Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Objective Optimization of Freeway Network Traffic Flow Using Particle Swarm Optimization

A multi-objective optimization problem of ramp metering and dynamic route guidance is presented. The problem domain, a freeway integration control application considers the efficiency and equity of system, is formulated as a multi-objective optimization problem. The Gini coefficient is adopted in this study as an indicator of equity. The control strategy’s effect is demonstrated through its application to the simple freeway network. Analyses of simulation results using this approach show the equity of the system have a significant improvement over traditional control, especially for the case of large traffic demand. Using the multi-objective optimization approach, the Gini coefficient of the network has been reduced by 55% compared to traditional method.
Image and Encoded Text Fusion for Deep Multi-Modal Clustering

Deep multimodal clustering is challenging since it needs to learn appropriate features for different modalities and find correct clusters by using consistency among the modalities such as textual and visual domains. In this paper, we proposed a fused deep multimodal clustering (FDMMC) approach that fuses image and associated text description into unified information enriched image. To cluster feature representations of resulting images in complex space, the data was compressed into a lower-dimensional by the convolutional autoencoders. Then, iteratively minimizes the Kullback-Leibler divergence between the distribution of the encoded data and the target distribution. To prevent feature space from being distorted by the clustering loss, we keep the decoders remained to preserve the data structure. By integrating the clustering and autoencoder's reconstruction loss, FDMMC can simultaneously optimize cluster labels assignment and features refinement. The experimental evaluations on Coco-cross datasets illustrated the proposed method has better performance and the effectiveness of our algorithm in the image-text pairs clustering task.
Worker flexibility in a parallel dual resource constrained job shop

In this paper we investigate cross-training policies in a dual resource constraint (DRC) parallel job shop where new part types are frequently introduced into the system. Each new part type introduction induces the need for workers to go through a learning curve. A cross-training policy relates to the level of multi-functionality, the pattern of skill overlaps, and the distribution of skills among workers. Our results show that the frequency of new part type introduction needs to be considered when selecting a cross-training policy. The frequency of new part type introduction has impact on the optimal level of cross-training, the relative advantage of skill overlaps, and the extent to which a cross-training policy should incorporate differences between workers. These findings are important for many DRC job shops, especially for manufacturing cells in which all changes, associated with a part type introduction, are concentrated in one cell.
Multi-prototype Morpheme Embedding for Text Classification

Representing a word into a continuous space, also known as a word vector, has been successful in various NLP tasks. The word-based embedding has two problems; one is the out-of-vocabulary problem and the other is does not take into account the context of a word, which is homonymy, and polysemy since word vector is represented as a single vector. To against out-of-vocabulary problem, the previous researches handle it as splitting smaller unit than word unit, in particular, mainly morphologically rich language is decomposed into morpheme unit as Korean. However, morpheme embedding has also a problem that doesn't take into account multiple senses of a morpheme although the morpheme mitigates the out-of-vocabulary problem. Therefore, we propose the Korean multi-prototype morpheme embedding method representing multiple senses of a morpheme. Connecting morphemes and POS vectors to handle multi-prototype morpheme. In the experiment, we found that our multi-prototype morpheme embedding makes morpheme in a similar context closer in the vector space than the previous morpheme embedding. Our method outperforms the previous morpheme embedding as well as a baseline.
Empirical Study on SAT-Encodings of the At-Most-One Constraint

Propositional satisfiability solving (SAT) has been one of the most successful automated reasoning methods in the last decade in computer science by solving a wide range of both industrial and academic problems. One of the most widely used constraint during the process of translating a practical problem into a SAT instance is the at-most-one (AMO) constraint. Many studies on SAT encodings of the AMO constraint have been reported in the literature, however thorough comparisons of these encodings are largely missing. This paper not only provide such comparisons, but also discusses the similarity of the SAT encodings of the AMO constraint and SAT encodings of more general CSPs. More specifically, the paper empirically evaluates the most well-known AMO encodings on three state-of-the-art SAT solvers of a number of benchmarks and provides some guidelines for efficient SAT encodings of the AMO constraint leading.
Counterfeiting Congestion Control Algorithms

Congestion Control Algorithms (CCAs) impact numerous desirable Internet properties such as performance, stability, and fairness. Hence, the networking community invests substantial effort into studying whether new algorithms are safe for wide-scale deployment. However, operators today are continuously innovating and some deployed CCAs are unpublished - either because the CCA is in beta or because it is considered proprietary. How can the networking community evaluate these new CCAs when their inner workings are unknown? In this paper, we propose 'counterfeit congestion control algorithms' - reverse-engineered implementations derived using program synthesis based on observations of the original implementation. Using the counterfeit (synthesized) CCA implementation, researchers can then evaluate the CCA using controlled empirical testbeds or mathematical analysis, even without access to the original implementation. Our initial prototype, 'Mister 880,' can synthesize several basic CCAs including a simplified Reno using only a few traces.
Complex information processing networks of EEG oscillations during voluntary movements

