Miracle-dz/UniFAR-FTUs huggingface dataset:
A force-control scheme for biped robots to walk over uneven terrain including partial footholds

The robustness of biped walking in unknown and uneven terrains is still a major challenge in research. Traversing such environments is usually solved through vision-based reasoning on footholds and feedback loops—such as ground force control. Uncertain terrains are still traversed slowly to keep inaccuracies in the perceived environment model low. In this article, we present a ground force-control scheme that allows for fast traversal of uneven terrain—including unplanned partial footholds—without using vision-based data. The approach is composed of an early-contact method, direct force control with an adaptive contact model, and a strategy to adapt the center of mass height based on contact force data. The proposed method enables the humanoid robot Lola to walk over a complex uneven terrain with 6 cm variation in ground height at a walking speed of 0.5 m/s. We consider our work a general improvement on the robustness to terrain uncertainties caused by inaccurate or even lacking information on the environment.
An Integrated Toolkit for Operating System Security

This thesis reports on joint research with Michael Rabin. Issues of operating system security occupy a central role in applied computer science; yet there has been no satisfactory complete solution to the problem of computer security. In the Security Toolkit Project we have developed a number of novel interlocking techniques which can be combined in many ways to provide tremendously enhanced security. The security toolkit, ITOSS, uses little overhead and is flexible; a security engineer can tailor a particular configuration to exactly satisfy the security needs demanded by the organizational structure at the site. It opens the way for further experimental work, rapid development, and simulation of new security schemes. The project consists of four phases: creating a new model of security, implementing the model by modifying UNIX (4.2 BSD) on a SUN-2 computer, inventing new algorithmic fences to validate the software by insuring that security violations are computationally infeasible, and developing software to permit easy exploitation of ITOSS’s features.
THE APPLICATION OF COMPUTER AIDED LEARNING TO LEARN BASIC CONCEPTS OF BRANCHING AND LOOPING ON LOGIC ALGORITHM

This paper reviews how to create an application based on Computer Aided Learning (CAL), which is the use of computers to deliver instructional materials and involve students / learners are active. This CAL application consists of 5 multimedia modules. Module 1 contains the basic concepts of data processing. Module 2 discusses the concept of flowcharts. Module 3 contains the testing applications using a flowchart. Module 4 contains the concept of nested selection. While the tutorial module 5 contains the concept of arrays and study case practice . To support this, CAL application is made as attractive as possible, by combining multimedia files, such as image files, sound files, and video files. With the CAL is equipped with modules of algorithms, students are expected to take computer courses, especially in STMIK STIKOM Surabaya can improve learning outcomes at the course logic algorithms.
Full Lifecycle Infrastructure Management System for Smart Cities: A Narrow Band IoT-Based Platform

The mobile telecom carriers have deployed massive infrastructures that support or carry the data signal transmission. Most of them are passive devices lacking the ability to actively monitor and automatically report information, which are called “dumb devices. ” At present, the dumb device management has problems, such as information incomplete or inaccurate, and lack of dynamic update mechanism. For catering to the construction of smart cities, we design an information management system considering the full lifecycle management for dumb devices to realize real-time or periodical context awareness and information transmission based on narrow band Internet of Things (NB-IoT). Compared to the existing radio frequency identification (RFID)-based solutions, which require RFID readers and electronic tags and have a limited sensing distance, the NB-IoT-based solution for dumb device management has advantages in transmission distance and communication stability. The NB-IoT terminal is attached to the dumb device, and a global positioning system module is installed on it to obtain the positioning information. The NB-IoT terminal is controlled by an real-time clock (RTC) alarm to periodically enter the low power mode and then wake up to automatically collect the location and battery information and upload it to the server. The application objects of this information management system can be extended to dumb devices in other industries.
Long-Distance Dependencies Don’t Have to Be Long: Simplifying through Provably (Approximately) Optimal Permutations

Neural models at the sentence level often operate on the constituent words/tokens in a way that encodes the inductive bias of processing the input in a similar fashion to how humans do. However, there is no guarantee that the standard ordering of words is computationally efficient or optimal. To help mitigate this, we consider a dependency parse as a proxy for the inter-word dependencies in a sentence and simplify the sentence with respect to combinatorial objectives imposed on the sentence-parse pair. The associated optimization results in permuted sentences that are provably (approximately) optimal with respect to minimizing dependency parse lengths and that are demonstrably simpler. We evaluate our general-purpose permutations within a fine-tuning schema for the downstream task of subjectivity analysis. Our fine-tuned baselines reflect a new state of the art for the SUBJ dataset and the permutations we introduce lead to further improvements with a 2.0% increase in classification accuracy (absolute) and a 45% reduction in classification error (relative) over the previous state of the art.
An Introduction of Myo Armband and Its Comparison with Motion Capture Systems

