Reviewerly/exHarmony huggingface dataset:
FIPAC: Thwarting Fault- and Software-Induced Control-Flow Attacks with ARM Pointer Authentication

With improvements in computing technology, more and more applications in the Internet-of-Things, mobile devices, or automotive area embed powerful ARM processors. These systems can be attacked by redirecting the control-flow to bypass critical pieces of code such as privilege checks or signature verifications or to perform other fault attacks on applications or security mechanisms like secure boot. Control-flow hijacks can be performed using classical software vulnerabilities, physical fault attacks, or software-induced faults. To cope with this threat and to protect the control-flow, dedicated countermeasures are needed. Control-flow integrity (CFI) aims to be a generic solution to counteract control-flow hijacks. However, software-based CFI typically either protects against software or fault attacks, but not against both. While hardware-assisted CFI can mitigate both, they require hardware changes, which are unrealistic for existing architectures. Thus, a wide range of systems remains unprotected and vulnerable to control-flow attacks. This work presents FIPAC, a software-based CFI scheme protecting the execution at basic block granularity against software and fault attacks. FIPAC exploits ARM pointer authentication of ARMv8.6-A to implement a cryptographically signed control-flow graph. We cryptographically link the correct sequence of executed basic blocks to enforce CFI at this level. We use a custom LLVM-based toolchain to automatically instrument programs. The evaluation on SPEC2017 with different security policies shows a geometric mean code overhead between 51–91% and a runtime overhead between 19–63%. For embedded benchmarks, we measured geometric mean runtime overheads between 49–168%. While these overheads are higher than for countermeasures against software attacks, FIPAC outperforms related work protecting the control-flow against faults. FIPAC is an efficient solution to protect software- and fault-based CFI attacks on basic block level on modern ARM devices.
Visualization Method for Decision-Making: A Case Study in Bibliometric Analysis

Data and information visualization have drawn an increasingly wide range of interest from several academic fields and industries. Concurrently, exploring a huge set of data to support feasible decisions needs an organized method of Multi-Criteria Decision Making (MCDM). The dramatic increasing of data producing during the past decade makes visualization necessary as a presentation layer on the top of MCDM process. This study aims to propose an integrated strategy to rank the alternatives in the dataset, by combining data, MCDM methods, and visualization layers. In fact, the well designed combination of Information Visualization and MCDM provides a more user-friendly approach than the traditional methods. We investigate a case study in bibliometric analyses, which have become an important dimension and tool for evaluating the impact and performance of researchers, departments, and universities. Hence, finding the best and most reliable papers, authors, and publishers considering diverse criteria is one of the important challenges in science world. Therefore, this text is presenting a new strategy on the bibliometric dataset as a case study and it demonstrates that this strategy can be more meaningful for the end users than the current tools. Finally, the presented simulations illustrate the performance and utilization of this combination. In other words, the researchers of this study could design and implement a tool that overcomes the biggest challenges of data analyzing and ranking via a combination of MCDM and visualization methodologies that can provide a tremendous amount of insight and information from a massive dataset in an efficient way.
ESL students’ perceptions of mobile applications for discipline-specific vocabulary acquisition for academic purposes

Undergraduates are often advised to expand their discipline-specific vocabulary in order to cope with the rigor of reading and writing academic texts. For the students studying in English-medium settings, it is in their interest to acquire vocabulary in their disciplines as this is vital to academic success. While students recognize the importance of discipline-specific vocabulary acquisition, they are typically unable to do so due to their heavy work on learning and assessment tasks. With the advent of mobile technology, one way to address this problem is to create vocabulary apps. To meet this need, this paper presents a gamified, discipline-specific vocabulary learning app Books vs Brains@PolyU. It was developed to help busy undergraduates to build a repertoire of the vocabulary across seven disciplines. We explored students’ perceptions and evaluation of this tool via interviews with sixteen student participants of the project. The results show that students found the tool useful and motivating, indicating the effectiveness of the app in helping busy undergraduates to build and expand their knowledge of discipline-specific vocabulary.
System for quantitative evaluation of DAB&amp;H-stained breast cancer biopsy digital images (CHISEL)

