Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving Dialogue Continuity using Inter-Robot Interaction

Research on conversational dialogue systems has attracted attention for achieving dialogue systems that can build social relationships with users. Although such systems are required for continuing long conversations with users to build relationships, they sometimes make sentences that are not related to the dialogue context, causing the dialogue to easily break down. The cause of the difficulty is simple. Unlike task-oriented dialogues, unexpected user utterances whose meaning the system cannot capture are frequently spoken in conversational dialogues, since the dialogue domain is much less restricted and the dialogue goal is less obvious. In this paper, we propose a novel strategy for dialogue systems through which two robots coordinate to create long conversations by avoiding dialogue breakdowns. If one of the two robots accepts user utterances with backchannel, the responsibility of responding to the user utterances is resolved and the other robot can change the dialogue topic, which decreases the risk of generating discontinuous utterances. Even if a dialogue nearly breaks down, such multiple robots can present predefined natural interactions among themselves to repair it. Our experiments show that the inter-robot interaction effectively improves the establishment of conversational dialogue that helps users continue the dialogue.
FlexCL: A Model of Performance and Power for OpenCL Workloads on FPGAs

Hardware acceleration is a promising trend for the energy and thermally constrained systems. The programmable nature of FPGAs allows it to deliver high performance and energy efficient solution. Unfortunately, the traditional RTL-based synthesis flow of FPGAs prevents its wide adoption. In response, recent adoption of OpenCL programming model has raised the possibility to program FPGAs in a software manner. To harness the power of FPGAs using OpenCL programming model, it is advantageous to design an analytical model for performance analysis, design space exploration and provide insights into the performance bottlenecks. To this end, this paper presents FlexCL, an analytical performance and power model for OpenCL workloads on FPGAs. FlexCL leverages static analysis to analyze the OpenCL kernels. As for the performance estimation, it first develops systematic computation models for processing elements, compute units and kernels by modeling the operation scheduling, work-item and work-group scheduling, and the resource constraints. Then, it models different global memory access patterns. Finally, FlexCL estimates the overall performance by tightly coupling the memory and computation models based on the communication mode. FlexCL can be also used to guide performance and power trade-off analysis. Experiments demonstrate that the average performance and power estimation errors of FlexCL are 9.5 and 12.6 percent for the Rodinia suite, respectively. The OpenCL model on FPGAs also exposes a rich optimization design space. With FlexCL, we can enable rapid exploration of the design space with respect to both performance and power within seconds instead of hours or days.
An Approach to Performance Evaluation Programming

We present an experimental concurrent programming language LPEP which supports performance evaluation programming. An LPEP program is a collection of modules which are executed concurrently. The structure of an LPEP program is similar to the structure of a Continuous Time Markov Chain (CTMC) model expressed in the PRISM language (of the PRISM probabilistic model checker). However, LPEP is a programming language, not a model checker. Activities are abstracted in a PRISM CTMC model by their rates. In the language LPEP an activity is the evaluation of a function (expressed in a functional sub-language of LPEP ) in a certain state of a module. Each LPEP module contains a number of variables (which describe the possible states of the module) and a list of commands that can specify activities. LPEP supports variables of both primitive types (booleans and integers) and non-primitive types, e.g., lists. For the purpose of performance evaluation the programmer must bound the ranges of variables. Any value of a non-primitive type is handled according to its complexity (rather than its value). For example, the complexity of a list could be the length of the list, which must also be bounded. It is also the responsibility of the programmer to design each LPEP function by induction on an appropriate complexity measure which always decreases upon a recursive call. Performance evaluation is supported in LPEP by constructing a CTMC model from an LPEP program, and by analyzing the CTMC model using the PRISM tool.
Alzheimer's disease DNA methylome of pyramidal layers in frontal cortex: laser-assisted microdissection study.

OBJECTIVE To study DNA methylation patterns of cortical pyramidal layers susceptible to late-onset Alzheimer's disease (LOAD) neurodegeneration. METHODS Laser-assisted microdissection to select pyramidal layers' cells in frontal cortex of 32 human brains (18 LOAD) and Infinium DNA Methylation 450K analysis were performed to find differential methylated positions and regions, in addition to the corresponding gene set functional enrichment analyses. RESULTS Differential hypermethylation in several genomic regions and genes mainly in HOXA3, GSTP1, CXXC1-3 and BIN1. The functional enrichment analysis revealed genes significantly related to oxidative-stress and synapsis. CONCLUSION The present results indicate the differentially methylated genes related to neural projections, synapsis, oxidative stress and epigenetic regulator genes and represent the first epigenome of cortical pyramidal layers in LOAD.
How the Devil Ray Got Its Horns: The Evolution and Development of Cephalic Lobes in Myliobatid Stingrays (Batoidea: Myliobatidae)

