Reviewerly/exHarmony huggingface dataset:
Improving Robustness of Deep Transfer Model by Double Transfer Learning

In recent years, deep learning models have been widely adopted for transfer learning tasks. The deep models, however, were shown to be easily attacked by adversarial examples, which is generated from original samples with the carefully designed small perturbations. Thus, the transfer learning models based on deep networks will also face this problem. Because of the particularity of transfer learning tasks, using the conventional adversarial training to improve the robustness of deep transfer models is very difficult. In this paper, we propose a novel Double Transfer Learning with Adversarial Training (DTLAT) method to enhance the robustness of deep transfer learning models. Our intuition is using the instances of the source domain and target domain to help with adversarial training. At the same time, we design a reverse transfer learning method to weaken the effect of attack methods and improve the performance of deep transfer models of target domain. We regard the adversarial examples from some kind of attack method, like FGSM, as an adversarial domain while try to improve the generalization ability on other attacks method. Experiments demonstrate that DTLAT exceed many other methods about improving the robustness of the deep transfer model on several benchmark datasets.
Capturing accelerometer outputs in healthy volunteers under normal and simulated-pathological conditions using ML classifiers

Wearable devices offer a possible solution for acquiring objective measurements of physical activity. Most current algorithms are derived using data from healthy volunteers. It is unclear whether such algorithms are suitable in specific clinical scenarios, such as when an individual has altered gait. We hypothesized that algorithms trained on healthy population will result in less accurate results when tested in individuals with altered gait. We further hypothesized that algorithms trained on simulated-pathological gait would prove better at classifying abnormal activity. We studied healthy volunteers to assess whether activity classification accuracy differed for those with healthy and simulated-pathological conditions. Healthy participants (n=30) were recruited from the University of Leeds to perform nine predefined activities under healthy and simulated-pathological conditions. Activities were captured using a wrist-worn MOX accelerometer (Maastricht Instruments, NL). Data were analyzed based on the Activity-Recognition-Chain process. We trained a Neural-Network, Random-Forests, k-Nearest-Neighbors (k-NN), Support-Vector-Machines (SVM) and Naive Bayes models to classify activity. Algorithms were trained four times; once with `healthy' data, and once with `simulated-pathological data' for each of activity-type and activity-task classification. In activity-type instances, the SVM provided the best results; the accuracy was 98.4% when the algorithm was trained and then tested with unseen data from the same group of healthy individuals. Accuracy dropped to 52.8% when tested on simulated-pathological data. When the model was retrained with simulated-pathological data, prediction accuracy for the corresponding test set was 96.7%. Algorithms developed on healthy data are less accurate for pathological conditions. When evaluating pathological conditions, classifier algorithms developed using data from a target sub-population can restore accuracy to above 95%.
A Novel Obstacle Detection and Tracking System Using Fisheye Vision

The large blind region behind the large body of a truck is the primary reason for reversing collisions. However, there are few truck reversing auxiliary approaches to avoid reversing accidents. In this paper, we present a novel heavy-duty truck safety approach for reversing based on pedestrian detection and obstacle tracking using fisheye stereo vision. The proposed system contains four primary steps: fisheye camera calibration and image matching, distortion correction and pedestrian identification, obstacle tracking with an improved CMT algorithm, and heavy-duty truck speed control method.
Sonar Inspired Optimization based Feature Selection

One of the problems that Machine Learning (ML) algorithms face in classification tasks is the Curse of Dimensionality, which refers to the sensitivity of their performance to the data dimensionality. The solution to this problem is to select the features that are of higher importance for the model produced. To improve the performance of a classifier, various meta-heuristic algorithms have been implemented due to their ability to provide optimal solutions in problems that there are multiple candidate solutions, such as in Feature Selection (FS). In this study, Sonar Inspired Optimization (SIO) algorithm is used to perform FS in order to improve the performance of a state-of-the-art classifier, i.e. k-NN. SIO’s performance is compared with other nature-inspired meta-heuristic algorithms that have been used for the same task.
Automatic Fingerprint Extraction of Mobile APP Users in Network Traffic

