Reviewerly/exHarmony huggingface dataset:
Scene Description for Visually Impaired People with Multi-Label Convolutional SVM Networks

In this paper, we present a portable camera-based method for helping visually impaired (VI) people to recognize multiple objects in images. This method relies on a novel multi-label convolutional support vector machine (CSVM) network for coarse description of images. The core idea of CSVM is to use a set of linear SVMs as filter banks for feature map generation. During the training phase, the weights of the SVM filters are obtained using a forward-supervised learning strategy unlike the backpropagation algorithm used in standard convolutional neural networks (CNNs). To handle multi-label detection, we introduce a multi-branch CSVM architecture, where each branch will be used for detecting one object in the image. This architecture exploits the correlation between the objects present in the image by means of an opportune fusion mechanism of the intermediate outputs provided by the convolution layers of each branch. The high-level reasoning of the network is done through binary classification SVMs for predicting the presence/absence of objects in the image. The experiments obtained on two indoor datasets and one outdoor dataset acquired from a portable camera mounted on a lightweight shield worn by the user, and connected via a USB wire to a laptop processing unit are reported and discussed.
Secure WiFi-Direct Using Key Exchange for IoT Device-to-Device Communications in a Smart Environment

With the rapid growth of Internet of Things (IoT) devices around the world, thousands of mobile users share many data with each other daily. IoT communication has been developed in the past few years to ensure direct connection among mobile users. However, wireless vulnerabilities exist that cause security concerns for IoT device-to-device (D2D) communication. This has become a serious debate, especially in smart environments where highly sensitive information is exchanged. In this paper, we study the security requirements in IoT D2D communication. In addition, we propose a novel authentication approach called Secure Key Exchange with QR Code (SeKeQ) to verify user identity by ensuring an automatic key comparison and providing a shared secret key using Diffie-Hellman key agreement with an SHA-256 hash. To evaluate the performance of SeKeQ, we ran a testbed using devices with a WiFi-Direct communication interface. The obtained results depict that our proposal can offer the required security functions including key exchange, data confidentiality, and integrity. In addition, our proposal can reach the same security performances as MANA (Manual Authentication) and UMAC (Universal-Hashing Message Authentication Code) but with 10 times fewer key computations and reduced memory occupancy.
A Proposal for Creation of Beverage Suited for User by Blending Juices based on Interactive Genetic Algorithm

Obtaining media contents suited to user’s preference and kansei is one of the important targets of kansei engineering (affective engineering). However, it is still difficult, because preference and kansei are quite different between users and they are difficult to describe as equation or function. As a creation method of a beverage, this study proposed Interactive Genetic Algorithm that blends source juices and creates new beverages suited to each user. A target of optimization was the ratio of the source juices. A concrete system was constructed with personal computer, Arduino, bottles containing source juices, and water pumps. To show efficiencies of the proposed IGA, a tasting experiment composed of two steps was conducted. In the search step, the subjects evaluated blended beverages as GA individuals. As results, continuous increase in the fitness values was observed and it was significant. In the progress of the ratios, different and common trends were observed. However, significant increase in the fitness value was not observed in the evaluation step.
Real-Time Object Detection Based on Unmanned Aerial Vehicle

In this paper, a real-time image detection system for unmanned aerial vehicle (UAV) is presented. First, the You Only Look Once (YOLO) detector is retrained to detect and recognize objects in UAV images fast and accurately. The retrained YOLO detector possesses a tradeoff between quickly objects identification and precisely objects localization, accounting for 4 general moving objects (i.e., car, bus, truck and pedestrian) in UAV images. Second, extra 4,000 UAV images shot by the embedded UAV camera are fed into the YOLO which is used to predict bounding-box with label probabilities from a full image. The YOLO is compared with the Faster Region Convolutional Neural Network, as well as other deep-learning frameworks for object detection. Field experiments on UAV videos shot in various backgrounds are performed to testify that the proposed system can detect objects in UAV images effectively and robustly, achieving a real-time detection speed at 15 frames per second and a satisfying accuracy on the wild test set of 400 UAV images with a Graphic Processing Unit (GPU) acceleration.
SVM-Based Multiple Instance Classification via DC Optimization

