Reviewerly/exHarmony huggingface dataset:
Classifiers Comparison for Convolutional Neural Networks (CNNs) in Image Classification

This paper presents a comparison between five different classifiers (Multi-class Logistic Regression (MLR), Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Random Forest (RF) and Gaussian Naive Bayes (GNB)) to be used in a Convolutional Neural Network (CNN) in order to perform images classification. For our experiments we have used a dataset composed of images of objects belonging to 256 widely varied categories called Caltech 256.
Programmatic Access to FAIRified Digital Plant Genetic Resources

Genetic variance within the genotype of population and its mapping to phenotype variance in a systematic and high throughput manner is of interest for biodiversity and breeding research. Beside the established and efficient high throughput genotype technologies, phenotype capabilities got increased focus in the last decade. This results in an increasing amount of phenotype data from well scaling, automated sensor platform. Thus, data stewardship is a central component to make experimental data from multiple domains interoperable and re-usable. To ensure a standard and comprehensive sharing of scientific and experimental data among domain experts, FAIR data principles are utilized for machine read-ability and scale-ability. In this context, BrAPI consortium, provides a comprehensive and commonly agreed FAIRed guidelines to offer a BrAPI layered scientific data in a RESTful manner. This paper presents the concepts, best practices and implementations to meet these challenges. As one of the worlds leading plant research institutes it is of vital interest for the IPK-Gatersleben to transform legacy data infrastructures into a bio-digital resource center for plant genetics resources (PGR). This paper also demonstrates the benefits of integrated database back-ends, established data stewardship processes, and FAIR data exposition in a machine-readable, highly scalable programmatic interfaces.
Mobile terminal identity authentication system based on behavioral characteristics

We propose a new type of authentication system based on behavioral characteristics for smartphone users. With the sensor and touch screen data in the smartphone, the combination of the motion state detection mode and the authentication mode can effectively distinguish between legitimate smartphone users and other users. The system deploys software on the smartphone to collect data from sensors and touch screens, and upload the data to the cloud. We apply random forest algorithm on the data to extract features and achieve motion state detection. Multilayer perceptron algorithm is used for user authentication in corresponding motion state. The system effectively implements an implicit and continuous authentication mode, which can achieve user identity authentication without users’ being aware of it. The system proposed in this article can achieve 95.96% accuracy with false rejection rate of 2.55% and false acceptance rate of 6.94%.
OmniTrack: Real-Time Detection and Tracking of Objects, Text and Logos in Video

The automatic detection and tracking of general objects (like persons, animals or cars), text and logos in a video is crucial for many video understanding tasks, and usually real-time processing as required. We propose OmniTrack, an efficient and robust algorithm which is able to automatically detect and track objects, text as well as brand logos in real-time. It combines a powerful deep learning based object detector (YoloV3) with high-quality optical flow methods. Based on the reference YoloV3 C++ implementation, we did some important performance optimizations which will be described.
AdSherlock: Efficient and Deployable Click Fraud Detection for Mobile Applications

Mobile advertising plays a vital role in the mobile app ecosystem. A major threat to the sustainability of this ecosystem is click fraud, i.e., ad clicks performed by malicious code or automatic bot problems. Existing click fraud detection approaches focus on analyzing the ad requests at the server side. However, such approaches may suffer from high false negatives since the detection can be easily circumvented, e.g., when the clicks are behind proxies or globally distributed. In this paper, we present AdSherlock, an efficient and deployable click fraud detection approach at the client side (inside the application) for mobile apps. AdSherlock splits the computation-intensive operations of click request identification into an offline procedure and an online procedure. In the offline procedure, AdSherlock generates both exact patterns and probabilistic patterns based on URL (Uniform Resource Locator) tokenization. These patterns are used in the online procedure for click request identification and further used for click fraud detection together with an ad request tree model. We implement a prototype of AdSherlock and evaluate its performance using real apps. The online detector is injected into the app executable archive through binary instrumentation. Results show that AdSherlock achieves higher click fraud detection accuracy compared with state of the art, with negligible runtime overhead.
A postmortem of suspended Twitter accounts in the 2016 U.S. presidential election

