Miracle-dz/UniFAR-FTUs huggingface dataset:
Superpolynomial Lower Bounds Against Low-Depth Algebraic Circuits

An Algebraic Circuit for a polynomial $P\ \ \in \mathbb{F}[x_{1}, \ldots, x_{N}]$ is a computational model for constructing the polynomial $P$ using only additions and multiplications. It is a syntactic model of computation, as opposed to the Boolean Circuit model, and hence lower bounds for this model are widely expected to be easier to prove than lower bounds for Boolean circuits. Despite this, we do not have superpolynomial lower bounds against general algebraic circuits of depth 3 (except over constant-sized finite fields) and depth 4 (over fields other than $\mathbb{F}_{2}$), while constant-depth Boolean circuit lower bounds have been known since the early 1980s. In this paper, we prove the first super polynomial lower bounds against general algebraic circuits of all constant depths over all fields of characteristic 0 (or large). We also prove the first lower bounds against homogeneous algebraic circuits of constant depth over any field. Our approach is surprisingly simple. We first prove superpolynomial lower bounds for constant-depth Set-Multilinear circuits. While strong lower bounds were already known against such circuits, most previous lower bounds were of the form $f(d)\cdot \text{poly}(N)$, where $d$ denotes the degree of the polynomial. In analogy with Parameterized complexity, we call this an FPT lower bound. We extend a well-known technique of Nisan and Wigderson (FOCS 1995) to prove non-FPT lower bounds against constant-depth set-multilinear circuits computing the Iterated Matrix Multiplication polynomial $\text{IMM}_{n, d}$ (which computes a fixed entry of the product of $d\ n\times n$ matrices). More precisely, we prove that any set-multilinear circuit of depth $\Delta$ computing $\text{IMM}_{n, d}$ must have size at least $n^{d^{\exp(-O(\Delta))}}$. This result holds over any field, as long as $d=o(\log n)$. We then show how to convert any constant-depth algebraic circuit of size $s$ to a constant-depth set-multilinear circuit with a blow-up in size that is exponential in $d$ but only polynomial in $s$ over fields of characteristic 0. (For depths greater than 3, previous results of this form increased the depth of the resulting circuit to $\Omega(\log s))$. This implies our constant-depth circuit lower bounds. Finally, we observe that our superpolynomial lower bound for constant-depth circuits implies the first deterministic sub-exponential time algorithm for solving the Polynomial Identity Testing (PIT) problem for all small depth circuits using the known connection between algebraic hardness and randomness.
Integrating Pattern Features to Sequence Model for Traffic Index Prediction

Intelligent traffic system (ITS) is one of the effective ways to solve the problem of traffic congestion. As an important part of ITS, traffic index prediction is the key of traffic guidance and traffic control. In this paper, we propose a method integrating pattern feature to sequence model for traffic index prediction. First, the pattern feature of traffic indices is extracted using convolutional neural network (CNN). Then, the extracted pattern feature, as auxiliary information, is added to the sequence-to-sequence (Seq2Seq) network to assist traffic index prediction. Furthermore, noticing that the prediction curve is less smooth than the ground truth curve, we also add a linear regression (LR) module to the architecture to make the prediction curve smoother. The experimentscomparingwithlongshort-termmemory(LSTM)andSeq2Seqnetworkdemonstratedadvantagesandeffectiveness ofourmethods.
Analysis of traps-related effects hindering GFETs performance

The effect of traps on DC and high-frequency behavior of a short channel single-layer graphene field-effect transistor (GFET) is discussed thoroughly in the present work. Trap-induced hysteresis is evident when a standard staircase measurement technique is applied while it is diminished when an opposing-pulse method is used. In both cases, forward and backward gate voltage (VGS) sweeps are utilized. A recently proposed analytical compact model accounting for traps activated both by vertical electric field and high-lateral electric field enabled by hot carriers, is accurately validated with both trap-affected and trap-reduced data. Important high-frequency figures of merit (FoM) such as cut-off and maximum oscillation frequencies as well as the intrinsic gain of the GFET under test, are also derived from the model, and exhibit a strong trap dependence through the DC operating point of the device. These FoM not only demonstrate VGS shifts, but also, they exhibit magnitude alterations due to traps impact even when the Dirac voltage of the GFET under test coincides in both forward and backward staircase measurement schemes.
RUSTInA: Automatically Checking and Patching Inline Assembly Interface Compliance (Artifact Evaluation): Accepted submission #992 – “Interface Compliance of Inline Assembly: Automatically Check, Patch and Refine”