A complex physical pattern of connections has been widely observed in many real-world systems, including social, biological, and technological networks. This ubiquitous characteristic of complex systems has also been observed in functional/structural networks of the human brain. In this study, we examined complex network topology of EEG-based functional connections while subjects performed voluntary movements. The complex system of the brain networks revealed an ideal balance between efficient information processing of local specialization and global integration, as reflected by small-world index across a wide range of EEG frequency-bands, i.e., theta, alpha, beta, and gamma oscillations. Further, directing subjective experience towards inner processes to voluntary movement altered the information processing of the brain networks wired by gamma oscillations. Brain functional networks demonstrate distinct small-world property depending on the strategy initiating voluntary movements while preserving the ubiquitous network characteristics found in many complex systems. Estimation of this alteration could prove helpful for understanding the brain mechanism of voluntary movements as well as for the evaluation of the efficiency of brain network.
Understanding Hierarchy in Contemporary Work

The chapter aims to bring out the dynamic nature or hierarchy in organizations and presents a conceptual framework for making sense of hierarchy in contemporary work. We describe hierarchy as the result of a contradictory dynamic that incorporates both vertical and horizontal practices of organizing. The vertical practice, verticalization, draws on and reproduces the formal organization, whereas the horizontal practice, horizontalization, orders people on the basis of their knowledge and initiatives. The dynamic between these two practices varies, we argue, depending on the social and epistemic distance of formal managers' from the operative work process. Three different dynamics between verticalization and horizontalization – loose coupling, translation, and integration – are identified and illustrated, drawing on three ethnographically inspired studies of knowledge work. Through these three dynamics, the chapter casts light on and provides nuances to the current discussion in the literature on postbureaucracy.
SNAP:Successor Entropy based Incremental Subgoal Discovery for Adaptive Navigation

Reinforcement learning (RL) has demonstrated great success in solving navigation tasks but often fails when learning complex environmental structures. One open challenge is to incorporate low-level generalizable skills with human-like adaptive path-planning in an RL framework. Motivated by neural findings in animal navigation, we propose a Successor eNtropy-based Adaptive Path-planning (SNAP) that combines a low-level goal-conditioned policy with the flexibility of a classical high-level planner. SNAP decomposes distant goal-reaching tasks into multiple nearby goal-reaching sub-tasks using a topological graph. To construct this graph, we propose an incremental subgoal discovery method that leverages the highest-entropy states in the learned Successor Representation. The Successor Representation encodes the likelihood of being in a future state given the current state and capture the relational structure of states based on a policy. Our main contributions lie in discovering subgoal states that efficiently abstract the state-space and proposing a low-level goal-conditioned controller for local navigation. Since the basic low-level skill is learned independent of state representation, our model easily generalizes to novel environments without intensive relearning. We provide empirical evidence that the proposed method enables agents to perform long-horizon sparse reward tasks quickly, take detours during barrier tasks, and exploit shortcuts that did not exist during training. Our experiments further show that the proposed method outperforms the existing goal-conditioned RL algorithms in successfully reaching distant-goal tasks and policy learning. To evaluate human-like adaptive path-planning, we also compare our optimal agent with human data and found that, on average, the agent was able to find a shorter path than the human participants.
Don't You Worry 'Bout a Packet: Unified Programming for In-Network Computing

In-network computing is gaining momentum as programmable switches are increasingly employed for compute acceleration. Designed for packet processing, data plane programming languages force developers to express compute in networking terms, resulting in a complex, error-prone practice. We envision the unification of switch and host programming and propose the Net Compute Language (NCL), a C/C++ extension for expressing computational kernels for switches to execute. NCL implements Compute Centric Communication (C3), our proposed programming model for INC under which, point-to-point primitives are augmented to carry out computations. We motivate our approach with real-world use cases and discuss the technical challenges for its realization.
Immunizing Systems from Distant Failures by Limiting Lamport Exposure

