Miracle-dz/UniFAR-FTUs huggingface dataset:
Analyzing "Not-a-Virus" Bundled Adware: The Wajam Case

Case studies on malicious code mostly focus on botnets and worms (recently revived with IoT devices), prominent pieces of malware or Advanced Persistent Threats, exploit kits, ransomware, yet very little has been done on adware. Previous studies on "unwanted" applications, including adware, favored breadth of analysis, uncovering ties between different actors and distribution methods. We investigate the evolution over nearly six years of a particularly successful and active adware business: Wajam. As of 2016, revealed by the Office of the Privacy Commissioner of Canada, Wajam had "hundreds of millions of installations" and collected 400TB of private information from users. We gather 52 samples of Wajam, released between 2013 to 2018, and analyze the technical evolution from a simple browser add-on to full-fledged obfuscated malware including rootkit, browser process injection, and antivirus evasion capabilities. We uncover its strategy to ensure a low detection rate, which heavily relies on numerous layers of encryption, and more recently on steganography. Furthermore, Wajam leaks the browsing histories of four major browsers, along with the keywords searched by users on highly popular websites. It is also vulnerable to arbitrary content injection on HTTPS webpages, and likely to remote code execution. We show evidence that Wajam is a widespread threat, actively maintained with daily obfuscated samples that are poorly detected by antivirus engines. More worrisome, we found the same evasion techniques in another piece of adware, suggesting that they could be provided by a third-party, and reused in other cases. Finally, we conclude that the adware problem has been overlooked for too long, which can reach (or even surplus) the complexity of advanced malware, and pose both privacy and security risks to users, more so than many well-known and thoroughly-analyzed malware families.
A Human-Inspired Recognition System for Pre-Modern Japanese Historical Documents

Recognition of historical documents is a challenging problem due to the noised, damaged characters, and background. However, in Japanese historical documents, not only contains the mentioned problems, pre-modern Japanese characters were written in cursive and are connected. Therefore, character segmentation-based methods do not work well. This leads to the idea of creating a new recognition system. In this paper, we propose a human-inspired document reading system to recognize multiple lines of pre-modern Japanese historical documents. During the reading, people employ eyes movement to determine the start of a text line. Then, they move the eyes from the current character/word to the next character/word. They can also determine the end of a line or skip a figure to move to the next line. The eyes movement integrates with visual processing to operate the reading process in the brain. We employ attention-based encoder–decoder to implement this recognition system. First, the recognition system detects were to start a text line. Second, the system scans and recognize character by character until the text line is completed. Then, the system continues to detect the start of the next text line. This process is repeated until reading the whole document. As results, the system is successful to recognize multiple lines, connected and cursive characters without performing character/line segmentation. Besides, we also employ a coverage model which stores the history of eyes movement to predict the next movement more precisely. We tested our human-inspired recognition system on the pre-modern Japanese historical document provided by the PRMU Kuzushiji competition. The results of the experiments demonstrate the superiority and effectiveness of our proposed system by achieving Sequence Error Rate of 9.87% and 53.81% on level 2 and level 3 of the dataset, respectively. These results outperform to any other systems participated in the PRMU Kuzushiji competition.
Single-Cell RNA-Sequencing Identifies Activation of TP53 and STAT1 Pathways in Human T Lymphocyte Subpopulations in Response to Ex Vivo Radiation Exposure

Detrimental health consequences from exposure to space radiation are a major concern for long-duration human exploration missions to the Moon or Mars. Cellular responses to radiation are expected to be heterogeneous for space radiation exposure, where only high-energy protons and other particles traverse a fraction of the cells. Therefore, assessing DNA damage and DNA damage response in individual cells is crucial in understanding the mechanisms by which cells respond to different particle types and energies in space. In this project, we identified a cell-specific signature for radiation response by using single-cell transcriptomics of human lymphocyte subpopulations. We investigated gene expression in individual human T lymphocytes 3 h after ex vivo exposure to 2-Gy gamma rays while using the single-cell sequencing technique (10X Genomics). In the process, RNA was isolated from ~700 irradiated and ~700 non-irradiated control cells, and then sequenced with ~50 k reads/cell. RNA in each of the cells was distinctively barcoded prior to extraction to allow for quantification for individual cells. Principal component and clustering analysis of the unique molecular identifier (UMI) counts classified the cells into three groups or sub-types, which correspond to CD4+, naïve, and CD8+/NK cells. Gene expression changes after radiation exposure were evaluated using negative binomial regression. On average, BBC3, PCNA, and other TP53 related genes that are known to respond to radiation in human T cells showed increased activation. While most of the TP53 responsive genes were upregulated in all groups of cells, the expressions of IRF1, STAT1, and BATF were only upregulated in the CD4+ and naïve groups, but were unchanged in the CD8+/NK group, which suggests that the interferon-gamma pathway does not respond to radiation in CD8+/NK cells. Thus, single-cell RNA sequencing technique was useful for simultaneously identifying the expression of a set of genes in individual cells and T lymphocyte subpopulation after gamma radiation exposure. The degree of dependence of UMI counts between pairs of upregulated genes was also evaluated to construct a similarity matrix for cluster analysis. The cluster analysis identified a group of TP53-responsive genes and a group of genes that are involved in the interferon gamma pathway, which demonstrate the potential of this method for identifying previously unknown groups of genes with similar expression patterns.
On Optimum Segment Combining Weight for ICI Self-Cancellation in OFDM Systems under Doubly Selective Fading Channels