The main goal of the artifact is to support the experimental claims of the paper #992 "Interface Compliance of Inline As-sembly: Automatically Check, Patch and Refine" by making both the prototype and data availableto the community. The expected result is the same output as the figures given in Table I and Table IV (appendix C) of the paper. In addition, we hope the released snapshot of our prototype is simple, documented and robust enough to have some uses for people dealing withinline assembly.
Modeling of Ultrafast Waveguided Electro-Absorption Modulator at Telecommunication Wavelength (λ = 1.55 μm) Based on Intersubband Transition in an InGaAs/AlAs/AlAsSb Asymmetric Coupled Double Quantum Well Lattice-Matched to InP

We investigated theoretically a waveguided EAM (Electro-Absorption Modulator) based on ISBT (Intersubband Transitions) in an In<sub>(0.53)</sub>Ga<sub>(0.47)</sub>As/AlAs/AlAs<sub>(0.56)</sub> Sb<sub>(0.44)</sub> A-CDQWs (Asymmetric Coupled Double Quantum Wells) lattice-matched to InP at telecommunication wavelength (<inline-formula> <tex-math notation="LaTeX">$\lambda =1.55~\mu \text{m}$ </tex-math></inline-formula>) which offers ultrahigh-speed and moderate voltage swing. Likewise, the temperature dependency in the In<sub>(0.53)</sub>Ga<sub>(0.47)</sub>As/AlAs/AlAs<sub>(0.56)</sub> Sb<sub>(0.44)</sub> A-CDQWs was investigated at different temperatures from 300 K to 400 K and evidently the InP-based ISB (Intersubband) modulator offers better temperature stability (~ 0.05 nm/C) compared to the InP-based IB (Interband) modulator. The EAM investigated here is anticipated to have a RC-limited speed (<inline-formula> <tex-math notation="LaTeX">$f_{3d\,B}$ </tex-math></inline-formula>) of ~300 GHz with insertion loss of 5.1 dB, 10 dB extinction ratio and 5.18 dB/V modulation efficiency at a peak-to-peak voltage of 2.0 V which can support a data rate of up to 600 Gbps and beyond.
Predicting the Stage of Non-small Cell Lung Cancer with Divergence Neural Network Using Pre-treatment Computed Tomography

Determining the stage of non-small cell lung cancer (NSCLC) is important for treatment and prognosis. Staging includes a professional interpretation of imaging, thus we aimed to build an automatic process with deep learning (DL). We proposed an end-to-end DL method that uses pre-treatment computer tomography images to classify the early- and advanced-stage of NSCLC. DL models were developed and tested to classify the early- and advanced-stage using training (n = 58), validation (n = 7), and testing (n = 17) cohorts obtained from public domains. The network consists of three parts of encoder, decoder, and classification layer. Encoder and decoder layers are trained to reconstruct original images. Classification layers are trained to classify early- and advanced-stage NSCLC patients with a dense layer. Other machine learning-based approaches were compared. Our model achieved accuracy of 0.8824, sensitivity of 1.0, specificity of 0.6, and area under the curve (AUC) of 0.7333 compared with other approaches (AUC 0.5500 ─ 0.7167) in the test cohort for classifying between early- and advanced-stages. Our DL model to classify NSCLC patients into early-stage and advanced-stage showed promising results and could be useful in future NSCLC research.
Securing IoT/IIoT from Software Attacks Targeting Hardware Vulnerabilities