Social media sites such as Twitter have faced significant pressure to mitigate spam and abuse on their platform in the aftermath of congressional investigations into Russian interference in the 2016 U.S. presidential election. Twitter publicly acknowledged the exploitation of their platform and has since conducted aggressive cleanups to suspend the involved accounts. To shed light on Twitter's countermeasures, we conduct a postmortem analysis of about one million Twitter accounts who engaged in the 2016 U.S. presidential election but were later suspended by Twitter. To systematically analyze coordinated activities of these suspended accounts, we group them into communities based on their retweet/mention network and analyze different characteristics such as popular tweeters, domains, and hashtags. The results show that suspended and regular communities exhibit significant differences in terms of popular tweeter and hashtags. Our qualitative analysis also shows that suspended communities are heterogeneous in terms of their characteristics. We further find that accounts suspended by Twitter's new countermeasures are tightly connected to the original suspended communities.
Not All Attributes are Created Equal: <i>d</i> <i> <sub>X</sub> </i> -Private Mechanisms for Linear Queries

Abstract Differential privacy provides strong privacy guarantees simultaneously enabling useful insights from sensitive datasets. However, it provides the same level of protection for all elements (individuals and attributes) in the data. There are practical scenarios where some data attributes need more/less protection than others. In this paper, we consider dX -privacy, an instantiation of the privacy notion introduced in [6], which allows this flexibility by specifying a separate privacy budget for each pair of elements in the data domain. We describe a systematic procedure to tailor any existing differentially private mechanism that assumes a query set and a sensitivity vector as input into its d X -private variant, specifically focusing on linear queries. Our proposed meta procedure has broad applications as linear queries form the basis of a range of data analysis and machine learning algorithms, and the ability to define a more flexible privacy budget across the data domain results in improved privacy/utility tradeoff in these applications. We propose several d X -private mechanisms, and provide theoretical guarantees on the trade-off between utility and privacy. We also experimentally demonstrate the effectiveness of our procedure, by evaluating our proposed d X -private Laplace mechanism on both synthetic and real datasets using a set of randomly generated linear queries.
An Expanded Vision of Breast Cancer Diagnosis Approaches Based on Machine Learning Techniques

Breast cancer is one of the leading causes of death among women all over the world, it is happened due to the uncontrollable growth of breast cells. The only rescue to reduce a breast cancer fatality is the early detection and appropriate diagnosis. In medical fields, the accurate classification of tumors is a very important task. Machine Learning techniques are widely employed in detecting and classifying different types of cancers because of their high performance and accurate results. In this paper, an overall expansion of breast cancer diagnosis based on different machine learning techniques is presented. Many techniques have been developed, techniques based on Artificial Neural Network (ANN), techniques based on Support Vector Machine (SVM) and techniques based on Relevance Vector Machine for diagnosing of breast cancer.
SE-IYOLOV3: An Accurate Small Scale Face Detector for Outdoor Security

Small scale face detection is a very difficult problem. In order to achieve a higher detection accuracy, we propose a novel method, termed SE-IYOLOV3, for small scale face in this work. In SE-IYOLOV3, we improve the YOLOV3 first, in which the anchorage box with a higher average intersection ratio is obtained by combining niche technology on the basis of the k-means algorithm. An upsampling scale is added to form a face network structure that is suitable for detecting dense small scale faces. The number of prediction boxes is five times more than the YOLOV3 network. To further improve the detection performance, we adopt the SENet structure to enhance the global receptive field of the network. The experimental results on the WIDERFACEdataset show that the IYOLOV3 network embedded in the SENet structure can significantly improve the detection accuracy of dense small scale faces.
Brahmi character recognition based on SVM (support vector machine) classifier using image gradient features

AbstractAbstractUnderstanding the ancient script can provide rich details of a civilization, like its cultural, political and social scenarios. Brahmi, an ancient mother script, has been a key to the development of many modern Indian scripts like Gurmukhi, Devanagari, Bangla etc. Inferring ancient writings can be a tedious job and moreover, if executed manually, it requires several language experts. The current paper presents a recognition system for Brahmi characters using linear Support Vector machine classifier. Gradient information of the character images pixels is extracted, and histogram of the gradients is stored as a feature vector for each character image. Character dataset includes both handwritten character images and images from the internet. Linear SVM classifier is trained on the feature set of 24 images of each character. The proposed recognition system is performed with an accuracy of 91.6% to recognize the Brahmi characters from the test images.Subject Classification: Pattern RecognitionKeywords: Brahmi ScriptImage GradientHistogram of Oriented Gradient (HOG)Support Vector Machine (SVM)
Improved Single Sample Per Person Face Recognition via Enriching Intra-Variation and Invariant Features