ICI self-cancellation algorithms are promising techniques to mitigate the ICI effect in OFDM systems caused by high mobility. In the area of ICI self-cancellation, the calculation of optimum combining weight is still an open question. In this paper, the problem of finding optimum combining weight to minimize the ICI-plus-noise power is formulated. We then derived a noniterative algorithm to find the optimum solution.We also compare the performance of the optimum combining weight with other existing combining weight methods.
Contribution of Radio Halos to the Foreground for SKA EoR Experiments

The overwhelming foreground contamination is one of the primary impediments to probing the Epoch of Reionization (EoR) through measuring the redshifted 21 cm signal. Among various foreground components, radio halos are less studied and their impacts on the EoR observations are still poorly understood. In this work, we employ the Press–Schechter formalism, merger-induced turbulent reacceleration model, and the latest SKA1-Low layout configuration to simulate the SKA “observed” images of radio halos. We calculate the one-dimensional power spectra from simulated images and find that radio halos can be about 104, 103, and 102.5 times more luminous than the EoR signal on scales of in the 120–128, 154–162, and 192–200 MHz bands, respectively. By examining the two-dimensional power spectra inside properly defined EoR windows, we find that the power leaked by radio halos can still be significant, as the power ratios of radio halos to the EoR signal on scales of can be up to about 230%–800%, 18%–95%, and 7%–40% in the three bands when the 68% uncertainties caused by the variation of the number density of bright radio halos are considered. Furthermore, we find that radio halos located inside the far side lobes of the station beam can also impose strong contamination within the EoR window. In conclusion, we argue that radio halos are severe foreground sources and need serious treatments in future EoR experiments.
Challenges in Building Intelligent Open-domain Dialog Systems

There is a resurgent interest in developing intelligent open-domain dialog systems due to the availability of large amounts of conversational data and the recent progress on neural approaches to conversational AI [33]. Unlike traditional task-oriented bots, an open-domain dialog system aims to establish long-term connections with users by satisfying the human need for communication, affection, and social belonging. This article reviews the recent work on neural approaches that are devoted to addressing three challenges in developing such systems: semantics, consistency, and interactiveness. Semantics requires a dialog system to not only understand the content of the dialog but also identify users’ emotional and social needs during the conversation. Consistency requires the system to demonstrate a consistent personality to win users’ trust and gain their long-term confidence. Interactiveness refers to the system’s ability to generate interpersonal responses to achieve particular social goals such as entertainment and conforming. The studies we select to present in this survey are based on our unique views and are by no means complete. Nevertheless, we hope that the discussion will inspire new research in developing more intelligent open-domain dialog systems.
DNA Methylation Indicates Susceptibility to Isoproterenol-Induced Cardiac Pathology and Is Associated With Chromatin States.

RATIONALE Only a small portion of the known heritability of cardiovascular diseases, such as heart failure, can be explained based on single-gene mutations. Chromatin structure and regulation provide a substrate through which genetic differences in noncoding regions may affect cellular function and response to disease, but the mechanisms are unknown. OBJECTIVE We conducted genome-wide measurements of DNA methylation in different strains of mice that are susceptible and resistant to isoproterenol-induced dysfunction to test the hypothesis that this epigenetic mark may play a causal role in the development of heart failure. METHODS AND RESULTS BALB/cJ and BUB/BnJ mice, determined to be susceptible and resistant to isoproterenol-induced heart failure, respectively, were administered the drug for 3 weeks via osmotic minipump. Reduced representational bisulfite sequencing was then used to compare the differences between the cardiac DNA methylomes in the basal state between strains and then after isoproterenol treatment. Single-base resolution DNA methylation measurements were obtained and revealed a bimodal distribution of methylation in the heart, enriched in lone intergenic CpGs and depleted from CpG islands around genes. Isoproterenol induced global decreases in methylation in both strains; however, the basal methylation pattern between strains shows striking differences that may be predictive of disease progression before environmental stress. The global correlation between promoter methylation and gene expression (as measured by microarray) was modest and revealed itself only with focused analyses of transcription start site and gene body regions (in contrast to when gene methylation was examined in toto). Modules of comethylated genes displayed correlation with other protein-based epigenetic marks, supporting the hypothesis that chromatin modifications act in a combinatorial manner to specify transcriptional phenotypes in the heart. CONCLUSIONS This study provides the first single-base resolution map of the mammalian cardiac DNA methylome and the first case-control analysis of the changes in DNA methylation with heart failure. The findings demonstrate marked genetic differences in DNA methylation that are associated with disease progression.
Patient Derived Chicken Egg Tumor Model (PDcE Model): Current Status and Critical Issues

