Reviewerly/exHarmony huggingface dataset:
Using a 3D CNN for Rejecting False Positives on Pedestrian Detection

Self-driving cars are becoming slowly but surely the future of transport. Nonetheless, in order to achieve fully automatic operation, several challenges are still needed to be tackled. One of the main goals that is currently being pursued is a very accurate scene understanding and object detection. In this regard, the most accurate object detectors are image-based. However, these methods yield critical flaws that make them prone to error in some specific scenarios. For instance, actual objects would be detected in depictions of such objects. The urban environment is strewn with these cases. Namely, in billboards and advertisements. However, most of the self-driving cars feature a lidar that provides 3D perception. This sensor could help to disambiguate the cases mentioned before.In this paper, we combine the accuracy of 2D deep learning object detectors with a 3D Convolutional Neural Network (3D CNN) for rejecting false positives on pedestrian detection. First, the object detector provides all the detected pedestrians in the scene, and then the 3D CNN is in charge of rejecting or verify the detections. Our proposal is tested on two well-known publicly available datasets and provides up to 84% accuracy.
RecNet: Deep Learning-Based OFDM Receiver with Semi-Blind Channel Estimation

This paper proposes a novel deep learning-based system for channel estimation and signal detection in an orthogonal frequency-division multiplexing (OFDM) system. Different from viewing the whole OFDM system as a black box in the literature, the proposed receiving system is divided into two low-complexity neural networks (NNs). The first NN is designed for semi-blind channel estimation and the second NN recovers the original signals based on the channel state information (CSI) obtained from the first network. Simulation results show that this system offers a competitive accuracy for channel estimation. Specifically, our simulations show better robustness when a very small number of pilots are used compared with traditional channel estimation methods. With the help of the estimated CSI, the NN for signal detection converges within much fewer epochs than data-driven solutions. Due to fast convergence and small size of the NNs, this system can be trained within a short time to adapt to different channel models. Compared with the traditional schemes, our whole system has a better performance under low signal-to-noise ratio (SNR).
Semi-supervised Training for Sequence-to-Sequence Speech Recognition Using Reinforcement Learning

This paper proposes a reinforcement learning based semi-supervised training approach for sequence-to-sequence automatic speech recognition (ASR) systems. Most recent semi-supervised training approaches are based on multi-loss functions such as cross-entropy loss for speech-to-text paired data and reconstruction loss for speech-text unpaired data.Although these approaches show promising results, some considerations still remain: (a) different loss functions are used for paired and unpaired data separately even though the purpose is classification accuracy improvement, and (b) several methods need auxiliary networks that increase the complexity of a semi-supervised training process.To address these issues, a reinforcement learning based approach is proposed. The proposed approach focuses on rewarding ASR to generate more correct sentences for both paired and unpaired speech data. The proposed approach is evaluated on the Wall Street Journal task domain. The experimental results show that the proposed method is effective by reducing the character error rate from 10.4% to 8.7%.
Evaluation of Increasing Camera Baseline on Depth Perception in Surgical Robotics

In this paper, we evaluate the effect of increasing camera baselines on depth perception in robot-assisted surgery. Restricted by the diameter of the surgical trocar through which they are inserted, current clinical stereo endoscopes have a fixed baseline of 5.5 mm. To overcome this restriction, we propose using a stereoscopic "pickup" camera with a side-firing design that allows for larger baselines. We conducted a user study with baselines of 10 mm, 15 mm, 20 mm, and 30 mm to evaluate the effect of increasing baseline on depth perception when used with the da Vinci surgical system. Subjects (N=28) were recruited and asked to rank differently sized poles, mounted at a distance of 200 mm from the cameras, according to their increasing order of height when viewed under different baseline conditions. The results showed that subjects performed better as the baseline was increased with the best performance at a 20 mm baseline. This preliminary proof-of-concept study shows that there is opportunity to improve depth perception in robot-assisted surgical systems with a change in endoscope design philosophy. In this paper, we present this change with our side-firing "pickup" camera and its flexible baseline design. Ultimately, this serves as the first step towards an adaptive baseline camera design that maximizes depth perception in surgery.
Thinking With Things: Landscapes, Connections, and Performances as Modes of Building Shared Understanding

