Reviewerly/exHarmony huggingface dataset:
Adaptive quantum state tomography with iterative particle filtering

Several Bayesian estimation-based heuristics have been developed to perform quantum state tomography (QST). Their ability to quantify uncertainties using region estimators and include a priori knowledge of the experimentalists makes this family of methods an attractive choice for QST. However, specialized techniques for pure states do not work well for mixed states and vice versa. In this paper, we present an adaptive particle filter (PF)-based QST protocol which improves the scaling of fidelity compared to nonadaptive Bayesian schemes for arbitrary multi-qubit states. This is due to the protocol’s unabating perseverance to find the states’ diagonal bases and more systematic handling of enduring problems in popular PF methods relating to the subjectivity of informative priors and the invalidity of particles produced by resamplers. Numerical examples and implementation on IBM quantum devices demonstrate improved performance for arbitrary quantum states and the application readiness of our proposed scheme.
Internal Symmetries and Linear Properties: Full-permutation Distinguishers and Improved Collisions on Gimli

$$\mathsf {Gimli}$$ is a family of cryptographic primitives (both a hash function and an AEAD scheme) that has been selected for the second round of the NIST competition for standardizing new lightweight designs. The candidate $$\mathsf {Gimli}$$ is based on the permutation $$\mathsf {Gimli}$$ , which was presented at CHES 2017. In this paper, we study the security of both the permutation and the constructions that are based on it. We exploit the slow diffusion in $$\mathsf {Gimli}$$ and its internal symmetries to build, for the first time, a distinguisher on the full permutation of complexity $$2^{64}$$ . We also provide a practical distinguisher on 23 out of the full 24 rounds of $$\mathsf {Gimli}$$ that has been implemented. Next, we give (full state) collision and semi-free start collision attacks on $$\mathsf {Gimli}$$ -Hash, reaching, respectively, up to 12 and 18 rounds. On the practical side, we compute a collision on 8-round $$\mathsf {Gimli}$$ -Hash. In the quantum setting, these attacks reach 2 more rounds. Finally, we perform the first study of linear trails in $$\mathsf {Gimli}$$ , and we find a linear distinguisher on the full permutation.
Measuring Product Similarity with Hesitant Fuzzy Set for Recommendation

The processing of a sparse matrix is a hot topic in the recommendation system. This paper applies the method of hesitant fuzzy set to study the sparse matrix processing problem. Based on the uncertain factors in the recommendation process, this paper applies hesitant fuzzy set theory to characterize the historical ratings embedded in the recommendation system and studies the data processing problem of the sparse matrix under the condition of a hesitant fuzzy set. The key is to transform the similarity problem of products in the sparse matrix into the similarity problem of two hesitant fuzzy sets by data conversion, data processing, and data complement. This paper further considers the influence of the difference of user ratings on the recommendation results and obtains a user’s recommendation list. On the one hand, the proposed method effectively solves the matrix in the recommendation system; on the other hand, it provides a feasible method for calculating similarity in the recommendation system.
Ensemble Learning With Manifold-Based Data Splitting for Noisy Label Correction

Label noise in training data can significantly degrade a model’s generalization performance for supervised learning tasks. Here we focus on the problem that noisy labels are primarily caused by mislabeled confusing samples, which tend to be concentrated near decision boundaries rather than uniformly distributed, and whose features should be equivocal. To address the problem, we propose an ensemble learning method to correct noisy labels by exploiting the local structures of feature manifolds. Different from typical ensemble strategies that increase the prediction diversity among sub-models via certain loss terms, our method trains sub-models on disjoint subsets, each being a union of randomly selected seed samples’ nearest-neighbors of the same class on the data manifold. As a result, only a limited number of sub-models will be affected by locally-concentrated noisy labels, and each sub-model can learn a coarse representation of the data manifold along with a corresponding graph. The constructed graphs are used to suggest a set of label correction candidates, and accordingly, our method determines label correction results by majority decisions. Our experiments on real-world noisy label datasets demonstrate the superiority of the proposed method over existing state-of-the-arts.
A New Optimization Approach for a Solar Tracker Based on an Inertial Measurement Unit

Improvements and applications of Inertial Measurement Unit (IMU) sensors have increased in several areas. They are generally used in equipment that measures orientation, gravitational force, and speed. Therefore, in this paper, we worked on improving the performance of IMU in an application on solar trackers of the Kalman filter. This work illustrates the design of an autonomous device with astronomical control of a photovoltaic (PV) panel, allowing the optimization of the orientation/energy gain ratio. The device is based on two concepts at the same time, the modeling of the solar trajectory adopted by an algorithm which calculates continuously the solar angles (elevation and azimuth) and the approval of these by the IMU in order to sweep away any climatic fluctuations and thus allow an almost perfect adjustment relative to the perpendicular axis of the rays. The tracking system is based on two joints controlled by an Arduino control board. Experiments have shown a better performance of the two-axis device: the net energy gains can be around 34% with an additional 1.1% when the Kalman filter is applied.
Differential Evolution Evolved RBFNN based automated recognition of Traffic Sign Images

