Reviewerly/exHarmony huggingface dataset:
A Hierarchical and Abstraction-Based Blockchain Model

In the nine years since its launch, amid intense research, scalability is always a serious concern in blockchain, especially in case of large-scale network generating huge number of transaction-records. In this paper, we propose a hierarchical blockchain model characterized by: (1) each level maintains multiple local blockchain networks, (2) each local blockchain records local transactional activities, and (3) partial views (tunable w.r.t. precision) of different subsets of local blockchain-records are maintained in the blockchains at next level of the hierarchy. To meet this objective, we apply abstractions on a set of transaction-records in a regular time interval by following the Abstract Interpretation framework, which provides a tunable precision in various abstract domain and guarantees the soundness of the system. While this model suitably fits to the real-worlds organizational structures, the proposal is powerful enough to scale when large number of nodes participate in a network resulting into an enormous growth of the network-size and the number of transaction-records. We discuss experimental results on a small-scale network with three sub networks at lower-level and by abstracting the transaction-records in the abstract domain of intervals. The results are encouraging and clearly indicate the effectiveness of this approach to control exponential growth of blockchain size w.r.t. the total number of participants in the network.
Efficient Weights Quantization of Convolutional Neural Networks Using Kernel Density Estimation based Non-uniform Quantizer

Convolutional neural networks (CNN) have achieved excellent results in the field of image recognition that classifies objects in images. A typical CNN consists of a deep architecture that uses a large number of weights and layers to achieve high performance. CNN requires relatively large memory space and computational costs, which not only increase the time to train the model but also limit the real-time application of the trained model. For this reason, various neural network compression methodologies have been studied to efficiently use CNN in small embedded hardware such as mobile and edge devices. In this paper, we propose a kernel density estimation based non-uniform quantization methodology that can perform compression efficiently. The proposed method performs efficient weights quantization using a significantly smaller number of sampled weights than the number of original weights. Four-bit quantization experiments on the classification of the ImageNet dataset with various CNN architectures show that the proposed methodology can perform weights quantization efficiently in terms of computational costs without significant reduction in model performance.
Dynamic Hand Gesture Recognition Based on 3D Convolutional Neural Network Models

Hand gesture is a natural communication method which could be used to create a more convenient interface for human-robot interaction. In this study, we use the simplest laptop camera as an input sensor. We designed a 3D hand gesture recognition model. The model is trained with the Jester dataset. After being trained about one day in a MacBook Pro (i5 2.3GHz), the model reached an average accuracy of 90%. We built a web application that implements the hand gesture recognition system and provides the recognition service to users.
Efficient Modular Adder Designs Based on Thermometer and One-Hot Coding

Residue number systems (RNSs) are efficient alternatives to positional number systems, providing fast and power-efficient computational systems. The key feature of the RNS benefitting modern embedded systems and the Internet-of-Thing (IoT) edge devices is its energy efficiency. Modular addition is the most important and frequent operation applied on the components of RNS, including arithmetic units in the channels as well as forward and reverse converters. The small and medium dynamic range requirements of low-power embedded and edge devices make the usage of the thermometer coding (TC) and one-hot coding (OHC) viable, reducing the power consumption and improving the energy efficiency of modulo addition in comparison to regular binary representations. Based on these techniques, this paper presents two new energy-efficient modular adders, which, due to the carry-free internal computations, are also highly performing. The proposed modular adders based on the TC and OHC result in average improvements of 38% and 34.5% for the delay, 27% and 14.5% for the circuit area, 29.5% and 6.3% for energy consumption, and about 54.9% and 44.2% for the area-delay product (ADP), respectively, in comparison with the related state of the art.
I Should Have Known Better: Development of a Self-Report Measure of Gullibility

The aim of this research was to explore the predictors of gullibility and to develop a self-report measure of the construct. In Studies 1 to 3, exploratory and confirmatory factor analyses were conducted on a large pool of items resulting in a 12-item scale with two factors: Persuadability and Insensitivity to cues of untrustworthiness. Study 4 confirmed the criterion validity of the scale using two distinct samples: scam victims and members of the Skeptics Society. Study 5 demonstrated positive relationships between gullibility and the self-reported persuasiveness of, and likelihood of responding to, unsolicited emails. Throughout the article, analyses of a variety of measures expected to converge with the scale provided evidence for its construct validity. Overall, these studies demonstrate that the construct of gullibility is distinct from trust, negatively related to social intelligence, and that the Gullibility Scale is a reliable and valid measure of gullibility.
Social Media for Social Good: Student engagement for the SDGs