Recently, ways for accurately measuring the three dimensional movements of hand are actively researched so as to utilize the measurement data for therapeutic and rehabilitation programs. This research paper aims to introduce a product called Myo Armband, a wearable device comprised of a 3-axis accelerometer, a 3 axis gyroscope, and electromyographic sensors. We compare Armband’s performance with that of the Motion Capture System, which is known as a device for providing fairly accurate measurements for angular movements of objects. Dart throwing and wrist winding motions comprised movement scenarios. This paper also discusses one of Armband’s advantages - portability, and suggests its potential as a substitute for previously used devices. Decent levels of measurement accuracy were obtained which were comparable to that of three dimensional measurement device.
AmI Context-based Cross-Layer Optimization of MAC Performance in WSNs

Direct information exchange between non-adjacent protocol layers or “cross-layer” (CL) interaction can optimize network performances such as energy efficiency and delay. This is particularly important for wireless sensor networks (WSNs) where sensor devices are energy-constrained and deployed for real-time monitoring applications. Existing CL schemes mainly exploit information exchange between physical, medium access control (MAC), and routing layers, with only a handful involving application layer. In this paper, we focus on CL optimization for WSNs in ambient intelligence (AmI) applications, where low-level sensor data on users and their surroundings are collected and processed to infer higher-level user context information for context-adaptive AmI applications. For the first time, a framework for CL optimization based on user context of AmI application and an ontology-based context modeling and reasoning mechanism, is proposed in this paper. We apply the proposed framework to a contention-based MAC protocol for WSNs to adapt its backoff behavior to the user context. Results show that the modified MAC protocol with CL interactions can yield appreciable performance improvement in terms of throughput, frame delay, and energy efficiency.
Comparison of methods for adaptive removal of motion artifact

Motion artifact is a significant source of noise in an ambulatory ECG monitor and and can occur frequently during Holter recording and stress testing. We have investigated methods for removing motion artifact from ECG signals using adaptive noise removal. We have investigated both the use of an electrode/skin impedance signal and a signal from a physical sensor (Measurand Shape Sensor (TM)) mounted on the electrode for adaptively modeling and removing motion artifact. The skin/electrode signal and physical sensor signals can both be used to produce equivalent noise reduction, but, in addition to requiring the sensors, higher order adaptive filters (5/sup th/ order vs. 3/sup rd/ order) were required when the physical sensor signal was used. Guided by the results of these tests, we have designed and implemented a portable ECG recorder using 120 Hz impedance based motion artifact removal to investigate the utility of motion artifact removal for Holter and stress recording.
Suboptimal Maximum Likelihood Detection of Integer Carrier Frequency Offset for Digital Terrestrial Television Broadcasting System

The integrated services digital broadcasting-terrestrial (ISDB-T) system adopts band segmented transmission orthogonal frequency division multiplexing (BST-OFDM) to support different services using hierarchical transmission. In the ISDB-T system, in particular, a hierarchical configuration depends on modulation type used in OFDM segment, which has to be decoded at the receiver. Therefore, it is necessary to detect integer carrier frequency offset (IFO) before decoding the modulation type carried through transmission and multiplexing configuration control (TMCC) signal. To address this issue, an effective IFO estimation scheme is presented for the ISDB-T system, considering information-carrying TMCC signal as pilot symbol without any need of additional pilots. In order to verify the performance of the proposed estimation scheme, theoretical detection error rate is derived. It is shown from simulation results that the redundantly transmitted TMCC signal can be efficiently utilized for IFO detection, offering an approximate performance of maximum likelihood detection.
Automatic Tag Recommendation Approach with Keyphrase Extraction and Word Embedding Techniques

The number of online videos made available has been increasing rapidly. The most common way to find interesting videos is to search for the relevant tags. However, a large number of video clips may not have tags which cover in every aspect their contents, therefore searching for the relevant videos may not be very effective. Recent development in topic modeling research shows good performance of the word embedding model [1-2]. Our work proposed an automatic video tag recommendation approach from video transcript using a hybrid of unsupervised keyphrase extraction and word embedding model considering the semantic similarity between keyphrase candidates. Our experiments were performed on TED Talk videos dataset. The results show improvements over a variety existing approach. The proposed approach can help video owners identify a set of tags presumably having good coverage of the video contents. Our work could be applied in many existing video sharing platforms like YouTube, Twitch, etc.
Boosting Dialog Response Generation

Neural models have become one of the most important approaches to dialog response generation. However, they still tend to generate the most common and generic responses in the corpus all the time. To address this problem, we designed an iterative training process and ensemble method based on boosting. We combined our method with different training and decoding paradigms as the base model, including mutual-information-based decoding and reward-augmented maximum likelihood learning. Empirical results show that our approach can significantly improve the diversity and relevance of the responses generated by all base models, backed by objective measurements and human evaluation.
Evaluating File System Reliability on Solid State Drives