This study presents CHISEL (Computer-assisted Histopathological Image Segmentation and EvaLuation), an end-to-end system capable of quantitative evaluation of benign and malignant (breast cancer) digitized tissue samples with immunohistochemical nuclear staining of various intensity and diverse compactness. It stands out with the proposed seamless segmentation based on regions of interest cropping as well as the explicit step of nuclei cluster splitting followed by a boundary refinement. The system utilizes machine learning and recursive local processing to eliminate distorted (inaccurate) outlines. The method was validated using two labeled datasets which proved the relevance of the achieved results. The evaluation was based on the IISPV dataset of tissue from biopsy of breast cancer patients, with markers of T cells, along with Warwick Beta Cell Dataset of DAB&H-stained tissue from postmortem diabetes patients. Based on the comparison of the ground truth with the results of the detected and classified objects, we conclude that the proposed method can achieve better or similar results as the state-of-the-art methods. This system deals with the complex problem of nuclei quantification in digitalized images of immunohistochemically stained tissue sections, achieving best results for DAB&H-stained breast cancer tissue samples. Our method has been prepared with user-friendly graphical interface and was optimized to fully utilize the available computing power, while being accessible to users with fewer resources than needed by deep learning techniques.
Towards Flexible Device Participation in Federated Learning

Traditional federated learning algorithms impose strict requirements on the participation rates of devices, which limit the potential reach of federated learning. This paper extends the current learning paradigm to include devices that may become inactive, compute incomplete updates, and depart or arrive in the middle of training. We derive analytical results to illustrate how allowing more flexible device participation can affect the learning convergence when data is not independently and identically distributed (non-IID). We then propose a new federated aggregation scheme that converges even when devices may be inactive or return incomplete updates. We also study how the learning process can adapt to early departures or late arrivals, and analyze their impacts on the convergence.
Machine Translation and Computer Aided English Translation

Abstract With the development of globalization, the contacts and exchanges between countries in the world are getting closer and more frequent. As the most widely used language in the world, English is favored and valued by many non-English speaking countries. In order to facilitate communication, people use computers technology has developed a computer translation tool. The purpose of this article is to explore the difference between machine translation and computer-assisted translation in computer translation, compare the advantages and characteristics of the two in English translation, and point out the direction for future English translation trends. This article uses Google Translator, Youdao Translator and Baidu Translator in machine translation software and Wordfast, WordFisher and iCAT Huoyun Translator in computer-aided translation software as the experimental research objects to compare and analyze the different features and advantages between machine translation and computer-aided translation, and compare the error rate and match rate of the two in English translation. Finally, the experimental results show that the total number of errors in computer-assisted translation software translation is less than that of machine translation software. Among them, WordFisher has the least number of errors, 31 errors in vocabulary, 25 errors in sentences, and 3 errors in punctuation, a total of 59 errors; with the improvement of translation matching rate, the number of translation errors of English professional terminology of machine translation software is increasing, and the error rate is getting higher and higher, when the matching rate is 90-100, Google Translate becomes the software with the most translation errors, and the number of errors reached 78. Studies have shown that computer-assisted English is better and more accurate than machine translation.
Waveforms Classification of Northern Sumatera Earthquakes for New Mini Region Stations Using Support Vector Machine

We develop and evaluate the new mini region station in Northern Sumatera for discrimination and feature extract seismic events form shallow and intermediate based on waveforms recorded. Machine learning approaches are employed to classification the waveforms and seismic features of the recoded signal in the time-frequency domain. The most issue of this study are the recurrence of the seismic tremors in January to April 2020 regularly happened, and exceptionally local in Northern Sumatra. This can be also in related to the establishment of modern sensors, for that it will be fundamental to create a high-performance technique for automated clustering of seismic tremors recorded of the modern smaller than expected locale sensors on a limited assortment of floor collectors based on their supply depths. We applied the technique to 25 earthquakes that started January to April 2020, with the depth are smaller than 100 km in the land. A selected set of features were then used to train the system to discriminate from events with a hypo-central depth between 10 to 100 km with 96.01 percent accuracy using the SVM model. The result shows that the spectral feature using wavelet-based with machine learning python (mlpy) package has the highest energy correlation. Wavelet spectral in the time-frequency domain is all-new mini region stations that are promising for seismic event classification. The used machine learning approaches have a good classification of low energy signals recorded at the new mini region station in Northern Sumatera.
Computer-generated Works under the CDPA 1988

Abstract Copyright protection in some commonwealth jurisdictions extends to computer-generated works. Although many scholars deem the right over computer-generated works to be a neighbouring right, it is still not clear under what circumstances a work is a computer-generated work. With the increasing application of artificial intelligence (AI), the copyright controversies associated with computer-generated works have become even more complicated. This chapter focuses on policy and legal issues surrounding the output of AI and copyright protection of computer-generated work under the Copyright, Designs and Patents Act (CDPA) 1988 in the UK. The CDPA approach to computer-generated work deviates from the mainstream international copyright practices, where human creativity is essential for authorship and copyright protection. From a policy perspective, it is important to explore whether this deviation can be justified. This chapter also investigates authorship issues concerning computer-generated works based on case law and its application, in particular who the person making the necessary arrangements is, and what the necessary arrangements in the AI environment are. Other issues relevant to computer-generated work, such as copyright term and joint authorship will be analysed as well.
Understanding Offline Password-Cracking Methods: A Large-Scale Empirical Study