Face recognition using a single sample per person is a challenging problem in computer vision. In this scenario, due to the lack of training samples, it is difficult to distinguish between inter-class variations caused by identity and intra-class variations caused by external factors such as illumination, pose, etc. To address this problem, we propose a scheme to improve the recognition rate by both generating additional samples to enrich the intra-variation and eliminating external factors to extract invariant features. Firstly, a 3D face modeling module is proposed to recover the intrinsic properties of the input image, i.e., 3D face shape and albedo. To obtain the complete albedo, we come up with an end-to-end network to estimate the full albedo UV map from incomplete textures. The obtained albedo UV map not only eliminates the influence of the illumination, pose, and expression, but also retains the identity information. With the help of the recovered intrinsic properties, we then generate images under various illuminations, expressions, and poses. Finally, the albedo and the generated images are used to assist single sample per person face recognition. The experimental results on Face Recognition Technology (FERET), Labeled Faces in the Wild (LFW), Celebrities in Frontal-Profile (CFP) and other face databases demonstrate the effectiveness of the proposed method.
Web service design defects detection: A bi-level multi-objective approach

Context: Web services frequently evolve to integrate new features, update existing operations and fix errors to meet the new requirements of subscribers. While this evolution is critical, it may have a negative impact on the quality of services (QoS) such as reduced cohesion, increased coupling, poor response time and availability, etc. Thus, the design of services could become hard to maintain and extend in future releases. Recent studies addressed the problem of web service design antipatterns detection, also called design defects, by either manually defining detection rules, as combination of quality metrics, or generating them automatically from a set of defect examples. The manual definition of these rules is time-consuming and difficult due to the subjective nature of design issues, especially to find the right thresholds value. The efficiency of the generated rules, using automated approaches, will depend on the quality of the training set since examples of web services antipatterns are limited. Furthermore, the majority of existing studies for design defects detection for web services are limited to structural information (interface/code static metrics) and they ignore the use of quality of services (QoS) or performance metrics, such as response time and availability, for this detection process or understanding the impact of antipatterns on these QoS attributes. Objective: To address these challenges, we designed a bi-level multi-objective optimization approach to enable the generation of antipattern examples that can improve the efficiency of detection rules. Method: The upper-level generates a set of detection rules as a combination of quality metrics with their threshold values maximizing the coverage of defect examples extracted from several existing web services and artificial ones generated by a lower level. The lower level maximizes the number of generated artificial defects that cannot be detected by the rules of the upper level and minimizes the similarity to well-designed web service examples. The generated detection rules, by our approach, are based on a combination of dynamic QoS attributes and structural information of web service (static interface/code metrics). Results: The statistical analysis of our results, based on a data-set of 662 web services, confirms the efficiency of our approach in detecting web service antipatterns comparing to the current state of the art in terms of precision and recall. Conclusion: The multi-objective search formulation at both levels helped to diversify the generated artificial web service defects which produced better quality of detection rules. Furthermore, the combination of dynamic QoS attributes and structural information of web services improved the efficiency of the generated detection rules.
Exploiting Temporality for Semi-Supervised Video Segmentation

In recent years, there has been remarkable progress in supervised image segmentation. Video segmentation is less explored, despite the temporal dimension being highly informative. Semantic labels, e.g. that cannot be accurately detected in the current frame, may be inferred by incorporating information from previous frames. However, video segmentation is challenging due to the amount of data that needs to be processed and, more importantly, the cost involved in obtaining ground truth annotations for each frame. In this paper, we tackle the issue of label scarcity by using consecutive frames of a video, where only one frame is annotated. We propose a deep, end-to-end trainable model which leverages temporal information in order to make use of easy to acquire unlabeled data. Our network architecture relies on a novel interconnection of two components: a fully convolutional network to model spatial information and temporal units that are employed at intermediate levels of the convolutional network in order to propagate information through time. The main contribution of this work is the guidance of the temporal signal through the network. We show that only placing a temporal module between the encoder and decoder is suboptimal (baseline). Our extensive experiments on the CityScapes dataset indicate that the resulting model can leverage unlabeled temporal frames and significantly outperform both the frame-by-frame image segmentation and the baseline approach.
Polygon-Based Random Tree Search Planning for Variable Geometry Truss Robot

This letter proposes the use of a polygon-based random tree path planning algorithm for a variable geometry topology system (VGT). By combining a path planning algorithm and our previous non-impact locomotion algorithm, the proposed VGT system reaches an objective point. The proposed path planning algorithm provides the desired set of support polygons with a modified rapid random tree algorithm. The algorithm can significantly reduce distortion of the VGT system while moving by limiting the deformation of the desired support polygon. With this algorithm feature, constraint violations of the system were significantly reduced with respect to using a normal rapid random tree algorithm for path planning. The performance of the algorithm was validated using the simulation results.
Authorship Clustering using TF-IDF weighted Word-Embeddings