As solid state drives (SSDs) are increasingly replacing hard disk drives, the reliability of storage systems depends on the failure modes of SSDs and the ability of the file system layered on top to handle these failure modes. While the classical paper on IRON File Systems provides a thorough study of the failure policies of three file systems common at the time, we argue that 13 years later it is time to revisit file system reliability with SSDs and their reliability characteristics in mind, based on modern file systems that incorporate journaling, copy-on-write and log-structured approaches, and are optimized for flash. This paper presents a detailed study, spanning ext4, Btrfs and F2FS, and covering a number of different SSD error modes. We develop our own fault injection framework and explore over a thousand error cases. Our results indicate that 16% of these cases result in a file system that cannot be mounted or even repaired by its system checker. We also identify the key file system metadata structures that can cause such failures and finally, we recommend some design guidelines for file systems that are deployed on top of SSDs.
Fuzzy Expert Systems to Control the Heating, Ventilating and Air Conditioning (HVAC) Systems

- This paper presents a fuzzy expert system for heating, ventilating and air-Conditioning (HVAC) system to provide a comfortable environment in terms of humidity, temperature and other environmental parameters for the occupants as well as to save energy. The current values of temperature, humidity and oxygen inside the building and the temperature outside the building is used as inputs variables for the introduced system, and the Heat valve, Cold valve, Speed of exhaust motor and Speed of water pump as output variables. Fuzzy logic Toolbox and Simulink in LABVIEW software are used to simulate the proposed system. The system shows good performance in controlling the HVAC system.
Unsupervised Rewriter for Multi-Sentence Compression

Multi-sentence compression (MSC) aims to generate a grammatical but reduced compression from multiple input sentences while retaining their key information. Previous dominating approach for MSC is the extraction-based word graph approach. A few variants further leveraged lexical substitution to yield more abstractive compression. However, two limitations exist. First, the word graph approach that simply concatenates fragments from multiple sentences may yield non-fluent or ungrammatical compression. Second, lexical substitution is often inappropriate without the consideration of context information. To tackle the above-mentioned issues, we present a neural rewriter for multi-sentence compression that does not need any parallel corpus. Empirical studies have shown that our approach achieves comparable results upon automatic evaluation and improves the grammaticality of compression based on human evaluation. A parallel corpus with more than 140,000 (sentence group, compression) pairs is also constructed as a by-product for future research.
Adaptive Fault-Tolerant Sliding-Mode Control for High-Speed Trains With Actuator Faults and Uncertainties

In this paper, a novel adaptive fault-tolerant sliding-mode control scheme is proposed for high-speed trains, where the longitudinal dynamical model is focused, and the disturbances and actuator faults are considered. Considering the disturbances in traction force generated by the traction system, a dynamic model with actuator uncertainties modeled as input distribution matrix uncertainty is established. Then, a new sliding-mode controller with design conditions is proposed for the healthy train system, which can drive the tracking error dynamical system to a predesigned sliding surface in finite time and maintain the sliding motion on it thereafter. In order to deal with the actuator uncertainties and unknown faults simultaneously, the adaptive technique is combined with the fault-tolerant sliding-mode control design together to guarantee that the asymptotical convergence of the tracking errors is achieved. Furthermore, the proposed adaptive fault-tolerant sliding-mode control scheme is extended to the cases of the actuator uncertainties with unknown bounds and the unparameterized actuator faults. Finally, the case studies on a real train dynamic model are presented to explain the developed fault-tolerant control scheme. The simulation results show the effectiveness and feasibility of the proposed method.
PV based Shunt Active Power Filter for harmonics mitigation using Decoupled DSRF theory

A PV based Shunt Active Power Filter (SAPF) with a DC-DC boost converter is proposed for the improvement of power quality, i.e., lessening current harmonics using Decoupled Double Synchronous Reference Frame (DDSRF) theory in this paper. DDSRF theory has the capability to extract the sequence components of currents when the loads are unbalanced and nonlinear, and eliminate double frequency oscillations created by injecting positive and negative-sequence currents. A three level Voltage Source Converter (VSC) configuration is used for SAPF realization which will provide compensation at Point of Common Coupling (PCC). The DC capacitor voltage is maintained constant with the aid of a battery connected to a PV panel, controlled by incremental conductance method algorithm. The simulation work has been performed for non linear load and the performance of the proposed theory is confirmed with the simulation results.
The grammar of focus at the interfaces

