Miracle-dz/UniFAR-FTUs huggingface dataset:
Reconfigurable Instruction-Based Multicore Parallel Convolution and Its Application in Real-Time Template Matching

Convolution is widely used in scientific computational fields such as digital image processing and machine learning. However, these applications are difficult to execute in realtime because they are computationally intensive. This paper introduces a high-speed convolution solution that runs on our self-developed multicore digital signal processor (DSP). To optimize the convolution capability, we propose a convolution instruction and a convolution micro architecture in the design of a subcore. As a coprocessor, the designed subcore is integrated into a network-on-chip (NoC)-based multicore DSP. In the implementation of the multicore parallel convolution, an independent convolution task-partitioning and mapping scheme is proposed. Datablock storage and software prefetching mechanisms are used to hide the data transmission time during the calculation, improving the computing efficiency. We also develop a data reuse strategy that effectively reduces the data bandwidth requirements of multicore parallel convolution. The proposed methods are applied to correlation-based template matching, with the results showing that our convolution computing approach greatly improves the performance compared with the same operations run on a personal computer, a TMS320C6678 processor and an NVIDIA Quadro 1000M graphics processing unit (GPU).
On the Impact of the Multiplication Decrease Factor of QUIC

QUIC is a new transport protocol for transferring web traffic proposed by Google employing CUBIC as the default congestion control algorithm with a small but important modification where it uses a smaller congestion window reduction than the one used in the original CUBIC. Motivated by this change, in this paper we address the robustness and sensitivity of the multiplication decrease factor. More specifically, we investigate the page load time of QUIC as an impact of the multiplication decrease factor in different network environments by changing the loss rate, delay, buffer size, web page size, and bandwidth. We have found that the region where the impact of the change of the multiplication decrease factor in QUIC is pronounced when large web pages are downloaded with low round-trip time achieving 22% decrease in the page load time.
Sequential Behavioral Modeling for Scalable IoT Devices and Systems

The Internet of Things (IoT) enables connectivity between devices, thereby allowing them to interact with each other. A recurring problem is the emergence of siloed IoT platforms due to proprietary standards. Recently, the World Wide Web Consortium (W3C) proposed a human-readable and machine- understandable format called Thing Description (TD). It allows to uniformly describe device and service interfaces of different IoT standards with syntactic and semantic information, and hence enables semantic interoperability. However, describing sequential behavior of devices, which is essential for many cyber-physical systems, is not covered. In this paper, we propose a systematic way to describe such sequential behavior as an extension within TDs, thereby increasing their semantic expressiveness through possible, valid state transitions. This enables safe and desired operation of devices as well as scalability by modeling systems as sequential compositions of Things. We show in a case study that previously unmodelable behavior can now be expressed and the overall manual intervention requirements of state-of-the-art implementations can be significantly reduced.
Distributed Spectrum Sharing for Enterprise Powerline Communication Networks

As powerline communication (PLC) technology does not require dedicated cabling and network setup, it can be used to easily connect multitude of IoT devices deployed in enterprise environments for sensing and control related applications. IEEE has standardized the PLC protocol in IEEE 1901, also known as HomePlug AV (HPAV), which is widely adopted PLC standard. A key weakness of HPAV protocol is that it does not support any spectrum sharing strategies. Currently, each link in an HPAV PLC network operates over the whole available spectrum, and only one link can operate at any time within a single collision domain. We conducted a large scale measurement study using commodity HPAV PLC devices and analyzed channel characteristics of PLC networks in a real enterprise environment across space, time, and spectral dimensions. Based on our findings, we propose a distributed spectrum sharing technique for enterprise PLC networks, and show that fine-grained distributed spectrum sharing on top of current HPAV MAC protocols can boost the aggregated and per-link throughput by up to 60% and 250% respectively, by allowing multiple PLC links to communicate concurrently, while requiring a few modifications to the existing HPAV PLC devices and protocols.
A New Ageing-Aware Approach Via Path Isolation

NBTI is becoming one of the major circuit reliability issues in nano-scale technologies. BTI can cause a threshold voltage shift in CMOS devices and consequently increase circuit delay. This paper proposed a novel ageing aware approach to improve circuit's lifetime. The vulnerable circuit paths against ageing effects are isolated. In addition, minimum area overhead is consumed by adopting proposed synthesis algorithm. The simulation results show that the proposed approach can save up to 67.7% area compared with the conventional over-design technique.
Design of a Wireless Sensor Network to Detect Car Accidents on Highways

