Miracle-dz/UniFAR-FTUs huggingface dataset:
Workload Management in Database Management System: A Taxonomy (Extended Abstract)

Workload management is the discipline of effectively monitoring, managing and controlling work flow across computing systems. In particular, workload management in database management systems (DBMSs) is the process or act of monitoring and controlling work (i.e., requests) executing on a database system in order to make efficient use of system resources in addition to achieving any performance objectives assigned to that work. In the past decade, workload management studies and practice have made considerable progress in both academia and industry. New techniques have been proposed by researchers, and new features of workload management facilities have been implemented in most commercial database products. In this paper, we provide a systematic study of workload management in today's DBMSs by developing a taxonomy of workload management techniques. We apply the taxonomy to evaluate and classify existing workload management techniques implemented in the commercial databases and available in the recent research literature. We also introduce the underlying principles of today's workload management technology for DBMSs, discuss open problems and outline some research opportunities in this research area.
Social Network Mining for Recommendation of Friends Based on Music Interests

With the rapid development of technology and software, social media have become a necessity in our daily lives as it is a way for people to keep in touch with friends and share about current events. Some of the most popular social media and social networking sites that people use include Facebook, Instagram, Snapchat, and Twitter. Finding compatible persons to be friends on social media can be a challenge as many of the people recommended to the user by social media are people who are already friends with them or have been followed. However, when users are looking for friends, the real concern is whether they have common interests or hobbies with each other and whether they often interact with one another. In this paper, we propose friend recommendation algorithms revolving around music interests and interactions in social media.
Language Modeling with Sparse Product of Sememe Experts

Most language modeling methods rely on large-scale data to statistically learn the sequential patterns of words. In this paper, we argue that words are atomic language units but not necessarily atomic semantic units. Inspired by HowNet, we use sememes, the minimum semantic units in human languages, to represent the implicit semantics behind words for language modeling, named Sememe-Driven Language Model (SDLM). More specifically, to predict the next word, SDLM first estimates the sememe distribution given textual context. Afterwards, it regards each sememe as a distinct semantic expert, and these experts jointly identify the most probable senses and the corresponding word. In this way, SDLM enables language models to work beyond word-level manipulation to fine-grained sememe-level semantics, and offers us more powerful tools to fine-tune language models and improve the interpretability as well as the robustness of language models. Experiments on language modeling and the downstream application of headline generation demonstrate the significant effectiveness of SDLM.
Mimetic vs Anchored Value Alignment in Artificial Intelligence

"Value alignment" (VA) is considered as one of the top priorities in AI research. Much of the existing research focuses on the "A" part and not the "V" part of "value alignment. " This paper corrects that neglect by emphasizing the "value" side of VA and analyzes VA from the vantage point of requirements in value theory, in particular, of avoiding the "naturalistic fallacy"--a major epistemic caveat. The paper begins by isolating two distinct forms of VA: "mimetic" and "anchored. " Then it discusses which VA approach better avoids the naturalistic fallacy. The discussion reveals stumbling blocks for VA approaches that neglect implications of the naturalistic fallacy. Such problems are more serious in mimetic VA since the mimetic process imitates human behavior that may or may not rise to the level of correct ethical behavior. Anchored VA, including hybrid VA, in contrast, holds more promise for future VA since it anchors alignment by normative concepts of intrinsic value.
Modeling Input Uncertainty in Neural Network Dependency Parsing

Recently introduced neural network parsers allow for new approaches to circumvent data sparsity issues by modeling character level information and by exploiting raw data in a semi-supervised setting. Data sparsity is especially prevailing when transferring to non-standard domains. In this setting, lexical normalization has often been used in the past to circumvent data sparsity. In this paper, we investigate whether these new neural approaches provide similar functionality as lexical normalization, or whether they are complementary. We provide experimental results which show that a separate normalization component improves performance of a neural network parser even if it has access to character level information as well as external word embeddings. Further improvements are obtained by a straightforward but novel approach in which the top-N best candidates provided by the normalization component are available to the parser.
A 2.3-MW, 950-MHz, 8-Bit Fully-Time-Based Subranging ADC Using Highly-Linear Dynamic VTC

A novel fully-time-based subranging analog-to-digital converter (ADC) is proposed. Two time-based ADCs (TB ADCs) are used for both coarse and fine ADCs, resulting in low power operation. They were pipelined to enhance the sampling frequency. Highly-linear voltage-to-time converter (HL VTC) is also proposed to ensure a wide input range for the coarse ADC. Moreover, the interpolation time-to-digital converter (TDC) with a dynamic delayer enables low-power operation. An 8-bit test chip fabricated with 65-nm CMOS technology had power dissipation of 2.3 mW at 950 MS/s. The figure of merit was 16.0 fJ/conversion step.
Application of Naive Bayes Classifier Theorem in Detecting Induction Motor Bearing Failure

