Miracle-dz/UniFAR-FTUs huggingface dataset:
EVALUASI WEB USABILITY PADA WEBSITE WIKI-BUDAYA BERDASARKAN NIELSEN MODEL DENGAN METODE USER TESTING DAN TEKNIK HEURISTIC EVALUATION

Website Wiki-Budaya merupakan ensiklopedia berbasis web yang khusus mengelola informasi pengetahuan budaya di Indonesia dalam bentuk artikel dan dikelola oleh Dinas Kebudayaan dan Pariwisata. Selama proses pengembangannya, software quality control belum dilakukan. Oleh karena itu, website Wiki-Budaya belum dirilis secara resmi dan saat ini berada pada tahap pengujian dalam Software Development Life Cycle. Maka, pada tahap pengujian ini akan dilakukan evaluasi untuk menerapkan software quality control pada website Wiki-Budaya. Software quality control merupakan cara untuk mengontrol penjaminan kualitas website yang dilaksanakan sebelum website dirilis. Software quality control yang akan dilakukan berfokus pada aspek web usability berdasarkan Nielsen Model, yaitu aspek kebutuhan non-fungsional yang memiliki tolak ukur paling penting untuk menentukan keberhasilan atau kegagalan website. Evaluasi web usability akan dilakukan dengan metode user testing dan akan menghasilkan penilaian statistik deskriptif yang menginterpretasikan masing-masing pengukuran faktor usability dan statistik inferensial yang akan menghasilkan uji hipotesis kerangka kerja usability. Hasil tersebut akan divalidasi dengan teknik Heuristic Evaluation yang melibatkan expert User Interface untuk memberikan judgement terhadap hasil evaluasi. Dengan melaksanakan evaluasi web usability, hasil yang diharapkan adalah 1) mengetahui sisi kekurangan website dalam aspek usability, 2) membuat rekomendasi untuk kekurangan tersebut berdasarkan hasil pengujian website sebagai feedback bagi pengembangan website untuk mengurangi bug, meningkatkan kepuasan pengguna dan produk website yang mudah digunakan.
A Review on WiMAX Multihop Relay Technology for Practical Broadband Wireless Access Network Design

This article provides insight and understanding on WiMAX multi-hop relay technology state-of-art, focusing on the most important aspects related to its system design and development. More specifically, QoS provisioning, types of relay station, relay station working schemes, relay station benefits and usage models, transparent and non-transparent relay operation modes, centralized and distributed scheduling modes and optimal number of hops are discussed. The various important aspects presented in this article, contribute an in-depth understanding of WiMAX relay and help the research community and Internet Service Providers (ISPs) towards designing practical broadband wireless access network based WiMAX relay technology.
Cache-based Side-Channel Attacks in Multi-Tenant Public Clouds and Their Countermeasures

Yinqian Zhang: Cache-based Side-Channel Attacks in Multi-Tenant Public Clouds and Their Countermeasures (Under the direction of Michael Reiter) Cloud computing is gaining traction due to the business agility, resource scalability and operational efficiency that it enables. However, the murkiness of the security assurances offered by public clouds to their tenants is one of the major impediments to enterprise and government adoption of cloud computing. This dissertation explores one of the major design flaws in modern public clouds, namely insufficient isolation among cloud tenants as evidenced by the cloud’s inability to prevent side-channel attacks between co-located tenants, in both Infrastructure-as-a-Service (IaaS) clouds and Platform-as-a-Service (PaaS) clouds. Specifically, we demonstrate that one virtual machine (VM) can successfully exfiltrate cryptographic private keys from another VM co-located on the same physical machine using a cache-based side-channel attack, which calls into question the established belief that the security isolation provided by modern virtualization technologies remains adequate under the new threat model in multi-tenant public IaaS clouds. We have also demonstrated in commercial PaaS clouds that cache-based side channels can penetrate container-based isolation by extracting sensitive information from the execution paths of the victim applications, thereby subverting their security. Finally, we devise two defensive techniques for the IaaS setting, which can be adopted by cloud tenants immediately on modern cloud platforms without extra help from cloud providers, to address side-channel threats: (1) for tenants requiring a high degree of security and physical isolation, a tool to facilitate cloud auditing of such isolation; and (2) for tenants who use multi-tenant cloud services, an operating-system-level defense to defend against cache-based side-channel threats on their own.
Modeling and control of three-phase regenerative AC-DC converters

