Miracle-dz/UniFAR-FTUs huggingface dataset:
SifrDB: A Unified Solution for Write-Optimized Key-Value Stores in Large Datacenter

Key-value (KV) stores based on multi-stage structures are widely deployed in the cloud to ingest massive amounts of easily searchable user data. However, current KV storage systems inevitably sacrifice at least one of the performance objectives, such as write, read, space efficiency etc., for the optimization of others. To understand the root cause of and ultimately remove such performance disparities among the representative existing KV stores, we analyze their enabling mechanisms and classify them into two models of data structures facilitating KV operations, namely, the multi-stage tree (MS-tree) as represented by LevelDB, and the multi-stage forest (MS-forest) as typified by the size-tiered compaction in Cassandra. We then build a KV store on a novel split MS-forest structure, called SifrDB, that achieves the lowest write amplification across all workload patterns and minimizes space reservation for the compaction. In addition, we design a highly efficient parallel search algorithm that fully exploits the access parallelism of modern flash-based storage devices to substantially boost the read performance. Evaluation results show that under both micro and YCSB benchmarks, SifrDB outperforms its closest competitors, i.e., the popular MS-forest implementations, making it a highly desirable choice for the modern large-dataset-driven KV stores.
A Multi-agent System Approach for Management of Industrial IoT Devices in Manufacturing Processes

Industry 4.0 is the new industrial revolution that uses technologies to enable communication and interaction between devices. In this new environment, applications are designed to ensure concepts like connection, data sharing, accessibility and process automation. In a process automation context, applications must coordinate and manage the execution of different tasks performed by autonomous devices. Aiming to support the development of such applications this paper presents an architecture for the development of an approach based on Multi-agent systems (MAS), Service-Oriented Architecture and Semantic Web technologies. The architecture uses MAS organizational approach and ontologies to ensure and automate the devices management in IoT environments. A case study in a simulated industrial environment aiming the manufacturing of metallic components is presented in order to show the proposal features and feasibility.
A Generalized Audio Identification System Using Adaptive Filters

From searching music with smartphones to broadcast monitoring by radio channels, audio identification systems are being used more in recent years. Design of such systems may differ when the problem domain changes, since each environment has special conflicting constraints to consider, like required speed and robustness to signal degradations. In this paper, a widely used audio identification system originally developed by Haitsma and Kalker is analyzed from a signal processing point of view and the fingerprint (audio feature) extraction method is modified. By adding a flexible filter to the fingerprint extraction method, the original system can be tuned to work in different domains. In order to optimize the filters for each domain, Pareto optimization is used with the multi-obj ective genetic algorithm. The novel approach to make an adaptive audio identification system leads to the fact that instead of making fundamentally different systems for different domains, one general system can be developed and then modified to meet each domain-specific constraints. By using optimized filters, the average accuracy was increased to 94.28% from 69.78% which is the average accuracy of the original system.
Spatial and Structured SVM for Multilabel Image Classification

We describe a novel multilabel classification approach based on a support vector machine (SVM) for the extremely high-resolution remote sensing images. Its underlying ideas consist to: 1) exploit inter-label relationships by means of a structured SVM and 2) incorporate spatial contextual information by adding to the cost function a term that encourages spatial smoothness into the structural SVM optimization process. The resulting formulation appears as an extension of the traditional SVM learning, in which our proposed model integrates the output structure and spatial information simultaneously during the training. Numerical experiments conducted on two different UAV- and airborne-acquired sets of images show the interesting properties of the proposed model, in particular, in terms of classification accuracy.
Pain Mirrors: Neural Correlates of Observing Self or Others’ Facial Expressions of Pain