The usage of the induction motor is getting attention in the critical industrial process and leads to the necessity of identifying the current state of the motor. Usually, early detection of an incipient fault is done using condition monitoring. Bearing failure is the most common fault among induction motor and vibration analysis is often used for fault detection. The research work is carried out by giving scrutiny to the recognition of the current state of the motor. The two types of holes are induced on the outer raceway of the bearing and fast Fourier transform (FFT) analysis is performed to the load current of the stator. Additionally, diagnosis is performed using Naive Bayes Classifier Theorem (NBC) without considering the rotating speed. Furthermore, the comparison is done with the Support Vector Machine (SVM) and the results are promising.
Calibration Techniques of Electrical Machines Thermal Models

Thermal analysis is a key aspect in electrical machine design and in particular for those operating with severe duty where a high-reliability thermal analysis is required. This paper presents a comprehensive analysis of techniques for calibrating generic thermal model of electrical machines. The proposed approach combines two existing experimental methods commonly used in thermal testing of electrical devices. The first method uses a short-duty transient excitation to derive the winding thermal parameters, whereas the second method uses a steady-state excitation to inform thermal parameters related with heat transfer across from the winding body into the machine periphery. Both testing methods are based on a well-defined heat source provided by a dc excitation. In contrast, the existing methods for calibrating thermal models usually combine the winding thermal data predictions and a set of experimentally derived temperatures from dc tests. Such approach however, might be inadequate if not supported by experimental data with a sufficient number of test points. This frequently leads to an under-defined calibration process, with several unknown factors, each of which has a significant impact on the reliability of temperature predictions. The main advantage of proposed approach is the combination of the short-transient and steady-state testes that allows reducing the number of free parameters and it provides a more systematic calibration process. In addition, a detailed test procedure for generic thermal model calibration is presented and discussed.
A Deep Survey on Human Activity Recognition in Video Surveillance

Recognition of the activities of human from videos has garnered numerous demands in Computer Vision This paper focuses on the literature reviews on different ways of Human Activity Recognition (HAR) in different types of videos by recognizing human activities in videos using different techniques such as, feature extraction, object segmentation, bag of word, Hidden markov model etc. Human Activity recognition is used in various applications such as Human-Computer Interaction, Human fall detection, Ambient Intelligence, Video Indexing, Content-based Video Analytics, Robotics, Visual Surveillance. The intention for this deep survey is to identify activities of humans in different environments using divergent techniques
Computation of Green’s Function of the Bounded Solutions Problem

Abstract It is well known that the equation x′⁢(t)=A⁢x⁢(t)+f⁢(t){x^{\prime}(t)=Ax(t)+f(t)}, where A is a square matrix, has a unique bounded solution x for any bounded continuous free term f, provided the coefficient A has no eigenvalues on the imaginary axis. This solution can be represented in the form x⁢(t)=∫-∞∞𝒢⁢(t-s)⁢f⁢(s)⁢𝑑s. x(t)=\int_{-\infty}^{\infty}\mathcal{G}(t-s)f(s)\,ds. The kernel 𝒢{\mathcal{G}} is called Green’s function. In this paper, for approximate calculation of 𝒢{\mathcal{G}}, the Newton interpolating polynomial of a special function gt{g_{t}} is used. An estimate of the sensitivity of the problem is given. The results of numerical experiments are presented.
The Guts Game: Towards Designing Ingestible Games

Ingestible sensors, such as capsule endoscopy and medication monitoring pills, are becoming increasingly popular in the medical domain, yet few studies have considered what experiences may be designed around ingestible sensors. We believe such sensors may create novel bodily experiences for players when it comes to digital games. To explore the potential of ingestible sensors for game designers, we designed a two-player game - the "Guts Game" - where the players play against each other by completing a variety of tasks. Each task requires the players to change their own body temperature measured by an ingestible sensor. Through a study of the Guts Game (N=14) that interviewed players about their experience, we derived four design themes: 1) Bodily Awareness, 2) Human-Computer Integration, 3) Agency, and 4) Uncomfortableness. We used the four themes to articulate a set of design strategies that designers can consider when aiming to develop engaging ingestible games.
Soft-Switching Analysis and PFM Control Method of Bidirectional DC/DC Converter Topology

