Reviewerly/exHarmony huggingface dataset:
Global and Adaptive Contrast Enhancement for Low Illumination Gray Images

Aiming at solving the problems of overall darkness, uneven illumination and low contrast of image under low illumination conditions, we present a global and adaptive contrast enhancement algorithm for low illumination gray images in this paper. The proposed algorithm is based on the Bilateral Gamma Adjustment function and combined with the particle swarm optimization (PSO). For the PSO, the gray standard variance is integrated into the evaluation function. To reconcile the dilemma of promoting the gray values of dark areas and suppressing the gray values of local bright areas at the same time, the information of entropy, edge content, and gray standard variance are used as the objective function for each particle to evaluate the gray image enhancement results. Then, the algorithm globally enhances the quality of the image by determining the optimal $\alpha $ value. Meanwhile, the learning factors of the PSO are updated during the iteration of optimization in the proposed algorithm. Compared with histogram equalization (HE), double plateau histogram equalization (DPHE), contrast limited adaptive histogram equalization (CLAHE), linear contrast stretching (LCS), adaptive gamma correction weighting distribution (AGCWD), the traditional PSO and MSF-PSO algorithm, the proposed algorithm significantly enhances the visual effect of the low illumination gray images. The experimental results demonstrate the superior capabilities of the proposed algorithm in enhancing the contrast of the image, such as improving the overall visual effect of the low illumination gray image and avoiding over-enhancement in the local area (s).
UA-CRNN: Uncertainty-Aware Convolutional Recurrent Neural Network for Mortality Risk Prediction

Accurate prediction of mortality risk is important for evaluating early treatments, detecting high-risk patients and improving healthcare outcomes. Predicting mortality risk from the irregular clinical time series data is challenging due to the varying time intervals in the consecutive records. Existing methods usually solve this issue by generating regular time series data from the original irregular data without considering the uncertainty in the generated data, caused by varying time intervals. In this paper, we propose a novel Uncertainty-Aware Convolutional Recurrent Neural Network (UA-CRNN), which incorporates the uncertainty information in the generated data to improve the mortality risk prediction performance. To handle the complex clinical time series data with sub-series of different frequencies, we propose to incorporate the uncertainty information into the sub-series level rather than the whole time series data. Specifically, we design a novel hierarchical uncertainty-aware decomposition layer (UADL) to adaptively decompose time series into different sub-series and assign them proper weights according to their reliabilities. Experimental results on two real-world clinical datasets demonstrate that the proposed UA-CRNN method significantly outperforms state-of-the-art methods in both short-term and long-term mortality risk predictions.
The Good, the Bad, and the Ethical Implications of Bridging Blockchain and Multi-Agent Systems

The agent based approach is a well established methodology to model distributed intelligent systems. Multi-Agent Systems (MAS) are increasingly employed in applications dealing with safety and information critical tasks (e.g., in eHealth, financial, and energy domains). Therefore, transparency and the trustworthiness of the agents and their behaviors must be enforced. For example, employing reputation based mechanisms can promote the development of trust. Nevertheless, besides recent early stage studies, the existing methods and systems are still unable to guarantee the desired accountability and transparency adequately. In line with the recent trends, we advocate that combining blockchain technology (BCT) and MAS can achieve the distribution of the trust, removing the need for trusted third parties (TTP), potential single points of failure. This paper elaborates on the notions of trust, BCT, MAS, and their integration. Furthermore, to attain a trusted environment, this manuscript details the design and implementation of a system reconciling MAS (based on the Java Agent DEvelopment Framework (JADE)) and BTC (based on Hyperledger Fabric). In particular, the agents’ interactions, computation, tracking the reputation, and possible policies for disagreement-management are implemented via smart contracts and stored on an immutable distributed ledger. The results obtained by the presented system and similar solutions are also discussed. Finally, ethical implications (i.e., opportunities and challenges) are elaborated before concluding the paper.
Anti-patterns for multi-language systems

Multi-language systems are common nowadays because most of the systems are developed using components written in different programming languages. These systems could arise from three different reasons: (1) to leverage the strengths and take benefits of each language, (2) to reduce the cost by reusing code written in other languages, (3) to include and accommodate legacy code. However, they also introduce additional challenges, including the increase in the complexity and the need for proper interfaces and interactions between the different languages. To address these challenges, the software-engineering research community, as well as the industry, should describe and provide common guidelines, idioms, and patterns to support the development, maintenance, and evolution of these systems. These patterns are an effective means of improving the quality of multi-language systems. They capture good practices to adopt and bad practices to avoid. In order to help to improve the quality of multi-language systems, we analysed open-source systems, developers' documentation, bug reports, and programming language specifications to extract bad practices of multi-language systems usage. We encoded and cataloged these practices in the form of design anti-patterns. We report here six anti-patterns. These results could help not only researchers but also professional developers considering the use of more than one programming language.
Dynamic Multi-Swarm Particle Swarm Optimization Based on Elite Learning

