Reviewerly/exHarmony huggingface dataset:
Private Multi-Group Aggregation

We study the differentially private multi-group aggregation (PMGA) problem. This setting involves a single server and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> users. Each user belongs to one of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$k$</tex> distinct groups and holds a discrete value representing his data. The goal is to design schemes that allow the server to find the aggregate (sum) of the values in each group (with high accuracy), under communication and local differential privacy constraints. The privacy constraint guarantees that the user's group remains private. This is motivated by applications where a user's group can reveal sensitive information, such as his religious and political beliefs, health condition, or race. We propose a novel scheme, dubbed Query and Aggregate (Q&A) for PMGA. The novelty of Q&A is that it is an interactive aggregation scheme. In Q&A, each user is assigned a random query matrix, to which he sends the server an answer based on his group and value. We characterize the Q&A scheme's performance in terms of accuracy (MSE), privacy, and communication. Private aggregation schemes for related settings in the literature are predominantly non-interactive and based on randomized response. We compare Q&A to the Randomized Group (RG) scheme, which adapts existing schemes to the PMGA setting. We observe that typically Q&A outperforms RG, in terms of utility vs. privacy, in the high privacy regime. Moreover, an attractive property of Q&A is that its communication cost per user does not depend on the number of groups.
Refining Pseudo Labels for Unsupervised Domain Adaptive Person Re-Identification

Unsupervised domain adaptive (UDA) person re-identification (re-ID) aims to generalize the model trained on a labeled source domain to an unlabeled target domain. Recently, the methods based on pseudo labels have achieved great success in the field of UDA person re-ID. However, pseudo label noise is still a problem to be solved. In this paper, we propose a novel framework to refine pseudo labels for UDA person re-ID. Firstly, in order to learn more discriminative features for better clustering, we propose a No-Reference Light Enhancement network (NRLE-Net) and a novel feature extraction model called Trans-Encoder. The NRLE-Net can reduce the inter-domain and intra-domain person image style differences caused by light condition changes simultaneously, which is helpful to improve the performance of the pre-trained model in target domain and reduce the noise of initial pseudo labels. The feature extraction model Trans-Encoder is designed based on Transformer. Compared with traditional Convolutional Neural Networks (CNN), the features extracted by Trans-Encoder are more robust to domain shift. On this base, the confidence of feature clustering can be improved. Secondly, to further reduce the noise of clustering pseudo labels, we design two noise refinement strategies. One is noise correction based on neighborhood consistency and the other is noise restraint based on label confidence. Through these two strategies, our model can be effectively optimized with the noise pseudo labels. Experimental results on Market-1501, DukeMTMC-reID and MSMT17 datasets demonstrate the effectiveness of our proposed UDA person re-ID framework.
J-score: a robust measure of clustering accuracy

Background. Clustering analysis discovers hidden structures in a data set by partitioning them into disjoint clusters. Robust accuracy measures that evaluate the goodness of clustering results are critical for algorithm development and model diagnosis. Common problems of current clustering accuracy measures include overlooking unmatched clusters, biases towards excessive clusters, unstable baselines, and difficult interpretation. In this study, we presented a novel accuracy measure, J-score, that addresses these issues. Methods. Given a data set with known class labels, J-score quantifies how well the hypothetical clusters produced by clustering analysis recover the true classes. It starts with bidirectional set matching to identify the correspondence between true classes and hypothetical clusters based on Jaccard index. It then computes two weighted sums of Jaccard indices measuring the reconciliation from classes to clusters and vice versa. The final J-score is the harmonic mean of the two weighted sums. Results. Via simulation studies, we evaluated the performance of J-score and compared with existing measures. Our results show that J-score is effective in distinguishing partition structures that differ only by unmatched clusters, rewarding correct inference of class numbers, addressing biases towards excessive clusters, and having a relatively stable baseline. The simplicity of its calculation makes the interpretation straightforward. It is a valuable tool complementary to other accuracy measures. We released an R/jScore package implementing the algorithm.
SeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery

Seam carving is a popular technique for content aware image retargeting. It can be used to deliberately manipulate images, for example, change the GPS locations of a building or displace/remove roads in a satellite image. This paper proposes a novel approach for detecting and localizing seams in such images. While there are methods to detect seam carving based manipulations, this is the first time that robust localization and detection of seam carving forgery is made possible. We also propose a seam localization score (SLS) metric to evaluate the effectiveness of localization. The proposed method is evaluated extensively on a large collection of images from different sources, demonstrating a high level of detection and localization performance across these datasets. The code and datasets curated during this work will be released to the public.
A Novel Database Watermarking Technique Using Blockchain as Trusted Third Party

