Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Deep Learning of ResNet50 and VGG16 with Pipeline Parallelism

Data parallel distributed deep learning has been used to accelerate the learning speed. The communication is becoming a bottleneck as the computation time is accelerated. Therefore, the communication time is needed to be reduced. As a new acceleration method, research activity for pipeline parallelism has increased since 2018. However, the existing methods generally combine data parallelism and pipeline parallelism, and there has been a little analysis of speed improvement in pipeline parallelism alone. This paper analyzes the effect of speed improvement by pipeline parallelism.We compared the execution time of data parallelism and pipeline parallelism with VGG16 and ResNet50, respectively. VGG16 includes more parameters comparing to ResNet50 when the size of input data is the same. A comparison of the execution time shows that data parallelism is 3.3 times faster than pipeline parallelism for ResNet50. Pipeline parallelism is 2.45 times faster than data parallelism for VGG16.The speed-up of pipeline parallelism was significant in a model with a large number of parameters. Therefore, pipeline parallelism is suitable for large-scale models that have a large number of parameters.
Semantic Image Inpainting via Maximum Likelihood

Current approaches involve deep learning in solving an image inpainting task. We propose a meta-algorithm in which we can set a probabilistic distribution on images. The set distribution can be a classic one, e.g. Normal distribution, or a modern one, e.g. PixelCNN++. Our first observation is relatively unexpected: if a learnt distribution generates reasonable images, then this does not make it a good candidate for inpainting via our proposed algorithm and vice-versa (if a learnt distribution gives reasonable results on image inpainting via our algorithm, then this does not make it is a good candidate for sampling a new image). Our second observation is that although the visual results of the state-of-the-art method are superior to ours, the training time for our method is lower. Hence, one can experiment faster with our method to see, for example, if the desired inpainting is learnable. Moreover, using a specific distribution, our algorithm can be trained also on high-quality RGB images, like 1024 times 1024 pixels. As for the experiments, we included visual results and some quantitative comparisons. The Google Colab notebook with the code and the demo is available at: https://github.com/aciobanusebi/mle-inpainting
Deep Learning-Based Automatic Monitoring Method for Grain Quantity Change in Warehouse Using Semantic Segmentation

The quantity security of stored grain in warehouses is crucial for a country. Since manual inspection of stored grain quantity is inefficient, some online automeasurement methods have been proposed recently, but they are either time-consuming or too expensive to be popularized. This article proposes a deep learning-based method to automatically monitor changes in grain quantity of granaries. First, the image of the same scene in a granary is taken at different moments using a device that integrates a camera and an infrared laser rangefinder. Then, a deep semantic segmentation model based on an encoder–decoder framework is developed to extract the grain loading line and grain surface of the image. Finally, the distance and area between the extracted grain loading line and grain surface are calculated and compared with the previous measured one to determine whether the grain quantity of the granary has changed. To improve the accuracy of the segmentation results, a novel reverse attention model is proposed to provide guidance information to fuse low-level features, which calculates multiscale attention maps based only on the output of the last layer of the encoder. Furthermore, the proposed method and infrared laser rangefinder-based method are combined to get more accurate grain volume after an abnormal change of grain quantity is detected by our method. Experimental results show that our method is effective and feasible for monitoring changes in grain quantity and outperforms the state-of-the-art methods on semantic segmentation.
Resolving automatic high availability operations and users commands to virtual machines on OpenStack

In recent years, server virtualization technology has become more and more popular. Many companies tend to build services on virtual machines (VMs) on cloud computing platforms. Therefore, high-availability (HA) technologies that improve system availability by reducing downtime have become critical. In previous research, we proposed a HA technology called High Availability Service System (HASS) for OpenStack. However, the operation of HASS conflicts with the operation of the VM user or administrator. For example, HASS incorrectly judges a user’s operation (such as live migrating a VM) as a fault and conducts the corresponding failover. There is no existing method that can be applied to OpenStack to solve this problem. Therefore, this paper aims to propose a new method to resolve the conflict between automatic operation of HA technology and VM operation. The proposed method collects VM operation information and automatically changing the fault detection strategy of OpenStack HA technology. According to the experimental results, even if the user operates the VM, the OpenStack HA technology using the proposed method can accurately detect the VM fault and failover.
A Switching-Coupled Backend for Simultaneous Localization and Dynamic Object Tracking

