Reviewerly/exHarmony huggingface dataset:
A Personalized Recommendation Framework for Social Internet of Things (SIoT)

Recommendation is inevitably crucial in human life, as almost every human daily activity involves decision and choice making from amongst various alternatives at our disposal. The use of computers has made automated decision making interesting, and beneficial in many areas of real-life activities, targeted at meeting different yet specific users' needs, such as in Social Internet of Things (SIoT). SIoT is defined as an emerging paradigm of IoT where intelligent devices are able to create social relationships among them in achieving a goal. Providing particular recommendation by using Social Network Services (SNS) with geographical location-aware feature is one of the interesting SIoT problems. This is mainly because the recommendation is usually constrained by several factors, which pose challenges to us on the available options, and hence having the right information that leads us in taking right decision at the right time is essential. The main contribution of this paper is providing a personalized recommendation framework which is suitable to be adopted in SIoT systems. We also analyzed the performances of the proposed framework with different datasets. Promising results are shown from the analysis.
Fault Detection using hybrid of KF-ELM for Wireless Sensor Networks

The adoption of data aggregation depending on data fusion and data acquisition for wireless sensor networks (WSN) is increasing these days. While in WSN, the sensor node senses data and send them to the end node. The application of WSN gets limited due to its features such as low-cost sensor nodes, limited battery backups. The usage of sensor nodes in WSN becomes prone to faulty behavior due to its resource constraint and easily gets defected. Predictive detection using data fusion can be a better choice in order to detect the fault with low transmission energy and low power usage. Considering the conditions of the sensor node with its limited capacity of storing and processing of data, a hybrid predictive classification technique is proposed by using the Kalman filter with Extreme learning machine. Here for data fusion Kalman filter is used to train the sink node with the faulty pattern of data in place of training it with the larger amount. In addition, Extreme learning machine (ELM) is used as a predictive classifier, which can provide a high prediction with low communication overhead. The proposed work is evaluated using standard WSN data by inserting random anomalies to it. The performance is measured in terms of detection accuracy and computational time.
Precise Separation of Adjacent Nuclei Using a Siamese Neural Network

Nuclei segmentation in digital histopathology images plays an important role in distinguishing stages of cancer. Recently, deep learning based methods for segmenting the nuclei have been proposed, but precise boundary delineation of adjacent nuclei is still challenging. To address this problem, we propose a post processing method which can accurately separate the adjacent nuclei when an image and a predicted nuclei segmentation are given. Specifically, we propose a novel deep neural network which can predict whether adjacent two instances belong to a single nuclei or separate nuclei. By borrowing the idea of decision making with Siamese networks, the proposed network learns the affinity between two adjacent instances and surrounding features from a large amount of adjacent nuclei even though the training data is limited. Furthermore, we estimate the segmentation of instances through a decoding network and then use their overlapping Dice score for class prediction to improve the classification accuracy. The proposed method effectively alleviates the over-fitting problem and compatible with any cell segmentation algorithms. Experimental results show that our proposed method significantly improves the cell separation accuracy.
Embedded Dynamic Modification for Efficient Watermarking Using Different Medical Inputs in IoT

Nowadays, the Internet of Things (IoT) occupies an effective and essential place, considerable progress has been made in the healthcare sector. This is valid in view of the spacious developments of the IoT environment, towards guaranteeing the reliability of reciprocal medical images. Due to the critical content in medical images, huge attention must be given to safeguarding again frequent cyber-attacks. Such attacks may change image content thereby, impacting a patient's diagnosis. In this respect, this paper elaborates on a robust medical watermarking based on digital watermarking to embed a physician's signature in a medical image within a hidden state. Statistical arguments methods employed to get the best effectiveness metric result and to avoid losing clinical information that appears in the medical image. The suggested watermarking algorithm is composed of Daubechies-3 and Daubechies-9 wavelet transform, utilizing three different medical images as a host image.
An Empirical Evaluation of k-Means Clustering Technique and Comparison

In this research work we have studied the behavior of k-means clustering technique and we have made an analysis. We have mentioned the limitations and some methods to improve it. Next we have discussed two improved versions of k-means algorithm - in the first version, a greedy method is being applied to overcome some of the limitation whereas in the second version, using some pre-computation, we can improve the traditional k-means technique to some extent. We have implemented these algorithms in python programming language and compared the results. We have also investigated the ability of four cluster validation technique in terms of these three clustering techniques. From the experimental outcomes, indications regarding the optimal validation method as well as optimal clustering algorithm is being presented.
Practical Fully Homomorphic Encryption for Fully Masked Neural Networks