Facial expressions of pain are able to elicit empathy and adaptive behavioral responses in the observer. An influential theory posits that empathy relies on an affective mirror mechanism, according to which emotion recognition relies upon the internal simulation of motor and interoceptive states triggered by emotional stimuli. We tested this hypothesis comparing representations of self or others’ expressions of pain in nineteen young healthy female volunteers by means of functional magnetic resonance imaging (fMRI). We hypothesized that one’s own facial expressions are more likely to elicit the internal simulation of emotions, being more strictly related to self. Video-clips of the facial expressions of each volunteer receiving either painful or non-painful mechanical stimulations to their right hand dorsum were recorded and used as stimuli in a 2 × 2 (Self/Other; Pain/No-Pain) within-subject design. During each trial, a 2 s video clip was presented, displaying either the subject’s own neutral or painful facial expressions (Self No-Pain, SNP; Self Pain, SP), or the expressions of other unfamiliar volunteers (Others’ No-Pain, ONP; Others’ Pain, OP), displaying a comparable emotional intensity. Participants were asked to indicate whether each video displayed a pain expression. fMRI signals were higher while viewing Pain than No-Pain stimuli in a large bilateral array of cortical areas including middle and superior temporal, supramarginal, superior mesial and inferior frontal (IFG) gyri, anterior insula (AI), anterior cingulate (ACC), and anterior mid-cingulate (aMCC) cortex, as well as right fusiform gyrus. Bilateral activations were also detected in thalamus and basal ganglia. The Self vs. Other contrast showed signal changes in ACC and aMCC, IFG, AI, and parietal cortex. A significant interaction between Self and Pain [(SP vs. SNP) >(OP vs. ONP)] was found in a pre-defined region of aMCC known to be also active during noxious stimulation. These findings demonstrate that the observation of one’s own and others’ facial expressions share a largely common neural network, but self-related stimuli induce generally higher activations. In line with our hypothesis, selectively greater activity for self pain-related stimuli was found in aMCC, a medial-wall region critical for pain perception and recognition.
HOP-rec: high-order proximity for implicit recommendation

Recommender systems are vital ingredients for many e-commerce services. In the literature, two of the most popular approaches are based on factorization and graph-based models; the former approach captures user preferences by factorizing the observed direct interactions between users and items, and the latter extracts indirect preferences from the graphs constructed by user-item interactions. In this paper we present HOP-Rec, a unified and efficient method that incorporates the two approaches. The proposed method involves random surfing on a graph to harvest high-order information among neighborhood items for each user. Instead of factorizing a transition matrix, our method introduces a confidence weighting parameter to simulate all high-order information simultaneously, for which we maintain a sparse user-item interaction matrix and enrich the matrix for each user using random walks. Experimental results show that our approach significantly outperforms the state of the art on a range of large-scale real-world datasets.
Combinatorial Approach Toward Multiparametric Quadratic Programming Based on Characterizing Adjacent Critical Regions

Several optimization-based control design techniques can be cast in the form of parametric optimization problems. The multiparametric quadratic programming (mpQP) represents a popular class often related to the control of constrained linear systems. The complete solution to mpQP takes the form of explicit feedback functions with a piecewise affine structure, valid in polyhedral partitions of the feasible parameter space known as critical regions. The recently proposed combinatorial approach for solving mpQP has shown better efficiency than geometric approaches in finding the complete solution to problems with high dimensions of the parameter vectors. The drawback of this method, on the other hand, is that it tends to become very slow as the number of constraints increases in the problem. This paper presents an alternative method for enumerating all optimal active sets in an mpQP based on theoretical properties of adjacent critical regions and their corresponding optimal active sets. Consequently, it results in excluding a noticeable number of feasible but not optimal candidate active sets from investigation. Therefore, the number of linear programs that should be solved decreases noticeably and the algorithm becomes faster. Simulation results confirm the reliability of the suggested method in finding the complete solution to the mpQPs while decreasing the computational time compared favorably with the best alternative approaches.
Continuous Sliding Mode Control Strategy for a Class of Nonlinear Underactuated Systems

Sliding mode control (SMC), which is known to present strong robustness against various disturbances, has been extensively employed on underactuated systems, whose control problem has been a research focus in recent years. However, though received great attention, the study on SMC for underactuated systems still remains quite open since it is very challenging to construct a proper manifold which can simultaneously stabilize both actuated and unactuated system states. Motivated to promote the corresponding research, we propose an improved second-order SMC (IS-SMC) method for a class of nonlinear underactuated systems in this paper, which guarantees that the closed-loop system's equilibrium point is asymptotically stable even in the presence of unknown nondiminishing disturbances. Different from traditional SMC methods presenting with chattering problem, the control inputs of the proposed method are essentially continuous, which brings much convenience for practical applications. Moreover, for the disturbances which are second-order differentiable, an enhanced IS-SMC (EIS-SMC) method is derived to guarantee the smoothness for both the control inputs and their derivatives. Finally, the proposed approach is applied to a practical underactuated system: The overhead crane system, with sufficient simulation results provided to validate the efficiency of the proposed method.
Generating fMRI-Enriched Acoustic Vectors using a Cross-Modality Adversarial Network for Emotion Recognition