In this paper, we propose a novel idea to solve the problem of Author Clustering which is introduced in PAN-2017 Author Identification task. First, we use word embeddings that brings semantic feature of the words along with the grammatical and syntactic information. Secondly, the documents are represented as the TF-IDF weighted sum of the embedding vectors corresponding to each word. A higher TF-IDF weight implies that the words have a stronger relationship in the documents in which they appear. Lastly, using hierarchical clustering all those documents written by each author are grouped together. Finally, we compare the proposed technique with the top submissions to PAN-2017 author clustering task. Through extensive experimentation, we show that the proposed technique outperforms various state-of-the-art models on the given task.
A Comparative Study on Recommender Systems Approaches

Recommender systems aim to generate interest items or products for web users. They offer useful information and adapted to users' profile based on their preferences and behaviors. Currently, they are proposed in several domains, namely: e-commerce, e-learning, research, music, social networks, etc. There are mainly three approaches that are used in the recommender systems, those based on content, those based on collaborative filtering, and finally the hybrid approaches, which merge different algorithms and provide more accurate and effective recommendations than a single algorithm, as the disadvantages of one simple technique can be overcome by another technique. This paper offers a comparative study of approaches in recommender systems, starting with a general presentation of each one, then it treats the advantages, the limitations, and the techniques, which give a high accuracy in each approach.
Interaction dynamics between hate and counter users on Twitter

Social media platforms usually tackle the proliferation of hate speech by blocking/suspending the message or account. One of the major drawback of such measures is the restriction of free speech. In this paper, we investigate the interaction of hatespeech and the responses that counter it (aka counter-speech). One of the prime contribution of this work is that we developed and released1 a dataset where we annotate pairs of hate and counter users.
Effects of Home-Based Working Memory Training on Visuo-Spatial Working Memory in Parkinson’s Disease: A Randomized Controlled Trial

Cognitive impairment is a very frequent and severe nonmotor symptom of Parkinson's disease (PD). Early intervention in this at-risk group for cognitive decline may be crucial for long-term preservation of cognitive functions. Computerized working memory training (WMT) has been proven beneficial in non-PD patient populations, but such evidence is still needed for patients with PD.This study aimed to evaluate the effect of WMT on visuo-spatial working memory (WM) in cognitively unimpaired patients with PD.A single-blind randomized controlled trial encompassing 76 patients with PD but no cognitive impairment according to level II diagnostic criteria was conducted. Thirty-seven patients engaged in home-based adaptive WMT 5 times per week for a period of 5 weeks, whereas the remaining patients were in the waiting list arm of the study (control group [CG]). Working memory performance was evaluated using a computerized task before and after intervention and at 14-week follow-up, allowing to quantify the precision of WM on a continuous scale, ie, to test not only if an item was remembered but also how well the location of this item was retained.Coincidently, the WMT group showed slightly worse WM performance compared with the CG at baseline, which was ameliorated after WMT. This training-induced effect remained stable until follow-up.Patients showing relatively low WM performance, despite not formally diagnosable as Parkinson's disease with mild cognitive impairment (PD-MCI), seem to benefit from home-based WMT. Thus, WMT could potentially be implemented in future trials as a time- and cost-efficient route to counteract subtle cognitive changes in early disease stages.German Clinical Trial Register (drks.de, DRKS00009379).
A power-type varying gain discrete-time recurrent neural network for solving time-varying linear system

Many practical engineering problems can be described as an online time-varying linear system (TVLS), and thus solving TVLS is very important in control theory and control engineering. In this paper, a novel power-type varying gain discrete-time recurrent neural network (PVG-DTRNN) is proposed to solve the TVLS problem. Compared with the state-of-art method, i.e., the fixed-parameter discrete-time zeroing neural network (FP-DTZNN), the proposed PVG-DTRNN has better convergent rate and higher accuracy. To do so, a vector error function is firstly defined. Secondly, a power-type gain implicit dynamic model is derived and needs to be further discretized. Thirdly, by using Euler forward-difference rule, a discretized dynamic model is designed. In order to get the explicit dynamic model, the Broyden-Fletcher-Goldfarb-Shanno (BFGS) quasi-Newton method is utilized to estimate the inverse of the Hessian matrix. Comparisons of computer simulations verify the effectiveness and superiority of the proposed PVG-DTRNN models.