This article explores the relatively underexplored potential for physicalizations to materialize qualitative data related to human experiences and knowledge domains. Our reading of “data” in this context extends from imperceptible systems and infrastructures to mental models and the phenomenological dimensions of experiences themselves. Physical objects can be regarded as a form of knowledge with which to inquire about human life, bring about improved conditions, and imagine alternative realities. Objects are made of materials, which are manipulated materials into various configurations. The materials used in the process of externalization have a profound influence on the resulting forms, and through them on how knowledge is constructed and internalized. We pay detailed attention to the characteristics of materials and how they are combined, in the context of interdisciplinary exchange. We are motivated by the need for a shared understanding of what work materials can do in the making of physicalisations. We suggest this work is useful in the analysis of physicalizations, specifically where they seek to articulate the phenomena of lived experience.
Line Coverage with Multiple Robots

The line coverage problem is the coverage of linear environment features (e.g., road networks, power lines), modeled as 1D segments, by one or more robots while respecting resource constraints (e.g., battery capacity, flight time) for each of the robots. The robots incur direction dependent costs and resource demands as they traverse the edges. We treat the line coverage problem as an optimization problem, with the total cost of the tours as the objective, by formulating it as a mixed integer linear program (MILP). The line coverage problem is NP-hard and hence we develop a heuristic algorithm, Merge-Embed-Merge (MEM). We compare it against the optimal MILP approach and a baseline heuristic algorithm, Extended Path Scanning. We show the MEM algorithm is fast and suitable for real-time applications. To tackle large-scale problems, our approach performs graph simplification and graph partitioning, followed by robot tour generation for each of the partitioned subgraphs. We demonstrate our approach on a large graph with 4,658 edges and 4,504 vertices that represents an urban region of about 16 sq. km. We compare the performance of the algorithms on several small road networks and experimentally demonstrate the approach using UAVs on the UNC Charlotte campus road network.
Adaptive Trust-Aware Decentralized Information Flow Control

Modern software systems are decentralized, distributed, and dynamic, and consequently, require decentralized mechanisms to enforce security. In this paper, we propose an adaptive approach using a combination of decentralized information flow control (DIFC) mechanisms, trust-based methods and decentralized control architectures to enforce security in open distributed systems. In our approach, adaptivity mitigates two aspects of the system dynamics that cause uncertainty: the ever-changing nature of trust and system openness. We formalize our trust-aware DIFC model and instantiate two decentralized control architectures to implement and evaluate it. We evaluate the effectiveness and performance of our method and decentralized control architectures on two case studies.
Triplet Loss for Knowledge Distillation

In recent years, deep learning has spread rapidly, and deeper, larger models have been proposed. However, the calculation cost becomes enormous as the size of the models becomes larger. Various techniques for compressing the size of the models have been proposed to improve performance while reducing computational costs. One of the methods to compress the size of the models is knowledge distillation (KD). Knowledge distillation is a technique for transferring knowledge of deep or ensemble models with many parameters (teacher model) to smaller shallow models (student model). Since the purpose of knowledge distillation is to increase the similarity between the teacher model and the student model, we propose to introduce the concept of metric learning into knowledge distillation to make the student model closer to the teacher model using pairs or triplets of the training samples. In metric learning, the researchers are developing the methods to build a model that can increase the similarity of outputs for similar samples. Metric learning aims at reducing the distance between similar and increasing the distance between dissimilar. The functionality of the metric learning to reduce the differences between similar outputs can be used for the knowledge distillation to reduce the differences between the outputs of the teacher model and the student model. Since the outputs of the teacher model for different objects are usually different, the student model needs to distinguish them. We think that metric learning can clarify the difference between the different outputs, and the performance of the student model could be improved. We have performed experiments to compare the proposed method with state-of-the-art knowledge distillation methods. The results show that the student model obtained by the proposed method gives higher performance than the conventional knowledge distillation methods.
Preventing Session Hijacking using Encrypted One-Time-Cookies

Hypertext Transfer Protocol (HTTP) cookies are pieces of information shared between HTTP server and client to remember stateful information for the stateless HTTP protocol or to record a user’s browsing activity. Cookies are often used in web applications to identify a user and corresponding authenticated session. Thus, stealing a cookie can lead to hijacking an authenticated user’s session. To prevent this type of attack, a cookie protection mechanism is required. In this paper, we have proposed a secure and efficient cookie protection system. We have used one time cookies to prevent attacker from performing cookie injection. To ensure cookie integrity and confidentiality, we have encrypted sensitive information in the cookie. We have verified that our proposed system can ensure confidentiality, authenticity and integrity through security analysis. Our proposed system can efficiently prevent session hijacking performed through replay attack and cookie poisoning attack.
Decision Tree Integration Using Dynamic Regions of Competence