In Latin America, the number of car accidents are alarming figures every year. Ecuador is one of the countries that leads the statistics. This paper presents an engineering design proposal to detect a car accident on roads or highways with the aim to reduce deaths caused by these events and to improve the response time of the corresponding emergency entities. This work analyzes a particular scenario and does not pretend to compare the performance of our proposal with prior related works. Our solution consists of several components: a wireless sensor network with elements based on open source tools (such as Arduino nano microcontroller), distance sensors (HC-SR04) and accelerometer sensor (MMA7361), a Raspberry PI 3 model B embedded system and the deployment of a Zigbee network. The system is connected by means of a wireless local area network (WLAN) to a database server and a web application. Sensors response (accelerometer and proximity) in a frontal vehicle collision was proven by implementing a prototype, in order to be tested in a real scenario. The numerical results demonstrate that the response time between the vehicle and the concentrator affects (either increasing or reducing) the total communication time between the vehicle and the web server.
Hadoop High Availability with Linux HA

This paper proposes The High Availability Method using Heartbeat and DRBD on Hadoop Namenode Failover. Implementation of Hadoop as computing process framework and media storage needs to be supported by the availability in case of failure. Cluster system utilization in Hadoop with many Datanode is one of the strength points. Stored file will be replicated in distributed datanode as blocks that makes if one the datanode server fails, the other datanodes server still has duplicates blocks. The problem in hadoop system, there is just one Namenode which is the communicator between users and Datanodes. While Namenode goes down then users can't access files stored in Datanode. This proposed method accomplishes the Namenode failover process. The use of this method is very effective because it can accelerate downtime with automatic failover formed by Heartbeat and real-time mirroring formed by DRBD. The results indicate that the use of DRBD and Heartbeat may accelerate failover time of less than 10 seconds.
Augmentation of Fingerprints for Indoor WiFi Localization Based on Gaussian Process Regression

WiFi-Fingerprint is extensively utilized for indoor localization with the advent of the high-density wireless networks deployment and research on ubiquitous intelligence. Nevertheless, establishing an elaborate radio map for localization is a highly time-consuming task. Aiming to alleviate this problem and enhance WiFi-based indoor localization accuracy, this paper has done the following contributions. First, we present Gaussian process regression models to predict the spatial distribution of signal strength in the uncalibrated domain with limited known labeled fingerprints in reference points. As a result, deployment effort for radio mapping can be greatly reduced. Second, in order to acquire fingerprints data with higher accuracy, the compound kernels for received signal strength (RSS) prediction models are presented. Finally, the definite position is determined with the weighted Similarity K-Nearest Neighbor localization algorithm when new observation RSS is collected. The experiments show that compared with the original reference fingerprints localization system, the proposed localization system explicitly reduces the localization error. The results further demonstrate that our method can augment the fingerprints and improve the accuracy of fingerprint-based indoor localization without extra manual calibration or adding dedicated infrastructure.
Reliability and Diagnosability Analysis of Hyper Bijective Connection Networks

Bijective connection (BC) networks, including a family of interconnection networks of multiprocessor systems, have been studied extensively due to its desirable properties, such as lower diameter, high reliability, and diagnosability. To meet the demand of processing integrating tasks with large-scale and complex architectures, it is significant to explore alternative interconnection networks for multiprocessor configuration. To this end, we propose a novel framework called <italic>hyper bijective connection network</italic> (HBC network) as an extension of BC networks, which allows to study the properties of other potential interconnection networks in unity rather than in individual. We prove that when <inline-formula> <tex-math notation="LaTeX">$n\geq 3, m\geq 2$ </tex-math></inline-formula>, every <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-dimensional HBC network <inline-formula> <tex-math notation="LaTeX">$H_{n}(m)$ </tex-math></inline-formula> has (edge) connectivity <inline-formula> <tex-math notation="LaTeX">$m+n-2$ </tex-math></inline-formula>, super connectivity <inline-formula> <tex-math notation="LaTeX">$2n+m-4$ </tex-math></inline-formula>, and super edge-connectivity <inline-formula> <tex-math notation="LaTeX">$2n+2m-6$ </tex-math></inline-formula>, and is super-connected and super-edge-connected. These results indicate the high reliability of HBC networks. Moreover, we analyze three classic diagnosabilities of a HBC network, including <inline-formula> <tex-math notation="LaTeX">$t_{p}$ </tex-math></inline-formula>-, <inline-formula> <tex-math notation="LaTeX">$t_{1}/t_{1}$ </tex-math></inline-formula>-, and <inline-formula> <tex-math notation="LaTeX">$t/k$ </tex-math></inline-formula>- diagnosability. We show that when <inline-formula> <tex-math notation="LaTeX">$n\geq 3$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$m\geq 2$ </tex-math></inline-formula>, an <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-dimensional HBC network <inline-formula> <tex-math notation="LaTeX">$H_{n}(m)$ </tex-math></inline-formula> is <inline-formula> <tex-math notation="LaTeX">$(m+n-2)$ </tex-math></inline-formula>-diagnosable, <inline-formula> <tex-math notation="LaTeX">$(2n+m-4)/ (2n+m-4)$ </tex-math></inline-formula>-diagnosable, and <inline-formula> <tex-math notation="LaTeX">$t(m,n,k)/~k$ </tex-math></inline-formula>-diagnosable, where <inline-formula> <tex-math notation="LaTeX">$0\leq k \leq m+n-2$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$t(m,n,k)=(k+1)n+(m-2)- ({(k+1)(k+2)}/{2})+1$ </tex-math></inline-formula>. Besides, it is shown that the corresponding properties for BC networks can be derived naturally as special cases of that for HBC networks.
A Model of Generating and Predicting Intention toward Human-Robot Cooperation

