Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing Cyber-Physical Threats on Robotic Platforms †

Robots are increasingly involved in our daily lives. Fundamental to robots are the communication link (or stream) and the applications that connect the robots to their clients or users. Such communication link and applications are usually supported through client/server network connection. This networking system is amenable of being attacked and vulnerable to the security threats. Ensuring security and privacy for robotic platforms is thus critical, as failures and attacks could have devastating consequences. In this paper, we examine several cyber-physical security threats that are unique to the robotic platforms; specifically the communication link and the applications. Threats target integrity, availability and confidential security requirements of the robotic platforms, which use MobileEyes/arnlServer client/server applications. A robot attack tool (RAT) was developed to perform specific security attacks. An impact-oriented approach was adopted to analyze the assessment results of the attacks. Tests and experiments of attacks were conducted in simulation environment and physically on the robot. The simulation environment was based on MobileSim; a software tool for simulating, debugging and experimenting on MobileRobots/ActivMedia platforms and their environments. The robot platform PeopleBotTM was used for physical experiments. The analysis and testing results show that certain attacks were successful at breaching the robot security. Integrity attacks modified commands and manipulated the robot behavior. Availability attacks were able to cause Denial-of-Service (DoS) and the robot was not responsive to MobileEyes commands. Integrity and availability attacks caused sensitive information on the robot to be hijacked. To mitigate security threats, we provide possible mitigation techniques and suggestions to raise awareness of threats on the robotic platforms, especially when the robots are involved in critical missions or applications.
Survey and Taxonomy of Lossless Graph Compression and Space-Efficient Graph Representations

Various graphs such as web or social networks may contain up to trillions of edges. Compressing such datasets can accelerate graph processing by reducing the amount of I/O accesses and the pressure on the memory subsystem. Yet, selecting a proper compression method is challenging as there exist a plethora of techniques, algorithms, domains, and approaches in compressing graphs. To facilitate this, we present a survey and taxonomy on lossless graph compression that is the first, to the best of our knowledge, to exhaustively analyze this domain. Moreover, our survey does not only categorize existing schemes, but also explains key ideas, discusses formal underpinning in selected works, and describes the space of the existing compression schemes using three dimensions: areas of research (e.g., compressing web graphs), techniques (e.g., gap encoding), and features (e.g., whether or not a given scheme targets dynamic graphs). Our survey can be used as a guide to select the best lossless compression scheme in a given setting.
Time-differentiated supply at customer sites: Analysing the service costs

Common in IT equipment support and emergency service operations, time-based service differentiation involves setting different service time windows to respond to different classes of service calls. These time windows can be associated with providing service parts (materials) at customer sites. It is reasonable to expect that the impact of different service time options on service costs is realized for strategic and operational planning. However, the question of how different service time limits impact on service costs has largely been overlooked in the existing literature. The paper focuses on this question and presents an estimate cost model based on a stylized system considering hierarchical and nonhierarchical organizations of service facilities. The impact of varying service time limits and the demand fractions for different service times on inventory, transportation and distribution network setup costs is investigated. The findings show that when service time requirements become stricter, requiring more decentralized distribution, the inventory levels do not increase in all cases, while travelling to reach customers reduces proportionally. The analysis highlights that a non-hierarchical setup of facilities, treating different service time requests in a uniform fashion, can perform better in certain cases.
B 1 +-insensitive slice-selective pulses constructed from optimized non-selective composite waveforms

Fig. 3: Images acquired at 7 T with a GRE sequence (TR = 5s) using sinc excitations (left) and the composite pulse excitations from Fig. 2 (middle). Line profiles (right) reflect signals from the two pulse types after adjustment for the slice profile differences reported in Fig. 2. Results for both 45° (top row) and 90° (bottom row) pulses are included. Fig. 2: Slice profiles as measured in a mineral oil phantom for a Gaussian-modulated sinc pulse (left), and the 45° (middle) and 90° (right) optimized pulses from Fig. 1. B1-insensitive slice-selective pulses constructed from optimized non-selective composite waveforms
Data-Driven Participation Factors for Nonlinear Systems Based on Koopman Mode Decomposition

This letter develops a novel data-driven technique to compute the participation factors for nonlinear systems based on the Koopman mode decomposition. Provided that certain conditions are satisfied, it is shown that the proposed technique generalizes the original definition of the linear mode-in-state participation factors. Two numerical examples are provided to demonstrate the performance of our approach: one relying on a canonical nonlinear dynamical system, and the other based on the two-area four-machine power system. The Koopman mode decomposition is capable of coping with a large class of nonlinearity, thereby making our technique able to deal with oscillations arising in practice due to nonlinearities while being fast to compute and compatible with real-time applications.
Adaptive learning‐based projection method for smoke simulation

