Reviewerly/exHarmony huggingface dataset:
Deep Transfer Learning for Alzheimer's disease detection

Early detection of Alzheimer's Disease (AD) is essential in order to initiate therapies that can reduce the effects of such a disease, improving both life quality and life expectancy of patients. Among all the activities carried out in our daily life, handwriting seems one of the first to be influenced by the arise of neurodegenerative diseases. For this reason, the analysis of handwriting and the study of its alterations has become of great interest in this research field in order to make a diagnosis as early as possible. In recent years, many studies have tried to use classification algorithms applied to handwriting to implement decision support systems for AD diagnosis. A key issue for the use of these techniques is the detection of effective features, that allow the system to distinguish the natural handwriting alterations due to age, from those caused by neurodegenerative disorders. In this context, many interesting results have been published in the literature in which the features have been typically selected by hand, generally considering the dynamics of the handwriting process in order to detect motor disorders closely related to AD. Features directly derived from handwriting generation models can be also very helpful for AD diagnosis. It should be remarked, however, that the above features do not consider changes in the shape of handwritten traces, which may occur as a consequence of neurodegenerative diseases, as well as the correlation among shape alterations and changes in the dynamics of the handwriting process. Moving from these considerations, the aim of this study is to verify if the combined use of both shape and dynamic features allows a decision support system to improve performance for AD diagnosis. To this purpose, starting from a database of on-line handwriting samples, we generated for each of them a synthetic off-line colour image, where the colour of each elementary trait encodes, in the three RGB channels, the dynamic information associated to that trait. Finally, we exploited the capability of Deep Neural Networks (DNN) to automatically extract features from raw images, following the Transfer Learning approach. The experimental comparison of the results obtained by using standard features and features extracted according the above procedure, confirmed the effectiveness of our approach.
Anticipatory Thinking: A Testing and Representation Challenge for Self-Driving Cars

Ensuring autonomous systems can reason and anticipate unknown (erroneous) futures is important for safety, trust, and reliability. Self-driving is an important domain because of the difficulty in testing: not all erroneous scenarios cannot be covered in training nor simulation experiments. I propose a series of tests for autonomous vehicles that require anticipatory thinking: the deliberate and divergent exploration of relevant possible futures. These stress tests include abstract thinking like theory of mind or self-introspection instead of memorization. Developing this capability, results in adaptive, autonomous vehicles that can reason and address the ever-growing, long tail of errors.
Application of Blockchain for the Security of Decentralized Cloud Computing

The demand for blockchain and its significant application has progressed in various sectors and practice areas. A blockchain is a digital technology act as a database that is shared across a network of computers. Although the blockchain is still in the initial stage of testing, in this platform the blockchain is considered as a progressive solution such as decentralization, identity, belief, character, ownership of data, and information about choices. The demand for cloud computing networks is also progressing higher in modern technology. This paper reviewed the application of blockchain technology where data kept in securing decentralized cloud storage which guarantees data of the security and credibility of the system. In a decentralized cloud, storage data are encrypted with a cryptographic algorithm technique with a private key from a user. The benefits and performance of the recent technique and system of cryptography motivated especially in decentralized cloud storage. Decentralized cloud storage using blockchain data makes it more secure and avoids many attacking challenges and single point of failure.
Symmetric Simplicial Neural Networks

Convolutional Neural Networks are capable of perform many complex tasks such as image classification. Recently morphological functions where introduced as a replacement of the first convolutional layers in any net, using their non-linearities to achieve better accuracy for classification Neural Networks, but in most cases the functions are fixed beforehand and can not be trained. We propose the use of Symmetric Simplicial algorithm that can be trained to perform many morphological computations and even more complex functions. We present the training of a certain topology that uses Symmetric Simplicials instead of morphological functions and the classification accuracy achieved during the training process.
A Privacy-Aware Digital Voting System employing Blockchain and Smart Contracts

Voting seem to be a very significant sign of political action, but it is very pathetic that a substantial majority of the world `s population do not have confidence in their voting system. There are different drawbacks and problems for a very long time in the conventional voting system. Several countries prefer to use a centralized voting process that may cause certain inconsistencies. The security and transparency issue is a challenge to the traditional structure from worldwide elections. One of the solutions to all of these challenges is Blockchain technology. New ideas are being provided to create new forms of digital services. Since it strains a decentralized structure and multiple people own the whole database system. Blockchain will reduce the deception of database abuse by implementing blockchain in a voting framework. In order to ensure a safe voting system, we have proposed a blockchain-based distributed infrastructure that offers fairness, transparency and flexibility over the current system. To assess our protocol, we have implemented it on Ethereum and done performance analysis based on security properties and gas cost.
A Scalable Extreme Learning Machine (S-ELM) for Class-Incremental ECG-Based User Identification

