OpenRaiser/Intern-Atlas huggingface dataset:
Comparing two deep learning sequence-based models for protein-protein interaction prediction
Venue: arXiv cs.LG 2019
Year: 2019

Biological data are extremely diverse, complex but also quite sparse. The recent developments in deep learning methods are offering new possibilities for the analysis of complex data. However, it is easy to be get a deep learning model that seems to have good results but is in fact either overfitting the training data or the validation data. In particular, the fact to overfit the validation data, called "information leak", is almost never treated in papers proposing deep learning models to predict protein-protein interactions (PPI). In this work, we compare two carefully designed deep learning models and show pitfalls to avoid while predicting PPIs through machine learning methods. Our best model predicts accurately more than 78% of human PPI, in very strict conditions both for training and testing. The methodology we propose here allow us to have strong confidences about the ability of a model to scale up on larger datasets. This would allow sharper models when larger datasets would be available, rather than current models prone to information leaks. Our solid methodological foundations shall be applicable to more organisms and whole proteome networks predictions.
Video Multimethod Assessment Fusion (VMAF) on 360VR contents
Venue: arXiv cs.MM 2019
Year: 2019

This paper describes the subjective experiments and subsequent analysis carried out to validate the application of one of the most robust and influential video quality metrics, Video Multimethod Assessment Fusion (VMAF), to 360VR contents. VMAF is a full reference metric initially designed to work with traditional 2D contents. Hence, at first, it cannot be assumed to be compatible with the particularities of the scenario where omnidirectional content is visualized using a Head-Mounted Display (HMD). Therefore, through a complete set of tests, we prove that this metric can be successfully used without any specific training or adjustments to obtain the quality of 360VR sequences actually perceived by users.
Detecting Behavioral Engagement of Students in the Wild Based on Contextual and Visual Data
Venue: arXiv cs.CY 2019
Year: 2019

To investigate the detection of students' behavioral engagement (On-Task vs. Off-Task), we propose a two-phase approach in this study. In Phase 1, contextual logs (URLs) are utilized to assess active usage of the content platform. If there is active use, the appearance information is utilized in Phase 2 to infer behavioral engagement. Incorporating the contextual information improved the overall F1-scores from 0.77 to 0.82. Our cross-classroom and cross-platform experiments showed the proposed generic and multi-modal behavioral engagement models' applicability to a different set of students or different subject areas.
On Distance Properties of Convolutional Polar Codes
Venue: arXiv cs.IT 2019
Year: 2019

A lower bound on minimum distance of convolutional polar codes is provided. The bound is obtained from the minimum weight of generalized cosets of the codes generated by bottom rows of the polarizing matrix. Moreover, a construction of convolutional polar subcodes is proposed, which provides improved performance under successive cancellation list decoding. For sufficiently large list size, the decoding complexity of convolutional polar subcodes appears to be lower compared to Arikan polar subcodes with the same performance. The error probability of successive cancellation list decoding of convolutional polar subcodes is lower than that of Arikan polar subcodes with the same list size.
Effectiveness Assessment of Cyber-Physical Systems
Venue: arXiv cs.AI 2019
Year: 2019

By achieving their purposes through interactions with the physical world, Cyber-Physical Systems (CPS) pose new challenges in terms of dependability. Indeed, the evolution of the physical systems they control with transducers can be affected by surrounding physical processes over which they have no control and which may potentially hamper the achievement of their purposes. While it is illusory to hope for a comprehensive model of the physical environment at design time to anticipate and remove faults that may occur once these systems are deployed, it becomes necessary to evaluate their degree of effectiveness in vivo. In this paper, the degree of effectiveness is formally defined and generalized in the context of the measure theory. The measure is developed in the context of the Transferable Belief Model (TBM), an elaboration on the Dempster-Shafer Theory (DST) of evidence so as to handle epistemic and aleatory uncertainties respectively pertaining the users' expectations and the natural variability of the physical environment. The TBM is used in conjunction with the Input/Output Hidden Markov Modeling framework (we denote by Ev-IOHMM) to specify the expected evolution of the physical system controlled by the CPS and the tolerances towards uncertainties. The measure of effectiveness is then obtained from the forward algorithm, leveraging the conflict entailed by the successive combinations of the beliefs obtained from observations of the physical system and the beliefs corresponding to its expected evolution. The proposed approach is applied to autonomous vehicles and show how the degree of effectiveness can be used for bench-marking their controller relative to the highway code speed limitations and passengers' well-being constraints, both modeled through an Ev-IOHMM.
An information theoretic model for summarization, and some basic results
Venue: arXiv cs.IT 2019
Year: 2019