The neural network performances depend entirely upon the quality of learning it has obtained in the past. The conventional gradient-based approach may trap in the local minima, and it may be the cause of premature convergence. To overcome this difficulty, in this paper, a differential evolution that establishes optimal learning of Radial basis function neural network (RBFNN) has been proposed where different parameters of kernel function and connection weights have been evolved simultaneously. The benefits of evolutionary learning of radial basis function have been evaluated extensively against the gradient-based learning in the classification of benchmark XOR problem. The model integrates principal component analysis (PCA); evolutionary self-adaptive Radial basis function neural network and correlation difference-based decision modules. The PCA has been applied in the pre-processing stage to retrieve the core details available at the input side of the image. A self-adaptive form of radial basis function neural network is used to develop a classifier. For making the learning process more precise and faster, the adjustment parameters considered. One-to-one mapping is used in the learning process to obtain the correlation between input and network parameters. The high recognition efficiency and a high level of decision confidence are the benefits of the proposed hybrid model. With an increase in iteration, the learning error approaches zero value for Differential evolution Adaptive radial basis function (DEARBF). Further, this method is computationally efficient and has shown robustness against visibility limitation because of weather and the aging effect of traffic signboards, proving its applicability in practical life.
Hands-On Deformation of Volumetric Anatomical Images on a Touchscreen

In this work, we propose a novel metaphor to interact with volumetric anatomical images, e.g., magnetic resonance imaging or computed tomography scans. Beyond simple visual inspection, we empower users to reach the visible anatomical elements directly with their hands, and then move and deform them through natural gestures, while respecting the mechanical behavior of the underlying anatomy. This interaction metaphor relies on novel technical methods that address three major challenges: selection of anatomical elements in volumetric images, mapping of 2D manipulation gestures to 3D transformations, and real-time deformation of the volumetric images. All components of the interaction metaphor have been designed to capture the user’s intent in an intuitive manner, solving the mapping from the 2D touchscreen to the visible elements of the 3D volume. As a result, users have the ability to interact with medical volume images much like they would do with physical anatomy, directly with their hands.
MPEG and DA-AD Resilient DCT-Based Video Watermarking Using Adaptive Frame Selection

We present a robust video watermarking scheme and report the detailed robustness of the video watermarking assessed based on standard criteria obtained from Information Hiding and its Criteria (IHC) Committee. Using discrete cosine transform domain spread-spectrum watermarking, our system achieves robustness under various non-hostile video processing techniques, including MPEG compression and digital/analog–analog/digital (DA-AD) conversion. The proposed system ensures that a 16-bit embedded sequence can be extracted through adaptive frame selection in any 15-s interval, even with a long video clip. To evaluate the performance of the proposed watermarking scheme, we conducted robustness tests under a DA-AD conversion environment, based on the MPEG-4 part 10 (H.264) codec. The experiment results obtained indicate that, in addition to being robust against non-hostile video processes, the proposed method achieves invisibility. The assessment of the developed watermarking scheme also satisfies the third edition of the IHC video watermarking evaluation criteria.
Gate Attentional Factorization Machines: An Efficient Neural Network Considering Both Accuracy and Speed

Nowadays, to deal with the increasing data of users and items and better mine the potential relationship between the data, the model used by the recommendation system has become more and more complex. In this case, how to ensure the prediction accuracy and operation speed of the recommendation system has become an urgent problem. Deep neural network is a good solution to the problem of accuracy, we can use more network layers, more advanced feature cross way to improve the utilization of data. However, when the accuracy is guaranteed, little attention is paid to the speed problem. We can only pursue better machine efficiency, and we do not pay enough attention to the speed efficiency of the model itself. Some models with advantages in speed, such as PNN, are slightly inferior in accuracy. In this paper, the Gate Attention Factorization Machine (GAFM) model based on the double factors of accuracy and speed is proposed, and the structure of gate is used to control the speed and accuracy. Extensive experiments have been conducted on data sets in various application scenarios, and the results show that the GAFM model is better than the existing factorization machines in both speed and accuracy.
Hardware Implementation Study of Particle Tracking Algorithm on FPGAs