Manta rays and their relatives of the family Myliobatidae have pectoral fins that have been modified for underwater flight, as well as a pair of fleshy projections at the anterior of the body called cephalic lobes, which are specialized for feeding. As a unique trait with a dedicated function, cephalic lobes offer an excellent opportunity to elucidate the processes by which diverse body plans and features evolve. To shed light on the morphological development and genetic underpinnings of cephalic lobes, we examined paired fin development in cownose rays, which represent the sister taxon to manta rays in the genus Mobula. We find that cephalic lobes develop as anterior pectoral fin domains and lack independent posterior patterning by 5’ HoxD genes and Shh, indicating that cephalic lobes are not independent appendages but rather are modified pectoral fins. In addition, by leveraging interspecies comparative transcriptomics and domain-specific RNA-sequencing, we identify shared expression of anterior patterning genes, including Alx1, Alx4, Pax9, Hoxa13, Hoxa2 and Hoxd4, in the pectoral fins of cownose ray (Rhinoptera bonasus) and little skate (Leucoraja erinacea), providing evidence in support of a homologous relationship between the cephalic lobes of myliobatids and the anterior pectoral fins of skates. We also suggest candidate genes that may be involved in development of myliobatid-specific features, including Omd, which is likely associated with development of thick anterior pectoral fin radials of myliobatids, and Dkk1, which may inhibit tissue outgrowth at the posterior boundary of the developing cephalic lobes. Finally, we observe that cephalic lobes share a surprising number of developmental similarities with another paired fin modification: the claspers of male cartilaginous fishes, including enrichment of Hand2, Hoxa13, and androgen receptor. These results suggest that cephalic lobes may have evolved by co-opting developmental pathways that specify novel domains in paired fins. Taken together, these data on morphological development and comparative gene expression patterns illustrate how distinct body plans and seemingly novel features can arise via subtle changes to existing developmental pathways.
Hybrid Precoding for Wideband Millimeter-Wave Systems With Finite Resolution Phase Shifters

In this paper, we study the hybrid analog-digital precoding for OFDM based-wideband millimeter wave (mmwave) communication systems. Since the analog precoding matrix is performed in the time domain and used for the entire bandwidth while the digital precoding matrices are performed on a per-subcarrier basis, the resulting wideband hybrid precoding problem becomes very complicated. Moreover, in practice phase shiftier (PS) usually has only finite resolution, which brings more difficulties. To solve the resulting difficult problem, we propose an efficient algorithm by leveraging the penalty decomposition method. Simulation results are finally provided to confirm the effectiveness and superiority of the proposed algorithm and show the performance improvement than the existing algorithm.
Named Data Networking’s Intrinsic Cyber-Resilience for Vehicular CPS

Modern vehicles equipped with a large number of electronic components, sensors, actuators, and extensive connectivity, are the classical example of cyber-physical systems (CPS). Communication as an integral part of the CPS has enabled and offered many value-added services for vehicular networks. The communication mechanism helps to share contents with all vehicular network nodes and the surrounding environment, e.g., vehicles, traffic lights, and smart road signs, to efficiently take informed and smart decisions. Thus, it opens the doors to many security threats and vulnerabilities. Traditional TCP/IP-based communication paradigm focuses on securing the communication channel instead of the contents that travel through the network. Nevertheless, for content-centered application, content security is more important than communication channel security. To this end, named data networking (NDN) is one of the future Internet architectures that puts the contents at the center of communication and offers embedded content security. In this paper, we first identify the cyberattacks and security challenges faced by the vehicular CPS (VCPS). Next, we propose the NDN-based cyber-resilient, the layered and modular architecture for VCPS. The architecture includes the NDN’s forwarding daemon, threat aversion, detection, and resilience components. A detailed discussion about the functionality of each component is also presented. Furthermore, we discuss the future challenges faced by the integration of NDN with VCPS to realize NDN-based VCPS.
Mechanical Stresses on Polymer Insulating Materials

The usage of polymer materials as the main insulating material for many electrical apparatuses has been dramatically increased since its first introduction for more than 40 years ago. Thanks to the great advantages of polymer materials, nowadays the polymer materials like the Poly Ethylene (PE), Ethylene Propylene Rubber (EPR) and the Cross-Linked Poly Ethylene (XLPE) are the common material used for the insulation purposes for low, medium and high voltage levels such as cables. The insulation is a very critical part that affects greatly the operation of all equipment. The polymer materials used for insulation are subjected to many stresses which have a notable effect on the characteristics of these materials. These stresses are not only electrical, thermal and environmental but also they suffer from different types of mechanical stresses. Since understanding the effect of the different stresses on the polymer insulating materials is a must then, this paper gives a brief review on the effect of mechanical stresses on these materials which is still out of reach.
Towards Real-Time Patient Prioritization in Hospital Emergency Services

