Miracle-dz/UniFAR-FTUs huggingface dataset:
Odess: Speeding up Resemblance Detection for Redundancy Elimination by Fast Content-Defined Sampling

Multiple data reduction techniques have been investigated to lower storage costs for a wide variety of customers. In this work, we focus on similarity-based delta compression, which calculates and stores the difference of very similar, but non-duplicate, chunks in storage systems. Delta compression is often implemented along with deduplication and has been shown to achieve a much higher compression ratio.Currently, the N-Transform method is the most popular and widely-used approach to generate features for data content (e.g. chunks) to detect similar candidates (and then apply delta compression). For delta compression systems, though, the throughput of N-Transform is often the bottleneck. Finesse is a high throughput variant of N-Transform, but it suffers from lower detection accuracy and compression ratio. The computation overhead of N-Transform consists of two parts: calculating the rolling hash across data and applying time-consuming transforms on each hash. In this work, we propose Odess, a fast resemblance detection approach, that uses a novel Content-Defined Sampling method to generate a much smaller proxy hash set and then applies transforms on this small hash set. This reduces the calculations in the transform step from being the bottleneck. Meanwhile, Odess also leverages the faster Gear hash to generate rolling hashes. Thus, Odess greatly reduces the computational overhead for resemblance detection while achieving high detection accuracy and high compression ratio.Our evaluation results show that Odess is ~ 5.4× (Finesse) and ~ 26.9× (N-Transform) faster (on average) at generating features for resemblance detection. When considering an end-to-end data reduction storage system, Odess increases throughput by ~ 1.36× (Finesse) and ~ 2.76× (N-Transform) while maintaining the compression ratio of N-Transform and increasing the compression ratio ~ 1.22× over Finesse.
Password Cracking of PDF 2.0 documents on GPU

PDF is one of the international documents standards and it is widely used in the world. Password cracking of the PDF documents has attracted the attention of forensic community. Some forensic tools can crack the password of PDF documents with GPU-acceleration only for PDF l.x documents. This paper studies the password cracking of PDF 2.0 documents, which is the newest PDF standard, on GPU. We analyze the complexity of the password cracking algorithm, and implement it on GPU. We adopt many optimization techniques and our experiments shows that our GPU implementation has about 47 times speedup, compared with the CPU implementation.
Nodal Sensitivity-Based Smart Inverter Control for Voltage Regulation in Distribution Feeder

Overvoltage is one of the issues in distribution grids with high penetration of photovoltaics (PVs). Centralized or droop-based methods of active power curtailment (APC) and/or reactive power control of PVs are viable solutions to prevent overvoltage. This article proposes two distributed methods to control PV inverters, which are based on nodal sensitivities. Then, the performance of the proposed methods is compared with two commonly used control methods, i.e., a distributed method that follows IEEE-1547 but uses arbitrarily chosen droops and a centralized optimal power flow (OPF) based method. Performance is evaluated using a 730-node feeder with up to 100% penetration of inverters. Based on the case studies, the key findings are: first, local droop setting as per IEEE-1547, whether the droops are arbitrarily chosen or systematically calculated using sensitivities, can eliminate overvoltage if reactive power control and APC are coordinated, second, the proposed sensitivity-based approach yields the best voltage performance index computed based on voltage profile compared to the maximum allowed upper bound, and third, OPF-based method is desirable if communication infrastructure exists and minimum energy curtailment is sought.
Efficient rectifier for piezoelectric energy harvester using active diode

Ambient energy harvesting using piezoelectric material needs a rectifier for ac to dc conversion. Piezoelectric energy harvester (PZEH) generates very low level output and therefore, the rectification efficiency of the rectifier needs to be high. The voltage drop across the rectifier components which commonly use diode connected MOSFETs, is a major source of power loss. To reduce the voltage drop, in this paper we propose use of active diode as the rectifier component of the bridge rectifier circuit used for PZEH. The active diode minimizes the forward voltage drop to 21 mV. The proposed bridge rectifier circuit can extract maximum 91% of the power provided by the piezoelectric energy harvesting device. The use of active diode in the bridge circuit improves the power conversion efficiency by 49% to that of the diode connected MOS based BR.
A Generic Ontology Framework for Indexing Keyword Search on Massive Graphs (Extended Abstract)

