Reviewerly/exHarmony huggingface dataset:
De-coding or de-colonising the technocratic university? Rural students’ digital transitions to South African higher education

Despite wide-ranging policies and practices intended to address historical inequalities in South African higher education, and calls for decolonisation to include more local relevance, little attention has been paid to the experiences of rural students, especially their digital participation once at university. Previous research has highlighted limitations in technological access in rural areas and the importance of mobile phones for transitions. Whilst universities offer wide-ranging digital support, there remains a tendency towards universalist mechanisms. Drawing on a longitudinal study across three universities, and employing Holland’s theory of figured worlds, we highlight rural students’ experiences of digital transitions across different cultural worlds, prior to university and once they arrive, including the bewildering technocratic systems and practices and resulting conflicts and positionings encountered. We show how students improvise to decode the digital university and figure out new practices. Decolonisation of universities involves rethinking the ‘technocratic consciousness’ (both colonialist and neoliberal) and its apparatus including digital systems and structures. For rural students to become successful digital practitioners in higher education, universities should acknowledge prior digital experience and forms of knowledge and focus on expanding individual and collective agency in supporting transitions, as mechanisms for shaping a decolonised digital education.
Weak zero-knowledge beyond the black-box barrier

The round complexity of zero-knowledge protocols is a long-standing open question, yet to be settled under standard assumptions. So far, the question has appeared equally challenging for relaxations such as weak zero-knowledge and witness hiding. Protocols satisfying these relaxed notions under standard assumptions have at least four messages, just like full-fledged zero-knowledge. The difficulty in improving round complexity stems from a fundamental barrier: none of these notions can be achieved in three messages via reductions (or simulators) that treat the verifier as a black box.
A sinogram inpainting method based on generative adversarial network for limited-angle computed tomography

Limited-angle computed tomography (CT) image reconstruction is a challenging reconstruction problem in the fields of CT. With the development of deep learning, the generative adversarial network (GAN) perform well in image restoration by approximating the distribution of training sample data. In this paper, we proposed an effective GAN-based inpainting method to restore the missing sinogram data for limited-angle scanning. To estimate the missing data, we design the generator and discriminator of the patch-GAN and train the network to learn the data distribution of the sinogram. We obtain the reconstructed image from the restored sinogram by filtered back projection and simultaneous algebraic reconstruction technique with total variation. Experimental results show that serious artifacts caused by missing projection data can be reduced by the proposed method, and it is hopeful to solve the reconstruction problem of 60° limited scanning angle.
New Binary Quantum Codes Derived From One-Generator Quasi-Cyclic Codes

In this paper, we consider a family of one-generator quasi-cyclic (QC) codes and their applications in quantum codes construction.We give a sufficient condition for one-generator QC codes to be self-orthogonal with respect to the symplectic inner product.As the computational results, five new binary quantum codes with parameters [[45,29,5]], [[63,36,7]], [[73,55,5]], [[105,71,7]], and [[105,72,7]] improving the best-known lower bounds on minimum distance in Grassl's code tables are constructed.
ARCog: An Aerial Robotics Cognitive Architecture

SUMMARY Efficient algorithm integration is a key issue in aerial robotics. However, only a few integration solutions rely on a cognitive approach. Cognitive approaches break down complex problems into independent units that may deal with progressively lower-level data interfaces, all the way down to sensors and actuators. A cognitive architecture defines information flow among units to produce emergent intelligent behavior. Despite the improvements in autonomous decision-making, several key issues remain open. One of these issues is the selection, coordination, and decision-making related to the several specialized tasks required for fulfilling mission objectives. This work addresses decision-making for the cognitive unmanned-aerial-vehicle architecture coined as ARCog. The proposed architecture lays the groundwork for the development of a software platform aligned with the requirements of the state-of-the-art technology in the field. The system is designed to provide high-level decision-making. Experiments prove that ARCog works correctly in its target scenario.
An Empirical Assessment of Machine Learning Approaches for Triaging Reports of a Java Static Analysis Tool

Despite their ability to detect critical bugs in software, developers consider high false positive rates to be a key barrier to using static analysis tools in practice. To improve the usability of these tools, researchers have recently begun to apply machine learning techniques to classify and filter false positive analysis reports. Although initial results have been promising, the long-term potential and best practices for this line of research are unclear due to the lack of detailed, large-scale empirical evaluation. To partially address this knowledge gap, we present a comparative empirical study of four machine learning techniques, namely hand-engineered features, bag of words, recurrent neural networks, and graph neural networks, for classifying false positives, using multiple ground-truth program sets. We also introduce and evaluate new data preparation routines for recurrent neural networks and node representations for graph neural networks, and show that these routines can have a substantial positive impact on classification accuracy. Overall, our results suggest that recurrent neural networks (which learn over a program's source code) outperform the other subject techniques, although interesting tradeoffs are present among all techniques. Our observations provide insight into the future research needed to speed the adoption of machine learning approaches in practice.
Assimilating remote sensing data into GIS-based all sky solar radiation modeling for mountain terrain

