Miracle-dz/UniFAR-FTUs huggingface dataset:
Interaction History Support for Web Applications

All users of complex software make decisions that they may later wish to change. Many computer systems have tools to support this need for revision, such as the undo command. However, the common history tools (like undo) do not support exploratory, epistemic interaction well. And there are common, non-specialized tasks that are difficult in common computer systems, but would be much easier with improved support for managing interaction history. Desktop computing environments have well-established norms for how undo works, but there is room to explore this in newer computing environments, such as the Web and surface computing, as their design culture has not stabilized to the same extent. We argue that history tracking needs to be more accessible to users. We developed a prototype JavaScript library for Web applications that lets users keep a history of all their interaction states, including those that would be discarded by using a traditional stack-model undo system. The history is presented to users in a tree structure similar to the model used in source control software. We ran a usability study of our system with two applications designed to encourage the kind of exploratory behaviour we wanted to support. We identified usability improvements that could be made, but the study suggests that this kind of system could be generally useful even in non-specialized fields.
Simulation pipeline for virtual clinical trials of dermatology images

Skin cancer is the most common cancer in the US; one in five Americans will develop it by the age of 70. Early diagnosis offers more favorable treatment options; currently available diagnostics, however, shows large readerdependence. The simulation approach (virtual trials) to development and validation offers advantages in terms of quantitative and objective assessment of system performance in the design of novel imaging methods, and validation of clinical trial designs prior to execution of real clinical trials. We have designed a pipeline for performing Virtual Clinical Trials of optical imaging of skin lesions. This pipeline includes modules to simulate healthy skin and subcutaneous tissue, to create skin lesions and insert those in the skin models, and to simulate the acquisition of optical images resulting in simulated/virtual images of skin. The last module of the pipeline performs virtual reading of simulated images, which is based on the clinical task-based performance analysis. The physical properties of the skin and lesions used in our simulations were selected to represent clinically plausible conditions. Skin lesion images were simulated assuming the ambient white light, and a linear camera model. We have utilized the standards for optimal data representation based upon the XML schema, adopted from the VCT pipeline developed for breast imaging research. This paper describes the principles used for designing the proposed VCT pipeline and presents preliminary simulation results, including visual and quantitative evaluation. Integration of pipeline modules and its validation is ongoing.
A Path Planning Algorithm for Loiter Maneuver: A Limit Cycle Approach

In this paper, we propose a path planning algorithm for loitering maneuver using limit cycles. Specifically, actual position of the aircraft is plugged in to nonlinear dynamics of a stable limit cycle. Then, the desired velocity to converge the desired loitering path is generated by the vector field around the limit cycle. Relying on the Jordan-Schnflies Curve Theorem, the method for loitering path planning is extended to any simple closed curve other than exact circular paths. Knowing the transformation between two topological spaces, one can achieve the desired path tracking performance with a user-defined loop frequency and simple closed loitering path. In addition, robustness against noise is achieved by shaping the vector field around the limit cycle without effecting the control performance. Efficacy of the proposed path following algorithm is illustrated through numerical simulations with planar motion of a point mass.
Blockchain-Based Verifiable Multi-Keyword Ranked Search on Encrypted Cloud With Fair Payment

In traditional cloud computing system, searchable encryption is deemed as a core technology to realize data confidentiality protection and information retrieval functions. However, the online payment problem and mutual distrust between cloud platforms and users may hinder the wide adoption of cloud service. In this paper, we construct a blockchain based multi-keyword ranked search with fair payment (BMFP) system, which leverages smart contracts to verify the correctness and completeness of the search result, and automatically execute the fair payment operations. The system realizes public verifiability on a multi-keyword ranked search result. The data owner manages the search authority, and a concrete fair payment smart contract is designed. The BMFP is compatible with Ethereum, and the verification algorithm executed by the smart contract is cost-efficient.
Rule-based Part of Speech Tagger for Indonesian Language