Traditional Eulerian‐based fluid simulations require much time and computational resources to solve the projection step, especially the large linear system produced by the Poisson equation. In this paper, we propose an adaptive machine‐learning‐based projection method combining deep neural network and incremental learning technique. We provide two modes: Fast Mode and Normal Mode to solve the most time‐consuming projection step and deal with various simulation scenes largely different from the training data set. We introduce patch‐based feature vectors to represent the whole velocity field and a loss function to keep the divergence‐free constraint. We have demonstrated the acceleration and extrapolation ability of our method by testing various smoke scenes far different from our training data set.
Polyethylene-Grafted Gold and Silver Nanoparticles Using Catalyzed Chain Growth (CCG)

We report an efficient synthesis route for the formation of gold/silver-core–PE-shell nanohybrids in a simple self-assembly approach using PE with strong aurophilicity and argentophilicity, via thiol- and trithiocarbonate terminated moieties. This united the unique properties of polyethylene (PE) with gold and silver nanoparticles, using the well-defined end-group design of PE. These nanocomposites showed a similar solubility as PE, as confirmed by dynamic light scattering, and could be fully incorporated into a polyethylene matrix with different particle contents, as visualized by transmission electron microscopy. Using UV/vis-spectroscopy, we observed reversible, thermoresponsive aggregation/deaggregation properties in the nanohybrids, validating the strong and effective anchoring of PE on gold/silver surfaces.
Second Language Acquisition Modeling: An Ensemble Approach

Accurate prediction of students’ knowledge is a fundamental building block of personalized learning systems. Here, we propose an ensemble model to predict student knowledge gaps. Applying our approach to student trace data from the online educational platform Duolingo we achieved highest score on all three datasets in the 2018 Shared Task on Second Language Acquisition Modeling. We describe our model and discuss relevance of the task compared to how it would be setup in a production environment for personalized education.
A novel refocusing distance measurement method using super‐resolved light field images

This paper proposes a novel distance measurement method using superresolved light‐field images. We first superresolve the light field using the hybrid cross‐resolution input based on PatchMatch and convolutional neural network method, which combines and takes advantage of these two methods. In this way, we can explore the similarity between images and deal with challenging scenes effectively. The scaling factor is up to eight times, which is much larger than the ordinary superresolution scaling factor, and our superresolution method can also maintain a satisfactory accuracy. With the traditional idea of focus ranging, the light‐field 3D measurement method is established. A proper definition evaluation function suitable for light‐field data is selected to evaluate the imaging quality, and the triangle barycenter interpolation method is proposed to measure 3D scenes. Experimental results demonstrate that the proposed method not only improves the quality of the reconstructed high‐resolution light field but also has the ability of the distance measurement. It indicates that the light‐field imaging is a promising 3D measurement technique.
High-Performance FV Somewhat Homomorphic Encryption on GPUs: An Implementation using CUDA

Homomorphic encryption (HE) offers great capabilities that can solve a wide range of privacy-preserving computing problems. This tool allows anyone to process encrypted data producing encrypted results that only the decryption key’s owner can decrypt. Although HE has been realized in several public implementations, its performance is quite demanding. The reason for this is attributed to the huge amount of computation required by secure HE schemes. In this work, we present a CUDAbased implementation of the Fan and Vercauteren (FV) Somewhat HomomorphicEncryption (SHE) scheme. We demonstrate several algebraic tools such as the Chinese Remainder Theorem (CRT), Residual Number System (RNS) and Discrete Galois Transform (DGT) to accelerate and facilitate FV computation on GPUs. We also show how the entire FV computation can be done on GPU without multi-precision arithmetic. We compare our GPU implementation with two mature state-of-the-art implementations: 1) Microsoft SEAL v2.3. 0-4 and 2) NFLlib-FV. Our implementation outperforms them and achieves on average 5.37x, 7.37x, 22.22x, 5.11x and 13.18x (resp. 2.03x, 2.94x, 27.86x, 8.53x and 18.69x) for key generation, encryption, decryption, homomorphic addition and homomorphic multiplication against SEAL-FVRNS (resp. NFLlib-FV).
NILC at CWI 2018: Exploring Feature Engineering and Feature Learning

This paper describes the results of NILC team at CWI 2018. We developed solutions following three approaches: (i) a feature engineering method using lexical, n-gram and psycholinguistic features, (ii) a shallow neural network method using only word embeddings, and (iii) a Long Short-Term Memory (LSTM) language model, which is pre-trained on a large text corpus to produce a contextualized word vector. The feature engineering method obtained our best results for the classification task and the LSTM model achieved the best results for the probabilistic classification task. Our results show that deep neural networks are able to perform as well as traditional machine learning methods using manually engineered features for the task of complex word identification in English.
The role of Galectin-3 in modulating tumor growth and immunosuppression within the tumor microenvironment