Due to the unstructuredness and the lack of schema information of knowledge graphs, social networks and RDF graphs, keyword search has been proposed for querying such graphs/networks. Recently, various keyword search semantics have been designed. In this work, we propose a generic ontology-based indexing framework for keyword search, called Bisimulation of Generalized Graph Index (BiG-index), to enhance the search performance. Novelties of BiG-index reside in using an ontology graph GOnt to summarize and index a data graph G iteratively, to form a hierarchical index structure ${\mathbb{G}}$. BiG-index is generic since it is applicable to keyword search algorithms that have two properties. BiG-index reduced the runtimes of popular keyword search work Blinks by 50.5% and r-clique by 29.5%.
MAC Contention Protocol Based on Reinforcement Learning for IoV Communication Environments

The Medium Access Control (MAC) layer contention protocol is closely related to the performance of network throughput, end-to-end delay, and access fairness on the Internet of Vehicles (IoV) communication. Based on the MAC layer protocol of the Wireless Access in Vehicular Environments (WAVE) standard system, this paper proposes a MAC layer contention window adaptive adjustment policy using Reinforcement Learning. Through the detection of the number of neighbors and the application of the Q-Learning algorithm, the vehicle can adjust the contention window according to the number of nodes competing for the same channel to adapt to the changing environments of the IoV. Three different MAC protocols are simulated and analyzed under the Vehicle in Network Simulation (Veins) platform. The results show that the proposed MAC protocol based on neighbor detection and Q-Learning performs better than WAVE MAC protocol and general MAC protocol based on Q-Learning.
Fairness in Rankings and Recommenders: Models, Methods and Research Directions

We increasingly depend on a variety of data-driven algorithmic systems to assist us in many aspects of life. Search engines and recommendation systems amongst others are used as sources of information and to help us in making all sort of decisions from selecting restaurants and books, to choosing friends and careers. This has given rise to important concerns regarding the fairness of such systems. This tutorial aims at presenting a toolkit of definitions, models and methods used for ensuring fairness in rankings and recommendations. Our objectives are three-fold: (a) to provide a solid framework on a novel, quickly evolving, and impactful domain, (b) to present related methods and put them into perspective, and (c) to highlight challenges and research paths for researchers and practitioners that work in data management and applications.
Improving Network Lifetime in WSN’s based on Maximum Residual Energy

WSN is an important area in the field of Networking. The sensors in the network communicate with each other and to the sink which is far from the network. In the process of this communication, energy dissipates by the sensors when they receive or transmit the information in the network. The main concern in the WSN is to reduce the consumption of energy by the sensors and to improve the lifetime of the network. Various protocols were proposed to enhance the network lifetime existing work. In this paper, we have concentrated to enhance the lifetime of network depending on the node’s residual energy. The results shows that the proposed Maximum LEACH protocol improved on an average 50% of life time while compared to the existing Energy based routing protocols in WSN.
Privacy-Preserving Sequential Publishing of Knowledge Graphs

Knowledge graphs (KGs) are widely shared because they can model both users’ attributes as well as their relationships. Unfortunately, adversaries can re-identify their victims in these KGs by using a rich background knowledge about not only the victims’ attributes but also their relationships. A preliminary work to deal with this issue has been proposed in [1] which anonymizes both user attributes and relationships, but this is not enough. Indeed, adversaries can still re-identify target users if data providers publish new versions of their anonymized KGs. We remedy this problem by presenting the kw-Time-Varying Attribute Degree (kw-tad) principle that prevents adversaries from re-identifying any user appearing in w continuous anonymized KGs with a confidence higher than $\frac{1}{k}$. Moreover, we introduce the Cluster-based Time-Varying Knowledge Graph Anonymization Algorithm to generate anonymized KGs satisfying kw-tad. Finally, we prove that even if data providers insert/re-insert/update/delete their users, the users are protected by kw-tad.
16 × 4 Linear Solar-Blind UV Photoconductive Detector Array Based on β-Ga2O3 Film