A basic information theoretic model for summarization is formulated. Here summarization is considered as the process of taking a report of $v$ binary objects, and producing from it a $j$ element subset that captures most of the important features of the original report, with importance being defined via an arbitrary set function endemic to the model. The loss of information is then measured by a weight average of variational distances, which we term the semantic loss. Our results include both cases where the probability distribution generating the $v$-length reports are known and unknown. In the case where it is known, our results demonstrate how to construct summarizers which minimize the semantic loss. For the case where the probability distribution is unknown, we show how to construct summarizers whose semantic loss when averaged uniformly over all possible distribution converges to the minimum.
Modular design to transform codes for the wiretap channel of type I into codes for the wiretap channel of type II
Venue: arXiv cs.IT 2019
Year: 2019

We construct a modular scheme which extends codes for wiretap channels of type I for use in wiretap channels of type II. This is done by using a concatenate and permute strategy, wherein multiple uses of the wiretap type I code are concatenated and then the joint sequence of symbols permuted. The choice of permutation is then encoded with a short code and appended to the transmitted sequence. Analysis shows essentially no degradation in operational parameters (rate, error rate, leakage) for the new code over the wiretap type II channel when compared to those of multiple uses of the original code over the wiretap type I channel.
Combating Fake News: A Survey on Identification and Mitigation Techniques
Venue: arXiv cs.LG 2019
Year: 2019

The proliferation of fake news on social media has opened up new directions of research for timely identification and containment of fake news, and mitigation of its widespread impact on public opinion. While much of the earlier research was focused on identification of fake news based on its contents or by exploiting users' engagements with the news on social media, there has been a rising interest in proactive intervention strategies to counter the spread of misinformation and its impact on society. In this survey, we describe the modern-day problem of fake news and, in particular, highlight the technical challenges associated with it. We discuss existing methods and techniques applicable to both identification and mitigation, with a focus on the significant advances in each method and their advantages and limitations. In addition, research has often been limited by the quality of existing datasets and their specific application contexts. To alleviate this problem, we comprehensively compile and summarize characteristic features of available datasets. Furthermore, we outline new directions of research to facilitate future development of effective and interdisciplinary solutions.
The Conditional Information Leakage Given Eavesdropper's Received Signals in Wiretap Channels
Venue: arXiv cs.IT 2019
Year: 2019

Information leakage in Wyner's wiretap channel model is usually defined as the mutual information between the secret message and the eavesdropper's received signal. We define a new quantity called "conditional information leakage given the eavesdropper's received signals," which expresses the amount of information that eavesdropper gains from his/her received signal. A benefit of introducing this quantity is that we can develop a fast algorithm for computing the conditional information leakage, which has linear complexity in the code length $n$, while the complexity for computing the usual information leakage is exponential in $n$. Validity of such a conditional information leakage as a security criterion is confirmed by studying the cases of binary symmetric channels and binary erasure channels.
A Reconfigurable High-Performance Optical Data Center Architecture
Venue: arXiv cs.NI 2019
Year: 2019

Optical data center network architectures are becoming attractive because of their low energy consumption, large bandwidth, and low cabling complexity. In\cite{Xu1605:PODCA}, an AWGR-based passive optical data center architecture (PODCA) is presented. Compared with other optical data center architectures, e.g., DOS \cite{ye2010scalable}, Proteus \cite{singla2010proteus}, and Petabit \cite{xia2010petabit}, PODCA can save up to 90$\%$ on power consumption and 88$\%$ in cost. Also, average latency can be low as 9 $μ$s at close to 100$\%$ throughput. However, PODCA is not reconfigurable and cannot optimize the network topology to dynamic traffic. In this paper, we present a novel, scalable and flexible reconfigurable architecture called RODCA. RODCA is built on and augments PODCA with a flexible localized intra-cluster optical network. With the reconfigurable intra-cluster network, racks with mutually large traffic can be located within the same cluster, and share the large bandwidth of the intra-cluster network. We present an algorithm for DCN topology reconfiguration, and present simulation results to demonstrate the effectiveness of reconfiguration.
Holographic Phase Retrieval and Reference Design
Venue: arXiv cs.IT 2019
Year: 2019

A general mathematical framework and recovery algorithm is presented for the holographic phase retrieval problem. In this problem, which arises in holographic coherent diffraction imaging, a "reference" portion of the signal to be recovered via phase retrieval is a priori known from experimental design. A generic formula is also derived for the expected recovery error when the measurement data is corrupted by Poisson shot noise. This facilitates an optimization perspective towards reference design and analysis. We employ this optimization perspective towards quantifying the performance of various reference choices.
Deep Time-Frequency Representation and Progressive Decision Fusion for ECG Classification
Venue: arXiv cs.LG 2019
Year: 2019