With widespread use of relational database in various real-life applications, maintaining integrity and providing copyright protection is gaining keen interest of the researchers.For this purpose, watermarking has been used for quite a long time.Watermarking requires the role of trusted third party and a mechanism to extract digital signatures (watermark) to prove the ownership of the data under dispute.This is often inefficient as lots of processing is required.Moreover, certain malicious attacks, like additive attacks, can give rise to a situation when more than one parties can claim the ownership of the same data by inserting and detecting their own set of watermarks from the same data.To solve this problem, we propose to use blockchain technology-as trusted third party-along with watermarking for providing a means of rights protection of relational databases.Using blockchain for writing the copyright information alongside watermarking helps to secure the watermark as changing the blockchain is very difficult.This way, we combined the resilience of our watermarking scheme and the strength of blockchain technology-for protecting the digital rights information from alteration-to design and implement a robust scheme for digital right protection of relational databases.Moreover, we also discuss how the proposed scheme can also be used for version control.The proposed technique works with nonnumeric features of relational database and does not target only selected tuple or portion (subset) from the database for watermark embedding unlike most of the existing techniques; as a result, the chances of subset selection containing no watermark decrease automatically.The proposed technique employs zerowatermarking approach and hence no intentional error (watermark) is added to the original dataset.The results of the experiments proved the effectiveness of the proposed scheme.
BERT-Based Multi-Task Learning for Aspect-Based Opinion Mining

Aspect-Based Opinion Mining (ABOM) mainly focuses on mining the aspect terms (product's features) and related opinion polarities (e.g., Positive, Negative, and Neutral) from user's reviews. The most prominent neural network-based methods to perform ABOM tasks include BERT-based approaches, such as BERT-PT and BAT. These approaches build separate models to complete each ABOM subtasks, such as aspect term extraction (e.g., pizza, staff member) and aspect sentiment classification. Both approaches use different training algorithms, such as Post-Training and Adversarial Training. Also, the BERT-LSTM/Attention approach uses different pooling strategies on the intermediate layers of the BERT model to achieve better results. Moreover, they do not consider the subtasks of aspect categories (e.g., a category of aspect pizza in a review is food) and related opinion polarity. This paper proposes a new system for ABOM, called BERT-MTL, which uses Multi-Task Learning (MTL) approach and differentiates from these previous approaches by solving two tasks such as aspect terms and categories extraction simultaneously by taking advantage of similarities between tasks and enhancing the model's accuracy as well as reduce the training time. Our proposed system also builds models to identify user's opinions for aspect terms and aspect categories by applying different pooling strategies on the last layer of the BERT model. To evaluate our model's performance, we have used the SemEval-14 task 4 restaurant dataset. Our model outperforms previous models in several ABOM tasks, and the experimental results support its validity.
Provisioning Differentiated QoS for NVMe over Fabrics

In this paper, we propose a quality of service (QoS) aware transport solution for storage access over data center network. Motivation for QoS differentiation comes from the emerging storage technologies which not only provide network comparable latency but also overwhelm the network bandwidth close to the storage servers. We consider both throughput and latency related QoS requirements and demonstrate how they can be enforced by Explicit Congestion Notification (ECN) enabled switches. The mechanism can be viewed as a way of adding QoS capability to the existing data center transport solutions. Our scheme can co-exist with existing congestion management schemes and ensures RTT fairness while providing service differentiation. We show that our scheme can provide differentiated treatment and besides achieving better throughput fairness during the congestion episode, our solution can reduce the target latency misses by up to 71% and 80% as compared to the existing TCP and RDMA transport.
Characterizing photon-number distribution of pulsed sources using non–photon-number-resolving detectors for quantum key distribution

Quantum key distribution (QKD) has a prospect to realize unconditional secure communication. However, many real system problems should be solved. Transmitters security is an important part of QKD security. And measuring photon number distribution could help improve it. This measurement is also useful for measuring unknown sources or testing newly designed sources. In this article, a method to measure the photon number distribution of pulses using non-photon-number-resolving detectors is proposed. Numerical simulation is conducted to test this scheme and performs well. Experiment on Poisson source is also used to test this method.
On the stability and generalization of neural networks with VC dimension and fuzzy feature encoders

