. Secondly, the approach automatically provides tool support so that method experts can actually execute the method. This position paper presents the approach through a proof of concept implementation.
An exploratory study on the relationship of smells and design issues with software vulnerabilities

Software vulnerabilities are one of the leading causes of the loss of confidential data resulting in financial damages in the industry. As a result, software companies strive to discover potential vulnerabilities before the software is deployed. While traditionally, software metrics have been widely used to uncover vulnerabilities, more recent studies have been looking at code smells to detect vulnerabilities. This preliminary study explores the relationship between smells, design issues, and software vulnerabilities. As smells and design issues are indicators of potential problems in the software, establishing a relationship with vulnerabilities can be helpful for vulnerability prediction. In this study, we analyzed 561 versions of nine open-source software by exploring the smells and design issues in the vulnerable and non-vulnerable classes. We found that some smells and design issues have a statistically significant relationship with the vulnerable classes. However, after a manual analysis of the code segments containing the vulnerabilities, we found no indication that smells or design issues induce the vulnerabilities. In fact, they were still present in those code segments even after the vulnerabilities were resolved.
DirtyCred

The kernel vulnerability DirtyPipe was reported to be present in nearly all versions of Linux since 5.8. Using this vulnerability, a bad actor could fulfill privilege escalation without triggering existing kernel protection and exploit mitigation, making this vulnerability particularly disconcerting. However, the success of DirtyPipe exploitation heavily relies on this vulnerability's capability (i.e., injecting data into the arbitrary file through Linux's pipes). Such an ability is rarely seen for other kernel vulnerabilities, making the defense relatively easy. As long as Linux users eliminate the vulnerability, the system could be relatively secure.
Hierarchical Bayesian multi-kernel learning for integrated classification and summarization of app reviews

App stores enable users to share their experiences directly with the developers in the form of app reviews. Recent studies have shown that the feedback received from users is a valuable source of information for requirements extraction, which encourages app developers to leverage the reviews for app update and maintenance purposes. Follow-up studies proposed automated techniques to help developers filter the large volume of daily and noisy reviews and/or summarize their content. However, all previous studies approached the app reviews classification and summarization as separate tasks, which complicated the process and introduced unnecessary overhead. Moreover, none of those approaches explored the potential of utilizing the hierarchical relationships that exist between the labels of app reviews for the purpose of building a more accurate model. In this work, we propose Hierarchical Multi-Kernel Relevance Vector Machines (HMK-RVM), a Bayesian multi-kernel technique that integrates app review classification and summarization using a unified model. Moreover, it can provide insights into the learned patterns and underlying data for easier model interpretation. We evaluated our proposed approach on two real-world datasets and showed that in addition to the gained insights, the model produces equal or better results than the state of the art.
The Paradox of the <i>DABUS</i> Judgment of the German Federal Patent Court

Abstract The judgment of the German Federal Patent Court (Bundespatentgericht) in the Food container case1 holds a unique place in the worldwide series of DABUS2 cases. On the one hand, it confirmed that an artificial intelligence (AI) system cannot be regarded as an inventor under patent law, which has so far been a dominant trend in other jurisdictions concerned with the matter.3 On the other hand, it allowed the applicant Stephen Thaler, who insisted on not having invented the food container, to be designated as the inventor in the patent application, thus, opening up an opportunity for him to become the patent owner. While the case outcome may appear as a constructive compromise, one may question the internal consistency of the court’s reasoning. This analysis shows that the judgment is paradoxical in that, by allowing Thaler to be deemed the inventor of the food container, it contradicts the very principle of human creativity underlying the inventor’s (moral) right to be named – the principle that was relied upon by the adjudicating Board when denying the designation of DABUS as the inventor.
The use and value of cultural historical activity theory in institutional educational technology policy

