Reviewerly/exHarmony huggingface dataset:
An Adaptive Social Spammer Detection Model With Semi-Supervised Broad Learning

Mobile social networks include a large number of social members who forward messages cooperatively. However, spammers post links to viruses and advertisements, or follow a large number of users, which produces many misleading messages in mobile social networks. In this paper, we propose an adaptive social spammer detection (ASSD) model. We build a spammer classifier by using a small number of labeled patterns and some unlabeled patterns. The prediction accuracy is high compared with some conventional supervised learning methods. Moreover, the time and energy required to label the identity of social members are reduced by applying ASSD. Because social spammers frequently change their behavior to deceive the spammer detection model, an incremental learning method is designed to update the spammer detection model adaptively, without retraining. We evaluate ASSD by comparing it with other supervised and semi-supervised machine learning methods using the Social Honeypot Dataset. Experimental results show that the proposed model outperforms the baseline methods in terms of recall and precision. Additionally, ASSD maintains a high detection accuracy by adaptively updating the model with newly generated social media data.
CNN-based Driver Activity Understanding: Shedding Light on Deep Spatiotemporal Representations

While deep Convolutional Neural Networks(CNNs) have become front-runners in the field of driver observation, they are often perceived as black boxes due to their end-to-end nature. Interpretability of such models is vital for building trust and is a serious concern for the integration of CNNs in real-life systems. In this paper, we implement a diagnostic framework for analyzing such models internally and shed light on the learned spatiotemporal representations in a comprehensive study. We examine prominent driver monitoring models from three points of view: (1) visually explaining the prediction by combining the gradient with respect to the intermediate features and the corresponding activation maps, (2) looking at what the network has learned by clustering the internal representations and discovering, how individual classes relate at the feature-level, and (3) conducting a detailed failure analysis with multiple metrics and evaluation settings (e.g. common versus rare behaviors). Among our findings, we show that most of the mistakes can be traced back to learning an object- or a specific movement bias, strong semantic similarity between classes (e.g. preparing food and eating) and underrepresentation in the training set. Besides, we demonstrate the advantages of the Inflated 3D Net compared to other CNNs as it results in more discriminative embedding clusters and in the highest recognition rates based on all metrics.
Deepening the IDA* algorithm for knowledge graph reasoning through neural network architecture

Inferring missing links in Knowledge Graphs (KGs) is a key evaluation task for KG reasoning, which aims to find relations for a given entity pair. Existing research often employs the IDA* (Iterative Deepening A*) algorithm for the path discovery task owing to its efficiency and accuracy. However, it relies on heuristics to set cost functions and is also difficult to utilize useful context information in the search process. In this paper, we propose the Deep-IDA* framework which applies neural networks and reinforcement learning (RL) to empower the IDA* algorithm to tackle the path discovery problem in KG reasoning. We model KG reasoning as a Markov Decision Process (MDP) and divide our Deep-IDA* framework and the resulting path into two parts: path-finding and path-reasoning. For path-finding, we propose a policy network to model the cost from the source to a candidate location. In this process, we employ the GCN (Graph Convolutional Network) to embed the observable sub-track, then employ the LSTM (Long Short-Term Memory) to record the historical trajectory, and introduce the attention to utilize the context information, and finally form policy. For path-reasoning with the searched candidate paths passed from the former process, we employ a value network to estimate the cost from the candidate to the destination entity, using the GNN (Graph Neural Networks) to learn a message-passing algorithm that solves the path inference problem, and using the GRU (Gated Recurrent Unit) to update the historical information. Finally, the actor-learner algorithm is utilized to minimize the sum of the losses of the two parts. Experiment results on three datasets demonstrate the effectiveness and efficiency of our framework.
Discovery Team at SemEval-2020 Task 1: Context-sensitive Embeddings Not Always Better than Static for Semantic Change Detection

This paper describes the approaches used by the Discovery Team to solve SemEval-2020 Task 1 -Unsupervised Lexical Semantic Change Detection.The proposed method is based on clustering of BERT contextual embeddings, followed by a comparison of cluster distributions across time.The best results were obtained by an ensemble of this method and static Word2Vec embeddings.According to the official results, our approach proved the best for Latin in Subtask 2.
Radio Access Technology characterisation through object detection

