Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterizing serverless platforms with serverlessbench

Serverless computing promises auto-scalability and cost-efficiency (in "pay-as-you-go" manner) for high-productive software development. Because of its virtue, serverless computing has motivated increasingly new applications and services in the cloud. This, however, also presents new challenges including how to efficiently design high-performance serverless platforms and how to efficiently program on the platforms. This paper proposes ServerlessBench, an open-source benchmark suite for characterizing serverless platforms. It includes test cases exploring characteristic metrics of serverless computing, e.g., communication efficiency, startup latency, stateless overhead, and performance isolation. We have applied the benchmark suite to evaluate the most popular serverless computing platforms, including AWS Lambda, Open-Whisk, and Fn, and present new serverless implications from the study. For example, we show scenarios where decoupling an application into a composition of serverless functions can be beneficial in cost-saving and performance, and that the "stateless" property in serverless computing can hurt the execution performance of serverless functions. These implications form several design guidelines, which may help platform designers to optimize serverless platforms and application developers to design their functions best fit to the platforms.
Overview of the Transformer-based Models for NLP Tasks

In 2017, Vaswani et al. proposed a new neural network architecture named Transformer. That modern architecture quickly revolutionized the natural language processing world. Models like GPT and BERT relying on this Transformer architecture have fully outperformed the previous state-of-theart networks. It surpassed the earlier approaches by such a wide margin that all the recent cutting edge models seem to rely on these Transformer-based architectures. In this paper, we provide an overview and explanations of the latest models. We cover the auto-regressive models such as GPT, GPT-2 and XLNET, as well as the auto-encoder architecture such as BERT and a lot of post-BERT models like RoBERTa, ALBERT, ERNIE 1.0/2.0.
Dynamic Clustering Personalization for Recommending Long Tail Items

Recommendation strategies are used in several contexts in order to bring potential users closer to products with a strong probability of interest. When recomendations focus on niche items, they are called recommendations in the long tail. In these cases, they also look for less popular items and try to find your target custumer, niche market. This paper proposes a long tail recommendation approach that prioritizes relevance, diversity and popularity of recommended items. For that, a hybrid approach based on two techniques are used. The first is clustering with dynamic parameters that adapt from according to the dataset used and the second is a type of Markov chains for to calculate the distance of interest of a user to an item of relevance for this user. The results show that the techniques used have a better relevance indexes at the same time more diverse and less popular recommendations.
Data Mining-Based Phishing Detection

Webpages can be faked easily nowadays and as there are many internet users, it is not hard to find some becoming victims of them. Simultaneously, it is not uncommon these days that more and more activities such as banking and shopping are being moved to the internet, which may lead to huge financial losses. In this paper, a developed Chrome plugin for data mining-based detection of phishing webpages is described. The plugin is written in JavaScript and it uses a C4.5 decision tree model created on the basis of collected data with eight describing attributes. The usability of the model is validated with 10-fold cross-validation and the computation of sensitivity, specificity and overall accuracy. The achieved results of experiments are promising.
On Finding the Optimal Tree of a Complete Weighted Graph

We want to find a tree where the path length between any two vertices on this tree is as close as possible to their corresponding distance in the complete weighted graph of vertices upon which the tree is built. We use the residual sum of squares as the optimality criterion to formulate this problem, and use the Cholesky decomposition to solve the system of linear equations to optimize weights of a given tree. We also use two metaheuristics, namely Simulated Annealing (SA) and Iterated Local Search (ILS) to optimize the tree structure. Our results suggest that SA and ILS both perform well at finding the optimal tree structure when the dispersion of distances in the complete graph is large. However, when the dispersion of distances is small, only ILS has a solid performance.
From Machine Translated NLI Corpus to Universal Sentence Representations in Czech

—Natural language inference (NLI) is a sentence-pair classiﬁcation task w.r. t. the entailment relation. As already shown, certain deep learning architectures for NLI task – I NFER S ENT in particular – may be exploited for obtaining (supervised) universal sentence embeddings. Although I NFER S ENT approach to sentence embeddings has been recently outperformed in different tasks by transformer-based architectures (like BERT and its derivatives), it still remains a useful tool in many NLP areas and it also serves as a strong baseline. One of the greatest advantages of this approach is its relative simplicity. Moreover, in contrast to other approaches, the training of I NFER S ENT models can be performed on a standard GPU within hours. Unfortunately, the majority of research on sentence embeddings in general is done in/for English, whereas other languages are apparently neglected. In order to ﬁll this gab, we propose a methodology for obtaining universal sentence embeddings in another language – arising from training I NFER S ENT -based sentence encoders on machine translated NLI corpus and present a transfer learning use-case on semantic textual similarity in Czech.
Peculiarities of Modern Street Addressing System toward to Implementation of the Smart City Conception

