Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Micro Service Architecture Recovery: An Empirical Study

Micro service architectures are rapidly establishing themselves in the software industry as a more efficient and effective substitute for monolithic applications. In a micro service architecture, the application is broken down into many small elements called micro services. These are managed in a distributed way and typically involve several development teams. In such an environment, an architectural model can get lost along the way, making it difficult to perform many downstream software engineering tasks, such as migration, audit, integration or impact analysis. To address this problem, we are developing support for Micro Service Architecture Recovery (MiSAR) using a Model Driven Engineering approach. In this paper, we describe an empirical study which aims to identify the core elements of our approach, by undertaking manual analysis on 8 micro service-based open source projects. From this analysis, we define a metamodel for micro service-based architectures and a set of mapping rules which map between the software and the metamodel. The resulting metamodel and mapping rules provide a solid foundation for any micro service architecture recovery approach and hence are a key first step towards managing the architectural integrity of micro service-based applications.
Learning and Interpreting Complex Distributions in Empirical Data

To fit empirical data distributions and then interpret them in a generative way is a common research paradigm to understand the structure and dynamics underlying the data in various disciplines. However, previous works mainly attempt to fit or interpret empirical data distributions in a case-by-case way. Faced with complex data distributions in the real world, can we fit and interpret them by a unified but parsimonious parametric model? In this paper, we view the complex empirical data as being generated by a dynamic system which takes uniform randomness as input. By modeling the generative dynamics of data, we showcase a four-parameter dynamic model together with inference and simulation algorithms, which is able to fit and generate a family of distributions, ranging from Gaussian, Exponential, Power Law, Stretched Exponential (Weibull), to their complex variants with multi-scale complexities. Rather than a black box, our model can be interpreted by a unified differential equation, which captures the underlying generative dynamics. More powerful models can be constructed by our framework in a principled way. We validate our model by various synthetic datasets. We then apply our model to $16$ real-world datasets from different disciplines. We show the systematic biases of fitting these datasets by the most widely used methods and show the superiority of our model. In short, our model potentially provides a framework to fit complex distributions in empirical data, and more importantly, to understand their generative mechanisms.
PTfuzz: Guided Fuzzing With Processor Trace Feedback

Greybox fuzzing, such as american fuzzy lop (AFL), is very efficient in finding software vulnerability, which makes it the state-of-the-art fuzzing technology. Greybox fuzzing leverages the branch information collected during program running as feedback to guide choosing seeds. Current greybox fuzzing generally uses two kinds of methods to collect branch information: compile-time instrumentation (AFL) and emulation [AFL extended with QEMU emulation (QAFL)]. Compile-time instrumentation is efficient, but it does not support binary programs. Meanwhile, emulation supports binary programs, but its efficiency is very low. In this paper, we propose a greybox fuzzing approach named PTfuzz, which leverages hardware mechanism (Intel Processor Trace) to collect branch information. Our approach supports binary programs, just like the emulation method, while it gains a comparable performance with the compile-time instrumentation method. Our experiments show that PTfuzz can fuzz the original binary programs without any modification, and we gain a $3\times $ performance improvement compared to QAFL.
Material Limit of Power Devices—Applied to Asymmetric 2-D Superjunction MOSFET

In spite of the reporting of several mathematical approaches dealing with the behavior of the superjunction MOSFET’s specific resistance, a study for the asymmetrical pillar (when the width of the n-pillar and the p-pillar are not the same at a given cellpitch) has not been carried out yet. When the width of one of the pillar (say n-pillar) is modified, the doping concentration (say donor) should be changed to maintain a charge balance condition. This in turn, changes the width of the depletion region, due to the parasitic JFET effect and as a result the effective on-state conduction path. This raises the question whether the best tradeoff between the specific on-state resistance and the breakdown voltage could be achieved by employing the conventional assumption of the same width of the n and p pillars. This paper clarifies the best option for the width of each pillar when designing a superjunction MOSFET and adapts the figures of merit to take into account the asymmetrical superjunction cell.
Attention Mechanisms for Object Recognition With Event-Based Cameras