Failures far away from a user should intuitively be less likely to affect that user. Today's ecosystem miserably fails this test, however, despite high-availability best practices. Correlated and cascading failures - triggered by misconfigurations, bugs, and network partitions - often invalidate assumptions of failure independence. We propose that distributed services need not and should not expose local activities to distant failures or partitions, no matter how severe. Limix is an exposure-limiting architecture, guaranteeing that neither the availability nor the performance of strongly-consistent accesses within a local area may be impacted by distant failures. Preliminary results suggest that infrastructures today could use Limix to limit exposure at a manageable cost.
Smartphone-based Indoor Navigation System Using Particle Filter and Map-Constraints

A modern smartphone is equipped with various types of sensors and modules such as accelerometer, magnetometer, gyroscope, Wi-Fi, Bluetooth low energy (BLE), etc. These sensors and modules can be utilized for a better localization estimation. The Particle filter (PF) has been widely accepted as a multisensory data fusion tool in indoor navigation. In this paper, we deployed PF by restricting the particle propagation with map-constraints and updating the weight of particle with the BLE beacon proximity. The proposed technique is experimentally accomplished on a smartphone with the real field deployment of BLE beacons. The results demonstrated that our system achieved a promising mean accuracy of 1.87 m in the testbed. Further, we represent the advantage of the map-constraints based PF over a typical PF for multisensory data fusion.
Locally Linear Embedding based Indoor Localization in Internet of Things

With the upcoming of smart cities, numerous indoor localization applications plays a significant role. In outdoor, a Global Positioning System (GPS) is majorly used as it’s easy to deploy and provides high accuracy. However, in indoor localization accuracy becomes a challenge due to poor signal strength. This invokes the necessity for a mechanism to get precise node location. In this paper, a multilateration and Locally Linear Embedding (LLE) based localization approach is proposed. In the proposed mechanism, distance-RSSI characterization is done at the initial stage to obtain distances between each pair of sensor nodes. The multilateration method is used to obtain the course grain location of sensor nodes. Finally, LLE is applied to refine locations. Simulation results show that the proposed mechanism is robust and accurately localizes sensor nodes as compared to existing algorithms in an indoor environment.
Estimated Time of Arrival Prediction via Modeling the Spatial-Temporal Interactions between Links and Crosses

The ACM SIGSPATIAL GIS CUP 2021 focuses on Estimated Time of Arrival (ETA) prediction, which is important to the travel scheduling and decision-making of ride-hailing platforms. Accurate ETA prediction is very challenging since ETA is affected by many heterogeneous influencing factors, including static features (e.g., number of links) and dynamic features (e.g., real-time road conditions). Meanwhile, ETA can also be affected by complex spatial-temporal dependencies between links and crosses in the route. To tackle the above challenges, we propose a deep learning method based on the Wide-Deep-Recurrent (WDR) architecture while modeling the interactions between links and crosses. We adopt Neural Factorization Machines (NFM) to memorize the historical patterns and a multiple layer perceptron (MLP) to integrate various heterogeneous influencing factors. We also model links and crosses jointly to learn their spatial-temporal dependencies in the route. Extensive experiments conducted on a real dataset show that our method achieves a high prediction accuracy. The source code is available at: https://github.com/wanhuaiyu/WDR-LC.
Coupled Monte Carlo simulation of Si and SiO/sub 2/ transport in MOS capacitors

We present a Monte Carlo (MC) model comprising SiO/sub 2/ and Si transport, suitable to simulate carrier multiplication in MOS structures. The code extends full-band density of states (DoS) and scattering rate calculations in silicon up to high energy. Simulations of 5-15 nm oxides for nonvolatile memory applications demonstrate the role of oxide transport on the distribution of the holes generated by impact ionization, which are often regarded as the origin of oxide degradation, SILC and breakdown.
Subword-based Sentence Representation Model for Sentiment Classification

While most embedding methods in the Korean language focus on morpheme unit to alleviate the out of vocabulary problem, recent researches in the English use the subword unit for embedding. Considering that a word is composed of subwords, which have a partial role in a word, we hypothesize that a sequence of subwords enriches the meaning of a sentence than a sequence of words or morphemes. We propose a sentence embedding method based on a sequence of subwords in the Korean language. We evaluate the effectiveness of our sentence embedding method on binary sentiment classification using Naver Sentiment Movie Corpus. By comparing the performance of sentence embedding based on a sequence of words, morphemes, and subwords, we verify that sentence embedding based on a sequence of subwords is more robust to the out of vocabulary problem than the others.
A Novel Threshold Secret Sharing Scheme for CP-ABE: A secret sharing approach for CP-ABE

