Miracle-dz/UniFAR-FTUs huggingface dataset:
Interleukin (IL)-2 Is a Key Regulator of T Helper 1 and T Helper 2 Cytokine Expression in Fish: Functional Characterization of Two Divergent IL2 Paralogs in Salmonids

Mammalian interleukin (IL)-2 is a cytokine centrally involved in the differentiation and survival of CD4+ T helper subsets and CD4+ T regulatory cells and in activation of cytotoxic effector lymphocytes. In bony fish, IL2 orthologs have been identified with an additional divergent IL2-Like gene on the same locus present in several fish species. We report here two divergent IL2 paralogs, IL2A and IL2B, in salmonids that originated from the whole genome duplication event in this fish lineage. The salmonid IL2 paralogs differ not only in sequence but also in exon sizes. The IL-2 isoforms that are encoded have disparate pI values and may have evolved to preferentially bind specific IL-2 receptors. Rainbow trout IL2 paralogs are highly expressed in thymus, spleen, gills, kidney and intestine, important tissues/organs in fish T cell development and function. Their expression in peripheral blood leukocytes (PBL) is low constitutively but can be upregulated by the mixed leukocyte reaction, by the T cell mitogen phytohemagglutinin and by signal mimics of T cell activation (phorbol 12-myristate 13-acetate and calcium ionophore). Both trout IL-2 isoforms promoted PBL proliferation and sustained high-level expression of CD4 and CD8, suggesting that trout IL-2 isoforms are T cell growth/survival factors mainly expressed by activated T cells. The recombinant proteins for these two trout IL2 paralogs have been produced in E. coli and possess shared but also distinct bioactivities. IL-2A, but not IL-2B, induced IL12P35A1 and CXCR1 expression in PBL. IL-2B had a stronger effect on upregulation of the T helper 1 (Th1) cytokine interferon-γ (IFNγ) and could sustain CD8α and CD8β expression levels. Nevertheless, both cytokines upregulated key Th1 (IFNγ1, IFNγ2, TNFα2 and IL12) and T helper 2 (Th2) cytokines (IL4/13B1 and IL4/13B2), cytokine and chemokine receptors and the antimicrobial peptide cathelicidin-1 but had limited effects on T helper 17 cytokines and TGFβ1 in PBL. They could also enhance PBL phagocytosis. These results suggest, for the first time in fish, that IL-2 isoforms may have an important role in regulating Th1 and Th2 cell development, and innate and adaptive host defenses in fish, and shed light on lineage-specific expansion, evolution, and functional diversification of IL2 in vertebrates.
105W Highly Efficient Ku band GaN HEMT Power Amplifier

In this paper design and simulation of Ku band 105W highly efficient GaN HEMT power amplifier is presented. A single stage design using 20 mm bare die packaged GaN HEMT device is discussed. Inductor based equivalent bondwire array model is used for this design. Simulated performance of power amplifier (PA) shows 105W output power with drain efficiency of 70% and power added efficiency (PAE) of 59% at design frequency of 11.09 GHz. Less than 0.5 dB variation in large signal gain is observed between 10.02-11.17 GHz band of frequency. Proposed GaN HEMT power amplifier can be used to repalce conventional travelling wave tube amplifiers (TWTAs) used in Ku-band satellite communication.
A Deep Learning Framework to Enhance Software Defined Networks Security

Software-Defined Networks (SDN) initiates a novel networking model. SDN proposes the separation of forward and control planes by introducing a new independent plane called network controller. The architecture enhances the network resilient, decompose management complexity, and support more straightforward network policies enforcement. However, the model suffers from severe security threats. Specifically, a centralized network controller is a precious target for two reasons. First, the controller is located at a central point between the application and data planes. Second, a controller is software which prone to vulnerabilities, e.g., buffer and stack overflow. Hence, providing security measures is a crucial procedure towards the fully unleash of the new model capabilities. Intrusion detection is an option to enhance the networking security. Several approaches were proposed, for instance, signature-based, and anomaly detection. Anomaly detection is a broad approach deployed by various methods, e.g., machine learning. For many decades intrusion detection solution suffers performance and accuracy deficiencies. This paper revisits network anomalies detection as recent advances in machine learning particularly deep learning proofed success in many areas like computer vision and speech recognition. The study proposes an intrusion detection framework based on unsupervised deep learning algorithms.
A Novel Multi Verifier Device Attestation Scheme for Swarm of Devices