Implementation of Smart City conception is direction of developing many modern cities. Rapid urbanization leaded to constant increasing urban population. At the same time technological growth is cause of increasing of density and complexity of urban infrastructure. Street Addressing system is an essential component of city management, quality of citizens’ life, and other spheres of city’s economy.This paper considers peculiarities of Street Addressing system from human and technologies perspectives to be an essential part of the Smart Cities concept implementation.
StarCraft agent strategic training on a large human versus human game replay dataset

Real-time strategy games are currently very popular as a testbed for AI research and education. StarCraft: Brood War (SC:BW) is one of such games. Recently, a new large, unlabeled human versus human SC:BW game replay dataset called STARDATA was published. This paper aims to prove that the player strategy diversity requirement of the dataset is met, i.e., that the diversity of player strategies in STARDATA replays is of sufficient quality. To this end, we built a competitive SC:BW agent from scratch and trained its strategic decision making process on STARDATA. The results show that in the current state of the competitive environment the agent is capable of keeping a stable rating and a decent win rate over a longer period of time. It also performs better than our other, simple rule-based agent. Therefore, we conclude that the strategy diversity requirement of STARDATA is met.
A New Optimized Stochastic Approach for Multiple Integrals in Option Pricing

—In the present paper we evaluate European style options with an exponential payoff function with an optimized lattice rule based on a new optimal generating vector. A brief introduction of the theory of lattice rule has been given. We compare the performance of the new stochastic approach with a new optimal generating vector for multiple integrals up to 50 dimensions.
A Social Robot-based Platform towards Automated Diet Tracking

Diet tracking via self-reports or manual taking of meal photos might be difficult, time-consuming, and discouraging, especially for children, which limits the potential of long-term dietary assessment. We present the design and development of a proof of concept of an automated and unobtrusive system for diet tracking integrating: a) a social robot programmed to automatically capture photos of food and motivate children, b) a deep learning model based on Google Inception V3, applied for the use case of image-based fruit recognition, c) a RESTful microservice architecture deployed to deliver the model outcomes to a platform aiming at childhood obesity prevention. We illustrate the feasibility and virtue of this approach, towards the development of the next-generation computer-assisted systems for automated diet tracking.
Context Clustering-based Recommender Systems

Recommender systems have gained lots of attention due to the rapid increase in the amount of data on the internet. Therefore, the demand for finding more advanced techniques to generate more useful recommendations becomes an urgent. The increasing need for generating more relevant recommendations led to the emergence of many novel recommendation systems, such as Context-aware Recommender System (CARS), which is based on incorporating the contextual information in recommendation systems. The goal of this paper is to propose new recommender systems that utilize the contextual information to find more relevant recommendations. In this paper, we propose CoC1, a novel Context Clustering-based recommender system. We introduce two approaches which utilize the contextual information and KMeans clustering algorithm to generate new forms of user-item matrices. We show that the accuracy of CoC1 which uses the new user-item matrices has been improved comparing with the accuracy of classical recommender system which uses the original user-item matrix.
Memristor based Oscillators with Controlled Threshold Parameters

The principle of controlling threshold parameters in memristor based oscillators circuit is discussed. The new control approach using two-threshold comparators is considered. It is shown that capabilities of memristor oscillators with control thresholds are sufficient for constructing the simplest elements of oscillatory computing architectures. The results of oscillator simulation confirm these capabilities.
Design of a ‘U’ Slot Substrate-Integrated Waveguide Cavity-Backed Self-Diplexing Antenna

In this paper, the simple design of a "U" slot substrate-integrated waveguide (SIW)-based single substrate self-diplexing antenna is proposed. Two longitudinal slots (similar length) and one transverse slot are designated for "U" slot representation respectively, which radiate at two separate resonant frequencies, being imprinted on the top surface of the antenna. Two different microstrip feed lines are used to feed the SIW antenna. A high degree of isolation is achieved between the two ports.
A Compact Model for the Electroforming Process of Memristive Devices

Memristive devices based on the valence change mechanism undergo an electroforming process before they can be switched repetitively between different resistance states. This electroforming process can already lead to device-to-device variability in a memristive array. Here, we propose a physics-based compact model for the electroforming process of filamentary memristive switches based on the valence change mechanism. The model is verified by experimental data of ZrOx-based devices. Using this model, the forming process of a whole memory array is investigated. It is shown that the parasitic IR-drop of the word and bit lines can influence the final state of the device after the electroforming process. In addition, the simulation results reveal that the location of the cell in the array influences the final state.
Massive MIMO Networks With Spatio-Temporal Traffic: Scheduling Mechanism Optimization