Event-based cameras are neuromorphic sensors capable of efficiently encoding visual information in the form of sparse sequences of events. Being biologically inspired, they are commonly used to exploit some of the computational and power consumption benefits of biological vision. In this paper we focus on a specific feature of vision: visual attention. We propose two attentive models for event based vision: an algorithm that tracks events activity within the field of view to locate regions of interest and a fully-differentiable attention procedure based on DRAW neural model. We highlight the strengths and weaknesses of the proposed methods on four datasets, the Shifted N-MNIST, Shifted MNIST-DVS, CIFAR10-DVS and N-Caltech101 collections, using the Phased LSTM recognition network as a baseline reference model obtaining improvements in terms of both translation and scale invariance.
A mechanism-based computational model to capture the interconnections among epithelial-mesenchymal transition, cancer stem cells and Notch-Jagged signaling

Epithelial-mesenchymal transition (EMT) and cancer stem cell formation (CSCs) are two fundamental and well-studied processes contributing to cancer metastasis and tumor relapse. Cells can undergo a partial EMT to attain a hybrid epithelial/mesenchymal (E/M) phenotype or a complete EMT to attain a mesenchymal one. Similarly, cells can reversibly gain or lose ‘stemness’. This plasticity in cell states is modulated by signaling pathways such as Notch. However, the interconnections among the cell states enabled by EMT, CSCs and Notch signaling remain elusive. Here, we devise a computational model to investigate the coupling among the core decision-making circuits for EMT, CSCs and the Notch pathway. Our model predicts that hybrid E/M cells are most likely to associate with stemness traits and exhibit enhanced Notch-Jagged signaling – a pathway that is implicated in therapeutic resistance. Further, we show that the position of the ‘stemness window’ on the ‘EMT axis’ is varied by altering the coupling strength between EMT and CSC circuits, and/or modulating Notch signaling. Finally, we analyze the gene expression profile of CSCs from several cancer types and observe a heterogeneous distribution along the ‘EMT axis’, suggesting that different subsets of CSCs may exist with varying phenotypes along the epithelial-mesenchymal plasticity axis. Our computational model offers insights into the complex EMT-stemness interplay and provides a platform to investigate the effects of therapeutic perturbations such as treatment with metformin, a common anti-diabetic drug that has been associated with decreased cancer incidence and increased lifespan of patients. Our mechanism-based model helps explain how metformin can both inhibit EMT and blunt the aggressive potential of CSCs simultaneously, by driving the cells out of a hybrid E/M stem-like state with enhanced Notch-Jagged signaling.
DCMPTCP: Host-Based Load Balancing for Datacenters

Load balancing in datacenter networks (DCNs) is an important and challenging task for datacenter managers. A number of sophisticated technologies have been proposed to improve load balancing performance in a complicated circumstance, i.e., with various traffic characteristics. Many approaches need a high cost to implement, such as changing switch hardware. The efficiency problem has not been well addressed. MPTCP was proposed as a low-cost approach to improve data transmission in DCNs, which uses subflows to balance workloads across multiple paths. However, current MPTCP is not satisfying, especially when there are rack-local flows or many-to-one short flows. In this paper, we propose DCMPTCP to improve the efficacy of MPTCP. We gradually develop three mechanisms. First, DCMPTCP identifies rack-local traffic and eliminates unnecessary subflows to reduce the overhead. Second, DCMPTCP estimates flow length and establishes subflows in a smarter way. Third, DCMPTCP strengthens explicit congestion notification to improve the congestion control performance on inter-rack manyto-one short flows. DCMPTCP has a good compatibility and is easy to deploy. We implement DCMPTCP in ns-3 simulator and evaluate the performance by comprehensive simulations. The results show that DCMPTCP achieves ~65-771X and ~10-15X better FCT than MPTCP for rack-local and inter-rack traffic respectively.
Self unbound: ego dissolution in psychedelic experience