Simultaneous localization and object tracking (SLOT) is essentially important for autonomous systems. Tightly-coupled and loosely-coupled methods are two commonly used back-end frameworks for the state-of-the-art solutions of SLOT problem. However, some inherent limitations exist in these two frameworks. In particular, the tightly-coupled method is usually disturbed by the poor observations of some dynamic objects, and the performance of a loosely-coupled one completely depends on that of classical static simultaneous localization and mapping (SLAM) process. Motivated by these observations, we propose a novel switching-coupled back-end solution and theoretically derive its concrete form using probability representation. Based on the switching strategy and the proposed objects classification criteria where the object uncertainty, observation quality and prior information are jointly considered, the dynamic objects’ states are flexibly coupled with camera's state and static landmarks’ states. For implementation, the measurement constraints of “good” dynamic objects and static landmarks are simultaneously leveraged to perform SLAM and good object tracking (SLAMGOT) process, and those of “bad” objects are used for bad object tracking (BOT) process based on the obtained camera state. Extensive evaluations on synthetic scenes, KITTI datasets and real-world experiments demonstrate the performance of the proposed method.
A Hyperspectral Image Classification Method Based on Weight Wavelet Kernel Joint Sparse Representation Ensemble and β-Whale Optimization Algorithm

Joint sparse representation (JSR) is a commonly used classifier that recognizes different objects with core features extracted from images. However, the generalization ability is weak for the traditional linear kernel, and the objects with similar feature values associated with different categories are not sufficiently distinguished especially for a hyperspectral image (HSI). In this article, an HSI classification technique based on the weight wavelet kernel JSR ensemble model and the <inline-formula><tex-math notation="LaTeX">$\beta$</tex-math></inline-formula>-whale optimization algorithm is proposed to conduct pixel-level classification, where the wavelet function is acted as the kernel of JSR. Moreover, ensemble learning is used to determine the category label of each sample by comprehensive decision of some subclassifiers, and the <inline-formula><tex-math notation="LaTeX">$\beta$</tex-math></inline-formula> function is utilized to enhance the exploration phase of the whale optimization algorithm and obtain the optimal weight of subclassifiers. Experimental results indicate that the performance of the proposed HSI classification method is better than that of other newly proposed and corresponding approaches, the misclassification and classified noise are eliminated to some extent, and the overall classification accuracy reaches 95% for all HSIs.
Research on Data Protection Algorithm Based on Social Network

In a social network, it needs to protect the network data effectively. To improve the security and privacy protection ability of the network data, a social network data protection algorithm is proposed based on dynamic cyclic encryption and link equilibrium configuration. The architecture model and routing control protocol of mobile social network are constructed. The mixed recommended values of user behavior attribution data of social network are calculated, and the data encryption in social network is realized by using sub-key random amplitude modulation method. The dynamic cyclic encryption algorithm is used to encrypt and transmit the data and the adaptive equalization scheduling of the data output of the social network is carried out by using the link equalization configuration method to improve the protection ability in the process of data transmission. The simulation results show that the proposed algorithm has good encryption ability, and the ability of data storage and transmission is improved.
Thread-Placement Learning

In a non-uniform memory access machine, the placement of software threads to hardware cores can have a significant effect on the performance of concurrent applications. Detecting the best possible placement for each application is a necessity for thread scheduling. Yet, due to the difficulty of this problem, operating-system schedulers do not really try to understand the needs of applications, but rather focus on (non-portable) scheduling heuristics.In this paper, we introduce thread-placement learning (TPLE), a technique for understanding the placement requirements of applications. TPLE utilizes machine learning and performance counters for choosing between different placement policies. To feed the machine learning model, TPLE requires a set of portable microbenchmarks that produce training data—i.e., performance counter measurements—for all the target placement policies. We use this data to train a classifier that is able to choose between these policies online in order to change the thread-placement of a running application.We demonstrate the practicality of TPLE by implementing a thread-placement algorithm, named Slate. Slate is able to automatically and online (i.e., in runtime) select between the two most commonly-used placement policies, namely locality and round-robin placement on the nodes of a multicore. To the best of our knowledge, Slate is the first online thread-placement algorithm that utilizes machine learning in combination with performance counters. We evaluate Slate and show that it achieves up to 93% accuracy in its decisions and outperforms the Linux scheduler by up to 16%.
Scale the Data Plane of Software-Defined Networks: a Lazy Rule Placement Approach

