Reviewerly/exHarmony huggingface dataset:
Matrix inference and estimation in multi-layer models*

Abstract We consider the problem of estimating the input and hidden variables of a stochastic multi-layer neural network (NN) from an observation of the output. The hidden variables in each layer are represented as matrices with statistical interactions along both rows as well as columns. This problem applies to matrix imputation, signal recovery via deep generative prior models, multi-task and mixed regression, and learning certain classes of two-layer NNs. We extend a recently-developed algorithm—multi-layer vector approximate message passing, for this matrix-valued inference problem. It is shown that the performance of the proposed multi-layer matrix vector approximate message passing algorithm can be exactly predicted in a certain random large-system limit, where the dimensions N × d of the unknown quantities grow as N → ∞ with d fixed. In the two-layer neural-network learning problem, this scaling corresponds to the case where the number of input features as well as training samples grow to infinity but the number of hidden nodes stays fixed. The analysis enables a precise prediction of the parameter and test error of the learning.
Penggerombolan Mutu SMA/MA per Provinsi Berdasarkan Hasil Akreditasi Menggunakan Metode Fuzzy C-Means

Mapping the quality of education in Indonesia needs to be studied so that the provincial government, as the institution responsible for secondary education management policies, can more easily determine priorities and what actions will be taken to improve the quality of education in Indonesia. One of the analytical methods that can be used to map the quality of education is fuzzy c-means. This research aims to classify the quality maps of provinces in Indonesia based on the results of SHS/MA accreditation using the fuzzy c-means method. The fuzzy c-means method can show the probability of objects entering a cluster with a degree of membership. The optimum cluster sizes obtained were 2 and 3. The final solution with cluster size 2 was 12 provinces categorized in cluster 1 and 22 provinces categorized in cluster 2. Clustering with cluster size 3 resulted in cluster 1 consisting of 11 provinces, cluster 2 consisting of 16 provinces, and cluster 3, which consists of 7 provinces. The main character of cluster 1 is a high national education standard score, while the main character of cluster 2 is a low national education standard score. Then the main character of group 3 is the national standard score, whose value is around the national average.
Randomized Signal Processing with Continuous Frames

Abstract This paper focuses on signal processing tasks in which the signal is transformed from the signal space to a higher dimensional coefficient space (also called phase space) using a continuous frame, processed in the coefficient space, and synthesized to an output signal. We show how to approximate such methods, termed phase space signal processing methods, using a Monte Carlo method. As opposed to standard discretizations of continuous frames, based on sampling discrete frames from the continuous system, the proposed Monte Carlo method is directly a quadrature approximation of the continuous frame. We show that the Monte Carlo method allows working with highly redundant continuous frames, since the number of samples required for a certain accuracy is proportional to the dimension of the signal space, and not to the dimension of the phase space. Moreover, even though the continuous frame is highly redundant, the Monte Carlo samples are spread uniformly, and hence represent the coefficient space more faithfully than standard frame discretizations.
Modeling Health Stage Development of Patients with Dynamic Attributed Graphs in Online Health Communities

In this paper, we propose a novel DynAttGraph2Seq framework to model complex dynamic transitions of an individual user's activities and the textual information of the posts over time in online health forums and learning how these correspond to his/her health stage. To achieve this, we first formulate the transition of user activities as a dynamic attributed graph with multi-attributed nodes that evolves over time, then formalize the health stage inference task as a dynamic attributed graph to sequence learning problem. Our proposed model consists of a novel dynamic graph encoder along with a two-level sequential encoder to capture the semantic features from user posts and an interpretable sequence decoder that learn the mapping between a sequence of time-evolving user activity graphs as well as user posts to a sequence of target health stages. We go on to propose new dynamic graph regularization and dynamic graph hierarchical attention mechanisms to facilitate the necessary multi-level interpretability. A comprehensive experimental analysis of its use for a health stage prediction task demonstrates both the effectiveness and the interpretability of the proposed models.
Smart-Thing Design by Children at a Distance: How to Engage Them and Make Them Learn