This paper presents a dynamic multi-swarm particle swarm optimization based on an elite learning strategy (DMS-PSO-EL). In DMS-PSO-EL, the whole evolutionary process is divided into a former stage and a later stage. The former and later stages are focus on the exploration and the exploitation, respectively. In the former stage, the entire population is divided into multiple dynamic sub-swarms and a following sub-swarm according to the particles' fitness values. In each generation, the dynamic sub-swarms evolve independently, which is beneficial for keeping population diversity, while particles in the following sub-swarm choose elites in the dynamic sub-swarms as their learning exemplars aiming to find out more promising solutions. To take full advantages of the different sub-swarms and then speed up the convergence, a randomly dynamic regrouping schedule is conducted on the entire population in each regrouping period. In the latter stage, all the particles select the historical best solution of the entire population as an exemplar aiming to enhance the exploitation ability. The comparison results among DMS-PSO-EL and other 9 well-known algorithms on CEC2013 and CEC2017 test suites suggest that DMS-PSO-EL demonstrates superior performance for solving different types of functions. Furthermore, the sensitivity and performance of the proposed strategies in DMS-PSO-EL are also testified by a set of experiments.
2D–3D Digital Image Correlation Comparative Analysis for Indentation Process

Nowadays, localized forming operations, such as incremental forming processes, are being developed as an alternative to conventional machining or forming techniques. An indentation process is the main action that takes places in these forming activities, allowing small, localized deformations. It is essential to have the knowledge of the material behavior under the punch and the transmitted forces to achieve correct control of the entire procedure. This paper presents the work carried out with the digital image correlation (DIC) technique applied to the study of the material flow that takes place under an indentation process. The material flow analysis is performed under 2D and 3D conditions, establishing the methodology for the calibration and implementation for each alternative. Two-dimensional DIC has been proven to be a satisfactory technique compared with the 3D method, showing results in good agreement with experimental tests and models developed by the finite element method. Notwithstanding, part of the indented material flows under the punch, emerging on the front surface and generating a dead zone that can only be addressed with a 3D technique. So, the main objective is to carry out a comparison between the 2D and 3D techniques to identify if the 3D application could be mandatory for this type of process. Also, a 2D–3D mix analysis is proposed for study cases in which it is necessary to know the material flow in that specific area of the workpiece.
The Relationship among University Students’ Trust, Self-Esteem, Satisfaction with Life and Social Media Use

The study is aimed at explaining the relationships among trust, self-esteem and satisfaction with life of university student with their social media use and some demographic variables.The demographic variables are; gender, socioeconomic status, grade point average (GPA) of students and their stated worldviews.The study employed a quantitative research approach.The subjects of the study were students of several social science departments in 12 different Turkish state universities located in several parts of Turkey.The total number of the students involved in the study was 2253.The data was collected by a categorical variable form and 3 other scales developed for the research project.The scales were Likert type scales.Their reliability and dimensions are explained in detail in method section of the study.The data were analyzed through one-way-ANOVA.The main findings are; the frequency of social media use creates a difference in trust levels of university students.It is found out that a higher average of the grades means higher self-esteem among students.There is significant difference between genders in students' satisfaction with life.
Ladder Loss for Coherent Visual-Semantic Embedding

For visual-semantic embedding, the existing methods normally treat the relevance between queries and candidates in a bipolar way – relevant or irrelevant, and all “irrelevant” candidates are uniformly pushed away from the query by an equal margin in the embedding space, regardless of their various proximity to the query. This practice disregards relatively discriminative information and could lead to suboptimal ranking in the retrieval results and poorer user experience, especially in the long-tail query scenario where a matching candidate may not necessarily exist. In this paper, we introduce a continuous variable to model the relevance degree between queries and multiple candidates, and propose to learn a coherent embedding space, where candidates with higher relevance degrees are mapped closer to the query than those with lower relevance degrees. In particular, the new ladder loss is proposed by extending the triplet loss inequality to a more general inequality chain, which implements variable push-away margins according to respective relevance degrees. In addition, a proper Coherent Score metric is proposed to better measure the ranking results including those “irrelevant” candidates. Extensive experiments on multiple datasets validate the efficacy of our proposed method, which achieves significant improvement over existing state-of-the-art methods.
POST: POlicy-Based Switch Tracking

