Miracle-dz/UniFAR-FTUs huggingface dataset:
Branch Location Problems with Maximum Satisfiability

Constrained location problems ﬁnd a wide range of practical applications. Recent work showed that dedicated brute-force algorithms and greedy approach enable solutions of reasonable efﬁ-ciency, for a restriction of the general constrained location problem, referred to as the branch location problem . This paper extends earlier work in several ways. First, the paper develops propositional encodings for the branch location problem. Second, given that the branch location problem is a restriction of the general constraint location problem, the paper shows that the restricted problem is still hard for NP. Third, the paper devises improved propositional encodings for the branch location problem, which in practice enable not only solving exactly a signiﬁcantly larger class of problems but also effectively approximating optimal problem solutions, using state-of-the-art (complete and incomplete) Maximum Satisﬁability (MaxSAT) solvers.
Learning Temporal Action Models via Constraint Programming

We present a solver-independent Constraint Programming (CP) formulation for learning action models in temporal planning scenarios beyond PDDL2.1. Inspired by the CP approach for temporal planning, our formulation bases on a temporal plan trace and represents observations (as time-stamped states), actions, causal-link relationships, condition threats and effect interferences. This formulation is very expressive and supports a wide range of input knowledge. It also evidences the connection between the tasks of: i) action model learning, ii) plan validation, and iii) plan synthesis. Our experiments evaluate the quality of the learned models under different learning scenarios and in different planning domains.
Semantic Point Completion Network for 3D Semantic Scene Completion

Semantic scene completion (SSC) is composed of scene completion (SC) and semantic segmentation. Most of the existing methods carry out SSC in a regular 3D grid space, where 3D CNNs cause unnecessary computational cost on empty voxels. In this work, a Semantic Point Completion Network (SPCNet) is proposed to address SSC in the point cloud space. Speciﬁcally, SPCNet is an Encoder-decoder architecture, in which an Observed Point Encoder is applied to extract the features of observed points, and an Observed to Occluded Point Decoder is responsible for mapping the features to the occluded points. Based on the SPCNet, we further introduce an Image-point Fused Semantic Point Completion Network (IPF-SPCNet), which aims to boost the performance of SSC by combining the texture with geometry information. Evaluations are conducted on two public datasets. Experimental results show that our method can address the SC problem in the point cloud space. Compared to state-of-the-art approaches, our method can achieve satisfying results on the SSC task.
The Homomorphism Lattice, Unique Characterizations, and Concept Learning

Various problems arising in the context of example-driven approaches to query discovery have turned out to be intimately related to basic structural properties of the homomorphism lattice of ﬁnite structures , such as density, or the existence of duals. In this keynote, I will review some such connections, and highlight some relevant recent results
Maximum Entropy Reinforced Single Object Visual Tracking

Single object visual tracking is a fundamental problem in computer vision and has many applications. Given only the location of the target of interest in the ﬁrst video frame, a visual tracking algorithm must track the target until the end of the video while having to face challenging factors such as illumination change and scale variation. In this paper, we formulate this tracking problem in a framework of maximum entropy reinforcement learning where the agent is our visual tracker and the goal is to learn a tracking policy that maximises both the expected reward and its entropy so as to achieve a balance between exploitation and exploration. The aim of our tracking framework is to improve the tracking accuracy while giving the tracking agent the ability to avoid getting stuck on a non-target object. Extensive experiments have been performed on a range of benchmarks where our method achieves state-of-the-art performance. Furthermore, we demonstrate that, in contrast to other visual trackers based on deep reinforcement learning, our method can run in real-time while maintaining high tracking accuracy.
Joint Network Selection and Service Placement Based on Particle Swarm Optimization for Multi-Access Edge Computing

With the popularity of mobile devices such as smartphones and tablets, the improvement of service of quality is an important issue facing great challenges. The improvement of user service of quality is mainly reflected in reducing the energy consumption of mobile devices and the delay of task execution. Multi-access edge computing sinks computing and storage capabilities from the remote cloud to the edge network, which can effectively reduce the high latency caused by the transmission of tasks between the mobile device and the remote cloud and the high energy consumption of tasks performed locally. Most of the previous work was limited to service of quality optimization through dynamic service layout, while ignoring the critical impact of access network selection on network congestion. This article studies the task offloading model of multiple tasks and services with several MEC servers, and jointly optimizes the MEC’s access network selection and service placement issues. Considering the delay and energy consumption caused by task offloading and execution, this article designs an effect function on delay and energy consumption, and aims to minimize this function to solve the MEC problem. Since this problem is NP-hard, this article designs a new optimization algorithm based on particle swarm optimization to solve this problem. Extensive simulation experiments show that the proposed optimization algorithm realize better performance than other algorithms. The algorithm has achieved good results in terms of time delay and energy consumption, which effectively reduces the system cost.
DFR: Differentiable Function Rendering for Learning 3D Generation from Images