Structuring a suitable depth and width based on the complexity of data is a difficult task in network training. Overparameterized deep networks with stochastic gradient descent optimization exhibit excellent accuracy on both training and validation set but are highly computationally expensive. The success of deep learning demands an efficient method to configure deep architectures based on the complexity of data. Here we developed a new strategy called FEVCFNN to structure a network based on sample complexity with fuzzy logic and VC Dimension for binary classification problems. Here preprocessing is done with a new technique called fuzzy feature encoders that transforms the data by increasing the dimension of input features based on the sample complexity evaluated through VC Dimension. VC Dimension is calculated on a class of least ϵ identifiable function space defined over the weight space identified by the processing state of the network. Using fuzzy set theory, VC Dimension evaluates a feasible bound for parameter size and structures a network based on sample complexity. Comparative study shows FEVCFNN gives high-performance results in minimum parameter size and less number of hidden nodes.
Information-Theoretic Bounds on the Moments of the Generalization Error of Learning Algorithms

Generalization error bounds are critical to understanding the performance of machine learning models. In this work, building upon a new bound of the expected value of an arbitrary function of the population and empirical risk of a learning algorithm, we offer a more refined analysis of the generalization behaviour of a machine learning models based on a characterization of (bounds) to their generalization error moments. We discuss how the proposed bounds - which also encompass new bounds to the expected generalization error - relate to existing bounds in the literature. We also discuss how the proposed generalization error moment bounds can be used to construct new generalization error high-probability bounds.
A MOOCs Recommender System Based on User’s Knowledge Background

Massive open online courses (MOOCs) are becoming a novel and flexible way for education. Learning on the MOOCs platform is a continuous process with multiple independent selections of courses. Meanwhile, students' choices of subsequent courses are affected by their knowledge background, i.e., their historical courses and their learning performance on these courses. However, existing MOOC recommender systems often neglect students' knowledge background. To fill this gap, in this paper we propose a knowledge-aware recommendation model which incorporates users' historical enrolled courses and learning feedback. Specifically, we first construct a Course Knowledge Graph to represent the learning sequence between MOOCs and a Keyword Knowledge Graph to model the relation between course titles. With these two knowledge graphs and the course titles, we can extract the content feature and knowledge graph feature of courses. Second, we combine the extracted features with the user's learning feedback to learn the representation of his knowledge background according to his historical enrollments. Third, to model the different impacts of historical courses, we use an attention module to calculate the weights of these courses and aggregate the user's historical representation, and finally predict the probability of the user enrolling a course. We conduct a series of experiments compared with state-of-the-art baseline methods across multiple popular metrics. The promising results show that our proposed method can effectively recommend MOOCs to students.
Performance-based Software Architecture Design and Blockchain as a Service for Peruvian E-government

This paper demonstrates the software architecture design and implementation of real-time processing based on a regulatory contribution system, Blockchain-as-a-Service platform, and financial transaction platform. Attribute-driven design was used to integrate affidavits and payment transactions as well as technological platforms using architectural patterns and tactics. The results show proper integration and a robust architecture without jeopardizing time response and while supporting quality attributes and maintaining interoperability using an enterprise service bus. A case study of an e-government implementation project for an environmental agency is used as a reference.
Design and implementation of a power supervisory of a controlled greenhouse in the north of Tunisia

The use of renewable and sustainable energy resources within the agricultural sector has been increasing, especially with the use of modern agricultural greenhouses to scale back the production cost. In this chapter, a hybrid power system (HPS) incorporating a wind turbine system (WTS), supported by a permanent magnetic synchronous generator and combined with an electric battery storage system (BSS), was proposed to cover the electrical power needed by an insulated agricultural greenhouse. This greenhouse was established, tested, and controlled via fuzzy logic controllers (FLCs) to attain the desired microclimate for plant growth. Besides, the use of FLCs presented a powerful technique that aimed to facilitate the management of the microclimate and avoid unnecessary energy consumption. An innovative thermodynamic model of an insulated type of greenhouse was developed and validated within the Research Center (CRTEn) in Tunisia. Additionally, a mathematical model of the HPS was developed and simulated using MATLAB, which was intended to cover the required energy for the greenhouse actuators. For this purpose, supervisory control was presented and implemented under STM32F4-Discovery by using the new technology processor-in-the-loop simulation to manage the power coordination in real time between the WTS and the BSS considering the load requirement with low energy operation. The proposed configuration was further simulated, and the results indicated the efficiency of the proposed thermodynamic model of the greenhouse controlled by FLCs to maintain a suitable growing environment. The ability of the proposed HPS fully satisfied the required electricity for the studied greenhouse actuators and therefore the robustness of the proposed power supervisory algorithm.
Interactive Dynamic Projection Mapping onto Thin Plants with Bioluminescent Effect Animations