Institutional educational technology policies in Higher Education Institutions can help or hinder the objectives of faculty and administration staff.In many national contexts, these policies typically result from a top-down unilateral canonical decision-making process and or/retroactive heuristic models of investigation.However, research utilizing and advocating multilateral non-canonical approaches and more sociocultural models of investigation in institutional educational technology policy decision-making are novel.This paper stems from a project which used a formative Change Laboratory intervention to affect real meaningful change in institutional educational technology policy at one university in South Korea.Participants, including Korean faculty, international faculty and administration staff participated in multilateral, non-canonical workshops over a period of 7 months to explore and redesign their own activity.Central to this formative Change Laboratory intervention was Engeström's Cultural Historical Activity Theory (CHAT).The study utilized CHAT as a practical lens/toolkit to expose/examine contradictions and collectively transform institutional educational technology policy-practice activity.CHAT's activity system models helped participants identify, shape and question 'normal' or 'routine' practices in shared
Detecting Languages Unintelligible to Multilingual Models through Local Structure Probes

Providing better language tools for low-resource and endangered languages is imperative for equitable growth. Recent progress with massively multilingual pretrained models has proven surprisingly effective at performing zero-shot transfer to a wide variety of languages. However, this transfer is not universal, with many languages not currently understood by multilingual approaches. It is estimated that only 72 languages possess a "small set of labeled datasets" on which we could test a model's performance, the vast majority of languages not having the resources available to simply evaluate performances on. In this work, we attempt to clarify which languages do and do not currently benefit from such transfer. To that end, we develop a general approach that requires only unlabelled text to detect which languages are not well understood by a cross-lingual model. Our approach is derived from the hypothesis that if a model's understanding is insensitive to perturbations to text in a language, it is likely to have a limited understanding of that language. We construct a cross-lingual sentence similarity task to evaluate our approach empirically on 350, primarily low-resource, languages.
Efficiently Scaling Transformer Inference

We study the problem of efficient generative inference for Transformer models, in one of its most challenging settings: large deep models, with tight latency targets and long sequence lengths. Better understanding of the engineering tradeoffs for inference for large Transformer-based models is important as use cases of these models are growing rapidly throughout application areas. We develop a simple analytical model for inference efficiency to select the best multi-dimensional partitioning techniques optimized for TPU v4 slices based on the application requirements. We combine these with a suite of low-level optimizations to achieve a new Pareto frontier on the latency and model FLOPS utilization (MFU) tradeoffs on 500B+ parameter models that outperforms the FasterTransformer suite of benchmarks. We further show that with appropriate partitioning, the lower memory requirements of multiquery attention (i.e. multiple query heads share single key/value head) enables scaling up to 32x larger context lengths. Finally, we achieve a low-batch-size latency of 29ms per token during generation (using int8 weight quantization) and a 76% MFU during large-batch-size processing of input tokens, while supporting a long 2048-token context length on the PaLM 540B parameter model.
A Hybrid Method of Enhancing Accuracy of Facial Recognition System Using Gabor Filter and Stacked Sparse Autoencoders Deep Neural Network

Face recognition has grown in popularity due to the ease with which most recognition systems can find and recognize human faces in images and videos. However, the accuracy of the face recognition system is critical in ascertaining the success of a person’s identification. A lack of sufficiently large training datasets is one of the significant challenges that limit the accuracy of face recognition systems. Meanwhile, machine learning (ML) algorithms, particularly those used for image-based face recognition, require large training data samples to achieve a high degree of face recognition accuracy. Based on the above challenge, this research proposes a method for improving face recognition precision and accuracy by employing a hybrid approach of the Gabor filter and a stacked sparse autoencoders (SSAE) deep neural network. The face image datasets from Olivetti Research Laboratory (OLR) and the Extended Yale-B databases were used to evaluate the proposed hybrid model’s performance. All face image datasets used in our experiments are grayscale image type with a resolution of 92 × 112 for the OLR database and a resolution 192 × 168 for the Extended Yale-B database. Our experimental results showed that the proposed method improved face recognition accuracy by approximately 100% for the two databases used at a significantly reduced feature extraction time compared to the current state-of-art face recognition methods for all test cases. The SSAE approach can explore large and complex datasets with minimal computation time. In addition, the algorithm minimizes the false acceptance rate and improves recognition accuracy. This implies that the proposed method is promising and has the potential to enhance the performance of face recognition systems.
Accurate 3D Single Object Tracker With Local-to-Global Feature Refinement