Solar radiation is the ultimate energy resource of earth surface energy balance and the main driving force of atmospheric, ecological and hydrological processes. Solar radiation over complex terrain has large spatial and temporal variation because of terrain shading and high cloud heterogeneity. While most existing GIS-based solar radiation models only work under clear sky condition, this research presents a solar radiation model which considers both terrain shading and anisotropic cloud attenuation and diffuse radiation using MODIS atmospheric products. Specifically, we use skyshed map, sky cloud maps, and sky weight map to represent angular distribution of sky obstruction, anisotropic cloud properties, and diffuse radiance over hemispherical sky, respectively. Combining skyshed map, sky weight map and sky cloud maps, we develop a solar radiation model where 3D geometrical relationships among sun, cloud, and terrain are considered and anisotropic diffuse radiance and cloud attenuation are modeled. Model results are evaluated using field observations in the Kunlun Mountains of western China. At Terra and Aqua overpass time, our model performs well with a mean relative bias (MRB) of −0.2%. It underestimates in clear and partly cloudy sky with a MRB of −5.86% and −4.79% and has a mean absolute relative bias (MARB) of 8.11% and 21.59% respectively. It overestimates under overcast sky with a MRB of 1.68% and has a MARB of 31.71%. Our model performs better when compared with existing instantaneous solar radiation products. For daily solar radiation, our model shows good performance with a MRB of 1.43% and MARB of 17.02%. Our model shows significant spatial variation of solar radiation within the study area with the influences from terrain and cloud. Our research provides a novel and improved approach to assimilating remote sensing data into GIS-based solar radiation modeling in mountainous terrain where observations are sparse and difficult to obtain.
A hybrid fuzzy quantum time series and linear programming model: Special application on TAIEX index dataset

The supremacy of quantum approach is able to provide the solutions which are not practically feasible on classical machines. This paper introduces a novel quantum model for time series data which depends on the appropriate length of intervals. In this study, the effects of these drawbacks are elaborately illustrated, and some significant measures to remove them are suggested, such as use of degree of membership along with mid-value of the interval. All these improvements signify the effective results in case of quantum time series, which are verified and validated with real-time datasets.
Personalized Educational Learning with Multi-Stakeholder Optimizations

Recommender systems (RS) have been introduced to educations as an effective technology-enhanced learning technique. Traditional RS produce recommendations by considering the preferences of the end users only. Multi-stakeholder recommender systems (MSRS) claim that it is necessary to consider the utility of the items from the perspective of other stakeholders in order to balance the needs of multiple stakeholders. Take book recommendations for example, the utility of items from the view of parents, instructors and even publishers may be also important in addition to the student preferences. In this paper, we propose and exploit utility-based MSRS for personalized learning. Particularly, we attempt to address the challenge of over-/under-expectations in the utility-based MSRS. Our experimental results based on an educational data demonstrate the effectiveness of our proposed models and solutions.
Reinforcement Learning Based Policies for Elastic Stream Processing on Heterogeneous Resources

Data Stream Processing (DSP) has emerged as a key enabler to develop pervasive services that require to process data in a near real-time fashion. DSP applications keep up with the high volume of produced data by scaling their execution on multiple computing nodes, so as to process the incoming data flow in parallel. Workloads variability requires to elastically adapt the application parallelism at run-time in order to avoid over-provisioning. Elasticity policies for DSP have been widely investigated, but mostly under the simplifying assumption of homogeneous infrastructures. The resulting solutions do not capture the richness and inherent complexity of modern infrastructures, where heterogeneous computing resources are available on-demand. In this paper, we formulate the problem of controlling elasticity on heterogeneous resources as a Markov Decision Process (MDP). The resulting MDP is not easily solved by traditional techniques due to state space explosion, and thus we show how linear Function Approximation and Tile Coding can be used to efficiently compute elasticity policies at run-time. In order to deal with parameters uncertainty, we integrate the proposed approach with Reinforcement Learning algorithms. Our numerical evaluation shows the efficacy of the presented solutions compared to standard methods in terms of accuracy and convergence speed.
Learning Space Partitions for Nearest Neighbor Search