How can I trust this device? This is an important concern among users in the digital world. In such a scenario a device needs to establish its credibility to its users and this is realized using Device Attestation schemes. Though attestation of single device is well studied, however for swarm of devices, lot needs to be done. Moreover, this is more relevant and fundamental requirement for IoT systems to establish trust among swarm of heterogeneous devices interacting with each other. In this direction, the first scheme to address the attestation of a swarm is Scalable Embedded Device Attestation (SEDA). However in SEDA a single device co-ordinates the attestation scheme. This makes scheme prone to frequent failures; specifically in swarm setup, where the probability of device failure is high. Hence to overcome this, we propose fault tolerant, Multi Verifier attestation scheme. Here we describe two approaches based on the trust levels of device operating environment. The first approach is for setup where the devices are semi-honest and second one is based on consensus paradigm for malicious environment. In addition to this, our attestation scheme supports attestation for multiple properties of device such as hash of the code, security parameters, device features, etc. Further, we extend our scheme for swarm-ofswarms. We implement and evaluate correctness and performance of proposed scheme analytically and experimentally.
Nonlinear State Estimation for Humanoid Robot Walking

This letter presents a novel cascade state estimation framework for the three-dimensional (3-D) center of mass (CoM) estimation of walking humanoid robots. The proposed framework, called State Estimation RObot Walking (SEROW), fuses effectively joint encoder, inertial, feet pressure, and visual odometry measurements. Initially, we consider the humanoid's Newton–Euler dynamics and rigorously derive the nonlinear CoM estimator. The latter accurately estimates the 3D-CoM position, velocity, and external forces acting on the CoM, while directly considering the presence of uneven terrain and the body's angular momentum rate and, thus, effectively coupling the frontal with the lateral plane dynamics. Furthermore, we extend an established floating mass estimator to take into account the support foot pose, yielding in such a way the mandatory, for CoM estimation, affine transformations and forming a cascade state estimation scheme. Subsequently, we quantitatively and qualitatively assess the proposed scheme by comparing it to other estimation structures in terms of accuracy and robustness to disturbances, both in simulation and on an actual NAO robot walking outdoors over an inclined terrain. To facilitate further research endeavors, our implementation is offered as an open-source ROS/C++ package.
Does Source Code Quality Reflect the Ratings of Apps?

In the past, bad code quality has been associated with higher bug-proneness. At the same time, the main reason why mobile users negatively rate an app is due to the presence of bugs leading to crashes. In this paper, we preliminarily investigate the extent to which code quality metrics can be exploited to predict the commercial success of mobile apps. Key results suggest the existence of a relation between code quality and commercial success; We found that inheritance and information hiding metrics represent important indicators and therefore should be carefully monitored by developers.
FAHES: A Robust Disguised Missing Values Detector

Missing values are common in real-world data and may seriously affect data analytics such as simple statistics and hypothesis testing. Generally speaking, there are two types of missing values: explicitly missing values (i.e. NULL values), and implicitly missing values (a.k.a. disguised missing values (DMVs)) such as "11111111" for a phone number and "Some college" for education. While detecting explicitly missing values is trivial, detecting DMVs is not; the essential challenge is the lack of standardization about how DMVs are generated. In this paper, we present FAHES, a robust system for detecting DMVs from two angles: DMVs as detectable outliers and as detectable inliers. For DMVs as outliers, we propose a syntactic outlier detection module for categorical data, and a density-based outlier detection module for numerical values. For DMVs as inliers, we propose a method that detects DMVs which follow either missing-completely-at-random or missing-at-random models. The robustness of FAHES is achieved through an ensemble technique that is inspired by outlier ensembles. Our extensive experiments using real-world data sets show that FAHES delivers better results than existing solutions.
Ranking CGANs: Subjective Control over Semantic Image Attributes

In this paper, we investigate the use of generative adversarial networks in the task of image generation according to subjective measures of semantic attributes. Unlike the standard (CGAN) that generates images from discrete categorical labels, our architecture handles both continuous and discrete scales. Given pairwise comparisons of images, our model, called RankCGAN, performs two tasks: it learns to rank images using a subjective measure; and it learns a generative model that can be controlled by that measure. RankCGAN associates each subjective measure of interest to a distinct dimension of some latent space. We perform experiments on UT-Zap50K, PubFig and OSR datasets and demonstrate that the model is expressive and diverse enough to conduct two-attribute exploration and image editing.
Logic Design Using Memristors: An Emerging Technology

This paper provides an introduction to memristor, which is considered as the fourth circuit element along with resistor, inductor and capacitor. Memristors possess some unique properties, i.e. it can change the resistance under voltage control and can retain its value even after the voltage is withdrawn. Another property of memristors is their small feature size which makes them useful for design of ultra-compact memory systems. In addition, the resistive switching property of memristors allows to execute logic primitives and thus can also be used for implementing logic functions using various logic design styles studied in this paper. The paper also discusses memristor fabrication, circuit models, methods for implementing logic functions, and the various computing methodologies that can be used viz. nearmemory computing and in-memory computing.
Predicting Disease Complications Using a Stepwise Hidden Variable Approach for Learning Dynamic Bayesian Networks