This paper presents and reflects on the evolution of an innovative, interdisciplinary module which engages undergraduate students with a range of non-profit actors, working in teams with the aim of developing and deploying business skills for social good The module combines experiential learning with service learning, and has proven to have very positive outcomes for students, partner organisations and the Business School, University and teaching team. We describe the context in which the module was developed, how it evolved over time, and its impact both within and outside of the University. The module is contextualised by reference to the PRME Principles and the commitment of the Business School, a PRME Champion Institution, to the realisation of the SDGs. Data is drawn from pre- and post-delivery surveys over a three year period as well as the student voice in the form of reflective journals and end-of-semester submissions and feedback from partner organisations. The offering is described at a level of detail intended to enable other institutions to replicate or develop this model further.
Generalizing identity-based string comparison metrics: Framework and techniques

In this paper, we propose a framework that aims at handling metrics among strings defined over heterogeneous alphabets. Furthermore, we illustrate in detail its application to generalize one of the most important string metrics, namely the edit distance. This last activity leads us to define the Multi-Parameterized Edit Distance (MPED). As for this last metric, we investigate its computational properties and solution algorithms, and we present several experiments for its evaluation. As a final contribution, we provide several notes about some possible applications of MPED and other generalized metrics in different scenarios.
The value of abstraction

• Agents with finite space, time, and data can benefit from abstract representations. • Both humans and artificial agents rely on abstractions to solve complex problems. • Recent work in reinforcement learning elucidates what makes abstractions useful. • Abstractions guide learning, facilitate tradeoffs, and simplify computation. • This structure provides a basis for a rational analysis of abstract representations. Agents that can make better use of computation, experience, time, and memory can solve a greater range of problems more effectively. A crucial ingredient for managing such finite resources is intelligently chosen abstract representations . But, how do abstractions facilitate problem solving under limited resources? What makes an abstraction useful? To answer such questions, we review several trends in recent reinforcement-learning research that provide insight into how abstractions interact with learning and decision making. During learning, abstraction can guide exploration and generalization as well as facilitate efficient tradeoffs—e.g., time spent learning versus the quality of a solution. During computation, good abstractions provide simplified models for reasoning that preserve information that is relevant for making good decisions. These features of abstraction are not only key for scaling up artificial problem solving, but can also shed light on what pressures shape the use of abstract representations in humans and other organisms.
Taking advantage of the software product line paradigm to generate customized user interfaces for decision-making processes: a case study on university employability

University employment and, specifically, employability has gained relevance since research in these fields can lead to improvement in the quality of life of individual citizens. However, empirical research is still insufficient to make significant decisions, and relying on powerful tools to explore data and reach insights on these fields is paramount. Information dashboards play a key role in analyzing and visually exploring data about a specific topic or domain, but end users can present several necessities that differ from each other, regarding the displayed information itself, design features and even functionalities. By applying a domain engineering approach (within the software product line paradigm), it is possible to produce customized dashboards to fit into particular requirements, by the identification of commonalities and singularities of every product that could be part of the product line. Software product lines increase productivity, maintainability and traceability regarding the evolution of the requirements, among other benefits. To validate this approach, a case study of its application in the context of the Spanish Observatory for University Employability and Employment system has been developed, where users (Spanish universities and administrators) can control their own dashboards to reach insights about the employability of their graduates. These dashboards have been automatically generated through a domain specific language, which provides the syntax to specify the requirements of each user. The domain language fuels a template-based code generator, allowing the generation of the dashboards' source code. Applying domain engineering to the dashboards' domain improves the development and maintainability of these complex software products given the variety of requirements that users might have regarding their graphical interfaces.
Automatic feature extraction and classification of Iberian ceramics based on deep convolutional networks

Accurate classification of pottery vessels is a key aspect in several archaeological inquiries, including documentation of changes in style and ornaments, inference of chronological and ethnic groups, trading routes analyses, and many other matters. We present an unsupervised method for automatic feature extraction and classification of wheel-made vessels. A convolutional neural network was trained with a profile image database from Iberian wheel made pottery vessels found in the upper valley of the Guadalquivir River (Spain). During the design of the model, data augmentation and regularization techniques were implemented to obtain better generalization outcomes. The resulting model is able to provide classification on profile images automatically, with an accuracy mean score of 0.9013. Such computation methods will enhance and complement research on characterization and classification of pottery assemblages based on fragments.
Contextual-boosted deep neural collaborative filtering model for interpretable recommendation