In visual object tracking, by reasonably fusing multiple experts, ensemble framework typically achieves superior performance compared to the individual experts. However, the necessity of parallelly running all the experts in most existing ensemble frameworks heavily limits their efficiency. In this paper, we propose POST, a POlicy-based Switch Tracker for robust and efficient visual tracking. The proposed POST tracker consists of multiple weak but complementary experts (trackers) and adaptively assigns one suitable expert for tracking in each frame. By formulating this expert switch in consecutive frames as a decision-making problem, we learn an agent via reinforcement learning to directly decide which expert to handle the current frame without running others. In this way, the proposed POST tracker maintains the performance merit of multiple diverse models while favorably ensuring the tracking efficiency. Extensive ablation studies and experimental comparisons against state-of-the-art trackers on 5 prevalent benchmarks verify the effectiveness of the proposed method.
Attending to Entities for Better Text Understanding

Recent progress in NLP witnessed the development of large-scale pre-trained language models (GPT, BERT, XLNet, etc.) based on Transformer (Vaswani et al. 2017), and in a range of end tasks, such models have achieved state-of-the-art results, approaching human performance. This clearly demonstrates the power of the stacked self-attention architecture when paired with a sufficient number of layers and a large amount of pre-training data. However, on tasks that require complex and long-distance reasoning where surface-level cues are not enough, there is still a large gap between the pre-trained models and human performance. Strubell et al. (2018) recently showed that it is possible to inject knowledge of syntactic structure into a model through supervised self-attention. We conjecture that a similar injection of semantic knowledge, in particular, coreference information, into an existing model would improve performance on such complex problems. On the LAMBADA (Paperno et al. 2016) task, we show that a model trained from scratch with coreference as auxiliary supervision for self-attention outperforms the largest GPT-2 model, setting the new state-of-the-art, while only containing a tiny fraction of parameters compared to GPT-2. We also conduct a thorough analysis of different variants of model architectures and supervision configurations, suggesting future directions on applying similar techniques to other problems.
Distributed Stochastic Gradient Descent with Event-Triggered Communication

We develop a Distributed Event-Triggered Stochastic GRAdient Descent (DETSGRAD) algorithm for solving non-convex optimization problems typically encountered in distributed deep learning. We propose a novel communication triggering mechanism that would allow the networked agents to update their model parameters aperiodically and provide sufficient conditions on the algorithm step-sizes that guarantee the asymptotic mean-square convergence. The algorithm is applied to a distributed supervised-learning problem, in which a set of networked agents collaboratively train their individual neural networks to perform image classification, while aperiodically sharing the model parameters with their one-hop neighbors. Results indicate that all agents report similar performance that is also comparable to the performance of a centrally trained neural network, while the event-triggered communication provides significant reduction in inter-agent communication. Results also show that the proposed algorithm allows the individual agents to classify the images even though the training data corresponding to all the classes are not locally available to each agent.
Mobile learning policy and practice in Africa: Towards inclusive and equitable access to higher education

This article presents the results of a review of practice and policy in relation to mobile learning and its potential to enhance inclusive and equitable access to higher education in Africa. We reviewed academic literature on potential barriers. Then, we explored the current state of the mobile learning policy environment in 10 African countries through an analysis of how these policies have tried to address the prominent challenges in the adoption of mobile learning as identified in the literature. The findings reveal that significant resourcing inequalities and epistemological, sociocultural, and institutional barriers remain and affect mobile learning adoption. The analysis also reveals that there is still a policy vacuum in relation to mobile learning specific policies within African higher education institutional and governmental policies. Thus, the formal integration of mobile learning in higher education to facilitate equitable access is very much in its infancy. This article suggests a strong need for institutional, cross-institutional, national and African-wide mobile learning specific policies to ensure better implementation of mobile learning. As interest in mobile learning continues to grow, this review will provide insights into policy and strategic planning for the adoption of mobile learning to achieve inclusive and equitable access to higher education.
Generally Speaking, Context Matters: Making the Case for a Change from Universal to Particular ISP Research