ABSTRACT The efficacy of cancer immunotherapy is limited, in part, by the multitude of immunosuppressive mechanisms present within the tumor microenvironment (TME). Galectin-3 (Gal-3) is a lectin that contributes to TME immunosuppression and regulates diverse functions including cellular homeostasis and cancer biology. Increased Gal-3 expression during cancer progression augments tumor growth, invasiveness, metastatic potential, and immune suppression, which highlights the potential use of Gal-3 as a therapeutic target capable of modulating anti-tumor immunity. Here, we discuss the mechanisms by which Gal-3 regulates lymphocytes, the role of Gal-3 in lung and prostate tumors, and the contribution of Gal-3 to TME immunosuppression.
Visual Relationship Detection Using Joint Visual-Semantic Embedding

Visual relationship detection can serve as the intermediate building block for higher level tasks such as image captioning, visual question answering, image-text matching. Due to the long tail of relationship distribution in real world images, zero-shot predication of relationships that it has never seen before can alleviate stress of collecting every possible relationship. Following zero-shot learning (ZSL) strategies, we propose a joint visual-semantic embedding model for visual relationship detection. In our model, the visual vector and semantic vector are projected to a shared latent space to learn the similarity between the two branches. In the semantic embedding, sequential features in terms of <sub, pred, obj> are learned to provide the context information and then concatenated with corresponding component vector of the relationship triplet. Experiments show that the proposed model achieves superior performance in zero-shot visual relationship detection and comparable results in non-zero-shot scenario.
Context Based Approach for Second Language Acquisition

SLAM 2018 focuses on predicting a student’s mistake while using the Duolingo application. In this paper, we describe the system we developed for this shared task. Our system uses a logistic regression model to predict the likelihood of a student making a mistake while answering an exercise on Duolingo in all three language tracks - English/Spanish (en/es), Spanish/English (es/en) and French/English (fr/en). We conduct an ablation study with several features during the development of this system and discover that context based features plays a major role in language acquisition modeling. Our model beats Duolingo’s baseline scores in all three language tracks (AUROC scores for en/es = 0.821, es/en = 0.790 and fr/en = 0.812). Our work makes a case for providing favourable textual context for students while learning second language.
Intra- and Interpersonal Factors in Adolescence Predicting Loneliness among Young Adults with Visual Impairments

Introduction: Youths with visual impairments (i.e., blindness or low vision) experience difficulties with forming and maintaining social relationships with peers. These difficulties challenge their psychosocial functioning and put them at risk of being lonelier later in life. The study’s primary goal was to investigate how intra- and interpersonal factors during adolescence influence Loneliness in young adulthood. Methods: Analyses were conducted on data from a national data set. Participants (N = 96) were interviewed at two different time points. General linear regression and mediation analyses were used to examine the role of social competence, personality, and satisfaction with social support, measured at mean age 17.83, and on Loneliness measured at mean age 23.45. Results: Analyses showed that adolescents with visual impairments who were more emotionally stable and had higher social competence during adolescence were less lonely later in life. In addition, the results showed that emotionally unstable adolescents reported lower social competence and, therefore, were lonelier in young adulthood. Discussion: These findings indicate that factors connected to Loneliness in young adulthood include people’s personality traits and their level of social competence at a younger age. Implications for practitioners: Knowing the underlying causes of an individual’s Loneliness assists practitioners in selecting what type of intervention would be suitable for addressing these issues. Those with low social skills benefit more from social skills training and those with negative biases of their own functioning profit more from interventions based on cognitive approaches. Screening methods could be used in order to determine these underlying issues and personality structure, before assigning persons to specific interventions.
CAMB at CWI Shared Task 2018: Complex Word Identification with Ensemble-Based Voting

This paper presents the winning systems we submitted to the Complex Word Identification Shared Task 2018. We describe our best performing systems’ implementations and discuss our key findings from this research. Our best-performing systems achieve an F1 score of 0.8792 on the NEWS, 0.8430 on the WIKINEWS and 0.8115 on the WIKIPEDIA test sets in the monolingual English binary classification track, and a mean absolute error of 0.0558 on the NEWS, 0.0674 on the WIKINEWS and 0.0739 on the WIKIPEDIA test sets in the probabilistic track.
Deep Learning Architecture for Complex Word Identification

We describe a system for the CWI-task that includes information on 5 aspects of the (complex) lexical item, namely distributional information of the item itself, morphological structure, psychological measures, corpus-counts and topical information. We constructed a deep learning architecture that combines those features and apply it to the probabilistic and binary classification task for all English sets and Spanish. We achieved reasonable performance on all sets with best performances seen on the probabilistic task, particularly on the English news set (MAE 0.054 and F1-score of 0.872). An analysis of the results shows that reasonable performance can be achieved with a single architecture without any domain-specific tweaking of the parameter settings and that distributional features capture almost all of the information also found in hand-crafted features.
Explaining Away Syntactic Structure in Semantic Document Representations