The microarchitecture of modern systems become more and more complicated. This increasing complexity gives rise to a new class of attacks which uses software code and targets hardware vulnerabilities of the system microarchitectures. Software attacks targeting hardware vulnerabilities (SATHVs) gain popularity. In particular, cache side channel attacks, Spectre, and Rowhammer are serious threats. They take advantage of microarchitectural vulnerabilities to extract secret information or harm the system. As these attacks target the system’s hardware, they can avoid traditional software antivirus protections. However, they modify the normal operation of the system’s hardware. Hardware Performance Counters (HPCs) are special registers that allow counting specific hardware events. These registers can help us monitor system’s execution at hardware level and detect this set of attacks. Many solutions in the literature use HPCs to detect SATHVs. Although, these solutions target detecting only a limited set of the available SATHVs. If security designers do not consider all the possibilities, attackers can bypass existing protections using SATHV variants. In this article, we investigate how the side effect selection proposed in the literature, could or could not help us detect the studied attacks in our testing platform. Our threat model includes Cache side channel and Rowhammer attacks. We also discuss the limitations of software monitoring and how hardware approaches can resolve them.
Anomaly Detection based on Traffic Monitoring for Secure Blockchain Networking

While the blockchain technology provides strong cryptographic protection on the ledger and the system operations, the underlying blockchain networking remains vulnerable due to potential threats such as denial of service (DoS), Eclipse, spoofing, and Sybil attacks. Effectively detecting such malicious events should thus be an essential task for securing blockchain networks and services. Due to its importance, several studies investigated anomaly detection in Bitcoin and blockchain networks, but their analyses mainly focused on the blockchain ledger in the application context (e.g., transactions) and targets specific types of attacks (e.g., double-spending, deanonymization, etc). In this study, we present a security mechanism based on the analysis of blockchain network traffic statistics (rather than ledger data) to detect malicious events, through the functions of data collection and anomaly detection. The data collection engine senses the underlying blockchain traffic and generates multi-dimensional data streams in a periodic manner. The anomaly detection engine then detects anomalies from the created data instances based on semi-supervised learning, which is capable of detecting previously unseen patterns, and we introduce our profiling-based detection engine implemented on top of AutoEncoder (AE). Our experimental results support the effectiveness of the presented security mechanism for accurate, online detection of malicious events from blockchain networking traffic data. We also show further reduction in time complexity (up to 66.8% for training and 85.7% for testing), without any performance degradation using feature prioritization compared to the utilization of the entire features.
A Practical Hybrid Automatic Transmission Model for Commercial Vehicles

This study proposes a practical hybrid automatic transmission model for commercial vehicles based on the first-principle modelling approach. The developed plant model consists of three base elements, i.e. hydraulic circuit, multi-plate wet clutches and planetary gear sets. In today's intelligent transmission control system development framework, plant model plays an important role. It can be used to valid the control algorithm as well as control system in an early stage of the development process, thus shortening development process and improving software quality.
Heterogeneous Information Network based Adaptive Social Influence Learning for Recommendation and Explanation

Collaborative filtering (CF)-based recommendation systems that rely on user-item history interactions often suffer from the data sparsity problem. Social-based recommendation methods have become one of the successful methods to address this problem. However, few works have focused on the sparsity problem of social data. As real-world social networks are usually sparse, the observed relationships in social networks can only represent a limited part of a person’s real social network. The sparse social data will degrade the performance of the existing social-based algorithms. Also, the influence of a user’s friends on their friends is dynamic: even the same friend may impact the target user in different decision-making processes. It is difficult for an the end-to-end deep learning-based model to provide underlying reasons for the recommendation results. To this end, we propose a novel deep learning-based model to extract useful missing links as auxiliary social information to enrich the users’ features for item recommendation. The framework is composed of two major components: a missing links identifier module that generates useful social links from a heterogeneous information network to enrich the user’s social profile and enhance the social-based recommendation model, and an attention-based recommendation module that assigns different scores for each friend with regard to different candidate items to adaptively evaluate the quality of different social links. An attention-based fusion strategy is proposed to improve the interpretability of the recommendation system by assigning non-uniform weight to different factors. Extensive experiments on three published datasets show that our proposed method achieves better performance than other state-of-the-art methods.
A contact resistance extraction method of 2D-FET technologies without test structures