User identification using electrocardiogram (ECG) is emerging due to the uniqueness and convenience of ECG signals. In addition, in real world applications, new subjects may enter the existed identification system and be authorized to access the private data. Therefore, we propose a scalable extreme learning machine (S-ELM) to meet the demand for class-incremental ECG-based user identification. We first prove that the output weight of the S-ELM learnt in a class-incremental manner is as same as that of a regular ELM which prior has the information of the number of total class. Therefore, in our experiment, S-ELM immunes from the catastrophic forgetting phenomenon, which is a common problem in class-incremental scenarios. Comparing to another class-incremental extreme learning machine such as progressive ELM, S-ELM outperforms progressive ELM by 7% accuracy in online dataset. Comparing to another commonly applied classifier, support vector machine (SVM) with linear and radial basis function (RBF) kernels, S-ELM shows its efficiency by 13.35% and 10.54% higher accuracy but only spends 5.09% and 3.48% of the inference time. Therefore, the proposed S-ELM is promising for the class-incremental ECG-based user identification.
Natureza Virtual: Enhancing Ecosystem Awareness by Using Virtual Reality in Educational Tourism

Virtual Reality (VR) has been applied in several business scenarios. The tourism industry is one of the sectors that have great potential to take advantage of VR technology. Especially the marketing strategy and the tourism-travel which has tremendous opportunities in recent studies in Technology Acceptance Model (TAM), Self-Determination Theory in combination with Virtual Learning Environment (VLE). However, few attempts have been made in Educational Tourism to examine the factors that enhance the visitor experience by using new methods of the trending technology regarding VR interactive application in combination with Augmented Virtuality (AV). This research aims to better understand the elements that can influence the flow-state and presence of the participants in the VR experience based on studies and how educational tourism and the industry can benefit from the trending technology. We cover the VR App development focused on the educational travel for the touristic region of the International Biological Station of Duero-Douro and its consideration to the visitors of the Bragança Ciência Viva Science Center (Centro Ciência Viva de Bragança—CCVB). Although the research is still under development we aim to analyze the impact on the attitude of the visitors towards the destination of the cross-border region between Portugal and Spain, and the validation of the facts related to theories addressed in this paper.
Adaptive Security via Deletion in Attribute-Based Encryption: Solutions from Search Assumptions in Bilinear Groups

One of the primary research challenges in Attribute-Based Encryption (ABE) is constructing and proving cryptosystems that are adaptively secure. To date the main paradigm for achieving adaptive security in ABE is dual system encryption. However, almost all such solutions in bilinear groups rely on (variants of) either the subgroup decision problem over composite order groups or the decision linear assumption. Both of these assumptions are decisional rather than search assumptions and the target of the assumption is a source or bilinear group element. This is in contrast to earlier selectively secure ABE systems which can be proven secure from either the decisional or search Bilinear Diffie-Hellman assumption. In this work we make progress on closing this gap by giving a new ABE construction for the subset functionality and prove security under the Search Bilinear Diffie-Hellman assumption.
ComBI: Compressed Binary Search Tree for Approximate k-NN Searches in Hamming Space

The space-partitioning based hashing techniques are widely used to represent high-dimensional data points as bit-codes. Although Binary Search Trees (BSTs) can be used for storing bit-codes, their size grows exponentially with code length. In practice, such a tree turns out to be highly sparse, increasing the miss-rate of nearest neighbor searches. We present Compressed BST of Inverted hash tables (ComBI), a geometrically motivated compression technique for BSTs. ComBI enables fast and approximate nearest neighbor searches without a significant memory footprint over BSTs. We show that approximate search in ComBI can perform competitively to an exact search algorithm in retrieving the nearest neighbors in a Hamming space. On a database containing ∼80M samples, ComBI yields an average precision of 0.90, at ∼4X - ∼296X improvements in run-time across different code lengths when compared to MIH, a widely used exact search method. On a database consisting of 1B samples, this value of precision (0.90) is reached at ∼4X - ∼19X improvements in run-time.
RUITE: Refining UI Layout Aesthetics Using Transformer Encoder