In this article, a <inline-formula> <tex-math notation="LaTeX">$16\times4$ </tex-math></inline-formula> linear array of <inline-formula> <tex-math notation="LaTeX">$\beta $ </tex-math></inline-formula>-Ga<sub>2</sub>O<sub>3</sub>-based metal-semiconductor-metal structured photodetector is described for solar-blind sensing operation at a wavelength of 254 nm. The <inline-formula> <tex-math notation="LaTeX">$\beta-\mathrm{Ga}_{2} \mathrm{O}_{3}$ </tex-math></inline-formula> film is grown by using metal-organic chemical vapor deposition (MOCVD) equipment, and the photodetectors are finished in constructing with standard photolithography and ion beam sputtering procedures. The results show that the dark current (<inline-formula> <tex-math notation="LaTeX">${\mathrm {I}}_{{dark }}$ </tex-math></inline-formula>), photo-to-dark current ratio (PDCR), photo responsivity (R), specific detectivity <inline-formula> <tex-math notation="LaTeX">$\left(D^{*}\right)$ </tex-math></inline-formula>, exterl quantum efficiency (EQE), and linear dynamic region (LDR) are 1.94 pA, <inline-formula> <tex-math notation="LaTeX">$2.95 \times 10^{7}$ </tex-math></inline-formula>, 139.56 A/W, <inline-formula> <tex-math notation="LaTeX">$2.55 \times 10^{15}$ </tex-math></inline-formula> Jones, <inline-formula> <tex-math notation="LaTeX">$6.8 \times 10^{4 \%}$ </tex-math></inline-formula>, and 149.4 dB, at 10 V bias and <inline-formula> <tex-math notation="LaTeX">$2000 \mu \mathrm{W} / \mathrm{cm}^{2}$ </tex-math></inline-formula> light intensity illumination. In addition, the standard deviation of <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula> for this photodetector array is 10%. Overall, such a <inline-formula> <tex-math notation="LaTeX">$16 \times 4$ </tex-math></inline-formula> linear array of <inline-formula> <tex-math notation="LaTeX">$\beta-\mathrm{Ga}_{2} \mathrm{O}_{3}$ </tex-math></inline-formula>-based photodetector array makes a progress in the field of Ga<sub>2</sub>O<sub>3</sub> photodetectors.
Unbalanced Bit-slicing Scheme for Accurate Memristor-based Neural Network Architecture

Emerging memristor-based computing has the potential to achieve higher computational efficiency over conventional architectures. Bit-slicing scheme, which represents a single neural weight using multiple memristive devices, is usually introduced in memristor-based neural networks to meet high bit-precision demands. However, the accuracy of such networks can be significantly degraded due to non-zero minimum conductance $(\mathrm{G}_{min})$ of memristive devices. This paper proposes an unbalanced bit-slicing scheme; it uses smaller slice sizes for more important bits to provide higher sensing margin and reduces the impact of non-zero $\mathrm{G}_{min}$. Moreover, the unbalanced bit-slicing is assisted by 2’s complement arithmetic which further improves the accuracy. Simulation results show that our proposed scheme can achieve up to $8.8 \times $ and $1.8 \times $ accuracy compared to state-of-the-art for single-bit and two-bit configurations respectively, at reasonable energy overheads.
An Adaptive Deep-Ensemble Anomaly-Based Intrusion Detection System for the Internet of Things