Space partitions of $\mathbb{R}^d$ underlie a vast and important class of fast nearest neighbor search (NNS) algorithms. Inspired by recent theoretical work on NNS for general metric spaces (Andoni et al. 2018b,c), we develop a new framework for building space partitions reducing the problem to balanced graph partitioning followed by supervised classification. We instantiate this general approach with the KaHIP graph partitioner (Sanders and Schulz 2013) and neural networks, respectively, to obtain a new partitioning procedure called Neural Locality-Sensitive Hashing (Neural LSH). On several standard benchmarks for NNS (Aumuller et al. 2017), our experiments show that the partitions obtained by Neural LSH consistently outperform partitions found by quantization-based and tree-based methods as well as classic, data-oblivious LSH.
BEN'FIT

Physical inactivity has been recognized as one of the leading causes of non-communicable diseases and mortality globally. Though persuasive technology has been identified as a potential tool for tackling physical inactivity and sedentary behaviors, very little attention has been paid to investigating the effectiveness of culture-tailored interventions in the wild. To bridge this gap, we designed and implemented two versions of a fitness app we called BEN'FIT [personal version (PV) and social version (SV)] targeted at encouraging regular bodyweight exercise behavior on the home front. The PV and SV versions are targeted at users from individualist and collectivist cultures, respectively. In this paper, we describe the empirical findings that informed the design and implementation of both versions of the BEN'FIT app, their features and how we intend to evaluate them in a pilot field study among our target audience once we complete the implementation of the app.
How (Not) to Use Welch’s T-Test in Side-Channel Security Evaluations

The Test Vector Leakage Assessment (TVLA) methodology is a qualitative tool relying on Welch’s T-test to assess the security of cryptographic implementations against side-channel attacks. Despite known limitations (e.g., risks of false negatives and positives), it is sometimes considered as a pass-fail test to determine whether such implementations are “safe” or not (without clear definition of what is “safe”). In this note, we clarify the limited quantitative meaning of this test when used as a standalone tool. For this purpose, we first show that the straightforward application of this approach to assess the security of a masked implementation is not sufficient. More precisely, we show that even in a simple (more precisely, univariate) case study that seems best suited for the TVLA methodology, detection (or lack thereof) with Welch’s T-test can be totally disconnected from the actual security level of an implementation. For this purpose, we put forward the case of a realistic masking scheme that looks very safe from the TVLA point-of-view and is nevertheless easy to break. We then discuss this result in more general terms and argue that this limitation is shared by all “moment-based” security evaluations. We conclude the note positively, by describing how to use moment-based analyses as a useful ingredient of side-channel security evaluations, to determine a “security order”.
Local optimality of self-organising neuro-fuzzy inference systems

Optimality of the premise, IF part is critical to a zero-order evolving intelligent system (EIS) because this part determines the validity of the learning results and overall system performance. Nonetheless, a systematic analysis of optimality has not been done yet in the state-of-the-art works. In this paper, we use the recently introduced self-organising neuro-fuzzy inference system (SONFIS) as an example of typical zero-order EISs and analyse the local optimality of its solutions. The optimality problem is firstly formulated in a mathematical form, and detailed optimality analysis is conducted. The conclusion is that SONFIS does not generate a locally optimal solution in its original form. Then, an optimisation method is proposed for SONFIS, which helps the system to attain local optimality in a few iterations using historical data. Numerical examples presented in this paper demonstrate the validity of the optimality analysis and the effectiveness of the proposed optimisation method. In addition, it is further verified numerically that the proposed concept and general principles can be applied to other types of zero-order EISs with similar operating mechanisms.
CTRAS: Crowdsourced Test Report Aggregation and Summarization