In recent years, the technological node used to implement FPGA devices has led to very high performance in terms of computational capacity and in some applications these can be much more efficient than CPUs or other programmable devices. The clock managers and the enormous versatility of communication technology through digital transceivers place FPGAs in a prime position for many applications. For example, from real-time medical image analysis to high energy physics particle trajectory recognition, where computation time can be crucial, the benefits of using frontier FPGA capabilities are even more relevant. This paper shows an example of FPGA hardware implementation, via a firmware design, of a complex analytical algorithm: The Hough transform. This is a mathematical spatial transformation used here to facilitate on-the-fly recognition of the trajectories of ionising particles as they pass through the so-called tracker apparatus within high-energy physics detectors. This is a general study to demonstrate that this technique is not only implementable via software-based systems, but can also be exploited using consumer hardware devices. In this context the latter are known as hardware accelerators. In this article in particular, the Xilinx UltraScale+ FPGA is investigated as it belongs to one of the frontier family devices on the market. These FPGAs make it possible to reach high-speed clock frequencies at the expense of acceptable energy consumption thanks to the 14 nm technological node used by the vendor. These devices feature a huge number of gates, high-bandwidth memories, transceivers and other high-performance electronics in a single chip, enabling the design of large, complex and scalable architectures. In particular the Xilinx Alveo U250 has been investigated. A target frequency of 250 MHz and a total latency of 30 clock periods have been achieved using only the 17 ÷ 53% of LUTs, the 8 ÷ 12% of DSPs, the 1 ÷ 3% of Block Rams and a Flip Flop occupancy range of 9 ÷ 28%.
Image Network Teaching Resource Retrieval Algorithm Based on Deep Hash Algorithm

In order to improve the retrieval ability of multiview attribute coded image network teaching resources, a retrieval algorithm of image network teaching resources based on depth hash algorithm is proposed. The pixel big data detection model of the multiview attribute coding image network teaching resources is constructed, the pixel information collected by the multiview attribute coding image network teaching resources is reconstructed, the fuzzy information feature components of the multiview attribute coding image are extracted, and the edge contour distribution image is combined. The distributed fusion result of the edge contour of the view image of the network teaching resources realizes the construction of the view feature parameter set. The gray moment invariant feature analysis method is used to realize information coding, the depth hash algorithm is used to realize the retrieval of multiview attribute coded image network teaching resources, and the information recombination is realized according to the hash coding result of multiview attribute coded image network teaching resources, thus improving the fusion. The simulation results show that this method has higher precision, better retrieval precision, and higher level of resource fusion for multiview coded image network teaching resource retrieval.
Electric Vehicle Automatic Charging System Based on Vision-force Fusion

Electric vehicles are an emerging means of transportation with environmental friendliness. The automatic charging is a hot topic in this field that is full of challenges. We introduce a complete automatic charging system based on vision-force fusion, which includes perception, planning and control for robot manipulations of the system. We design the whole system in simulation and transfer it to the real world. The experimental results prove the effectiveness of our system.
Implementation of Pure Pursuit Algorithm for Nonholonomic Mobile Robot using Robot Operating System

In this study, a differential wheeled mobile robot was controlled in real time using pure pursuit algorithm (PPA). The robot was obtained in a simulation environment by using Gazebo simulator which offer the ability to accurately and efficiently simulate various robots in complex indoor/outdoor environments. This simulator was operated with robot operating system (ROS) that allows the use of Python, C++, MATLAB or various programming languages. In this paper, MATLAB/Simulink environment was used to control the robot with communication interface between MATLAB and ROS. Thus, it is possible to study more comprehensively by using multiple the features of MATLAB. The robot was traveled around a 4m x 4m area with random waypoints. The position of the robot was measured based on odometer sensor in order to determine the robot’s location. The performance of the control algorithm was analyzed using various information of the robot such as robot’s velocity, motors’ speed, the position of the robot, etc.
Digital Sex Crimes and Victim Protection - Centered on police business -

As the use of Internet-related devices became more common, a new form of sex crime called digital sex crime emerged in life. As people’s Internet access has become more active, the harmful effects of Internet access have begun to emerge in the shadow. It is a digital sex crime. According to the government’s measures, the National Police Agency set up a “digital sex crime investigation team” at the National Police Agency and the National Police Agency in March 2018 to reinforce the expertise of digital sex crime investigation and unify the investigation system. In particular, when the Webhard cartel collapsed during the police’s arrest and crackdown, those who handled sexual exploitation attempted to find a direction and found a “telegram room.” This is the “N-Bang incident,” or “Telegram sexual exploitation incident,” which shocked the entire nation in 2019. Since then, the government has expanded and operated the Special Investigation Headquarters for Digital Sexual Crimes, which investigates sexual violence, to provincial police and frontline police stations under the joint measures to eradicate digital sex crimes.<BR> But still, digital sex crime is on the rise, and the damage is very serious. In particular, victims of digital sex crimes are anxious about the second crime that the trauma caused by the trauma is severe, the damage persists, and they can be retaliated at any time. In response, the main paper examined the types and conditions of digital sex crimes that victims of digital sex crimes experience, and proposed effective measures against digital sex crimes. The contents include expanding the scope of identity confidentiality investigations, expanding the scope of punishment and developing technologies for illegal filming, unifying the investigation department for camera and other crimes, and integrating the victim protection support system.
Applying Sentiment Product Reviews and Visualization for BI Systems in Vietnamese E-Commerce Website: Focusing on Vietnamese Context