Network operators often need a clear visibility of the mobile APPs and their user scales running in the network traffic. This is critical for network management and network security. Analysis of the network traffic using the extracted APP features and user fingerprints is helpful for effective network operations, management, and security monitoring of LANs, MANs, and WANs. In China, the number of mobile APP users continues to increase, and the proportion of Internet users using mobile APPs to access the Internet far exceeds that of computers, making this task significant and difficult. The traditional methods are mainly APP identifications or identifications of specific APP users, which cannot satisfy the requirements of globally monitoring of APPs and their user scales at the same time. Especially when many users share the same network IPs (4G, home broadband, NAT), this work becomes challenging and time-consuming. This paper proposes an automatic fingerprint extraction approach of mobile APP users in network traffic. By analyzing the plaintext of the HTTP requests initiated by APPs in training datasets, we extract the APPs' features and the users' fingerprints simultaneously. Both of them are the combinations of strings which are distinguishable of APPs and their users in the network traffic. The proposed method is evaluated with the top 49 popular APPs in Huawei App Store. The experimental results show that the recalls of the extractions of APPs' features and users' fingerprints are respectively 77.5% and 55.1% in total.
A Multi-Level Environment for the Improvement of the Multi-Swarm Cooperative PSO Algorithm

In recent years, we assist to growing up interest of PSO algorithm in engineering applications and especially in machine learning. However, the PSO algorithm stills suffer from some weakness as a global search method harming its use in these fields. Many authors have introduced a lot of strategies to improve this algorithm, where the use of multiple swarms seems more promoting. Even if this strategy has significantly improved the performance of the PSO algorithm, it stills not yet enough. In this paper, we introduce a particular environment based on a hierarchical society coordinating the exploration and exploitation mechanisms to enhance the multi-swarm approach. Subjected to well-known optimization benchmark functions, the introduced strategy has proved its efficiency and effectiveness among the most popular versions to improve the potential of the PSO algorithm as a global optimization technique.
Model-Based Algorithm Configuration with Default-Guided Probabilistic Sampling

In recent years, general-purpose automated algorithm configuration procedures have enabled impressive improvements in the state of the art in solving a wide range of challenging problems from AI, operations research and other areas. To search vast combinatorial spaces of parameter settings for a given algorithm as efficiently as possible, the most successful configurators combine techniques such as racing, estimation of distribution algorithms, Bayesian optimisation and model-free stochastic search. Two of the most widely used general-purpose algorithm configurators, SMAC and irace, can be seen as combinations of Bayesian optimisation and racing, and of racing and an estimation of distribution algorithm, respectively. Here, we propose a first approach that combines all three of these techniques into one single configurator, while exploiting prior knowledge contained in expert-chosen default parameter values. We demonstrate significant performance improvements over irace and SMAC on a broad range of running time optimisation scenarios from AClib.
Equality of Arms and Automatedly Generated Evidence

This Chapter addresses the trial dimension of evidence generated in an automated way. In Chap. 3 we focused, in particular, on the issues of digital intrusion into the individuals’ private life, for the purpose of investigation. Here the spotlight moves to the courtroom, where evidence is presented, discussed, challenged by the parties and evaluated by the court. In this scenario, reconstructing how evidence was generated is crucial to evaluate its reliability, eventually allowing the judge to use it for her decision. But what if evidence is generated by a black box? How do the basic conditions of the parties in trial change if there is no chance to challenge the counterpart’s evidence, because it was generated by an inaccessible algorithm? Fundamental rights, other than privacy, come to the attention and, in particular, the primary and crucial condition for a fair trial, the equality of arms between the parties.
Research and Implementation of Real-Time Motion Control of Robot Based on Kinect

With the development of human-computer interaction technology, the interactive experience between human and machine becomes more and more simple and natural. The ideal state of human-computer interaction can be operated by natural devices such as voice, gestures and expressions, which have been tried and applied in the Kinect field. Kinect, as a new generation of motion sensing device, can achieve the input function by capturing the user’s motion and speech recognition in real time, so as to control the movement of the robot and effectively improve its intelligence. The research of this paper mainly includes skeleton tracking and robot control. Kinect1 generation was used to obtain the human body depth and skeleton images. VS software was used for programming. OpenNI and Kinect SDK libraries were called to identify the human arm joints and obtain the spatial coordinates. The robot controller USES the STMF series 32-bit processor, accepts serial port data, controls the robot arm steering gear, and completes the human body movement imitation. The motion trajectory is smooth and stable, and the motion effect is good.
A Literature Review: To predict the Problem of Post Analysis in Breast Cancer Determination using the Wearable Device

Cancer is one of the life threatening and painful disease, especially breast cancer is most aching disease for women. Statistics says that all over the world for every year 42,260 deaths are due to breast cancer. The major cause of death is late diagnosis. All around the globe mammogram is the standard diagnosing technique which is used, but the diagnosing process was bit difficult and seems painful. Later, wearable diagnosis device were implemented but due to air-gaps that was not so successful. So, in this project our aim is to over come the drawbacks in wearable device. This can be achieved by the help of microwave sensors and controllers. The technique we are going to use for diagnosing is image processing. Also this would be a comfortable and cost effective wearable device which can be conveniently used as breast cancer monitoring technique.
DLDDO: Deep Learning to Detect Dummy Operations