3D single object tracking in point clouds is an essential task in robotics and autonomous driving. Many astonished trackers only adopt the voting-based region proposal network (RPN) to regress the object's location. However, they suffer from heavy outlier votes and ignore the global semantic features of targets. To resolve the problems, we propose a two-stage RPN module with local-to-global feature refinement for accurate tracking in point clouds. Specifically, deep Hough voting is applied to obtain coarse proposals in the first stage. In the second stage, we design a local feature refinement (LFR) module and a global feature refinement (GFR) module to realize accurate localization jointly. The LFR module excludes noisy outliers in disordered point clouds and obtains refined local features for coarse proposals. After that, the GFR module explores the relationships among all proposals to weigh the proposal-wise global context features. Integrating the proposed two-stage RPN module into the previous method BAT Zheng et al. (2021), we develop a coarse-to-fine 3D single object tracker in point clouds abbreviated as C2FT. Extensive experiments on KITTI and nuScene benchmarks demonstrate that C2FT achieves favorable performance with a real-time speed ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 50 FPS). Furthermore, the proposed LFR and GFR modules are generalized and can be easily integrated into other trackers.
Unsupervised Deep Learning-based clustering for Human Activity Recognition

One of the main problems in applying deep learning techniques to recognize activities of daily living (ADLs) based on inertial sensors is the lack of appropriately large labelled datasets to train deep learning-based models. A large amount of data would be available due to the wide spread of mobile devices equipped with inertial sensors that can collect data to recognize human activities. Unfortunately, this data is not labelled. The paper proposes DISC (Deep Inertial Sensory Clustering), a DL-based clustering architecture that automatically labels multi-dimensional inertial signals. In particular, the architecture combines a recurrent AutoEncoder and a clustering criterion to predict unlabelled human activities-related signals. The proposed architecture is evaluated on three publicly available HAR datasets and compared with four well-known end-to-end deep clustering approaches. The experiments demonstrate the effectiveness of DISC on both clustering accuracy and normalized mutual information metrics.
A3D: Physical Adversarial Attack on Visual Perception Module of Self-driving Cars

Abstract Advancements in machine learning, especially tech­niques such as deep neural networks, are promoting a range of high-stakes applications, including autonomous driving, which often relies on deep learning for perception. While deep learning for perception has been shown to be vulnerable to a host of subtle adversarial manipulations of images, end-to-end demonstrations of successful attacks, which manipulate the physical environment and result in physical consequences, are scarce. We demonstrate the first end-to-end attacks on autonomous driving in simulation, using simple physically realizable attacks: the painting of black lines on the road. These attacks target deep neural network models for end-to-end autonomous driving control. A systematic investigation shows that such attacks are surprisingly easy to engineer, and we describe scenarios (e.g., right turns) in which they are highly effective, and others that are less vulnerable Our result shows how that physical adversarial attack decreased the accuracy of the state of the art network by up to 30%.
FAIRer Data through Digital Recording: The FAIMS Mobile Experience

Critical data and metadata must be captured or created in the field, or shortly thereafter, to avoid loss. For the past 10 years, the Field Acquired Information Management Systems (FAIMS) project has developed and operated a customisable field data capture platform. Over time, we built features and approaches that incorporated the Findable, Accessible, Interoperable, and Reusable (FAIR) principles into born-digital datasets created during fieldwork. This paper synthesises our experience helping more than 40 projects adapt the FAIMS platform to nearly 70 research workflows in archaeology and other fieldwork domains. We review what elements of the FAIR Data Principles FAIMS was able to build into our software, how users received these capabilities, and what sociotechnical challenges impeded creation of FAIRer field data. Based on our experience, we argue that field data capture software can facilitate the production of FAIRer data, making those data much more Findable and Reusable, and somewhat more Accessible and Interoperable. Any such improvements, however, depend upon (1) making FAIR-data features an integral part of field data collection systems, minimising the burden imposed on researchers, and (2) researchers’ willingness to spend time and resources implementing FAIR Data Principles that do not provide immediate benefits to their research.
Geo-environmental assessment of geochemistry of groundwater and associated human health risks in the dry zone terrain of Sri Lanka

The Journal of the National Science Foundation of Sri Lanka publishes the results of research in all aspects of Science and Technology. The journal also has a website at http://www.nsf.gov.lk/. 2021 Impact Factor: 0.682The JNSF provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.