Automatic emotion recognition has long been developed by concentrating on modeling human expressive behavior. At the same time, neuro-scientific evidences have shown that the varied neuro-responses (i.e., blood oxygen level-dependent (BOLD) signals measured from the functional magnetic resonance imaging (fMRI)) is also a function on the types of emotion perceived. While past research has indicated that fusing acoustic features and fMRI improves the overall speech emotion recognition performance, obtaining fMRI data is not feasible in real world applications. In this work, we propose a cross modality adversarial network that jointly models the bi-directional generative relationship between acoustic features of speech samples and fMRI signals of human percetual responses by leveraging a parallel dataset. We encode the acoustic descriptors of a speech sample using the learned cross modality adversarial network to generate the fMRI-enriched acoustic vectors to be used in the emotion classifier. The generated fMRI-enriched acoustic vector is evaluated not only in the parallel dataset but also in an additional dataset without fMRI scanning. Our proposed framework significantly outperform using acoustic features only in a four-class emotion recognition task for both datasets, and the use of cyclic loss in learning the bi-directional mapping is also demonstrated to be crucial in achieving improved recognition rates.
New Charge Pumping Current Model Assuming Exponential Tails in the Trap Energy Distribution

In this letter, a new charge pumping current expression is derived by assuming an exponential distribution of traps. This modified expression leads to a different method of extracting the trap emission time constant. This approach is applied on FinFET transistors with high-<inline-formula> <tex-math notation="LaTeX">$\kappa $ </tex-math></inline-formula> replacement metal gate stack to evaluate experimentally the impact of this correction compared to the original expressions from the previous works. It is found that the difference with customary methods is small, although it may potentially distort the temperature dependence of the trap emission rate.
A Semantically Aware Explainable Recommender System using Asymmetric Matrix Factorization

: Matrix factorization is an accurate collaborative ﬁltering method for predicting user preferences. However, it is a black box system that lacks transparency, providing little information about both users and items in comparison with white box systems. White box systems can easily generate explanations, relying on the rich information foundation that these systems exploit in an explicit manner. However, the accuracy of recommendations is generally low. In this work, we take advantage of the Semantic Web in the process of building a black box model which can make recommendations that can be explained. Our experiments show that our proposed method succeeds in producing lower error rates and in explaining its outputs.
Deadline-aware Memory Scheduler and Governor for Heterogeneous Processors

Mobile systems are usually equipped with heterogeneous processors and memories to trade off power and performance. The energy-efficient scheduling of such system is difficulty from two folders: tasks executed on such system with the end-to-end deadline and with varied power consumption from the heterogeneous component. A deadline-aware scheduler is proposed to deal with the performance degradation from memory bandwidth contention. A DVFS governor to manage processors and memory, and the run-time memory overclocking reclaiming are presented to minimize the energy consumption while maintaining the performance. Evaluation results show considerable schedulability and energy conservation using this framework.
Privacy Improvement Architecture for IoT

The Internet of Things (IoT) is inspired by network interconnectedness of humans, objects, and cloud services to facilitate new use cases and new business models across multiple enterprise domains including healthcare. This creates the need for continuous data streaming in IoT architectures which are mainly designed following the broadcast model. As more devices communicate with each other via the Internet, it will be crucial to determine the origins of requests and responses. Being able to store records related to the life cycle of requests and responses in an immutable form will provide documentary evidence that will help to establish transparency and accountability within the IoT. Previous works employed provenance techniques to address this problem but focus on the request perspective. However, little or nothing has been done regarding the response perspective. Consequently, this paper proposes and develops a blockchain-based provenance system to trace bi-directionally the sources of requests and responses in the IoT. This is achieved through the investigation of historical communication records. Several empirical evaluations are conducted in a real-world IoT ecosystem to prove the superiority of the proposed work.
Sentiment Analysis Approaches based on Granularity Levels

The evolution of web 2.0 has enabled the emergence of social media where users can post, share and discuss their opinions about products, events, peoples and organizations. This increase of the user generated content (UGC) has allowed the publication of several works during the last decade in the scientific community working on sentiment analysis. Sentiment analysis, also known as opinion mining is the field of extraction and analysis of opinions, feelings and attitudes of users on the web. In this paper, we provide an overview of the field of sentiment analysis by discussing the workflow of mining opinions in different granularity levels and covering common and recent approaches and techniques used to solve tasks related to sentiment analysis process at
Gamma‐aminobutyric acid transaminase genetic polymorphism is a candidate locus for responsiveness to opioid analgesics in patients with cancer pain: An exploratory study