Early recognition of abnormal rhythms in ECG signals is crucial for monitoring and diagnosing patients' cardiac conditions, increasing the success rate of the treatment. Classifying abnormal rhythms into exact categories is very challenging due to the broad taxonomy of rhythms, noises and lack of large-scale real-world annotated data. Different from previous methods that utilize hand-crafted features or learn features from the original signal domain, we propose a novel ECG classification method by learning deep time-frequency representation and progressive decision fusion at different temporal scales in an end-to-end manner. First, the ECG wave signal is transformed into the time-frequency domain by using the Short-Time Fourier Transform. Next, several scale-specific deep convolutional neural networks are trained on ECG samples of a specific length. Finally, a progressive online decision fusion method is proposed to fuse decisions from the scale-specific models into a more accurate and stable one. Extensive experiments on both synthetic and real-world ECG datasets demonstrate the effectiveness and efficiency of the proposed method.
Endoscopic vs. volumetric OCT imaging of mastoid bone structure for pose estimation in minimally invasive cochlear implant surgery
Venue: arXiv cs.CV 2019
Year: 2019

Purpose: The facial recess is a delicate structure that must be protected in minimally invasive cochlear implant surgery. Current research estimates the drill trajectory by using endoscopy of the unique mastoid patterns. However, missing depth information limits available features for a registration to preoperative CT data. Therefore, this paper evaluates OCT for enhanced imaging of drill holes in mastoid bone and compares OCT data to original endoscopic images. Methods: A catheter-based OCT probe is inserted into a drill trajectory of a mastoid phantom in a translation-rotation manner to acquire the inner surface state. The images are undistorted and stitched to create volumentric data of the drill hole. The mastoid cell pattern is segmented automatically and compared to ground truth. Results: The mastoid pattern segmented on images acquired with OCT show a similarity of J = 73.6 % to ground truth based on endoscopic images and measured with the Jaccard metric. Leveraged by additional depth information, automated segmentation tends to be more robust and fail-safe compared to endoscopic images. Conclusion: The feasibility of using a clinically approved OCT probe for imaging the drill hole in cochlear implantation is shown. The resulting volumentric images provide additional information on the shape of caveties in the bone structure, which will be useful for image-to-patient registration and to estimate the drill trajectory. This will be another step towards safe minimally invasive cochlear implantation.
Guaranteeing Recoverability via Partially Constrained Transaction Logs
Venue: arXiv cs.DB 2019
Year: 2019

Transaction logging is an essential constituent to guarantee the atomicity and durability in online transaction processing (OLTP) systems. It always has a considerable impact on performance, especially in an in-memory database system. Conventional implementations of logging rely heavily on a centralized design, which guarantees the correctness of recovery by enforcing a total order of all operations such as log sequence number (LSN) allocation, log persistence, transaction committing and recovering. This strict sequential constraint seriously limits the scalability and parallelism of transaction logging and recovery, especially in the multi-core hardware environment. In this paper, we define recoverability for transaction logging and demonstrate its correctness for crash recovery. Based on recoverability, we propose a recoverable logging scheme named Poplar, which enables scalable and parallel log processing by easing the restrictions. Its main advantages are that (1) Poplar enables the parallel log persistence on multiple storage devices; (2) it replaces the centralized LSN allocation by calculating a partially ordered sequence number in a distributed manner, which allows log records to only track RAW and WAW dependencies among transactions; (3) it only demands transactions with RAW dependencies to be committed in serial order; (4) Poplar can concurrently restore a consistent database state based on the partially constrained logs after a crash. Experimental results show that Poplar scales well with the increase of IO devices and outperforms other logging approaches on both SSDs and emulated non-volatile memory.
Writer Independent Offline Signature Recognition Using Ensemble Learning
Venue: arXiv cs.CV 2019
Year: 2019

The area of Handwritten Signature Verification has been broadly researched in the last decades, but remains an open research problem. In offline (static) signature verification, the dynamic information of the signature writing process is lost, and it is difficult to design good feature extractors that can distinguish genuine signatures and skilled forgeries. This verification task is even harder in writer independent scenarios which is undeniably fiscal for realistic cases. In this paper, we have proposed an Ensemble model for offline writer, independent signature verification task with Deep learning. We have used two CNNs for feature extraction, after that RGBT for classification & Stacking to generate final prediction vector. We have done extensive experiments on various datasets from various sources to maintain a variance in the dataset. We have achieved the state of the art performance on various datasets.
A Two-Layer Component-Based Allocation for Embedded Systems with GPUs
Venue: arXiv cs.SE 2019
Year: 2019