A vital aspect of the Multiple Classifier Systems construction process is the base model integration. For example, the Random Forest approach used the majority voting rule to fuse the base classifiers obtained by bagging the training dataset. In this paper we propose the algorithm that uses partitioning the feature space whose split is determined by the decision rules of each decision tree node which is the base classification model. After dividing the feature space, the centroid of each new subspace is determined. This centroids are used in order to determine the weights needed in the integration phase based on the weighted majority voting rule. The proposal was compared with other Multiple Classifier Systems approaches. The experiments regarding multiple open-source benchmarking datasets demonstrate the effectiveness of our method. To discuss the results of our experiments, we use micro and macro-average classification performance measures.
Multi-Instance Learning Algorithm Based on LSTM for Chinese Painting Image Classification

Aiming at the problem of weakly supervised learning in traditional Chinese painting image classification, a novel multi-instance learning algorithm based on Long and Short-Term Memory neural network with attention mechanism (ALSTM-MIL) is proposed. Firstly, by using the Pyramid Overlapping Grid Division (POGP), a multi-instance modeling scheme is designed to convert Chinese painting images into multi-instance bag, thereby transforming the problem of Chinese painting image classification into a MIL problem. Secondly, an efficient sequence generator is designed. It selects discriminative instances from the positive bags, construct a discriminative instance set (DIS), and convert multi-instance bags into equal-length ordered sequences. Thirdly, an LSTM network model with an attention mechanism is designed to perform semantic analysis on multi-instance bags to obtain their memory coding features, and then combined with the Softmax classifier to achieve semantic classification of traditional Chinese painting images. Experimental results on the Chinese painting (CP) image set show that the LSTM network built on the visual feature set is feasible, and the performance of the proposed MIL algorithm is also superior to other classification algorithms.
Dronecaps: Recognition Of Human Actions In Drone Videos Using Capsule Networks With Binary Volume Comparisons

Understanding human actions from videos captured by drones is a challenging task in computer vision due to the unfamiliar viewpoints of individuals and changes in their size due to the camera's location and motion. This work proposes DroneCaps, a capsule network architecture for multi-label human action recognition (HAR) in videos captured by drones. DroneCaps uses features computed by 3D convolution neural networks plus a new set of features computed by a novel Binary Volume Comparison layer. All these features, in conjunction with the learning power of CapsNets, allow understanding and abstracting the different viewpoints and poses of the depicted individuals very efficiently, thus improving multi-label HAR. The evaluation of the DroneCaps architecture's performance for multi-label classification shows that it outperforms state-of-the-art methods on the Okutama-Action dataset.
Innovation and active learning for training mobile app developers

The mobile market is growing every year, creating a demand for mission-critical and innovative enterprises, and personal applications. This demand implies the need for skills training necessary for students to enter the professional development market. The Innovation, Development and Teaching in Complex Software Environments approach (IDEAS) was developed for the training of mobile developers to meet this need, through an innovative process for student selection, training through the active methodology Challenge Based Learning, software production, and startups creation. The approach was created through action-research applied during two cycles of a real project in a university environment, where 398 students applied for the first cycle, of which 110 were selected, and 900 applied for the second cycle, of which 100 were selected. We undertook evaluations with students and teachers through periodic reflections, questionnaires, and interviews at the end of each cycle. Some of the positive results included the prominence of 125 professional-looking apps that were finalized and published, report of dropout reduction and disapproval in the programming courses and experience in entrepreneurship and pre-acceleration startup program.
Comparative Study of Different Machine Learning Techniques in the Diagnosis of Dementia

Machine learning techniques play an important role in solving real world problems. Prediction in the field of medical science using these techniques has helped the experts in making their diagnosis accurate in recent years. Implementation of these techniques leads to the advancement in the production of efficient diagnostic procedures. In our study, four machine learning techniques: Logistic Regression, k-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Neuro-Fuzzy System (NFS) have been evaluated on the basis of five most important parameters, i.e., RMSE (Root Mean Square Error), Accuracy, Recall, Precision, and F-score. Experimentation is done to reveal the most accurate forecasting technique for dementia diagnosis. The data set containing 336 records was undertaken for the study and subsequently divided into three different ratios of 90:10, 75:25, and 60:40 for training and testing. Results obtained shows that SVM outperformed all other techniques in all cases, and optimum results were predicted for all the techniques in the selection of 75:25 split into train-test.
Effective Control of Photon Statistics from Electroluminescence by Fano-like Interference Effect