Radio Access Technology (RAT) classification and monitoring are essential for efficient coexistence of different communication systems in shared spectrum. Shared spectrum, including operation in license-exempt bands, is envisioned in the fifth generation of wireless technology (5G) standards (e.g., 3GPP Rel. 16). In this paper, we propose a Machine Learning (ML) approach to characterise the spectrum utilisation and facilitate the dynamic access to it. Recent advances in Convolutional Neural Networks (CNNs) enable us to perform waveform classification by processing spectrograms as images. In contrast to other ML methods that can only provide the class of the monitored RATs, the solution we propose can recognise not only different RATs in shared spectrum, but also identify critical parameters such as inter-frame duration, frame duration, centre frequency, and signal bandwidth by using object detection and a feature extraction module to extract features from spectrograms. We have implemented and evaluated our solution using a dataset of commercial transmissions, as well as in a Software-Defined Radio (SDR) testbed environment. The scenario evaluated was the coexistence of WiFi and LTE transmissions in shared spectrum. Our results show that our approach has an accuracy of 96% in the classification of RATs from a dataset that captures transmissions of regular user communications. It also shows that the extracted features can be precise within a margin of 2%, and can detect above 94% of objects under a broad range of transmission power levels and interference conditions.
Grammar-compressed indexes with logarithmic search time

Let a text T[1..n] be the only string generated by a context-free grammar with g (terminal and nonterminal) symbols, and of size G (measured as the sum of the lengths of the right-hand sides of the rules). Such a grammar, called a grammar-compressed representation of T, can be encoded using Glg⁡G bits. We introduce the first grammar-compressed index that uses O(Glg⁡n) bits (precisely, Glg⁡n+(2+ϵ)Glg⁡g for any constant ϵ>0) and can find the occ occurrences of patterns P[1..m] in time O((m2+occ)lg⁡G). We implement the index and demonstrate its practicality in comparison with the state of the art, on highly repetitive text collections.
Cyber Security Threats to Bitcoin Exchanges: Adversary Exploitation and Laundering Techniques

Bitcoin is gaining traction as an alternative store of value. Its market capitalization transcends all other cryptocurrencies in the market. But its high monetary value also makes it an attractive target to cyber criminal actors. Hacking campaigns usually target an ecosystem’s weakest points. In Bitcoin, the exchange platforms are one of them. Each exchange breach is a threat not only to direct victims, but to the credibility of Bitcoin’s entire ecosystem. Based on an extensive analysis of 36 breaches of Bitcoin exchanges, we show the attack patterns used to exploit Bitcoin exchange platforms using an industry standard for reporting intelligence on cyber security breaches. Based on this we are able to provide an overview of the most common attack vectors, showing that all except three hacks were possible due to relatively lax security. We show that while the security regimen of Bitcoin exchanges is subpar compared to other financial service providers, the use of stolen credentials, which does not require any hacking, is decreasing. We also show that the amount of BTC taken during a breach is decreasing, as well as the exchanges that terminate after being breached. Furthermore we show that overall security posture has improved, but still has major flaws. To discover adversarial methods post-breach, we have analyzed two cases of BTC laundering. Through this analysis we provide insight into how exchange platforms with lax cyber security even further increase the intermediary risk introduced by them into the Bitcoin ecosystem.
AiSee

People with visual impairments (PVI) experience simple tasks, such as grocery shopping, to be an essential difficulty. Although the recent emergence of AI-technology has been dramatically improving visual recognition capabilities, the application to the daily life of PVI is still complex and erroneous. For example, image recognition engines require a clear shot of the targeted object and a contextual understanding of the information the user requires. In this paper, we aimed to understand the PVI's needs and their pain points in the task of identifying grocery items. Following a user-centered design process, we iteratively
Developing a standardized protocol for computational sentiment analysis research using health-related social media data

Sentiment analysis is a popular tool for analyzing health-related social media content. However, existing studies exhibit numerous methodological issues and inconsistencies with respect to research design and results reporting, which could lead to biased data, imprecise or incorrect conclusions, or incomparable results across studies. This article reports a systematic analysis of the literature with respect to such issues. The objective was to develop a standardized protocol for improving the research validity and comparability of results in future relevant studies.We developed the Protocol of Analysis of senTiment in Health (PATH) based on a systematic review that analyzed common research design choices and how such choices were made, or reported, among eligible studies published 2010-2019.Of 409 articles screened, 89 met the inclusion criteria. A total of 16 distinctive research design choices were identified, 9 of which have significant methodological or reporting inconsistencies among the articles reviewed, ranging from how relevance of study data was determined to how the sentiment analysis tool selected was validated. Based on this result, we developed the PATH protocol that encompasses all these distinctive design choices and highlights the ones for which careful consideration and detailed reporting are particularly warranted.A substantial degree of methodological and reporting inconsistencies exist in the extant literature that applied sentiment analysis to analyzing health-related social media data. The PATH protocol developed through this research may contribute to mitigating such issues in future relevant studies.
Machine-Made Synthesis of Stabilization System by Modified Cartesian Genetic Programming