Cancer pain impairs not only physical functions but also social functions and roles. Consequently, the overall health‐related quality of life of patients with cancer pain deteriorates. Opioid analgesics are recommended for treating moderate to strong cancer pain. Advances in human genome research have fueled a growing interest to understand individual differences in responsiveness to opioid analgesics. This study aimed to explore and identify novel loci for genes predisposing an individual to opioid analgesic responsiveness.
Learning Convolutional Neural Network Using Data from Other Domains in case of Insufficient Data

In this paper, we describe a training methodology of convolutional neural networks(CNNs) using data from a different domain when the number of training data in the test domain is small. Training a CNN for classification without enough data might lead to serious problems of overfitting and thus fail to generalize. In this case, if large data of the same object categories is available in another domain, this problem can be alleviated. We propose a method to train a CNN with small data in the test domain and large data in another. Since training a single network using data from different domains could lead to performance degradation, we consider this problem as cross-domain image similarity learning. In our experiment, we train a Siamese network to compute similarity between a pair of images from different domains, which are natural photos and 3D model projections. We design the network to output the probability that the input image pair belongs to the same category. Thus, the network can calculate similarity between the input pair and also classify a natural photo by comparing it with each images in the 3D model database. Since the network output represents similarity, we can greatly reduce testing time for classification compared to other methods (such as NN classification) in which distances between feature vectors must be calculated for every pair of images.
Factors to Evaluate Capability of Map for Vehicle Localization

Recently, autonomous vehicle’s self-localization based on the matching of laser scanner data to the high definition (HD) map become more popular due to the availability of HD map and price down of light detection and ranging technologies. Many types of research have been done to achieve locally and globally accurate HD map for accurate localization. However, the global accuracy of the map does not guarantee accurate self-localization within the map. To achieve accurate self-localization, the map should satisfy some requirements. In this paper, the focus is made on the map, as one of the high potential sources of error in localization. By investigating the erroneous scenarios in the map and comparing their characteristics, we introduced four criteria for the self-localization ability of the map. These criteria are feature sufficiency, layout, local similarity, and representation quality of the map. Then, in order to quantify these criteria, we introduce several factors for each criterion. Unlike evaluation criteria which are defined regardless of the map formats, factors are defined based on normal distribution map which is a map format of normal distribution transformation scan-matching. These factors are calculated for each position in the map, based on the map features within its local vicinity. By conducting the experiments in Shinjuku, Japan, we have evaluated these factors in a different part of the map with different scenarios by comparing them with the self-localization error.
CaTDet: Cascaded Tracked Detector for Efficient Object Detection from Video

Detecting objects in a video is a compute-intensive task. In this paper we propose CaTDet, a system to speedup object detection by leveraging the temporal correlation in video. CaTDet consists of two DNN models that form a cascaded detector, and an additional tracker to predict regions of interests based on historic detections. We also propose a new metric, mean Delay(mD), which is designed for latency-critical video applications. Experiments on the KITTI dataset show that CaTDet reduces operation count by 5.1-8.7x with the same mean Average Precision(mAP) as the single-model Faster R-CNN detector and incurs additional delay of 0.3 frame. On CityPersons dataset, CaTDet achieves 13.0x reduction in operations with 0.8% mAP loss.
Comparison of Different Multicast Approaches in Elastic Optical Networks

Internet traffic is experiencing an enormous growth and increased demand for new services. To enhance flexibility and improved spectral efficiency of traditional WDM optical networks that use ITU-T fixed frequency grid, Elastic Optical Networks (EONs) have been proposed. Moreover, multicast services, including live Internet video delivery and inter-datacenter database synchronization, gain popularity, and they usually generate large volume of traffic. To address the growth of multicast services, we focus here on routing and spectrum allocation for the multicast traffic in EONs considering distance-adaptive transmission. For the accommodation of multicast traffic, existing studies focus on using light-tree and lightpath technologies while the light-trail technology was not considered for EONs. In this paper, we compare the effectiveness, in term of spectrum and transmitter usage, among light-trail, lightpath and the light-tree in multicast accommodation. We also evaluate, in EONs, the benefit of having distance-adaptive transmission for these approaches. Numerical results illustrate their performances for a range of cases.