The photon blockade induced by optical nonlinearity has been widely used to generate single-photon emission under optical driving in quantum optics. However, the same approach is difficult to achieve in electrically driven molecular junctions. Here we propose a scheme for tuning photon statistics via Fano-like interference effect in a system consisting of two molecules within one optical cavity. Under electrical pumping, a transition from photon bunching to antibunching takes place as a manifestation of the Fano-like interference. This effect persists even in the presence of the dipole-dipole interaction between molecules based on the parameters extracted from the experiments. Our proposal can be realized in current-carrying scanning tunneling microscope junctions.
Phishing Website Prediction by Using Cuckoo Search as a Feature Selection and Random Forest and BF-Tree Classifier as a Classification Method

Phishing attacks are the threats that are ongoing in a campus and have become sophisticated as the time passes by. Target of phishing attacks are victims to affect their financial transactions and expose their personal information prone to risks. Social engineering techniques help the phishing attackers to gain and access the sensitive and personal information about a user. This may include personal details, username, password, financial details, etc. It has become very common to impersonate legitimate businesses, trick users by indulging them in business activities, and get their legitimate data as provided by them. Associative and classification algorithms can be extremely valuable in predicting phishing websites. It can give us replies about what are the most imperative cybercrime phishing site attributes and markers and how they identify with one another. WEKA tool is utilized for the usage of classifiers on an open dataset from NASA store. The inspiration driving this examination is to utilize data mining techniques and algorithms for the prediction motivation behind phishing sites and look at their viability as far as accuracy and errors. We evaluate and improve accuracy of predicting phishing websites and also reduce different types of errors, i.e., Mean Absolute Error (MAE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), and Mean Squared Error (MSE), using cuckoo search algorithm as a feature selection technique, where random forest and BF-tree are used as classifiers.
Smart Virtual Product Development (SVPD) System to Support Product Inspection Planning in Industry 4.0

This paper presents the idea of supporting product inspection planning process during the early stages of product life cycle for the experts working on product development. Aim of this research is to assist a collaborative product development process by using Smart Virtual Product Development (SVPD) system, which is based on Set of Experience Knowledge Structure (SOEKS) and Decisional DNA (DDNA). The proposed system is developed to support three key aspects of industrial product development i.e. design, manufacturing, and product inspection. Therefore, it comprises of three main modules; design knowledge management (DKM), manufacturing capability and process planning (MCAPP), and product inspection planning (PIP). It collects, stores, and uses experiential knowledge from formal decisional events in the form of set of experience (SOE). This research enlightens the working mechanism of the PIP module, and shows how experiential knowledge related to product inspection can be used during the early stages of product development process. This experiential knowledge is extracted and stored from similar products having some common features and functions. First, the basic description and principles of the approach are introduced, then the prototype version of the system is developed and tested for product inspection planning (PIP) module for the case study, which verifies the feasibility of the proposed approach. The presented system successfully supports smart manufacturing and can play a vital role in Industry 4.0.
Defending Deep Learning-Based Biomedical Image Segmentation from Adversarial Attacks: A Low-Cost Frequency Refinement Approach

Deep learning has demonstrated superb performance and efficiency in medical image segmentation. However, recently the community has also found the first practical adversarial example crafting algorithm dedicated to misleading deep learning-based biomedical image segmentation models. The generated segmentation-oriented adversarial examples, while almost indistinguishable by human eyes, can always produce target incorrect segmentation prediction with high intersection-over-union (IoU) rate, significantly concerning the safe use of such an emerging technique in medical diagnosis tasks. On the other hand, research on defending such an emerging attack in the context of medical image segmentation is lacking. In this work, we make the very first attempt to develop a low-cost and effective input-transformation based defense technique. To maximize the defense efficiency (or recovered segmentation results) of adversarial samples while minimizing the segmentation performance loss of benign samples after applying defense, we propose a novel low-cost image compression-based defense approach guided by fine-grained frequency refinement (FR). Extensive experimental results on various deep learning segmentation models show that our defense can offer very high defense efficiency against adversarial examples with very marginal segmentation performance loss of benign images on both ISIC skin lesion segmentation challenge and the problem of glaucoma optic disc segmentation. To further validate our method’s effectiveness, we also extend our evaluation to the image classification model. We show the influence of our recovered segmentation prediction by our defense on disease prediction in adversarial settings. The code is released at: https://github.com/qiliu08/frequency-refinement-defense.