A numerical solution of the problem of the general synthesis of a stabilization system by a symbolic regression method is considered. The goal is to automatically find a feedback control function using a computer so that the control object can reach a given terminal position from anywhere in a given region of the initial conditions with an optimal value of the quality criterion. Usually, the control synthesis problem is solved analytically or technically taking into account the specific properties of the mathematical model. We suppose that modern numerical approaches of symbolic regression can be applied to find a solution without reference to specific model equations. It is proposed to use the numerical method of Cartesian genetic programming (CGP). It was developed for automatic writing of programs but has never been used to solve the synthesis problem. In the present work, the method was modified with the principle of small variations in order to reduce the search area and increase the rate of convergence. To apply the general principle of small variations to CGP, we developed special types of variations and coding. The modified CGP searches for the mathematical expression of the feedback control function in the form of a code and, at the same time, the optimal value of the parametric vector which is also a new feature-simultaneous tuning of the parameters inside the search process. This approach enables working with objects and functions of any type, which is not always possible with analytical methods. The need to use the received solution on the onboard processor of the control object imposes certain restrictions on the used basic set of elementary functions. This article proposes the theoretical foundations of the study of these functions, and the concept of the space of machine-made functions is introduced. The capabilities of the approach are demonstrated on the numerical solution of the control system synthesis problems for a mobile robot and a Duffing model.
Defending Against Package Typosquatting

Software repositories based on a single programming language are common. Examples include npm (JavaScript) and PyPI (Python). They encourage code reuse, making it trivial for developers to import external packages. Unfortunately, the ease with which packages can be published also facilitates typosquatting: uploading a package with name similar to that of a highly popular package, with the aim of capturing some of the popular package's installs. Typosquatting frequently occurs in the wild, is difficult to detect manually, and has resulted in developers importing incorrect and sometimes malicious packages. We present TypoGard, a tool for identifying and reporting potentially typosquatted imports to developers. TypoGard implements a novel detection technique, based on the analysis of npm and PyPI. It leverages a model of lexical similarity between names, and incorporates the notion of package popularity. It flags cases where unknown/scarcely used packages would be installed in place of popular ones with similar names, before installation occurs. We evaluated TypoGard on both npm, PyPI and RubyGems, with encouraging results: TypoGard flags up to 99.4% of known typosquatting cases while generating limited warnings (up to 0.5% of package installs), and low overhead (2.5% of package install time).
A Sparse Gaussian Approach to Region-Based 6DoF Object Tracking

We propose a novel, highly efficient sparse approach to region-based 6DoF object tracking that requires only a monocular RGB camera and the 3D object model. The key contribution of our work is a probabilistic model that considers image information sparsely along correspondence lines. For the implementation, we provide a highly efficient discrete scale-space formulation. In addition, we derive a novel mathematical proof that shows that our proposed likelihood function follows a Gaussian distribution. Based on this information, we develop robust approximations for the derivatives of the log-likelihood that are used in a regularized Newton optimization. In multiple experiments, we show that our approach outperforms state-of-the-art region-based methods in terms of tracking success while being about one order of magnitude faster. The source code of our tracker is publicly available (https://github.com/DLR-RM/RBGT).
New methods and ways of organizing the educational process in the context of digitalization

Abstract Education is a fundamental requirement for each person and digital education is the current pattern and needs for each student or student to be increasingly engaged in their learning. Digital education encourages students to accumulate information in simpler ways. It additionally decreases the learning time. Achieving effective learning by means of digital media keeps on being a significant concern in contemporary education. The everyday utilization of all types of digital media is a piece of our lives and in this manner turns into a key segment of education. The challenge for instructors is to continually audit and structure educational methodologies supporting the students towards utilizing the instruments meaningfully and within a characterized learning experience, proceeding to assist students to become active members and authors of their own personality and inventiveness. Instructors need to locate the best use of digital media innovation, giving students each preferred position towards arriving at a universal perspective.
Uncertainty depth estimation with gated images for 3D reconstruction

Gated imaging is an emerging sensor technology for self-driving cars that provides high-contrast images even under adverse weather influence. It has been shown that this technology can even generate high-fidelity dense depth maps with accuracy comparable to scanning LiDAR systems. In this work, we extend the recent Gated2Depth framework with aleatoric uncertainty providing an additional confidence measure for the depth estimates. This confidence can help to filter out uncertain estimations in regions without any illumination. Moreover, we show that training on dense depth maps generated by LiDAR depth completion algorithms can further improve the performance.
Edge-preserved fringe-order correction strategy for code-based fringe projection profilometry