Predicting Diabetes Type 2 Mellitus (T2DM) complications such as retinopathy and liver disease is still a challenge despite being a growing public health concern worldwide. This is due to the complex interactions between complications and other features, as well as between the different complications, themselves. What is more, there are likely to be many unmeasured effects that impact the disease progression of different patients. Probabilistic graphical models such as Dynamic Bayesian Networks (DBNs) have demonstrated much promise in the modeling of disease progression and they can naturally incorporate hidden (latent) variables using the EM algorithm. Unlike deep learning approaches that attempt to model complex interactions in data by using a large number of hidden variables, we adopt a different approach. We are interested in models that not only capture unmeasured effects but are also transparent in how they model data so that knowledge about disease processes can be extracted and trust in the model can be maintained by clinicians. As a result, we have developed a step-wise hidden variable structure learning process that incrementally adds hidden variables based on the IC* algorithm. To the best of our knowledge, this is the first study for classifying disease complication using a step-wise learning methodology for identifying hidden and T2DM features with a DBN structure from clinical data. Our extensive set of experiments show that the proposed method improves classification accuracy, identifying the correct number of hidden variables, and targeting their precise location within the network structure.
TBEENISH: Threshold Balanced Energy Efficient Network Integrated Super Heterogeneous Protocol for WSNs

Number of wireless sensing device protocols have been introduced in last decade due to enhancement in automation of battle field and other commercial applications. Main focus was to improve power efficiency and life span of the network to ensure smooth communication across the network. Protocols for heterogeneous networks have been mainly working on power level quantization. TBEENISH protocol will improve threshold criteria to elect cluster head for improving life span as well as it will also present complete visibility of sensing device. Life time of complete network which has never been shown by researchers previously. Which helps to devise more effective protocols to decrease latency and increase fault tolerance.
Architecting a Software-Based Ecosystem for the Automotive Aftermarket: An Experience Report

Software-based ecosystems comprise multiple software systems developed by a multitude of organizations. They are on the one hand technically integrated, often via a dedicated platform forming the center of the ecosystem. On the other hand, the organizations and their systems interact in a way that provides (business) benefits for all participants and leads to new forms of businesses. The ecosystem platform is typically defined, developed and operated by the ecosystem initiator. In the past two years, we have been working on the initiation of an ecosystem and the development of a platform for the automotive aftermarket: a data and service marketplace. As core contributions in this paper, we share the experiences and lessons learned from the early phases from an architect’s point of view. As a background, we first describe our key architecture drivers, the current state of the architecture, and how architecture work is performed. We experienced a substantially extended scope for ecosystem architects, working on the overall ecosystem and the platform. Especially in the beginning, architects have to live with a high degree of uncertainty and fuzziness and have to help shaping and aligning business, technical, and legal aspects. Besides these key insights, we share lessons learned in the following categories: Requirements and Priorities, Architecture and Architecture Work, Platform Releases and Time-to-Market, Partners, Communication, Learning from other Ecosystems.
NetRS: Cutting Response Latency in Distributed Key-Value Stores with In-Network Replica Selection

In distributed key-value stores, performance fluctuations generally occur across servers, especially when the servers are deployed in a cloud environment. Hence, the replica selected for a request will directly affect the response latency. In the context of key-value stores, even the state-of-the-art algorithm of replica selection still has considerable room for improving the response latency. In this paper, we present the fundamental factors that prevent replica selection algorithms from being effective. We address these factors by proposing NetRS, a framework that enables in-network replica selection for key-value stores. NetRS exploits emerging network devices, including programmable switches and network accelerators, to select replicas for requests. NetRS supports diverse algorithms of replica selection and is suited to the network topology of modern data centers. Compared with the conventional scheme of clients selecting replicas for requests, NetRS could effectively cut the response latency according to our extensive evaluations. Specifically, NetRS reduces the average latency by up to 48.4%, and the 99th latency by up to 68.7%.
Identifying User Intent and Context in Graph Queries

Graph querying is the task of finding similar embeddings of a given query graph in a large target graph. Existing techniques employ the use of structural as well as node and edge label similarities to find matches of a query in the target graph. However, these techniques have ignored the presence of context (usually manifested in the form of node/edge label distributions) in the query. In this paper, we propose CGQ (Contextual Graph Querying), a context-aware subgraph matching technique for querying real-world graphs. We introduce a novel sub-graph searching paradigm, which involves learning the context prevalent in the query graph. Under the proposed paradigm, we formulate the most contextually-similar subgraph querying problem that, given a query graph and a target graph, aims to identify the (top-k) maximal common subgraph(s) between the query and the target graphs with the highest contextual similarity. The quality of a match is quantized using our proposed contextual similarity function. We prove that the problem is NP-hard and also hard to approximate. Therefore, to efficiently solve the problem, we propose a hierarchical index, CGQ-Tree, with its associated search algorithm. Our experiments show that the proposed CGQ index is effective, efficient and scalable.
Scalable Aspects Learning for Intent-Aware Diversified Search on Social Networks