Nowadays, IoT technology has become an essential part of many aspects of life and business. Nevertheless, such widespread application has come at the cost of many security concerns that threaten data privacy and diminish IoT utilization momentum in critical applications such as the smart grid and intelligent transportation systems. To address this challenge, several approaches have been proposed to detect and prevent IoT cyberthreats from materializing. Anomaly detection is one of these approaches that defines the boundaries of legitimate (normal) behavior. Any behavior that falls outside these boundaries is considered anomalous. However, these solutions should have the capability to adapt and adjust to environmental changes that prompt IoT nodal behavioral aberrations, except they only assume that these nodes show the same behavior. This assumption does not hold due to the heterogeneity of IoT nodes and the dynamic nature of an IoT network topology. Furthermore, existing adaptive solutions rely on static (pre-defined) thresholds to control the moment for retraining updates. The cost is heavy for highly dynamic environments like IoT as it leads to an unnecessary higher frequency of retraining. Consequently, the model becomes unstable and adversely affects its accuracy and robustness. This paper addresses these problems by offering an improved Adaptive Anomaly Detection (AAD) methodology that resolves the heterogeneity issues by building local profiles that define normal behavior at each IoT node. The One Class Support Vector Machines (OC-SVM) was used to build these profiles. Then, K-Means clustering was used to build a global profile that represents all network nodes. A Local-Global Ratio-Based (LGR) Anomaly Detection scheme is advanced and was enlisted to control the adaptation process by adjusting the threshold of adaptive functionality dynamically based on the “current” situation to prevent unnecessary retraining. An Ensemble of Deep Belief Networks (EDBN) is developed and used to train the anomaly detection model. Additionally, this study's proposes a new Minimized Redundancy Discriminative Feature Selection (MRD-FS) technique to resolve the issue of redundant features. The MRD-FS experimental evaluation shows detection accuracy higher than those of the related solutions including lower false alarm rates. This validates the efficacy of the proposed model for various IoT applications such as smart grids, smart homes, smart cities and intelligent transportation systems.
The Role of Defects on the Performance of Quantum Dot Intermediate Band Solar Cells

Electrically active defects present in three InAs/GaAs quantum dots (QDs) intermediate band solar cells grown by metalorganic vapor phase epitaxy have been investigated. The devices’ structures are almost identical, differing only in the growth temperature and thickness of the GaAs layers that cover each InAs QD layer. These differences induce significant changes in the solar energy conversion efficiency of the photovoltaic cells, as previously reported. In this work, a systematic investigation was carried out using deep level transient spectroscopy (DLTS) and Laplace DLTS measurements on control samples and solar cell devices, which have clearly shown that electrically active traps play an important role in the device figures of merit, such as open circuit voltage, short circuit current, and shunt resistance. In particular, it was found that the well-known EL2 defect negatively affects both the open circuit voltage and shunt resistance, more in structures containing QDs, as a consequence of the temperature cycle required to deposit them. Other unidentified defects, that are absent in samples in which the QDs were annealed at 700 °C, contribute to a reduction of the short circuit current, as they increase the Shockley-Read-Hall recombination. Photoluminescence results further support the DLTS-based assignments.
Multiphysics Modelling and Real Time Simulation of a Permanent-Magnet Machine for Electric Vehicles

This paper presents a multiphysics modelling and real-time simulation of PMSMs. The study includes electromagnetic modelling, thermal modelling, and finally, a real-time reduced-order model simulation is developed. The resulting model is capable of online estimation of machine parameters and temperature. The results have been compared with dedicated software and a good agreement has been noticed.
Implementation of Trigger Unit for Generation of High-Current-Density Electron Beam

In this article, a trigger unit has been implemented to generate a high-current-density electron beam from a pseudospark (PS) discharge-based electron gun. A trigger pulse power supply unit has been designed and developed to provide the seed electrons from the ferroelectric cathode (FEC). The FEC has been used inside the hollow cathode cavity of a single-gap PS discharge-based electron gun. The electron gun has been operated in the self-breakdown as well as the trigger-based breakdown mode. A comparative analysis has also been performed between self-breakdown and trigger breakdown modes with the developed trigger unit’s help for different applied gap voltages ranging between 0 and 16 kV. The designed trigger unit is based on a high-voltage-gain isolated dc–dc converter and a pulse-forming network. It is compact and capable of generating a negative pulse of ~–5 kV with a pulsewidth of ~500 ns. It provides better control over PS discharge to produce a high-current-density electron beam.
Review of Feature Selection, Dimensionality Reduction and Classification for Chronic Disease Diagnosis