A mathematical model of the power circuit of a three phase voltage source converter (VSC) was developed in the synchronous reference frame. The mathematical model was then used to design an algorithm to estimate position of line voltage essential for operation of regenerative three-phase PWM controlled VSC. The PI current regulator in the D axis of VSC current control was modified to obtain the angle error signal. The angle error signal drives the observer (similar in structure to PLL) which provides position or angle of line voltages. To achieve fast and reliable lock with the utility system with minimal duration of transients, initial line voltage position was estimated first. The initial line voltage position estimation was done by measuring amount of current change when zero state voltage vectors were applied at the output of the inverter for defined time intervals.
Wearable ECG signal processing for automated cardiac arrhythmia classification using CFASE‐based feature selection

Classification of electrocardiogram (ECG) signals is obligatory for the automatic diagnosis of cardiovascular disease. With the recent advancement of low‐cost wearable ECG device, it becomes more feasible to utilize ECG for cardiac arrhythmia classification in daily life. In this paper, we propose a lightweight approach to classify five types of cardiac arrhythmia, namely, normal beat (N), atrial premature contraction (A), premature ventricular contraction (V), left bundle branch block beat (L), and right bundle branch block beat (R). The combined method of frequency analysis and Shannon entropy is applied to extract appropriate statistical features. Information gain criterion is employed to select features that the results show that 10 highly effective features can obtain performance measures comparable to those obtained by using the complete features. The selected features are then fed to the input of Random Forest, K‐Nearest Neighbour, and J48 for classification. To evaluate classification performance, tenfold cross validation is used to verify the effectiveness of our method. Experimental results show that Random Forest classifier demonstrates significant performance with the highest sensitivity of 98.1%, the specificity of 99.5%, the precision of 98.1%, and the accuracy of 98.08%, outperforming other representative approaches for automated cardiac arrhythmia classification.
3D sound in the helicopter environment: localisation performance

At IRBA, we are carrying out studies into acoustic protection and enhancement of the audio performances of combat helicopter pilots. As part of this work, we present here the results of a laboratory study concerning the localisation of spatialized sound sources using head mounted equipment ( ight helmet and customized wireless ear plugs). The study involved 11 participants. Participant pointed out at the perceived position of an onboard alarm using a plastic sphere representing the proximal auditory space. Two factors were tested: silence versus helicopter type noise and individualized HRTF versus non individualized ones. Each subject ful lled all the conditions, i.e. took part in all 4 sessions. In each session 45 positions were tested randomly 3 times each. Before recording the results, a procedural training was followed by the participants concerning the pointing task and the localisation task. Following correction of front/rear inversions, the mean centroid aiming point error for all positions taken together, was of the order of 35° (standard deviation about 20°). With customized HRTF, the subjects achieved better performances both silence and noise conditions (difference of 4°).
A Modular Multilevel Converter With Self-Voltage Balancing Part I: Mathematical Proof

The modular feature of the modular multilevel converter (MMC) distinguishes itself from other topologies for medium-/high-voltage applications. However, as the count of submodules increases, the control complexity of voltage balancing for each submodule sharply increases. Conventionally, the MMC is deemed to have no inherit voltage balancing property without voltage monitoring and control. This paper mathematically proves that MMC capacitor voltage is self-balanced by nature if considering certain submodule patterns. This implies that MMC could achieve the submodule capacitor voltage balancing without any monitoring or control. The mathematical proof starts from observing the two- and three-level MMCs. Mathematically, two- and three-level MMCs are proved to be self-balanced. Then, a similar observation is extended to <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>-level MMC. And <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula>-level MMC is proven to be self-balanced mathematically. The modulation which utilizes this MMC merit and secures the self-voltage balancing for MMC will be fully explained in Part II.
A comparison of workload traces from two production parallel machines