We normally tend to think that we, as human beings, act as if we have intention to achieve a goal. However, in the situation of cooperating with other people, it is difficult to come up with a mechanism for predicting and adjusting each other's intention when we think that the goal is given top-down. Thinking that intention comes from context may help to solve that problem. In this study, we attempt to model an action learning mechanism that generates/predicts self-intention using deep Q-network and recurrent neural network. And then we show such a mechanism makes it possible for the agent to predict intention of others and goals.
Mechanical design and robust tracking control of a class of antagonistic variable stiffness actuators based on the equivalent nonlinear torsion springs

The novel conceptual model of the antagonistic variable stiffness actuator based on the equivalent nonlinear torsion spring and the friction damper is demonstrated. For the dynamic model of the antagonistic variable stiffness actuator in the presence of parametric uncertainties, unknown bounded friction torques, unknown bounded external disturbance, and input saturation constraints, using the coordinate transformation, the state space model of the antagonistic variable stiffness actuator with composite disturbances and input saturation constraints is transformed into an extended integral chain–type pseudo-linear system with input saturation constraints. Subsequently, a combination of the linear extended state observer, sliding mode control, and adaptive input saturation compensation law is adopted for the design of the robust tracking controller that simultaneously regulates the position and stiffness of the antagonistic equivalent nonlinear torsion spring-based variable stiffness actuator. Under the proposed controller, the semi-global uniformly ultimately bounded stability of the closed-loop system has been proved via Lyapunov stability analysis. Simulation studies demonstrate the effectiveness and the robustness of the proposed robust adaptive tracking control method for the antagonistic variable stiffness actuator.
Comparative Transcriptomics of Rat and Axolotl After Spinal Cord Injury Dissects Differences and Similarities in Inflammatory and Matrix Remodeling Gene Expression Patterns

Following spinal cord injury in mammals, maladaptive inflammation, and matrix deposition drive tissue scarring and permanent loss of function. In contrast, axolotls regenerate their spinal cord after severe injury fully and without scarring. To explore previously unappreciated molecules and pathways that drive tissue responses after spinal cord injury, we performed a 4-way intersection of rat and axolotl transcriptomics datasets and isolated shared genes with similar or differential expression at days 1, 3, and 7 after spinal cord injury in both species. Systems-wide differences and similarities between the two species are described in detail using public-domain computational tools and key differentially regulated genes are highlighted. Amongst persistent differential expression in matching neuronal genes (upregulated in axolotls but downregulated in rats) and nucleic acid metabolism genes (downregulated in axolotls but upregulated in rats), we found multiple extracellular matrix genes that were upregulated in both species after spinal cord injury and all time-points (days 1, 3, and 7), indicating the importance of extracellular matrix remodeling in wound healing. Moreover, the archetypal transcription factor SP1, which was consistently upregulated in rats but was unchanged in axolotls, was predicted as a potential transcriptional regulator of classic inflammatory response genes in rats most of which were not regulated in regenerating axolotls. This analysis offers an extensive comparative platform between a non-regenerating mammal and a regenerating urodele after spinal cord injury. To better understand regeneration vs. scarring mechanisms it is important to understand consistent molecular differences as well as similarities after experimental spinal cord injury.
The Relationship of the Teaching, Empowering, Leading and Learning (TELL) Survey Responses to Student Smarter Balanced Assessment (SBA) in Math and English: Teacher Professional Development and Student Achievement