Machine learning applications are spreading in many fields and more often than not manipulate private data in order to derive classifications impacting the lives of many individuals. In this context, it becomes important to work on privacy preserving mechanisms associated to different privacy scenarios: protecting the training data, the classification data, the weights of a neural network. In this paper, we study the possibility of using FHE techniques to address the above issues. In particular, we are able to evaluate a neural network where both its topology and its weights as well as the user data it operates on remain sealed in the encrypted domain. We do so by relying on Hopfield neural networks which are much more "FHE friendly" than their feed-forward counterparts. In doing so, we thus also argue the case of considering different (yet existing) Neural Network models better adapted to FHE, in order to more efficiently address real-world applications.The paper is concluded by experimental results on a face recognition application demonstrating the ability of the approach to provide reasonable recognition timings ( $${\approx }0.6$$ s) on a single standard processor core.
Staged abstract interpreters: fast and modular whole-program analysis via meta-programming

It is well known that a staged interpreter is a compiler: specializing an interpreter to a given program produces an equivalent executable that runs faster. This connection is known as the first Futamura projection. It is even more widely known that an abstract interpreter is a program analyzer: tweaking an interpreter to run on abstract domains produces a sound static analysis. What happens when we combine these two ideas, and apply specialization to an abstract interpreter? In this paper, we present a unifying framework that naturally extends the first Futamura projection from concrete interpreters to abstract interpreters. Our approach derives a sound staged abstract interpreter based on a generic interpreter with type-level binding-time abstractions and monadic abstractions. By using different instantiations of these abstractions, the generic interpreter can flexibly behave in one of four modes: as an unstaged concrete interpreter, a staged concrete interpreter, an unstaged abstract interpreter, or a staged abstract interpreter. As an example of abstraction without regret, the overhead of these abstraction layers is eliminated in the generated code after staging. We show that staging abstract interpreters is practical and useful to optimize static analysis, all while requiring less engineering effort and without compromising soundness. We conduct three case studies, including a comparison with Boucher and Feeley’s abstract compilation, applications to various control-flow analyses, and a demonstration for modular analysis. We also empirically evaluate the effect of staging on the execution time. The experiment shows an order of magnitude speedup with staging for control-flow analyses.
A multimodal vision sensor for autonomous driving

This paper describes a multimodal vision sensor that integrates three types of cameras, including a stereo camera, a polarization camera and a panoramic camera. Each sensor provides a specific dimension of information: the stereo camera measures depth per pixel, the polarization obtains the degree of polarization, and the panoramic camera captures a 360&deg; landscape. Data fusion and advanced environment perception could be built upon the combination of sensors. Designed especially for autonomous driving, this vision sensor is shipped with a robust semantic segmentation network. In addition, we demonstrate how cross-modal enhancement could be achieved by registering the color image and the polarization image. An example of water hazard detection is given. To prove the multimodal vision sensor’s compatibility with different devices, a brief runtime performance analysis is carried out.
Multi-View Fusion with Extreme Learning Machine for Clustering

Unlabeled, multi-view data presents a considerable challenge in many real-world data analysis tasks. These data are worth exploring because they often contain complementary information that improves the quality of the analysis results. Clustering with multi-view data is a particularly challenging problem as revealing the complex data structures between many feature spaces demands discriminative features that are specific to the task and, when too few of these features are present, performance suffers. Extreme learning machines (ELMs) are an emerging form of learning model that have shown an outstanding representation ability and superior performance in a range of different learning tasks. Motivated by the promise of this advancement, we have developed a novel multi-view fusion clustering framework based on an ELM, called MVEC. MVEC learns the embeddings from each view of the data via the ELM network, then constructs a single unified embedding according to the correlations and dependencies between each embedding and automatically weighting the contribution of each. This process exposes the underlying clustering structures embedded within multi-view data with a high degree of accuracy. A simple yet efficient solution is also provided to solve the optimization problem within MVEC. Experiments and comparisons on eight different benchmarks from different domains confirm MVEC’s clustering accuracy.
Echocardiography Segmentation by Quality Translation Using Anatomically Constrained CycleGAN

Segmentation of an echocardiogram (echo) is favorable for assessment of cardiac functionality and disease. The quality of the captured echo is a key factor that affects the segmentation accuracy. In this paper, we propose a novel generative adversarial network architecture, which aims to improve echo quality for the segmentation of the left ventricle (LV). The proposed model is anatomically constrained to the structure of the LV in apical four chamber (AP4) echo view. A set of discriminative features are learned through unpaired translation of low to high quality echo using adversarial training. The anatomical constraint regularizes the model during end-to-end training to preserve the corresponding shape of the LV in the translated echo. Experiments show that leveraging information in the translated high quality echocardiograms by the proposed method improves the robustness of the segmentation, where the worst-case Dice similarity score is improved by a margin of 15% over the baseline.
Query Expansion With Local Conceptual Word Embeddings in Microblog Retrieval