Abstract Users of psychedelic drugs often report that their sense of being a self or ‘I’ distinct from the rest of the world has diminished or altogether dissolved. Neuroscientific study of such ‘ego dissolution’ experiences offers a window onto the nature of self-awareness. We argue that ego dissolution is best explained by an account that explains self-awareness as resulting from the integrated functioning of hierarchical predictive models which posit the existence of a stable and unchanging entity to which representations are bound. Combining recent work on the ‘integrative self' and the phenomenon of self-binding with predictive processing principles yields an explanation of ego dissolution according to which self-representation is a useful Cartesian fiction: an ultimately false representation of a simple and enduring substance to which attributes are bound which serves to integrate and unify cognitive processing across levels and domains. The self-model is not a mere narrative posit, as some have suggested; it has a more robust and ubiquitous cognitive function than that. But this does not mean, as others have claimed, that the self-model has the right attributes to qualify as a self. It performs some of the right kinds of functions, but it is not the right kind of entity. Ego dissolution experiences reveal that the self-model plays an important binding function in cognitive processing, but the self does not exist.
Consume Local: Towards Carbon Free Content Delivery

P2P sharing amongst consumers has been proposed as a way to decrease load on Content Delivery Networks. This paper develops an analytical model that shows an additional benefit of sharing content locally: Selecting close by peers to share content from leads to shorter paths compared to traditional CDNs, decreasing the overall carbon footprint of the system. Using data from a month-long trace of over 3 million monthly users in London accessing TV shows online, we show that local sharing can result in a decrease of 24-48% in the system-wide carbon footprint of online video streaming, despite various obstacle factors that can restrict swarm sizes. We confirm the robustness of the savings by using realistic energy parameters drawn from two widely used settings. We also show that if the energy savings of the CDN servers are transferred as carbon credits to the end users, over 70% of users can become carbon positive, i.e., are able to support their content consumption without incurring any carbon footprint, and are able to offset their other carbon consumption. We suggest carbon credit transfers from CDNs to end users as a novel way to incentivise participation in peer-assisted content delivery.
Blind Nonlinear Self-Interference Cancellation for Wireless Full-Duplex Transceivers

As the transmission power increases for high-power full-duplex (FD) communications, the nonlinear effects of the transmit chain become more significant, resulting in severe nonlinear self-interference (SI) at the receiver of the FD transceiver and hence demodulation performance degradation for the desired signal from a remote user. In this paper, a blind nonlinear SI cancellation method, which consists of a cancellation stage and a recovery stage, is proposed to cancel the nonlinear SI along with the linear SI signal for an orthogonal frequency division multiplexed (OFDM)-modulated FD transceiver. By taking the advantage of two symbols on adjacent subcarriers received in one OFDM symbol, the cancellation stage cancels both the linear SI and its nonlinear SI components of the received signal without estimating the transmitter nonlinearity as well as the SI channel response. Subsequently, the recovery stage is performed on the resulting signal of the cancellation stage to eliminate any impacts on the desired signal. Simulations are performed on a nonlinear model extracted from a practical class AB power amplifier with transmission power ranging from 10 to 30 dBm to demonstrate different nonlinear distortions. The effectiveness of the proposed nonlinear cancellation method is verified in terms of the computation complexity, the cancellation capability for the SI signal, and the bit error rate of the desired signal.
Are we underestimating the richness of visual experience?