Learning‐based 3D generation is a popular research field in computer graphics. Recently, some works adapted implicit function defined by a neural network to represent 3D objects and have become the current state‐of‐the‐art. However, training the network requires precise ground truth 3D data and heavy pre‐processing, which is unrealistic. To tackle this problem, we propose the DFR, a differentiable process for rendering implicit function representation of 3D objects into 2D images. Briefly, our method is to simulate the physical imaging process by casting multiple rays through the image plane to the function space, aggregating all information along with each ray, and performing a differentiable shading according to every ray's state. Some strategies are also proposed to optimize the rendering pipeline, making it efficient both in time and memory to support training a network. With DFR, we can perform many 3D modeling tasks with only 2D supervision. We conduct several experiments for various applications. The quantitative and qualitative evaluations both demonstrate the effectiveness of our method.
GCN-ALP: Addressing Matching Collisions in Anchor Link Prediction

Nowadays online users prefer to join multiple social media for the purpose of socialized online service. The problem anchor link prediction is formalized to link user data with the common ground on user profile, content and network structure across social networks. Most of the traditional works concentrated on learning matching function with explicit or implicit features on observed user data. However, the low quality of observed user data confuses the judgment on anchor links, resulting in the matching collision problem in practice. In this paper, we explore local structure consistency and then construct a matching graph in order to circumvent matching collisions. Furthermore, we propose graph convolution networks with mini-batch strategy, efficiently solving anchor link prediction on matching graph. The experimental results on three real application scenarios show the great potentials of our proposed method in both prediction accuracy and efficiency. In addition, the visualization of learned embeddings provides us a qualitative way to understand the inference of anchor links on the matching graph.
Reducing NoC Energy Consumption Exploring Asynchronous End-to-end GALS Communication

Systems-on-Chip (SoCs) with a large number of cores adopt Networks-on-chip (NoCs) as the communication infrastructure due to its scalability. The complexity to distribute a skew-free synchronous clock signal over the entire chip increases in current fabrication technologies due to the process variability. Thus, designers may choose among fully asynchronous and Globally Asynchronous, Locally Synchronous (GALS) NoCs. This work proposes an intermediate solution. Each Intellectual Property (IP) core may have its clock domain, and the NoC supports both synchronous and asynchronous communication. The NoC has its own clock domain, with the synchronous communication used to establish end-to-end paths. Once the end-to-end path is established, the NoC connects the IPs asynchronously, as wires with repeaters. The message is transmitted using handshake protocol at the source IP frequency. The benefit of the proposal is the reduction in the communication energy consumption (up to 52%). The cost is a latency increase (16% to 30%) and area overhead (4%).
Neural Approach for Modeling and Optimizing Si-MOSFET Manufacturing

An optimal design of semiconductor device and its process uniformity are critical factors affecting desired figure-of-merits as well as reducing fabrication cost of fixing possible malfunctioning in semiconductor manufacturing. Two main tasks in optimal device design for semiconductor manufacturing, i.e., parameter optimization and modeling, have been typically used either to characterize the devices by understanding how each parameter affects the device performances or to calibrate the parameters for SPICE circuit simulation. However, there still remains limitations in describing the relationship between all manufacturing parameters and figure-of-merits using several simple equations human experts can utilize. Even with the best model currently available, the optimal design of semiconductor device heavily relies on experiences of human experts and deals with time-consuming ad-hoc trials and non-holistic approaches. In this paper, we propose a new approach for data-based accurate electrical modeling of transistor, which is the most fundamental unit device of semiconductor, and fast optimization of its manufacturing parameters. Instead of the previous analytic approaches, finding finite equations derived from semiconductor physics, we utilize machine learning technique and neural networks to find appropriate modeling functions from data pairs of parameters and figure-of-merits. And for given desired figure-of-merits, we find optimal manufacturing parameters in holistic manner by using the learned functions of neural networks and fast gradient-based optimization method. Experimental results show that our neural-network-based-model directly estimate figure-of-merits with competitive accuracy and that our holistic optimization technique accurately and rapidly adapts the manufacturing parameters to meet desired figure-of-merits.
Evaluation of Cache-Based Memory Hierarchy for HEVC Video Decoding