The analysis of workload traces from real production parallel machines can aid a wide variety of parallel processing research, providing a realistic basis for experimentation in the management of resources over an entire workload. We analyze a five-month workload trace of an Intel Paragon machine supporting a production parallel workload at the San Diego Supercomputer Center (SDSC), comparing and contrasting it with a similar workload study of an Intel iPSC/860 machine at NASA Ames NAS. Our analysis of workload characteristics takes into account the job scheduling policies of the sites and focuses on characteristics such as job size distribution (job parallelism), resource usage, runtimes, submission patterns, and wait times. Despite fundamental differences in the two machines and their respective usage environments, we observe a number of interesting similarities with respect to job size distribution, system utilization, runtime distribution, and interarrival time distribution. We hope to gain insight into the potential use of workload traces for evaluating resource management policies at supercomputing sites and for providing both real-world job streams and accurate stochastic workload models for use in simulation analysis of resource management policies.
An Empirical Study on the Rewritability of the with Statement in JavaScript

As JavaScript gets more popular in web programming, a demand for better analysis of JavaScript programs becomes higher. However, many dynamic features of JavaScript make its analysis, especially static analysis, particularly difficult. One of the main dynamic features of JavaScript is the with statement, which invalidates lexical scoping by introducing a new scope at run time. To simplify the problem, many researchers leave the with statement out of their consideration and major web service companies force their web application developers to use sub-languages of JavaScript which do not include the with statement. While deprecating a potentially dangerous feature might be the easiest solution, it could be too restrictive for application development. To decide whether to include the with statement, we should better understand the actual usage patterns of the with statement. In this paper, we present the usage patterns of the with statement in real-world JavaScript applications currently used in the 98 most popular web sites. We investigate whether we can rewrite the with statements in each pattern to other statements not using with. We show that we can rewrite all the static occurrences of the with statement which does not have any dynamic code generating functions. Even though the rewriting process is not applicable to any dynamically instrumented with statements either, our result is still promising. Because all the static approaches that disallow the with statement also disallow dynamic code instrumentation, such static approaches can allow the with statement using our rewriting process: JavaScript developers use the with statement in their applications and the rewriting process desugars it away. Automating the rewriting process is under progress and we believe that it will make static analysis of JavaScript programs more feasible while imposing less restriction.
Spatial Keyword Query of Region-Of-Interest Based on the Distributed Representation of Point-Of-Interest

The tremendous advance in information technology has promoted the rapid development of location-based services (LBSs), which play an indispensable role in people’s daily lives. Compared with a traditional LBS based on Point-Of-Interest (POI), which is an isolated location point, an increasing number of demands have concentrated on Region-Of-Interest (ROI) exploration, i.e., geographic regions that contain many POIs and express rich environmental information. The intention behind the POI is to search the geographical regions related to the user’s requirements, which contain some spatial objects, such as POIs and have certain environmental characteristics. In order to achieve effective ROI exploration, we propose an ROI top-k keyword query method that considers the environmental information of the regions. Specifically, the Word2Vec model has been introduced to achieve the distributed representation of POIs and capture their environmental semantics, which are then leveraged to describe the environmental characteristic information of the candidate ROI. Given a keyword query, different query patterns are designed to measure the similarities between the query keyword and the candidate ROIs to find the k candidate ROIs that are most relevant to the query. In the verification step, an evaluation criterion has been developed to test the effectiveness of the distributed representations of POIs. Finally, after generating the POI vectors in high quality, we validated the performance of the proposed ROI top-k query on a large-scale real-life dataset where the experimental results demonstrated the effectiveness of our proposals.
A differential 2.4 GHz switched-gain CMOS LNA for 802.11b and Bluetooth