A multiple instance learning problem consists of categorizing objects, each represented as a set (bag) of points. Unlike the supervised classification paradigm, where each point of the training set is labeled, the labels are only associated with bags, while the labels of the points inside the bags are unknown. We focus on the binary classification case, where the objective is to discriminate between positive and negative bags using a separating surface. Adopting a support vector machine setting at the training level, the problem of minimizing the classification-error function can be formulated as a nonconvex nonsmooth unconstrained program. We propose a difference-of-convex (DC) decomposition of the nonconvex function, which we face using an appropriate nonsmooth DC algorithm. Some of the numerical results on benchmark data sets are reported.
Accountable authority identity-based broadcast encryption with constant-size private keys and ciphertexts

Identity-based broadcast encryption (IBBE) enables a sender to broadcast a message to multiple identities efficiently. Nevertheless, since IBBE is based on identity-based cryptography (IBC), it suffers from the inherent key escrow problem. As a consequence, not only the user knows its private key, but also the private key generator (PKG). This property leads to that the creator of a given pirated private key, named a private key from an unknown source, is untraceable since both the PKG and suspected user can generate such a pirated private key for this identity. To mitigate this problem, accountable authority IBBE (A-IBBE) was proposed to provide accountability for IBBE, where white-box A-IBBE can distinguish the creator of a given pirated private key between the PKG and suspected user and black-box A-IBBE can further trace the creator of a decoder box. However, all prior constructions of black-box A-IBBE do not capture constant-size private keys and ciphertexts simultaneously. In this paper, to fill this gap, we propose a weak black-box A-IBBE scheme with constant-size private keys and ciphertexts. Our construction supports public traceability such that tracing can be performed with the public tracing key of suspected user instead of its secret key. We first define the weak black-box A-IBBE with public traceability. Then, we give our construction where the private key and ciphertext consist of two and five group elements respectively. Furthermore, the proposed scheme is proven to be secure with random oracles.
A Study of Persistent Fault Analysis

Persistent faults mark a new class of injections that perturb lookup tables within block ciphers with the overall goal of recovering the encryption key. Unlike earlier fault types persistent faults remain intact over many encryptions until the affected device is rebooted, thus allowing an adversary to collect a multitude of correct and faulty ciphertexts. It was shown to be an efficient and effective attack against substitution-permutation networks. In this paper, the scope of persistent faults is further broadened and explored. More specifically, we show how to construct a key-recovery attack on generic Feistel schemes in the presence of persistent faults. In a second step, we leverage these faults to reverse-engineer AES- and PRESENT-like ciphers in a chosen-key setting, in which some of the computational layers, like substitution tables, are kept secret. Finally, we propose a novel, dedicated, and low-overhead countermeasure that provides adequate protection for hardware implementations against persistent fault injections.
A Modularized Architecture of Multi-Branch Convolutional Neural Network for Image Captioning

Image captioning is a comprehensive task in computer vision (CV) and natural language processing (NLP). It can complete conversion from image to text, that is, the algorithm automatically generates corresponding descriptive text according to the input image. In this paper, we present an end-to-end model that takes deep convolutional neural network (CNN) as the encoder and recurrent neural network (RNN) as the decoder. In order to get better image captioning extraction, we propose a highly modularized multi-branch CNN, which could increase accuracy while maintaining the number of hyper-parameters unchanged. This strategy provides a simply designed network consists of parallel sub-modules of the same structure. While traditional CNN goes deeper and wider to increase accuracy, our proposed method is more effective with a simple design, which is easier to optimize for practical application. Experiments are conducted on Flickr8k, Flickr30k and MSCOCO entities. Results demonstrate that our method achieves state of the art performances in terms of caption quality.
Blockchain-based Peer-to-Peer Energy Trade: A Critical Review of Disruptive Potential

Motivated by numerous drivers, blockchain-based peer-to-peer energy trade whitepapers surged in the past two years. Assuming disruption through blockchain technology, they envisioned a transformation of energy systems through technosocio-economic solutions. Few impartial and sober assessments of blockchain-based energy projects exist, and many publications praise disruptive potential without further examination. A more distant and critical perspective, however, is imperative for a responsible use of a novel, in particular disruptive, technology. This review aims at surveying the energy system envisioned by the projects through discussing the projects by their characteristics, their perspective on the transactive energy lifecycle and the energy ecosystem envisioned in the white papers. This review is descriptive and comparative in nature, and attempts to synthesize topics raised in the white papers through methods of grounded theory, as well as assessing the disruptive potential of blockchain technology in energy systems. Through this and a critical and neutral perspective, it strives to (soberly) contribute to a discussion on the digitization of elements of the energy system, and how blockchain-based use cases can contribute constructively to the problems at hand.
Neighborhood-Correction Algorithm for Classification of Normal and Malignant Cells