Researchers proposed several data-driven methods to efficiently guess user-chosen passwords for password strength metering or password recovery in the past decades. However, these methods are usually evaluated under ad hoc scenarios with limited data sets. Thus, this motivates us to conduct a systematic and comparative investigation with a very large-scale data corpus for such state-of-the-art cracking methods. In this paper, we present the large-scale empirical study on password-cracking methods proposed by the academic community since 2005, leveraging about 220 million plaintext passwords leaked from 12 popular websites during the past decade. Specifically, we conduct our empirical evaluation in two cracking scenarios, i.e., cracking under extensive-knowledge and limited-knowledge. The evaluation concludes that no cracking method may outperform others from all aspects in these offline scenarios. The actual cracking performance is determined by multiple factors, including the underlying model principle along with dataset attributes such as length and structure characteristics. Then, we perform further evaluation by analyzing the set of cracked passwords in each targeting dataset. We get some interesting observations that make sense of many cracking behaviors and come up with some suggestions on how to choose a more effective password-cracking method under these two offline cracking scenarios.
Using Deep Image Priors to Generate Counterfactual Explanations

Through the use of carefully tailored convolutional neural network architectures, a deep image prior (DIP) can be used to obtain pre-images from latent representation encodings. Though DIP inversion has been known to be superior to conventional regularized inversion strategies such as total variation, such an over-parameterized generator is able to effectively reconstruct even images that are not in the original data distribution. This limitation makes it challenging to utilize such priors for tasks such as counterfactual reasoning, wherein the goal is to generate small, interpretable changes to an image that systematically leads to changes in the model prediction. To this end, we propose a novel regularization strategy based on an auxiliary loss estimator jointly trained with the predictor, which efficiently guides the prior to re-cover natural pre-images. Our empirical studies with a real-world ISIC skin lesion detection problem clearly evidence the effectiveness of the proposed approach in synthesizing meaningful counterfactuals. In comparison, we find that the standard DIP inversion often proposes visually imperceptible perturbations to irrelevant parts of the image, thus providing no additional insights into the model behavior.
Secure Software Development in the Era of Fluid Multi-party Open Software and Services

Pushed by market forces, software development has become fast-paced. As a consequence, modern development projects are assembled from 3rd-party components. Security & privacy assurance techniques once designed for large, controlled updates over months or years, must now cope with small, continuous changes taking place within a week, and happening in sub-components that are controlled by third-party developers one might not even know they existed. In this paper, we aim to provide an overview of the current software security approaches and evaluate their appropriateness in the face of the changed nature in software development. Software security assurance could benefit by switching from a process-based to an artefact-based approach. Further, security evaluation might need to be more incremental, automated and decentralized. We believe this can be achieved by supporting mechanisms for lightweight and scalable screenings that are applicable to the entire population of software components albeit there might be a price to pay.
Evaluation of geochemical anomaly classification models based on the relevant uncertainties and error propagation per class to select the most robust model(s) for the follow-up exploration&amp;#160;