Component-based development is a software engineering paradigm that can facilitate the construction of embedded systems and tackle its complexities. The modern embedded systems have more and more demanding requirements. One way to cope with such versatile and growing set of requirements is to employ heterogeneous processing power, i.e., CPU-GPU architectures. The new CPU-GPU embedded boards deliver an increased performance but also introduce additional complexity and challenges. In this work, we address the component-to-hardware allocation for CPU-GPU embedded systems. The allocation for such systems is much complex due to the increased amount of GPU-related information. For example, while in traditional embedded systems the allocation mechanism may consider only the CPU memory usage of components to find an appropriate allocation scheme, in heterogeneous systems, the GPU memory usage needs also to be taken into account in the allocation process. This paper aims at decreasing the component-to-hardware allocation complexity by introducing a 2-layer component-based architecture for heterogeneous embedded systems. The detailed CPU-GPU information of the system is abstracted at a high-layer by compacting connected components into single units that behave as regular components. The allocator, based on the compacted information received from the high-level layer, computes, with a decreased complexity, feasible allocation schemes. In the last part of the paper, the 2-layer allocation method is evaluated using an existing embedded system demonstrator; namely, an underwater robot.
An Optimal Vector Clock Algorithm for Multithreaded Systems
Venue: arXiv cs.DC 2019
Year: 2019

Tracking causality (or happened-before relation) between events is useful for many applications such as debugging and recovery from failures. Consider a concurrent system with $n$ threads and $m$ objects. For such systems, either a vector clock of size $n$ is used with one component per thread or a vector clock of size $m$ is used with one component per object. A natural question is whether one can use a vector clock of size strictly less than the minimum of $m$ and $n$ to timestamp events. We give an algorithm in this paper that uses a hybrid of thread and object components. Our algorithm is guaranteed to return the minimum number of components necessary for vector clocks. We first consider the case when the interaction between objects and threads is statically known. This interaction is modeled by a thread-object bipartite graph. Our algorithm is based on finding the maximum bipartite matching of such a graph and then applying König-Egerváry Theorem to compute the minimum vertex cover to determine the optimal number of components necessary for the vector clock. We also propose two mechanisms to compute such an vector clock when computation is revealed in an online fashion. Evaluation on different types of graphs indicates that our offline algorithm generates a size vector clock which is significantly less than the minimum of $m$ and $n$. These mechanisms are more effective when the underlying bipartite graph is not dense.
Synthesizing facial photometries and corresponding geometries using generative adversarial networks
Venue: arXiv cs.CG 2019
Year: 2019

Artificial data synthesis is currently a well studied topic with useful applications in data science, computer vision, graphics and many other fields. Generating realistic data is especially challenging since human perception is highly sensitive to non realistic appearance. In recent times, new levels of realism have been achieved by advances in GAN training procedures and architectures. These successful models, however, are tuned mostly for use with regularly sampled data such as images, audio and video. Despite the successful application of the architecture on these types of media, applying the same tools to geometric data poses a far greater challenge. The study of geometric deep learning is still a debated issue within the academic community as the lack of intrinsic parametrization inherent to geometric objects prohibits the direct use of convolutional filters, a main building block of today's machine learning systems. In this paper we propose a new method for generating realistic human facial geometries coupled with overlayed textures. We circumvent the parametrization issue by imposing a global mapping from our data to the unit rectangle. We further discuss how to design such a mapping to control the mapping distortion and conserve area within the mapped image. By representing geometric textures and geometries as images, we are able to use advanced GAN methodologies to generate new geometries. We address the often neglected topic of relation between texture and geometry and propose to use this correlation to match between generated textures and their corresponding geometries. We offer a new method for training GAN models on partially corrupted data. Finally, we provide empirical evidence demonstrating our generative model's ability to produce examples of new identities independent from the training data while maintaining a high level of realism, two traits that are often at odds.
Face Detection and Face Recognition In the Wild Using Off-the-Shelf Freely Available Components
Venue: arXiv cs.CV 2019
Year: 2019

This paper presents an easy and efficient face detection and face recognition approach using free software components from the internet. Face detection and face recognition problems have wide applications in home and office security. Therefore this work will helpful for those searching for a free face off-the-shelf face detection system. Using this system, faces can be detected in uncontrolled environments. In the detection phase, every individual face is detected and in the recognition phase the detected faces are compared with the faces in a given data set and recognized.