Collaborative filtering (CF) is one of the most successful recommendation techniques due to its simplicity and attractive accuracy. However, existing CF methods fail to interpret the reasons why they recommend a new item. In this paper, we propose a Contextual-boosted Deep Neural Collaborative filtering (CDNC) model for item recommendation, which simultaneously exploits both item introductions (textual features) and user ratings (collaborative features) to alleviate the cold-start problem and provide interpretable item recommendation. Specifically, we propose an interactive attention mechanism to learn the user representation, which makes use of the mutual information from both the user ratings and item introductions to supervise the representation learning of each other. With the learned attention weights, we can obtain the importance of each historical item among the historical list. Meanwhile, the attention model can assign different weights to the words in item introductions according to their importance. Therefore, CDNC can provide interpretations for the recommendations by assigning different attention weights to the historically interacted items and the words in the item introductions. On the other hand, we also learn the distributed representations of new-coming items with deep neural networks (i.e., LSTM), considering both rating and item introduction information. Finally, the user representation and the representations of new-coming item are concatenated to perform recommendation score prediction. Extensive experiments on four public benchmarks demonstrate the effectiveness of CDNC. In addition, CDNC has the advantage of interpreting the recommendations and providing user profiles for down-stream applications.
Self-Taught Semisupervised Dictionary Learning With Nonnegative Constraint

This paper investigates classification by dictionary learning. A novel unified framework termed self-taught semisupervised dictionary learning with nonnegative constraint is proposed for simultaneously optimizing the components of a dictionary and a graph Laplacian. Specifically, an atom graph Laplacian regularization is built by using sparse coefficients to effectively capture the underlying manifold structure. It is more robust to noisy samples and outliers because atoms are more concise and representative than training samples. A nonnegative constraint imposed on the sparse coefficients guarantees that each sample is in the middle of its related atoms. In this way, the dependency between samples and atoms is made explicit. Furthermore, a self-taught mechanism is introduced to effectively feed back the manifold structure induced by atom graph Laplacian regularization and the supervised information hidden in unlabeled samples in order to learn a better dictionary. An efficient algorithm, combining a block coordinate descent method with the alternating direction method of multipliers, is derived to optimize the unified framework. Experimental results on several benchmark datasets show the effectiveness of the proposed model.
Refining Arabic text stego-techniques for shares memorization of counting-based secret sharing

Sensitive information requires many security systems to be protected by secret keys. One of these practical security systems is the so-called counting-based secret sharing, in which certain key portions are distributed to allow sharing access by specified number of real participants. Secret shares are hidden adopting a humanized remembrance tool to serve uncontrolled assigned shares, which are generated from the security system via an automatic authentic target key generation process. While in their counting-based secret sharing process, the shares become challenging to be memorized unlike the normal password assignment that enjoys full personal selection. Therefore, our secret shares models are proposed to be hidden inside the personally chosen texts utilizing improved Arabic text steganography. The current steganography models' study is based on Kashida extension character that is redundant within Arabic writing text. We refined new models to hide data via Arabic text steganography used within counting-based secret sharing technique. The research tests our two proposed modifications to original Arabic text steganography and all are serving secret sharing on the same text database. The comparisons examined the different models on the same benchmark of 40 standard text statements (40 Prophet Hadiths). The results are significant and present promising research contributions.
Kullback-Leibler Averaging for Multitarget Density Fusion

This paper addresses the linear and log-linear fusion approaches to multitarget density fusion which yield arithmetic average (AA) and geometric average (GA), respectively. We reaffirm Abbas’s finding in 2009 that both AA and GA can be related to the minimization of the Kullback-Leibler divergence (KLD) between the fusing densities and the fused result, which differ from each other in the reference used to measure the KLD: the AA uses the fusing densities while the GA uses the fused density. We derive the explicit AA expressions for fusing some known multitarget densities and discuss the implementation issues. The results serve as the theoretical basis for designing distributed random finite set filters for distributed multitarget tracking.
Enhancing Arabic text steganography for personal usage utilizing pseudo-spaces

Steganography is the process of hiding data within multimedia communication between parties by embedding the secret data inside a carrier file to be protected during its transmission. This paper addresses the problem of hiding information within the Arabic text as it is currently a challenging research area. Two methods are presented for text steganography: utilizing pseudo-spaces alone and combining them with Kashida (extension character). Experimental results have shown that the proposed algorithms achieved high capacity and security ratio as compared to state-of-the-art steganography methods presented for the Arabic language. The proposed techniques are of great benefit and can be further used for languages similar to Arabic, such as Urdu and Persian, as well as opening the direction of text-stego research for other languages of the world.
A difficulty ranking approach to personalization in E-learning

