Reviewerly/exHarmony huggingface dataset:
Non-personal Data Collection for Toy User Interfaces

Toy-user-interfaces (ToyUI) are computing devices or peripherals that leverage interactivity and connectivity with other devices to promote physical and social play.ToyUI products may collect both personal and non-personal data (NPD) on their users.We propose nine data patterns for NPD collection as part of ToyUI design based on the study of 297 ToyUI items from both the literature and industry.In addition, we introduce a printed circuit board (PCB) used for rapid prototyping that enabled NPD data collection concerning both objects and users by gathering nonpersonal identification, positioning system, and motion tracking.We demonstrate the effectiveness of our hardware architecture by embedding it into two design scenarios, namely, closed rules and open-ended rules solutions.The objectives here are to assist the ToyUI makers in creating more meaningful play experiences while ensuring the privacy of children's and their parents' data.
11 ways to avert a data-storage disaster

Hard-drive failures are inevitable, but data loss doesn’t have to be. Hard-drive failures are inevitable, but data loss doesn’t have to be.
Is Virtual Reality the Future of Learning? A Critical Reflection

The year 2016 marks the so-called second wave of VR, which was initiated by the first consumer VR-HMD, Oculus Rift (development kit), entering the market.There are four practical advantages in the field of virtual reality learning: a shift from abstract to tangible settings, interactivity rather than passive observations, using desirable but practically infeasible methods, and breaking the bounds of reality.In contrast, current VR technologies also feature certain limitations.The most common negative factor is motion sickness, which distracts the user.We conducted a multiple case study and invited 41 people to participate in two different scenarios.One was a self-developed 360° video and the other was a selfdeveloped interactive scenario.We investigate different barriers which hamper individual learning in VR and we point out that there is a potential for implicit learning in virtual reality.
A Novel Multi-Scale Adaptive Convolutional Network for Single Image Super-Resolution

Recently, the methods based on deep convolutional neural networks have shown superior performance on image restoration. By using a faster and deeper network, the existing methods have achieved the breakthroughs in speed and accuracy. But, they still work hard to recover sharper edge and finer texture details for the large upscaling factor. In this paper, we propose a novel multi-scale adaptive convolutional network to solve this problem. The proposed network is mainly built on the multi-scale adaptive convolutional block which is composed of three different scales sub-blocks. Each sub-block consists of two convolutional layers, one parametric rectified linear unit layer, and some adaptive shortcuts. Different scaling parameters are assigned to these shortcuts to extract and connect diversified and complicated features adaptively. Due to the special design of MACB, the proposed algorithm can enhance the combination of various levels features and provide different ranges of image context for super-resolution (SR) reconstruction. Besides the stack of MACB, the skip connections with identity mapping are used to further aggregate the features of two specific layers in the proposed network. Moreover, in order to infer photo-realistic natural images, a perceptual loss function is proposed to supervise the reconstruction, which consists of four parts of loss: feature loss, style loss, total variation loss, and pixel loss. These losses push our reconstructed image to the target image. The experimental results on the public benchmark datasets demonstrate that the proposed algorithm achieves the best super-resolution reconstruction among the state-of-the-art SR methods.
Buyers of Lemons: Addressing Buyers’ Needs in the Market for Lemons with Blockchain Technology

The automotive market is in the top three markets with the least trust from consumers.In particular, in the second-hand car market, consumers suffer from such problems as the car being in worse condition than initially indicated, accident damage that is not disclosed, fraud, etc.Akerlof, described the market for used cars as an example of the problem of information asymmetries and resulting quality uncertainty.In order to cope with quality uncertainties, used car buyers actively engage themselves in information seeking.Blockchain technology promises to automatize the tracking of cars through their lifecycles and provide reliable information at any point in time it is needed.In our study, we investigate the problems car buyers face during information seeking and propose requirements for the design of a blockchain-based system to address these.
Context-Aware Dual-Attention Network for Natural Language Inference

Natural Language Inference (NLI) is a fundamental task in natural language understanding. In spite of the importance of existing research on NLI, the problem of how to exploit the contexts of sentences for more precisely capturing the inference relations (i.e. by addressing the issues such as polysemy and ambiguity) is still much open. In this paper, we introduce the corresponding image into inference process. Along this line, we design a novel Context-Aware Dual-Attention Network (CADAN) for tackling NLI task. To be specific, we first utilize the corresponding images as the Image Attention to construct an enriched representation for sentences. Then, we use the enriched representation as the Sentence Attention to analyze the inference relations from detailed perspectives. Finally, a sentence matching method is designed to determine the inference relation in sentence pairs. Experimental results on large-scale NLI corpora and real-world NLI alike corpus demonstrate the superior effectiveness of our CADAN model.
Smart Packaging in Intralogistics: An Evaluation Study of Human-Technology Interaction in Applying New Collaboration Technologies