Classification of normal and malignant cells observed under a microscope is an essential and challenging step in the development of a cost-effective computer-aided diagnosis tool for acute lymphoblastic leukemia. In this paper, we propose the neighborhood-correction algorithm (NCA) to address this challenge, which consists of three major steps, including (1) fine-tuning a pretrained residual network using training data and producing initial labels and feature maps for test data, (2) constructing a Fisher vector for each cell image based on its feature maps, and (3) correcting the initial label of each test cell image via the weighted majority voting based on its most similar neighbors. We have evaluated this algorithm on the database provided by the grand challenge on the classification of normal and malignant cells (C-NMC) in B-ALL white blood cancer microscopic images. Experimental results demonstrate that our proposed NCA achieves the weighted F1-score of 92.50% and balanced accuracy of 91.73% in the preliminary testing and achieves weighted F1-score of 91.04% in the final testing, which ranks the first in C-NMC. Associated code is available at https://github.com/YongshengPan/ISBI-NMC .
Document Classification of Filipino Online Scam Incident Text using Data Mining Techniques

The increasing number of online transactions and other internet activities give rise to the proliferation of online scam. The Philippine National Police - Anti Cybercrime Group (PNP-ACG) reported an increasing number of complaints from a double digit figure in 2013 to a triple digit figure in 2017. The challenge of addressing this problem in the Philippines is shared by other developing countries in Southeast Asia and other parts of the world. Since 2013 when the PNP-ACG was established, cybercrime data continue to be accumulated but were not given much attention and significance in research. Previous studies highlight the importance of taking advantage of data mining. However, the absence of empirical studies on cybercrime analytics in the country connotes the lack of exploitation of data mining in facilitating cybercrime investigations. This study exploits Weka text mining tool in order to draw insights by classifying a given online scam dataset. Online scam unstructured data were considered as dataset containing a total of 14,098 mainly Filipino words. J48 Decision Tree, Naïve Bayes, and Sequential Minimal Optimization were used to build classification models. All these three classifiers or algorithms were compared in terms of performance and prediction accuracy. The results show that J48 achieves the highest accuracy and the lowest error rate, followed by Naïve Bayes and then the SMO classifier. Also, the responses during validation reveal that J48 is preferred over the other classifiers as it easy to understand and apply in cybercrime investigations. This demonstrates how text mining can assist the PNP-ACG in analyzing online scam criminal data as it also highlights the importance of employing data mining tools in the legal and criminal investigation domains in the Philippines. Further work can be carried out in the future using different and a more inclusive cybercrime datasets and other classification techniques in Weka or any other data mining tool, and other data mining tasks such as crime prevention and prediction, clustering, finding leads, trends and patterns of criminal activities, among others.
Rapid multispectral data sampling using Google Earth Engine

The advent of cloud-based GIS tools has enabled the rapid exploration and processing of geospatial datasets. The Google Earth Engine (GEE) platform provides a library of algorithms and a powerful application programming interface (API) to produce flexible cloud-based applications that leverage Google's computing infrastructure for geospatial analysis. We introduce "Spectral Point", a new GUI tool developed in GEE that allows users to explore, process and extract multispectral data rapidly within a single browser window. The ability to access and measure spectral signals from surface deposits using the entire available Landsat and Sentinel 2 archive is of tremendous benefit to geomorphic research, removing the need to download and process terabytes worth of imagery. Spectral values from composite imagery collected in GEE that relate to changes in surface mineral composition agree with corresponding point values using conventional desktop Landsat processing. The "Spectral Point" tool makes it fast and simple to extract quantitative, contrast-corrected brightness data from multispectral imagery compared conventional desktop-based approaches. At the same time, the user needs no experience developing code, proprietary third-party software or dedicated high-performance computing and only a modern web browser. The "Spectral Point" tool has many potential applications in the remote study of Earth's surface; for example, we explore a case study from the western United States that demonstrates how the tool can be used for mapping, geochronology, and estimating weathering rates for Quaternary landforms. With increasing numbers of satellites, we are now faced with a growing deluge of geospatial data. Cloud-based solutions to mapping, field reconnaissance and image processing will be increasingly necessary to handle this valuable but untapped satellite image resource. "Spectral Point" is an example of a new generation of web-based remote sensing tools available for Earth scientists, leveraging the revolution in cloud-based processing power and access to entire satellite image archives.
Quality-related English text classification based on recurrent neural network