Recently, research on deep learning based side-channel analysis (DLSCA) has received a lot of attention. Deep learning-based profiling methods similar to template attacks as well as non-profiling-based methods similar to differential power analysis have been proposed. DLSCA methods have been proposed for targets to which masking schemes or jitter-based hiding schemes are applied. However, most of them are methods for finding the secret key, except for methods for preprocessing, and there are no studies on the target to which the dummy-based hiding schemes or shuffling schemes are applied. In this paper, we propose a DLSCA for detecting dummy operations. In the previous study, dummy operations were detected using the method called BCDC, but there is a disadvantage in that it is impossible to detect dummy operations for commercial devices such as an IC card. We consider the detection of dummy operations as a multi-label classification problem and propose a deep learning method based on CNN to solve it. As a result, it is possible to successfully perform detection of dummy operations on an IC card, which was not possible in the previous study.
Parameter-Less Population Pyramid for Permutation-Based Problems

Linkage learning is frequently employed in state-of-the-art methods dedicated to discrete optimization domains. Information about linkage identifies a subgroup of genes that are found dependent on each other. If such information is precise and properly used, it may significantly improve a method's effectiveness. The recent research shows that to solve problems with so-called overlapping blocks, it is not enough to use linkage of high quality – it is also necessary to use many different linkages that are diverse. Taking into account that the overlapping nature of problem structure is typical for practical problems, it is important to propose methods that are capable of gathering many different linkages (preferably of high quality) to keep them diverse. One of such methods is a Parameter-less Population Pyramid (P3) that was shown highly effective for overlapping problems in binary domains. Since P3 does not apply to permutation optimization problems, we propose a new P3-based method to fill this gap. Our proposition, namely the Parameter-less Population Pyramid for Permutations (P4), is compared with the state-of-the-art methods dedicated to solving permutation optimization problems: Generalized Mallows Estimation of Distribution Algorithm (GM-EDA) and Linkage Tree Gene-pool Optimal Mixing Evolutionary Algorithm (LT-GOMEA) for Permutation Spaces. As a test problem, we use the Permutation Flowshop Scheduling problem (Taillard benchmark). Statistical tests show that P4 significantly outperforms GM-EDA for almost all considered problem instances and is superior compared to LT-GOMEA for large instances of this problem.
Edge Detection Using Modified Third Order Edge Mask for Grayscale Medical Image Segmentation

Image segmentation, which partitions image into distinct regions, plays a significant role in digital image processing. Edge detection is one of the discontinuity-based image segmentation approaches that segment the images based on abrupt changes in its intensity or gray level. Edges of an object are characterized by pointed change in intensity. Edge detection is widely used in image processing applications, namely object recognition, segmentation, and matching. This paper introduces a new edge detection technique called Modified Third Order Differential Edge Mask (MTODEM) based on third-order differential equation. MTODEM is tested on real-time image samples obtained from patients. Experimental results show that the new edge detector, MTODEM, performs better with higher peak signal-to-noise ratio, lower mean square error, and larger entropy over other edge detection operators like Sobel, Prewitt, Laplacian, and TODEM.
A New Path Planning Algorithm for Forced Landing of UAVs in Emergency Using Velocity Prediction Method

This paper aims to propose a waypoint path planning for the forced landing of an unmanned aerial vehicle in an emergency based on the terminal velocity prediction method, in order to satisfy the approach conditions. In the proposed path planning method, a flight path is represented by a set of waypoints and a combination of rectilinear path and curvilinear path. Based on this representation with some approximations, the terminal velocity prediction method is analytically derived. By using the terminal velocity prediction method, the path planning problem can be converted into a problem finding appropriate positions of the waypoints in order to satisfy the approach conditions for a safe landing. Since the problem is given by a nonlinear system of equations, it can be readily solved using a simple numerical method. Compared to other methods, the proposed method can allow to satisfying the approach conditions with a low computational load. Numerical simulation is performed to conduct the performance of the proposed method.
Blockchain based Protocols for IoT Security using ABE Cryptosystems

This paper gives a study on existing techniques for providing security in the Internet of Things (IoT) networks. First, an overview of IoT is given and it is followed by the security concerns in IoT networks. Then, existing methodologies and techniques for security in IoT are discussed. From this study, it is found that Attribute Based Encryption schemes provide security in a network of resource limited end devices. Comparison of existing methods is done by considering the key security objectives. In order to overcome the disadvantages of a centralized environment, it is seen that IoT implementation supported with ABE in a distributed environment would be better. Finally, the feasibility of implementing such an environment using one of the latest paradigms, called blockchain is also discussed.
A Novel Authentication Approach Towards Secured Storage of Electronic Patient Record Using 3D Dynamic Order Template