&amp;lt;p&amp;gt;A significant issue in all geochemical anomaly classification methods is uncertainty in the identification of different populations and allocation of samples to those populations, including the critical category of geochemical anomalies or patterns that are associated with the effects of mineralisation. This is a major challenge where the effects of mineralisation are subtle. There are various possible sources of such uncertainty, such as (i) gaps in coverage of geochemical sampling within a study area; (ii) errors in geochemical data analysis, spatial measurement, interpolation; (iii) misunderstanding of geological and geochemical processes; (iv) fuzziness or vagueness of the threshold between geochemical background and geochemical anomalies. In this research, the well-established concentration-area (C-A) and the newly established concentration-concentration (C-C) fractal models were applied to centered-logratio (clr) transformed data, and highly correlated elements of Cu-Te, respectively. Such models were applied to the available till samples (2578 samples) collected by the Geological Survey of Sweden (SGU) from 75% of the country area, to generate the Cu volcanic massive sulfide (VMS) geochemical anomaly classified map and define the highly promising areas for further exploration. However, to be confident more about the robustness of each class recognised by the thresholds obtained from the C-A and C-C log-log plots, Monte Carlo simulation (MCSIM) was applied to each class to simulate a higher number of values per class and consider the relevant error propagation. Under the MCSIM approach, the P50 value (the average 50&amp;lt;sup&amp;gt;th&amp;lt;/sup&amp;gt; percentile of the multiple simulated distributions represents a neutral probability in decision-making) is defined as the expected &amp;amp;#8216;return&amp;amp;#8217;. The uncertainty is calculated, in this approach, as 1/(P90-P10) for which P10 (lower decile) and P90 (upper decile) are the average 10&amp;lt;sup&amp;gt;th&amp;lt;/sup&amp;gt; and 90&amp;lt;sup&amp;gt;th&amp;lt;/sup&amp;gt; percentiles of the multiple simulated values, associated with each class. The most reliable classes are those with high returns and low risks. Based on the results obtained, C-A could not provide robust enough results since in the defined classes, the risk was almost equal or even higher than the return, however, the C-C model provided high returns and very low uncertainties, demonstrating the robustness of C-C compared to C-A. This approach can improve the quality of the decision-making in choosing the most robust classification models, and consequently getting more reliable results.&amp;lt;/p&amp;gt;
A Methodology to Enhance Learning Processes with Augmented Reality Glasses

Although Augmented Reality (AR) encourages self-managed learning and enhances the learner’s reflection only few companies and schools use AR glasses in vocational training. In our research project we aim to support the dissemination of AR and speed up the augmentation of existing learning processes. But we did not come across methodologies for transferring a conventional learning processes into learning processes for AR Glasses. Therefore, we developed a methodology to enhance conventional learning processes with AR glasses by supporting the creation of AR enrichments for the individual process steps. We describe a use case of an electro engineering lesson that is enriched with AR elements using our methodology.
Secrecy in Open Innovation and Open Innovation in Secrecy

Previous research have opposed secrecy and openness as mutually exclusive processes. In this paper, we suggest a more nuanced approach to secrecy in open innovation, drawing from Philosophy and Sociology. We have conducted a two-year research program in the defense industry, collecting data on the practices of concealment in open innovation activities. We found that focal actors utilize distinct cognitive techniques to decontextualize knowledge in the course of open innovation projects in order to safeguard secrecy while preserving mutual learning. Further, focal actors design contrasted relational approaches to secrecy with their open innovation partners. Such approaches are based on the positions of secrecy boundaries, which are internal or external to the relationship. In fact, actors intentionally make use of the reversible nature of secrecy, balancing inclusion and exclusion of knowledge in partnerships, to meet objectives that sometimes go beyond knowledge protection. Finally, we build on our findings to introduce a capability-based framework of secrecy management in openness processes. This framework, which we call Knowledge Discretion, suggests that actors overcome tensions stemming from secrecy and openness playing on the contextual depth and relational breadth of external knowledge sharing.
Saw It or Triggered It: Exploring the Threshold of Implicit and Explicit Interaction for Eye-tracking Technique in Virtual Reality

With eye-tracking techniques, the virtual reality (VR) system can acquire what the user is looking at in the virtual environment (VE). Most previous works focused on eye-tracking interaction in VR with explicit interactions. In this paper, we tried to determine whether the eye-tracking techniques can be applied as implicit interactions. With implicit eye-tracking interactions, users could trigger the story events unconsciously without missing them. The approaches could be interactive techniques for enhancing VR storytelling. We conducted a user study to investigate the threshold of implicit and explicit interaction for the eye-tracking technique in VR. We designed three interfaces: objects lighting, objects movement, and objects sound, for users to judge whether they trigger objects in the VE or not when seeing them. The result provides a parameter in the design guideline for immersive storytelling with the eye-tracking device and brings a new VR storytelling technique.
Locally orderless tensor networks for classifying two- and three-dimensional medical images

Tensor networks are factorisations of high rank tensors into networks of lower rank tensors and have primarily been used to analyse quantum many-body problems. Tensor networks have seen a recent surge of interest in relation to supervised learning tasks with a focus on image classification. In this work, we improve upon the matrix product state (MPS) tensor networks that can operate on one-dimensional vectors to be useful for working with 2D and 3D medical images. We treat small image regions as orderless, squeeze their spatial information into feature dimensions and then perform MPS operations on these locally orderless regions. These local representations are then aggregated in a hierarchical manner to retain global structure. The proposed locally orderless tensor network (LoTeNet) is compared with relevant methods on three datasets. The architecture of LoTeNet is fixed in all experiments and we show it requires lesser computational resources to attain performance on par or superior to the compared methods.