Lexical processing, such as detection of root words (Stemming) and type of words (Part of Speech tagging) is the important determinant for language computing systems that requires the detection of sentence structure or pattern. In Indonesian language, a problem that was encountered in lexical processing is lack of annotated corpus in Indonesian. Thus, POS tagger built in this research did not use annotated corpus, but utilize KBBI (Indonesian large dictionary) and some morphological rules that affect changes in word form (morphology). The method used in this study begin with change input text into tokens and do stemming. Each token is checked whether it is available in the baseword dictionary or not. If the token is not found in the baseword dictionary, it will go through stemming and affix detection. By doing this step, it can produce output list of POS tag for basewords and their affixes. By collate the output based on the rules of grammar, we can determine the type of affixed-word. Testing is done by comparing the detection results and the type of wordlist available in KBBI, for every input token. Accuracy score is obtained by calculate number of true results divided by total number of tokens being examined. Based on result of test performed, the achievement of accuracy is quite good (average rate of 87.4% for 4 parts of PAN Localization corpus in Indonesian). False results were caused by some mistake tags in existing KBBI and presence of ambigous word (word with more than one POS tag). So, improvement will be possible by using more complete Indonesian dictionary and adding word-sense disambiguation.
Multimodal Comparable Corpora for Machine Translation

The construction of a statistical machine translation (SMT) system requires parallel corpus for training the translation model and monolingual data to build the target language model. A parallel corpus, also called bitext, consists in bilingual/multilingual texts. Unfortunately, parallel texts are a sparse resource for many language pairs. One way to overcome this lack of data is to exploit comparable corpora which are much more easily available. In this paper, we present the corpus developed for automatic parallel data extraction from multimodal comparable corpora, from Euronews and TED web sites. We describe the content of each corpus and how we extracted the parallel data with our new extraction system. We present the methods considered for using multimodal corpora and discuss the results on bitext extraction.
Adaptive Collaborative Position Control of a Tendon-Driven Robotic Finger

This paper presents a collaborative control scheme that adaptively combines two different control strategies in order to optimize the trajectory-tracking performance and the angular-position response of the motor, installed at the metacarpophalangeal joint of each finger, in a robotic hand. The scheme contains an adaptive neuro-fuzzy inference system (ANFIS) in the feed-forward path, and a proportional-integral-derivative (PID) controller in the feedback path. The ANFIS supervises the motion-control and efficiently tracks the reference trajectory. The PID controller improves the transient response and robustly nullifies the effects of exogenous disturbances. The final control output is taken as the weighted sum of individual outputs from the ANFIS and PID controller. A hyperbolic tangent function (HTF) of the error dynamics is used to adaptively vary the weightages in the controller combination. This feature dynamically varies the contributions of individual control effort, delivered by ANFIS and PID controller, with respect to the variations in the angular-error dynamics. The PID parameters and the variation-rate of HTF are meta-heuristically tuned via an adaptive particle swarm optimization (APSO) algorithm. The collaborative controller effectively tracks the desired trajectory and improves the overall system response. The results of real-time experiments are presented to validate the efficacy of the proposed controller.
INFaaS: A Model-less Inference Serving System.

Despite existing work in machine learning inference serving, ease-of-use and cost efficiency remain key challenges. Developers must manually match the performance, accuracy, and cost constraints of their applications to decisions about selecting the right model and model optimizations, suitable hardware architectures, and auto-scaling configurations. These interacting decisions are difficult to make for users, especially when the application load varies, applications evolve, and the available resources vary over time. Thus, users often end up making decisions that overprovision resources. This paper introduces INFaaS, a model-less inference-as-a-service system that relieves users of making these decisions. INFaaS provides a simple interface allowing users to specify their inference task, and performance and accuracy requirements. To implement this interface, INFaaS generates and leverages model-variants, versions of a model that differ in resource footprints, latencies, costs, and accuracies. Based on the characteristics of the model-variants, INFaaS automatically navigates the decision space on behalf of users to meet user-specified objectives: (a) it selects a model, hardware architecture, and any compiler optimizations, and (b) it makes scaling and resource allocation decisions. By sharing models across users and hardware resources across models, INFaaS achieves up to 150x cost savings, 1.5x higher throughput, and violates latency objectives 1.5x less frequently, compared to Clipper and TensorFlow Serving.
Music Visualization Based on Spherical Projection With Adjustable Metrics

Development of techniques for music visualization is important and still open problem in analysis and creation of the quantitative profiles of single or multiple compositions, which could be used as required constraints in music generation or music classification processes. When generating creative data with no objective function, it is hard to select or to find appropriate measurable features. This paper proposes a method to normalize data in MIDI files by 12 dimensional vector descriptors extracted from tonality as well as a novel technique for dimensionality reduction and visualization of extracted music data by 3D projections is discussed. Employing a non-overlapping sliding window through the composition, the harmonic features are found in a music piece. Then a self-similarity matrix is computed using distance metrics to analyze and project the resulting 3D feature vectors. Three dimensional projection creates a quantitative profile of a composition, which correlates the tone similarities along the music piece. The dimensionality reduction is compared with well-known autoencoder. Conducted tests show that our method preserves up to 90% of original data in the projection of reduced dimension. The advantages of the proposed method consist in a novel technique that provides interactive visualization and dynamically adjusts different metrics to observe the behavior of data during music information retrieval and recognition.
New Perspectives on Serialism and Parallelism in Oculomotor Control During Reading: The Multi-Constituent Unit Hypothesis