We propose a system to generate an interactive projection mapping onto thin plants automatically. Recently, projection mapping targets have included a wide variety of objects, ranging from buildings to fish. However, it is difficult to project onto dynamic and deformable targets such as thin plants. Our system tracks dynamic and deformable plants and registers the target projection area automatically. Our system also detects and recalculates vertices in the occlusion area for the interaction, e.g., a hand touching the plant. Therefore, our system can acquire the shape of occluded area. We also propose a system in which effect animations change interactively using occlusion detection with touch event detection via a capacitive sensor. In our system, four effect animations are applied to the targets.
Clustering Massive-Categories and Complex Documents via Graph Convolutional Network

In recent years, a significant amount of text data are being generated on the Internet and in digital applications. Clustering the unlabeled documents becomes an essential task in many areas such as automated document management and information retrieval. A typical approach of document clustering consists of two major steps, where step one extracts proper features to model documents for clustering and step two applies the clustering methods to categorize the documents. Recent research document clustering algorithms are mostly focusing on step one to finding high-quality embedding or vector representation, after which adopting traditional clustering methods for the second step. Or infer the document representation based on the predetermined k clusters. However, the traditional clustering methods are designed with simplistic assumption of the data distribution that fails to cope with the documents with complex distribution and a small number of clusters i.e. , less than 50. In addition to this, the previous need a predetermined k. In this paper, we introduce Graph Convolutional Network into the document clustering (instead of using the traditional clustering methods) and propose a supervised GCN-based document clustering algorithm, DC-GCN which is able to handle documents in noisy, huge and complex distribution by a learnable similarity estimator. Our proposed algorithm first adopts a GCN-based confidence estimator to learn the document position in a cluster via the affinity graph, and then adopts a GCN-based similarity estimator to learn the document similarity by constructing the doc-word graphs integrating the local neighbor documents and its keywords. Based on the confidence and similarity, the document clusters are finally formed. Our experimental evaluations show that DC-GCN achieves 21.88%, 17.35% and 15.58% performance improvement on $$F_p$$ over the best baseline algorithms in three different datasets.
Two-Step Fine-Tuned Convolutional Neural Networks for Multi-label Classification of Children’s Drawings

Developmental psychologists employ several drawing-based tasks to measure the cognitive maturity of a child. Manual scoring of such tests is time-consuming and prone to scorer bias. A computerized analysis of digitized samples can provide efficiency and standardization. However, the inherent variability of hand-drawn traces and lack of sufficient training samples make it challenging for both feature engineering and feature learning. In this paper, we present a two-step fine-tuning based method to train a multi-label Convolutional Neural Network (CNN) architecture, for the scoring of a popular drawing-based test 'Draw-A-Person' (DAP). Our proposed two-step fine-tuned CNN architecture outperforms conventional pre-trained CNNs by achieving an accuracy of 81.1% in scoring of Gross Details, 99.2% in scoring of Attachments, and 79.3% in scoring of Head Details categories of DAP samples.
Information-theoretic Key Encapsulation and its Application to Secure Communication

A hybrid encryption scheme consists of a public-key part called the key encapsulation mechanism (KEM), that is used to generate and establish a shared secret key between two parties, and a (symmetric) secret-key part called the data encapsulation mechanism (DEM) that encrypts the data using the shared key. Hybrid encryption schemes are widely used for securing Internet communication. In this paper, we initiate the study of hybrid encryption in preprocessing model which assumes access to initial correlated variables by all parties (including the eavesdropper), and define information-theoretic KEM (iKEM) that together with a (computationally) secure DEM, results in a hybrid encryption scheme in preprocessing model. We define security of each building block, and prove a composition theorem that guarantees (computational) q-chosen-plaintext attack (CPA) security of the hybrid encryption system if the iKEM and the DEM satisfy q-chosen-encapsulation attack security and one-time security, respectively. We show that an iKEM can be realized by an information-theoretic one-way secret key agreement (OW-SKA) protocol where a single message is transmitted from Alice to Bob, with a new security definition that allows <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$q$</tex> queries to Alice. Using a OW-SKA that satisfies this new definition of security, effectively allows the established secret key to be used with a onetime symmetric key encryption system that can be implemented, for example, by XORing the output of a (computationally) secure pseudorandom generator with the message, to provide secure encryption of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$q$</tex> arbitrary messages (polynomially bounded length). We discuss our results and directions for future work.