A $Y$ -function based method (YFM) is used here to extract the contact resistance $R_{\mathrm{c}}$ of different two-dimensional (2D) field-effect transistor (FET) technologies. The methodology relies on individual transfer characteristics, at a single drain-to-source voltage, of devices from a same technology with different channel lengths. In contrast to the widely used transfer length method where a global-back gated test structure is required, the YFM presented here can be applied to 2D-FETs regardless the gate architecture. This method does not require the fabrication of dedicated test structures and hence it can be a useful and immediate tool for device characterization and scaling studies. $R_{\mathrm{c}}$ is extracted here for graphene-, black phosphorus-, WS2 and MoS2-FETs using the proposed methodology and considering the mobility degradation coefficient in the underlying model. The extracted values are in good agreement with the ones obtained with other approaches. An accurate description of the experimental drain current and channel resistance is achieved by using the extracted parameters in the corresponding equation.
Temperature Dependent Local Structure of NaxCoO2 Cathode Material for Rechargeable Sodium-Ion Batteries

We have investigated the local structure of differently charged NaxCoO2 cathode material as a function of temperature by Co K-edge X-ray absorption fine structure (EXAFS) measurements. We have found that the charge/discharge process has a direct effect on the bond characteristics of the cathode in the Na-ion batteries. The results reveal that the local Co–O bonds get softer, while the Co–Co bonds hardly show any change during discharge (sodiation). The present study underlines the key role of local atomic displacements in diffusion and the reversibility of ions in cathodes for batteries and points toward the feasibility of NaxCoO2 to be used as a cathode material. The results are discussed in comparison with the lithiation/delithiation of LixCoO2 battery materials.
Attitude Tracking Control of 3-DOF Helicopter Based on Command Filter and Neural Network Techniques

This paper proposes the attitude tracking controller design scheme for the 3-DOF helicopter with unknown model uncertainties and external disturbances by combining command filter with neural network techniques. On the basis of backstepping design method, the unknown model uncertainties and external disturbances are firstly reconstructed by adopting command filter. Then, neural network technique is used to approximate the reconstructed model uncertainties and external disturbances to realize the attitude tracking control of the 3-DOF helicopter. Finally, the stability of the closed-loop tracking control system of the 3-DOF helicopter is verified by Lyapunov stability theory. Simulation results verify the effectiveness of the proposed method.
Fake News Classification and Topic Modeling in Brazilian Portuguese

All over the world, people receive daily news on many subjects through web-based information sharing platforms such as social networks. However, some of such news are false (fake) with the potential to deceive them. Thus, the automatic detection of false news is a major issue and is gaining careful attention from the scientific community. In this paper, we present experimental analysis using both supervised and unsupervised learning on the Fake.Br corpus, a fake news dataset in Brazilian Portuguese. We propose a classification method for fake news detection based on distinct types of features, and deep learning supervised algorithms. Our best classification model achieved F1 scores up to 96% and was compared with other non-deep learning classifiers. Furthermore, we provide a complementary analysis of the same dataset by performing topic modeling based on both uni-grams and bi-grams.
Content-based image retrieval: Colorfulness and Depth visual perception quantification

This study focuses specifically on content-based image retrieval. The objective being to characterize the images according to their content, in the form of an index, a score which will be used for the manipulation of the images.We propose two visual perception quantification methods, one for colorfulness and one for depth. In terms of quantifying an impression, it was necessary to set up objective and rigorous criteria reflecting a sometimes subjective concept.Based on saturation, lightness and color disparity, we propose a framework that assigns to image an index between 0 and 100 to convey the impression that the image is very colorful or not. While in the case of depth, the estimate of a depth map will serve as a starting point. We estimate that the impression of depth is linked to the spread of the elements that make up the image from the farthest to the closest.
Unconstrained Lower Limb Motion Recognition by Angle Assisted Annotation-Based Surface EMG Sequential Labelling