In this letter, we develop a framework for the analysis of massive multiple-input multiple-output (MIMO) networks with both spatial and temporal random arrival of traffic, which is important in the next generation network but has not been investigated before. Using stochastic geometry and queueing theory, we derive a tight closed-form approximation for the average number of packets successfully transmitted at any unit time slot and area, referred to as spatial mean packet throughput (<inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula>). To maximize <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula>, we find that when the number of BS antennas (<inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>) is much larger than the average number of devices per cell (<inline-formula> <tex-math notation="LaTeX">${\mathcal{ N}}_{\tt S}$ </tex-math></inline-formula>), the BS should schedule as many devices as possible; however, when <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> is smaller or comparable with <inline-formula> <tex-math notation="LaTeX">${\mathcal{ N}}_{\tt S}$ </tex-math></inline-formula>, scheduling too many devices will bring significant impairment on <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula>, and the optimal number of scheduled devices should be obtained from our proposed solution. To further quantify this conclusion, we also present <inline-formula> <tex-math notation="LaTeX">$M/{\mathcal{ N}}_{\tt S}$ </tex-math></inline-formula> as the criteria to choose proper scheduling mechanisms. The effectiveness of our optimal solutions has been verified via simulations.
Switch-Centric Byzantine Fault Tolerance Mechanism in Distributed Software Defined Networks

Supporting byzantine fault tolerance (BFT) in distributed software-defined networks (SDNs) may lead to increased consensus delay and traffic load since all messages should be verified and multicasted among controllers. To address this problem, we propose a switch-centric byzantine fault tolerant (SC-BFT) mechanism, in which key BFT functions (e.g., message authentication and comparison) are implemented at the programmable switches. Thus, SC-BFT can accelerate the consensus procedure and mitigate the communication overhead. We implemented SC-BFT at BMv2 using P4. Analytical and simulation results show that SC-BFT provides 80% reduced response time compared to conventional BFT consensus mechanisms with significantly reduced communication overhead.
Exploiting Symmetry in Dependency Graphs for Model Reduction in Supervisor Synthesis

Supervisor synthesis enables the design of supervisory controllers for large cyber-physical systems, with high guarantees for functionality and safety. The complexity of the synthesis problem, however, increases exponentially with the number of system components in the cyber-physical system and the number of models of this system, often resulting in lengthy or even unsolvable synthesis procedures. In this paper, a new method is proposed for reducing the model of the system before synthesis to decrease the required computational time and effort. The method consists of three steps for model reduction, that are mainly based on symmetry in dependency graphs of the system. Dependency graphs visualize the components in the system and the relations between these components. The proposed method is applied in a case study on the design of a supervisory controller for a road tunnel. In this case study, the model reduction steps are described, and results are shown on the effectiveness of model reduction in terms of model size and synthesis time.
CUGR: Detailed-Routability-Driven 3D Global Routing with Probabilistic Resource Model

Many competitive global routers adopt the technique of compressing the 3D routing space into 2D in order to handle today’s massive circuit scales. It has been shown as an effective way to shorten the routing time, however, quality will inevitably be sacrificed to different extents. In this paper, we propose two routing techniques that directly operate on the 3D routing space and can maximally utilize the 3D structure of a grid graph. The first technique is called 3D pattern routing, by which we combine pattern routing and layer assignment, and we are able to produce optimal solutions with respect to the patterns under consideration in terms of a cost function in wire length and routability. The second technique is called multi-level 3D maze routing. Two levels of maze routing with different cost functions and objectives are designed to maximize the routability and to search for the minimum cost path efficiently. Besides, we also designed a cost function that is sensitive to resources changes and a post-processing technique called patching that gives the detailed router more flexibility in escaping congested regions. Finally, the experimental results show that our global router outperforms all the contestants in the ICCAD’19 global routing contest.
On Countermeasures Against the Thermal Covert Channel Attacks Targeting Many-core Systems

Although it has been demonstrated in multiple studies that serious data leaks could occur to many-core systems thanks to the existence of the thermal covert channels (TCC), little has been done to produce effective countermeasures that are necessary to fight against such TCC attacks. In this paper, we propose a three-step countermeasure to address this critical defense issue. Specifically, the countermeasure includes detection based on signal frequency scanning, positioning affected cores, and blocking based on Dynamic Voltage Frequency Scaling (DVFS) technique. Our experiments have confirmed that on average 98% of the TCC attacks can be detected, and with the proposed defense, the bit error rate of a TCC attack can soar to 92%, literally shutting down the attack in practical terms. The performance penalty caused by the inclusion of the proposed countermeasures is only 3% for an 8×8 system.
Challenges of Return-Oriented-Programming on the Xtensa Hardware Architecture