In the User Interface (UI) design process, designers sketch the UI design (low fidelity prototype) with minimal focus on visual appearances before converting them to higher fidelities. Contrary to low-fidelity, higher fidelity prototypes require better layout and aesthetic quality, during which designers adhere to design laws and conventions. Numerous research studies attempt to automate this transformation of low fidelity sketches to higher fidelities using Deep Neural Networks. However, these studies seldom focus on the layout quality and aesthetics of the generated higher fidelity prototype. As a solution, this paper proposes RUITE, a UI layout refinement engine that optimizes layouts using a Transformer Encoder. We trained RUITE by adding noise to misalign 35,369 UI layouts from the RICO dataset as input and the original aligned layout annotation as ground-truth. Upon evaluation with 4,421 misaligned UI layouts, RUITE provides 77% accuracy in aligning them. RUITE improves the existing research on transforming low-fidelity sketches to higher fidelities by beautifying generated UI layouts.
Learning Fine-Grained Semantics in Spoken Language Using Visual Grounding

In the case of unwritten languages, acoustic models cannot be trained in the standard way, i.e., using speech and textual transcriptions. Recently, several methods have been proposed to learn speech representations using images, i.e., using visual grounding. Existing studies have focused on scene images. Here, we investigate whether fine-grained semantic information, reflecting the relationship between attributes and objects, can be learned from spoken language. To this end, a Fine-grained Semantic Embedding Network (FSEN) for learning semantic representations of spoken language grounded by fine-grained images is proposed. For training, we propose an efficient objective function, which includes a matching constraint, an adversarial objective, and a classification constraint. The learned speech representations are evaluated using two tasks, i.e., speech-image cross-modal retrieval and speech-to-image generation. On the retrieval task, FSEN outperforms other state-of-the-art methods on both a scene image dataset and two fine-grained datasets. The image generation task shows that the learned speech representations can be used to generate high-quality and semantic-consistent fine-grained images. Learning fine-grained semantics from spoken language via visual grounding is thus possible.
An Effective Approach for Neural Network Training Based on Comprehensive Learning

Multi-layer feed-forward neural networks have been used to tackle many complex practical applications. Their performance is closely related to the success of training algorithms which adapt the weights in the network. Although conventional algorithms such as back-propagation are widely used, they suffer from drawbacks such as a tendency to get trapped in local optima. Stochastic optimisation algorithms, and in particular population-based metaheuristics, represent a useful alternative in this context. In this paper, we have proposed an effective hybrid algorithm, CLPSO-LM, which is based on particle swarm optimisation (PSO), a population-based metaheuristic algorithm, the Levenberg- Marquardt (LM) algorithm as a local search algorithm, and a comprehensive learning (CL) strategy. The CL strategy in our algorithm is responsible for improving the exploration ability of the algorithm and preventing premature convergence using neighbour candidate solutions in PSO. The best position found by comprehensive learning PSO is then used as the initial network weights for the LM algorithm. An extensive set of experiments on different classification benchmark datasets and comparison to various conventional and population-based algorithms shows CLPSO-LM to yield very competitive performance.
Efficiency and fairness in recurring data-driven risk assessments of violent recidivism

In this paper, we consider the prediction of violent recidivism in criminal justice as currently done through machine learning methods. Specifically, we consider sequential evaluations performed on jail inmates with a state-of-the-art risk assessment instrument, RisCanvi. In this protocol, evaluations are done periodically every six months to all inmates. We study a scenario in which the inter-evaluation period depends on the characteristics of each inmate. In this scenario, only a fraction of the inmates, those with the highest probability of having changed risk, are selected for the next evaluation. Our work is based on a cost-benefit analysis which leads to fewer evaluations in exchange for some missed/undetected changes. When modeling risk change, we obtain prediction models with AUC in the order of 0.74-0.78, which can be used to schedule evaluations leading to a small number of missed changes (about 14%) by performing half of the evaluations (50%). This allows freeing resources and staff for other tasks. Importantly, we analyze if this method leads to discriminatory outcomes across some characteristics, including disparate impact in the evaluation rates along nationality and age. By adjusting decision boundaries we are able to mitigate the disparate impact and ensure equality in the rate of evaluation. Even after mitigation, missed changes remain small (about 15%) while still halving the number of evaluations needed.
Prediction of Industrial Power Consumption and Air Pollutant Emission in Energy Internet

