Miracle-dz/UniFAR-FTUs huggingface dataset:
ACCF: Learning Attentional Conformity for Collaborative Filtering

In recent years, Collaborative Filtering (CF) methods have yielded immense success on recommender systems. They mainly use the similarity between users and items, or the interactions between users and items to predict the unknown ratings. However, the social conformity phenomenon received little notice, which means: 1) individuals in a social network can have multiple characteristics and hence tend to belong to multiple overlapping groups or communities and 2) when confronted with conformity pressure, people often adjust their responses to conform to others’ opinions to obtain social approval and belonging in the community. In this paper, we propose a new collaborative filtering-based recommendation framework, called ACCF, which explicitly exploits social conformity of users. We incorporate such social conformity phenomenon into the latent factor model, using a weighted average of community preference profiles as the adjusting factor, and learn the weight of each community’s influence through an attention network. Compared with the seven state-of-the-art methods on three real-world datasets, our method achieves the best performance.
Attention and Lexicon Regularized LSTM for Aspect-based Sentiment Analysis

Abstract Attention based deep learning systems have been demonstrated to be the state of the art approach for aspect-level sentiment analysis, however, end-to-end deep neural networks lack flexibility as one can not easily adjust the network to fix an obvious problem, especially when more training data is not available: e.g. when it always predicts positive when seeing the word disappointed. Meanwhile, it is less stressed that attention mechanism is likely to “over-focus” on particular parts of a sentence, while ignoring positions which provide key information for judging the polarity. In this paper, we describe a simple yet effective approach to leverage lexicon information so that the model becomes more flexible and robust. We also explore the effect of regularizing attention vectors to allow the network to have a broader “focus” on different parts of the sentence. The experimental results demonstrate the effectiveness of our approach.
Post-Earthquake Performance Assessment and Decision-Making for Tall Buildings: Integrating Statistical Modeling, Machine Learning, Stochastic Simulation and Optimization

Author(s): Zhang, Yu | Advisor(s): Burton, Henry V | Abstract: With the embrace of the seismic resilience concept as a measure of the ability of constructed facilities and communities to contain the effects of an earthquake and achieve a timely recovery, the critical role of tall buildings in supporting community functionality, has been brought to the forefront. This study presents a series of frameworks for performing post-earthquake assessment and optimal decision-making for tall buildings. A machine learning framework to assess structural safety is first proposed and applied to a low-rise frame building. A similar methodology is then adapted for tall buildings, while incorporating robust techniques to deal with the high-dimension feature space that arises because of the large number of structural components. Seismic risk assessment is then carried out for the tall building by comparing the time-dependent probability of exceeding various response demand limits over a pre-defined period considering both mainshock-only and mainshock-aftershock hazard. The risk-based consistency of the limit state acceptance criteria for engineering demand parameters is also examined. Finally, a methodology to support optimal decision-making following the mainshock is developed with the goal of minimizing expected financial losses and, at the same time, ensuring life safety. The proposed prediction model, risk assessment methodology and optimal decision-making strategy can provide critical insights into the seismic performance of mainshock-damaged tall buildings and inform the post-earthquake actions of occupants, structural engineers, insurance companies and policymakers.
WiMAX structured combat network – performance analysis

The elements of a battlefield nowadays are the unexhausted source of information. Modern battle environment requires extremely mobile armed systems what leads to the fact that network infrastructure needs to be more agile. The information exchange in these conditions is done mostly using the radio. The node mobility is imperative and is the beginning of all the beginnings when the network analysis is ought to be performed. Movement of the nodes is rarely measured below 60 km/h and required bit rate is mostly always above 1 Mb/s. To achieve this, the migration to new technologies, like the one described by IEEE 802.16e standard is required. The paper gives the battle operation information exchange analysis which takes place into the flat, intercepted urban area. The information exchange is done using the cell structured network consisted of WiMAX base stations. The transceiver system described by ETSI TS 102 177 V1.5. 1 (2010-05) is analyzed in detail thanks to MATLAB program package. The real battle situation simulation, coverage and interference analysis is achieved using ATDI HTZ software. At the end, the basic assumptions review of the simulation is given. The behavior of the transmitted signal modulated using various modulation schemes in jamming environment is analyzed considering different Signal to Noise Ratios (SNR). The performance of the WiMAX base station transceiver network system doing fast handover of a given subscriber is observed. The simulated network gives the emergent data on which basis the final conclusion about network performances in rough electromagnetic environment is given.
Drones in IoT-enabled Spaces