The prime contribution of this work is the development of an integrated framework to create a single platform on consolidating the patient’s data as an Electronic Patient Record (EPR), improve the storage security of EPR over the network, and authenticate the retrieval of EPR using a biomedical key. An HL7 standard, OpenEMR has been customized to create an integrated single platform for EPR, which aids in faster access of patient medical data placed under a single unique identification number (ID). To address the privacy of the EPR, the work includes implementation of hybrid watermarking algorithm to embed the entire EPR inside the facial photograph of the patient. A 3D dynamic order template key built using demographic and diagnosis information of the patient addresses the security issue of the EPR. The augmentation index (AI) of the photoplethysmogram (PPG) signals and a homomorphic encryption mechanism doubles the security of the hidden EPR. The processing of PPG signals from 200 patients has revealed that AI is distinct, the peak signal-to-noise ratio (PSNR) is 86 dB, and the structural content of dewatermarked EPR is close to 1. The results have been validated from the doctors of Dental College and Hospital are promising for further treatment of the patients. It is concluded that rotation factor AI upholds the uniqueness of the authentication key.
Tinker: A Middleware for Deploying Multiple NN-Based Applications on a Single Machine

Currently, deep learning technology is widely used in various fields, such as face recognition, object recognition, and image classification. Multiple application instances sharing GPU resources can deploy more applications with limited GPU resources. However, this will lead to resource competition problems, resulting in application switching, timeouts, and other phenomena. Therefore, how to deploy these applications to a single machine with limited resources and properly schedule tasks while maximizing system performance is a new challenge. In this article, we propose Tinker, a middleware, to solve multiple CNN-based application deployment problems on a single machine. Tinker has two phases: 1) offline analysis and 2) runtime scheduling. Offline analysis generates the best application deployment configuration information of the current system. Runtime scheduling can properly schedule tasks to ensure that they are completed normally and efficiently utilize resources to improve system performance. Our experiment proved that Tinker boosts system performance and ensures that most tasks are completed efficiently.
Query Intent Detection from the SEO Perspective

Google users have different intents from their queries such as acquiring information, buying products, comparing or simulating services, looking for products and so on. Understanding the right intention of users helps to provide i) better content on web pages from the Search Engine Optimization (SEO) (Search engine optimization is the process of increasing the quality and quantity of website traffic by increasing the visibility of a website [1]) perspective and ii) more user-satisfying results from the search engine perspective. In this study, we aim to identify the user query's intent by taking advantage of Google results and machine learning methods. Our proposed approach is a clustering model that exploits some features to detect query's intent. A list of keywords extracted from the clustered queries is used to identify the intent of a new given query. Comparing the clustering results with the intents predicted by filtered keywords show the efficiency of the extracted keywords for detecting intents.
Analytical Classifications of Side Channel Attacks, Glitch Attacks and Fault Injection Techniques: Their Countermeasures

Cryptographic devices have many encrypted and secured solutions to protect them against attacks. Hardware engineers infuse lot of time and effort in implementing cryptographic algorithms, keeping the analysis of design constraints into consideration. Engineer's face a challenge for building resistant free embedded system against attacks called as side channel attacks. Therefore, there is a strong need to address issues related to side channel attacks. This paper is a review into the field of hardware security that will provide a deep investigation of types of side channel attacks & fault injection techniques with some real life examples further enhancing the researcher's vision to build efficient and secure systems in order to thwart attacks. Researchers will also be acquainted with some countermeasures against various attacks. Lastly, we have also discussed some future perspective that can give upcoming researchers a new domain to work on.
Evaluation of Word and Sub-word Embeddings for isiZulu on Semantic Relatedness and Word Sense Disambiguation Tasks

Morphologically rich languages, such as isiZulu have a large number of surface words due to their highly productive (agglutinative) nature. This results in Natural Language Processing (NLP) models learnt from training corpora failing to generalize to the large number of words that would not have be seen in the training data. Some researchers believe that the use of morphemes for NLP for morphologically rich languages results in better models. This belief is based on two premises; (i) morphemes are the most basic meaning bearing units of a language (ii) the space of morphemes is much smaller than the space of word-forms, hence models based on morphemes are more likely to generalise better to unseen words than word-based models. In this paper we investigate the veracity of these premises by comparing morpheme-level embeddings to word-level embeddings through (i) a semantic relatedness task (ii) a Word Sense Disambiguation (WSD) task. The results obtained showed that morpheme-level embeddings were outperformed by word-level embeddings for the semantic relatedness task, but they fared much better than their word level counterpart on the WSD task.