Esta tesis estudia el diseno de la arquitectura de la gramatica y las interacciones entre los diferentes modulos gramaticales mediante un estudio innovador y pormenorizado de la naturaleza del foco. Para ello realizo un estudio de las propiedades sintacticas, semanticas, morfologicas y fonologicas del foco y ofrezco un analisis explicativo de ellas dentro de la tradicional concepcion "sintactocentrica" de la arquitectura de la. Con ese objetivo ofrezco un analisis detallado de la gramatica del foco en vasco, una lengua donde el foco se realiza mediante una serie de efectos fonologicos, semanticos y morfosintactico, lo que la hace especialmente interesantes para la investigacion del foco y las interacciones entre los diferentes modulos. Ademas, para obtener una imagen mas general de la naturaleza del foco, discuto varios fenomenos caracteristicos de otras lenguas como el ingles, bengali, italiano, griego, portugues, japones, sueco, coreano, chichewa, holandes, hungaro, ruso, zapoteco, tuki o manding. Asi, propongo capturar la naturaleza del foco en un sistema netamente derivacional que construye estructuras focales en la gramatica estrecha, permitiendo, por tanto, a los modulos interpretativos operar sobre estas estructuras. La composicion de la estructural focal sera anterior a e independiente de cualquier asignacion de acento. Para las interfaces, propongo que en PF existe (dependiendo de la lengua) una regla de acento nuclear focal que asigna acento nuclear al elemento mas hendido dentro del foco. Esto permite dar cuenta de la posicion del acento a la vez que evita los problemas de desajustes entre el foco y la posicion del acento. En LF el foco recaera en el abarque de un cuantificador sobre eventos. Propongo dos tipos de cuantificadores: un existencial para oraciones sin contexto y un cuantificador delta para oraciones contextualizadas, lo que explica de una forma natural las lecturas presuposiconales y exhaustivas de cada tipo de construccion. Ademas, propongo que la interfaz sintaxis-semantica de las oraciones sin contexto supone una simple traslacion mientras que en las oraciones contextualizadas involucra una reproyeccion del cuantificador binario para tomar abarque. La reproyeccion, asi como la posibilidad de movimiento sintactico se derivaran de las diferencias en los complementantes de estas construcciones. Por ultimo, tambien analizo las respuestas a preguntas multiples como casos de estructuras focales disjuntas.
Ten Criteria for Evaluating Synthesis Techniques

A synthesis technique (Figure ib) is a strategy for generating sound samples based on some control information called parameters. Parameters generally change at a rate that is significantly slower than the audio sampling rate. Whereas sound samples are usually produced at a constant rate, parameter-setting messages are often sporadic and irregular. For example, in the familiar Yamaha DX7 synthesizer (Chowning and Bristow 1986), the synthesis technique is frequency modulation (FM) synthesis, which produces a steady stream of audio samples, whereas MIDI pitch-bend is a parameter that changes only when the performer moves the instrument's pitch wheel or foot pedal. For a historical summary of synthesis techniques, see, for example, Smith (1991). A processing technique (Figure la) is similar to a synthesis technique except that it includes an additional input of one or more audio sample streams at the audio sampling rate. An example of a common processing technique is reverberation. Here, the parameters are controls, such as the initial delay before the first reflections, the balance between the reverberant and dry signals, and the decay rate of the echoes. A wide variety of synthesis and processing techniques have been invented. The question may arise as to which technique is best. As it turns out, there is no simple answer. The best technique depends on the priorities of the user and the problem to be solved.
Improving Neural Entity Disambiguation with Graph Embeddings

Entity Disambiguation (ED) is the task of linking an ambiguous entity mention to a corresponding entry in a knowledge base. Current methods have mostly focused on unstructured text data to learn representations of entities, however, there is structured information in the knowledge base itself that should be useful to disambiguate entities. In this work, we propose a method that uses graph embeddings for integrating structured information from the knowledge base with unstructured information from text-based representations. Our experiments confirm that graph embeddings trained on a graph of hyperlinks between Wikipedia articles improve the performances of simple feed-forward neural ED model and a state-of-the-art neural ED system.
Chinese Relation Extraction with Multi-Grained Information and External Linguistic Knowledge

Chinese relation extraction is conducted using neural networks with either character-based or word-based inputs, and most existing methods typically suffer from segmentation errors and ambiguity of polysemy. To address the issues, we propose a multi-grained lattice framework (MG lattice) for Chinese relation extraction to take advantage of multi-grained language information and external linguistic knowledge. In this framework, (1) we incorporate word-level information into character sequence inputs so that segmentation errors can be avoided. (2) We also model multiple senses of polysemous words with the help of external linguistic knowledge, so as to alleviate polysemy ambiguity. Experiments on three real-world datasets in distinct domains show consistent and significant superiority and robustness of our model, as compared with other baselines. We will release the source code of this paper in the future.