Abstract It has been argued that the bandwidth of perceptual experience is low—that the richness of experience is illusory and that the amount of visual information observers can perceive and remember is extremely limited. However, the evidence suggests that this postulated poverty of experiential content is illusory and that visual phenomenology is immensely rich. To properly estimate perceptual content, experimentalists must move beyond the limitations of binary alternative-forced choice procedures and analyze reports of experience more broadly. This will open our eyes to the true richness of experience and to its neuronal substrates.
Design of a Low-Power MTJ-Based True Random Number Generator Using a Multi-voltage/Current Converter

In this paper, we introduce a multi-V/I converter for low-power true random number generator (TRNG) using a three-terminal magnetic tunnel junction (MTJ) device. As MTJ devices are probabilistically switched by current, the desired probability of 50% is digitally controlled by digital-to-analog (D/A) and V/I converters. In the conventional MTJ-based TRNG a highly accurate (and large-power) D/A is required to be tolerate to large temperature variation of MTJ devices. By changing the characteristics dynamically according to the temperature variation, the proposed circuit can reduce the bit precision of the D/A converter while generating the quality of random number as the conventional V/I converter. The circuit is designed with a 65nm CMOS/three-terminal MTJ model, and the simulation is carried out using HSPICE. As a result, the number of bits of the D/A converter is reduced from 10 bits to 7 bits.
Cooperative Look-Ahead Control of Vehicle Platoon for Maximizing Fuel Efficiency Under System Constraints

The fuel consumption and greenhouse gas emissions can be reduced by organizing a group of vehicles into a platoon at a short inter-vehicular distance. Additionally, the eco-driving technology has the potential to further increase fuel efficiency by optimizing the speed trajectories of vehicles. However, little research has been done into the eco-driving of a vehicle platoon. This paper proposes a cooperative look-ahead control strategy for maximizing the fuel efficiency of vehicle platoon travelling on a road with varying slopes. In this paper, the aerodynamic drag, nonlinear engine fuel consumption model, discrete gear ratios, and three engine operating modes are considered in the control strategy; under system constraints, a cooperative optimal fuel consumption problem of vehicle platoon based on distributed model predictive control is formulated; to obtain the optimal solution of the formulated nonlinear optimization problem, after the minimum fuel consumption table and the corresponding optimal control variable tables are obtained, the nonlinear optimization problem with discrete control variables is transformed into a 0–1 binary mixed linear programming problem. Simulation results show that, compared with benchmarks, the proposed control strategy can significantly improve fuel efficiency under different passenger comfort requirements, allowed speed ranges, and predictive control horizons.
Explanation Mining: Post Hoc Interpretability of Latent Factor Models for Recommendation Systems

The widescale use of machine learning algorithms to drive decision-making has highlighted the critical importance of ensuring the interpretability of such models in order to engender trust in their output. The state-of-the-art recommendation systems use black-box latent factor models that provide no explanation of why a recommendation has been made, as they abstract their decision processes to a high-dimensional latent space which is beyond the direct comprehension of humans. We propose a novel approach for extracting explanations from latent factor recommendation systems by training association rules on the output of a matrix factorisation black-box model. By taking advantage of the interpretable structure of association rules, we demonstrate that predictive accuracy of the recommendation model can be maintained whilst yielding explanations with high fidelity to the black-box model on a unique industry dataset. Our approach mitigates the accuracy-interpretability trade-off whilst avoiding the need to sacrifice flexibility or use external data sources. We also contribute to the ill-defined problem of evaluating interpretability.
Analysis and Design of Commutation-Based Circulator-Receivers for Integrated Full-Duplex Wireless

Previously, we presented a non-magnetic, non-reciprocal N-path-filter-based circulator-receiver (circ.-RX) architecture for single-frequency full-duplex (SF-FD) wireless, which merges a commutation-based linear periodically time-varying (LPTV) non-magnetic circulator with a down-converting mixer and directly provides the baseband (BB) RX signals at its output, while suppressing the noise contribution of one set of the commutating switches. The architecture also incorporates an on-chip balance network to enhance the transmitter (TX)-RX isolation. In this paper, we present a detailed analysis of the architecture, including a noise analysis and an analysis of the effect of the balance network. The analyses are verified by the simulation and measurement results of a 65-nm CMOS 750-MHz circ. -RX prototype. The circ. -RX can handle up to +8 dBm of TX power with 8-dB noise figure (NF) and 40-dB average isolation over 20-MHz radio frequency (RF) bandwidth (BW). In conjunction with digital self-interference (SI) and its third-order intermodulation (IM3) cancellation, the SF-FD circ. -RX demonstrates 80-dB overall SI suppression for up to +8-dBm TX average output power. The claims are also verified through an SF-FD demonstration where a −50-dBm weak desired received signal is recovered while transmitting a 0-dBm average power orthogonal frequency-division multiplexing (OFDM)-like TX signal.
Applicability of Deep Soil Mixing for Use in Embankments and Foundation Support