In recent years, research in child-computer interaction shifted the focus from design with children for giving them a voice in the design process, to design by children so as to bring child participants different benefits, such as engagement and learning. Design workshops, encompassing different stages, are however challenging in terms of engagement and learning, e.g., they take a prolonged commitment and concentration capabilities. They are potentially more challenging when held at a distance, as it was the case in recent years due to the COVID-19 pandemic. This paper explores at-a-distance smart-thing design with children, and how it can engage different children and support their learning in programming.The paper reports on a series of design workshops with 20 children, aged from 8 to 16 years old, all held at a distance. They were uniformly framed with the [anonymous name] design framework and toolkit. The first workshop enabled children to explore what smart things are, start ideating their own and scaffold their programming. The other workshops enabled children to evolve their own smart-thing ideas and programs. Data were gathered in relation to children's engagement and learning, from different sources. Results are promising for future editions of smart-thing design at a distance or in hybrid modality. They are discussed in the conclusions of the paper, along with guidelines and difficulties faced with design by children at a distance.
Oriented Splits Network to Distill Background for Vehicle Re-Identification

Vehicle re-identification (re-id) is a challenging task due to the presence of high intra-class and low inter-class variations in the visual data acquired from monitoring camera networks. Unique and discriminative feature representations are needed to overcome the existence of several variations including color, illumination, orientation, background and occlusion. The orientations of the vehicles in the images make the learned models unable to learn multiple parts of the vehicle and relationship between them. The combination of global and partial features is one of the solutions to improve the discriminative learning of deep learning models. Leveraging on such solutions, we propose an Oriented Splits Network (OSN) for an end to end learning of multiple features along with global features to form a strong descriptor for vehicle re-identification. To capture the orientation variability of the vehicles, the proposed network introduces a partition of the images into several oriented stripes to obtain local descriptors for each part/region. Such a scheme is therefore exploited by a camera based feature distillation (CBD) training strategy to remove the background features. These are filtered out from oriented vehicles representations which yield to a much stronger unique representation of the vehicles. We perform experiments on two benchmark vehicle re-id datasets to verify the performance of the proposed approach which show that the proposed solution achieves better result with respect to the state of the art with margin.
ReFlat: A Robust Access Pattern Hiding Solution for General Cloud Query Processing Based on K-Isomorphism and Hardware Enclave

The access frequency pattern leakage reveals sensitive information over encrypted cloud data, such as query inclinations and interests. Even worse, adversaries can infer the content of storage with the help of auxiliary knowledge. It jeopardizes the mutual trust between the client users and the cloud platform as reported in many cases. In this paper, we study the threats model in which adversaries know both the exact in-memory flow of accessed blocks and the processing boundary of each request. Under these settings, he can precisely observe the access frequency patterns in both aggregated and independent perspectives over queries. We then propose the ReFlat module as a counter solution through the <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -duplication obfuscation mechanism. ReFlat securely runs inside the hardware enclave provided by Intel SGX and requires no modifications on query processors. The <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> -duplication mechanism is further optimized with two working functions to practically deal with point and range queries. Comparing with the state-of-the-art schemes using the similar idea, that is, fake query injection, ReFlat eliminates the security risk of involving intermediate proxy and achieves higher robustness under the proposed threat model. We exhibit comparative experiment results showing that ReFlat exceeds existing schemes providing equal security level in multiple system performance metrics.
Extending the Design of Smart Mobile Application to Detect Fraud Theft of E-Banking Access Using Big Data Analytic and SOA

For the current situation in which digital banking services are becoming more popular, fraud detection applications are required. Banks are among the other businesses with confidential data assets that must be safeguarded because they have financial information. E-banking channel (mobile banking, internet banking, ATM) is required in the digital era for managing financial activities such as international remittance, transfer fund, and other banking activities. Banking industry facing how the fraudster try to breach the mobile banking services, it will be the biggest challenge for banks in order to maintain the confidence, integrity, and availability of their IT security systems. Potential attacks on confidential information may be carried out by the employee as well as by external parties. Banks attempt to engineer a software to prevent a higher loss due to fraud action in order to maintain the balance of security and convenience of customers when using a banking services. One of the most common attacks is social engineering and data intervention by internal employees; the focus of this research is to discuss the two potential attacks on accounts that have been dormant for a long time and deal with them using an application based on the SOA and Big data approach.
A Three-Phase Framework for Global Path Planning for Nonholonomic Autonomous Vehicles on 3D Terrains

This paper presents the development and evaluation of a novel three-phase global path planning framework that combines and modifies D* Lite, Rapidly Exploring Random Tree Star (RRT*), and local path optimization for nonholonomic autonomous off-road navigation on 3D terrains. This hierarchical algorithm inherits the advantage of D* Lite + RRT* algorithm proposed by Brunner in terms of sampling efficiency and incorporates the local path optimization used by Krüsi to further improve the path’s quality. To demonstrate the necessity for all three phases, a High Mobility Multipurpose Wheeled Vehicle (HMMWV) is simulated on 3D terrain and under various obstacle configurations, and the tracking performance of the proposed three-phase algorithm is compared with that of RRT* and D* Lite + RRT* as one-phase and two-phase benchmarks. The results show that the new framework offers a better path quality and higher success rate. Although the improvement comes at the expense of longer computation time, real-time performance is still ensured in the implementation.
Optimal Subgraph Matching Queries over Distributed Knowledge Graphs Based on Partial Evaluation