Industrial cyber-physical systems are becoming increasingly popular and starting to emerge as a response to society’s problems using the Internet of Things to create real-time solutions capable of analyzing large amounts of data. In this article, taking advantage of this type of technology, we present a patient prioritization system for hospital emergency services. Our system responds to the problems that occur in hospitals when there is a peak in the number of people using the emergency service, increasing the waiting times. In such a situation, the clinical condition of waiting patients may change after the initial triage, requiring immediate care or at least their re-prioritization. The proposed system consists of a combination of a Smart Priority Recommendation and Patient Control System and an Hospital Emergency Smart Band placed in each patient, allowing to detect changes in the vital signs of waiting patients that may indicate that they need immediate care. This system not only improves the quality of emergency services by ensuring a more efficient prioritization of patients, but in extreme cases can help save human lives.
A Feature Selection Approach for Network Intrusion Detection Based on Tree-Seed Algorithm and K-Nearest Neighbor

Network intrusion detection is one of the hottest and most difficult issues in the field of network security. K-nearest neighbor technique is a kind of lazy classification algorithm which has been successfully applied to network intrusion detection. However, with the increase of the characteristic dimensions of network data, classification performance is significantly reduced. Considering that tree-seed algorithm(TSA)has good classification ability in reducing feature redundancy, it can be used in network intrusion detection. In the paper, the tree-seed algorithm(TSA) is introduced to extract the effective feature of the input data, and KNN is used for classification. A novel network intrusion detection model (KNN-TSA) based on K-nearest neighbor (KNN) and tree-seed algorithm (TSA) algorithm is proposed to select features to improve improve Classification efficiency of intrusion detection. This paper uses some data from UCI repository and KDD CUP 99 datasets to test the performance of the proposed model. Experimental results testify that the proposed model is able to remove the redundant features and reduce the input dimensions of classifier. In addition, it can improve the accuracy and efficiency of network intrusion detection.
The Rising Cost of Diabetes: Can DiRECT Tip the Scales?

The cost of treating diabetes mellitus has risen sharply in recent years because of an increasing number of patients with type 2 diabetes and obesity and a rapidly expanding repertoire of expensive nongeneric medications. Despite the call for a new treatment paradigm by the American Diabetes Association to mitigate this cost, recent Medicare Part D expenditure data show that this call remains unanswered. The Diabetes Remission Clinical Trial, published in The Lancet, adds support to a different path, demonstrating success with intensive lifestyle intervention followed by structured support for long‐term weight loss maintenance. Nearly half of patients enrolled were able to achieve remission of their diabetes with hemoglobin A1c < 6.5% and an improvement of their quality of life. Although only from one trial, and of limited duration, these results should provide optimism for providers and patients alike and support future work toward an evidence base for non–medication‐based management of diabetes.
Effect of Ankle Weight Variation on Skin Surface Temperature Profile Using Thermography Technique

Sport exercise can be classified into static and dynamic. Exercise for athletes is an activity that requires physical effort to be carried out in order to build up their stamina, muscle strength, and to improve their fitness level and performance. During the exercise, body will emit infrared radiation due to human thermoregulation process which could be detected using infrared thermography. However, the effect of various weights on low intensity dynamic exercise is still unclear. Therefore, this study is conducted to explore and investigate the effect of various loads to the changes of skin surface temperature in low intensity dynamic exercise using infrared thermography. A set of test was conducted on 10 healthy men by performing repetitive calf raise exercise with 0 kg, 0.9 kg and 2.72 kg individual ankle weight within 3 minutes. Muscle surface temperature was registered from gastrocnemius muscle and the analysis was focused on the changes of the muscle temperature for the various loads given. Average temperature and temperature difference were extracted from thermal images. Statistical analyses on the effect of loads on temperature parameters were then analysed. Results obtained show that although higher temperature difference changes were observed in the higher load exercise however statistically there is no effect of various loads on the temperature parameters in the beginning and along the exercise duration. Therefore, this study concluded that thermography can be used as a method for assessing human thermoregulation process during low intensity dynamic exercise.
Research on Advertising Click-Through Rate Prediction Based on CNN-FM Hybrid Model

Click through rate prediction is one of the hot topics in machine learning. The single structure model does not take into account the characteristics including highly nonlinear association for features. Aiming at this problem, this paper presents a click through rate prediction model based on CNN (Convolutional Neural Networks) and FM (factorization machine). This model uses CNN to extract high-impact features, and predicts and classifies them by FM, which can learn the relationship between mutually distinct feature components. The experimental results show that compared with the single structure model, the CNN-FM hybrid model can effectively improve the accuracy of advertising click through rate prediction.
Auction-Based Design and Analysis of Wireless Power Transfer Network With Critical Users