Chorioallantoic membrane assay (CAM assay) using fertilized chicken eggs has been used for the study of tumor formation, angiogenesis and metastasis. Recently, there is growing realization that this system provides a valuable assay for a patient-derived tumor model. Several reports establish that tumor samples from cancer patients can be used to reproduce tumor in the chicken egg. High transplantation efficiency has been achieved. In this review, we discuss examples of transplanting patient tumors. We then discuss critical issues that need to be addressed to pursue this line of experiments. The patient-derived chicken egg model (PDcE model) has an advantage over other models in its rapid tumor formation. This raises the possibility that the PDcE model is valuable for identifying optimum drug for each individual patient.
RLS-Based Detection for Massive Spatial Modulation MIMO

Most detection algorithms in spatial modulation (SM) are formulated as linear regression via the regularized least-squares (RLS) method. In this method, the transmit signal is estimated by minimizing the residual sum of squares penalized with some regularization. This paper studies the asymptotic performance of a generic RLS-based detection algorithm employed for recovery of SM signals. We derive analytically the asymptotic average mean squared error and the error rate for the class of bi-unitarily invariant channel matrices.The analytic results are employed to study the performance of SM detection via the box-LASSO. The analysis demonstrates that the performance characterization for i.i. d. Gaussian channel matrices is valid for matrices with non-Gaussian entries, as well. This justifies the partially approved conjecture given in [1]. The derivations further extend the former studies to scenarios with non-i.i. d. channel matrices. Numerical investigations validate the analysis, even for practical system dimensions.
Bayesian Analysis of Privacy Attacks on GPS Trajectories

The success of applications for sharing GPS trajectories raises serious privacy concerns, in particular about users' home addresses. In this paper we show that a Bayesian approach is natural and effective for a rigorous analysis of home-identification attacks and their countermeasures, in terms of privacy. We focus on a family of countermeasures named "privacy-region strategies", consisting in publishing each trajectory from the first exit to the last entrance from/into a privacy region. Their performance is studied through simulations on Brownian motions.
Lightweight Monocular Depth Estimation Model by Joint End-to-End Filter Pruning

Convolutional neural networks (CNNs) have emerged as the state-of-the-art in multiple vision tasks including depth estimation. However, memory and computing power requirements remain as challenges to be tackled in these models. Monocular depth estimation has significant use in robotics and virtual reality that requires deployment on low-end devices. Training a small model from scratch results in a significant drop in accuracy and it does not benefit from pre-trained large models. Motivated by the literature of model pruning, we propose a lightweight monocular depth model obtained from a large trained model. This is achieved by removing the least important features with a novel joint end-to-end filter pruning. We propose to learn a binary mask for each filter to decide whether to drop the filter or not. These masks are trained jointly to exploit relations between filters at different layers as well as redundancy within the same layer. We show that we can achieve around 5x compression rate with small drop in accuracy on the KITTI driving dataset. We also show that masking can improve accuracy over the baseline with fewer parameters, even without enforcing compression loss.
Learning and Exploiting Multiple Subgoals for Fast Exploration in Hierarchical Reinforcement Learning

Hierarchical Reinforcement Learning (HRL) exploits temporally extended actions, or options, to make decisions from a higher-dimensional perspective to alleviate the sparse reward problem, one of the most challenging problems in reinforcement learning. The majority of existing HRL algorithms require either significant manual design with respect to the specific environment or enormous exploration to automatically learn options from data. To achieve fast exploration without using manual design, we devise a multi-goal HRL algorithm, consisting of a high-level policy Manager and a low-level policy Worker. The Manager provides the Worker multiple subgoals at each time step. Each subgoal corresponds to an option to control the environment. Although the agent may show some confusion at the beginning of training since it is guided by three diverse subgoals, the agent's behavior policy will quickly learn how to respond to multiple subgoals from the high-level controller on different occasions. By exploiting multiple subgoals, the exploration efficiency is significantly improved. We conduct experiments in Atari's Montezuma's Revenge environment, a well-known sparse reward environment, and in doing so achieve the same performance as state-of-the-art HRL methods with substantially reduced training time cost.
Live demonstration: An FPGA based hardware compression accelerator for Hadoop system