Currently there are several computational models of eye movement control that provide a good account of oculomotor behavior during reading of English and other alphabetic languages. I will provide an overview of two dominant models: E-Z Reader and SWIFT, as well as a recently proposed model: OB1-Reader. I will evaluate a critical issue of controversy among models, namely, whether words are lexically processed serially or in parallel. I will then consider reading in Chinese, a character-based, unspaced language with ambiguous word boundaries. Finally, I will evaluate the concepts of serialism and parallelism of process central to these models, and how these models might function in relation to lexical processing that is operationalized over parafoveal multi-constituent units.
Hybrid Precoding for Beamspace MIMO Systems With Sub-Connected Switches: A Machine Learning Approach

By employing lens antenna arrays, the number of radio frequency (RF) chains in millimeter-wave (mmWave) communications can be significantly reduced. However, most existing studies consider the phase shifters (PSs) as the main components of the analog beamformer, which may result in a significant loss of energy efficiency (EE). In this paper, we propose a switch selecting network to solve this issue, where the analog part of the beamspace MIMO system is realized by a sub-connected switch selecting network rather than the PS network. Based on the proposed architecture and inspired by the cross-entropy (CE) optimization developed in machine learning, an optimal hybrid cross-entropy (HCE)-based hybrid precoding scheme is designed to maximize the achievable sum rate, where the probability distribution of the hybrid precoder is updated by minimizing CE with unadjusted probabilities and smoothing constant. Simulation results show that the proposed HCE-based hybrid precoding can not only effectively achieve the satisfied sum-rate, but also outperform the PSs schemes concerning energy efficiency.
Queuing approach and optimal inventory decisions in a stochastic supply chain network design

This paper addresses an integrated multi-echelon location-allocation-inventory problem in a ‎stochastic supply chain. In a bid to be more realistic, the demand and lead time are considered ‎to be hemmed in by uncertainty. To tackle the proposed supply chain network design problem, ‎a two-phase approach based on queuing and optimization models is devised. The queuing ‎approach is first deployed, which is able to cope with inherent uncertainty of parameters. ‎Afterwards, the proposed supply chain network design problem is formulated using a mixed‏-‏integer nonlinear model. Likewise, the convexity of the model is proved and the optimal ‎inventory policy as closed-form is acquired. Inasmuch as the concerned problem belongs to ‎NP-hard problems, two meta-heuristic algorithms are employed, which are capable of ‎circumventing the complexity burden of the model. The numerical examples evince the efficient ‎and effective performance of the solving algorithms. Lastly, sensitivity analyses are conducted ‎through which interesting insights are gained.
Low-Rank Constrained Latent Domain Adaptation Co-Regression for Robust Depression Recognition

Focusing on the facial-based depression recognition where the feature distribution could be shifted due to unlimited variations in facial image acquisition, we propose a novel Low-rank constrained latent Domain Adaptation Depression Recognition (LDADR) framework by jointly utilizing facial appearance and dynamics features. Under this framework, to alleviate the domain distribution bias in depression recognition, we devote to uncover a compact and more informative latent space on appearance feature representation to minimize the domain distribution divergence as well as to share more discriminative structures between domains. In this optimal latent space, both source and target classification loss functions are incorporated as parts of its co-regression function by encoding the common components of the classifier models as a low-rank constraint term. Moreover, the target prediction results on both appearance features and dynamics features are constrained to be consistent for better fusing the discriminative information from different representations. We specially adopt the ${l_{2,1}}$ -norm based loss function for learning robust classifiers on different feature representations. Different from the state of the arts, our algorithm can adapt knowledge from another source for Automated Depression Recognition (ADR) even if the features of the source and target domains are partially different but overlapping. The proposed methods are evaluated on three depression databases, and the outstanding performance for almost all learning tasks has been achieved compared with several representative algorithms.
Deep Learning in Gesture Recognition Based on sEMG Signals