We propose and analyze a self-controlled playing strategy, named reneging strategy, for users competing for receiving wireless power. In contrast to a non-reneging strategy, users can leave the game if their received power is not up to their expectation. The performance of the reneging strategy is studied and compared with the conventional non-reneging strategy for different network operation scenarios, particularly in the presence of critical users with stringent service requirements.
Credibility of System Self-Checking When Tests are Performed Randomly

The paper deals with the problem of system level self-diagnosis. Usually it is assumed that a system unit is tested when it is idle and, thus. it can participate in a test. It means that a system unit cannot be tested continuously. In view of this, there is a probability of system unit fault omission. In the paper, we show how the tests of the system units are organized and proposed the method of evaluating the credibility of the results of system checking.
Parallel K-Medoids Improved Algorithm Based on MapReduce

K-Medoids algorithm is a partition-based algorithm, which has the characteristics of simple implementation, strong robustness, and high accuracy. However, it has some disadvantages, such as strong dependence on the selection of initial center, the unknown number of classification K, high resource cost of frequent iteration of the algorithm, and poor clustering effect for mass data. In order to solve these problems, the original K-Medoids algorithm was improved by introducing the Canopy algorithm and the Max-Min distance algorithm, and K points were selected as the initial center of the cluster. In the era of big data, we use the MapReduce computing framework to parallelize the algorithm. The experimental results show that: the improved clustering algorithm not only has a good speedup, but also improves the clustering accuracy and convergence, and shows a large performance advantage in dealing with large-scale data.
Simulation of Stochastic Blockchain Models

This paper build the foundations of a simulation tool for blockchain-based applications. It takes advantage of the huge expressiveness and extensibility of PyCATSHOO framework to deal with the important variability of blockchain implémentations and properties of interest. A simple stochastic model of generic blockchain-style distributed consensus system and associated performance indicators are proposed (performance in terms of consistency and ability to discard double-spending attacks). Monte Carlo simulations are applied to assess the indicators and determine their sensitivity to the variation of input parameters.
Sound Reduction of Vibration Feedback by Perceptually Similar Modulation

The transmission of high-frequency collision vibration can effectively deliver tactile characteristics in teleoperation of remote robots and the virtual environment. However, high-frequency vibrations also introduces audible noise. To address this issue, we modulated the frequency and amplitude of collision vibration to keep the perceptually similar while reducing the sound of the vibrations. Our experimental results showed that the sound pressure level of the collision vibrations (f = 675 Hz and 1012 Hz) is higher than the perceptual similar collision vibrations (f = 300 Hz and 450 Hz). These results suggest that our modulation method is able to reduce the sound level of the collision vibrations while maintaining the perceptual similarity.
ON EXACTITUDE IN SCIENCE: A MAP OF THE EMPIRE THE SIZE OF THE EMPIRE.

In the service of children's best interests, we argue for a sharpening of the evidentiary standards used in family court decision making, from preponderance of (occasionally substandard) evidence to "beyond a reasonable doubt. " Second, we call for a move in child protection cases from static diagnoses (e.g., attachment classifications) to assessments of the potential for enhanced parenting. Finally, informed by the implications of the replication crisis in the biomedical and psychological sciences, we applaud the move of the attachment field forward to large-scale, collective research agendas and goals.
Model of Warehouse Performance Measurement Based on Sustainable Warehouse Design

In sustainable supply chain management (SSCM), sustainability aspects, i.e. economic, social and environmental are integrated in managing supply chain. This concept has elicited government regulations that requiring every company to prevent the occurrence of environmental pollution. However, the concept of sustainability has not been implemented in operational processes of most industries in Indonesia particularly warehouse. This study aims to develop a model of warehouse performance measurement, to measure the warehouse performance, and to identify the indicators needed to be improved. The Balanced Scorecard (BSC) method with Sustainable Supply Chain Management (SSCM) approach is applied in developing the model. Balanced scorecard is a performance measurement method based on four perspectives: financial, customer satisfaction, internal process, and learning growth. BSC is appropriate for overcoming the problems related to performance assessment in SCs according to several researches. There are 12 indicators used in warehouse performance measurement, i.e. warehousing costs, labor cost, perfect quality items, on time delivery, inventory accuracy, environmental friendly tools, waste management, turn over, 5s implementation, worker competency improvement and environmental awareness. The warehouse performance measurement model based on sustainable warehouse design has been successfully demonstrated in medium scale textile industry.