Product reviews become more important in the buying decision-making process of customers. Exploiting and analyzing customer product reviews in sentiments also become an advantage for businesses and researchers in e-commerce platforms. This study proposes a sentiment evaluation model of customer reviews by extracting objects, emotional words for emotional level analysis, using machine learning algorithms. The research object is the Vietnamese language, which has special semantic structures and characteristics. In this research model, emotional dictionaries and sets of extract rules are combined to build a data training data set based on the semantic dependency relationship between words in sentences of the given Vietnamese context. The recurrent neural network model (RNN) solves the emotional analysis issue, specifically, the long short-term memory neural network (LSTMs). This analysis model combines the vector representations of words with a continuous bag-of-words (CBOW) architecture. Our system is designed to crawl realistic data in an e-commerce website and automatically aggregate them. These data will be stored in MongoDB before processing and input into our model on the server. Then, the system can exploit the features in products reviews and classify customer reviews. These features extracted from different feedback on each shopping step and depending on the kinds of products. Finally, there is a web-app to connect to a server and visualize all the research results. Based on the research results, enterprises can follow up their customers in real-time and receive recommendations to understand their customers. From there, they can improve their services and provide sustainable consumer service.
Monotonic Takagi–Sugeno models with cubic spline membership functions

In the paper we present sufficient monotonicity conditions for zero order Takagi–Sugeno fuzzy systems with cubic spline membership functions. Those fuzzy models have some advantages comparing with commonly used membership functions. In contrast to triangular membership functions the corresponding mapping is smooth. Comparing to Gaussian membership functions the monotonicity conditions are less conservative, more intuitive and admit membership functions with different width. Finally, the derived monotonicity conditions are formulated as linear constraints on the parameters of fuzzy system that can be easily incorporated into related optimization problems solvable by efficient algorithms. Performance of the proposed fuzzy system is tested on two benchmark datasets and output prediction of a nonlinear dynamical system.
Damage Assessment of Recent Indian Earthquakes: Review of Existing Rapid Visual Screening Schemes

To comprehend the seismic vulnerability of a particular region, seismic damage assessment of a large number of buildings needs to be conducted.However, a realistic representation of structural damage can only be obtained through post-earthquake field observations and reconnaissance survey reports.Post-earthquake damage survey reports for Nepal (Gorkha) earthquake (2015) and Imphal (India) earthquake ( 2016) presented in this study are utilized to give an overall idea about the nature of seismic damage prevalent for the widespread civil engineering infrastructural developments in the particular geographical regions.Further, the study applies the existing rapid visual screening (RVS) schemes for predicting seismic vulnerability of the structures to judge the sanctity of the schemes.An extensive state-of-the-art review of the existing RVS schemes reported in the literature is presented.A comparative study exhibiting the efficacy of the existing RVS schemes is conducted on the basis of damage survey reports obtained from the Nepal and Imphal earthquakes.Finally, a modified RVS scheme is proposed here for seismic damage assessment of masonry and low-rise reinforced concrete buildings located in hilly regions of the Indian subcontinent and other developing countries.Excerpts from the study can be useful for researchers and practising engineers to perform seismic damage assessment of buildings using the proposed RVS scheme.
LCCG

In modern data centers, massive concurrent graph processing jobs are being processed on large graphs. However, existing hardware/-software solutions suffer from irregular graph traversal and intense resource contention. In this paper, we propose LCCG, a <u>L</u>ocality-<u>C</u>entric programmable accelerator that augments the many-core processor for achieving higher throughput of <u>C</u>oncurrent <u>G</u>raph processing jobs. Specifically, we develop a novel topology-aware execution approach into the accelerator design to regularize the graph traversals for multiple jobs on-the-fly according to the graph topology, which is able to fully consolidate the graph data accesses from concurrent jobs. By reusing the same graph data among more jobs and coalescing the accesses of the vertices' states for these jobs, LCCG can improve the core utilization. We conduct extensive experiments on a simulated 64-core processor. The results show that LCCG improves the throughput of the cutting-edge software system by 11.3~23.9 times with only 0.5% additional area cost. Moreover, LCCG gains the speedups of 4.7~10.3, 5.5~13.2, and 3.8~8.4 times over state-of-the-art hardware graph processing accelerators (namely, HATS, Minnow, and PHI, respectively).