Most generative document models act on bag-of-words input in an attempt to focus on the semantic content and thereby partially forego syntactic information. We argue that it is preferable to keep the original word order intact and explicitly account for the syntactic structure instead. We propose an extension to the Neural Variational Document Model (Miao et al., 2016) that does exactly that to separate local (syntactic) context from the global (semantic) representation of the document. Our model builds on the variational autoencoder framework to define a generative document model based on next-word prediction. We name our approach Sequence-Aware Variational Autoencoder since in contrast to its predecessor, it operates on the true input sequence. In a series of experiments we observe stronger topicality of the learned representations as well as increased robustness to syntactic noise in our training data.
An integromic signature for lung cancer early detection

We previously developed three microRNAs (miRs-21, 210, and 486-5p), two long noncoding RNAs (lncRNAs) (SNHG1 and RMRP), and two fucosyltransferase (FUT) genes (FUT8 and POFUT1) as potential plasma biomarkers for lung cancer. However, the diagnostic performance of the individual panels is not sufficient to be used in the clinics. Given the heterogeneity of lung tumors developed from multifactorial molecular aberrations, we determine whether integrating the different classes of molecular biomarkers can improve diagnosis of lung cancer. By using droplet digital PCR, we analyze expression of the seven genes in plasma of a development cohort of 64 lung cancer patients and 33 cancer-free individuals. The panels of three miRNAs (miRs-21, 210, and 486-5p), two lncRNAs (SNHG1 and RMRP), and two FUTs (FUT8 and POFUT1) have a sensitivity of 81-86% and a specificity of 84-87% for diagnosis of lung cancer. From the seven genes, an integromic plasma signature comprising miR-210, SNHG1, and FUT8 is developed that produces higher sensitivity (95.45%) and specificity (96.97%) compared with the individual biomarker panels (all p<0.05). The diagnostic value of the signature was confirmed in a validation cohort of 40 lung cancer patients and 29 controls, independent of stage and histological type of lung tumor, and patients’ age, sex, and smoking status (all p>0.05). The integration of the different categories of biomarkers might improve diagnosis of lung cancer.
CLUF: a Neural Model for Second Language Acquisition Modeling

Second Language Acquisition Modeling is the task to predict whether a second language learner would respond correctly in future exercises based on their learning history. In this paper, we propose a neural network based system to utilize rich contextual, linguistic and user information. Our neural model consists of a Context encoder, a Linguistic feature encoder, a User information encoder and a Format information encoder (CLUF). Furthermore, a decoder is introduced to combine such encoded features and make final predictions. Our system ranked in first place in the English track and second place in the Spanish and French track with an AUROC score of 0.861, 0.835 and 0.854 respectively.
Sketch‐based shape‐preserving tree animations

We present a novel and intuitive sketch‐based tree animation technique, targeting on generating a new type of special effect of smoothly transforming leafy trees into morphologically different new shapes. Both topological consistencies of branches and meaningful in‐between crown shapes are preserved during the transformation. Specifically, it takes a leafy tree and a user's sketch describing the silhouette of the desired crown shape under a certain viewpoint as the input. Based on a self‐adaptive multiscale cage tree representation, branches are locally transformed through a series of topology‐aware deformations, and the resulting tree conforms to the user‐designed shape, demonstrating better aesthetics compared to global single‐cage‐based methods. By interpolating the transformations, we are able to create visually pleasing shape‐preserving animations of trees transforming between two crown shapes. Our proposed framework also provides an efficient way to interactively edit leafy trees toward desired shapes, demonstrating its potential to leverage existing tree modeling frameworks by providing flexible and intuitive tree editing operations.
C-BPMN: A Context Aware BPMN for Modeling Complex Business Process

A complex business process demands adaptability as it has been highly influenced by the contextual information. The contextual information declares the underlying semantics on which the process logic depends. Thus one of the challenges of a business process modeling is to include the context sensitivity within the modeling itself. BPMN is the widely accepted tool in this field. All the process modeling languages like EPC, UML, BPMN are not able to express the context awareness as required. In this paper an attempt has been made to offer a means for modeling a complex business process with necessary contextual information. We have proposed a context model in terms of a graph, extended the existing BPMN by adding new construct and integrated the said components to achieve our goal. The methodology as stated certainly offers necessary understandability, maintainability and the adaptability as a whole. Moreover the model is validated using Colored Petri Net and is expected to behave properly in a real life environment.