This paper shows how the Xtensa architecture can be attacked with Return-Oriented-Programming (ROP). The presented techniques include possibilities for both supported Application Binary Interfaces (ABIs). Especially for the windowed ABI a powerful mechanism is presented that not only allows to jump to gadgets but also to manipulate registers without relying on specific gadgets. This paper purely focuses on how the properties of the architecture itself can be exploited to chain gadgets and not on specific attacks or a gadget catalog.
Adaptive Control of Hard Disk Drive Servo System

This paper focuses on the design of an adaptive controller for read/write (R/W) head servo system in a hard disk drive (HDD). In this control system a reference model-based adaptive loop is added to the inner feedback loop with the PID controller. The parameters of the PID controller are computed using PID tuner within Matlab/Simulink. The normalized MIT rule is then used to tune the controller parameters in order to reduce an error between the reference model and actual system. The main objective is to provide accurate and fast positioning of the R/W head and to ensure a fast response of the arm actuator. The performance of the designed system is evaluated through simulations taking into account an influence of changes in the system parameters as well as the effect of external disturbances acting.
Optimistic Causal Consistency for Geo-Replicated Key-Value Stores

Causal consistency (CC) is an attractive consistency model for geo-replicated data stores because it hits a sweet spot in the ease-of-programming versus performance trade-off. We present a new approach for implementing CC in geo-replicated data stores, which we call Optimistic Causal Consistency (OCC). OCC's main design goal is to maximize data freshness. The optimism in our approach lies in the fact that the updates replicated to a remote data center are made visible immediately, without checking if their causal dependencies have been received. Servers perform the dependency check needed to enforce CC only upon serving a client operation, rather than on receipt of a replicated data item as in existing systems. OCC offers a significant gain in data freshness, which is of crucial importance for various types of applications, such as real-time systems. OCC's potentially blocking behavior makes it vulnerable to network partitions. We therefore propose a recovery mechanism that allows an OCC system to fall back on a pessimistic protocol to continue operating during network partitions. We implement POCC, the first causally consistent geo-replicated multi-master key-value data store designed to maximize data freshness. We show that POCC improves data freshness, while offering comparable or better performance than its pessimistic counterparts.
Service-Driven Resource Management in Vehicular Networks Based on Deep Reinforcement Learning

This paper studies a joint communication, computing and caching resource allocation problem in vehicular networks to improve user satisfaction and reduce costs. We propose a double-scale deep reinforcement learning (DSDRL) framework that combines on-policy strategy and off-policy strategy to enable dynamic resources allocation, which considers not only the diversity and difference of services but also the costs of network operators. Simulation results show that the proposed scheme can effectively improve the long-term revenue of network operators.
Adaptive on Time Controlled Double-Line- Frequency Ripple Suppressor With Fast Dynamic Response and High Efficiency

Due to the input power and output power do not match in real time, single-stage power factor correction converters have large double-line-frequency ripple at the output voltage. The double-line-frequency ripple voltage will cause some electronic devices to work abnormally, and limit the control loop bandwidth of power factor correction converter. In order to reduce the output double-line-frequency ripple voltage, a single-stage Flyback power factor correction converter with Buck ripple suppressor is used in this paper, the Buck ripple suppressor can generate same magnitude but 180° phase shifted voltage as Flyback power factor correction converter output voltage ripple. Adaptive on-time control is adopted in the Buck ripple suppressor benefiting with its wider bandwidth and fast dynamic response. The switching frequency range and stability of Buck ripple suppressor under constant on-time control and adaptive on-time control are discussed. By establishing the input-output audio susceptibility model, the output double-line-frequency ripple suppression performance is analyzed. Buck ripple suppressor using adaptive on-time control can suppress double-line-frequency ripple voltage effectively with the fast dynamic response and high efficiency. Simulation and experimental results are given to verify the theoretical analysis.
User and Traffic Allocation in Wireless Single-Tier Dense Networks with Multi-Connectivity

In dense single tier networks, users can have access to multiple cells. Multi-connectivity (MC) enhances user through-puts and the reliability and robustness of the transmission. The work presented here aims at the cell selection process as well as traffic management to further enhance the throughput and QoS for all users, in a single-tier dense network. Using a bin packing problem formulation, several user association and traffic allocation algorithms have been assessed through simulations in a realistic deployment with 3 frequency channels and packet transmission. MC enhances the packet throughputs to an extend depending on the traffic load and its possible asymmetry between cells. SINR based user association is a robust choice in such a scenario. When using MC, the QoS and fairness must be done at the network level and not left at the resource scheduler at the cell level. The effect of the scheduler can be countered by transferring the right amount of data to the cell for each packet.