Surface electromyography(sEMG)-based lower limb motion recognition has developed rapidly in a variety of applications and attracts extensive attentions. However, most of existing work focuses on single lower limb motion recogition(SLLMR) and ignores the importance of unconstrained lower limb motion recognition(ULLMR). In this paper, we propose a novel angle assisted annotation-based surface electromyography sequential labelling (A3-SESL) framework to solve ULLMR task. Specifically, A3-SESL is composed of labelling phase and learning phase. In term of labelling phase, due to difficulty of direct sEMG annotation, we introduce joint angle information to assist sEMG labelling. In term of learning phase, we first use labelled sEMG data to establish unconstrained lower limb motion sEMG (ULLM-sEMG) dataset, and then an light-weight temporal concolutional network is designed to perceive the latent difference information in supervised learning manner. Comprehensive comparision experimental results demonstrate the ability of proposed framework in recognizing lower limb motion.
Phenotype Anomaly Detection in Early C. elegans Embryos by Variational Auto-Encoder

Variational auto encoder (VAE) is used to detect and quantify the phenotype anomaly in the nuclear division of the early embryo of C. elegans. The latent space of VAE, on which the normal data distribution is obtained through the training, is used to characterize not only the morphological anomaly, but also the temporal anomaly of the time series data, based on the position in the latent space. The proposed method is applied to the time series of three dimensional DIC data of nuclear division process during two-cell stage of C. elegans. Wild type data is used as the normal data for the training, and then an anomaly is evaluated on an embryo, for which one of the lethal genes is silenced by RNAi. First, Morphological anomaly is quantified by the reconstruction error. Then, for the well-reconstructed data, the trajectory in the latent space corresponding to the input time series is used to characterize the time development of the division process. Anomaly score is defined based on the normal time distribution in the latent space, and the proposed method successfully obtains a list of lethal genes, which cause the temporal anomaly by the knocking down.
Modeling and Optimal Motion Planning for the Omnidirectional Mobile Robot with Three Orthogonal Wheels

Motion planning optimization in the robotics field is the series of actions that are taken to generate the optimal path so that it improves the overall performance of the robot properties or reduces the consumption of the resources where the restriction system remains maintained. In this paper, first, a new compact mathematical model for the three-wheeled omnidirectional robot is derived by using the Network Model Approach (NMA) method developed to analyze the mechanical and electrical systems of the robot. The advantage of this method is without switching from one method to another so that the same method can be used in mechanical and electrical systems. Then, the hybridized algorithm of Constrained Dynamic Inversion Based (CDIB) Method with the Simulated Annealing (SA) Algorithm (CDIB-SA) has been experienced in collision free motion planning by incorporation of the path constraints using the Voronoi diagram. Numerical tests and simulations are presented aiming to ensure the efficiency of the optimal motion planning proposed algorithm.
PROMAL: Precise Window Transition Graphs for Android via Synergy of Program Analysis and Machine Learning

Mobile apps have been an integral part in our daily life. As these apps become more complex, it is critical to provide automated analysis techniques to ensure the correctness, security, and performance of these apps. A key component for these automated analysis techniques is to create a graphical user interface (GUI) model of an app, i.e., a window transition graph (WTG), that models windows and transitions among the windows. While existing work has provided both static and dynamic analysis to build the WTG for an app, the constructed WTG misses many transitions or contains many infeasible transitions due to the coverage issues of dynamic analysis and over-approximation of the static analysis. We propose ProMal, a “tribrid” analysis that synergistically combines static analysis, dynamic analysis, and machine learning to construct a precise WTG. Specifically, ProMal first applies static analysis to build a static WTG, and then applies dynamic analysis to verify the transitions in the static WTG. For the unverified transitions, ProMal further provides machine learning techniques that leverage runtime information (i.e., screenshots, UI layouts, and text information) to predict whether they are feasible transitions. Our evaluations on 40 real-world apps demonstrate the superiority of ProMal in building WTGs over static analysis, dynamic analysis, and machine learning techniques when they are applied separately.