The early diagnosis of chronic diseases plays a vital role in the field of healthcare communities and biomedical, where it is necessary for detecting the disease at an initial phase to reduce the death rate. This paper investigates the use of feature selection, dimensionality reduction and classification techniques to predict and diagnose chronic disease. The appropriate selection of attributes plays a crucial role in improving the classification accuracy of the diagnosis systems. Additionally, dimensionality reduction techniques effectively improve the overall performance of the machine learning algorithms. On chronic disease databases, the classification techniques deliver efficient predictive results by developing intelligent, adaptive and automated system. Parallel and adaptive classification techniques are also analyzed in chronic disease diagnosis which is used to stimulate the classification procedure and to improve the computational cost and time. This survey article represents the overview of feature selection, dimensionality reduction and classification techniques and their inherent benefits and drawbacks.
A Review on Machine Learning Approaches for Predicting the Effect of Device Parameters on Performance of Nanoscale MOSFETs

This review investigates the possibility of using Machine Learning as a replacement for numerical TCAD device simulation. As the chip design is getting complex to incorporate more and more functionality in the devices, many chipmakers started exploring advanced techniques of machine learning to get rid of some big challenges faced by IC industry. In Machine learning, advanced algorithms are utilized to identify patterns in data and to predict about the required information. Machine Learning finds its application in semiconductor fabrication as well as parameter extraction in device modeling. It is also used in prediction of device reliability and its analysis. This work proposes to utilize machine learning method to establish mapping between the performance parameters and structural parameters of the nanoscale MOSFETs. Methods using Machine Learning are fast, highly efficient and computing resource saving over traditional methods.
Compressed Indexes for Fast Search of Semantic Data (Extended Abstract)

The sheer increase in volume of RDF data demands efficient solutions for the triple indexing problem, that is devising a compressed data structure to compactly represent RDF triples by guaranteeing, at the same time, fast pattern matching operations. This problem lies at the heart of delivering good practical performance for the resolution of complex SPARQL queries on large RDF datasets. We propose a trie-based index layout to solve the problem and introduce two novel techniques to reduce its space of representation for improved effectiveness. The extensive experimental analysis reveals that our best space/time trade-off configuration substantially outperforms existing solutions at the state-of-the-art, by taking 30–60% less space and speeding up query execution by a factor of 2–81 times.
CruiseDB: An LSM-Tree Key-Value Store with Both Better Tail Throughput and Tail Latency

Due to excellent performance, LSM-tree key-value stores have been widely used in various applications in recent years. However, LSM-tree’s inherent batched data processing approach makes it suffer from poor SLA behaviors, such as a very unstable throughput and high tail latency. Unlike the I/O isolation or prioritization methods that cannot solve the SLA problem thoroughly, we have designed and implemented a new SLA-oriented LSM-tree KV store, i.e., CruiseDB, to solve both the essential and the direct SLA problems of LSM-tree KV stores by introducing an adaptive admission mechanism and improving the LSM-tree structure. According to reliable estimation of the service capacity of the LSM-tree, CruiseDB adaptively admits only an appropriate number of user requests to enter the LSM-tree memory buffer in unit time and removes the internal roadblocks of the request processing, with the advantages of preventing the write stall phenomenon, which leads to SLA declines. CruiseDB can promote the guaranteed throughput by 2.08 times on average compared with the state-of-the-art LSM-tree or B-tree KV stores.
Dynamic Power Allocation for Cooperative Multi- Antenna Networks in Presence of Mutual Coupling

This paper provides an overview of the influence of mutual antenna coupling on the massive MIMO and its effects on gaining energy efficiency and power allocation schemes. As massive MIMO downlink cooperation becomes more prevalent, it becomes challenging to design and manufacture antenna arrays. We derive expressions for the multi-objective optimum power allocation strategy under the influence of mutual antenna coupling. When joint antenna coupling is taken into account or ignored, the numerical analysis and simulation results indicate a substantial gap in the measured results for the optimized average channel power. The results suggest that antenna common coupling effects need to be carefully considered to obtain individual rate or sum-rate capability and the right optimal power allocation strategy. Computer simulation instances back this argument.