The objective of our paper is to conceptually and empirically challenge the idea of general information security policy (ISP) compliance. Conceptually, we argue that general ISP compliance is an ill-defined concept that has minimal theoretical usefulness because the policy-directed security actions vary considerably from threat to threat in terms of time, difficulty, diligence, knowledge, and effort. Yet, our senior IS scholars’ basket of journals has a strong preference to publish models in which the authors speculate that their findings are generalizable across all (or many) threats and controls contained in an organization’s ISP document. In our paper, we argue that compliance with each of the mandatory threat-specific security actions may require different (as opposed to similar) explanatory models, which makes constructing a universal model of ISP compliance problematic. Therefore, we argue that future ISP compliance literature will be more valuable if it focuses on the mechanisms, treatments, and behavioral antecedents associated with the required actions around specific threats instead of attempting to build a model that purportedly covers all (or many) threat-specific security actions (or intentions thereof). To support this claim empirically, we conducted two studies comparing general compliance intentions (i.e., undefined security action) and threat-specific compliance intentions. In both studies, our data show that compliance intentions vary significantly across general compliance measures and multiple threat-specific security measures or scenarios. Our results indicate that it is problematic to generalize about the behavioral antecedents from general compliance intentions to threat-specific security compliance intentions, from one threat-specific security action to other threat-specific security actions, and from one threat-specific security action to general compliance intentions.
Malware Guard Extension: abusing Intel SGX to conceal cache attacks

Abstract In modern computer systems, user processes are isolated from each other by the operating system and the hardware. Additionally, in a cloud scenario it is crucial that the hypervisor isolates tenants from other tenants that are co-located on the same physical machine. However, the hypervisor does not protect tenants against the cloud provider and thus, the supplied operating system and hardware. Intel SGX provides a mechanism that addresses this scenario. It aims at protecting user-level software from attacks from other processes, the operating system, and even physical attackers.In this paper, we demonstrate fine-grained software-based side-channel attacks from a malicious SGX enclave targeting co-located enclaves. Our attack is the first malware running on real SGX hardware, abusing SGX protection features to conceal itself. Furthermore, we demonstrate our attack both in a native environment and across multiple Docker containers. We perform a Prime+Probe cache side-channel attack on a co-located SGX enclave running an up-to-date RSA implementation that uses a constant-time multiplication primitive. The attack works, although in SGX enclaves, there are no timers, no large pages, no physical addresses, and no shared memory. In a semi-synchronous attack, we extract 96 % of an RSA private key from a single trace. We extract the full RSA private key in an automated attack from 11 traces within 5 min.
Timespan-Aware Dynamic Knowledge Graph Embedding by Incorporating Temporal Evolution

Recently, Knowledge Graph Embedding (KGE) has attracted considerable research efforts, since it simplifies the manipulation while preserving the inherent structure of the KG.However to some extent, most existing KGE approaches ignore the historical changes of structural information involved in dynamic knowledge graphs (DKGs).To deal with this problem, this paper presents a Timespan-aware Dynamic knowledge Graph Embedding Evolution (TDG2E) method that considers temporal evolving process of DKGs.The major innovations of our paper are two-fold.Firstly, a Gated Recurrent Units (GRU) based model is utilized in TDG2E to deal with the dependency among sub-KGs that is inevitably involved in the learning process of the dynamic knowledge graph embedding.Furthermore, we incorporate an auxiliary loss to supervise the learning process of the next sub-KG by utilizing previous structural information (i.e., the hidden state of GRU).In contrast with existing approaches in the literature (e.g., HyTE and t-TransE), TDG2E preserves structural information of current sub-KG and the temporal evolving process of the DKG simultaneously.Secondly, to further deal with the time unbalance issue underlying the DKGs, a Timespan Gate is designed in GRU.It makes TDG2E possible to model the temporal evolving process of DKGs more effectively by incorporating the timespan between adjacent sub-KGs.Extensive experiments on two large temporal datasets (i.e., YAGO11k and Wikidata12k) extracted from real-world KGs validate that the proposed TDG2E significantly outperforms traditional KGE methods in terms of Mean Rank and Hit Rate.
Experimental demonstration of quantum-enhanced machine learning in a nitrogen-vacancy-center system

We demonstrate the quantum-enhanced supervised classification of vectors in a nitrogen-vacancy ($NV$)--center system using the algorithm of Lloyd, Mohseni, and Rebentrost (arXiv:1307.0411). A $^{13}\mathrm{C}$ nuclear spin is employed to encode the vectors with the spin-triplet state of the $NV$ center as an ancilla. We design efficient methods to prepare the initial electron-nuclear entangled state within the coherence time ${T}_{2}^{*}$ of the electron spin, and then compute the distance between the test vector and the center of each class by measuring the level population through maximum likelihood estimation. Our experiment allows more than one reference vector in each class, thus forming an important enabling step toward quantum-enhanced machine learning.