Search result diversification on networks aims at selecting a set of representative nodes in response to a given query node so that the result is able to meet users’ ambiguous query intents. Previous work mainly tackles this problem based on global diversity metrics, such as the expansion ratio and the expanded relevance, according to which the potential diversity needs of different query are interpreted as an unchanged criterion. While with various side information in real-world social networks, the intents of users often have more than one interpretation underlying the same query. In this paper, we therefore adopt an intent-aware perspective on this problem, based on network representation learning. With the hypothesis that a search result being aware of multiple intents of query is more likely to satisfy the information needs of users, we propose an intent-aware method that first encodes the possible query aspects and nodes as vectors, and then diversifies the search result based on these vectors. In particular, we present aspect2vec, a scalable and flexible network representation learning model, which maps nodes into low-dimensional vector spaces while preserving the network structure, the node attribute, and the query-oriented proximity. An attribute augmented sampling approach is proposed to sample corpus for the three contexts to train the model. Finally, we perform a comprehensive evaluation on our method with various baselines. The results show that our proposed method outperforms the state-of-the-art diversification algorithms.
Cross Version Defect Prediction with Representative Data via Sparse Subset Selection

Software defect prediction aims at detecting the defect-prone software modules by mining historical development data from software repositories. If such modules are identified at the early stage of the development, it can save large amounts of resources. Cross Version Defect Prediction (CVDP) is a practical scenario by training the classification model on the historical data of the prior version and then predicting the defect labels of modules of the current version. However, software development is a constantly-evolving process which leads to the data distribution differences across versions within the same project. The distribution differences will degrade the performance of the classification model. In this paper, we approach this issue by leveraging a state-of-the-art Dissimilarity-based Sparse Subset Selection (DS3) method. This method selects a representative module subset from the prior version based on the pairwise dissimilarities between the modules of two versions and assigns each module of the current version to one of the representative modules. These selected modules can well represent the modules of the current version, thus mitigating the distribution differences. We evaluate the effectiveness of DS3 for CVDP performance on total 40 cross-version pairs from 56 versions of 15 projects with three traditional and two effort-aware indicators. The extensive experiments show that DS3 outperforms three baseline methods, especially in terms of two effort-aware indicators.
MIX: Multi-Channel Information Crossing for Text Matching

Short Text Matching plays an important role in many natural language processing tasks such as information retrieval, question answering, and conversational system. Conventional text matching methods rely on predefined templates and rules, which are not applicable to short text with limited numebr of words and limit their ability to generalize to unobserved data. Many recent efforts have been made to apply deep neural network models to natural language processing tasks, which reduces the cost of feature engineering. In this paper, we present the design of Multi-Channel Information Crossing , a multi-channel convolutional neural network model for text matching, with additional attention mechanisms from sentence and text semantics. MIX compares text snippets at varied granularities to form a series of multi-channel similarity matrices, which are crossed with another set of carefully designed attention matrices to expose the rich structures of sentences to deep neural networks. We implemented MIX and deployed the system on Tencent's Venus distributed computation platform. Thanks to carefully engineered multi-channel information crossing, evaluation results suggest that MIX outperforms a wide range of state-of-the-art deep neural network models by at least 11.1% in terms of the normalized discounted cumulative gain (NDCG@3), on the English WikiQA dataset. Moreover, we also performed online A/B tests with real users on the search service of Tencent QQ Browser. Results suggest that MIX raised the number of clicks on the returned results by 5.7%, due to an increased accuracy in query-document matching, which demonstrates the superior performance of MIX in production environments.
On the Analysis of Content Dissemination through Real Vehicular Boards

The advances in wireless technologies and the automotive industry lead to the appearance of Vehicular Ad-hoc NETwork (VANET). Consequently, new interests have emerged regarding the introduction of new applications and services for non-urgent content (e.g. videos, ads, touristic information) dissemination for vehicular network environments. The nodes of a VANET are equipped with On-Board Units (OBUs) to enable the communication with other vehicles and to the infrastructure through Road Side Units (RSUs). OBUs enable content dissemination by the mobile nodes while RSUs are static boards deployed along the road or/and strategic locations (e.g. intersections and high buildings). This paper studies and designs how data can be efficiently disseminated using real vehicular boards (RSUs and OBUs) equipped with Dedicated Short Range Communications (DSRC) interfaces in vehicular networks. It evaluates a content distribution strategy (Local Rarest Bundle First (LRBF)) through a laboratory testbed composed by real RSUs and OBUs equipped with IEEE 802.11p (WAVE) communication interfaces. The experimentation results, performed using real vehicular boards, show that the LRBF strategy provides a high delivery of content through the vehicles, with a limited amount of resources.