Bidirectional DC/DC converters have been widely used as an interface for two-way energy flow with the development of new energy technologies. Soft-switching technology is often integrated in bidirectional converters to reduce switching loss, but analysis and design of circuit are usually complex. This paper proposes a method for soft-switching, and a closed-loop control analysis. The soft-switching conditions, the influence of parameter on the output and the design of PFM closed-loop control system are analyzed in detail. A sweep frequency analysis of the designed circuit was carried out in this study, and the PI regulator was designed according to the frequency characteristic of the system. The proposed 1000 W-closed-loop prototype realizes a bidirectional energy flow under control of the program. The prototype can realize a mutual conversion of 200 V and 400 V, with an efficiency of 96.65% in Boost mode and 97.04% in Buck mode under full load.
Systematic Review and Validation of the Game Experience Questionnaire (GEQ) - Implications for Citation and Reporting Practice

Despite lacking a formal peer-reviewed publication, the Game Experience Questionnaire (GEQ) is widely applied in games research, which might risk the proliferation of erroneous study implications. This concern motivated us to conduct a systematic literature review of 73 publications, analysing how and why the GEQ and its variants have been employed in current research. Besides inconsistent reporting of psychometric properties, we found that misleading citation practices with regards to the source, rationale and number of items reported were prevalent, which in part seem to stem from confusion over the "manuscript in preparation" status. Additionally, we present the results of a validation study (N = 633), which found no evidence for the originally postulated 7-factor structure of the GEQ. Based on these findings, we discuss the challenges inherent to the "manuscript in preparation" status and provide recommendations for authors, researchers, educators, and reviewers on how to improve reporting, citation and publication practices.
Container Live Migration for Latency Critical Industrial Applications on Edge Computing

A new level of factory automation demands processing vast amounts of data, complex orchestration of cyber-physical systems, and coordination of computation as well as communication resources in real-time. Virtualization and decentralized computation is becoming a de-facto solution for factory automation. Edge Computing (EC) is a promising approach to achieve the low latencies required by many industrial systems. It employs resource rich edge servers distributed within a factory that are placed close to end devices and assist them in executing computation intensive tasks and also in coordinating with each other. This paper discusses the requirements and challenges of EC for factory automation applications. In a distributed EC infrastructure, safe and timely operation of industrial applications require load balancing and mobility support and thus a seamless service migration between the edge servers. With the recent advances in virtualization and due to its advantages, virtual machine (VM) and container technologies are pavings its way into factory. Though containers have some distinctive advantages over VMs in EC, the service live migration has comparatively high downtime. This paper proposes a novel live migration scheme called redundancy migration that reduces the downtime by a factor of 1.8 compared to the stock migration in linux containers.
Sparse Recovery over Graph Incidence Matrices

Classical results in sparse recovery guarantee the exact reconstruction of s-sparse signals under assumptions on the dictionary that are either too strong or NP-hard to check. Moreover, such results may be pessimistic in practice since they are based on a worst-case analysis. In this paper, we consider the sparse recovery of signals defined over a graph, for which the dictionary takes the form of an incidence matrix. We derive necessary and sufficient conditions for sparse recovery, which depend on properties of the cycles of the graph that can be checked in polynomial time. We also derive support-dependent conditions for sparse recovery that depend only on the intersection of the cycles of the graph with the support of the signal. Finally, we exploit sparsity properties on the measurements and the structure of incidence matrices to propose a specialized sub-graph-based recovery algorithm that outperforms the standard $\ell_{1}$ -minimization approach.
Circuit Models for Printed Multiresonant FSSs

Frequency selective surfaces, the unit cell of which shows a multiresonant behavior, are analyzed in this work by means of an equivalent circuit approach. The derivation of the topology and the computation of thevalues of the elements in the equivalent circuits is achieved through the extension of a previous formulation developed by the authors for multiresonant discontinuities in hollow-pipe waveguides. This formulation results in the obtaining of canonical circuits formed by a series connectionof resonant LC tanks. The results provided by the circuit models for the scattering behavior of a frequency selective surface printed on a dielectric substrate are compared with those computed using a commercial electromagnetic simulator.
A High-Efficiency Power Supply from Magnetic Energy Harvesters

Magnetic energy harvesting is a promising technology for a self-powered sensor, because it rarely depends on the weather condition. In order to increase the power density, maximizing the harvested power is important. In this paper, the analysis for power harvesting according to varying primary current is prevailed, and a new design guideline of primary voltage for maximizing harvested power is suggested. This analysis is distinct in that the effect of magnetizing inductance was taken into the consideration. To confirm the validity of this paper, the experiments were prevailed with a prototype of 4 ~ 7 Arms primary current.
Identifying Locus of Control in Social Media Language