Hadoop is an emerging data application for the big data processing. In Hadoop system, data compression is a significant part in processing big data effectively. Achieving this in software requires significant compute processing. In this paper we present the detailed design of a hardware compression accelerators. We also measure the performance of the hardware accelerators. Our analysis shows that hardware acceleration has the potential to improve the data compression by as much as 12x. As a result, our hardware accelerator provides a real time data processing of big data.
Preparation of Thermoplastic Polyurethane (TPU) Perforated Membrane via CO2 Foaming and Its Particle Separation Performance

The way in which a perforated structure is formed has attracted much interest in the porous membrane research community. This novel structure gives materials an excellent antifouling property as well as a low operating pressure and other benefits. Unfortunately, the current membrane fabrication methods usually involve multi-step processes and the use of organic solvents or additives. Our study is the first to offer a way to prepare perforated membrane by using a physical foaming technique with CO2 as the blowing agent. We selected thermoplastic polyurethane (TPU) as the base material because it is a biocompatible elastomer with excellent tensility, high abrasion resistance, and good elastic resilience. Various processing parameters, which included the saturation pressure, the foaming temperature, and the membrane thickness, were applied to adjust the TPU membrane’s perforated morphology. We proposed a possible formation mechanism of the perforated membrane. The as-prepared TPU membrane had good mechanical properties with a tensile strength of about 5 MPa and an elongation at break above 100%. Such mechanical properties make this novel membrane usable as a self-standing filter device. In addition, its straight-through channel structure can separate particles and meet different separation requirements.
Design and Experimental Validation of a Software-Defined Radio Access Network Testbed with Slicing Support

Network slicing is a fundamental feature of 5G systems to partition a single network into a number of segregated logical networks, each optimized for a particular type of service or dedicated to a particular customer or application. The realization of network slicing is particularly challenging in the Radio Access Network (RAN) part, where multiple slices can be multiplexed over the same radio channel and Radio Resource Management (RRM) functions shall be used to split the cell radio resources and achieve the expected behaviour per slice. In this context, this paper describes the key design and implementation aspects of a Software-Defined RAN (SD-RAN) experimental testbed with slicing support. The testbed has been designed consistently with the slicing capabilities and related management framework established by 3GPP in Release 15. The testbed is used to demonstrate the provisioning of RAN slices (e.g., preparation, commissioning, and activation phases) and the operation of the implemented RRM functionality for slice-aware admission control and scheduling.
Determining the Neural Network Topology: A Review

One of the challenges in the successful implementation of deep neural network (DNN) is setting the value for various hyper-parameters, one of which is the network topology, which is closely related to the number of hidden layers and the number of hidden neurons. Determining the number of hidden layers and the number of neurons is very important and has a large influence on DNN performance. Determining these two numbers manually (usually through trial and error methods) to find fairly optimal arrangement is a time-consuming process, while the automatic approach is divided into two, they are a model-based approach and a non-model based approach. The non-model-based approach, for example, is grid search and random search, whereas model-based approaches, for example, are using particle swarm optimization (PSO) algorithms. In some researches, how to determine the number of hidden layers or number of neurons, often the guidelines are unclear, even the roles and functions of both are explained minimally. Although it is still a difficult area of research, research to determine the number of hidden layers and the number of neurons must continue to be carried out, because these two numbers will greatly determine the performance of DNN.
Dimensionality Reduction for Tukey Regression

We give the first dimensionality reduction methods for the overconstrained Tukey regression problem. The Tukey loss function $\|y\|_M = \sum_i M(y_i)$ has $M(y_i) \approx |y_i|^p$ for residual errors $y_i$ smaller than a prescribed threshold $\tau$, but $M(y_i)$ becomes constant for errors $|y_i| > \tau$. Our results depend on a new structural result, proven constructively, showing that for any $d$-dimensional subspace $L \subset \mathbb{R}^n$, there is a fixed bounded-size subset of coordinates containing, for every $y \in L$, all the large coordinates, with respect to the Tukey loss function, of $y$. Our methods reduce a given Tukey regression problem to a smaller weighted version, whose solution is a provably good approximate solution to the original problem. Our reductions are fast, simple and easy to implement, and we give empirical results demonstrating their practicality, using existing heuristic solvers for the small versions. We also give exponential-time algorithms giving provably good solutions, and hardness results suggesting that a significant speedup in the worst case is unlikely.
Designing Far Memory Data Structures: Think Outside the Box

Technologies like RDMA and Gen-Z, which give access to memory outside the box, are gaining in popularity. These technologies provide the abstraction of far memory, where memory is attached to the network and can be accessed by remote processors without mediation by a local processor. Unfortunately, far memory is hard to use because existing data structures are mismatched to it. We argue that we need new data structures for far memory, borrowing techniques from concurrent data structures and distributed systems. We examine the requirements of these data structures and show how to realize them using simple hardware extensions.