Crowdsourced testing has been widely adopted to improve the quality of various software products. Crowdsourced workers typically perform testing tasks and report their experiences through test reports. While the crowdsourced test reports provide feedbacks from real usage scenarios, inspecting such a large number of reports becomes a time-consuming yet inevitable task. To improve the efficiency of this task, existing widely used issue-tracking systems, such as JIRA, Bugzilla, and Mantis, have provided keyword-search-based methods to assist users in identifying duplicate test reports. However, on mobile devices (such as mobile phones), where the crowdsourced test reports often contain insufficient text descriptions but instead rich screenshots, these text-analysis-based methods become less effective because the data has fundamentally changed. In this paper, instead of focusing on only detecting duplicates based on textual descriptions, we present CTRAS: a novel approach to leveraging duplicates to enrich the content of bug descriptions and improve the efficiency of inspecting these reports. CTRAS is capable of automatically aggregating duplicates based on both textual information and screenshots, and further summarizes the duplicate test reports into a comprehensive and comprehensible report. To validate CTRAS, we conducted quantitative studies using more than 5000 test reports, collected from 12 industrial crowdsourced projects. The experimental results reveal that CTRAS can reach an accuracy of 0.87, on average, regarding automatically detecting duplicate reports, and it outperforms the classic Max-Coverage-based and MMR summarization methods under Jensen Shannon divergence metric. Moreover, we conducted a task-based user study with 30 participants, whose result indicates that CTRAS can save nearly 30% time cost on average without loss of correctness.
Oil-source correlation in the slope of the Qikou Depression in the Bohai Bay Basin with discriminant analysis

The commonly-used tools for oil-source correlation, such as mass chromatograms of biomarkers and bivariate cross-plots of geochemical parameters, cannot deal with multiple geochemical parameters and plenty of samples simultaneously, leading to uncertainties in the results and even failures sometimes. In this paper, the discriminant analysis (DA) is selected from supervised machine learning algorithms, as it is superior to the commonly-used tools as well as other multivariate statistical methods, with the accumulation of geochemical data of source rocks and oils. In the slope of the Qikou Depression, the main source rocks in the third (Es3) and first members (Es1) of the Paleogene Shahejie Formation were deposited in similar depositional environments. The source rocks cannot be distinguished with the commonly-used tools. We firstly extended geochemical parameters and then used stepwise DA to select informative parameters and to develop a discriminant model for oil-source correlation. The 22 selected parameters are supported by geochemical characteristics of the source rocks in the study area. The DA of these parameters for oil-source correlation achieved a high correct rate of original validation (96.8%) and leave-one-out cross-validation (89.4%), indicating a sufficient discriminatory power. The oil-source correlation results with high posterior probabilities, showing strong similarity between the sources and oils, coincide with geological conditions and illustrate that there is still much exploration potential in the study area, especially for the Es3 petroleum system. All these illustrate that DA is one of the most useful tools for oil-source correlation with the accumulation of geochemical data of source rocks and oils.
Customizable text generation via conditional text generative adversarial network

Automatically generating meaningful and coherent text has many applications, such as machine translation, dialogue systems, BOT application, etc. Text generation technology has attracted more attention over the past decades. A bunch of excellent methods are proposed; however, there are still challenges to generate text rivals the real one by human, such as most machines output fixed length text, or can only generate text quite the same with the input training text. In this paper, we put forward a novel text generation system, called customizable conditional text generative adversarial network, which is capable of generating diverse text content of variable length with customizable emotion label. It is more convenient for generating actual original text with specific sensitive orientation. We propose a conditional text generative adversarial network (CTGAN), in which emotion label is adopted as an input channel to specify the output text, and variable length text generation strategy is put forward. After generating initial texts by CTGAN, to make the generated text data match the real scene, we design an automated word-level replacement strategy, which extracts the keywords (e.g. nouns) from the training texts and replaces the specific keywords in the generated texts. Finally, we design a comprehensive evaluation metric based on various text evaluations, called mixed evaluation metric. Comprehensive experiments on real-world datasets testify that our proposed CTGAN behaves better than other text generation methods, i.e., generated text are more real compared with the real text than other generation methods, achieving state-of-the-art generation performance.
A comparative evaluation of order-revealing encryption schemes and secure range-query protocols

Database query evaluation over encrypted data can allow database users to maintain the privacy of their data while outsourcing data processing. Order-Preserving Encryption (OPE) and Order-Revealing Encryption (ORE) were designed to enable efficient query execution, but provide only partial privacy. More private protocols, based on Searchable Symmetric Encryption (SSE), Oblivious RAM (ORAM) or custom encrypted data structures, have also been designed. In this paper, we develop a framework to provide the first comprehensive comparison among a number of range query protocols that ensure varying levels of privacy of user data. We evaluate five ORE-based and five generic range query protocols. We analyze and compare them both theoretically and experimentally and measure their performance over database indexing and query evaluation. We report not only execution time but also I/O performance, communication amount, and usage of cryptographic primitive operations. Our comparison reveals some interesting insights concerning the relative security and performance of these approaches in database settings.
A Prototype Earthquake Early Warning System for Northern India

Part of North-Western Himalaya particularly Uttarakhand has been identified by several eminent seismologists as gap area where large size earthquake (greater than magnitude 7) is expected. The regi...