Individuals express their locus of control, or “control”, in their language when they identify whether or not they are in control of their circumstances. Although control is a core concept underlying rhetorical style, it is not clear whether control is expressed by how or by what authors write. We explore the roles of syntax and semantics in expressing users’ sense of control –i.e. being “controlled by” or “in control of” their circumstances– in a corpus of annotated Facebook posts. We present rich insights into these linguistic aspects and find that while the language signaling control is easy to identify, it is more challenging to label it is internally or externally controlled, with lexical features outperforming syntactic features at the task. Our findings could have important implications for studying self-expression in social media.
Tratamiento artroscópico del choque femoroacetabular

El choque femoroacetabular es una deformidad de reciente descripción que ocasiona un conflicto mecánico en la cadera. Este conflicto provoca una alteración estructural que causará una artrosis de la cadera. La resección de la deformidad y la reparación de las estructuras lesionadas se ha propuesto para eliminar el dolor ocasionado por dicho conflicto. Este tratamiento puede ser llevado a cabo por artroscopia. De una casuística de 45 casos, se presenta la técnica y resultados de 15 casos con seguimiento mínimo de 6 meses evaluados con test de ‘impingement’ y escalas WOMAC y Merle d’Aubigne.
Multi-Task Neural Learning Architecture for End-to-End Identification of Helpful Reviews

Helpful reviews play a pivotal role in recommending desirable goods and accelerating purchase decisions of customers in e-commercial services. Given a large proportion of product reviews with unknown helpfulness/unhelpfulness, the research on automatic identification of helpful reviews has drawn much attention in recent years. However, state-of-the-art approaches still rely heavily on extracting heuristic text features from reviews with domain-specific knowledge. In this paper, we first introduce a multi-task neural learning (MTNL) architecture for identifying helpful reviews. The end-to-end neural architecture can learn to reconstruct effective features upon the raw input of words and even characters, and the multi-task learning paradigm helps to make more accurate predictions of helpful reviews based on a secondary task which fits the star ratings of reviews. We also build two datasets containing helpful/unhelpful reviews from different product categories in Amazon, and compare the performance of MTNL with several mainstream methods on both datasets. Experimental results confirm that MTNL outperforms the state-of-the-art approaches by a significant margin.
A Comparison of Average Model, Sampled-data Model and Multi-frequency Model Based on DC/DC Converters

The widely-used state-space average model based on DC/DC converters loses its accuracy in high frequency domain, which makes it ineffective to predict the stability of some specific systems. In order to solve this problem, several modified models were provided. This paper mainly focuses on two modified models, i.e. the sampled-data model and the multi-frequency model. By comparing them with traditional average model, a unified model structure is proposed to show the key differences of these models. Through this structure, the basic reason for the frequency limitation of average model can be revealed clearly. And the accuracy and possible application sceneries of these models are discussed. Simulation results are presented as verifications.
A Smart TLVC-Based Traffic Light Scheduling for Preventing YLD-related Accidents in Smart City

The paper proposes a novel adaptive traffic light scheduling scheme based on two-way traffic-light-to-vehicle communication (TLVC). The target of the paper is to use VANETs technologies to prevent traffic accidents caused by the rapid growth in the number of vehicles worldwide. In the proposed scheme, if vehicles are detected in a yellow-light-dilemma (YLD) zone when traffic light's color is yellow, a traffic light rescheduling algorithm is executed and speed guidance messages are sent to the vehicles to prevent YLD-related accidents happening. The preliminary simulation results show that the number of traffic light violation case caused by the YLD problem is greatly reduced by approximately 63% to 84%, which consequently reduces the number of YLD-related accidents at signalized intersections.
On the Assessment of the Voltage Drop between two Lightning Protection Zones Connected by a Signal Line

the transient performance of a simple communication wire located inside metallic cable tray interconnecting a main Lightning Protection Zone (LPZ) to a second LPZ is analyzed using the electromagnetic field theory approach. The main LPZ is subjected to a lightning strike at one of its aboveground corner. The field theory approach used is essentially an extension to arbitrary frequencies of antenna theory while accounting for the complex electrical characteristics of soil. The computation of transient voltages is carried out using the MultiFields software package. The article focuses on the transient voltage drop along the metallic cable tray that connects the two different systems with the same LPZ level. Comparison are performed between a simple metallic interconnection conductor parallel to the communication wire and two other metallic cable trays. The article shows that the voltage drop along the interconnection cables can be evaluated to determine if Surge Protection Diverters (SPD) are required. Measurements and simulations are compared to validate the computation results, and compare them to the requirement of the pertinent IEC standard