The energy internet integrated the information technology into the renewable energy can solve the energy shortage and environmental pollution problems. This paper studies the prediction of the power consumption in the energy internet based on the linear regression and random forest algorithms. Based on the predicted power consumption and the emission factors, the emission of the major air pollutants, i.e., PM, NOx and SO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> , in the cement industry are predicted. Simulation results show that these two predicted algorithms can achieve the accuracy performance as much as 89.4% and 97.6%, respectively. It also demonstrates that the predicted amount of PM emission is much more than NOx and SO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> .
Exploring Entrepreneurial Activities in Marginalized Widows

In some developing countries, widows are looked down upon and are often considered inauspicious especially in rural regions. Some societies even consider them and their issues invisible. This paper presents findings from a qualitative study focused on understanding how technology could facilitate entrepreneurial and DIY activities of widows from rural Sri Lanka. We conducted semi-structured interviews and field observations with thirteen widows from low socio-economic backgrounds, who were involved in various small-scale entrepreneurial activities. Our findings showed three central aspects associated with their entrepreneurial activities which can be supported through technology: initial stages of entrepreneurship, balancing work with life, and dealing with exploitations. This paper explores how gender inequality in a social context affects marginalized women in rural Sri Lanka in conducting their entrepreneurial efforts. In particular, we highlight resilient practices that the participants apply to support their entrepreneurial activities. With an "assets-based approach" we conclude by providing implications for policymakers, media, and HCI practitioners to support this inbuilt resilience by leveraging their current assets.
CT Guided Diagnosis: Cascaded U-Net for 3D Segmentation of Liver and Tumor

Abstract Volumetric estimation of the liver tumor is the first step to identifying the livers critical disorder. The liver and its tumor ratio prerequisite measures to select the therapeutic procedure. 3D printing and virtual reality platform require a segmented liver entity mask to evaluate the pre and post-treatment analysis. A cascaded U-Net model is proposed for automatic segmentation of liver and tumor in CT images. LiTS CT data set utilized for this study. The images were pre-processed using the windowing technique for contrast enhancement. Two U-Net models were modified for liver and tumor segmentation, respectively and connected in a cascaded manner. U-Net decoder end was modified in comparison to the original U-Net. The probability map of the first U-Net fed to the second U-Net and the input image to segment out the liver tumor. Eight subject volumetric CT datasets were utilized to test the cascaded U-Net performance and achieved average Dice coefficient for liver and tumor 0.95 and 0.69, respectively. Liver tumor diagnosis and treatment accuracy depend upon the precision of segmentation algorithms. Designed model segmented liver almost accurately and tumor segmented with limited accuracy. A further modification is required for the tumor segmentation cause of the occurrence of false negative.
Estimating the Size of Crowds through Deep Learning

Automatically counting the number of people in a crowd from a photographic image must address numerous potential issues: partial occlusion, differences in illumination, varying poses, and potential blurring caused by the different distances from individuals in the crowd to the camera. This is especially true with large crowds where close-up counting of faces is not easy/possible. Deep learning offers some opportunities to tackle these issues. In this paper, we describe the implementation of a crowd counting application based on a Multi-task Convolutional Neural Network. This approach enables the model to detect and gradually refine the identification and counting of faces. We tailor the model to make it suitable for the counting of individuals in larger-scale crowds using non-trivial and hence realistic and challenging crowd images. We provide details of the practical realization of the model through two mobile applications (iPhone/Android) and a web application for statistical analysis.
An Identity Authentication Method Based on Multi-modal Feature Fusion

Abstract At present, many judicial organs have adopted the daily attendance system based on face recognition to strengthen the supervision of community correction personnels. In order to prevent a few personnels from using pre-prepared photos and videos to deceive the face recognition system, this paper proposes an identity verification scheme with liveness detection based on dynamic combination of multimodal features. The main idea is as follows. Firstly, during face verification, the user is required to read out random numbers on the screen. Secondly, generating dynamic combination of speech, voiceprint, lip-reading and other verification methods according to the user’s risk personas, so as to achieve a balance between convenience and security. In addition, in view of the low accuracy of lip-reading recognition in practice, this paper changes the traditional lip-reading recognition based on morpheme to lip-reading recognition based on classifier. By optimizing the interactive content, the distinction of pronunciation between different Chinese characters is increased, and the accuracy of lip-reading recognition is significantly improved.