With the rapid development of artificial intelligence technology, text categorization technology is becoming more and more mature. However, text categorization in real situations still faces various unconstrained conditions. English text is an important part of text information, it is also an important way for people to get information from abroad. How can everyone get the desired content from the massive data quickly and accurately, it has become a hot issue in current research. This paper improves the current text categorization algorithm based on English quality-related text categorization. The design and implementation of text categorization system are illustrated with an example of English quality-related text categorization system, complete the research work of text categorization algorithm. The core work of this paper is to mine, classify and analyze large amounts of data in English text by using the method of combining cyclic neural network with quality. Finally, the essential features of high quality English texts are obtained. Traditional English text categorization algorithm if the amount of training data is large, it is easy to show some defects such as unclear feature items. In view of these problems, in order to improve the accuracy and flexibility of English text categorization, this paper proposes a quality-related English text categorization method based on cyclic neural network. A mechanism combining attention is proposed to improve the problem of label disorder and make the structure of the model more flexible. The model proposed in this paper is compared and optimized. Experiments show that the accuracy of neural text classification based on quality classification can reach about 96%.
Model predictive approach to integrated path planning and tracking for autonomous vehicles

In the field of path planning for autonomous vehicle, the existing studies separately consider the path planning and path tracking problem. To fill in this research gap, this study proposes an integrated path planning and path tracking control method. In addition, this paper studies the collision avoidance problem of autonomous vehicles by considering static and dynamic obstacles. Simulation results show that the proposed method can generate a collision-free path and control the autonomous vehicle to avoid the obstacles simultaneously.
Text Input Performance with a Mixed Reality Head-Mounted Display (HMD)

Past research has shown that text input methods must be quick, efficient, and involve little to no learning in order for them to be accepted in a consumer market. The Microsoft HoloLens is a mixed reality head-mounted display (HMD) that requires gestures or a physical clicker for interaction, including text entry. In this study, we investigated performance, perceived usability, and workload of these text input methods. Participants were able to type only 5.41WPM with the gesture method, gave it a poor usability score. The clicker method resulted in a WPM rate of 6.58, was given an “OK” usability score, and lower perceived exertion scores in the bicep and index finger. This study demonstrates the need for further research to develop more optimal text entry methods for MR environments.
CamLoc: Pedestrian Location Estimation through Body Pose Estimation on Smart Cameras

Advances in hardware and algorithms are driving the exponential growth of Internet of Things (IoT), with increasingly more pervasive computations being performed near the data generation sources. With this wave of technology, a range of intelligent devices can perform local inferences (activity recognition, fitness monitoring, etc.), which have obvious advantages: reduced inference latency for interactive (real-time) applications and better data privacy by processing user data locally. Video processing can benefit many applications and data labelling systems, although performing this efficiently at the edge of the Internet is not trivial. In this paper, we show that accurate pedestrian location estimation is achievable using deep neural networks on fixed cameras with limited computing resources. Our approach, CamLoc, uses pose estimation from key body points detection to extend pedestrian skeleton when the entire body is not in view (occluded by obstacles or partially outside the frame). Our evaluation dataset contains over 2100 frames from surveillance cameras (including two cameras simultaneously pointing at the same scene from different angles), in 42 different scenarios of activity and occlusion. We make this dataset available together with annotations indicating the exact 2D position of person in frame as ground-truth information. CamLoc achieves good location estimation accuracy in these complex scenarios with high levels of occlusion, matching the performance of state-of-the-art solutions, but using less computing resources and attaining a higher inference throughput.
Internet of Things (IoT) Fall Detection using Wearable Sensor

Abstract The IoT fall detection system detects the fall through the data classification of falling and daily living activity. It includes microcontroller board (Arduino Mega 2560), Inertial Measurement Unit sensor (Gy-521 mpu6050) and WI-FI module (ESP8266-01). There total ten (10) subjects in this project. The data of falling and non-falling (daily living activity) can be identified. The falling is the frontward fall, while the daily living activity includes standing, sitting, walking and crouching. K-nearest neighbour (k-NN) classifiers were used in the data classification. The accuracy of k-NN classifiers were 100% between falling and non-falling class. The feature was selected based on the percentage of accuracy of the k-NN classifier. The features of the Aareal.z (97.14%) and Angle.x (97.24%) were selected due to the good performance during the classification of the falling and non-falling class. The performance of the Aareal.z (58.41%) and Angle.x (57.78%) were satisfactory during the sub-classification of the non-falling class. Hence, the feature of Aareal.z and Angle.x were selected as the features which were implemented in the IoT fall detection device.