The purpose of this study is to identify the relationship between changes in teachers’ professional learning conditions and changes in student achievement. The study examined conditions as measured by three state-wide administrations of the New Teacher Center’s Teaching, Empowering, Leading, and Learning (TELL) Survey in Oregon from 2014 to 2018 and contemporaneous student achievement data from Oregon state achievement tests, the Smarter Balanced Assessment (SBA) in Math and English. Participants were all 274 schools in Oregon with data for all administrations of relevant instruments. Data were disaggregated based on district and school size, poverty level, and English Language Learner (ELL) population. Analysis of study data were guided by three research questions: (a) how have measures of student achievement and teacher professional development changed over time in districts and schools in Oregon, (b) how do the changes in professional development correlate to student outcomes on the Smarter Balanced Assessment in Math and English, and (c) which professional development factor(s) are most closely related to or predictive of subsequent changes in student outcomes? Analyses included repeated measures analysis of variance (RM-ANOVA) for all instruments, analysis of variance (ANOVA) for both raw scores and calculated change scores, and correlation analysis among both raw and change scores within and between instruments. The study found strong within instrument correlations but few and weak correlations among SBA and
An Integrated MAV-RFID System for Geo-referenced Monitoring of Harsh Environments

Micro Aerial Vehicles (MAVs) equipped with lightweight Radio Frequency Identification (RFID) sensor dataloggers, have the potential to assist in achieving environmental awareness in a large range of situations. However, in order to gain such insight, the system must be able to accurately localize itself and fuse any readings of its surroundings into a consistent map. In this paper we demonstrate how camera, IMU and environmental data obtained with an RFID-enabled temperature sensor may be merged together to create accurate 3D maps along the MAV curvilinear trajectories in unknown locations. The idea is demonstrated through experimentations in both indoor and outdoor harsh environments.
Protograph-Based EXIT Analysis and Optimization of LDPC Coded DCO-OFDM in VLC Systems

The direct current (DC)-biased optical orthogonal frequency division multiplexing (DCO-OFDM) with low-density parity-check coding is a popular scheme for visible light communication systems. Under the optical power constraint for dimming control, the protograph-based extrinsic information transfer (PEXIT) analysis is utilized to optimize the DC bias and signal power jointly by searching the lowest optical threshold. To reduce the search space, an approximate method is proposed to simplify the PEXIT procedure of the optimal solution. Both the optimal configuration and simplified method are in good agreement with the simulation results.
Android Malware Detection Based on Useful API Calls and Machine Learning

Accurate malware detection can benefit Android users significantly considering the growing number of sophisticated malwares recently. In this paper, we propose a machine learning based malware detection methodology that identifies the subset of Android APIs that is effective as features and classifies Android apps as benign or malicious apps. The proposed methodology first constructs two ranked lists of popular Android APIs. One is benign_API_list that contains the top popular APIs commonly used in benign apps, and the other malicious_API_list that contains the top popular APIs commonly used in malicious apps. We observe that the set of APIs in benign_API_list is quite different from the set of APIs in malicious_API_list. We apply Random Forest classifier on a dataset of 60,243 apps by using each list as the features of the classifier. To evaluate the proposed methodology, we build top50_benign_API_list and top50_malicious_API_list by only selecting the first 50 APIs in each ranked list. Our evaluation shows that the Random Forest classifier with top50_benign_API_list is more accurate than the one with top50_malicious_API_list. The Random Forest classifier with top50_benign_API_list can achieve high accuracy of 99.98%.
Sentiment Analysis Tool on Cloud: Software as a Service Model

In this paper we have proposed an open source tool for sentiment analysis. This tool can be accessed over cloud platform in the form of Software as a Service (SaaS) model. We have used dynamic sentiment extraction and analysis mechanism to derive opinions from the social media data. In order to provide wide range of input data options, our tool encompasses various Application Programming Interfaces (APIs). Most of the tools available in the market pivot around only particular type of data or input source. This proposed tool consolidates the functionality of such sentiment analysis tools and provides a single platform to obtain sentiment analysis from any type of data or input source.
[Research Paper] CroLSim: Cross Language Software Similarity Detector Using API Documentation

In today's open source era, developers look forsimilar software applications in source code repositories for anumber of reasons, including, exploring alternative implementations, reusing source code, or looking for a better application. However, while there are a great many studies for finding similarapplications written in the same programming language, there isa marked lack of studies for finding similar software applicationswritten in different languages. In this paper, we fill the gapby proposing a novel modelCroLSimwhich is able to detectsimilar software applications across different programming lan-guages. In our approach, we use the API documentation tofind relationships among the API calls used by the differentprogramming languages. We adopt a deep learning based word-vector learning method to identify semantic relationships amongthe API documentation which we then use to detect cross-language similar software applications. For evaluating CroLSim, we formed a repository consisting of 8,956 Java, 7,658 C#, and 10,232 Python applications collected from GitHub. Weobserved thatCroLSimcan successfully detect similar softwareapplications across different programming languages with a meanaverage precision rate of 0.65, an average confidence rate of3.6 (out of 5) with 75% high rated successful queries, whichoutperforms all related existing approaches with a significantperformance improvement.