The intra-frame and inter-frame prediction techniques for video encoding, although not exclusively, are key factors in the architectural design of many codec specifications. Adding to the intense exploitation of the processing resources, video codecs carry a very significant energy footprint when it comes to the data flow between the main memory and the processing unit. The present work provides an evaluation of the cache and main memory hierarchy taking the perspective of the energy-related resources demanded in order to perform video decoding. This paper considers various encoding configurations, video sequences, and cache memory parameters. Our methodology uses HEVC state-of-the-art codecs as a case study and employs memory architecture and power models to properly evaluate the memory infrastructure efficiency in terms of hit ratios and energy consumption. Beyond the data reuse comparison between HEVC encoder and decoder sides (28% lower hit ratios at the decoder on average), our results demonstrate the poor effectiveness of increasing cache capacity for providing energy and performance expressive improvements, as its resources are increased, in a video decoding scenario: 5%–76% of higher memory energy consumption when the cache capacity is increased, on almost all the cases. In view of this result, the exploration of novel memory organizations seems to be advised towards the optimization of video encoding techniques.
Research on death caused by secondary bacterial infection of Cryptocaryon irritans on Pseudosciaena crocea

The research separated bacteria from livers of Pseudosciaena crocea suffering from Cryptocaryon irritans,and the result showed that bacteria couldn't be separated from Pseudosciaena crocea with relatively light disease,but could be separated with severe disease.Separated virus was tested after purifying as Vibrio vulnificus.Regression infection experiment of Vibrio vulnificus demonstrated that the morbidity and mortality were 100% and 80%,respectively.The research showed that the bacteria in Pseudosciaena crocea suffering from Cryptocaryon irritans were secondary infection.It was a nosogenetic virus causing the death of Pseudosciaena crocea.Its infection way was from water to vulnificus to bodies.
Stabilizing Effect of Iminodisuccinic Acid on Metal Ions

Iminodisuccinic acid is able to stabilize Cu 2+ and Mn 2+ in aqueous s olutions of pH=7 and 11, with effect close to EDTA. It shows therefore a promising application in stabilization of H 2O 2 so lution used in pulp bleaching.
Effect of Dynamic Folding with Varying Fold Orientations and C-rates on Flexible Power Source Capacity Degradation

The major contributor to the boost in flexible power source research is the growing need for wearable devices, fitness accessories and biomedical equipment. Flexible batteries are required to sustain repetitive mechanical stresses during motion in addition to the usual desirable features such as high capacity, fast charge capability and low susceptibility towards degradation. The investigation of cyclic deformation of batteries is limited and the reported studies are conducted for a shorter number of flex cycles and that too with manual flexing instead of a deformation setup. The purpose of this research is to understand the degradation behavior of lithium ion batteries subjected to cyclic flexing deformation along with accelerated deep charge-discharge life cycling. Furthermore, the power sources are tested for the combined effect of mechanical and electrical loads by varying the charge C-Rate. By measuring the battery current and terminal voltage, assessment of its capacity and battery state of health is conducted. Finally, the state of health of the battery is correlated to these parameters with a regression model.
Intelligent Instruction-Based IoT Framework for Smart Home Applications using Speech Recognition

Design of a smart home using Internet of Things (IoT) and Machine Learning technology has been presented in this paper. This design is primarily based on LoRaWAN protocol and the main objective of this work was to establish an IoT network that is based on integration of sensors, gateway, network server and data visualization system. More importantly, intelligent speech recognition system is designed and presented here in detail as part of this work to achieve a novel futuristic smart home system design framework with intelligent instruction-based operation mechanism. In the case of low noise, the success rate of speaker recognition is above 90% based on THCHS-30 dataset.
Increasing the EMI-resilience of Triplication-based Error Correction Codes for PAM-4 Line Coding in Harsh Electromagnetic Environments

When transmitting data within a harsh electromagnetic environment, Error Correction Codes have been used for many years. However, not all systems are able to overcome the incurred overhead by those Error Correction Codes. As a solution to the overhead, engineers opted for a Pulse Amplitude Modulation (PAM) technique to encode two bits into a single symbol. This line-coding technique, called PAM-4, reduces the overhead that is imposed by the Error Correction Codes. A trade-off for the reduced overhead is a reduced noise margin between each individual symbol. It is the goal of this paper to assess if different techniques and measures can be applied towards both the Error Correction Codes and the line coding to provide resilience against harsh electromagnetic conditions. This paper focuses on the occurrence of False Negatives: corrupted data deemed valid. By using a combination of techniques, it is possible to significantly reduce the amount of False Negatives within the Error Correction Codes.
Impedance Metrology: Bridging the LF-RF Gap