Handling and packaging of heterogeneous products with different weights and sizes with optimal packaging schemes is a challenging task for the e-commerce industry.Furthermore, to keep the packaging process on a standardized level independent of the experience level of the employee, the demand of digital humancentered solutions is increasing.Against this background, two different digital assistance systems to indicate packaging order and scheme -Augmented Reality (AR) based data glasses and a LED based packaging assistant -were developed.In a laboratory study the interaction between human and both digital devices regarding subjective workload, usability, user experience, physical complaints and objective measurements was evaluated -with a conventional paper list as control group.Results indicate that both the AR and LED interface are appropriate solutions to assist warehouse workers in packaging.However, it can be supposed that the LED interface seems to be a better method in terms of physical and especially visual strains.
Hematological Changes in Sudanese Patients with Falciparum Malaria Attending Elnihoud Teaching Hospital

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Self-Adapting Patch Strategies for Face Recognition

In this paper, we propose two self-adapting patch strategies, which are obtained by employing the integral projection technique on images’ edge images, while the edge images are recovered by the two-dimensional discrete wavelet transform. The patch strategies are equipped with the advantage of considering the single image’s unique properties and maintaining the integrity of some particular local information. Combining the self-adapting patch strategies with local binary pattern feature extraction and the classifier of the forward and backward greedy algorithms under strong sparse constraint, we propose two new face recognition methods. Experiments are run on the Georgia Tech, LFW and AR face databases. The obtained numerical results show that the new methods outperform some related patch-based methods to a larger extent.
Multilevel thresholding hyperspectral image segmentation based on independent component analysis and swarm optimization methods

High dimensional problems are often encountered in studies related to hyperspectral data. One of the challenges that arise is how to find representations that are accurate so that important structures can be clearly easily. This study aims to process segmentation of hyperspectral image by using swarm optimization techniques. This experiments use Aviris Indian Pines hyperspectral image dataset that consist of 103 bands. The method used for segmentation image is particle swarm optimization (PSO), Darwinian particle swarm optimization (DPSO) and fractional order Darwinian particle swarm optimization (FODPSO). Before process segmentation image, the dimension of the hyperspectral image data set are first reduced by using independent component analysis (ICA) technique to get first independent component. The experimental show that FODPSO method is better than PSO and DPSO, in terms of the average CPU processing time and best fitness value. The PSNR and SSIM values when using FODPSO are better than the other two swarm optimization method. It can be concluded that FODPSO method is better in order to obtain better segmentation results compared to the previous method.
Designing Authentic Cybersecurity Learning Experiences: Lessons from the Cybermatics Playable Case Study

This paper reports our work on an educational simulation that we call the Playable Case Study (PCS).A PCS is characterized by a fictitious narrative integrated with real-world learning activities, helping students learn skills, knowledge, and dispositions relevant to a professional career.We describe a recent pilot test of a PCS focused on the discipline of cybersecurity, emphasizing the kinds of tensions and difficulties that can arise during the development of immersive, experiential learning experiences: a) challenges accompanying the work of interdisciplinary PCS teams, particularly maintaining technical accuracy while still developing an authentic and engaging narrative; b) reconciling the opportunities provided by the philosophy of the simulation with the need to scaffold educational experiences to support students' capabilities; and c) integrating the PCS into the classroom environment.We also provide design recommendations, in the form of questions that others can consider if they are attempting to create similar educational experiences.
Asymmetric Deep Semantic Quantization for Image Retrieval

Due to its fast retrieval and storage efficiency capabilities, hashing has been widely used in nearest neighbor retrieval tasks. By using deep learning-based techniques, hashing can outperform non-learning-based hashing technique in many applications. However, we argue that the current deep learning-based hashing methods ignore some critical problems (e.g., the learned hash codes are not discriminative due to the hashing methods being unable to discover rich semantic information and the training strategy having difficulty optimizing the discrete binary codes). In this paper, we propose a novel image hashing method, termed as asymmetric deep semantic quantization (ADSQ). The ADSQ is implemented using three stream frameworks, which consist of one LabelNet and two ImgNets. The LabelNet leverages the power of three fully-connected layers, which are used to capture rich semantic information between image pairs. For the two ImgNets, they each adopt the same convolutional neural network structure but with different weights (i.e., asymmetric convolutional neural networks). The two ImgNets are used to generate discriminative compact hash codes. Specifically, the function of the LabelNet is to capture rich semantic information that is used to guide the two ImgNets in minimizing the gap between the real-continuous features and the discrete binary codes. Furthermore, the ADSQ can utilize the most critical semantic information to guide the feature learning process and consider the consistency of the common semantic space and Hamming space. The experimental results on three benchmarks (i.e., CIFAR-10, NUS-WIDE, and ImageNet) demonstrate that the proposed ADSQ can outperform current state-of-the-art methods.
FraudMemory: Explainable Memory-Enhanced Sequential Neural Networks for Financial Fraud Detection