This report presents a fully differential 2.4 GHz LNA with step gain control for use in IEEE 802.11b and Bluetooth systems. The LNA was fabricated using a commercial 0.25 um 1P5M mixed signal CMOS process. Measured results show a gain of 14.7 dB, noise figure of 2.88 dB and IIP3 of -0.5 dBm. A gain of -14.2 dB and IIP3 of +19 dBm were measured in the switched low gain mode, which will provide an additional 29 dB of dynamic range to a fully integrated receiver.
CNN-Based Facial Expression Recognition from Annotated RGB-D Images for Human-Robot Interaction

Facial expression recognition has been widely used in human computer interaction (HCI) systems. Over the years, researchers have proposed different feature descriptors, implemented different classification methods, and carried out a number of experiments on various datasets for automatic facial expression recognition. However, most of them used 2D static images or 2D video sequences for the recognition task. The main limitations of 2D-based analysis are problems associated with variations in pose and illumination, which reduce the recognition accuracy. Therefore, an alternative way is to incorporate depth information acquired by 3D sensor, because it is invariant in both pose and illumination. In this paper, we present a two-stream convolutional neural network (CNN)-based facial expression recognition system and test it on our own RGB-D facial expression dataset collected by Microsoft Kinect for XBOX in unspontaneous scenarios since Kinect is an inexpensive and portable device to capture both RGB and depth information. Our fully annotated dataset includes seven expressions (i.e., neutral, sadness, disgust, fear, happiness, anger, and surprise) for 15 subjects (9 males and 6 females) aged from 20 to 25. The two individual CNNs are identical in architecture but do not share parameters. To combine the detection results produced by these two CNNs, we propose the late fusion approach. The experimental results demonstrate that the proposed two-stream network using RGB-D images is superior to that of using only RGB images or depth images.
Action-driven and prediction-driven contagions in human actions

Human actions are affected by "motor contagions"; implicit effects on an observer's actions caused by viewing other people's actions. Though these contagions are ubiquitous, affecting speech, gestures, everyday movements and sports, the mechanisms underlying them are still not fully understood. In this chapter we will review the known forms of motor contagion and show that they can be characterized into two types. First, action driven contagions, which include most previous contagion reports and which manifest as similarities between specific features (like the kinematics, outcome or goal) of an individual's action, and an action they observe. Second, prediction driven contagions, which we define here and which have only recently begun to be explored, are driven by differences between predictions of how another will act and observations of how they actually do act. These lead to implicit modifications of the motor representations in an individual but may not manifest as similarities between action features. We will explore the conceptual mechanisms that can explain these contagions and propose an extension to the previously suggested Dual Route Model of sensorimotor processing so as to explain the entire contagion spectrum. Finally, in the light of this characterization, we will discuss possible strategies to control, prevent and compensate for motor contagions, and optimize performance by athletes.
Signal averaging of ventricular late potentials using Holter recordings

Holter 24-h ECG tape recordings have been used to identify patients susceptible to ventricular arrhythmias. The authors record ventricular late potentials from an ambulatory ECG using the technique of signal averaging. To commence signal averaging, a trigger template and a time interval, relative to the fiducial point, from which to measure noise are visually selected. The trigger template is used to define the shaping filter, and the ECG data file is continuously deconvolved with the shaping filter. As beats are accepted into the signal average they are graphically displayed, and a record of individual beat noise estimates is made. The results demonstrate that with an enhanced signal-averaging technique it is technically feasible to obtain high-resolution ambulatory ECG recordings. <<ETX>>
Development of an MEG-based brain–computer interface