Problematic soils present a difficult challenge for geotechnical engineering due to the high settlement of the soil structure and the often associated high water table and moisture content. The Deep Soil Mixing (DSM) method, which is a soil stabilization technique, has been commonly applied to improve Different parameters of soil. The application covers on-land and in-water constructions ranging from strengthening the foundation ground of buildings, embankment supports, earth retaining structures, retrofit and renovation of urban infrastructures, liquefaction hazards mitigation, man-made island constructions and seepage control. In this paper important parameters that effect in performance of deep soil mixing (DSM) and different design steps of DSM is examined. In this regard embankment investigates when use of DSM with finite element method. the results shows by increase of diameter and height of columns safety factor and settlement decrease but this parameters effects Up to a certain amount Then it is negligible Effect.
Three hypergraph eigenvector centralities

Eigenvector centrality is a standard network analysis tool for determining the importance of (or ranking of) entities in a connected system that is represented by a graph. However, many complex systems and datasets have natural multi-way interactions that are more faithfully modeled by a hypergraph. Here we extend the notion of graph eigenvector centrality to uniform hypergraphs. Traditional graph eigenvector centralities are given by a positive eigenvector of the adjacency matrix, which is guaranteed to exist by the Perron-Frobenius theorem under some mild conditions. The natural representation of a hypergraph is a hypermatrix (colloquially, a tensor). Using recently established Perron-Frobenius theory for tensors, we develop three tensor eigenvectors centralities for hypergraphs, each with different interpretations. We show that these centralities can reveal different information on real-world data by analyzing hypergraphs constructed from n-gram frequencies, co-tagging on stack exchange, and drug combinations observed in patient emergency room visits.
Multicolumn Networks for Face Recognition

The objective of this work is set-based face recognition, i.e. to decide if two sets of images of a face are of the same person or not. Conventionally, the set-wise feature descriptor is computed as an average of the descriptors from individual face images within the set. In this paper, we design a neural network architecture that learns to aggregate based on both "visual" quality (resolution, illumination), and "content" quality (relative importance for discriminative classification). To this end, we propose a Multicolumn Network (MN) that takes a set of images (the number in the set can vary) as input, and learns to compute a fix-sized feature descriptor for the entire set. To encourage high-quality representations, each individual input image is first weighted by its "visual" quality, determined by a self-quality assessment module, and followed by a dynamic recalibration based on "content" qualities relative to the other images within the set. Both of these qualities are learnt implicitly during training for set-wise classification. Comparing with the previous state-of-the-art architectures trained with the same dataset (VGGFace2), our Multicolumn Networks show an improvement of between 2-6% on the IARPA IJB face recognition benchmarks, and exceed the state of the art for all methods on these benchmarks.
TrainWare: A Memory Optimized Weight Update Architecture for On-Device Convolutional Neural Network Training

Training convolutional neural network on device has become essential where it allows applications to consider user's individual environment. Meanwhile, the weight update operation from the training process is the primary factor of high energy consumption due to its substantial memory accesses. We propose a dedicated weight update architecture with two key features: (1) a specialized local buffer for the DRAM access deduction (2) a novel dataflow and its suitable processing element array structure for weight gradient computation to optimize the energy consumed by internal memories. Our scheme achieves 14.3%-30.2% total energy reduction by drastically eliminating the memory accesses.