The rapid development of electronic financial services brings significant convenience to our daily life.However, it also offers criminals the opportunity to exploit financial systems to do fraudulent transactions.Previous studies on fraud detection only deal with single type transactions and cannot adapt well to evolving environment in reality.In addition, their black box models pay less attention on the interpretability of fraud detection results.Here we propose a novel fraud detection algorithm called FraudMemory.It adopts state-of-art feature representation methods to better depict users and logs with multiple types in financial systems.Our model innovatively uses sequential model to capture the sequential patterns of each transaction and leverages memory networks to improve both the performance and interpretability.Also, with the incorporation of memory components, FraudMemory possesses high adaptability to the existence of concept drift.The empirical study proves that our model is a potential tool for financial fraud detection.
Radical technologies: Blockchain as an organizational movement

The emergence of blockchain technology has created a debate regarding technologies’ socio-cultural symbolism. Prevailing as alternative or complementary to internet technology, blockchain’s decentralized radical architecture reflects organizational change, enhancement of degrees of freedom, for individual identities and communities, new schemes of distributed trust and privacy, transformation of power relations and social reality perception. The current paper aims to contribute to the ongoing debate, from an organizational and socio-psychological perspective, discussing the key elements of a socially grounded technology, like any other technological product within the history of humanity. Through an evolutionary lens, blockchain technology is examined as a decentralized grassroots organizational movement at birth, influencing and, at the same time, be influenced, by science, culture, as well as by other aspects of individual and collective networked life, apart from the economy. Social sciences and cyber sciences are in a crossroad where society and technology integrate creating a mixed socio-technological or techno-social reality. Therefore, it is of high importance for them, to address the new epistemological challenges by developing new methodologies and tools, independently from any utopian or dystopian predictions.
Data-Driven Selection of Security Application Frameworks During Architectural Design

The selection of application frameworks is an important aspect of architectural design.Selection often requires satisficing, that is, searching a potentially large space of design alternatives until an acceptable solution is found.There is, however, little help for architects in selecting software frameworks.In this paper we investigate the criteria used by practicing software architects in selecting security frameworks.We also propose how information associated with some of the criteria that are important to architects can be obtained manually or in an automated way from online sources such as GitHub.Our ultimate goal is to identify measures associated with these criteria that can be helpful in providing support for architects to select software frameworks.
An Online Expert System for Psychiatric Diagnosis

Expert systems are programs that use artificial intelligence techniques, and simulate the performance of the human expert in a given area of expertise by collecting and using one or more expert information and expertise in a particular field.We developed declarative, online procedural rule-based expert system models for psychological diseases diagnosis and classification.The constructed system exploited computer as an intelligent and deductive tool.This system diagnoses and treats more than four types of psychiatric diseases, i.e., depression, anxiety disorder, obsessive-compulsive disorder, and hysteria.The system helps psychology practitioner and doctors to diagnose the condition of a patient efficiently and in short time.It is also very useful for the patients who cannot go to a doctor because they cannot afford the cast, or they do not have a psychological clinic in their area, or they are ashamed of discussing their situation with a doctor.The system consists of program codes that make a logic decision to classify the problem of the patient.The methodology for developing the declarative model was based on the backward chaining, also called goal-driven reasoning, where knowledge is represented by a set of IF-THEN production rules.The declarative programs were written in the PROLOG.While the design of the procedural model was based on using common languages like PHP, JavaScript, CSS, and HTML.The user of the system will enter the symptoms of the patients through the user interface and the program executes.Then the program links the symptoms to the pre-programmed psychological diseases, and will classify the disease and recommend treatment.
A cryptanalytic attack of simplified-AES using ant colony optimization

Ant colony Optimization is a nature-inspired meta-heuristic optimization algorithm that gained a great interest in resolution of combinatorial and numerical optimization problems in many science and engineering domains. The aim of this work was to investigate the use of Ant Colony Optimization in cryptanalysis of Simplified Advanced Encryption Standard (S-AES), using a known plaintext attack. We have defined the essential components of our algorithm such as heuristic value, fitness function and the strategy to update pheromone trails. It is shown from the experimental results that our proposed algorithm allow us to break S-AES cryptosystem after exploring a minimum search space when compared with others techniques and requiring only two plaintext-ciphertext pairs.
Movie Recommender System Comparison of User-based and Item-based Collaborative Filtering Systems