Over the past years, Deep Learning methods have shown promising results to a wide range of research fields including image classification and natural language processing. Their increased success rates have drawn the attention of many researchers from various domains. This chapter investigates the application of Deep Learning methods to the problem of electromyography-based gesture recognition. A signal processing pipeline based on Deep Learning is presented through examples taken from the literature, whereas the details of state-of-the-art neural network architectures are discussed. In addition, this chapter illustrates a few ways adopted from image classification tasks that visualize what the neural network learns. Finally, new approaches are proposed and evaluated with publicly available datasets. Keywords— sEMG, gesture recognition, Deep Learning, signal processing
Penerapan Trainer Human Machine Interface (HMI) Berbasis CX-Designer Sebagai Media Pembelajaran Programmable Logic Controller (PLC)

The purpose of this research is to create a learning media design in the Programmable Logic Controller (PLC) lecture in the form of CX-Designer-based Human Machine Interface (HMI) trainer integrated with CX-Programmer. The appropriate HMI design will make the program trial work easier and more efficient. The effectiveness of HMI as a learning media provides the information needed, so that the planning can run with maximum efficiency. The research method used is an experimental method with a design to make a product in the form of a trainer to test a program that has been designed by the operator. The results of this study can be concluded that the use of CX-Designer-based HMI trainers can be used in understanding the logic circuit of a program very easily and quickly in practicing programming skills in the PLC.
CoNNOR: Convolutional Neural Network for Outsole Recognition

A common goal of forensic shoeprint analysis is to identify shoe models or designs that are similar to that of a given print, such as a print found at a crime scene. Quantifying similarity between shoe outsole patterns is difficult because it requires both a set of well-defined features and an accurate method to classify outsoles according to those features. A set of geometric features was developed based on common geometric shapes, such as circles and quadrilaterals. A new classifier was then trained for the pretrained convolutional neural network base of VGG16 to create a model, named CoNNOR (Convolutional Neural Network for Outsole Recognition), to automatically classify portions of outsole images into the new geometric scheme of outsole class characteristics. During the analysis of CoNNOR’s performance, new diagnostic plots were developed which provide a better method to assess classification errors of multi-class, multi-label models. In general, CoNNOR performs well on images with unambiguous shapes and moderate color contrast; additional improvements may be realized by preprocessing the images to improve contrast as well as by integrating spatial relationships between geometric features. CoNNOR represents a significant improvement to the current manual classification of footwear tread patterns, facilitating new modes of data collection and automatic processing that will expand the data available for assessment of footwear class characteristic frequency in the population.
HTTP and contact‐based features for Botnet detection

Over the past decade, Botnets have been prevailing as a relevant threat on the Internet. A Botnet is compound by many infected computers connected to the global network and subdued to a controller, which uses the computational resources for malicious purposes. Many techniques are used to make difficult the identification of Botnet communications on networks. However, these communications are automated and usually have typical behavioral patterns, which have been used by detection approaches. In this context, we noted that HTTP connections established as command and control (C&C) channels of Botnets commonly have specific implementations, which deviate from the patterns used by legitimate HTTP accesses. Also, C&C channels over other protocols, such as IRC or TCP, typically are compound by similar chunks of data transmission interleaved with times with negligible traffic. We used both particularities to propose new features to distinguish C&C channels from benign traffic. The proposed detection method uses a random forest classifier implemented over Apache Spark, a Big Data processing framework. In the presented experiments, the approach achieved more than 99% of accuracy with virtually zero false positives, which are good results when compared with other available similar approaches. Also, the proposed features can be extracted before the communication end, which enables a premature response.
Gambaran Kondisi Wilayah dan Jalur Tata Niaga Susu Kambing di Kabupaten Sleman

Penelitian ini bertujuan untuk mengetahui gambaran kondisi wilayah dan jalur tata niaga susu kambing di Kabupaten Sleman. Pengambilan data dilakukan secara purposive sampling. Data terdiri atas data primer dan data sekunder. Pengumpulan data primer dilakukan melalui kombinasi teknik observasi dan wawancara sedangkan data sekunder diperoleh dengan cara mencatat data serta statistik yang telah ada yang berasal dari instansi maupun lembaga terkait. Analisis data yang digunakan dalam penelitian adalah analisis deskriptif. Hasil penelitian menunjukkan bahwa kecamatan Turi, Pakem dan Kalasan merupakan daerah sentra peternakan kambing perah dan pengolahan susu kambing bubuk. Pihak yang terlibat dalam tata niaga susu kambing di Kabupaten Sleman terdiri dari industri pengolahan susu yang bertindak sebagai industri inti; peternak, kelompok, dan koperasi susu sebagai pemasok. Jenis olahan yang dilakukan oleh pelaku industri olahan susu mayoritas adalah susu bubuk.