Brain–computer interfaces (BCI) have recently gained interest both in basic neuroscience and clinical interventions. The majority of noninvasive BCIs measure brain activity with electroencephalography (EEG). However, the real-time signal analysis and decoding of brain activity suffer from low signalto-noise ratio and poor spatial resolution of EEG. These limitations could be overcome by using magnetoencephalography (MEG) as an alternative measurement modality. The aim of this thesis is to develop an MEG-based BCI for decoding hand motor imagery, which could eventually serve as a therapeutic method for patients recovering from e.g. cerebral stroke. Here, machine learning methods for decoding motor imagery -related brain activity are validated with healthy subjects’ MEG measurements. The first part of the thesis (Study I) involves a comparison of feature extraction methods for classifying leftvs right-hand motor imagery (MI), and MI vs rest. It was found that spatial filtering and further extraction of bandpower features yield better classification accuracy than time–frequency features extracted from parietal gradiometers. Furthermore, prior spatial filtering improved the discrimination capability of time–frequency features. The training data for a BCI is typically collected in the beginning of each measurement session. However, as this can be time-consuming and exhausting for the subject, the training data from other subjects’ measurements could be used as well. In the second part of the thesis (Study II), methods for across-subject classification of MI were compared. The results showed that a classifier based on multi-task learning with a l2,1-norm regularized logistic regression was the best method for across-subject decoding for both MEG and EEG. In Study II, we also compared the decoding results of simultaneously measured EEG and MEG data, and investigated whether the MEG responses to passive hand movements could be used to train a classifier to detect MI. MEG yielded altogether slightly, but not significantly, better results than EEG. Training the classifiers with subject’s own or other subjects’ passive movements did not result in high accuracy, which indicates that passive movements should not be used for calibrating an MI-BCI. The methods presented in this thesis are suitable for a real-time MEG-based BCI. The decoding results can be used as a benchmark when developing other classifiers specifically for motor imagery -related MEG data.
Smart Roles: Inferring Professional Roles in Email Networks

Email is ubiquitous in the workplace. Naturally, machine learning models that make third-party email clients "smarter" can dramatically impact employees' productivity and efficiency. Motivated by this potential, we study the task of professional role inference from email data, which is crucial for email prioritization and contact recommendation systems. The central question we address is: Given limited data about employees, as is common in third-party email applications, can we infer where in the organizational hierarchy these employees belong based on their email behavior? Toward our goal, in this paper we study professional role inference on a unique new email dataset comprising billions of email exchanges across thousands of organizations. Taking a network approach in which nodes are employees and edges represent email communication, we propose EMBER, or EMBedding Email-based Roles, which finds email-centric embeddings of network nodes to be used in professional role inference tasks. EMBER automatically captures behavioral similarity between employees in the email network, leading to embeddings that naturally distinguish employees of different hierarchical roles. EMBER often outperforms the state-of-the-art by 2-20% in role inference accuracy and 2.5-344x in speed. We also use EMBER with our unique dataset to study how inferred professional roles compare between organizations of different sizes and sectors, gaining new insights into organizational hierarchy.
Nothing to Fear but Fear Itself? Fear of Fear, Fear of Greed and Gender Effects in Two-person Asymmetric Social Dilemmas

This article extends Simpson's (2003) research on sex differences in social dilemmas. To test the hypotheses that men defect in response to greed and women to fear, Simpson created Fear and Greed Dilemmas, but experiments using these games supported the greed hypothesis only. In this article I focus on why the fear hypothesis failed and suggest that fear was actually absent in the Fear Dilemma. To retest Simpson's hypotheses, I propose a new asymmetric game, the Fear-of-Greed Dilemma. The asymmetry is important for two reasons. First, it creates the risk of exploitation that Simpson's Fear Dilemma lacked. Second, it exposes a critical limitation in Rapoport's (1964) K-index and suggests a re-specification. Laboratory studies supported the fear hypothesis and found mediating effects of expectations about partners on sex differences in cooperation.