Collaborative Filtering (CF) is a method that is widely used in recommendation systems.There are two approaches that are often used in CF, namely User-based CF and Item-based CF.The User-based CF approach requires several similar users to predict the rating of a new item.Meanwhile, Item-based CF requires several similar items to predict the rating of a new item.The number of similar users or similar items involved in predicting ratings will affect the computing load.This research aims to see the effect of the number of neighbors (similar user or similar items) used on the level of accuracy of rating predictions for an item.By using different numbers of neighbors for both User-based CF and Item-based CF, the results of the experiment show that the number of neighbors affects the level of accuracy although not too significant.
Blockchains and Smart Contracts for the Internet of Things

ABSTRACT: Motivated by the recent explosion of interest around Blockchains, we examine whether they make a good t for the Internet of Things (IoT) sector. Blockchains allow us to have a distributed peer-to-peer network where non-trusting members can interact with each other without a trusted intermediary, in  a variable manner. We review how this  mechanism works and also look into smart contracts scripts that reside on the Blockchain that allow for the automation of multi-step processes. We then move into the IoT domain, and describe how a Blockchain-IoT  combination: 1) facilitates the sharing of services and resources leading to the creation of a marketplace of services between devices and 2) allows us to automate in a cryptographically variable manner several existing, time- consuming work owns. We also point out certain issues that should be considered before the deployment of a Blockchain network in an IoT setting: from transactional privacy to the expected value of the digitized assets traded on the network. Wherever applicable, we identify solutions and workarounds. Our conclusion is that the Blockchain-IoT combination is powerful and can cause sign cant transformations across several industries, paving the way for new business models and novel, distributed applications.
Optimal Data File Allocation for All-to-All Comparison in Distributed System: A Case Study on Genetic Sequence Comparison

In order to solve the problem of unbalanced load of data les in large-scale data all-to-all comparison under distributed system environment, the differences of les themselves arefully considered. This paper aims to fully utilize the advantages of distributed system to enhance the le allocation of all-to-all comparison between the data les in a large dataset. For this purpose, the author formally described the all-to-all comparison problem, and con-structed a data allocation model via mixed integer linear programming (MILP). Meanwhile, a data allocation algorithm was developed on the Matlab using the intlinprog function of branch-and-bound method. Finally, our model and algorithm were veried through several experiments. The results show that the proposed le allocation strategy can achieve the basic load balance of each node in the distributed system without exceeding the storage capacity of any node, and completely localize the data le. The research ndings can be applied to such elds as bioinformatics, biometrics and data mining.
SOCIOECONOMIC IMPACTS OF DIGITISATION IN SAUDI ARABIA

The aim of this paper is to measure and analyse the socioeconomic effects of digitisation in Saudi Arabia by using data for the period 1981-2016.Adopting the autoregressive distributed lag and the fully modified least squares approaches, the results reveal significant relationships between the digitisation and socioeconomic variables.Notably, digitisation contributes to the gross domestic product, reduces unemployment, and enhances transparency.Thus, digitisation can be considered a new impetus growth and a powerful instrument of economic diversification that permits the transition to a knowledge-based economy.
Unsupervised Learning of Total Variability Embedding for Speaker Verification with Random Digit Strings

Recently, the increasing demand for voice-based authentication systems has encouraged researchers to investigate methods for verifying users with short randomized pass-phrases with constrained vocabulary. The conventional i-vector framework, which has been proven to be a state-of-the-art utterance-level feature extraction technique for speaker verification, is not considered to be an optimal method for this task since it is known to suffer from severe performance degradation when dealing with short-duration speech utterances. More recent approaches that implement deep-learning techniques for embedding the speaker variability in a non-linear fashion have shown impressive performance in various speaker verification tasks. However, since most of these techniques are trained in a supervised manner, which requires speaker labels for the training data, it is difficult to use them when a scarce amount of labeled data is available for training. In this paper, we propose a novel technique for extracting an i-vector-like feature based on the variational autoencoder (VAE), which is trained in an unsupervised manner to obtain a latent variable representing the variability within a Gaussian mixture model (GMM) distribution. The proposed framework is compared with the conventional i-vector method using the TIDIGITS dataset. Experimental results showed that the proposed method could cope with the performance deterioration caused by the short duration. Furthermore, the performance of the proposed approach improved significantly when applied in conjunction with the conventional i-vector framework.