The wireless cellular communications infrastructure mainly depends on base station systems (BSS) that are responsible for ensuring communications of ass ociated nodes and user equipment (UE). Under normal circumstances, the cellular and infrastructure-based systems work effectively. However, in events of unexpected conditions and natural disasters, such systems are relatively fragile and can easily be disrupted. During a natural calamity, the wireless communications infrastructure can be severely affected, where one or more BSS can stop working. The disruption in the operation of BSS affects the communications of inter-connected devices. In such circumstances, flying ad hoc networks can assist as a substitute to provide structureless communications framework for communicating emergency and safety information using unmanned aerial vehicles (UAVs). Recent developments in microelectromechanical systems (MEMS) technology and very large-scale integration have been influential in transforming large BSS to minute structures, which enables the adaptation of small-sized drones (or UAVs). UAVs are capable of the replicating technology features of BSS and can be used to form a small coverage area. UAVs, with the ability to move autonomously and to hover over the affected area, can function as a small cell to establish communications with the active UE in the designated emergency coverage area. Hypothetically, with the presence of sufficient UAVs, the communications outage
Bound the Peak Performance of SGEMM on GPU with software-controlled fast memory

In this paper, we studied the NVIDIA GPU architecture characteristics concerning the SGEMM routine and the potential peak performance of SGEMM on Fermi GPU. Guiding by the analysis, our SGEMM routine achieved about 11% (NN), 4.5% (TN), 3% (NT) and 9% (TT) better performance than cublas in CUDA 4.1 package for large matrices on GTX580 Fermi Card. We also described how to use native assembly language directly in the CUDA runtime source code.
Improving Document Representation Using Retrofitting

Data-driven learning of document vectors that capture linkage between them is of immense importance in natural language processing (NLP). These document vectors can, in turn, be used for tasks like information retrieval, document classification, and clustering. Inherently, documents are linked together in the form of links or citations in case of web pages or academic papers respectively. Methods like PV-DM or PVDBOW try to capture the semantic representation of the document using only the text information. These methods ignore the network information altogether while learning the representation. Similarly, methods developed for network representation learning like node2vec or DeepWalk, capture the linkage information between the documents but they ignore the text information altogether. In this thesis, we proposed a method based on Retrofit for learning word embeddings using a semantic lexicon, which tries to incorporate both the text and network information together while learning the document representation. We also analyze the optimum weight for adding network information that will give us the best embedding. Our experimentation result shows that our method improves the classification score by 4% and we also introduce a new dataset containing both network and content information.
Penerapan Aplikasi Senayan Library Management System (Slims) Dalam Meningkatkan Layanan Pengguna Di Perpustakaan Unika De La Salle Manado

Perkembangan teknologi saat ini memberikan dampak besar bagi semua bidang kehidupan. Perpustakaan menerapkan teknologi informasi untuk memberikan pelayanan yang lebih baik kepada pemustakanya. Penerapan teknologi informasi dibutuhkan agar informasi dapat diperoleh dengan cepat, tepat dan mudah, system manual yang diterapkan diperpustakaan dirasakan tidak lagi memadai. Oleh karena itu , perpustakaan membutuhkan penerapan teknologi informasi untuk meningkatkan kualitas layanan agar lebih efektif dan efisien. Pentingnya penerapan teknologi informasi maka perpustakaan Unika De La Salle Manado sudah menyesuaikan dengan perkembangan saat ini, dengan memanfatkan aplikasi Senayan Library Management System (SLiMS). Tujuannya untuk dapat meningkatkan layanan yang lebih prima. Metode yang digunakan daam penelitian ini adalah metode penelitian kualitatif. Kata Kunci : SLiMS, layanan Pengguna
How the design process can benefit from Kansei

This MA thesis tries to analyze how and in what way expected emotional reactions, in other words Kansei, of a target audience, influences a designers decision making process in the field of print design. The first part of this thesis is concerned with the broader understanding of design and the creative process (based on Karl Aspelund’s Design Process model) and its prospective influence point concerning the implementation of the Japanese Kansei philosophy, that bares great potential for the improvement of User Experience (UX). This part of the thesis is concluded with a modified Design Process model named "Emotional Response Sensitive Design Process”, that provides guidance and tools supporting a more Kansei-oriented design process approach. The second part of this thesis has the goal to gain greater insight into how designers try to convey emotional messages through print design choices and which elements of design they utilize, in order to do so. Or, do they even consider the emotional response of the audience, when designing, and if so to what extent, do they let it influence their decision making process? Based on a survey conducted amongst designers, data was generated in order to support findings and conclusions. Understanding if there is a need for a higher predictability of the emotional viewer response and a corresponding design process model, will be the main focus of this thesis survey analysis.
A survey of recent work in adaptive flight control

This paper is a survey of recent literature in adaptive flight control. Although there are many published articles using classical control techniques, the trend appears to be toward using intelligent control techniques, particularly neural networks based methods. The survey is not presented in a chronological order, and it is not claimed to be exhaustive. In this rapidly progressing field, the only claim to be made is that it is a work in progress.
A Brief Discussion on Counter Attack Tactics and Application Technique in the Sanda Competition