Data plane programming languages enable administrators of Software-Defined Networks (SDNs) to perform fine-grained flow control by compiling high-level policies into low-level rules and deploying rules in the data plane. However, it is difficult to scale the data plane with the dynamics of network traffic and the limited storage space of switches. In this paper, we propose a lazy OpenFlow Rule Placement (ORP) framework to enforce control polices and scale the SDN data plane by placing and reusing wildcard rules. We provide an offline rule placement scheme to meet performance objectives under real-world constraints. To handle dynamic traffic and perform incremental rule updates, we design an online matching rule deployment algorithm to place rules in polynomial time and prove it to be conditionally-optimal. Furthermore, to address the rule dependency problem during online rule placement, we extend the algorithm to deploy dependent rules and present lightweight heuristics to guarantee the fast reaction to the new flows. Extensive experiments are conducted on diverse network topologies and datasets to show that the lazy ORP framework significantly reduces the storage cost, improves data plane scalability and is flexible enough to accomplish different optimization goals.
HPT-RL: Calibrating Power System Models based on Hierarchical Parameter Tuning and Reinforcement Learning

Power system models need regular calibration to reflect the current system status, identify and mitigate potential issues. However, traditional calibration approaches usually have local minima issues that require intervention from domain experts. In this study, we propose a reinforcement learning algorithm based on hierarchical parameter tuning (HPT-RL) for power system model calibration. The proposed HPT-RL is inspired by the natural hierarchy in parameter tuning where its higher level learns which parameter to tune and its lower level learns the tuning step size. We implement HPT-RL using the Deep Deterministic Policy Gradient (DDPG) architecture. We evaluate and compare the performance of HPT-RL, traditional DDPG, Bayesian Optimization, and Random Search for a generator model calibration on a four-bus system simulated with PSS®E. The performance of HPT-RL was evaluated based on 100 repeated experiments for two scenarios, namely low randomization and high randomization, based on where a simulated disturbance occurs on the bus system. The results show that HPT-RL outperforms the other three benchmark methods.
A Study on Handover Mechanism in 5G Non-standalone Network

3GPP divides the deployment of 5G networks into an initial version of a non-standalone network (NSN) and a mid-to-late version of a standalone network (SN). It further subdivides it into eight 5G candidate network architectures. The non-standalone network is recognized as the most suitable 5G version for initial deployment. The 5G network integrates other heterogeneous networks to provide higher bandwidth. Among them, the number of small cells will increase by multiples due to their low cost and rapid deployment characteristics. It implies that the density of base stations and the number of base station handovers in heterogeneous networks will increase significantly. However, too many handovers may be caused by the failure to effectively control the ping-pong effect, resulting in a severe drop in system performance. Traditionally, the handover decision is only based on the received signal strength. However, the only handover decision criterion cannot ensure the connection service quality facing the increasingly complex heterogeneous network environment. This paper proposes a new handover mechanism based on the 5G non-standalone networking, including a measuring module, a scoring module, a forwarding module, and a ping-pong effect module. The handover mechanism considers the base station bandwidth, signal strength, UE moving direction, and base station locations. The system will score and classify each candidate base station to reduce the ping-pong effect in a high-density base station environment, thus ensuring that the handover is performed under the commitment of QoS requirements to improve the throughput of user services.
A New Feature to Secure Web Applications

Web application security is one of essential components of any web-based systems. As becoming popular of the Internet makes many web sites be attacked by many kinds of attacks. So, we have to propose a secure framework for web applications. For this reason, we have to propose a web application framework which not only analyzes the source code implemented by web developers, also it can detects the vulnerabilities in the source code dynamically. Although a lot of research works have already proposed detection methods of vulnerabilities in web application attacks, but those are not fully detected because their methods do not use the information of the web applications. Therefore, we propose a new method which analyzes the source code of a web application, and then modifies it if needed, in addition, our method has a detection method of an application’s vulnerabilities that are difficult to detect by previous methods. According to our implementation and experiments, it is possible to detect actual attacks, which have been considered difficult to detect, against authentication leaks and SQL injection attacks using dynamic queries.
Forecasting Attrition from the Canadian Armed Forces using Multivariate LSTM

Using 15 years of monthly release volume of Canadian Armed Forces Regular Force members, we showed that a Long Short-Term Memory (LSTM) multivariate machine learning model performs better than univariate statistical models (Holt-Winters) for the same number of observations. For a 3-year forecast, our data supports a prediction error of less than 0.5%.
Collecting Indoor Environmental Sensor Values for Machine Learning Based Smart Building Control

This research presents a solution for collecting indoor environmental sensor values and how the gathered sensor values then could be used for green building certification and in turn also machine learning based smart building control. We have created and implemented a proof of concept system consisting of a sensor collecting device using off the shelf hardware to complement the existing sensor information from buildings, as well as a cloud system for persistently storing this data for later usage. We have measured and evaluated our implemented system for our envisioned scenarios. In which we could observe that our proof-of-concept could scale to handle almost four sensor value updates per second at maximum stress, as well as having a latency for uploading a sensor value from our sensor of about 130 ms. Finally, we present our future and ongoing work based on these results which outlines our work for smart building control, green building certification, and the energy signature of buildings.
A Current and Temperature Limiting System in a 7-nm Hexagon™ Compute Digital Signal Processor