The partial evaluation and assembly framework has recently been applied for processing subgraph matching queries over large-scale knowledge graphs in the distributed environment. The framework is implemented on the master-slave architecture, endowed with outstanding scalability. However, there exists a drawback of partial evaluation, that if the volume of intermediate results is large, assembly computation which is handled by the master would be a bottleneck. In this paper, we propose an efficient assembly algorithm by exploring the characteristics of intermediate results generated during the partial matching stage in each fragment. (1) The partial matching index (PM-Index) structure are constructed by utilizing the incoming and outgoing vertices in parallel to improve the searching efficiency of the assembling phase. (2) The time and space complexity of PM-Index construction are analysed. (3) The experimental results over benchmark datasets show that our approach outperforms the state-of-the-art methods.
Neural Architecture Search based on Genetic Algorithm and Deployed in a Bare-Metal Kubernetes Cluster

The interest in Deep Neural Networks has dramatically increased, especially e. g. in Computer Vision or Neural Language Processing tasks. Due to the heavy influence of the Neural Networks architecture on its predictive accuracy, Neural Architecture Search has gained much attention in recent years. Neural Architecture Search typically comes along with a high computational demand and thus, requires scalability as well as high availability to ensure no data loss or waste of computational power. Hence, we developed a scalable and highly available multi-objective Neural Architecture Search and adopted it to the modern thinking of developing applications by subdividing an already existing, monolithic approach – based on a Genetic Algorithm – into microservices. Moreover, we adjusted the initial population creation by mutating each individual 1,000 times, extended the approach by inception layers, implemented it as island model and achieved on MNIST, Fashion-MNIST and CIFAR-10 dataset 99.75%, 94.35% and 89.90% test accuracy, respectively. Furthermore, we analyzed nine different configurations of the Genetic Algorithm – with only one subpopulation – to identify well performing settings. Besides, our model is strongly focused on high availability empowered by the deployment in our bare-metal Kubernetes cluster. Our results show that the introduced Neural Architecture Search can easily handle and recover – without the necessity of human interaction – from the exceptional loss of Kubernetes pods within seconds and no loss of results or the algorithms state.
Surrogate Supervision-based Deep Weakly-supervised Anomaly Detection

Many anomaly detection applications can provide partially observed anomalies, but only limited work is for this setting. Additionally, a number of anomaly detectors focus on learning a particular model of normal/abnormal class. However, the intra-class model might be too complicated to be accurately learned. It is still a non-trivial task to handle data with anomalies/inliers in skewed and heterogeneous distributions. To address these problems, this paper proposes an anomaly detection method to leverage Partially Labeled anomalies via Surrogate supervision-based Deviation learning (denominated PLSD). The original supervision (i.e., known anomalies and a set of explored inliers) is transferred to semantic-rich surrogate supervision signals (i.e., anomaly-inlier and inlier-inlier class) via vector concatenation. Then different relationships and interactions between anomalies and inliers are directly and efficiently learned thanks to the neural network’s connection property. Anomaly scoring is processed via the trained network and the high-efficacy inliers. Extensive experiments show that PLSD significantly prevails state-of-the-art semi/weakly-supervised anomaly detectors.
A Case Study on Combining Agile and User-Centered Design

Agile is a dominating software developing technique throughout the world. It promotes high development speed, work in short iterations, and delivering working software as soon as possible.User-Centered Design is a more traditional approach that puts users and their needs first. It advocates spending more time understanding your users, their needs and wants, while the implementation process can be delayed and last longer. Both approaches try to deliver the best possible software for the users but utilize different means. This paper examines through a case study how Agile and User-Centered Design can be combined and whether this combination brings additional value. A development method is then suggested, combining the two philosophies.
EvoBench: Benchmarking Schema Evolution in NoSQL