The prevalence of e-learning systems and on-line courses has made educational material widely accessible to students of varying abilities and backgrounds. There is thus a growing need to accommodate for individual differences in e-learning systems. This paper presents an algorithm called EduRank for personalizing educational content to students that combines a collaborative filtering algorithm with voting methods. EduRank constructs a difficulty ranking for each student by aggregating the rankings of similar students using different aspects of their performance on common questions. These aspects include grades, number of retries, and time spent solving questions. It infers a difficulty ranking directly over the questions for each student, rather than ordering them according to the student’s predicted score. The EduRank algorithm was tested on two data sets containing thousands of students and a million records. It was able to outperform the state-of-the-art ranking approaches as well as a domain expert. EduRank was used by students in a classroom activity, where a prior model was incorporated to predict the difficulty rankings of students with no prior history in the system. It was shown to lead students to solve more difficult questions than an ordering by a domain expert, without reducing their performance.
Unpaired Mr to CT Synthesis with Explicit Structural Constrained Adversarial Learning

In medical imaging such as PET-MR attenuation correction and MRI-guided radiation therapy, synthesizing CT images from MR plays an important role in obtaining tissue density properties. Recently deep-learning-based image synthesis techniques have attracted much attention because of their superior ability for image mapping. However, most of the current deep-learning-based synthesis methods require large scales of paired data, which greatly limits their usage. Efforts have been made to relax such a restriction, and the cycle-consistent adversarial networks (Cycle-GAN) is an example to synthesize medical images with unpaired data. In Cycle-GAN, the cycle consistency loss is employed as an indirect structural similarity metric between the input and the synthesized images and often leads to mismatch of anatomical structures in the synthesized results. To overcome this shortcoming, we propose to (1) use the mutual information loss to directly enforce the structural similarity between the input MR and the synthesized CT image and (2) to incorporate the shape consistency information to improve the synthesis result. Experimental results demonstrate that the proposed method can achieve better performance both qualitatively and quantitatively for whole-body MR to CT synthesis with unpaired training images compared to Cycle-GAN.
Efficient constructions for progressive visual cryptography with meaningful shares

In a progressive visual cryptography scheme (PVCS), the clarity of recovered images can be enhanced by increasing the number of stacking shares. Noise-like shares are hard to identify, therefore, user-friendly progressive visual cryptography schemes (FPVCSs) generate meaningful shares. This paper proposes two encryption approaches to construct FPVCSs. One is a general OR-based FPVCS called (2, n)-FPVCS, and the other is an XOR-based FPVCS called (2, n)-XFPVCS. The common issues of the two models include: (1) systematic approaches, (2) avoiding the pixel-expansion, (3) providing adjustable visual quality of meaningful shares, and (4) eliminating residual traces of cover images. Moreover, the paper theoretically analyzes and carries out experiments to verify the visual qualities of the proposed two models. The experiment results indicate that the proposed approaches can accomplish these objectives simultaneously. In addition, (2, n)-FPVCS can adjust the threshold of trace-elimination by setting a parameter, which improves the flexibility of the proposed approach. As well as (2, n)-XFPVCS obtains a fully decrypted final recovered image.
A New Blind ECDSA Scheme for Bitcoin Transaction Anonymity

In this paper, we consider a scenario where a bitcoin liquidity provider sells bitcoins to clients. When a client pays for a bitcoin online, the provider is able to link the client's payment information to the bitcoin sold to that client. To address the privacy concerns of clients, we require that the bitcoin provider cannot tell the relationship between the real identities of clients and the sold bitcoins in the blockchain. This requirement can be effectively achieved by using blind signatures. However, existing blind signature schemes are incompatible with the Elliptic Curve Digital Signature Algorithm (ECDSA) which is used by most of the existing bitcoin protocol, thus cannot be applied directly in Bitcoin. In this paper, we propose a new blind signature scheme that allows generating a blind signature compatible with the standard ECDSA. Afterwards, we make use of the new scheme to achieve bitcoin transaction anonymity. The new scheme is built on a variant of the Paillier cryptosystem and its homomorphic properties. As long as the modified Paillier cryptosystem is semantically secure, the new blind signature scheme has blindness and unforgeability.