Code-based fringe projection profilometry (CBFPP) is widely used because of its simple structure, robustness, and potential high speed. One of the most important steps in this technique is to obtain the fringe orders correctly. However, fringe orders tend to fail easily, especially around the phase-jump regions. First, this paper analyzes the formation mechanisms of different types of fringe-order noise. Then, an edge-preserved fringe-order correction strategy has been proposed to tackle the fringe order noise problem. Unlike the proprietary denoising algorithms, the correction approach is suitable for all types of CBFPP. Furthermore, the present algorithm is fast and can process in a short time. Moreover, the entire filtering procedure is noninvasive and can deal with dense point artifacts. The simulation and experimental results are presented to verify the feasibility and effectiveness of the proposed method.
A trust-aware latent space mapping approach for cross-domain recommendation

Cross-domain recommendation is becoming increasingly popular recently. Existing cross-domain recommendation often assumes that, a sufficient set of bridged users across domains is given in advance which disregards the scenario with insufficient bridged users. In this paper, we propose a novel Trust-aware Latent Space Mapping approach for Cross-domain Recommendation, called TLSM-CDR. This represents one of the first attempts to address the challenge of insufficient bridged users from the perspective of users’ trust relationships to facilitate user sharing cross-domain recommendation. First, our model employs the Probabilistic Matrix Factorization (PMF) to generate user and item matrices. Then, Deep Neural Network (DNN) and graph Laplacian are seamlessly incorporated into our trust-aware non-linear mapping function to capture the latent space relationships between both bridged and non-bridged users. Finally, we predict the optimized users’ rating matrix in the target domain. Extensive experiments conducted on two real-world datasets demonstrate that, our TLSM-CDR model significantly outperforms several state-of-the-art methods.
A Dynamic Virtual Machine Resource Consolidation Strategy Based on a Gray Model and Improved Discrete Particle Swarm Optimization

With the continuous expansion of the scale of cloud datacenters, high energy consumption has become a problem. Virtual machine (VM) consolidation is an effective method of energy management, but overly aggressive consolidation has caused issues such as high service level agreement violation (SLA) rates and excessive migrations. To reduce energy consumption while ensuring the quality of service of a datacenter, a VM dynamic consolidation strategy based on a gray model and an improved discrete particle swarm algorithm (GM-DPSO) is proposed. This strategy uses a gray prediction algorithm for load detection and adjusts the size of the threshold according to the load condition. Then, we select VMs for overloaded and underloaded hosts and complete the placement of VMs through a VM placement strategy based on an improved discrete particle swarm algorithm. This method aims to improve the load balance, establish a mapping between VMs and hosts, use particles to search for the best VM placement in the global scope, and reduce the probability of SLAV. Our algorithm considers the impact of VM migration on system energy consumption and service quality and takes sufficient measures to reduce the number of migrations. To verify the effectiveness and practicality of this strategy, experiments were conducted on actual workloads, and the results were compared with those of other strategies. The experimental results show that the GM-DPSO greatly improves service quality while reducing energy consumption and SLAV by 34.53% and 97.53%, respectively. The methods and theories in this paper provide strong theoretical and practical engineering guidance for large-scale cloud deployment.
Lesion segmentation in breast ultrasound images using the optimized marked watershed method

Abstract Background Breast cancer is one of the most serious diseases threatening women’s health. Early screening based on ultrasound can help to detect and treat tumours in the early stage. However, due to the lack of radiologists with professional skills, ultrasound-based breast cancer screening has not been widely used in rural areas. Computer-aided diagnosis (CAD) technology can effectively alleviate this problem. Since breast ultrasound (BUS) images have low resolution and speckle noise, lesion segmentation, which is an important step in CAD systems, is challenging. Results Two datasets were used for evaluation. Dataset A comprises 500 BUS images from local hospitals, while dataset B comprises 205 open-source BUS images. The experimental results show that the proposed method outperformed its related classic segmentation methods and the state-of-the-art deep learning model RDAU-NET. Its accuracy (Acc), Dice similarity coefficient (DSC) and Jaccard index (JI) reached 96.25%, 78.4% and 65.34% on dataset A, and its Acc, DSC and sensitivity reached 97.96%, 86.25% and 88.79% on dataset B, respectively. Conclusions We proposed an adaptive morphological snake based on marked watershed (AMSMW) algorithm for BUS image segmentation. It was proven to be robust, efficient and effective. In addition, it was found to be more sensitive to malignant lesions than benign lesions. Methods The proposed method consists of two steps. In the first step, contrast limited adaptive histogram equalization (CLAHE) and a side window filter (SWF) are used to preprocess BUS images. Lesion contours can be effectively highlighted, and the influence of noise can be eliminated to a great extent. In the second step, we propose adaptive morphological snake (AMS). It can adjust the working parameters adaptively according to the size of the lesion. Its segmentation results are combined with those of the morphological method. Then, we determine the marked area and obtain candidate contours with a marked watershed (MW). Finally, the best lesion contour is chosen by the maximum average radial derivative (ARD).