In the actual Sanda competition, counter attack is a kind of common tactics for athletes. The athletes can win scores in the relatively passive case if applying the counter attack tactics flexibly. This paper first introduces the basic concept of counter attack and its importance in the Sanda competition and then emphatically introduces the strategy for applying the counter attack tactics in the Sanda competition.
Extended Linear Vulnerability Discovery Process

Numerous software vulnerabilities have been found in the popular operating systems. And recently, robust linear behaviors in software vulnerability discovery process have been noticeably observed among the many popular systems having multi-versions released. Software users need to estimate how much their software systems are risk enough so that they need to take an action before it is too late. Security vulnerabilities are discovered throughout the life of a software system by both the developers, and normal end-users. So far there have been several vulnerability discovery models are proposed to describe the vulnerability discovery pattern for determining readiness for patch release, optimal resource allocations or evaluating the risk of vulnerability exploitation. Here, we apply a linear vulnerability discovery model into Windows operating systems to see the linear discovery trends currently observed often. The applicability of the observation form the paper show that linear discovery model fits very well with aggregate version rather than each version.
Dynamic leakage cut-off scheme for low-voltage SRAM's

The operation voltage of VLSIs is ever decreasing due to the strong needs for low-power consumption. In order to achieve low-voltage, high-speed operation, the CMOS process tends to be optimized for low-voltage operation using thinner gate oxide and shorter effective channel length. The low-voltage operation is also important in the future VLSIs, where scaled MOSFETs can be operated only in low V/sub DD/ environments with sufficient reliability. Low-voltage SRAM schemes have been proposed, including source voltage driving and dynamic boost of the supply voltage and word line. However, in these schemes the gate voltage of MOSFETs goes up to over 1.4 V even though the V/sub DD/ is 0.8 V, which gives rise to reliability issues in these cases. In this paper, a sub-volt SRAM circuit scheme is presented which speeds up the conventional low-voltage SRAM by more than a factor of two without applying excessive voltage to gate oxide and with maintaining the subthreshold leakage current to a tolerable level.
Language Development and Literacy: Comments on Beitchman and Cohen*

Beitchman and Cohen both address the issues of language development and its impact on academic, psychosocial and emotional development, by focusing on the poor outcomes of children with primary difficulties in speech and language impairments (i.e. problems that cannot be accounted for by any other condition, known as specific language impairments or SLI). Both also focus on impairments in structural aspects of receptive and expressive language skills (phonology, semantics, syntax, morphosyntax, narrative discourse, auditory verbal information processing) and accord little attention to the outcome of impairments in pragmatic aspects (the appropriate use of language within social, situational and communicative contexts). Nonetheless, it is important to understand that speech and language impairments may also occur as secondary difficulties to a primary condition such as autism, hearing impairment, neurological impairment, general developmental difficulties, behavioural or emotional difficulties, psychosocial adversity (e.g. adverse rearing conditions associated with growing up in poverty, orphanages, refugee camps or war zones) or immigration (English language learners).
Creating A More Ecologically Valid Decision Task

There has been a standing debate in the field regarding the effectiveness of frequencies and probabilities to accurately convey information. This study utilized a naturalistic sampling procedure to determine risk preferences on binary choice decision using time, sex, and goal deficit variables as predictors. Participants consisted of 110 undergraduate students enrolled in core psychology courses at Fort Hays State University. A web application was created to conduct this specific study that will be made available to other researchers. Significant effects existed for time and goal deficit variables that predicted risk preferences.
Challenges in developing a global gradient-based groundwater model (G3M v1.0) for the integration into a global hydrological model

Abstract. In global hydrological models, groundwater (GW) is typically represented by a bucket-like linear groundwater reservoir. Reservoir models, however, (1) can only simulate GW discharge to surface water (SW) bodies but not recharge from SW to GW, (2) provide no information on the location of the GW table, and (3) assume that there is no GW flow among grid cells. This may lead, for example, to an underestimation of groundwater resources in semiarid areas where GW is often replenished by SW or to an underestimation of evapotranspiration where the GW table is close to the land surface. To overcome these limitations, it is necessary to replace the reservoir model in global hydrological models with a hydraulic head gradient-based GW flow model. We present G3M, a new global gradient-based GW model with a spatial resolution of 5′ (arcminutes), which is to be integrated into the 0.5∘ WaterGAP Global Hydrology Model (WGHM). The newly developed model framework enables in-memory coupling to WGHM while keeping overall runtime relatively low, which allows sensitivity analyses, calibration, and data assimilation. This paper presents the G3M concept and model design decisions that are specific to the large grid size required for a global-scale model. Model results under steady-state naturalized conditions, i.e., neglecting GW abstractions, are shown. Simulated hydraulic heads show better agreement to observations around the world compared to the model output of de Graaf et al. (2015). Locations of simulated SW recharge to GW are found, as is expected, in dry and mountainous regions but areal extent of SW recharge may be underestimated. Globally, GW discharge to rivers is by far the dominant flow component such that lateral GW flows only become a large fraction of total diffuse and focused recharge in the case of losing rivers, some mountainous areas, and some areas with very low GW recharge. A strong sensitivity of simulated hydraulic heads to the spatial resolution of the model and the related choice of the water table elevation of surface water bodies was found. We suggest to investigate how global-scale groundwater modeling at 5′ spatial resolution can benefit from more highly resolved land surface elevation data.
A Fast Object Recognition Using Edge Texture Analysis for Image Retrieval