Since NoSQL database schema evolution is an important cost and time factor in the development of software applications, a standardized benchmark is essential for the comparison and evaluation of different schema evolution management systems. This benchmark should be simple to be set up, its design and usage ergonomic, its results straightforward for interpretation and reproduction – even after decades. Therefore, we present the implementation of a benchmark using Docker containers. By using containers with databases that already contain the test data, containers with the schema evolution system to be measured and the possibility to use the benchmark system itself in a container, it is very convenient to get the benchmark up and running. We also provide a data generator that creates individual data sets and/or reproduces real data once the schema is known. We demonstrate the flexibility and easy application of our approach by means of several experiments and discuss their results.
Local and Global Feature Aggregation-Aware Network for Salient Object Detection

With the rise of deep learning technology, salient object detection algorithms based on convolutional neural networks (CNNs) are gradually replacing traditional methods. The majority of existing studies, however, focused on the integration of multi-scale features, thereby ignoring the characteristics of other significant features. To address this problem, we fully utilized the features to alleviate redundancy. In this paper, a novel CNN named local and global feature aggregation-aware network (LGFAN) has been proposed. It is a combination of the visual geometry group backbone for feature extraction, an attention module for high-quality feature filtering, and an aggregation module with a mechanism for rich salient features to ease the dilution process on the top-down pathway. Experimental results on five public datasets demonstrated that the proposed method improves computational efficiency while maintaining favorable performance.
The Accuracy vs. Sampling Overhead Trade-off in Quantum Error Mitigation Using Monte Carlo-Based Channel Inversion

Quantum error mitigation (QEM) is a class of promising techniques for reducing the computational error of variational quantum algorithms. In general, the computational error reduction comes at the cost of a sampling overhead due to the variance-boosting effect caused by the channel inversion operation, which ultimately limits the applicability of QEM. Existing sampling overhead analysis of QEM typically assumes exact channel inversion, which is unrealistic in practical scenarios. In this treatise, we consider a practical channel inversion strategy based on Monte Carlo sampling, which introduces additional computational error that in turn may be eliminated at the cost of an extra sampling overhead. In particular, we show that when the computational error is small compared to the dynamic range of the error-free results, it scales with the square root of the number of gates. By contrast, the error exhibits a linear scaling with the number of gates in the absence of QEM under the same assumptions. Hence, the error scaling of QEM remains to be preferable even without the extra sampling overhead. Our analytical results are accompanied by numerical examples.
Extend, don’t rebuild: Phrasing conditional graph modification as autoregressive sequence labelling

Deriving and modifying graphs from natural language text has become a versatile basis technology for information extraction with applications in many subfields, such as semantic parsing or knowledge graph construction.A recent work used this technique for modifying scene graphs (He et al., 2020), by first encoding the original graph and then generating the modified one based on this encoding.In this work, we show that we can considerably increase performance on this problem by phrasing it as graph extension instead of graph generation.We propose the first model for the resulting graph extension problem based on autoregressive sequence labelling.On three scene graph modification data sets, this formulation leads to improvements in accuracy over the state-of-the-art between 13 and 26 percentage points.Furthermore, we introduce a novel data set from the biomedical domain which has much larger linguistic variability and more complex graphs than the scene graph modification data sets.For this data set, the state-of-the art fails to generalize, while our model can produce meaningful predictions.
Conclusions and Policy Recommendations

The private sector is the engine of economic growth and sustainable development in Africa. But most African private businesses are small, and the sector has few firms in the medium and large categories— the “missing middle” and “missing large.” Mainly due to financial constraints, small and medium enterprises (SMEs) in Africa struggle to survive and grow into large firms, with most collapsing within the first three years—the “valley of death.”
An Agriculture Supply Chain Model for Improving Farmer Income Using Blockchain Smart Contract

Farmers are the backbone of our society, and they support the whole world's population. Though, farmers are not getting adequate profit due to the intermediaries and inefficient supply chain system. Farmers will begin producing more once they stand to benefit from higher farm produce rates, which is not the case at the moment. Technology innovation in the agricultural sector is essential for the development of efficient and productive food systems. In this paper, we propose a novel system for automating and controlling the supply chain process through blockchain and smart contracts, while maintaining total trust between the parties. In particular, we design smart contracts with two parameters, Minimum Purchase Rate (MPR) and Percentage Profit Share (PPS), to monitor the cost of product and to increase the farmer’s income. The payment history is recorded in a distributed ledger and can be used to trace back to the source of the products. Furthermore, the proposed framework is constructed and tested in an Ethereum blockchain network. Finally, the analysis results show that our solution is feasible and improves the farmer's income.
Namespaces

Today, containers are the de facto cloud software provision mechanism. They provide fast spin-up times and have less overhead than a virtual machine. There are certain specific reasons behind these features.