Secret sharing is an integral part of CP-ABE implementation. Currently, Linear Secret Sharing Scheme (LSSS) and Lagrange interpolation are used in existing CP-ABE implementation. Implementation is complex and computational cost is high for generating the access tree structure using these schemes. In this paper, a novel construction of CP-ABE has been developed which implements counting based secret share generation and reconstruction method to generate access tree structure and reconstruct secret shares. The proposed development reduces the cost of access structure generation and reconstruction of secret and improves security by generating the large number of secret shares for small-sized secret. Hence, the new construction enhances the efficiency of encryption and decryption operations of CP-ABE.
MonoFi: Efficient Indoor Localization based on Single Radio Source And Minimal Fingerprinting

Indoor localization is a key component of pervasive and mobile computing. Due to the widespread use of WiFi technology, WiFi fingerprinting is one of the most widely utilized approaches for indoor localization. Despite advancements in WiFi-based positioning approaches, existing solutions necessitate a dense deployment of access points, time-consuming manual fingerprinting, and/or special hardware. In this paper, we propose MonoFi, a novel WiFi-based indoor localization system relying only on the received signal strength from a single access point. To compensate for the low amount of information available for learning, the system trains a recurrent neural network with sequences of signal measurements. MonoFi incorporates different modules to reduce the data collection overhead, boost the scalability and improves the deep model's generalization. The proposed system is deployed and assessed in comparison to existing WiFi indoor localization systems. Our experiments with different mobile phones show that the system can achieve a median localization error of 0.80 meters, surpassing the state-of-the-art results by at least 140%.
STAR: A Cache-based Distributed Warehouse System for Spatial Data Streams

The proliferation of mobile phones and location-based services has given rise to an explosive growth in spatial data. In order to enable spatial data analytics, spatial data needs to be streamed into a data stream warehouse system that can provide real-time analytical results over the most recent and historical spatial data in the warehouse. Existing data stream warehouse systems are not tailored for spatial data. In this paper, we introduce the STAR (Spatial Data Stream Warehouse) system. STAR is a distributed in-memory data stream warehouse system that provides low-latency and up-to-date analytical results over a fast-arriving spatial data stream. STAR supports queries that are composed of aggregate functions and ad hoc query constraints over spatial, textual, and temporal data attributes. STAR implements a cache-based mechanism to facilitate the processing of queries that collectively utilizes the techniques of query-based caching (i.e., view materialization) and object-based caching. Extensive experiments over real data sets demonstrate the superior performance of STAR over existing systems.
Simulating Human Mobility with Agent-based Modeling and Particle Filter Following Mobile Spatial Statistics

Human mobility datasets collected from various sources are indispensable for analyzing, predicting, and solving emerging urbanization and population issues. However, such datasets are only available to the public after aggregation and anonymous processing. In recent years, agent-based modeling approaches have addressed this problem by reproducing synthetic human mobility data through simulation. However, the development of such agent models typically requires a large amount of personal location histories as training data for parameter learning, leading to cost and privacy concerns. To overcome this disadvantage, we attempted to explore optimal parameters using a particle filter to alleviate the strict requirement of the data. We tested our method in a local city in Japan using aggregated real-time observation data collected from mobile phone service companies. The results show that the proposed model can achieve satisfactory accuracy using low-resolution data and can therefore be easily used by local governments for municipal applications.
Performance Simulation Tools for MAC Protocols in the Internet of Things

The success of any newly designed protocol lies on the fact of the accurate and efficient use of simulation tools. The real testing of proposed algorithms and models are expensive and time consuming. In this scenario, simulation tools play a vital role to measure the effectiveness and correctness of any new models or schemes. On the other hand, the Internet of things (IoT) is one of the emerging technologies around the world. IoT is a technological ecosystem where devices are connected to each other and can exchange information without any human intervention. An IoT system generally suffers from two major problems: device resource restriction and protocol design. To handle these problems, a robust and resilient medium access control (MAC) protocol is crucial for numerous machine-type devices to concurrently access the channel in IoT. A MAC protocol affects not only system performance but also energy efficiency. In this paper, various performance evaluation tools, which are used for evaluating MAC protocols for IoT, are discussed and compared with each other in terms of major features, characteristics, pros, and cons.
JUST-Traj: A Distributed and Holistic Trajectory Data Management System

With the rapid development of the Internet of Things (IoT), massive trajectories have been generated. Trajectory data is beneficial for many urban applications. This demo presents a holistic trajectory data management system based on distributed platforms, such as Spark and HBase, namely JUST-Traj. It provides a variety of indexes to efficiently support spatio-temporal queries and analyses on massive trajectories. Additionally, it provides a convenient SQL engine to execute all operations (storage, queries, analyses) through a SQL-like statement. Finally, we design a web portal for developers and demonstrate different operations in the portal.