A Robust Object Recognition for Content Based Image Retrieval (CBIR) based on Discriminative Robust Local Binary Pattern (DRLBP) and Local Ternary Pattern (LTP) analysis. The Robust Object Recognition using edge and texture feature extraction. The extension of Local Binary Pattern (LBP) is called DRLBP. The category recognition system will be developed for application to image retrieval. The category recognition is to classify an object into one of several predefined categories. LBP is defined as an ordered set of binary comparisons of pixel intensities between the center pixel and its eight surrounding pixels . DRLBP features identifying the contrast information of image patterns. The proposed features preserve the contrast information of image patterns. The DRLBP discriminates an object like the object surface texture and the object shape formed by its boundary.
Iterated multi-channel filtering of SAR images

Optimised methods for filtering sequences of SAR intensity image are known, but are expensive in computer time and memory. This paper shows how filtering can be carried out sequentially. Under the assumption that the current image is uncorrelated with the rest of the data sequence, all previously filtered images can be updated without redoing the whole calculation. The only new quantity needing to be calculated is the local mean in the latest image (using spatially adaptive methods). Although applicable to any data sequence, this approach is most naturally applied to multitemporal data, and has the major advantage that archived data can be used to provide a cheap platform on which to base filtering of later data. The algorithm is illustrated using ERS PRI data.
A Hierarchical Career-Path-Aware Neural Network for Job Mobility Prediction

The understanding of job mobility can benefit talent management operations in a number of ways, such as talent recruitment, talent development, and talent retention. While there is extensive literature showing the predictability of the organization-level job mobility patterns (e.g., in terms of the employee turnover rate), there are no effective solutions for supporting the understanding of job mobility at an individual level. To this end, in this paper, we propose a hierarchical career-path-aware neural network for learning individual-level job mobility. Specifically, we aim at answering two questions related to individuals in their career paths: 1) who will be the next employer? 2) how long will the individual work in the new position? Specifically, our model exploits a hierarchical neural network structure with embedded attention mechanism for characterizing the internal and external job mobility. Also, it takes personal profile information into consideration in the learning process. Finally, the extensive results on real-world data show that the proposed model can lead to significant improvements in prediction accuracy for the two aforementioned prediction problems. Moreover, we show that the above two questions are well addressed by our model with a certain level of interpretability. For the case studies, we provide data-driven evidence showing interesting patterns associated with various factors (e.g., job duration, firm type, etc.) in the job mobility prediction process.
PROCEEDINGS of the HUMAN FACTORS AND ERGONOMICS SOCIETY 45th ANNUAL MEETING- 2001 USING THE CUSTOMER-CENTRIC APPROACH TO DESIGN INTERACTIVE VOICE RESPONSE SYSTEMS

This paper introduces and illustrates the practical application of the CustomerCentric approach to designing Interactive Voice Response (IVR) systems, illustrated here as an auto-attendant function. This five-step approach to IVR menu design produces menu options that match the tasks customers are trying to accomplish. In addition, the options are grouped and ordered by the frequency of customers' tasks, and they are worded in the language of the customer. Consequently, customers make the correct selection and reach the correct service entity without additional assistance. Results indicate that the Customer-Centric approach, relative to more traditional IVR design approaches, is associated with measurable improvements in call-routing accuracy and customer satisfaction. Such improvements significantly reduce the volume of misdirected calls and enhance a company's relationship with its customers.
Relaxation Labeling Using Non-linear Polya Processes

Relaxation Labeling (RL) processes are a class of iterative processes that use contextual information to reduce or eliminate ambiguity in the labeling or the interpretation of objects. There are many existing algorithms that fall under the category of probabilistic relaxation. In this paper, a new relaxation algorithm is given based on non-linear Polya processes. The algorithm was used to classify soft tissue types in brain Magnetic Resonance (MR) images.