Since the length of microblog texts, such as tweets, is strictly limited to 140 characters, traditional Information Retrieval techniques suffer from the vocabulary mismatch problem severely and cannot yield good performance in the context of microblogosphere. To address this critical challenge, in this paper, we focus on the use of local conceptual word embeddings for enhance microblog retrieval effectiveness. In particular, we propose a novel k-Nearest Neighbor (kNN) based Query Expansion (QE) algorithm to generate words from local word embeddings to expand the original query, which leads to better understanding of the information need. Besides, in order to further satisfy users' real-time information need, we incorporate temporal evidences into the expansion algorithm, which can boost recent tweets in the retrieval results with respect to a given topic. Experimental results on the official TREC Twitter corpora demonstrate the significant superiority of our approach over baseline methods.
An SNR Estimation Technique Based on Deep Learning

Signal-to-noise ratio (SNR) is a priori information necessary for many signal processing algorithms or techniques. However, there are many problems exsisting in conventional SNR estimation techniques, such as limited application range of modulation types, narrow effective estimation range of signal-to-noise ratio, and poor ability to accommodate non-zero timing offsets and frequency offsets. In this paper, an SNR estimation technique based on deep learning (DL) is proposed, which is a non-data-aid (NDA) technique. Second and forth moment (M2M4) estimator is used as a benchmark, and experimental results show that the performance and robustness of the proposed method are better, and the applied ranges of modulation types is wider. At the same time, the proposed method is not only applicable to the baseband signal and the incoherent signal, but can also estimate the SNR of the intermediate frequency signal.
JusticeChain: Using Blockchain to Protect Justice Logs

The auditability of information systems plays an essential role in public administration. Information system accesses are saved in log files so auditors can later inspect them. However, there are often distinct stakeholders with different roles and different levels of trust, namely the IT Department that manages the system and the government ministries that access the logs for auditing. This scenario happens at the Portuguese judicial system, where stakeholders utilize an information system managed by third-parties. This paper proposes using blockchain technology to make the storage of access logs more resilient while supporting such a multi-stakeholder scenario, in which different entities have different access rights to data. This proposal is implemented in the Portuguese Judicial System through JusticeChain. JusticeChain comprises the blockchain components and blockchain client components. The blockchain components grant log integrity and redundancy, while the blockchain client component is responsible for saving logs on behalf of an information system. The client allows end-users to access the blockchain, allowing audits mediated by the blockchain.
Segmentation and Evaluation of White Blood Cells using Segmentation Algorithms

Image Segmentation is an important step in Image processing applications. Microscopic images of White Blood Cells (WBC) enable hematologists to predict the vulnerability to several diseases. Automatic segmentation of different types of WBCs is the most challenging task in the prediction of the presence of disease. Our objective is to segment the blood cells in microscopic images using segmentation algorithms followed by evaluation of their performance. Color is a major cue to discern segmented WBCs from microscopic images. This paper appraises the performance of various color based segmentation techniques, and compare the results against ground truth image. Analysis of the results using dice similarity, shows that saliency based segmentation is the most suitable model to segment WBC cells.
On-Off Attack on a Blockchain-based IoT System

There is a growing interest in using the Blockchain for resolving IoT security and trustworthiness issues existing in today's complex systems. Blockchain concerns trust in peer to peer networks by providing a distributed tamper-resistant ledger. However, the combination of these two emerging technologies might create new problems and vulnerabilities that attackers might abuse. In this paper, we aim to investigate the trust mechanism of Lightweight Scalable BlockChain (LSB), that is a Blockchain specifically designed for Internet of Things networks, to show that a malicious participant in a Blockchain architecture have possibility to pursue an On-Off attack and downgrade the integrity of the distributed ledger. We choose a remote software update process as an instance to represent this violation. Finally, using the actor-based language Rebeca, we provide a model of a system under attack and verify the described attack scenario.
Visual Correspondences for Unsupervised Domain Adaptation on Electron Microscopy Images