A Qualcomm® Hexagon™ compute digital signal processor (CDSP) enhances reliability by integrating a current and temperature limiting system to avoid circuit failures from operating at excessive current levels for a sustained duration or from thermal runaway. The current and temperature limiting system consists of on-die current and temperature sensors, a limits evaluation (LE) circuit, and a randomized pulse modulation (RPM) circuit to control the instruction-issue rate. After the on-die sensors detect current or temperature exceeding a target threshold, the RPM circuit precisely adjusts the instruction-issue rate to maximize the performance while operating below the current and temperature specifications. The RPM circuit adapts performance in approximately five CDSP clock cycles after accounting for the clock-domain-crossing synchronization overhead to satisfy the 1- $\mu \text{s}$ latency requirement for the entire limiting system. From silicon measurements in a 7-nm CDSP, the RPM instruction-issue control circuit enables a 0.4% performance resolution across a wide range of operation from 100% to 0.4%. In addition, the RPM circuit avoids thread starvation during multi-threaded execution to maintain the CDSP quality of service (QoS). In comparison to a clock control for a limiting system, the RPM instruction-issue control enables CDSP performance gains ranging from ~2% to ~50%, depending on the workload and adjustment level, while satisfying the current and temperature specifications.
Multiagent Multi-Armed Bandit Techniques for Millimeter Wave Concurrent Beamforming

This paper leverages multiagent multi-armed bandit (MA-MAB) schemes to handle efficiently the millimeter wave (mmWave) concurrent transmission problem. MA-MAB selects the beams with the maximum long-term reward, mostly data rate, from the concurrent links. The mmWave access points (APs) are the agents of the bandit game, while the arms are the existing beam directions. Towards that, MA-KLUCB and MA-EXP3 are proposed and tested via applying them within each AP in a selfish concurrent beamforming scenario. It turns out that the proposed algorithms provide near-optimal performances as compared to the exhaustive search of all concurrent beams’ combinations. Also, they demonstrate reasonable fast convergence rates too.
Reinforcement Learning based Evolutionary Metric Filtering for High Dimensional Problems

Metric learning algorithms create distance metrics to capture the important relationships among features. These algorithms have been successful in low dimensional data. However, it is a challenge to handle high dimensional problems in real-world applications. This paper applies in high dimensional clustering problems a Reinforcement Learning (RL) based metric filtering approach in Evolutionary Distance Metric Learning (EDML), which relies on an evolutionary approach to optimize its distance metric. The proposed framework called High Dimensional Reinforced (HDR)-EDML has two novelties. The first is using a function approximation RL method called Least-Squares Policy Iteration (LSPI) to create a feature selection control strategy in high-dimensional input space to filter the metric. The second is adopting a two-way information exchange approach between LSPI and EDML Evolutionary Algorithm (EA) in metric learning. In the first way, LSPI will learn and send feedback that will affect the EDML metric creation. In the second way, the evolutionary feature prioritizing of EDML is utilized by LSPI in its learning process. These two novelties aim to reduce the number of selected features while maintaining the clustering performance. HDR-EDML is compared to conventional K-means, Information-Theoretic Metric Learning (ITML), normal EDML, and EDML with l1 norm regularization for feature selection as baselines. Moreover, 3 different HDR-EDML approaches are examined to explore different ways of information exchange. Results show a significant decrease in the number of features while maintaining accuracy as well as reduced computational time and memory compared to another RL-based filtering method.
Distributed Hierarchical File Systems strike back in the Cloud

Cloud service providers have aligned on availability zones as an important unit of failure and replication for storage systems. An availability zone (AZ) has independent power, networking, and cooling systems and consists of one or more data centers. Multiple AZs in close geographic proximity form a region that can support replicated low latency storage services that can survive the failure of one or more AZs. Recent reductions in inter-AZ latency have made synchronous replication protocols increasingly viable, instead of traditional quorum-based replication protocols. We introduce HopsFS-CL, a distributed hierarchical file system with support for high-availability (HA) across AZs, backed by AZ-aware synchronously replicated metadata and AZ-aware block replication. HopsFS-CL is a redesign of HopsFS, a version of HDFS with distributed metadata, and its design involved making replication protocols and block placement protocols AZ-aware at all layers of its stack: the metadata serving, the metadata storage, and block storage layers. In experiments on a real-world workload from Spotify, we show that HopsFS-CL, deployed in HA mode over 3 AZs, reaches 1.66 million ops/s, and has similar performance to HopsFS when deployed in a single AZ, while preserving the same semantics.