This paper describes two approaches for modeling impedances along the physical gap existing between the low and the high frequency range. For this purpose, the physics of a Haddad type resistor standard has been investigated taking into account the EM-field propagation as well as the effect of connectors. In addition, a new capacitor standard has been designed by means of physical simulations. Preliminary results show a good agreement with measured data at low and high frequency.
Graph-based Keyphrase Extraction Using Word and Document Em beddings*

With the increasing amount of text data in applications, the task of keyphrase extraction receives more attention that aims to extract concise and important information from a document. In this paper, we propose a novel graph-based keyphrase extraction method using word and document embedding vectors. Two graph construction schemes named GKE-w and GKE-p are designed in which candidate words and phrases are represented as nodes respectively. By calculating the similarity between a word/phrase and the document, each node is assigned an initial weight that reflects the preference to be a keyphrase. Then, we calculate the score of each candidate word/phrase using a semantic biased random walk strategy. Finally, the Top N scored candidate phrases are selected as the final keyphrases. Experiments on two widely used datasets show that the proposed keyphrase extraction algorithm outperforms the state-of-the-art keyphrase extraction methods in terms of precision, recall, and F1 measures.
LocoGear: Locomotion Analysis of Robotic Landing Gear for Multicopters

Nowadays, there are few unmanned aerial vehicles (UAVs) that are capable of landing on uneven surfaces or moving on the complex ground. A drone having both of these functionalities can significantly improve its performance in accomplishing difficult tasks, such as monitoring and exploring various types of terrain, searching for survivors, and delivering medical aid or repair kits in an unstructured dynamic environment, e.g., rescue operation following an earthquake. This article presents a motion analysis of the gait of an adaptive landing platform for multicopters. This landing gear has four robotic legs facilitated with torque sensors in the knee joints. Though it had been developed to provide adaptive landing on uneven surfaces, we decided to exploit the system for terrain locomotion purposes. For this purpose, we have analyzed the center of mass trajectory to evaluate the possible trajectory of the robot. Based on the result of this experimental analysis, we propose a LocoGear, a novel algorithm for locomotion of UAV equipped with the robotic landing gear. The servomotor torques were estimated using Lagrangian dynamic formulation and kinetostatic methods. We conducted experiments to verify the LocoGear approach based on feedforward control that proves the capability of landing gear to move along the desired trajectory. We achieved the robot motion along the straight line with the standard deviation of length in each step of 9.8 mm and standard deviation in yaw of 9°.
Robust and Efficient Image Alignment Method Using the Student-t Distribution

Pixel-based image alignment has always struggled to simultaneously reject outliers, avoid local minima and run quickly. There are many robust cost functions that perform well in terms of rejecting outliers, but they can yield unstable results during long image sequences as a result of their inability to adjust to changes in image content. In this paper, we propose a parameterised student-t cost function that can interpolate between two cost functions that are amongst the most widely used ones in image alignment problems, the $L2$ norm (quadratic function) and the Cauchy-Lorentzian function. We also propose a parameter estimation method that helps to find optimal parameters for the proposed cost function for a video. Experiments prove that the proposed approach can estimate the alignment variable accurately relative to the existing cost functions without demanding a higher computational cost.
Next-Generation Smart Electric Vehicles Cyber Physical System for Charging Slots Booking in Charging Stations

Owing to the increased worldwide awareness regarding pollution caused by the consumption of fossil fuels, Battery-powered vehicles are bound to take over the conventional Internal Combustion Engine. Keeping the difficulties faced by the economy of the city and the populace of adapting to an entirely grid-run charging infrastructure in mind, a framework incorporating electric vehicles to everything (EV2X) communication and Charge Slot booking based on data got from a survey conducted has been developed in this literature. The conclusions drawn from the survey develop key insights into developing statisticorating the use of LTE to support the conventional OCPP and promote user controlal models that are further explored in this context. Algorithms and strategies to implement next-generation efficient EV2X communications have been implemented and developed for the city. Further, we have established a priority order for slot booking and incorp over charge-cycles. Introducing IPMUs using an LTE connection to act as a supplement to the conventional OCPP is explored in this context. Besides that, we have built the M/M/m queuing model of EVs in the charging station and its optimisation. We have done the exhaustive evaluation of the robustness of the proposed system in a fairly large-scale network in a discrete-time event simulator. The proposed system’s results (simulation, analytical, and comparison) show the reduction of waiting time, good accuracy, and saving of charging time and costs. These performances measures improve shows the real-time applicability of the proposed system.