We present an Unsupervised Domain Adaptation strategy to compensate for domain shifts on Electron Microscopy volumes. Our method aggregates visual correspondences-motifs that are visually similar across different acquisitions-to infer changes on the parameters of pretrained models, and enable them to operate on new data. In particular, we examine the annotations of an existing acquisition to determine pivot locations that characterize the reference segmentation, and use a patch matching algorithm to find their candidate visual correspondences in a new volume. We aggregate all the candidate correspondences by a voting scheme and we use them to construct a consensus heatmap: a map of how frequently locations on the new volume are matched to relevant locations from the original acquisition. This information allows us to perform model adaptations in two different ways: either by a) optimizing model parameters under a Multiple Instance Learning formulation, so that predictions between reference locations and their sets of correspondences agree, or by b) using high-scoring regions of the heatmap as soft labels to be incorporated in other domain adaptation pipelines, including deep learning ones. We show that these unsupervised techniques allow us to obtain high-quality segmentations on unannotated volumes, qualitatively consistent with results obtained under full supervision, for both mitochondria and synapses, with no need for new annotation effort.
The Role of Serious Gaming using Virtual Reality Applications for 3D Architectural Visualization

This paper investigates the advantage of VR technology in architectural education.Different studies showed that VR increases the engagement and motivation of students by creating an interactive environment and provides deeper learning.On the other hand, Precedents in architecture are the first and the most important step toward architectural education.One needs to study these precedents and store them in the experiential/episodic memory.Visiting precedents is more beneficial than conventional ways of studying them such as hand sketching but it is costly and not available to all students.Therefore, we believe that the VR technology can take the place of visiting precedents since it resembles the precedents exactly and offers a better, cheaper and easier way for accumulating highly specific architectural knowledge than a local visit due to providing architectural interactivity.We tested our hypothesis by using Therme Vals and an HTC Vive headset among architecture students.This paper shows that VR is an advantage in architectural education because it offers cheap and easy solutions for studying precedents.Also, it motivates the students and leads to deeper learning because of the interactivity that it offers.The students were able to recognize a complex plan clearly and remember the spatial configuration.
Aadhaar and data privacy: biometric identification and anxieties of recognition in India

The data privacy debate in India has evolved with respect to the government’s biometric identity programme, Aadhaar that enrols welfare-dependent, poor populations to grant them access to government benefits. While legal challenges to Aadhaar by civil society groups argued that the biometric identity infrastructure creates conditions of mass surveillance and violation of individual privacy, the Indian Supreme Court in 2018 ruled that the government was justified in restricting individual privacy for the collective good of providing welfare in a transparent and corruption-free manner. Given the disproportionate burden on these populations to prove their identities to the state, this paper draws on a close reading of legal and policy texts, and activist documentation to argue that there is a need to move beyond the narrative of mass surveillance as privacy violation. Data privacy interests of the welfare-dependent emerge in the moment of biometric authentication, which creates anxieties of recognition when their authentication attempts fail or are deliberately falsified. Often, to have better social mobility, they are compelled to be physically mobile in order to enrol or update their records under conditions of physical disability and meagre socioeconomic means. These anxieties illuminate their privacy interests through a compromise of dignity or dignified living, a formulation articulated in the 2018 Aadhaar verdict. The paper makes a critical contribution to the global conversation on data privacy through a discussion of the Indian case that demonstrates the privacy-recognition nexus in local contexts.
Object Detection in Shelf Images with YOLO

Object detection in shelf images can solve many problems in retails sales such as monitoring the number of products on the shelves, completing the missing products and matching the planogram continuously. This study aims to detect object in shelf images with deep learning algorithms. Firstly, object detection algorithms and datasets are examined in the literature. Then, experimental study is performed using Coca Cola images obtained from Imagenet and Grocery dataset with YOLO (You Only Look Once) algorithm. Results of the study are discussed from different sides such as number of classes, threshold values and numder of iteration.
SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentation

Deep learning has largely reduced the need for manual feature selection in image segmentation. Nevertheless, network architecture optimization and hyperparameter tuning are mostly manual and time consuming. Although there are increasing research efforts on network architecture search in computer vision, most works concentrate on image classification but not segmentation, and there are very limited efforts on medical image segmentation especially in 3D. To remedy this, here we propose a framework, SegNAS3D, for network architecture search of 3D image segmentation. In this framework, a network architecture comprises interconnected building blocks that consist of operations such as convolution and skip connection. By representing the block structure as a learnable directed acyclic graph, hyperparameters such as the number of feature channels and the option of using deep supervision can be learned together through derivative-free global optimization. Experiments on 43 3D brain magnetic resonance images with 19 structures achieved an average Dice coefficient of 82%. Each architecture search required less than three days on three GPUs and produced architectures that were much smaller than the state-of-the-art manually created architectures.